diff --git a/02_end_to_end_machine_learning_project.ipynb b/02_end_to_end_machine_learning_project.ipynb
index 8d48fbf..924981d 100644
--- a/02_end_to_end_machine_learning_project.ipynb
+++ b/02_end_to_end_machine_learning_project.ipynb
@@ -1,10227 +1,11105 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Chapter 2 – End-to-End Machine Learning Project**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*This notebook contains all the sample code and solutions to the exercises in chapter 2.*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<table align=\"left\">\n",
-    "  <td>\n",
-    "    <a href=\"https://colab.research.google.com/github/ageron/handson-mlp/blob/main/02_end_to_end_machine_learning_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
-    "  </td>\n",
-    "  <td>\n",
-    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-mlp/blob/main/02_end_to_end_machine_learning_project.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
-    "  </td>\n",
-    "</table>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Welcome to Machine Learning!\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Welcome to Machine Learning!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This project requires Python 3.10 or above:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "\n",
-    "assert sys.version_info >= (3, 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It also requires Scikit-Learn ≥ 1.6.1:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from packaging.version import Version\n",
-    "import sklearn\n",
-    "\n",
-    "assert Version(sklearn.__version__) >= Version(\"1.6.1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Get the Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*Welcome to Machine Learning Housing Corp.! Your task is to predict median house values in Californian districts, given a number of features from these districts.*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download the Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "import pandas as pd\n",
-    "import tarfile\n",
-    "import urllib.request\n",
-    "\n",
-    "def load_housing_data():\n",
-    "    tarball_path = Path(\"datasets/housing.tgz\")\n",
-    "    if not tarball_path.is_file():\n",
-    "        Path(\"datasets\").mkdir(parents=True, exist_ok=True)\n",
-    "        url = \"https://github.com/ageron/data/raw/main/housing.tgz\"\n",
-    "        urllib.request.urlretrieve(url, tarball_path)\n",
-    "        with tarfile.open(tarball_path) as housing_tarball:\n",
-    "            housing_tarball.extractall(path=\"datasets\", filter=\"data\")\n",
-    "    return pd.read_csv(Path(\"datasets/housing/housing.csv\"))\n",
-    "\n",
-    "housing_full = load_housing_data()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Take a Quick Look at the Data Structure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"housing_full\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0035317235025802,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -123.39,\n          -122.08,\n          -116.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.135952397457107,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          36.16,\n          36.28,\n          32.68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.585557612111748,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2181.6152515827785,\n        \"min\": 2.0,\n        \"max\": 39320.0,\n        \"num_unique_values\": 5926,\n        \"samples\": [\n          913.0,\n          1759.0,\n          6434.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 421.38507007403194,\n        \"min\": 1.0,\n        \"max\": 6445.0,\n        \"num_unique_values\": 1923,\n        \"samples\": [\n          1296.0,\n          3493.0,\n          2035.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.4621217653341,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          3622.0,\n          3232.0,\n          2291.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 382.329752831612,\n        \"min\": 1.0,\n        \"max\": 6082.0,\n        \"num_unique_values\": 1815,\n        \"samples\": [\n          745.0,\n          1248.0,\n          1166.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452694,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          2.5211,\n          4.2454,\n          2.1681\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115395.61587441301,\n        \"min\": 14999.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          400700.0,\n          264900.0,\n          119100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"<1H OCEAN\",\n          \"ISLAND\",\n          \"INLAND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe",
-       "variable_name": "housing_full"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-2309612c-b95b-4446-b5fb-b5b3dcf5ab73\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "      <th>median_house_value</th>\n",
-       "      <th>ocean_proximity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-122.23</td>\n",
-       "      <td>37.88</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>880.0</td>\n",
-       "      <td>129.0</td>\n",
-       "      <td>322.0</td>\n",
-       "      <td>126.0</td>\n",
-       "      <td>8.3252</td>\n",
-       "      <td>452600.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-122.22</td>\n",
-       "      <td>37.86</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>7099.0</td>\n",
-       "      <td>1106.0</td>\n",
-       "      <td>2401.0</td>\n",
-       "      <td>1138.0</td>\n",
-       "      <td>8.3014</td>\n",
-       "      <td>358500.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-122.24</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>1467.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>177.0</td>\n",
-       "      <td>7.2574</td>\n",
-       "      <td>352100.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>1274.0</td>\n",
-       "      <td>235.0</td>\n",
-       "      <td>558.0</td>\n",
-       "      <td>219.0</td>\n",
-       "      <td>5.6431</td>\n",
-       "      <td>341300.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>1627.0</td>\n",
-       "      <td>280.0</td>\n",
-       "      <td>565.0</td>\n",
-       "      <td>259.0</td>\n",
-       "      <td>3.8462</td>\n",
-       "      <td>342200.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2309612c-b95b-4446-b5fb-b5b3dcf5ab73')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-2309612c-b95b-4446-b5fb-b5b3dcf5ab73 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-2309612c-b95b-4446-b5fb-b5b3dcf5ab73');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-558a9ae4-e2b5-43f1-a20b-c601349db1f1\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-558a9ae4-e2b5-43f1-a20b-c601349db1f1')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-558a9ae4-e2b5-43f1-a20b-c601349db1f1 button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/bertramwooster/Geron-handson-ml-2025/blob/new_branch/02_end_to_end_machine_learning_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "88jJUL9mtgdC"
+      },
+      "source": [
+        "**Chapter 2 – End-to-End Machine Learning Project**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "q4_cUYdntgdq"
+      },
+      "source": [
+        "*This notebook contains all the sample code and solutions to the exercises in chapter 2.*"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D07-_WsFtgd7"
+      },
+      "source": [
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a href=\"https://colab.research.google.com/github/ageron/handson-mlp/blob/main/02_end_to_end_machine_learning_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-mlp/blob/main/02_end_to_end_machine_learning_project.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EoehbLP4tgeT",
+        "outputId": "d719dfea-e3a9-46f6-9063-90ea09835826"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Welcome to Machine Learning!\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
-       "0    -122.23     37.88                41.0        880.0           129.0   \n",
-       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
-       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
-       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
-       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
-       "\n",
-       "   population  households  median_income  median_house_value ocean_proximity  \n",
-       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
-       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
-       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
-       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
-       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_full.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 20640 entries, 0 to 20639\n",
-      "Data columns (total 10 columns):\n",
-      " #   Column              Non-Null Count  Dtype  \n",
-      "---  ------              --------------  -----  \n",
-      " 0   longitude           20640 non-null  float64\n",
-      " 1   latitude            20640 non-null  float64\n",
-      " 2   housing_median_age  20640 non-null  float64\n",
-      " 3   total_rooms         20640 non-null  float64\n",
-      " 4   total_bedrooms      20433 non-null  float64\n",
-      " 5   population          20640 non-null  float64\n",
-      " 6   households          20640 non-null  float64\n",
-      " 7   median_income       20640 non-null  float64\n",
-      " 8   median_house_value  20640 non-null  float64\n",
-      " 9   ocean_proximity     20640 non-null  object \n",
-      "dtypes: float64(9), object(1)\n",
-      "memory usage: 1.6+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "housing_full.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ocean_proximity</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>&lt;1H OCEAN</th>\n",
-       "      <td>9136</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>INLAND</th>\n",
-       "      <td>6551</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NEAR OCEAN</th>\n",
-       "      <td>2658</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NEAR BAY</th>\n",
-       "      <td>2290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ISLAND</th>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div><br><label><b>dtype:</b> int64</label>"
+      "source": [
+        "print(\"Welcome to Machine Learning!\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kvozyINBtgel"
+      },
+      "source": [
+        "This project requires Python 3.10 or above:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "UVpUzuFDtge0"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "assert sys.version_info >= (3, 10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F9ugos62tge2"
+      },
+      "source": [
+        "It also requires Scikit-Learn ≥ 1.6.1:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "dAvOK9G5tge4"
+      },
+      "outputs": [],
+      "source": [
+        "from packaging.version import Version\n",
+        "import sklearn\n",
+        "\n",
+        "assert Version(sklearn.__version__) >= Version(\"1.6.1\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9QaOf0_Stge9"
+      },
+      "source": [
+        "# Get the Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FHPr06wStgfM"
+      },
+      "source": [
+        "*Welcome to Machine Learning Housing Corp.! Your task is to predict median house values in Californian districts, given a number of features from these districts.*"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CrICfyRLtgfV"
+      },
+      "source": [
+        "## Download the Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "5GKl01aUtgfX"
+      },
+      "outputs": [],
+      "source": [
+        "from pathlib import Path\n",
+        "import pandas as pd\n",
+        "import tarfile\n",
+        "import urllib.request\n",
+        "\n",
+        "def load_housing_data():\n",
+        "    tarball_path = Path(\"datasets/housing.tgz\")\n",
+        "    if not tarball_path.is_file():\n",
+        "        Path(\"datasets\").mkdir(parents=True, exist_ok=True)\n",
+        "        url = \"https://github.com/ageron/data/raw/main/housing.tgz\"\n",
+        "        urllib.request.urlretrieve(url, tarball_path)\n",
+        "        with tarfile.open(tarball_path) as housing_tarball:\n",
+        "            housing_tarball.extractall(path=\"datasets\", filter=\"data\")\n",
+        "    return pd.read_csv(Path(\"datasets/housing/housing.csv\"))\n",
+        "\n",
+        "housing_full = load_housing_data()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ep-sdTOWtgfy"
+      },
+      "source": [
+        "## Take a Quick Look at the Data Structure"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "RR8TuemRtggG",
+        "outputId": "292da207-9f76-48c5-813a-31f40bc88ec8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "20635    -121.09     39.48                25.0       1665.0           374.0   \n",
+              "20636    -121.21     39.49                18.0        697.0           150.0   \n",
+              "20637    -121.22     39.43                17.0       2254.0           485.0   \n",
+              "20638    -121.32     39.43                18.0       1860.0           409.0   \n",
+              "20639    -121.24     39.37                16.0       2785.0           616.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \\\n",
+              "20635       845.0       330.0         1.5603             78100.0   \n",
+              "20636       356.0       114.0         2.5568             77100.0   \n",
+              "20637      1007.0       433.0         1.7000             92300.0   \n",
+              "20638       741.0       349.0         1.8672             84700.0   \n",
+              "20639      1387.0       530.0         2.3886             89400.0   \n",
+              "\n",
+              "      ocean_proximity  \n",
+              "20635          INLAND  \n",
+              "20636          INLAND  \n",
+              "20637          INLAND  \n",
+              "20638          INLAND  \n",
+              "20639          INLAND  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d7bae99c-93dc-4728-bd76-44c4a4fa850a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "      <th>ocean_proximity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>20635</th>\n",
+              "      <td>-121.09</td>\n",
+              "      <td>39.48</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>1665.0</td>\n",
+              "      <td>374.0</td>\n",
+              "      <td>845.0</td>\n",
+              "      <td>330.0</td>\n",
+              "      <td>1.5603</td>\n",
+              "      <td>78100.0</td>\n",
+              "      <td>INLAND</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20636</th>\n",
+              "      <td>-121.21</td>\n",
+              "      <td>39.49</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>697.0</td>\n",
+              "      <td>150.0</td>\n",
+              "      <td>356.0</td>\n",
+              "      <td>114.0</td>\n",
+              "      <td>2.5568</td>\n",
+              "      <td>77100.0</td>\n",
+              "      <td>INLAND</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20637</th>\n",
+              "      <td>-121.22</td>\n",
+              "      <td>39.43</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>2254.0</td>\n",
+              "      <td>485.0</td>\n",
+              "      <td>1007.0</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1.7000</td>\n",
+              "      <td>92300.0</td>\n",
+              "      <td>INLAND</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20638</th>\n",
+              "      <td>-121.32</td>\n",
+              "      <td>39.43</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1860.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>741.0</td>\n",
+              "      <td>349.0</td>\n",
+              "      <td>1.8672</td>\n",
+              "      <td>84700.0</td>\n",
+              "      <td>INLAND</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20639</th>\n",
+              "      <td>-121.24</td>\n",
+              "      <td>39.37</td>\n",
+              "      <td>16.0</td>\n",
+              "      <td>2785.0</td>\n",
+              "      <td>616.0</td>\n",
+              "      <td>1387.0</td>\n",
+              "      <td>530.0</td>\n",
+              "      <td>2.3886</td>\n",
+              "      <td>89400.0</td>\n",
+              "      <td>INLAND</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7bae99c-93dc-4728-bd76-44c4a4fa850a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d7bae99c-93dc-4728-bd76-44c4a4fa850a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d7bae99c-93dc-4728-bd76-44c4a4fa850a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-3b654816-a4a6-4644-aecb-764502f0259b\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3b654816-a4a6-4644-aecb-764502f0259b')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-3b654816-a4a6-4644-aecb-764502f0259b button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"housing_full\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08264381404557382,\n        \"min\": -121.32,\n        \"max\": -121.09,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -121.21,\n          -121.24,\n          -121.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.047958315233128025,\n        \"min\": 39.37,\n        \"max\": 39.49,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          39.49,\n          39.37,\n          39.48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5637059362410923,\n        \"min\": 16.0,\n        \"max\": 25.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          18.0,\n          16.0,\n          25.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 774.7823565363373,\n        \"min\": 697.0,\n        \"max\": 2785.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          697.0,\n          2785.0,\n          2254.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 170.95818202121828,\n        \"min\": 150.0,\n        \"max\": 616.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          150.0,\n          616.0,\n          485.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 376.6566075352987,\n        \"min\": 356.0,\n        \"max\": 1387.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          356.0,\n          1387.0,\n          1007.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.41729177783168,\n        \"min\": 114.0,\n        \"max\": 530.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          114.0,\n          530.0,\n          433.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43616087857578417,\n        \"min\": 1.5603,\n        \"max\": 2.5568,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.5568,\n          2.3886,\n          1.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6716.546731766258,\n        \"min\": 77100.0,\n        \"max\": 92300.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          77100.0,\n          89400.0,\n          92300.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"INLAND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
       ],
-      "text/plain": [
-       "ocean_proximity\n",
-       "<1H OCEAN     9136\n",
-       "INLAND        6551\n",
-       "NEAR OCEAN    2658\n",
-       "NEAR BAY      2290\n",
-       "ISLAND           5\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_full[\"ocean_proximity\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"housing_full\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7333.554670164394,\n        \"min\": -124.35,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -119.56970445736432,\n          -118.49,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7286.333552413666,\n        \"min\": 2.135952397457107,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.63186143410853,\n          34.26,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7288.35672120143,\n        \"min\": 1.0,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.639486434108527,\n          29.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13944.990983306394,\n        \"min\": 2.0,\n        \"max\": 39320.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2635.7630813953488,\n          2127.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7106.427031043755,\n        \"min\": 1.0,\n        \"max\": 20433.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          537.8705525375618,\n          435.0,\n          20433.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13192.258841737372,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1425.4767441860465,\n          1166.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7167.532601135343,\n        \"min\": 1.0,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          499.5396802325581,\n          409.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7295.721435853639,\n        \"min\": 0.4999,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8706710029069766,\n          3.5347999999999997,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156160.28379826449,\n        \"min\": 14999.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          206855.81690891474,\n          179700.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-a15eed7c-bc95-4ae5-ac0a-d00265017999\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "      <th>median_house_value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20433.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>-119.569704</td>\n",
-       "      <td>35.631861</td>\n",
-       "      <td>28.639486</td>\n",
-       "      <td>2635.763081</td>\n",
-       "      <td>537.870553</td>\n",
-       "      <td>1425.476744</td>\n",
-       "      <td>499.539680</td>\n",
-       "      <td>3.870671</td>\n",
-       "      <td>206855.816909</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>2.003532</td>\n",
-       "      <td>2.135952</td>\n",
-       "      <td>12.585558</td>\n",
-       "      <td>2181.615252</td>\n",
-       "      <td>421.385070</td>\n",
-       "      <td>1132.462122</td>\n",
-       "      <td>382.329753</td>\n",
-       "      <td>1.899822</td>\n",
-       "      <td>115395.615874</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>-124.350000</td>\n",
-       "      <td>32.540000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.499900</td>\n",
-       "      <td>14999.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>-121.800000</td>\n",
-       "      <td>33.930000</td>\n",
-       "      <td>18.000000</td>\n",
-       "      <td>1447.750000</td>\n",
-       "      <td>296.000000</td>\n",
-       "      <td>787.000000</td>\n",
-       "      <td>280.000000</td>\n",
-       "      <td>2.563400</td>\n",
-       "      <td>119600.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>-118.490000</td>\n",
-       "      <td>34.260000</td>\n",
-       "      <td>29.000000</td>\n",
-       "      <td>2127.000000</td>\n",
-       "      <td>435.000000</td>\n",
-       "      <td>1166.000000</td>\n",
-       "      <td>409.000000</td>\n",
-       "      <td>3.534800</td>\n",
-       "      <td>179700.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>-118.010000</td>\n",
-       "      <td>37.710000</td>\n",
-       "      <td>37.000000</td>\n",
-       "      <td>3148.000000</td>\n",
-       "      <td>647.000000</td>\n",
-       "      <td>1725.000000</td>\n",
-       "      <td>605.000000</td>\n",
-       "      <td>4.743250</td>\n",
-       "      <td>264725.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>-114.310000</td>\n",
-       "      <td>41.950000</td>\n",
-       "      <td>52.000000</td>\n",
-       "      <td>39320.000000</td>\n",
-       "      <td>6445.000000</td>\n",
-       "      <td>35682.000000</td>\n",
-       "      <td>6082.000000</td>\n",
-       "      <td>15.000100</td>\n",
-       "      <td>500001.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a15eed7c-bc95-4ae5-ac0a-d00265017999')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-a15eed7c-bc95-4ae5-ac0a-d00265017999 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-a15eed7c-bc95-4ae5-ac0a-d00265017999');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-3f82bf27-126e-49f3-9861-44b3605d37e9\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3f82bf27-126e-49f3-9861-44b3605d37e9')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-3f82bf27-126e-49f3-9861-44b3605d37e9 button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "housing_full.tail()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rAo9xsJ5tggI",
+        "outputId": "35f0af53-3951-44bb-b5f2-1759e02ce387"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 20640 entries, 0 to 20639\n",
+            "Data columns (total 10 columns):\n",
+            " #   Column              Non-Null Count  Dtype  \n",
+            "---  ------              --------------  -----  \n",
+            " 0   longitude           20640 non-null  float64\n",
+            " 1   latitude            20640 non-null  float64\n",
+            " 2   housing_median_age  20640 non-null  float64\n",
+            " 3   total_rooms         20640 non-null  float64\n",
+            " 4   total_bedrooms      20433 non-null  float64\n",
+            " 5   population          20640 non-null  float64\n",
+            " 6   households          20640 non-null  float64\n",
+            " 7   median_income       20640 non-null  float64\n",
+            " 8   median_house_value  20640 non-null  float64\n",
+            " 9   ocean_proximity     20640 non-null  object \n",
+            "dtypes: float64(9), object(1)\n",
+            "memory usage: 1.6+ MB\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
-       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
-       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
-       "std        2.003532      2.135952           12.585558   2181.615252   \n",
-       "min     -124.350000     32.540000            1.000000      2.000000   \n",
-       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
-       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
-       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
-       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
-       "\n",
-       "       total_bedrooms    population    households  median_income  \\\n",
-       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
-       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
-       "std        421.385070   1132.462122    382.329753       1.899822   \n",
-       "min          1.000000      3.000000      1.000000       0.499900   \n",
-       "25%        296.000000    787.000000    280.000000       2.563400   \n",
-       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
-       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
-       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
-       "\n",
-       "       median_house_value  \n",
-       "count        20640.000000  \n",
-       "mean        206855.816909  \n",
-       "std         115395.615874  \n",
-       "min          14999.000000  \n",
-       "25%         119600.000000  \n",
-       "50%         179700.000000  \n",
-       "75%         264725.000000  \n",
-       "max         500001.000000  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_full.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1200x800 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# extra code – the next 5 lines define the default font sizes\n",
-    "plt.rc('font', size=14)\n",
-    "plt.rc('axes', labelsize=14, titlesize=14)\n",
-    "plt.rc('legend', fontsize=14)\n",
-    "plt.rc('xtick', labelsize=10)\n",
-    "plt.rc('ytick', labelsize=10)\n",
-    "\n",
-    "housing_full.hist(bins=50, figsize=(12, 8))\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a Test Set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "def shuffle_and_split_data(data, test_ratio, rng):\n",
-    "    shuffled_indices = rng.permutation(len(data))\n",
-    "    test_set_size = int(len(data) * test_ratio)\n",
-    "    test_indices = shuffled_indices[:test_set_size]\n",
-    "    train_indices = shuffled_indices[test_set_size:]\n",
-    "    return data.iloc[train_indices], data.iloc[test_indices]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To ensure that this notebook's outputs remain the same every time we run it, we need to set the random seed:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16512"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rng = np.random.default_rng(seed=42)\n",
-    "train_set, test_set = shuffle_and_split_data(housing_full, 0.2, rng)\n",
-    "len(train_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4128"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(test_set)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sadly, this won't guarantee that this notebook will output exactly the same results as in the book, since there are other possible sources of variation. The most important is the fact that algorithms get tweaked over time when libraries evolve. So please tolerate some minor differences: hopefully, most of the outputs should be the same, or at least in the right ballpark."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: another source of randomness is the order of Python sets: it is based on Python's `hash()` function, which is randomly \"salted\" when Python starts up (this started in Python 3.3, to prevent some denial-of-service attacks). To remove this randomness, the solution is to set the `PYTHONHASHSEED` environment variable to `\"0\"` _before_ Python even starts up. Nothing will happen if you do it after that. Luckily, if you're running this notebook on Colab, the variable is already set for you."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from zlib import crc32\n",
-    "\n",
-    "def is_id_in_test_set(identifier, test_ratio):\n",
-    "    return crc32(np.int64(identifier)) < test_ratio * 2**32\n",
-    "\n",
-    "def split_data_with_id_hash(data, test_ratio, id_column):\n",
-    "    ids = data[id_column]\n",
-    "    in_test_set = ids.apply(lambda id_: is_id_in_test_set(id_, test_ratio))\n",
-    "    return data.loc[~in_test_set], data.loc[in_test_set]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing_with_id = housing_full.reset_index()  # adds an `index` column\n",
-    "train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"index\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing_with_id[\"id\"] = (housing_full[\"longitude\"] * 1000\n",
-    "                         + housing_full[\"latitude\"])\n",
-    "train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"id\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "train_set, test_set = train_test_split(housing_full, test_size=0.2,\n",
-    "                                       random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.int64(44)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_set[\"total_bedrooms\"].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To find the probability that a random sample of 1,000 people contains less than 49% female or more than 54% female when the population's female ratio is 51.6%, we use the [binomial distribution](https://en.wikipedia.org/wiki/Binomial_distribution). The `cdf()` method of the binomial distribution gives us the probability that the number of females will be equal or less than the given value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.10727422667455677\n"
-     ]
-    }
-   ],
-   "source": [
-    "# extra code – shows how to compute the 10.7% proba of getting a bad sample\n",
-    "\n",
-    "from scipy.stats import binom\n",
-    "\n",
-    "sample_size = 1000\n",
-    "ratio_female = 0.516\n",
-    "proba_too_small = binom(sample_size, ratio_female).cdf(490 - 1)\n",
-    "proba_too_large = 1 - binom(sample_size, ratio_female).cdf(540)\n",
-    "print(proba_too_small + proba_too_large)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you prefer simulations over maths, here's how you could get roughly the same result:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(0.1077)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# extra code – shows another way to estimate the probability of bad sample\n",
-    "\n",
-    "rng = np.random.default_rng(seed=42)\n",
-    "samples = (rng.random((100_000, sample_size)) < ratio_female).sum(axis=1)\n",
-    "((samples < 490) | (samples > 540)).mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing_full[\"income_cat\"] = pd.cut(housing_full[\"median_income\"],\n",
-    "                                    bins=[0., 1.5, 3.0, 4.5, 6., np.inf],\n",
-    "                                    labels=[1, 2, 3, 4, 5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cat_counts = housing_full[\"income_cat\"].value_counts().sort_index()\n",
-    "cat_counts.plot.bar(rot=0, grid=True)\n",
-    "plt.xlabel(\"Income category\")\n",
-    "plt.ylabel(\"Number of districts\")\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import StratifiedShuffleSplit\n",
-    "\n",
-    "splitter = StratifiedShuffleSplit(n_splits=10, test_size=0.2, random_state=42)\n",
-    "strat_splits = []\n",
-    "for train_index, test_index in splitter.split(housing_full,\n",
-    "                                              housing_full[\"income_cat\"]):\n",
-    "    strat_train_set_n = housing_full.iloc[train_index]\n",
-    "    strat_test_set_n = housing_full.iloc[test_index]\n",
-    "    strat_splits.append([strat_train_set_n, strat_test_set_n])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "strat_train_set, strat_test_set = strat_splits[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's much shorter to get a single stratified split:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "strat_train_set, strat_test_set = train_test_split(\n",
-    "    housing_full, test_size=0.2, stratify=housing_full[\"income_cat\"],\n",
-    "    random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>income_cat</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.350533</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.318798</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.176357</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.114341</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.039971</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div><br><label><b>dtype:</b> float64</label>"
+      "source": [
+        "housing_full.info()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        },
+        "id": "GlVyitLltggO",
+        "outputId": "170e2dc8-a764-4ac9-adbf-f32fb2eb8a9d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "ocean_proximity\n",
+              "<1H OCEAN     9136\n",
+              "INLAND        6551\n",
+              "NEAR OCEAN    2658\n",
+              "NEAR BAY      2290\n",
+              "ISLAND           5\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ocean_proximity</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>&lt;1H OCEAN</th>\n",
+              "      <td>9136</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>INLAND</th>\n",
+              "      <td>6551</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>NEAR OCEAN</th>\n",
+              "      <td>2658</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>NEAR BAY</th>\n",
+              "      <td>2290</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ISLAND</th>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
       ],
-      "text/plain": [
-       "income_cat\n",
-       "3    0.350533\n",
-       "2    0.318798\n",
-       "4    0.176357\n",
-       "5    0.114341\n",
-       "1    0.039971\n",
-       "Name: count, dtype: float64"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"(compare_props * 100)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Income Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Overall %\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.261493882666464,\n        \"min\": 3.98,\n        \"max\": 35.06,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          31.88,\n          11.44,\n          35.06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stratified %\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.253786251483007,\n        \"min\": 4.0,\n        \"max\": 35.05,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          31.88,\n          11.43,\n          35.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Random %\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.645669219143763,\n        \"min\": 4.24,\n        \"max\": 34.52,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          30.74,\n          12.09,\n          34.52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Strat. Error %\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.174441967427566,\n        \"min\": -0.08,\n        \"max\": 0.36,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.02,\n          -0.08,\n          -0.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rand. Error %\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.5322157936267775,\n        \"min\": -3.59,\n        \"max\": 6.45,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -3.59,\n          5.63,\n          -1.53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-fd09dffd-80fe-497f-a1c0-24bfb8ff9d4b\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Overall %</th>\n",
-       "      <th>Stratified %</th>\n",
-       "      <th>Random %</th>\n",
-       "      <th>Strat. Error %</th>\n",
-       "      <th>Rand. Error %</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Income Category</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3.98</td>\n",
-       "      <td>4.00</td>\n",
-       "      <td>4.24</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>6.45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>31.88</td>\n",
-       "      <td>31.88</td>\n",
-       "      <td>30.74</td>\n",
-       "      <td>-0.02</td>\n",
-       "      <td>-3.59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>35.06</td>\n",
-       "      <td>35.05</td>\n",
-       "      <td>34.52</td>\n",
-       "      <td>-0.01</td>\n",
-       "      <td>-1.53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>17.63</td>\n",
-       "      <td>17.64</td>\n",
-       "      <td>18.41</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>4.42</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>11.44</td>\n",
-       "      <td>11.43</td>\n",
-       "      <td>12.09</td>\n",
-       "      <td>-0.08</td>\n",
-       "      <td>5.63</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd09dffd-80fe-497f-a1c0-24bfb8ff9d4b')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-fd09dffd-80fe-497f-a1c0-24bfb8ff9d4b button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-fd09dffd-80fe-497f-a1c0-24bfb8ff9d4b');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-4a029694-c41d-4bf9-8d4a-a8bf24de2fb0\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a029694-c41d-4bf9-8d4a-a8bf24de2fb0')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-4a029694-c41d-4bf9-8d4a-a8bf24de2fb0 button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "housing_full[\"ocean_proximity\"].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 320
+        },
+        "id": "iHvXNrOLtggW",
+        "outputId": "2169203c-8d13-40e5-9c09-57a4393e0d9f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+              "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
+              "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
+              "std        2.003532      2.135952           12.585558   2181.615252   \n",
+              "min     -124.350000     32.540000            1.000000      2.000000   \n",
+              "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
+              "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
+              "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
+              "max     -114.310000     41.950000           52.000000  39320.000000   \n",
+              "\n",
+              "       total_bedrooms    population    households  median_income  \\\n",
+              "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
+              "mean       537.870553   1425.476744    499.539680       3.870671   \n",
+              "std        421.385070   1132.462122    382.329753       1.899822   \n",
+              "min          1.000000      3.000000      1.000000       0.499900   \n",
+              "25%        296.000000    787.000000    280.000000       2.563400   \n",
+              "50%        435.000000   1166.000000    409.000000       3.534800   \n",
+              "75%        647.000000   1725.000000    605.000000       4.743250   \n",
+              "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+              "\n",
+              "       median_house_value  \n",
+              "count        20640.000000  \n",
+              "mean        206855.816909  \n",
+              "std         115395.615874  \n",
+              "min          14999.000000  \n",
+              "25%         119600.000000  \n",
+              "50%         179700.000000  \n",
+              "75%         264725.000000  \n",
+              "max         500001.000000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-dcf254dc-c6d1-4bd0-a89a-067747fabda8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20433.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>-119.569704</td>\n",
+              "      <td>35.631861</td>\n",
+              "      <td>28.639486</td>\n",
+              "      <td>2635.763081</td>\n",
+              "      <td>537.870553</td>\n",
+              "      <td>1425.476744</td>\n",
+              "      <td>499.539680</td>\n",
+              "      <td>3.870671</td>\n",
+              "      <td>206855.816909</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.003532</td>\n",
+              "      <td>2.135952</td>\n",
+              "      <td>12.585558</td>\n",
+              "      <td>2181.615252</td>\n",
+              "      <td>421.385070</td>\n",
+              "      <td>1132.462122</td>\n",
+              "      <td>382.329753</td>\n",
+              "      <td>1.899822</td>\n",
+              "      <td>115395.615874</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-124.350000</td>\n",
+              "      <td>32.540000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.499900</td>\n",
+              "      <td>14999.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-121.800000</td>\n",
+              "      <td>33.930000</td>\n",
+              "      <td>18.000000</td>\n",
+              "      <td>1447.750000</td>\n",
+              "      <td>296.000000</td>\n",
+              "      <td>787.000000</td>\n",
+              "      <td>280.000000</td>\n",
+              "      <td>2.563400</td>\n",
+              "      <td>119600.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>-118.490000</td>\n",
+              "      <td>34.260000</td>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>2127.000000</td>\n",
+              "      <td>435.000000</td>\n",
+              "      <td>1166.000000</td>\n",
+              "      <td>409.000000</td>\n",
+              "      <td>3.534800</td>\n",
+              "      <td>179700.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>-118.010000</td>\n",
+              "      <td>37.710000</td>\n",
+              "      <td>37.000000</td>\n",
+              "      <td>3148.000000</td>\n",
+              "      <td>647.000000</td>\n",
+              "      <td>1725.000000</td>\n",
+              "      <td>605.000000</td>\n",
+              "      <td>4.743250</td>\n",
+              "      <td>264725.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>-114.310000</td>\n",
+              "      <td>41.950000</td>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>39320.000000</td>\n",
+              "      <td>6445.000000</td>\n",
+              "      <td>35682.000000</td>\n",
+              "      <td>6082.000000</td>\n",
+              "      <td>15.000100</td>\n",
+              "      <td>500001.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dcf254dc-c6d1-4bd0-a89a-067747fabda8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-dcf254dc-c6d1-4bd0-a89a-067747fabda8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-dcf254dc-c6d1-4bd0-a89a-067747fabda8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-4ae64904-e02c-44c8-8010-6a8514e90763\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ae64904-e02c-44c8-8010-6a8514e90763')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-4ae64904-e02c-44c8-8010-6a8514e90763 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"housing_full\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7333.554670164394,\n        \"min\": -124.35,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -119.56970445736432,\n          -118.49,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7286.333552413666,\n        \"min\": 2.135952397457107,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.63186143410853,\n          34.26,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7288.35672120143,\n        \"min\": 1.0,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.639486434108527,\n          29.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13944.990983306394,\n        \"min\": 2.0,\n        \"max\": 39320.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2635.7630813953488,\n          2127.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7106.427031043755,\n        \"min\": 1.0,\n        \"max\": 20433.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          537.8705525375618,\n          435.0,\n          20433.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13192.258841737372,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1425.4767441860465,\n          1166.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7167.532601135343,\n        \"min\": 1.0,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          499.5396802325581,\n          409.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7295.721435853639,\n        \"min\": 0.4999,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8706710029069766,\n          3.5347999999999997,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156160.28379826449,\n        \"min\": 14999.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          206855.81690891474,\n          179700.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
       ],
-      "text/plain": [
-       "                 Overall %  Stratified %  Random %  Strat. Error %  \\\n",
-       "Income Category                                                      \n",
-       "1                     3.98          4.00      4.24            0.36   \n",
-       "2                    31.88         31.88     30.74           -0.02   \n",
-       "3                    35.06         35.05     34.52           -0.01   \n",
-       "4                    17.63         17.64     18.41            0.03   \n",
-       "5                    11.44         11.43     12.09           -0.08   \n",
-       "\n",
-       "                 Rand. Error %  \n",
-       "Income Category                 \n",
-       "1                         6.45  \n",
-       "2                        -3.59  \n",
-       "3                        -1.53  \n",
-       "4                         4.42  \n",
-       "5                         5.63  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# extra code – computes the data for Figure 2–10\n",
-    "\n",
-    "def income_cat_proportions(data):\n",
-    "    return data[\"income_cat\"].value_counts() / len(data)\n",
-    "\n",
-    "train_set, test_set = train_test_split(housing_full, test_size=0.2,\n",
-    "                                       random_state=42)\n",
-    "\n",
-    "compare_props = pd.DataFrame({\n",
-    "    \"Overall %\": income_cat_proportions(housing_full),\n",
-    "    \"Stratified %\": income_cat_proportions(strat_test_set),\n",
-    "    \"Random %\": income_cat_proportions(test_set),\n",
-    "}).sort_index()\n",
-    "compare_props.index.name = \"Income Category\"\n",
-    "compare_props[\"Strat. Error %\"] = (compare_props[\"Stratified %\"] /\n",
-    "                                   compare_props[\"Overall %\"] - 1)\n",
-    "compare_props[\"Rand. Error %\"] = (compare_props[\"Random %\"] /\n",
-    "                                  compare_props[\"Overall %\"] - 1)\n",
-    "(compare_props * 100).round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for set_ in (strat_train_set, strat_test_set):\n",
-    "    set_.drop(\"income_cat\", axis=1, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Discover and Visualize the Data to Gain Insights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing = strat_train_set.copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualizing Geographical Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True, alpha=0.2)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True,\n",
-    "            s=housing[\"population\"] / 100, label=\"population\",\n",
-    "            c=\"median_house_value\", cmap=\"jet\", colorbar=True,\n",
-    "            legend=True, sharex=False, figsize=(10, 7))\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next cell generates the first figure in the chapter (this code is not in the book). It's just a beautified version of the previous figure, with an image of California added in the background, nicer label names and no grid."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading california.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# extra code – this cell generates the first figure in the chapter\n",
-    "\n",
-    "# Download the California image\n",
-    "filename = \"california.png\"\n",
-    "filepath = Path(f\"my_{filename}\")\n",
-    "if not filepath.is_file():\n",
-    "    homlp_root = \"https://github.com/ageron/handson-mlp/raw/main/\"\n",
-    "    url = homlp_root + \"images/end_to_end_project/\" + filename\n",
-    "    print(\"Downloading\", filename)\n",
-    "    urllib.request.urlretrieve(url, filepath)\n",
-    "\n",
-    "housing_renamed = housing.rename(columns={\n",
-    "    \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n",
-    "    \"population\": \"Population\",\n",
-    "    \"median_house_value\": \"Median house value (ᴜsᴅ)\"})\n",
-    "housing_renamed.plot(\n",
-    "             kind=\"scatter\", x=\"Longitude\", y=\"Latitude\",\n",
-    "             s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n",
-    "             c=\"Median house value (ᴜsᴅ)\", cmap=\"jet\", colorbar=True,\n",
-    "             legend=True, sharex=False, figsize=(10, 7))\n",
-    "\n",
-    "california_img = plt.imread(filepath)\n",
-    "axis = -124.55, -113.95, 32.45, 42.05\n",
-    "plt.axis(axis)\n",
-    "plt.imshow(california_img, extent=axis)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Looking for Correlations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corr_matrix = housing.corr(numeric_only=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>median_house_value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>median_house_value</th>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>median_income</th>\n",
-       "      <td>0.688380</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>total_rooms</th>\n",
-       "      <td>0.137455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <td>0.102175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>households</th>\n",
-       "      <td>0.071426</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <td>0.054635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>population</th>\n",
-       "      <td>-0.020153</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>longitude</th>\n",
-       "      <td>-0.050859</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>latitude</th>\n",
-       "      <td>-0.139584</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div><br><label><b>dtype:</b> float64</label>"
+      "source": [
+        "housing_full.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "housing_full[\"total_bedrooms\"].isnull().sum()"
       ],
-      "text/plain": [
-       "median_house_value    1.000000\n",
-       "median_income         0.688380\n",
-       "total_rooms           0.137455\n",
-       "housing_median_age    0.102175\n",
-       "households            0.071426\n",
-       "total_bedrooms        0.054635\n",
-       "population           -0.020153\n",
-       "longitude            -0.050859\n",
-       "latitude             -0.139584\n",
-       "Name: median_house_value, dtype: float64"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1200x800 with 16 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from pandas.plotting import scatter_matrix\n",
-    "\n",
-    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
-    "              \"housing_median_age\"]\n",
-    "scatter_matrix(housing[attributes], figsize=(12, 8))\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n",
-    "             alpha=0.1, grid=True)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Experimenting with Attribute Combinations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing[\"rooms_per_house\"] = housing[\"total_rooms\"] / housing[\"households\"]\n",
-    "housing[\"bedrooms_ratio\"] = housing[\"total_bedrooms\"] / housing[\"total_rooms\"]\n",
-    "housing[\"people_per_house\"] = housing[\"population\"] / housing[\"households\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>median_house_value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>median_house_value</th>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>median_income</th>\n",
-       "      <td>0.688380</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rooms_per_house</th>\n",
-       "      <td>0.143663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>total_rooms</th>\n",
-       "      <td>0.137455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <td>0.102175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>households</th>\n",
-       "      <td>0.071426</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <td>0.054635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>population</th>\n",
-       "      <td>-0.020153</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>people_per_house</th>\n",
-       "      <td>-0.038224</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>longitude</th>\n",
-       "      <td>-0.050859</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>latitude</th>\n",
-       "      <td>-0.139584</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bedrooms_ratio</th>\n",
-       "      <td>-0.256397</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div><br><label><b>dtype:</b> float64</label>"
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Aa4tDdzJLSaq",
+        "outputId": "af166fa8-955d-4098-b67d-aac59321e723"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.int64(207)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "housing_full.isna().sum()"
       ],
-      "text/plain": [
-       "median_house_value    1.000000\n",
-       "median_income         0.688380\n",
-       "rooms_per_house       0.143663\n",
-       "total_rooms           0.137455\n",
-       "housing_median_age    0.102175\n",
-       "households            0.071426\n",
-       "total_bedrooms        0.054635\n",
-       "population           -0.020153\n",
-       "people_per_house     -0.038224\n",
-       "longitude            -0.050859\n",
-       "latitude             -0.139584\n",
-       "bedrooms_ratio       -0.256397\n",
-       "Name: median_house_value, dtype: float64"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corr_matrix = housing.corr(numeric_only=True)\n",
-    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Prepare the Data for Machine Learning Algorithms"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's revert to the original training set and separate the target (note that `strat_train_set.drop()` creates a copy of `strat_train_set` without the column, it doesn't actually modify `strat_train_set` itself, unless you pass `inplace=True`):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
-    "housing_labels = strat_train_set[\"median_house_value\"].copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Cleaning"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the book 3 options are listed to handle the NaN values:\n",
-    "\n",
-    "```python\n",
-    "housing.dropna(subset=[\"total_bedrooms\"], inplace=True)    # option 1\n",
-    "\n",
-    "housing.drop(\"total_bedrooms\", axis=1, inplace=True)       # option 2\n",
-    "\n",
-    "median = housing[\"total_bedrooms\"].median()  # option 3\n",
-    "housing[\"total_bedrooms\"] = housing[\"total_bedrooms\"].fillna(median)\n",
-    "```\n",
-    "\n",
-    "For each option, we'll create a copy of `housing` and work on that copy to avoid breaking `housing`. We'll also show the output of each option, filtering on the rows that originally contained a NaN value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "      <th>ocean_proximity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>14452</th>\n",
-       "      <td>-120.67</td>\n",
-       "      <td>40.50</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>5343.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2503.0</td>\n",
-       "      <td>902.0</td>\n",
-       "      <td>3.5962</td>\n",
-       "      <td>INLAND</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18217</th>\n",
-       "      <td>-117.96</td>\n",
-       "      <td>34.03</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>2093.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1755.0</td>\n",
-       "      <td>403.0</td>\n",
-       "      <td>3.4115</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11889</th>\n",
-       "      <td>-118.05</td>\n",
-       "      <td>34.04</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>1348.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1098.0</td>\n",
-       "      <td>257.0</td>\n",
-       "      <td>4.2917</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20325</th>\n",
-       "      <td>-118.88</td>\n",
-       "      <td>34.17</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>4260.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1701.0</td>\n",
-       "      <td>669.0</td>\n",
-       "      <td>5.1033</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14360</th>\n",
-       "      <td>-117.87</td>\n",
-       "      <td>33.62</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1266.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>375.0</td>\n",
-       "      <td>183.0</td>\n",
-       "      <td>9.8020</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 397
+        },
+        "id": "Bug90EmtLzWk",
+        "outputId": "4ed690c0-e6e1-4d7c-d57d-42f39452aeb9"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "longitude               0\n",
+              "latitude                0\n",
+              "housing_median_age      0\n",
+              "total_rooms             0\n",
+              "total_bedrooms        207\n",
+              "population              0\n",
+              "households              0\n",
+              "median_income           0\n",
+              "median_house_value      0\n",
+              "ocean_proximity         0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>longitude</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>latitude</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_rooms</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <td>207</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>population</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>households</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>median_income</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>median_house_value</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ocean_proximity</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 482
+        },
+        "id": "6rbAepurtggX",
+        "outputId": "322c468f-26ff-4690-ef89-1e8e4d6484b7"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 6 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
-       "14452    -120.67     40.50                15.0       5343.0             NaN   \n",
-       "18217    -117.96     34.03                35.0       2093.0             NaN   \n",
-       "11889    -118.05     34.04                33.0       1348.0             NaN   \n",
-       "20325    -118.88     34.17                15.0       4260.0             NaN   \n",
-       "14360    -117.87     33.62                 8.0       1266.0             NaN   \n",
-       "\n",
-       "       population  households  median_income ocean_proximity  \n",
-       "14452      2503.0       902.0         3.5962          INLAND  \n",
-       "18217      1755.0       403.0         3.4115       <1H OCEAN  \n",
-       "11889      1098.0       257.0         4.2917       <1H OCEAN  \n",
-       "20325      1701.0       669.0         5.1033       <1H OCEAN  \n",
-       "14360       375.0       183.0         9.8020       <1H OCEAN  "
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "null_rows_idx = housing.isnull().any(axis=1)\n",
-    "housing.loc[null_rows_idx].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "repr_error": "Out of range float values are not JSON compliant: nan",
-       "type": "dataframe"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-fe37e6ad-b0ec-4b76-9a47-7585c364030b\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "      <th>ocean_proximity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe37e6ad-b0ec-4b76-9a47-7585c364030b')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-fe37e6ad-b0ec-4b76-9a47-7585c364030b button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-fe37e6ad-b0ec-4b76-9a47-7585c364030b');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# extra code – the next 5 lines define the default font sizes\n",
+        "plt.rc('font', size=14)\n",
+        "plt.rc('axes', labelsize=14, titlesize=14)\n",
+        "plt.rc('legend', fontsize=14)\n",
+        "plt.rc('xtick', labelsize=10)\n",
+        "plt.rc('ytick', labelsize=10)\n",
+        "\n",
+        "housing_full.hist(bins=50, figsize=(12, 8),\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "sns.pairplot(housing_full, hue = \"ocean_proximity\")"
       ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity]\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_option1 = housing.copy()\n",
-    "\n",
-    "housing_option1.dropna(subset=[\"total_bedrooms\"], inplace=True)  # option 1\n",
-    "\n",
-    "housing_option1.loc[null_rows_idx].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"housing_option2\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1801821893250222,\n        \"min\": -120.67,\n        \"max\": -117.87,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -117.96,\n          -117.87,\n          -118.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9298412926300292,\n        \"min\": 33.62,\n        \"max\": 40.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.03,\n          33.62,\n          34.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.049896265113654,\n        \"min\": 8.0,\n        \"max\": 35.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          35.0,\n          8.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1839.8735010864198,\n        \"min\": 1266.0,\n        \"max\": 5343.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2093.0,\n          1266.0,\n          1348.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 796.5141555553172,\n        \"min\": 375.0,\n        \"max\": 2503.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1755.0,\n          375.0,\n          1098.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 299.012039891373,\n        \"min\": 183.0,\n        \"max\": 902.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          403.0,\n          183.0,\n          257.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6351133776367193,\n        \"min\": 3.4115,\n        \"max\": 9.802,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.4115,\n          9.802,\n          4.2917\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"<1H OCEAN\",\n          \"INLAND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-c092e84e-18d2-47a6-9aa3-7693a41a8c12\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "      <th>ocean_proximity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>14452</th>\n",
-       "      <td>-120.67</td>\n",
-       "      <td>40.50</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>5343.0</td>\n",
-       "      <td>2503.0</td>\n",
-       "      <td>902.0</td>\n",
-       "      <td>3.5962</td>\n",
-       "      <td>INLAND</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18217</th>\n",
-       "      <td>-117.96</td>\n",
-       "      <td>34.03</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>2093.0</td>\n",
-       "      <td>1755.0</td>\n",
-       "      <td>403.0</td>\n",
-       "      <td>3.4115</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11889</th>\n",
-       "      <td>-118.05</td>\n",
-       "      <td>34.04</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>1348.0</td>\n",
-       "      <td>1098.0</td>\n",
-       "      <td>257.0</td>\n",
-       "      <td>4.2917</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20325</th>\n",
-       "      <td>-118.88</td>\n",
-       "      <td>34.17</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>4260.0</td>\n",
-       "      <td>1701.0</td>\n",
-       "      <td>669.0</td>\n",
-       "      <td>5.1033</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14360</th>\n",
-       "      <td>-117.87</td>\n",
-       "      <td>33.62</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1266.0</td>\n",
-       "      <td>375.0</td>\n",
-       "      <td>183.0</td>\n",
-       "      <td>9.8020</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c092e84e-18d2-47a6-9aa3-7693a41a8c12')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-c092e84e-18d2-47a6-9aa3-7693a41a8c12 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-c092e84e-18d2-47a6-9aa3-7693a41a8c12');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-79447d74-4e89-4b00-9e78-eebd9be4c60c\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79447d74-4e89-4b00-9e78-eebd9be4c60c')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-79447d74-4e89-4b00-9e78-eebd9be4c60c button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "metadata": {
+        "id": "2dUE1cwAh4Wm",
+        "outputId": "ebda81a7-21d0-4dbf-9f4f-bbd6743d8094",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        }
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<seaborn.axisgrid.PairGrid at 0x7bdcffdb4740>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 39
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2449x2250 with 90 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ytWmYOp8tggZ"
+      },
+      "source": [
+        "## Create a Test Set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "DcTHARTbtggd"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "def shuffle_and_split_data(data, test_ratio, rng):\n",
+        "    shuffled_indices = rng.permutation(len(data))\n",
+        "    test_set_size = int(len(data) * test_ratio)\n",
+        "    test_indices = shuffled_indices[:test_set_size]\n",
+        "    train_indices = shuffled_indices[test_set_size:]\n",
+        "    return data.iloc[train_indices], data.iloc[test_indices]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Y6yPCPC1tgge"
+      },
+      "source": [
+        "To ensure that this notebook's outputs remain the same every time we run it, we need to set the random seed:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YQCBbZpYtgge",
+        "outputId": "50af09fa-faa4-4691-9a2e-4de86b280303"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "16512"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
       ],
-      "text/plain": [
-       "       longitude  latitude  housing_median_age  total_rooms  population  \\\n",
-       "14452    -120.67     40.50                15.0       5343.0      2503.0   \n",
-       "18217    -117.96     34.03                35.0       2093.0      1755.0   \n",
-       "11889    -118.05     34.04                33.0       1348.0      1098.0   \n",
-       "20325    -118.88     34.17                15.0       4260.0      1701.0   \n",
-       "14360    -117.87     33.62                 8.0       1266.0       375.0   \n",
-       "\n",
-       "       households  median_income ocean_proximity  \n",
-       "14452       902.0         3.5962          INLAND  \n",
-       "18217       403.0         3.4115       <1H OCEAN  \n",
-       "11889       257.0         4.2917       <1H OCEAN  \n",
-       "20325       669.0         5.1033       <1H OCEAN  \n",
-       "14360       183.0         9.8020       <1H OCEAN  "
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_option2 = housing.copy()\n",
-    "\n",
-    "housing_option2.drop(\"total_bedrooms\", axis=1, inplace=True)  # option 2\n",
-    "\n",
-    "housing_option2.loc[null_rows_idx].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"housing_option3\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1801821893250222,\n        \"min\": -120.67,\n        \"max\": -117.87,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -117.96,\n          -117.87,\n          -118.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9298412926300292,\n        \"min\": 33.62,\n        \"max\": 40.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.03,\n          33.62,\n          34.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.049896265113654,\n        \"min\": 8.0,\n        \"max\": 35.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          35.0,\n          8.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1839.8735010864198,\n        \"min\": 1266.0,\n        \"max\": 5343.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2093.0,\n          1266.0,\n          1348.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 434.0,\n        \"max\": 434.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          434.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 796.5141555553172,\n        \"min\": 375.0,\n        \"max\": 2503.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1755.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 299.012039891373,\n        \"min\": 183.0,\n        \"max\": 902.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          403.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6351133776367193,\n        \"min\": 3.4115,\n        \"max\": 9.802,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.4115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"<1H OCEAN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-9f74d271-c2cb-4c14-8db2-30aabc5ae4c9\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "      <th>ocean_proximity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>14452</th>\n",
-       "      <td>-120.67</td>\n",
-       "      <td>40.50</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>5343.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>2503.0</td>\n",
-       "      <td>902.0</td>\n",
-       "      <td>3.5962</td>\n",
-       "      <td>INLAND</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18217</th>\n",
-       "      <td>-117.96</td>\n",
-       "      <td>34.03</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>2093.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>1755.0</td>\n",
-       "      <td>403.0</td>\n",
-       "      <td>3.4115</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11889</th>\n",
-       "      <td>-118.05</td>\n",
-       "      <td>34.04</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>1348.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>1098.0</td>\n",
-       "      <td>257.0</td>\n",
-       "      <td>4.2917</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20325</th>\n",
-       "      <td>-118.88</td>\n",
-       "      <td>34.17</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>4260.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>1701.0</td>\n",
-       "      <td>669.0</td>\n",
-       "      <td>5.1033</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14360</th>\n",
-       "      <td>-117.87</td>\n",
-       "      <td>33.62</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1266.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>375.0</td>\n",
-       "      <td>183.0</td>\n",
-       "      <td>9.8020</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f74d271-c2cb-4c14-8db2-30aabc5ae4c9')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-9f74d271-c2cb-4c14-8db2-30aabc5ae4c9 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-9f74d271-c2cb-4c14-8db2-30aabc5ae4c9');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-3ecd88a0-d644-4223-bcdd-f89f7bcd0526\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3ecd88a0-d644-4223-bcdd-f89f7bcd0526')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-3ecd88a0-d644-4223-bcdd-f89f7bcd0526 button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "rng = np.random.default_rng(seed=42)\n",
+        "train_set, test_set = shuffle_and_split_data(housing_full, 0.2, rng)\n",
+        "len(train_set)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nCMhktGWtggf",
+        "outputId": "b5ab972f-0574-4e72-ad35-315f5039c723"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "4128"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
       ],
-      "text/plain": [
-       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
-       "14452    -120.67     40.50                15.0       5343.0           434.0   \n",
-       "18217    -117.96     34.03                35.0       2093.0           434.0   \n",
-       "11889    -118.05     34.04                33.0       1348.0           434.0   \n",
-       "20325    -118.88     34.17                15.0       4260.0           434.0   \n",
-       "14360    -117.87     33.62                 8.0       1266.0           434.0   \n",
-       "\n",
-       "       population  households  median_income ocean_proximity  \n",
-       "14452      2503.0       902.0         3.5962          INLAND  \n",
-       "18217      1755.0       403.0         3.4115       <1H OCEAN  \n",
-       "11889      1098.0       257.0         4.2917       <1H OCEAN  \n",
-       "20325      1701.0       669.0         5.1033       <1H OCEAN  \n",
-       "14360       375.0       183.0         9.8020       <1H OCEAN  "
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_option3 = housing.copy()\n",
-    "\n",
-    "median = housing[\"total_bedrooms\"].median()\n",
-    "housing_option3[\"total_bedrooms\"] = housing_option3[\"total_bedrooms\"].fillna(median)  # option 3\n",
-    "\n",
-    "housing_option3.loc[null_rows_idx].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.impute import SimpleImputer\n",
-    "\n",
-    "imputer = SimpleImputer(strategy=\"median\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Separating out the numerical attributes to use the `\"median\"` strategy (as it cannot be calculated on text attributes like `ocean_proximity`):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing_num = housing.select_dtypes(include=[np.number])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SimpleImputer(strategy='median')"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "imputer.fit(housing_num)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-118.51  ,   34.26  ,   29.    , 2125.    ,  434.    , 1167.    ,\n",
-       "        408.    ,    3.5385])"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "imputer.statistics_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check that this is the same as manually computing the median of each attribute:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-118.51  ,   34.26  ,   29.    , 2125.    ,  434.    , 1167.    ,\n",
-       "        408.    ,    3.5385])"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_num.median().values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Transform the training set:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = imputer.transform(housing_num)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
-       "       'total_bedrooms', 'population', 'households', 'median_income'],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "imputer.feature_names_in_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
-    "                          index=housing_num.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"housing_tr\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1801821893250222,\n        \"min\": -120.67,\n        \"max\": -117.87,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -117.96,\n          -117.87,\n          -118.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9298412926300292,\n        \"min\": 33.62,\n        \"max\": 40.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.03,\n          33.62,\n          34.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.049896265113654,\n        \"min\": 8.0,\n        \"max\": 35.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          35.0,\n          8.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1839.8735010864198,\n        \"min\": 1266.0,\n        \"max\": 5343.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2093.0,\n          1266.0,\n          1348.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 434.0,\n        \"max\": 434.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          434.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 796.5141555553172,\n        \"min\": 375.0,\n        \"max\": 2503.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1755.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 299.012039891373,\n        \"min\": 183.0,\n        \"max\": 902.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          403.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6351133776367193,\n        \"min\": 3.4115,\n        \"max\": 9.802,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.4115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-bc8b6ed8-94a5-4fa2-a2bf-0aaa3899c1c2\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>14452</th>\n",
-       "      <td>-120.67</td>\n",
-       "      <td>40.50</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>5343.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>2503.0</td>\n",
-       "      <td>902.0</td>\n",
-       "      <td>3.5962</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18217</th>\n",
-       "      <td>-117.96</td>\n",
-       "      <td>34.03</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>2093.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>1755.0</td>\n",
-       "      <td>403.0</td>\n",
-       "      <td>3.4115</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11889</th>\n",
-       "      <td>-118.05</td>\n",
-       "      <td>34.04</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>1348.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>1098.0</td>\n",
-       "      <td>257.0</td>\n",
-       "      <td>4.2917</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20325</th>\n",
-       "      <td>-118.88</td>\n",
-       "      <td>34.17</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>4260.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>1701.0</td>\n",
-       "      <td>669.0</td>\n",
-       "      <td>5.1033</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14360</th>\n",
-       "      <td>-117.87</td>\n",
-       "      <td>33.62</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1266.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>375.0</td>\n",
-       "      <td>183.0</td>\n",
-       "      <td>9.8020</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc8b6ed8-94a5-4fa2-a2bf-0aaa3899c1c2')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-bc8b6ed8-94a5-4fa2-a2bf-0aaa3899c1c2 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-bc8b6ed8-94a5-4fa2-a2bf-0aaa3899c1c2');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-7948471e-0685-4a4d-9348-8ff3785e1c1f\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7948471e-0685-4a4d-9348-8ff3785e1c1f')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-7948471e-0685-4a4d-9348-8ff3785e1c1f button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "len(test_set)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qgNglqMwtggi"
+      },
+      "source": [
+        "Sadly, this won't guarantee that this notebook will output exactly the same results as in the book, since there are other possible sources of variation. The most important is the fact that algorithms get tweaked over time when libraries evolve. So please tolerate some minor differences: hopefully, most of the outputs should be the same, or at least in the right ballpark."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9SCbd2gVtggj"
+      },
+      "source": [
+        "Note: another source of randomness is the order of Python sets: it is based on Python's `hash()` function, which is randomly \"salted\" when Python starts up (this started in Python 3.3, to prevent some denial-of-service attacks). To remove this randomness, the solution is to set the `PYTHONHASHSEED` environment variable to `\"0\"` _before_ Python even starts up. Nothing will happen if you do it after that. Luckily, if you're running this notebook on Colab, the variable is already set for you."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xfWcZX82tggn"
+      },
+      "outputs": [],
+      "source": [
+        "from zlib import crc32\n",
+        "\n",
+        "def is_id_in_test_set(identifier, test_ratio):\n",
+        "    return crc32(np.int64(identifier)) < test_ratio * 2**32\n",
+        "\n",
+        "def split_data_with_id_hash(data, test_ratio, id_column):\n",
+        "    ids = data[id_column]\n",
+        "    in_test_set = ids.apply(lambda id_: is_id_in_test_set(id_, test_ratio))\n",
+        "    return data.loc[~in_test_set], data.loc[in_test_set]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ukJyfW7Stggo"
+      },
+      "outputs": [],
+      "source": [
+        "housing_with_id = housing_full.reset_index()  # adds an `index` column\n",
+        "train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"index\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "X6R3Sc3htggo"
+      },
+      "outputs": [],
+      "source": [
+        "housing_with_id[\"id\"] = (housing_full[\"longitude\"] * 1000\n",
+        "                         + housing_full[\"latitude\"])\n",
+        "train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"id\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "za2tUt5ttggp"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "train_set, test_set = train_test_split(housing_full, test_size=0.2,\n",
+        "                                       random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "buuAclJztggv",
+        "outputId": "4a6ef6b1-50cf-47f1-a169-a52bc6047be2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "np.int64(44)"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
-       "14452    -120.67     40.50                15.0       5343.0           434.0   \n",
-       "18217    -117.96     34.03                35.0       2093.0           434.0   \n",
-       "11889    -118.05     34.04                33.0       1348.0           434.0   \n",
-       "20325    -118.88     34.17                15.0       4260.0           434.0   \n",
-       "14360    -117.87     33.62                 8.0       1266.0           434.0   \n",
-       "\n",
-       "       population  households  median_income  \n",
-       "14452      2503.0       902.0         3.5962  \n",
-       "18217      1755.0       403.0         3.4115  \n",
-       "11889      1098.0       257.0         4.2917  \n",
-       "20325      1701.0       669.0         5.1033  \n",
-       "14360       375.0       183.0         9.8020  "
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_tr.loc[null_rows_idx].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "string"
-      },
-      "text/plain": [
-       "'median'"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "imputer.strategy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
-    "                          index=housing_num.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"housing_tr\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1801821893250222,\n        \"min\": -120.67,\n        \"max\": -117.87,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -117.96,\n          -117.87,\n          -118.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9298412926300292,\n        \"min\": 33.62,\n        \"max\": 40.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.03,\n          33.62,\n          34.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.049896265113654,\n        \"min\": 8.0,\n        \"max\": 35.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          35.0,\n          8.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1839.8735010864198,\n        \"min\": 1266.0,\n        \"max\": 5343.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2093.0,\n          1266.0,\n          1348.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 434.0,\n        \"max\": 434.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          434.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 796.5141555553172,\n        \"min\": 375.0,\n        \"max\": 2503.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1755.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 299.012039891373,\n        \"min\": 183.0,\n        \"max\": 902.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          403.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6351133776367193,\n        \"min\": 3.4115,\n        \"max\": 9.802,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.4115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-d81b3678-b335-4baa-8d0b-2f5228b1f170\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>14452</th>\n",
-       "      <td>-120.67</td>\n",
-       "      <td>40.50</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>5343.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>2503.0</td>\n",
-       "      <td>902.0</td>\n",
-       "      <td>3.5962</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18217</th>\n",
-       "      <td>-117.96</td>\n",
-       "      <td>34.03</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>2093.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>1755.0</td>\n",
-       "      <td>403.0</td>\n",
-       "      <td>3.4115</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11889</th>\n",
-       "      <td>-118.05</td>\n",
-       "      <td>34.04</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>1348.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>1098.0</td>\n",
-       "      <td>257.0</td>\n",
-       "      <td>4.2917</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20325</th>\n",
-       "      <td>-118.88</td>\n",
-       "      <td>34.17</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>4260.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>1701.0</td>\n",
-       "      <td>669.0</td>\n",
-       "      <td>5.1033</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14360</th>\n",
-       "      <td>-117.87</td>\n",
-       "      <td>33.62</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1266.0</td>\n",
-       "      <td>434.0</td>\n",
-       "      <td>375.0</td>\n",
-       "      <td>183.0</td>\n",
-       "      <td>9.8020</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d81b3678-b335-4baa-8d0b-2f5228b1f170')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-d81b3678-b335-4baa-8d0b-2f5228b1f170 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-d81b3678-b335-4baa-8d0b-2f5228b1f170');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-1c171308-fe30-4430-8d52-f5aea8bd7bc6\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1c171308-fe30-4430-8d52-f5aea8bd7bc6')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-1c171308-fe30-4430-8d52-f5aea8bd7bc6 button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "test_set[\"total_bedrooms\"].isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UINYdcoAtggz"
+      },
+      "source": [
+        "To find the probability that a random sample of 1,000 people contains less than 49% female or more than 54% female when the population's female ratio is 51.6%, we use the [binomial distribution](https://en.wikipedia.org/wiki/Binomial_distribution). The `cdf()` method of the binomial distribution gives us the probability that the number of females will be equal or less than the given value."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RYqwb3QKtgg0",
+        "outputId": "166b7824-205a-40fd-c2fe-7f6acc5a3d5f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0.10727422667455677\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
-       "14452    -120.67     40.50                15.0       5343.0           434.0   \n",
-       "18217    -117.96     34.03                35.0       2093.0           434.0   \n",
-       "11889    -118.05     34.04                33.0       1348.0           434.0   \n",
-       "20325    -118.88     34.17                15.0       4260.0           434.0   \n",
-       "14360    -117.87     33.62                 8.0       1266.0           434.0   \n",
-       "\n",
-       "       population  households  median_income  \n",
-       "14452      2503.0       902.0         3.5962  \n",
-       "18217      1755.0       403.0         3.4115  \n",
-       "11889      1098.0       257.0         4.2917  \n",
-       "20325      1701.0       669.0         5.1033  \n",
-       "14360       375.0       183.0         9.8020  "
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_tr.loc[null_rows_idx].head()  # not shown in the book"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#from sklearn import set_config\n",
-    "#\n",
-    "# set_config(transform_output=\"pandas\")  # scikit-learn >= 1.2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's drop some outliers:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import IsolationForest\n",
-    "\n",
-    "isolation_forest = IsolationForest(random_state=42)\n",
-    "outlier_pred = isolation_forest.fit_predict(X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1,  1,  1, ...,  1,  1,  1])"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outlier_pred"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you wanted to drop outliers, you would run the following code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#housing = housing.iloc[outlier_pred == 1]\n",
-    "#housing_labels = housing_labels.iloc[outlier_pred == 1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Handling Text and Categorical Attributes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's preprocess the categorical input feature, `ocean_proximity`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"housing_cat\",\n  \"rows\": 16512,\n  \"fields\": [\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"<1H OCEAN\",\n          \"ISLAND\",\n          \"INLAND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe",
-       "variable_name": "housing_cat"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-98f67d70-735c-4c21-9aa8-bae05ce3f4e8\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ocean_proximity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>13096</th>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14973</th>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3785</th>\n",
-       "      <td>INLAND</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14689</th>\n",
-       "      <td>INLAND</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20507</th>\n",
-       "      <td>NEAR OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1286</th>\n",
-       "      <td>INLAND</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18078</th>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4396</th>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98f67d70-735c-4c21-9aa8-bae05ce3f4e8')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-98f67d70-735c-4c21-9aa8-bae05ce3f4e8 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-98f67d70-735c-4c21-9aa8-bae05ce3f4e8');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-79e24e6e-be29-4116-a8b4-c11ef61a77d2\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79e24e6e-be29-4116-a8b4-c11ef61a77d2')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-79e24e6e-be29-4116-a8b4-c11ef61a77d2 button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "# extra code – shows how to compute the 10.7% proba of getting a bad sample\n",
+        "\n",
+        "from scipy.stats import binom\n",
+        "\n",
+        "sample_size = 1000\n",
+        "ratio_female = 0.516\n",
+        "proba_too_small = binom(sample_size, ratio_female).cdf(490 - 1)\n",
+        "proba_too_large = 1 - binom(sample_size, ratio_female).cdf(540)\n",
+        "print(proba_too_small + proba_too_large)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t7SK0Xvqtgg2"
+      },
+      "source": [
+        "If you prefer simulations over maths, here's how you could get roughly the same result:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kTnLyUMStgg2",
+        "outputId": "96c02d1e-0845-496d-aa9a-225b6ecf91f4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "np.float64(0.1077)"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "      ocean_proximity\n",
-       "13096        NEAR BAY\n",
-       "14973       <1H OCEAN\n",
-       "3785           INLAND\n",
-       "14689          INLAND\n",
-       "20507      NEAR OCEAN\n",
-       "1286           INLAND\n",
-       "18078       <1H OCEAN\n",
-       "4396         NEAR BAY"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_cat = housing[[\"ocean_proximity\"]]\n",
-    "housing_cat.head(8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import OrdinalEncoder\n",
-    "\n",
-    "ordinal_encoder = OrdinalEncoder()\n",
-    "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[3.],\n",
-       "       [0.],\n",
-       "       [1.],\n",
-       "       [1.],\n",
-       "       [4.],\n",
-       "       [1.],\n",
-       "       [0.],\n",
-       "       [3.]])"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_cat_encoded[:8]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
-       "       dtype=object)]"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ordinal_encoder.categories_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import OneHotEncoder\n",
-    "\n",
-    "cat_encoder = OneHotEncoder()\n",
-    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Compressed Sparse Row sparse matrix of dtype 'float64'\n",
-       "\twith 16512 stored elements and shape (16512, 5)>"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_cat_1hot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By default, the `OneHotEncoder` class returns a sparse array, but we can convert it to a dense array if needed by calling the `toarray()` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0., 0., 0., 1., 0.],\n",
-       "       [1., 0., 0., 0., 0.],\n",
-       "       [0., 1., 0., 0., 0.],\n",
-       "       ...,\n",
-       "       [0., 0., 0., 0., 1.],\n",
-       "       [1., 0., 0., 0., 0.],\n",
-       "       [0., 0., 0., 0., 1.]])"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_cat_1hot.toarray()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Alternatively, you can set `sparse_output=False` when creating the `OneHotEncoder` (note: the `sparse` hyperparameter was renamned to `sparse_output` in Scikit-Learn 1.2):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0., 0., 0., 1., 0.],\n",
-       "       [1., 0., 0., 0., 0.],\n",
-       "       [0., 1., 0., 0., 0.],\n",
-       "       ...,\n",
-       "       [0., 0., 0., 0., 1.],\n",
-       "       [1., 0., 0., 0., 0.],\n",
-       "       [0., 0., 0., 0., 1.]])"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat_encoder = OneHotEncoder(sparse_output=False)\n",
-    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
-    "housing_cat_1hot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
-       "       dtype=object)]"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat_encoder.categories_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"ocean_proximity_INLAND\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR BAY\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-e51e8d85-bc0d-4627-9ad4-23676c8accca\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ocean_proximity_INLAND</th>\n",
-       "      <th>ocean_proximity_NEAR BAY</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e51e8d85-bc0d-4627-9ad4-23676c8accca')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-e51e8d85-bc0d-4627-9ad4-23676c8accca button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-e51e8d85-bc0d-4627-9ad4-23676c8accca');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-f669e8fe-3d15-40e3-8d38-e7322a5d30a5\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f669e8fe-3d15-40e3-8d38-e7322a5d30a5')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-f669e8fe-3d15-40e3-8d38-e7322a5d30a5 button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "# extra code – shows another way to estimate the probability of bad sample\n",
+        "\n",
+        "rng = np.random.default_rng(seed=42)\n",
+        "samples = (rng.random((100_000, sample_size)) < ratio_female).sum(axis=1)\n",
+        "((samples < 490) | (samples > 540)).mean()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ytS8xUDhtgg4"
+      },
+      "outputs": [],
+      "source": [
+        "housing_full[\"income_cat\"] = pd.cut(housing_full[\"median_income\"],\n",
+        "                                    bins=[0., 1.5, 3.0, 4.5, 6., np.inf],\n",
+        "                                    labels=[1, 2, 3, 4, 5])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bRlGGhu4tgg5",
+        "outputId": "f5796718-9a22-442a-cc17-80d24781ccf4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "   ocean_proximity_INLAND  ocean_proximity_NEAR BAY\n",
-       "0                    True                     False\n",
-       "1                   False                      True"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_test = pd.DataFrame({\"ocean_proximity\": [\"INLAND\", \"NEAR BAY\"]})\n",
-    "pd.get_dummies(df_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0., 1., 0., 0., 0.],\n",
-       "       [0., 0., 0., 1., 0.]])"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat_encoder.transform(df_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"ocean_proximity_<2H OCEAN\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_ISLAND\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-e900d25a-fc07-4211-96de-6ebd75138751\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ocean_proximity_&lt;2H OCEAN</th>\n",
-       "      <th>ocean_proximity_ISLAND</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e900d25a-fc07-4211-96de-6ebd75138751')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-e900d25a-fc07-4211-96de-6ebd75138751 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-e900d25a-fc07-4211-96de-6ebd75138751');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-ba225230-b686-4c24-bb39-0e4af56a87ea\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ba225230-b686-4c24-bb39-0e4af56a87ea')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-ba225230-b686-4c24-bb39-0e4af56a87ea button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "cat_counts = housing_full[\"income_cat\"].value_counts().sort_index()\n",
+        "cat_counts.plot.bar(rot=0, grid=True)\n",
+        "plt.xlabel(\"Income category\")\n",
+        "plt.ylabel(\"Number of districts\")\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DTZn7N-ntgg6"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import StratifiedShuffleSplit\n",
+        "\n",
+        "splitter = StratifiedShuffleSplit(n_splits=10, test_size=0.2, random_state=42)\n",
+        "strat_splits = []\n",
+        "for train_index, test_index in splitter.split(housing_full,\n",
+        "                                              housing_full[\"income_cat\"]):\n",
+        "    strat_train_set_n = housing_full.iloc[train_index]\n",
+        "    strat_test_set_n = housing_full.iloc[test_index]\n",
+        "    strat_splits.append([strat_train_set_n, strat_test_set_n])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "o1jLfi9Ztgg6"
+      },
+      "outputs": [],
+      "source": [
+        "strat_train_set, strat_test_set = strat_splits[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_fYwjg_Ltgg8"
+      },
+      "source": [
+        "It's much shorter to get a single stratified split:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MLRyVCxgtghN"
+      },
+      "outputs": [],
+      "source": [
+        "strat_train_set, strat_test_set = train_test_split(\n",
+        "    housing_full, test_size=0.2, stratify=housing_full[\"income_cat\"],\n",
+        "    random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VWtGE7ZutghS",
+        "outputId": "a71a7051-f06e-4185-f5f9-97c0ec540c59"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>income_cat</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.350533</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.318798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.176357</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>0.114341</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.039971</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ],
+            "text/plain": [
+              "income_cat\n",
+              "3    0.350533\n",
+              "2    0.318798\n",
+              "4    0.176357\n",
+              "5    0.114341\n",
+              "1    0.039971\n",
+              "Name: count, dtype: float64"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "   ocean_proximity_<2H OCEAN  ocean_proximity_ISLAND\n",
-       "0                       True                   False\n",
-       "1                      False                    True"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_test_unknown = pd.DataFrame({\"ocean_proximity\": [\"<2H OCEAN\", \"ISLAND\"]})\n",
-    "pd.get_dummies(df_test_unknown)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0., 0., 0., 0., 0.],\n",
-       "       [0., 0., 1., 0., 0.]])"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat_encoder.handle_unknown = \"ignore\"\n",
-    "cat_encoder.transform(df_test_unknown)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['ocean_proximity'], dtype=object)"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat_encoder.feature_names_in_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['ocean_proximity_<1H OCEAN', 'ocean_proximity_INLAND',\n",
-       "       'ocean_proximity_ISLAND', 'ocean_proximity_NEAR BAY',\n",
-       "       'ocean_proximity_NEAR OCEAN'], dtype=object)"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat_encoder.get_feature_names_out()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_output = pd.DataFrame(cat_encoder.transform(df_test_unknown),\n",
-    "                         columns=cat_encoder.get_feature_names_out(),\n",
-    "                         index=df_test_unknown.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"df_output\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"ocean_proximity_<1H OCEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_INLAND\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_ISLAND\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7071067811865476,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR BAY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR OCEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe",
-       "variable_name": "df_output"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-b48a6f0c-6e57-49b4-adb1-22d06d4366cf\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ocean_proximity_&lt;1H OCEAN</th>\n",
-       "      <th>ocean_proximity_INLAND</th>\n",
-       "      <th>ocean_proximity_ISLAND</th>\n",
-       "      <th>ocean_proximity_NEAR BAY</th>\n",
-       "      <th>ocean_proximity_NEAR OCEAN</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b48a6f0c-6e57-49b4-adb1-22d06d4366cf')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-b48a6f0c-6e57-49b4-adb1-22d06d4366cf button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-b48a6f0c-6e57-49b4-adb1-22d06d4366cf');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-89cbd3ac-51a0-45e6-9984-158208da42f2\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-89cbd3ac-51a0-45e6-9984-158208da42f2')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-89cbd3ac-51a0-45e6-9984-158208da42f2 button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "  <div id=\"id_d916aad7-0e99-4ec8-8543-46c62722f5e3\">\n",
-       "    <style>\n",
-       "      .colab-df-generate {\n",
-       "        background-color: #E8F0FE;\n",
-       "        border: none;\n",
-       "        border-radius: 50%;\n",
-       "        cursor: pointer;\n",
-       "        display: none;\n",
-       "        fill: #1967D2;\n",
-       "        height: 32px;\n",
-       "        padding: 0 0 0 0;\n",
-       "        width: 32px;\n",
-       "      }\n",
-       "\n",
-       "      .colab-df-generate:hover {\n",
-       "        background-color: #E2EBFA;\n",
-       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "        fill: #174EA6;\n",
-       "      }\n",
-       "\n",
-       "      [theme=dark] .colab-df-generate {\n",
-       "        background-color: #3B4455;\n",
-       "        fill: #D2E3FC;\n",
-       "      }\n",
-       "\n",
-       "      [theme=dark] .colab-df-generate:hover {\n",
-       "        background-color: #434B5C;\n",
-       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "        fill: #FFFFFF;\n",
-       "      }\n",
-       "    </style>\n",
-       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_output')\"\n",
-       "            title=\"Generate code using this dataframe.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "       width=\"24px\">\n",
-       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "    <script>\n",
-       "      (() => {\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#id_d916aad7-0e99-4ec8-8543-46c62722f5e3 button.colab-df-generate');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      buttonEl.onclick = () => {\n",
-       "        google.colab.notebook.generateWithVariable('df_output');\n",
-       "      }\n",
-       "      })();\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4EWnPtRmtghT",
+        "outputId": "5f4953c1-ea40-442c-c6be-674808a468fc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"(compare_props * 100)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Income Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Overall %\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.261493882666464,\n        \"min\": 3.98,\n        \"max\": 35.06,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          31.88,\n          11.44,\n          35.06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stratified %\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.253786251483007,\n        \"min\": 4.0,\n        \"max\": 35.05,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          31.88,\n          11.43,\n          35.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Random %\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.645669219143763,\n        \"min\": 4.24,\n        \"max\": 34.52,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          30.74,\n          12.09,\n          34.52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Strat. Error %\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.174441967427566,\n        \"min\": -0.08,\n        \"max\": 0.36,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.02,\n          -0.08,\n          -0.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rand. Error %\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.5322157936267775,\n        \"min\": -3.59,\n        \"max\": 6.45,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -3.59,\n          5.63,\n          -1.53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fd09dffd-80fe-497f-a1c0-24bfb8ff9d4b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Overall %</th>\n",
+              "      <th>Stratified %</th>\n",
+              "      <th>Random %</th>\n",
+              "      <th>Strat. Error %</th>\n",
+              "      <th>Rand. Error %</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Income Category</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3.98</td>\n",
+              "      <td>4.00</td>\n",
+              "      <td>4.24</td>\n",
+              "      <td>0.36</td>\n",
+              "      <td>6.45</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>31.88</td>\n",
+              "      <td>31.88</td>\n",
+              "      <td>30.74</td>\n",
+              "      <td>-0.02</td>\n",
+              "      <td>-3.59</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>35.06</td>\n",
+              "      <td>35.05</td>\n",
+              "      <td>34.52</td>\n",
+              "      <td>-0.01</td>\n",
+              "      <td>-1.53</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>17.63</td>\n",
+              "      <td>17.64</td>\n",
+              "      <td>18.41</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>4.42</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>11.44</td>\n",
+              "      <td>11.43</td>\n",
+              "      <td>12.09</td>\n",
+              "      <td>-0.08</td>\n",
+              "      <td>5.63</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd09dffd-80fe-497f-a1c0-24bfb8ff9d4b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fd09dffd-80fe-497f-a1c0-24bfb8ff9d4b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fd09dffd-80fe-497f-a1c0-24bfb8ff9d4b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-4a029694-c41d-4bf9-8d4a-a8bf24de2fb0\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a029694-c41d-4bf9-8d4a-a8bf24de2fb0')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-4a029694-c41d-4bf9-8d4a-a8bf24de2fb0 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                 Overall %  Stratified %  Random %  Strat. Error %  \\\n",
+              "Income Category                                                      \n",
+              "1                     3.98          4.00      4.24            0.36   \n",
+              "2                    31.88         31.88     30.74           -0.02   \n",
+              "3                    35.06         35.05     34.52           -0.01   \n",
+              "4                    17.63         17.64     18.41            0.03   \n",
+              "5                    11.44         11.43     12.09           -0.08   \n",
+              "\n",
+              "                 Rand. Error %  \n",
+              "Income Category                 \n",
+              "1                         6.45  \n",
+              "2                        -3.59  \n",
+              "3                        -1.53  \n",
+              "4                         4.42  \n",
+              "5                         5.63  "
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "   ocean_proximity_<1H OCEAN  ocean_proximity_INLAND  ocean_proximity_ISLAND  \\\n",
-       "0                        0.0                     0.0                     0.0   \n",
-       "1                        0.0                     0.0                     1.0   \n",
-       "\n",
-       "   ocean_proximity_NEAR BAY  ocean_proximity_NEAR OCEAN  \n",
-       "0                       0.0                         0.0  \n",
-       "1                       0.0                         0.0  "
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Feature Scaling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import MinMaxScaler\n",
-    "\n",
-    "min_max_scaler = MinMaxScaler(feature_range=(-1, 1))\n",
-    "housing_num_min_max_scaled = min_max_scaler.fit_transform(housing_num)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler\n",
-    "\n",
-    "std_scaler = StandardScaler()\n",
-    "housing_num_std_scaled = std_scaler.fit_transform(housing_num)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 800x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# extra code – this cell generates Figure 2–17\n",
-    "fig, axs = plt.subplots(1, 2, figsize=(8, 3), sharey=True)\n",
-    "housing[\"population\"].hist(ax=axs[0], bins=50)\n",
-    "housing[\"population\"].apply(np.log).hist(ax=axs[1], bins=50)\n",
-    "axs[0].set_xlabel(\"Population\")\n",
-    "axs[1].set_xlabel(\"Log of population\")\n",
-    "axs[0].set_ylabel(\"Number of districts\")\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What if we replace each value with its percentile?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# extra code – just shows that we get a uniform distribution\n",
-    "percentiles = [np.percentile(housing[\"median_income\"], p)\n",
-    "               for p in range(1, 100)]\n",
-    "flattened_median_income = pd.cut(housing[\"median_income\"],\n",
-    "                                 bins=[-np.inf] + percentiles + [np.inf],\n",
-    "                                 labels=range(1, 100 + 1))\n",
-    "flattened_median_income.hist(bins=50)\n",
-    "plt.xlabel(\"Median income percentile\")\n",
-    "plt.ylabel(\"Number of districts\")\n",
-    "plt.show()\n",
-    "# Note: incomes below the 1st percentile are labeled 1, and incomes above the\n",
-    "# 99th percentile are labeled 100. This is why the distribution below ranges\n",
-    "# from 1 to 100 (not 0 to 100)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics.pairwise import rbf_kernel\n",
-    "\n",
-    "age_simil_35 = rbf_kernel(housing[[\"housing_median_age\"]], [[35]], gamma=0.1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# extra code – this cell generates Figure 2–18\n",
-    "\n",
-    "ages = np.linspace(housing[\"housing_median_age\"].min(),\n",
-    "                   housing[\"housing_median_age\"].max(),\n",
-    "                   500).reshape(-1, 1)\n",
-    "gamma1 = 0.1\n",
-    "gamma2 = 0.03\n",
-    "rbf1 = rbf_kernel(ages, [[35]], gamma=gamma1)\n",
-    "rbf2 = rbf_kernel(ages, [[35]], gamma=gamma2)\n",
-    "\n",
-    "fig, ax1 = plt.subplots()\n",
-    "\n",
-    "ax1.set_xlabel(\"Housing median age\")\n",
-    "ax1.set_ylabel(\"Number of districts\")\n",
-    "ax1.hist(housing[\"housing_median_age\"], bins=50)\n",
-    "\n",
-    "ax2 = ax1.twinx()  # create a twin axis that shares the same x-axis\n",
-    "color = \"blue\"\n",
-    "ax2.plot(ages, rbf1, color=color, label=\"gamma = 0.10\")\n",
-    "ax2.plot(ages, rbf2, color=color, label=\"gamma = 0.03\", linestyle=\"--\")\n",
-    "ax2.tick_params(axis='y', labelcolor=color)\n",
-    "ax2.set_ylabel(\"Age similarity\", color=color)\n",
-    "\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "target_scaler = StandardScaler()\n",
-    "scaled_labels = target_scaler.fit_transform(housing_labels.to_frame())\n",
-    "\n",
-    "model = LinearRegression()\n",
-    "model.fit(housing[[\"median_income\"]], scaled_labels)\n",
-    "some_new_data = housing[[\"median_income\"]].iloc[:5]  # pretend this is new data\n",
-    "\n",
-    "scaled_predictions = model.predict(some_new_data)\n",
-    "predictions = target_scaler.inverse_transform(scaled_predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[131997.15275877],\n",
-       "       [299359.35844434],\n",
-       "       [146023.37185694],\n",
-       "       [138840.33653057],\n",
-       "       [192016.61557639]])"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.compose import TransformedTargetRegressor\n",
-    "\n",
-    "model = TransformedTargetRegressor(LinearRegression(),\n",
-    "                                   transformer=StandardScaler())\n",
-    "model.fit(housing[[\"median_income\"]], housing_labels)\n",
-    "predictions = model.predict(some_new_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([131997.15275877, 299359.35844434, 146023.37185694, 138840.33653057,\n",
-       "       192016.61557639])"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Custom Transformers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To create simple transformers:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import FunctionTransformer\n",
-    "\n",
-    "log_transformer = FunctionTransformer(np.log, inverse_func=np.exp)\n",
-    "log_pop = log_transformer.transform(housing[[\"population\"]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rbf_transformer = FunctionTransformer(rbf_kernel,\n",
-    "                                      kw_args=dict(Y=[[35.]], gamma=0.1))\n",
-    "age_simil_35 = rbf_transformer.transform(housing[[\"housing_median_age\"]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2.81118530e-13],\n",
-       "       [8.20849986e-02],\n",
-       "       [6.70320046e-01],\n",
-       "       ...,\n",
-       "       [9.55316054e-22],\n",
-       "       [6.70320046e-01],\n",
-       "       [3.03539138e-04]])"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "age_simil_35"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sf_coords = 37.7749, -122.41\n",
-    "sf_transformer = FunctionTransformer(rbf_kernel,\n",
-    "                                     kw_args=dict(Y=[sf_coords], gamma=0.1))\n",
-    "sf_simil = sf_transformer.transform(housing[[\"latitude\", \"longitude\"]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.999927  ],\n",
-       "       [0.05258419],\n",
-       "       [0.94864161],\n",
-       "       ...,\n",
-       "       [0.00388525],\n",
-       "       [0.05038518],\n",
-       "       [0.99868067]])"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sf_simil"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.5 ],\n",
-       "       [0.75]])"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ratio_transformer = FunctionTransformer(lambda X: X[:, [0]] / X[:, [1]])\n",
-    "ratio_transformer.transform(np.array([[1., 2.], [3., 4.]]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.base import BaseEstimator, TransformerMixin\n",
-    "from sklearn.utils.validation import check_array, check_is_fitted\n",
-    "\n",
-    "class StandardScalerClone(BaseEstimator, TransformerMixin):\n",
-    "    def __init__(self, with_mean=True):  # no *args or **kwargs!\n",
-    "        self.with_mean = with_mean\n",
-    "\n",
-    "    def fit(self, X, y=None):  # y is required even though we don't use it\n",
-    "        X = check_array(X)  # checks that X is an array with finite float values\n",
-    "        self.mean_ = X.mean(axis=0)\n",
-    "        self.scale_ = X.std(axis=0)\n",
-    "        self.n_features_in_ = X.shape[1]  # every estimator stores this in fit()\n",
-    "        return self  # always return self!\n",
-    "\n",
-    "    def transform(self, X):\n",
-    "        check_is_fitted(self)  # looks for learned attributes (with trailing _)\n",
-    "        X = check_array(X)\n",
-    "        assert self.n_features_in_ == X.shape[1]\n",
-    "        if self.with_mean:\n",
-    "            X = X - self.mean_\n",
-    "        return X / self.scale_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.cluster import KMeans\n",
-    "\n",
-    "class ClusterSimilarity(BaseEstimator, TransformerMixin):\n",
-    "    def __init__(self, n_clusters=10, gamma=1.0, random_state=None):\n",
-    "        self.n_clusters = n_clusters\n",
-    "        self.gamma = gamma\n",
-    "        self.random_state = random_state\n",
-    "\n",
-    "    def fit(self, X, y=None, sample_weight=None):\n",
-    "        self.kmeans_ = KMeans(self.n_clusters, random_state=self.random_state)\n",
-    "        self.kmeans_.fit(X, sample_weight=sample_weight)\n",
-    "        return self  # always return self!\n",
-    "\n",
-    "    def transform(self, X):\n",
-    "        return rbf_kernel(X, self.kmeans_.cluster_centers_, gamma=self.gamma)\n",
-    "\n",
-    "    def get_feature_names_out(self, names=None):\n",
-    "        return [f\"Cluster {i} similarity\" for i in range(self.n_clusters)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's cluster the districts based only on the latitude and longitude:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\n",
-    "similarities = cluster_simil.fit_transform(housing[[\"latitude\", \"longitude\"]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.  , 0.98, 0.  , 0.  , 0.  , 0.  , 0.13, 0.55, 0.  , 0.56],\n",
-       "       [0.64, 0.  , 0.11, 0.04, 0.  , 0.  , 0.  , 0.  , 0.99, 0.  ],\n",
-       "       [0.  , 0.65, 0.  , 0.  , 0.01, 0.  , 0.49, 0.59, 0.  , 0.28]])"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "similarities[:3].round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1000x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# extra code – this cell generates Figure 2–19\n",
-    "\n",
-    "housing_renamed = housing.rename(columns={\n",
-    "    \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n",
-    "    \"population\": \"Population\",\n",
-    "    \"median_house_value\": \"Median house value (ᴜsᴅ)\"})\n",
-    "housing_renamed[\"Max cluster similarity\"] = similarities.max(axis=1)\n",
-    "\n",
-    "housing_renamed.plot(kind=\"scatter\", x=\"Longitude\", y=\"Latitude\", grid=True,\n",
-    "                     s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n",
-    "                     c=\"Max cluster similarity\",\n",
-    "                     cmap=\"jet\", colorbar=True,\n",
-    "                     legend=True, sharex=False, figsize=(10, 7))\n",
-    "plt.plot(cluster_simil.kmeans_.cluster_centers_[:, 1],\n",
-    "         cluster_simil.kmeans_.cluster_centers_[:, 0],\n",
-    "         linestyle=\"\", color=\"black\", marker=\"X\", markersize=20,\n",
-    "         label=\"Cluster centers\")\n",
-    "plt.legend(loc=\"upper right\")\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Transformation Pipelines"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's build a pipeline to preprocess the numerical attributes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.pipeline import Pipeline\n",
-    "\n",
-    "num_pipeline = Pipeline([\n",
-    "    (\"impute\", SimpleImputer(strategy=\"median\")),\n",
-    "    (\"standardize\", StandardScaler()),\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.pipeline import make_pipeline\n",
-    "\n",
-    "num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
-       "                ('standardscaler', StandardScaler())])"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn import set_config\n",
-    "\n",
-    "set_config(display='diagram')\n",
-    "\n",
-    "num_pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-1.42,  1.01,  1.86,  0.31,  1.37,  0.14,  1.39, -0.94],\n",
-       "       [ 0.6 , -0.7 ,  0.91, -0.31, -0.44, -0.69, -0.37,  1.17]])"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_num_prepared = num_pipeline.fit_transform(housing_num)\n",
-    "housing_num_prepared[:2].round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_housing_num_prepared = pd.DataFrame(\n",
-    "    housing_num_prepared, columns=num_pipeline.get_feature_names_out(),\n",
-    "    index=housing_num.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"df_housing_num_prepared\",\n  \"rows\": 16512,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.00003028238323,\n        \"min\": -2.3877646847945297,\n        \"max\": 2.5408465499717225,\n        \"num_unique_values\": 817,\n        \"samples\": [\n          -1.5280069440442305,\n          -0.6282604711660115,\n          1.361178952198056\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383244,\n        \"min\": -1.4474519456998403,\n        \"max\": 2.959013528420613,\n        \"num_unique_values\": 835,\n        \"samples\": [\n          2.424612396452814,\n          2.3496087288082084,\n          1.5808211354510233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383234,\n        \"min\": -2.1912096612263676,\n        \"max\": 1.8611187475164104,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          1.146001969502979,\n          1.3049168090615193,\n          1.7816613277371403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000302823832317,\n        \"min\": -1.2069271002738067,\n        \"max\": 16.785758645406965,\n        \"num_unique_values\": 5473,\n        \"samples\": [\n          0.4066394580269817,\n          0.45972332664946935,\n          -0.1278601846546181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000302823832243,\n        \"min\": -1.2727767935135679,\n        \"max\": 13.446810448110993,\n        \"num_unique_values\": 1827,\n        \"samples\": [\n          -0.25575264684632076,\n          1.2567448020434313,\n          4.115791703863245\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383224,\n        \"min\": -1.2993815273147138,\n        \"max\": 13.591521979866151,\n        \"num_unique_values\": 3640,\n        \"samples\": [\n          -0.21330102982740873,\n          0.8965289150397702,\n          0.026933764822853052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383228,\n        \"min\": -1.3033426245281265,\n        \"max\": 12.688929356290272,\n        \"num_unique_values\": 1712,\n        \"samples\": [\n          -0.031317898999181186,\n          0.8489127632251608,\n          1.6847400982749814\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000302823832292,\n        \"min\": -1.7815774618066682,\n        \"max\": 5.882868113005668,\n        \"num_unique_values\": 10930,\n        \"samples\": [\n          -0.11984286517386168,\n          -0.4482466137863569,\n          0.9520547073535472\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe",
-       "variable_name": "df_housing_num_prepared"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-b4a7d327-e438-49bc-bbe0-2295aaaf86ca\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>13096</th>\n",
-       "      <td>-1.423037</td>\n",
-       "      <td>1.013606</td>\n",
-       "      <td>1.861119</td>\n",
-       "      <td>0.311912</td>\n",
-       "      <td>1.368167</td>\n",
-       "      <td>0.137460</td>\n",
-       "      <td>1.394812</td>\n",
-       "      <td>-0.936491</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14973</th>\n",
-       "      <td>0.596394</td>\n",
-       "      <td>-0.702103</td>\n",
-       "      <td>0.907630</td>\n",
-       "      <td>-0.308620</td>\n",
-       "      <td>-0.435925</td>\n",
-       "      <td>-0.693771</td>\n",
-       "      <td>-0.373485</td>\n",
-       "      <td>1.171942</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4a7d327-e438-49bc-bbe0-2295aaaf86ca')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-b4a7d327-e438-49bc-bbe0-2295aaaf86ca button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-b4a7d327-e438-49bc-bbe0-2295aaaf86ca');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-691de7e9-26d8-4da9-b8ed-38663f3faacc\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-691de7e9-26d8-4da9-b8ed-38663f3faacc')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-691de7e9-26d8-4da9-b8ed-38663f3faacc button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "# extra code – computes the data for Figure 2–10\n",
+        "\n",
+        "def income_cat_proportions(data):\n",
+        "    return data[\"income_cat\"].value_counts() / len(data)\n",
+        "\n",
+        "train_set, test_set = train_test_split(housing_full, test_size=0.2,\n",
+        "                                       random_state=42)\n",
+        "\n",
+        "compare_props = pd.DataFrame({\n",
+        "    \"Overall %\": income_cat_proportions(housing_full),\n",
+        "    \"Stratified %\": income_cat_proportions(strat_test_set),\n",
+        "    \"Random %\": income_cat_proportions(test_set),\n",
+        "}).sort_index()\n",
+        "compare_props.index.name = \"Income Category\"\n",
+        "compare_props[\"Strat. Error %\"] = (compare_props[\"Stratified %\"] /\n",
+        "                                   compare_props[\"Overall %\"] - 1)\n",
+        "compare_props[\"Rand. Error %\"] = (compare_props[\"Random %\"] /\n",
+        "                                  compare_props[\"Overall %\"] - 1)\n",
+        "(compare_props * 100).round(2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NIziwu36tghU"
+      },
+      "outputs": [],
+      "source": [
+        "for set_ in (strat_train_set, strat_test_set):\n",
+        "    set_.drop(\"income_cat\", axis=1, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "senpgUgwtghg"
+      },
+      "source": [
+        "# Discover and Visualize the Data to Gain Insights"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fEYUScS1tghh"
+      },
+      "outputs": [],
+      "source": [
+        "housing = strat_train_set.copy()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OUG56K--tghh"
+      },
+      "source": [
+        "## Visualizing Geographical Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "F7ZTwLvdtghj",
+        "outputId": "13e9198b-ad07-4a7e-b95f-7fc855e3e4a8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
-       "13096  -1.423037  1.013606            1.861119     0.311912        1.368167   \n",
-       "14973   0.596394 -0.702103            0.907630    -0.308620       -0.435925   \n",
-       "\n",
-       "       population  households  median_income  \n",
-       "13096    0.137460    1.394812      -0.936491  \n",
-       "14973   -0.693771   -0.373485       1.171942  "
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_housing_num_prepared.head(2)  # extra code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('simpleimputer', SimpleImputer(strategy='median')),\n",
-       " ('standardscaler', StandardScaler())]"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "num_pipeline.steps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "StandardScaler()"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "num_pipeline[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median'))])"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "num_pipeline[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SimpleImputer(strategy='median')"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "num_pipeline.named_steps[\"simpleimputer\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
-       "                ('standardscaler', StandardScaler())])"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "num_pipeline.set_params(simpleimputer__strategy=\"median\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.compose import ColumnTransformer\n",
-    "\n",
-    "num_attribs = [\"longitude\", \"latitude\", \"housing_median_age\", \"total_rooms\",\n",
-    "               \"total_bedrooms\", \"population\", \"households\", \"median_income\"]\n",
-    "cat_attribs = [\"ocean_proximity\"]\n",
-    "\n",
-    "cat_pipeline = make_pipeline(\n",
-    "    SimpleImputer(strategy=\"most_frequent\"),\n",
-    "    OneHotEncoder(handle_unknown=\"ignore\"))\n",
-    "\n",
-    "preprocessing = ColumnTransformer([\n",
-    "    (\"num\", num_pipeline, num_attribs),\n",
-    "    (\"cat\", cat_pipeline, cat_attribs),\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.compose import make_column_selector, make_column_transformer\n",
-    "\n",
-    "preprocessing = make_column_transformer(\n",
-    "    (num_pipeline, make_column_selector(dtype_include=np.number)),\n",
-    "    (cat_pipeline, make_column_selector(dtype_include=object)),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing_prepared = preprocessing.fit_transform(housing)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"housing_prepared_fr\",\n  \"rows\": 16512,\n  \"fields\": [\n    {\n      \"column\": \"pipeline-1__longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.00003028238323,\n        \"min\": -2.3877646847945297,\n        \"max\": 2.5408465499717225,\n        \"num_unique_values\": 817,\n        \"samples\": [\n          -1.5280069440442305,\n          -0.6282604711660115,\n          1.361178952198056\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383244,\n        \"min\": -1.4474519456998403,\n        \"max\": 2.959013528420613,\n        \"num_unique_values\": 835,\n        \"samples\": [\n          2.424612396452814,\n          2.3496087288082084,\n          1.5808211354510233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383234,\n        \"min\": -2.1912096612263676,\n        \"max\": 1.8611187475164104,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          1.146001969502979,\n          1.3049168090615193,\n          1.7816613277371403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000302823832317,\n        \"min\": -1.2069271002738067,\n        \"max\": 16.785758645406965,\n        \"num_unique_values\": 5473,\n        \"samples\": [\n          0.4066394580269817,\n          0.45972332664946935,\n          -0.1278601846546181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000302823832243,\n        \"min\": -1.2727767935135679,\n        \"max\": 13.446810448110993,\n        \"num_unique_values\": 1827,\n        \"samples\": [\n          -0.25575264684632076,\n          1.2567448020434313,\n          4.115791703863245\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383224,\n        \"min\": -1.2993815273147138,\n        \"max\": 13.591521979866151,\n        \"num_unique_values\": 3640,\n        \"samples\": [\n          -0.21330102982740873,\n          0.8965289150397702,\n          0.026933764822853052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383228,\n        \"min\": -1.3033426245281265,\n        \"max\": 12.688929356290272,\n        \"num_unique_values\": 1712,\n        \"samples\": [\n          -0.031317898999181186,\n          0.8489127632251608,\n          1.6847400982749814\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000302823832292,\n        \"min\": -1.7815774618066682,\n        \"max\": 5.882868113005668,\n        \"num_unique_values\": 10930,\n        \"samples\": [\n          -0.11984286517386168,\n          -0.4482466137863569,\n          0.9520547073535472\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-2__ocean_proximity_<1H OCEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.49646552791533155,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-2__ocean_proximity_INLAND\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.46688997586710157,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-2__ocean_proximity_ISLAND\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011005303042079697,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-2__ocean_proximity_NEAR BAY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3151266698441346,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-2__ocean_proximity_NEAR OCEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33243787156331894,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe",
-       "variable_name": "housing_prepared_fr"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-b9cf8dd7-5ed5-42d3-a085-f5d64acc5748\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pipeline-1__longitude</th>\n",
-       "      <th>pipeline-1__latitude</th>\n",
-       "      <th>pipeline-1__housing_median_age</th>\n",
-       "      <th>pipeline-1__total_rooms</th>\n",
-       "      <th>pipeline-1__total_bedrooms</th>\n",
-       "      <th>pipeline-1__population</th>\n",
-       "      <th>pipeline-1__households</th>\n",
-       "      <th>pipeline-1__median_income</th>\n",
-       "      <th>pipeline-2__ocean_proximity_&lt;1H OCEAN</th>\n",
-       "      <th>pipeline-2__ocean_proximity_INLAND</th>\n",
-       "      <th>pipeline-2__ocean_proximity_ISLAND</th>\n",
-       "      <th>pipeline-2__ocean_proximity_NEAR BAY</th>\n",
-       "      <th>pipeline-2__ocean_proximity_NEAR OCEAN</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>13096</th>\n",
-       "      <td>-1.423037</td>\n",
-       "      <td>1.013606</td>\n",
-       "      <td>1.861119</td>\n",
-       "      <td>0.311912</td>\n",
-       "      <td>1.368167</td>\n",
-       "      <td>0.137460</td>\n",
-       "      <td>1.394812</td>\n",
-       "      <td>-0.936491</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14973</th>\n",
-       "      <td>0.596394</td>\n",
-       "      <td>-0.702103</td>\n",
-       "      <td>0.907630</td>\n",
-       "      <td>-0.308620</td>\n",
-       "      <td>-0.435925</td>\n",
-       "      <td>-0.693771</td>\n",
-       "      <td>-0.373485</td>\n",
-       "      <td>1.171942</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b9cf8dd7-5ed5-42d3-a085-f5d64acc5748')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-b9cf8dd7-5ed5-42d3-a085-f5d64acc5748 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-b9cf8dd7-5ed5-42d3-a085-f5d64acc5748');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-58cad9b8-f58e-4e21-9bf3-92ee851f7074\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-58cad9b8-f58e-4e21-9bf3-92ee851f7074')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-58cad9b8-f58e-4e21-9bf3-92ee851f7074 button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True)\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vilTfBy5tghk",
+        "outputId": "372412a1-d3c4-45fa-a27e-8d6075e3acf5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "       pipeline-1__longitude  pipeline-1__latitude  \\\n",
-       "13096              -1.423037              1.013606   \n",
-       "14973               0.596394             -0.702103   \n",
-       "\n",
-       "       pipeline-1__housing_median_age  pipeline-1__total_rooms  \\\n",
-       "13096                        1.861119                 0.311912   \n",
-       "14973                        0.907630                -0.308620   \n",
-       "\n",
-       "       pipeline-1__total_bedrooms  pipeline-1__population  \\\n",
-       "13096                    1.368167                0.137460   \n",
-       "14973                   -0.435925               -0.693771   \n",
-       "\n",
-       "       pipeline-1__households  pipeline-1__median_income  \\\n",
-       "13096                1.394812                  -0.936491   \n",
-       "14973               -0.373485                   1.171942   \n",
-       "\n",
-       "       pipeline-2__ocean_proximity_<1H OCEAN  \\\n",
-       "13096                                    0.0   \n",
-       "14973                                    1.0   \n",
-       "\n",
-       "       pipeline-2__ocean_proximity_INLAND  pipeline-2__ocean_proximity_ISLAND  \\\n",
-       "13096                                 0.0                                 0.0   \n",
-       "14973                                 0.0                                 0.0   \n",
-       "\n",
-       "       pipeline-2__ocean_proximity_NEAR BAY  \\\n",
-       "13096                                   1.0   \n",
-       "14973                                   0.0   \n",
-       "\n",
-       "       pipeline-2__ocean_proximity_NEAR OCEAN  \n",
-       "13096                                     0.0  \n",
-       "14973                                     0.0  "
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# extra code – shows that we can get a DataFrame out if we want\n",
-    "housing_prepared_fr = pd.DataFrame(\n",
-    "    housing_prepared,\n",
-    "    columns=preprocessing.get_feature_names_out(),\n",
-    "    index=housing.index)\n",
-    "housing_prepared_fr.head(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def column_ratio(X):\n",
-    "    return X[:, [0]] / X[:, [1]]\n",
-    "\n",
-    "def ratio_name(function_transformer, feature_names_in):\n",
-    "    return [\"ratio\"]  # feature names out\n",
-    "\n",
-    "def ratio_pipeline():\n",
-    "    return make_pipeline(\n",
-    "        SimpleImputer(strategy=\"median\"),\n",
-    "        FunctionTransformer(column_ratio, feature_names_out=ratio_name),\n",
-    "        StandardScaler())\n",
-    "\n",
-    "log_pipeline = make_pipeline(\n",
-    "    SimpleImputer(strategy=\"median\"),\n",
-    "    FunctionTransformer(np.log, feature_names_out=\"one-to-one\"),\n",
-    "    StandardScaler())\n",
-    "cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\n",
-    "default_num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n",
-    "                                     StandardScaler())\n",
-    "preprocessing = ColumnTransformer([\n",
-    "        (\"bedrooms\", ratio_pipeline(), [\"total_bedrooms\", \"total_rooms\"]),\n",
-    "        (\"rooms_per_house\", ratio_pipeline(), [\"total_rooms\", \"households\"]),\n",
-    "        (\"people_per_house\", ratio_pipeline(), [\"population\", \"households\"]),\n",
-    "        (\"log\", log_pipeline, [\"total_bedrooms\", \"total_rooms\", \"population\",\n",
-    "                               \"households\", \"median_income\"]),\n",
-    "        (\"geo\", cluster_simil, [\"latitude\", \"longitude\"]),\n",
-    "        (\"cat\", cat_pipeline, make_column_selector(dtype_include=object)),\n",
-    "    ],\n",
-    "    remainder=default_num_pipeline)  # one column remaining: housing_median_age"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(16512, 24)"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_prepared = preprocessing.fit_transform(housing)\n",
-    "housing_prepared.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['bedrooms__ratio', 'rooms_per_house__ratio',\n",
-       "       'people_per_house__ratio', 'log__total_bedrooms',\n",
-       "       'log__total_rooms', 'log__population', 'log__households',\n",
-       "       'log__median_income', 'geo__Cluster 0 similarity',\n",
-       "       'geo__Cluster 1 similarity', 'geo__Cluster 2 similarity',\n",
-       "       'geo__Cluster 3 similarity', 'geo__Cluster 4 similarity',\n",
-       "       'geo__Cluster 5 similarity', 'geo__Cluster 6 similarity',\n",
-       "       'geo__Cluster 7 similarity', 'geo__Cluster 8 similarity',\n",
-       "       'geo__Cluster 9 similarity', 'cat__ocean_proximity_<1H OCEAN',\n",
-       "       'cat__ocean_proximity_INLAND', 'cat__ocean_proximity_ISLAND',\n",
-       "       'cat__ocean_proximity_NEAR BAY', 'cat__ocean_proximity_NEAR OCEAN',\n",
-       "       'remainder__housing_median_age'], dtype=object)"
-      ]
-     },
-     "execution_count": 115,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preprocessing.get_feature_names_out()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Select and Train a Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training and Evaluating on the Training Set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Pipeline(steps=[('columntransformer',\n",
-       "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
-       "                                                              SimpleImputer(strategy='median')),\n",
-       "                                                             ('standardscaler',\n",
-       "                                                              StandardScaler())]),\n",
-       "                                   transformers=[('bedrooms',\n",
-       "                                                  Pipeline(steps=[('simpleimputer',\n",
-       "                                                                   SimpleImputer(strategy='median')),\n",
-       "                                                                  ('functiontransformer',\n",
-       "                                                                   FunctionTransformer(feature_names_out=<function ratio_name at 0x7ff...\n",
-       "                                                   'median_income']),\n",
-       "                                                 ('geo',\n",
-       "                                                  ClusterSimilarity(random_state=42),\n",
-       "                                                  ['latitude', 'longitude']),\n",
-       "                                                 ('cat',\n",
-       "                                                  Pipeline(steps=[('simpleimputer',\n",
-       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
-       "                                                                  ('onehotencoder',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
-       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7ffb95e12f90>)])),\n",
-       "                ('linearregression', LinearRegression())])"
-      ]
-     },
-     "execution_count": 116,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "lin_reg = make_pipeline(preprocessing, LinearRegression())\n",
-    "lin_reg.fit(housing, housing_labels)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's try the full preprocessing pipeline on a few training instances:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([246000., 372700., 135700.,  91400., 330900.])"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_predictions = lin_reg.predict(housing)\n",
-    "housing_predictions[:5].round(-2)  # -2 = rounded to the nearest hundred"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compare against the actual values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([458300., 483800., 101700.,  96100., 361800.])"
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_labels.iloc[:5].values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-46.3%, -23.0%, 33.4%, -4.9%, -8.5%\n"
-     ]
-    }
-   ],
-   "source": [
-    "# extra code – computes the error ratios discussed in the book\n",
-    "error_ratios = housing_predictions[:5].round(-2) / housing_labels.iloc[:5].values - 1\n",
-    "print(\", \".join([f\"{100 * ratio:.1f}%\" for ratio in error_ratios]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "68972.88910758484"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import root_mean_squared_error\n",
-    "\n",
-    "lin_rmse = root_mean_squared_error(housing_labels, housing_predictions)\n",
-    "lin_rmse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Pipeline(steps=[('columntransformer',\n",
-       "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
-       "                                                              SimpleImputer(strategy='median')),\n",
-       "                                                             ('standardscaler',\n",
-       "                                                              StandardScaler())]),\n",
-       "                                   transformers=[('bedrooms',\n",
-       "                                                  Pipeline(steps=[('simpleimputer',\n",
-       "                                                                   SimpleImputer(strategy='median')),\n",
-       "                                                                  ('functiontransformer',\n",
-       "                                                                   FunctionTransformer(feature_names_out=<function ratio_name at 0x7ff...\n",
-       "                                                  ClusterSimilarity(random_state=42),\n",
-       "                                                  ['latitude', 'longitude']),\n",
-       "                                                 ('cat',\n",
-       "                                                  Pipeline(steps=[('simpleimputer',\n",
-       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
-       "                                                                  ('onehotencoder',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
-       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7ffb95e12f90>)])),\n",
-       "                ('decisiontreeregressor',\n",
-       "                 DecisionTreeRegressor(random_state=42))])"
-      ]
-     },
-     "execution_count": 121,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "\n",
-    "tree_reg = make_pipeline(preprocessing, DecisionTreeRegressor(random_state=42))\n",
-    "tree_reg.fit(housing, housing_labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_predictions = tree_reg.predict(housing)\n",
-    "tree_rmse = root_mean_squared_error(housing_labels, housing_predictions)\n",
-    "tree_rmse"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Better Evaluation Using Cross-Validation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import cross_val_score\n",
-    "\n",
-    "tree_rmses = -cross_val_score(tree_reg, housing, housing_labels,\n",
-    "                              scoring=\"neg_root_mean_squared_error\", cv=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>10.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>66573.734600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1103.402323</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>64607.896046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>66204.731788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>66388.272499</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>66826.257468</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>68532.210664</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div><br><label><b>dtype:</b> float64</label>"
+      "source": [
+        "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True, alpha=0.2)\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_GCDb7KGtgh1",
+        "outputId": "97e0a662-d97d-43ae-ecd4-9a525ecaf276"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 1000x700 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "count       10.000000\n",
-       "mean     66573.734600\n",
-       "std       1103.402323\n",
-       "min      64607.896046\n",
-       "25%      66204.731788\n",
-       "50%      66388.272499\n",
-       "75%      66826.257468\n",
-       "max      68532.210664\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(tree_rmses).describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>10.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>70003.404818</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>4182.188328</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>65504.765753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>68172.065831</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>68743.995249</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>70344.943988</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>81037.863741</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div><br><label><b>dtype:</b> float64</label>"
+      "source": [
+        "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True,\n",
+        "            s=housing[\"population\"] / 100, label=\"population\",\n",
+        "            c=\"median_house_value\", cmap=\"jet\", colorbar=True,\n",
+        "            legend=True, sharex=False, figsize=(10, 7))\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F2icHx34tgiC"
+      },
+      "source": [
+        "The next cell generates the first figure in the chapter (this code is not in the book). It's just a beautified version of the previous figure, with an image of California added in the background, nicer label names and no grid."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DagINU8ZtgiD",
+        "outputId": "76a67de1-a67f-42d4-8012-2d0827cc9db1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading california.png\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 1000x700 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "count       10.000000\n",
-       "mean     70003.404818\n",
-       "std       4182.188328\n",
-       "min      65504.765753\n",
-       "25%      68172.065831\n",
-       "50%      68743.995249\n",
-       "75%      70344.943988\n",
-       "max      81037.863741\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# extra code – computes the error stats for the linear model\n",
-    "lin_rmses = -cross_val_score(lin_reg, housing, housing_labels,\n",
-    "                              scoring=\"neg_root_mean_squared_error\", cv=10)\n",
-    "pd.Series(lin_rmses).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Warning:** the following cell may take a few minutes to run:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "\n",
-    "forest_reg = make_pipeline(preprocessing,\n",
-    "                           RandomForestRegressor(random_state=42))\n",
-    "forest_rmses = -cross_val_score(forest_reg, housing, housing_labels,\n",
-    "                                scoring=\"neg_root_mean_squared_error\", cv=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>10.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>47038.092799</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1021.491757</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>45495.976649</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>46510.418013</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>47118.719249</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>47480.519175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>49140.832210</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div><br><label><b>dtype:</b> float64</label>"
+      "source": [
+        "# extra code – this cell generates the first figure in the chapter\n",
+        "\n",
+        "# Download the California image\n",
+        "filename = \"california.png\"\n",
+        "filepath = Path(f\"my_{filename}\")\n",
+        "if not filepath.is_file():\n",
+        "    homlp_root = \"https://github.com/ageron/handson-mlp/raw/main/\"\n",
+        "    url = homlp_root + \"images/end_to_end_project/\" + filename\n",
+        "    print(\"Downloading\", filename)\n",
+        "    urllib.request.urlretrieve(url, filepath)\n",
+        "\n",
+        "housing_renamed = housing.rename(columns={\n",
+        "    \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n",
+        "    \"population\": \"Population\",\n",
+        "    \"median_house_value\": \"Median house value (ᴜsᴅ)\"})\n",
+        "housing_renamed.plot(\n",
+        "             kind=\"scatter\", x=\"Longitude\", y=\"Latitude\",\n",
+        "             s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n",
+        "             c=\"Median house value (ᴜsᴅ)\", cmap=\"jet\", colorbar=True,\n",
+        "             legend=True, sharex=False, figsize=(10, 7))\n",
+        "\n",
+        "california_img = plt.imread(filepath)\n",
+        "axis = -124.55, -113.95, 32.45, 42.05\n",
+        "plt.axis(axis)\n",
+        "plt.imshow(california_img, extent=axis)\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6FYAUyRPtgie"
+      },
+      "source": [
+        "## Looking for Correlations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0YvJfjP3tgif"
+      },
+      "outputs": [],
+      "source": [
+        "corr_matrix = housing.corr(numeric_only=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xevITb5Mtgij",
+        "outputId": "25380a8f-801a-4e60-8895-672b8e3bf272"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>median_house_value</th>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>median_income</th>\n",
+              "      <td>0.688380</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_rooms</th>\n",
+              "      <td>0.137455</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <td>0.102175</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>households</th>\n",
+              "      <td>0.071426</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <td>0.054635</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>population</th>\n",
+              "      <td>-0.020153</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>longitude</th>\n",
+              "      <td>-0.050859</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>latitude</th>\n",
+              "      <td>-0.139584</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ],
+            "text/plain": [
+              "median_house_value    1.000000\n",
+              "median_income         0.688380\n",
+              "total_rooms           0.137455\n",
+              "housing_median_age    0.102175\n",
+              "households            0.071426\n",
+              "total_bedrooms        0.054635\n",
+              "population           -0.020153\n",
+              "longitude            -0.050859\n",
+              "latitude             -0.139584\n",
+              "Name: median_house_value, dtype: float64"
+            ]
+          },
+          "execution_count": 34,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "count       10.000000\n",
-       "mean     47038.092799\n",
-       "std       1021.491757\n",
-       "min      45495.976649\n",
-       "25%      46510.418013\n",
-       "50%      47118.719249\n",
-       "75%      47480.519175\n",
-       "max      49140.832210\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(forest_rmses).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's compare this RMSE measured using cross-validation (the \"validation error\") with the RMSE measured on the training set (the \"training error\"):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "17551.2122500877"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest_reg.fit(housing, housing_labels)\n",
-    "housing_predictions = forest_reg.predict(housing)\n",
-    "forest_rmse = root_mean_squared_error(housing_labels, housing_predictions)\n",
-    "forest_rmse"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The training error is much lower than the validation error, which usually means that the model has overfit the training set. Another possible explanation may be that there's a mismatch between the training data and the validation data, but it's not the case here, since both came from the same dataset that we shuffled and split in two parts."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fine-Tune Your Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Grid Search"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Warning:** the following cell may take a few minutes to run:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "GridSearchCV(cv=3,\n",
-       "             estimator=Pipeline(steps=[('preprocessing',\n",
-       "                                        ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
-       "                                                                                     SimpleImputer(strategy='median')),\n",
-       "                                                                                    ('standardscaler',\n",
-       "                                                                                     StandardScaler())]),\n",
-       "                                                          transformers=[('bedrooms',\n",
-       "                                                                         Pipeline(steps=[('simpleimputer',\n",
-       "                                                                                          SimpleImputer(strategy='median')),\n",
-       "                                                                                         ('functiontransformer',\n",
-       "                                                                                          FunctionTransformer(feature_names_out=<f...\n",
-       "                                                                         <sklearn.compose._column_transformer.make_column_selector object at 0x7ffb95e12f90>)])),\n",
-       "                                       ('random_forest',\n",
-       "                                        RandomForestRegressor(random_state=42))]),\n",
-       "             param_grid=[{'preprocessing__geo__n_clusters': [5, 8, 10],\n",
-       "                          'random_forest__max_features': [4, 6, 8]},\n",
-       "                         {'preprocessing__geo__n_clusters': [10, 15],\n",
-       "                          'random_forest__max_features': [6, 8, 10]}],\n",
-       "             scoring='neg_root_mean_squared_error')"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import GridSearchCV\n",
-    "\n",
-    "full_pipeline = Pipeline([\n",
-    "    (\"preprocessing\", preprocessing),\n",
-    "    (\"random_forest\", RandomForestRegressor(random_state=42)),\n",
-    "])\n",
-    "param_grid = [\n",
-    "    {'preprocessing__geo__n_clusters': [5, 8, 10],\n",
-    "     'random_forest__max_features': [4, 6, 8]},\n",
-    "    {'preprocessing__geo__n_clusters': [10, 15],\n",
-    "     'random_forest__max_features': [6, 8, 10]},\n",
-    "]\n",
-    "grid_search = GridSearchCV(full_pipeline, param_grid, cv=3,\n",
-    "                           scoring='neg_root_mean_squared_error')\n",
-    "grid_search.fit(housing, housing_labels)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can get the full list of hyperparameters available for tuning by looking at `full_pipeline.get_params().keys()`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dict_keys(['memory', 'steps', 'transform_input', 'verbose', 'preprocessing', 'random_forest', 'preprocessing__force_int_remainder_cols', 'preprocessing__n_jobs', 'preprocessing__remainder__memory', 'preprocessing__remainder__steps', 'preprocessing__remainder__transform_input', 'preprocessing__remainder__verbose', 'preprocessing__remainder__simpleimputer', 'preprocessing__remainder__standardscaler', 'preprocessing__remainder__simpleimputer__add_indicator', 'preprocessing__remainder__simpleimputer__copy', 'preprocessing__remainder__simpleimputer__fill_value', 'preprocessing__remainder__simpleimputer__keep_empty_features', 'preprocessing__remainder__simpleimputer__missing_values', 'preprocessing__remainder__simpleimputer__strategy', 'preprocessing__remainder__standardscaler__copy', 'preprocessing__remainder__standardscaler__with_mean', 'preprocessing__remainder__standardscaler__with_std', 'preprocessing__remainder', 'preprocessing__sparse_threshold', 'preprocessing__transformer_weights', ...\n"
-     ]
-    }
-   ],
-   "source": [
-    "# extra code – shows part of the output of get_params().keys()\n",
-    "print(str(full_pipeline.get_params().keys())[:1000] + \"...\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The best hyperparameter combination found:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'preprocessing__geo__n_clusters': 15, 'random_forest__max_features': 6}"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grid_search.best_params_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Pipeline(steps=[('preprocessing',\n",
-       "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
-       "                                                              SimpleImputer(strategy='median')),\n",
-       "                                                             ('standardscaler',\n",
-       "                                                              StandardScaler())]),\n",
-       "                                   transformers=[('bedrooms',\n",
-       "                                                  Pipeline(steps=[('simpleimputer',\n",
-       "                                                                   SimpleImputer(strategy='median')),\n",
-       "                                                                  ('functiontransformer',\n",
-       "                                                                   FunctionTransformer(feature_names_out=<function ratio_name at 0x7ffb95e...\n",
-       "                                                  ClusterSimilarity(n_clusters=15,\n",
-       "                                                                    random_state=42),\n",
-       "                                                  ['latitude', 'longitude']),\n",
-       "                                                 ('cat',\n",
-       "                                                  Pipeline(steps=[('simpleimputer',\n",
-       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
-       "                                                                  ('onehotencoder',\n",
-       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
-       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7ffb9c38bb60>)])),\n",
-       "                ('random_forest',\n",
-       "                 RandomForestRegressor(max_features=6, random_state=42))])"
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grid_search.best_estimator_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's look at the score of each hyperparameter combination tested during the grid search:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"cv_res\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"n_clusters\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 5,\n        \"max\": 15,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10,\n          5,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_features\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 10,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8,\n          10,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1183,\n        \"min\": 42725,\n        \"max\": 46840,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          46473,\n          45568,\n          42725\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 965,\n        \"min\": 43708,\n        \"max\": 46905,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          46384,\n          45323,\n          43708\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1054,\n        \"min\": 44335,\n        \"max\": 47813,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          47621,\n          46134,\n          44335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_rmse\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1058,\n        \"min\": 43590,\n        \"max\": 47186,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          46826,\n          45675,\n          43590\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe",
-       "variable_name": "cv_res"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-95709831-262a-4817-84d2-0c317b41d9be\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>n_clusters</th>\n",
-       "      <th>max_features</th>\n",
-       "      <th>split0</th>\n",
-       "      <th>split1</th>\n",
-       "      <th>split2</th>\n",
-       "      <th>mean_test_rmse</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>15</td>\n",
-       "      <td>6</td>\n",
-       "      <td>42725</td>\n",
-       "      <td>43708</td>\n",
-       "      <td>44335</td>\n",
-       "      <td>43590</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>15</td>\n",
-       "      <td>8</td>\n",
-       "      <td>43486</td>\n",
-       "      <td>43820</td>\n",
-       "      <td>44900</td>\n",
-       "      <td>44069</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>10</td>\n",
-       "      <td>4</td>\n",
-       "      <td>43798</td>\n",
-       "      <td>44036</td>\n",
-       "      <td>44961</td>\n",
-       "      <td>44265</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>43710</td>\n",
-       "      <td>44163</td>\n",
-       "      <td>44967</td>\n",
-       "      <td>44280</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>43710</td>\n",
-       "      <td>44163</td>\n",
-       "      <td>44967</td>\n",
-       "      <td>44280</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-95709831-262a-4817-84d2-0c317b41d9be')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-95709831-262a-4817-84d2-0c317b41d9be button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-95709831-262a-4817-84d2-0c317b41d9be');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-2e49b3f2-4f9a-46a1-82e4-eb3c51a91833\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2e49b3f2-4f9a-46a1-82e4-eb3c51a91833')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-2e49b3f2-4f9a-46a1-82e4-eb3c51a91833 button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HWsUe7brtgil",
+        "outputId": "5880fabd-caf1-406d-ef33-72352df2b45d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 1200x800 with 16 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "    n_clusters  max_features  split0  split1  split2  mean_test_rmse\n",
-       "12          15             6   42725   43708   44335           43590\n",
-       "13          15             8   43486   43820   44900           44069\n",
-       "6           10             4   43798   44036   44961           44265\n",
-       "9           10             6   43710   44163   44967           44280\n",
-       "7           10             6   43710   44163   44967           44280"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cv_res = pd.DataFrame(grid_search.cv_results_)\n",
-    "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n",
-    "\n",
-    "# extra code – these few lines of code just make the DataFrame look nicer\n",
-    "cv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n",
-    "                 \"param_random_forest__max_features\", \"split0_test_score\",\n",
-    "                 \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n",
-    "score_cols = [\"split0\", \"split1\", \"split2\", \"mean_test_rmse\"]\n",
-    "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n",
-    "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n",
-    "\n",
-    "cv_res.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Randomized Search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.experimental import enable_halving_search_cv\n",
-    "from sklearn.model_selection import HalvingRandomSearchCV"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Try 30 (`n_iter` × `cv`) random combinations of hyperparameters:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Warning:** the following cell may take a few minutes to run:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RandomizedSearchCV(cv=3,\n",
-       "                   estimator=Pipeline(steps=[('preprocessing',\n",
-       "                                              ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
-       "                                                                                           SimpleImputer(strategy='median')),\n",
-       "                                                                                          ('standardscaler',\n",
-       "                                                                                           StandardScaler())]),\n",
-       "                                                                transformers=[('bedrooms',\n",
-       "                                                                               Pipeline(steps=[('simpleimputer',\n",
-       "                                                                                                SimpleImputer(strategy='median')),\n",
-       "                                                                                               ('functiontransformer',\n",
-       "                                                                                                FunctionTransformer(feature_names_...\n",
-       "                                             ('random_forest',\n",
-       "                                              RandomForestRegressor(random_state=42))]),\n",
-       "                   param_distributions={'preprocessing__geo__n_clusters': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7ffb95e65670>,\n",
-       "                                        'random_forest__max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7ffb9c355be0>},\n",
-       "                   random_state=42, scoring='neg_root_mean_squared_error')"
-      ]
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import RandomizedSearchCV\n",
-    "from scipy.stats import randint\n",
-    "\n",
-    "param_distribs = {'preprocessing__geo__n_clusters': randint(low=3, high=50),\n",
-    "                  'random_forest__max_features': randint(low=2, high=20)}\n",
-    "\n",
-    "rnd_search = RandomizedSearchCV(\n",
-    "    full_pipeline, param_distributions=param_distribs, n_iter=10, cv=3,\n",
-    "    scoring='neg_root_mean_squared_error', random_state=42)\n",
-    "\n",
-    "rnd_search.fit(housing, housing_labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"cv_res\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"n_clusters\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 4,\n        \"max\": 45,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          21,\n          32,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_features\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 2,\n        \"max\": 16,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          12,\n          7,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2165,\n        \"min\": 41342,\n        \"max\": 48955,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          43481,\n          41825,\n          42502\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1881,\n        \"min\": 42242,\n        \"max\": 48833,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          43828,\n          42275,\n          43694\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1997,\n        \"min\": 43057,\n        \"max\": 49929,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          44501,\n          43241,\n          44234\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_rmse\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2007,\n        \"min\": 42214,\n        \"max\": 49239,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          43936,\n          42447,\n          43477\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe",
-       "variable_name": "cv_res"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-b19551b9-3ea6-4a3d-b852-261e02b9945b\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>n_clusters</th>\n",
-       "      <th>max_features</th>\n",
-       "      <th>split0</th>\n",
-       "      <th>split1</th>\n",
-       "      <th>split2</th>\n",
-       "      <th>mean_test_rmse</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>45</td>\n",
-       "      <td>9</td>\n",
-       "      <td>41342</td>\n",
-       "      <td>42242</td>\n",
-       "      <td>43057</td>\n",
-       "      <td>42214</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>32</td>\n",
-       "      <td>7</td>\n",
-       "      <td>41825</td>\n",
-       "      <td>42275</td>\n",
-       "      <td>43241</td>\n",
-       "      <td>42447</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>41</td>\n",
-       "      <td>16</td>\n",
-       "      <td>42238</td>\n",
-       "      <td>42938</td>\n",
-       "      <td>43354</td>\n",
-       "      <td>42843</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>42</td>\n",
-       "      <td>4</td>\n",
-       "      <td>41869</td>\n",
-       "      <td>43362</td>\n",
-       "      <td>43664</td>\n",
-       "      <td>42965</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>23</td>\n",
-       "      <td>8</td>\n",
-       "      <td>42490</td>\n",
-       "      <td>42928</td>\n",
-       "      <td>43718</td>\n",
-       "      <td>43046</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b19551b9-3ea6-4a3d-b852-261e02b9945b')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-b19551b9-3ea6-4a3d-b852-261e02b9945b button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-b19551b9-3ea6-4a3d-b852-261e02b9945b');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-dab67d90-91bb-47ae-8db6-77feef3236b4\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dab67d90-91bb-47ae-8db6-77feef3236b4')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-dab67d90-91bb-47ae-8db6-77feef3236b4 button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
+      "source": [
+        "from pandas.plotting import scatter_matrix\n",
+        "\n",
+        "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
+        "              \"housing_median_age\"]\n",
+        "scatter_matrix(housing[attributes], figsize=(12, 8))\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sxWz6Oh3tgir",
+        "outputId": "0184770a-3adf-4d0a-c4fe-b9d670284f39"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n",
+        "             alpha=0.1, grid=True)\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dJEvqXwZtgis"
+      },
+      "source": [
+        "## Experimenting with Attribute Combinations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qVmnVB9ytgiu"
+      },
+      "outputs": [],
+      "source": [
+        "housing[\"rooms_per_house\"] = housing[\"total_rooms\"] / housing[\"households\"]\n",
+        "housing[\"bedrooms_ratio\"] = housing[\"total_bedrooms\"] / housing[\"total_rooms\"]\n",
+        "housing[\"people_per_house\"] = housing[\"population\"] / housing[\"households\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XMuApnRBtgiv",
+        "outputId": "a622cd12-280a-4ac2-9615-f117abfb87d2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>median_house_value</th>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>median_income</th>\n",
+              "      <td>0.688380</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>rooms_per_house</th>\n",
+              "      <td>0.143663</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_rooms</th>\n",
+              "      <td>0.137455</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <td>0.102175</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>households</th>\n",
+              "      <td>0.071426</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <td>0.054635</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>population</th>\n",
+              "      <td>-0.020153</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>people_per_house</th>\n",
+              "      <td>-0.038224</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>longitude</th>\n",
+              "      <td>-0.050859</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>latitude</th>\n",
+              "      <td>-0.139584</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bedrooms_ratio</th>\n",
+              "      <td>-0.256397</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ],
+            "text/plain": [
+              "median_house_value    1.000000\n",
+              "median_income         0.688380\n",
+              "rooms_per_house       0.143663\n",
+              "total_rooms           0.137455\n",
+              "housing_median_age    0.102175\n",
+              "households            0.071426\n",
+              "total_bedrooms        0.054635\n",
+              "population           -0.020153\n",
+              "people_per_house     -0.038224\n",
+              "longitude            -0.050859\n",
+              "latitude             -0.139584\n",
+              "bedrooms_ratio       -0.256397\n",
+              "Name: median_house_value, dtype: float64"
+            ]
+          },
+          "execution_count": 38,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "corr_matrix = housing.corr(numeric_only=True)\n",
+        "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YUklTIixtgiz"
+      },
+      "source": [
+        "# Prepare the Data for Machine Learning Algorithms"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PP-gMxhStgiz"
+      },
+      "source": [
+        "Let's revert to the original training set and separate the target (note that `strat_train_set.drop()` creates a copy of `strat_train_set` without the column, it doesn't actually modify `strat_train_set` itself, unless you pass `inplace=True`):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1O41vkAxtgi0"
+      },
+      "outputs": [],
+      "source": [
+        "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
+        "housing_labels = strat_train_set[\"median_house_value\"].copy()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zF_MnSq_tgi1"
+      },
+      "source": [
+        "## Data Cleaning"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wL9v2swgtgi1"
+      },
+      "source": [
+        "In the book 3 options are listed to handle the NaN values:\n",
+        "\n",
+        "```python\n",
+        "housing.dropna(subset=[\"total_bedrooms\"], inplace=True)    # option 1\n",
+        "\n",
+        "housing.drop(\"total_bedrooms\", axis=1, inplace=True)       # option 2\n",
+        "\n",
+        "median = housing[\"total_bedrooms\"].median()  # option 3\n",
+        "housing[\"total_bedrooms\"] = housing[\"total_bedrooms\"].fillna(median)\n",
+        "```\n",
+        "\n",
+        "For each option, we'll create a copy of `housing` and work on that copy to avoid breaking `housing`. We'll also show the output of each option, filtering on the rows that originally contained a NaN value."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6pCdIKQjtgi2",
+        "outputId": "286fb118-0b8d-4d90-a133-d7774c579b05"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>ocean_proximity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>14452</th>\n",
+              "      <td>-120.67</td>\n",
+              "      <td>40.50</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>5343.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2503.0</td>\n",
+              "      <td>902.0</td>\n",
+              "      <td>3.5962</td>\n",
+              "      <td>INLAND</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18217</th>\n",
+              "      <td>-117.96</td>\n",
+              "      <td>34.03</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2093.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1755.0</td>\n",
+              "      <td>403.0</td>\n",
+              "      <td>3.4115</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11889</th>\n",
+              "      <td>-118.05</td>\n",
+              "      <td>34.04</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>1348.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1098.0</td>\n",
+              "      <td>257.0</td>\n",
+              "      <td>4.2917</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20325</th>\n",
+              "      <td>-118.88</td>\n",
+              "      <td>34.17</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>4260.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1701.0</td>\n",
+              "      <td>669.0</td>\n",
+              "      <td>5.1033</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14360</th>\n",
+              "      <td>-117.87</td>\n",
+              "      <td>33.62</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1266.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>375.0</td>\n",
+              "      <td>183.0</td>\n",
+              "      <td>9.8020</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "14452    -120.67     40.50                15.0       5343.0             NaN   \n",
+              "18217    -117.96     34.03                35.0       2093.0             NaN   \n",
+              "11889    -118.05     34.04                33.0       1348.0             NaN   \n",
+              "20325    -118.88     34.17                15.0       4260.0             NaN   \n",
+              "14360    -117.87     33.62                 8.0       1266.0             NaN   \n",
+              "\n",
+              "       population  households  median_income ocean_proximity  \n",
+              "14452      2503.0       902.0         3.5962          INLAND  \n",
+              "18217      1755.0       403.0         3.4115       <1H OCEAN  \n",
+              "11889      1098.0       257.0         4.2917       <1H OCEAN  \n",
+              "20325      1701.0       669.0         5.1033       <1H OCEAN  \n",
+              "14360       375.0       183.0         9.8020       <1H OCEAN  "
+            ]
+          },
+          "execution_count": 40,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "null_rows_idx = housing.isnull().any(axis=1)\n",
+        "housing.loc[null_rows_idx].head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FDHEx5q2tgi2",
+        "outputId": "ec106057-f717-4e98-d432-cd84b1842176"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "repr_error": "Out of range float values are not JSON compliant: nan",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fe37e6ad-b0ec-4b76-9a47-7585c364030b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>ocean_proximity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe37e6ad-b0ec-4b76-9a47-7585c364030b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fe37e6ad-b0ec-4b76-9a47-7585c364030b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fe37e6ad-b0ec-4b76-9a47-7585c364030b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "Empty DataFrame\n",
+              "Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity]\n",
+              "Index: []"
+            ]
+          },
+          "execution_count": 41,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_option1 = housing.copy()\n",
+        "\n",
+        "housing_option1.dropna(subset=[\"total_bedrooms\"], inplace=True)  # option 1\n",
+        "\n",
+        "housing_option1.loc[null_rows_idx].head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pWJgmyMstgi3",
+        "outputId": "9b93eb29-18bc-4d98-f674-96750dcb9f18"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"housing_option2\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1801821893250222,\n        \"min\": -120.67,\n        \"max\": -117.87,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -117.96,\n          -117.87,\n          -118.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9298412926300292,\n        \"min\": 33.62,\n        \"max\": 40.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.03,\n          33.62,\n          34.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.049896265113654,\n        \"min\": 8.0,\n        \"max\": 35.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          35.0,\n          8.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1839.8735010864198,\n        \"min\": 1266.0,\n        \"max\": 5343.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2093.0,\n          1266.0,\n          1348.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 796.5141555553172,\n        \"min\": 375.0,\n        \"max\": 2503.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1755.0,\n          375.0,\n          1098.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 299.012039891373,\n        \"min\": 183.0,\n        \"max\": 902.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          403.0,\n          183.0,\n          257.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6351133776367193,\n        \"min\": 3.4115,\n        \"max\": 9.802,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.4115,\n          9.802,\n          4.2917\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"<1H OCEAN\",\n          \"INLAND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c092e84e-18d2-47a6-9aa3-7693a41a8c12\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>ocean_proximity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>14452</th>\n",
+              "      <td>-120.67</td>\n",
+              "      <td>40.50</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>5343.0</td>\n",
+              "      <td>2503.0</td>\n",
+              "      <td>902.0</td>\n",
+              "      <td>3.5962</td>\n",
+              "      <td>INLAND</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18217</th>\n",
+              "      <td>-117.96</td>\n",
+              "      <td>34.03</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2093.0</td>\n",
+              "      <td>1755.0</td>\n",
+              "      <td>403.0</td>\n",
+              "      <td>3.4115</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11889</th>\n",
+              "      <td>-118.05</td>\n",
+              "      <td>34.04</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>1348.0</td>\n",
+              "      <td>1098.0</td>\n",
+              "      <td>257.0</td>\n",
+              "      <td>4.2917</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20325</th>\n",
+              "      <td>-118.88</td>\n",
+              "      <td>34.17</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>4260.0</td>\n",
+              "      <td>1701.0</td>\n",
+              "      <td>669.0</td>\n",
+              "      <td>5.1033</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14360</th>\n",
+              "      <td>-117.87</td>\n",
+              "      <td>33.62</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1266.0</td>\n",
+              "      <td>375.0</td>\n",
+              "      <td>183.0</td>\n",
+              "      <td>9.8020</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c092e84e-18d2-47a6-9aa3-7693a41a8c12')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c092e84e-18d2-47a6-9aa3-7693a41a8c12 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c092e84e-18d2-47a6-9aa3-7693a41a8c12');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-79447d74-4e89-4b00-9e78-eebd9be4c60c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79447d74-4e89-4b00-9e78-eebd9be4c60c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-79447d74-4e89-4b00-9e78-eebd9be4c60c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  population  \\\n",
+              "14452    -120.67     40.50                15.0       5343.0      2503.0   \n",
+              "18217    -117.96     34.03                35.0       2093.0      1755.0   \n",
+              "11889    -118.05     34.04                33.0       1348.0      1098.0   \n",
+              "20325    -118.88     34.17                15.0       4260.0      1701.0   \n",
+              "14360    -117.87     33.62                 8.0       1266.0       375.0   \n",
+              "\n",
+              "       households  median_income ocean_proximity  \n",
+              "14452       902.0         3.5962          INLAND  \n",
+              "18217       403.0         3.4115       <1H OCEAN  \n",
+              "11889       257.0         4.2917       <1H OCEAN  \n",
+              "20325       669.0         5.1033       <1H OCEAN  \n",
+              "14360       183.0         9.8020       <1H OCEAN  "
+            ]
+          },
+          "execution_count": 42,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_option2 = housing.copy()\n",
+        "\n",
+        "housing_option2.drop(\"total_bedrooms\", axis=1, inplace=True)  # option 2\n",
+        "\n",
+        "housing_option2.loc[null_rows_idx].head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1JOqqSWbtgi5",
+        "outputId": "228a1d39-4f1d-48f3-c884-1b0600a028e5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"housing_option3\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1801821893250222,\n        \"min\": -120.67,\n        \"max\": -117.87,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -117.96,\n          -117.87,\n          -118.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9298412926300292,\n        \"min\": 33.62,\n        \"max\": 40.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.03,\n          33.62,\n          34.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.049896265113654,\n        \"min\": 8.0,\n        \"max\": 35.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          35.0,\n          8.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1839.8735010864198,\n        \"min\": 1266.0,\n        \"max\": 5343.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2093.0,\n          1266.0,\n          1348.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 434.0,\n        \"max\": 434.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          434.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 796.5141555553172,\n        \"min\": 375.0,\n        \"max\": 2503.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1755.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 299.012039891373,\n        \"min\": 183.0,\n        \"max\": 902.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          403.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6351133776367193,\n        \"min\": 3.4115,\n        \"max\": 9.802,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.4115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"<1H OCEAN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9f74d271-c2cb-4c14-8db2-30aabc5ae4c9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>ocean_proximity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>14452</th>\n",
+              "      <td>-120.67</td>\n",
+              "      <td>40.50</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>5343.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>2503.0</td>\n",
+              "      <td>902.0</td>\n",
+              "      <td>3.5962</td>\n",
+              "      <td>INLAND</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18217</th>\n",
+              "      <td>-117.96</td>\n",
+              "      <td>34.03</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2093.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1755.0</td>\n",
+              "      <td>403.0</td>\n",
+              "      <td>3.4115</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11889</th>\n",
+              "      <td>-118.05</td>\n",
+              "      <td>34.04</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>1348.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1098.0</td>\n",
+              "      <td>257.0</td>\n",
+              "      <td>4.2917</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20325</th>\n",
+              "      <td>-118.88</td>\n",
+              "      <td>34.17</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>4260.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1701.0</td>\n",
+              "      <td>669.0</td>\n",
+              "      <td>5.1033</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14360</th>\n",
+              "      <td>-117.87</td>\n",
+              "      <td>33.62</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1266.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>375.0</td>\n",
+              "      <td>183.0</td>\n",
+              "      <td>9.8020</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f74d271-c2cb-4c14-8db2-30aabc5ae4c9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9f74d271-c2cb-4c14-8db2-30aabc5ae4c9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9f74d271-c2cb-4c14-8db2-30aabc5ae4c9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-3ecd88a0-d644-4223-bcdd-f89f7bcd0526\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3ecd88a0-d644-4223-bcdd-f89f7bcd0526')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-3ecd88a0-d644-4223-bcdd-f89f7bcd0526 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "14452    -120.67     40.50                15.0       5343.0           434.0   \n",
+              "18217    -117.96     34.03                35.0       2093.0           434.0   \n",
+              "11889    -118.05     34.04                33.0       1348.0           434.0   \n",
+              "20325    -118.88     34.17                15.0       4260.0           434.0   \n",
+              "14360    -117.87     33.62                 8.0       1266.0           434.0   \n",
+              "\n",
+              "       population  households  median_income ocean_proximity  \n",
+              "14452      2503.0       902.0         3.5962          INLAND  \n",
+              "18217      1755.0       403.0         3.4115       <1H OCEAN  \n",
+              "11889      1098.0       257.0         4.2917       <1H OCEAN  \n",
+              "20325      1701.0       669.0         5.1033       <1H OCEAN  \n",
+              "14360       375.0       183.0         9.8020       <1H OCEAN  "
+            ]
+          },
+          "execution_count": 43,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_option3 = housing.copy()\n",
+        "\n",
+        "median = housing[\"total_bedrooms\"].median()\n",
+        "housing_option3[\"total_bedrooms\"] = housing_option3[\"total_bedrooms\"].fillna(median)  # option 3\n",
+        "\n",
+        "housing_option3.loc[null_rows_idx].head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lztmZASitgi7"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.impute import SimpleImputer\n",
+        "\n",
+        "imputer = SimpleImputer(strategy=\"median\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W6TylWiytgi7"
+      },
+      "source": [
+        "Separating out the numerical attributes to use the `\"median\"` strategy (as it cannot be calculated on text attributes like `ocean_proximity`):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LWuPZhf2tgi7"
+      },
+      "outputs": [],
+      "source": [
+        "housing_num = housing.select_dtypes(include=[np.number])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5Xv0BHnutgi8",
+        "outputId": "15178c9e-b00f-4153-de15-343130a83cd8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "SimpleImputer(strategy='median')"
+            ]
+          },
+          "execution_count": 46,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "imputer.fit(housing_num)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8xyYDSv5tgi9",
+        "outputId": "8c9e9ab4-ad7a-49d6-8d34-c74e7b405ce6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([-118.51  ,   34.26  ,   29.    , 2125.    ,  434.    , 1167.    ,\n",
+              "        408.    ,    3.5385])"
+            ]
+          },
+          "execution_count": 47,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "imputer.statistics_"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GZ__Jw4etgi_"
+      },
+      "source": [
+        "Check that this is the same as manually computing the median of each attribute:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2msihSf3tgjE",
+        "outputId": "3a979a75-72c3-447e-9214-b7d901cf544d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([-118.51  ,   34.26  ,   29.    , 2125.    ,  434.    , 1167.    ,\n",
+              "        408.    ,    3.5385])"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_num.median().values"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rtghocxHtgjI"
+      },
+      "source": [
+        "Transform the training set:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RGs18XyhtgjK"
+      },
+      "outputs": [],
+      "source": [
+        "X = imputer.transform(housing_num)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1Rf9M6Dvtgjn",
+        "outputId": "8dc000a4-eb04-4373-ec74-d11282c5b507"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
+              "       'total_bedrooms', 'population', 'households', 'median_income'],\n",
+              "      dtype=object)"
+            ]
+          },
+          "execution_count": 50,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "imputer.feature_names_in_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zJ7QSiCntgjr"
+      },
+      "outputs": [],
+      "source": [
+        "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
+        "                          index=housing_num.index)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "efT8Nu2_tgjs",
+        "outputId": "81d45525-b6f7-42bc-ca79-11af0ed3752b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"housing_tr\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1801821893250222,\n        \"min\": -120.67,\n        \"max\": -117.87,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -117.96,\n          -117.87,\n          -118.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9298412926300292,\n        \"min\": 33.62,\n        \"max\": 40.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.03,\n          33.62,\n          34.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.049896265113654,\n        \"min\": 8.0,\n        \"max\": 35.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          35.0,\n          8.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1839.8735010864198,\n        \"min\": 1266.0,\n        \"max\": 5343.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2093.0,\n          1266.0,\n          1348.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 434.0,\n        \"max\": 434.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          434.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 796.5141555553172,\n        \"min\": 375.0,\n        \"max\": 2503.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1755.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 299.012039891373,\n        \"min\": 183.0,\n        \"max\": 902.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          403.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6351133776367193,\n        \"min\": 3.4115,\n        \"max\": 9.802,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.4115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-bc8b6ed8-94a5-4fa2-a2bf-0aaa3899c1c2\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>14452</th>\n",
+              "      <td>-120.67</td>\n",
+              "      <td>40.50</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>5343.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>2503.0</td>\n",
+              "      <td>902.0</td>\n",
+              "      <td>3.5962</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18217</th>\n",
+              "      <td>-117.96</td>\n",
+              "      <td>34.03</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2093.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1755.0</td>\n",
+              "      <td>403.0</td>\n",
+              "      <td>3.4115</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11889</th>\n",
+              "      <td>-118.05</td>\n",
+              "      <td>34.04</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>1348.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1098.0</td>\n",
+              "      <td>257.0</td>\n",
+              "      <td>4.2917</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20325</th>\n",
+              "      <td>-118.88</td>\n",
+              "      <td>34.17</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>4260.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1701.0</td>\n",
+              "      <td>669.0</td>\n",
+              "      <td>5.1033</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14360</th>\n",
+              "      <td>-117.87</td>\n",
+              "      <td>33.62</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1266.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>375.0</td>\n",
+              "      <td>183.0</td>\n",
+              "      <td>9.8020</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc8b6ed8-94a5-4fa2-a2bf-0aaa3899c1c2')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-bc8b6ed8-94a5-4fa2-a2bf-0aaa3899c1c2 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-bc8b6ed8-94a5-4fa2-a2bf-0aaa3899c1c2');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-7948471e-0685-4a4d-9348-8ff3785e1c1f\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7948471e-0685-4a4d-9348-8ff3785e1c1f')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-7948471e-0685-4a4d-9348-8ff3785e1c1f button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "14452    -120.67     40.50                15.0       5343.0           434.0   \n",
+              "18217    -117.96     34.03                35.0       2093.0           434.0   \n",
+              "11889    -118.05     34.04                33.0       1348.0           434.0   \n",
+              "20325    -118.88     34.17                15.0       4260.0           434.0   \n",
+              "14360    -117.87     33.62                 8.0       1266.0           434.0   \n",
+              "\n",
+              "       population  households  median_income  \n",
+              "14452      2503.0       902.0         3.5962  \n",
+              "18217      1755.0       403.0         3.4115  \n",
+              "11889      1098.0       257.0         4.2917  \n",
+              "20325      1701.0       669.0         5.1033  \n",
+              "14360       375.0       183.0         9.8020  "
+            ]
+          },
+          "execution_count": 52,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_tr.loc[null_rows_idx].head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gE47383ctgjs",
+        "outputId": "0b740c64-379e-42c3-9604-55ef4ff97e89"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'median'"
+            ]
+          },
+          "execution_count": 53,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "imputer.strategy"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5xDTeW1Ltgjt"
+      },
+      "outputs": [],
+      "source": [
+        "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
+        "                          index=housing_num.index)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d2wg0mUytgjx",
+        "outputId": "62e693b6-25c7-4a47-8575-092ec384c960"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"housing_tr\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1801821893250222,\n        \"min\": -120.67,\n        \"max\": -117.87,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -117.96,\n          -117.87,\n          -118.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9298412926300292,\n        \"min\": 33.62,\n        \"max\": 40.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.03,\n          33.62,\n          34.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.049896265113654,\n        \"min\": 8.0,\n        \"max\": 35.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          35.0,\n          8.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1839.8735010864198,\n        \"min\": 1266.0,\n        \"max\": 5343.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2093.0,\n          1266.0,\n          1348.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 434.0,\n        \"max\": 434.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          434.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 796.5141555553172,\n        \"min\": 375.0,\n        \"max\": 2503.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1755.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 299.012039891373,\n        \"min\": 183.0,\n        \"max\": 902.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          403.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6351133776367193,\n        \"min\": 3.4115,\n        \"max\": 9.802,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.4115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d81b3678-b335-4baa-8d0b-2f5228b1f170\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>14452</th>\n",
+              "      <td>-120.67</td>\n",
+              "      <td>40.50</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>5343.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>2503.0</td>\n",
+              "      <td>902.0</td>\n",
+              "      <td>3.5962</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18217</th>\n",
+              "      <td>-117.96</td>\n",
+              "      <td>34.03</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2093.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1755.0</td>\n",
+              "      <td>403.0</td>\n",
+              "      <td>3.4115</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11889</th>\n",
+              "      <td>-118.05</td>\n",
+              "      <td>34.04</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>1348.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1098.0</td>\n",
+              "      <td>257.0</td>\n",
+              "      <td>4.2917</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20325</th>\n",
+              "      <td>-118.88</td>\n",
+              "      <td>34.17</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>4260.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1701.0</td>\n",
+              "      <td>669.0</td>\n",
+              "      <td>5.1033</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14360</th>\n",
+              "      <td>-117.87</td>\n",
+              "      <td>33.62</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1266.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>375.0</td>\n",
+              "      <td>183.0</td>\n",
+              "      <td>9.8020</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d81b3678-b335-4baa-8d0b-2f5228b1f170')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d81b3678-b335-4baa-8d0b-2f5228b1f170 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d81b3678-b335-4baa-8d0b-2f5228b1f170');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-1c171308-fe30-4430-8d52-f5aea8bd7bc6\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1c171308-fe30-4430-8d52-f5aea8bd7bc6')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-1c171308-fe30-4430-8d52-f5aea8bd7bc6 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "14452    -120.67     40.50                15.0       5343.0           434.0   \n",
+              "18217    -117.96     34.03                35.0       2093.0           434.0   \n",
+              "11889    -118.05     34.04                33.0       1348.0           434.0   \n",
+              "20325    -118.88     34.17                15.0       4260.0           434.0   \n",
+              "14360    -117.87     33.62                 8.0       1266.0           434.0   \n",
+              "\n",
+              "       population  households  median_income  \n",
+              "14452      2503.0       902.0         3.5962  \n",
+              "18217      1755.0       403.0         3.4115  \n",
+              "11889      1098.0       257.0         4.2917  \n",
+              "20325      1701.0       669.0         5.1033  \n",
+              "14360       375.0       183.0         9.8020  "
+            ]
+          },
+          "execution_count": 55,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_tr.loc[null_rows_idx].head()  # not shown in the book"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OtsmyyNFtgj4"
+      },
+      "outputs": [],
+      "source": [
+        "#from sklearn import set_config\n",
+        "#\n",
+        "# set_config(transform_output=\"pandas\")  # scikit-learn >= 1.2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JUrdpksctgj-"
+      },
+      "source": [
+        "Now let's drop some outliers:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3pKUKkSytgkA"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import IsolationForest\n",
+        "\n",
+        "isolation_forest = IsolationForest(random_state=42)\n",
+        "outlier_pred = isolation_forest.fit_predict(X)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UcM3uTMStgkE",
+        "outputId": "69adac58-e70b-4bef-eeb4-1a8525c1931e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([-1,  1,  1, ...,  1,  1,  1])"
+            ]
+          },
+          "execution_count": 58,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "outlier_pred"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1Tvhpc0EtgkG"
+      },
+      "source": [
+        "If you wanted to drop outliers, you would run the following code:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eCoUogajtgkH"
+      },
+      "outputs": [],
+      "source": [
+        "#housing = housing.iloc[outlier_pred == 1]\n",
+        "#housing_labels = housing_labels.iloc[outlier_pred == 1]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R5i93yGctgkN"
+      },
+      "source": [
+        "## Handling Text and Categorical Attributes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-qanL78_tgkP"
+      },
+      "source": [
+        "Now let's preprocess the categorical input feature, `ocean_proximity`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "n-gRjhfvtgkP",
+        "outputId": "0c0d7096-6ccd-4144-adc0-6853eb4a11f3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"housing_cat\",\n  \"rows\": 16512,\n  \"fields\": [\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"<1H OCEAN\",\n          \"ISLAND\",\n          \"INLAND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "housing_cat"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-98f67d70-735c-4c21-9aa8-bae05ce3f4e8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ocean_proximity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>13096</th>\n",
+              "      <td>NEAR BAY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14973</th>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3785</th>\n",
+              "      <td>INLAND</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14689</th>\n",
+              "      <td>INLAND</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20507</th>\n",
+              "      <td>NEAR OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1286</th>\n",
+              "      <td>INLAND</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18078</th>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4396</th>\n",
+              "      <td>NEAR BAY</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98f67d70-735c-4c21-9aa8-bae05ce3f4e8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-98f67d70-735c-4c21-9aa8-bae05ce3f4e8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-98f67d70-735c-4c21-9aa8-bae05ce3f4e8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-79e24e6e-be29-4116-a8b4-c11ef61a77d2\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79e24e6e-be29-4116-a8b4-c11ef61a77d2')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-79e24e6e-be29-4116-a8b4-c11ef61a77d2 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "      ocean_proximity\n",
+              "13096        NEAR BAY\n",
+              "14973       <1H OCEAN\n",
+              "3785           INLAND\n",
+              "14689          INLAND\n",
+              "20507      NEAR OCEAN\n",
+              "1286           INLAND\n",
+              "18078       <1H OCEAN\n",
+              "4396         NEAR BAY"
+            ]
+          },
+          "execution_count": 60,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_cat = housing[[\"ocean_proximity\"]]\n",
+        "housing_cat.head(8)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ktkUpkaptgkQ"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import OrdinalEncoder\n",
+        "\n",
+        "ordinal_encoder = OrdinalEncoder()\n",
+        "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Je4tgbdmtgkV",
+        "outputId": "db8513f1-4c50-49e4-de81-f5fc357ab108"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[3.],\n",
+              "       [0.],\n",
+              "       [1.],\n",
+              "       [1.],\n",
+              "       [4.],\n",
+              "       [1.],\n",
+              "       [0.],\n",
+              "       [3.]])"
+            ]
+          },
+          "execution_count": 62,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_cat_encoded[:8]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VhI7CPqbtgkY",
+        "outputId": "3392a256-5a52-40f3-8c53-45144779a903"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
+              "       dtype=object)]"
+            ]
+          },
+          "execution_count": 63,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "ordinal_encoder.categories_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5cg9iIGltgkZ"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "\n",
+        "cat_encoder = OneHotEncoder()\n",
+        "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3VAPwp8utgkZ",
+        "outputId": "ddaf86a9-9f37-4af9-f297-e356973cbcab"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Compressed Sparse Row sparse matrix of dtype 'float64'\n",
+              "\twith 16512 stored elements and shape (16512, 5)>"
+            ]
+          },
+          "execution_count": 65,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_cat_1hot"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0m5XeSgJtgkc"
+      },
+      "source": [
+        "By default, the `OneHotEncoder` class returns a sparse array, but we can convert it to a dense array if needed by calling the `toarray()` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9TTH1pmntgkd",
+        "outputId": "ae9ec514-e0bd-4e3f-e6d1-4127bd4b847f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[0., 0., 0., 1., 0.],\n",
+              "       [1., 0., 0., 0., 0.],\n",
+              "       [0., 1., 0., 0., 0.],\n",
+              "       ...,\n",
+              "       [0., 0., 0., 0., 1.],\n",
+              "       [1., 0., 0., 0., 0.],\n",
+              "       [0., 0., 0., 0., 1.]])"
+            ]
+          },
+          "execution_count": 66,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_cat_1hot.toarray()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JuKk5BPbtgkj"
+      },
+      "source": [
+        "Alternatively, you can set `sparse_output=False` when creating the `OneHotEncoder` (note: the `sparse` hyperparameter was renamned to `sparse_output` in Scikit-Learn 1.2):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7EWNapZCtgkk",
+        "outputId": "98e02bfe-a108-484c-a899-4e0c593ed007"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[0., 0., 0., 1., 0.],\n",
+              "       [1., 0., 0., 0., 0.],\n",
+              "       [0., 1., 0., 0., 0.],\n",
+              "       ...,\n",
+              "       [0., 0., 0., 0., 1.],\n",
+              "       [1., 0., 0., 0., 0.],\n",
+              "       [0., 0., 0., 0., 1.]])"
+            ]
+          },
+          "execution_count": 67,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "cat_encoder = OneHotEncoder(sparse_output=False)\n",
+        "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
+        "housing_cat_1hot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DvOsyHZWtgkl",
+        "outputId": "5c104ae5-1cf9-4580-e0c2-e838826c5f10"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
+              "       dtype=object)]"
+            ]
+          },
+          "execution_count": 68,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "cat_encoder.categories_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vvyQU8GRtgkl",
+        "outputId": "97add2b5-a861-47ef-ff98-a76291843412"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"ocean_proximity_INLAND\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR BAY\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e51e8d85-bc0d-4627-9ad4-23676c8accca\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ocean_proximity_INLAND</th>\n",
+              "      <th>ocean_proximity_NEAR BAY</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e51e8d85-bc0d-4627-9ad4-23676c8accca')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e51e8d85-bc0d-4627-9ad4-23676c8accca button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e51e8d85-bc0d-4627-9ad4-23676c8accca');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f669e8fe-3d15-40e3-8d38-e7322a5d30a5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f669e8fe-3d15-40e3-8d38-e7322a5d30a5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-f669e8fe-3d15-40e3-8d38-e7322a5d30a5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "   ocean_proximity_INLAND  ocean_proximity_NEAR BAY\n",
+              "0                    True                     False\n",
+              "1                   False                      True"
+            ]
+          },
+          "execution_count": 69,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_test = pd.DataFrame({\"ocean_proximity\": [\"INLAND\", \"NEAR BAY\"]})\n",
+        "pd.get_dummies(df_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "R8KfXuZutgkm",
+        "outputId": "be743d5e-c0fc-4ef3-ce0d-b79ec00c6c51"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[0., 1., 0., 0., 0.],\n",
+              "       [0., 0., 0., 1., 0.]])"
+            ]
+          },
+          "execution_count": 70,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "cat_encoder.transform(df_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VbVkRpl_tgkn",
+        "outputId": "0b7105cc-a8c6-48cd-abbc-ad1814cd011a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"ocean_proximity_<2H OCEAN\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_ISLAND\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e900d25a-fc07-4211-96de-6ebd75138751\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ocean_proximity_&lt;2H OCEAN</th>\n",
+              "      <th>ocean_proximity_ISLAND</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e900d25a-fc07-4211-96de-6ebd75138751')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e900d25a-fc07-4211-96de-6ebd75138751 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e900d25a-fc07-4211-96de-6ebd75138751');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-ba225230-b686-4c24-bb39-0e4af56a87ea\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ba225230-b686-4c24-bb39-0e4af56a87ea')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-ba225230-b686-4c24-bb39-0e4af56a87ea button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "   ocean_proximity_<2H OCEAN  ocean_proximity_ISLAND\n",
+              "0                       True                   False\n",
+              "1                      False                    True"
+            ]
+          },
+          "execution_count": 71,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_test_unknown = pd.DataFrame({\"ocean_proximity\": [\"<2H OCEAN\", \"ISLAND\"]})\n",
+        "pd.get_dummies(df_test_unknown)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "J6-zaD92tgko",
+        "outputId": "fa298480-3233-45f0-9bc4-eeec3c0fb9b5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[0., 0., 0., 0., 0.],\n",
+              "       [0., 0., 1., 0., 0.]])"
+            ]
+          },
+          "execution_count": 72,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "cat_encoder.handle_unknown = \"ignore\"\n",
+        "cat_encoder.transform(df_test_unknown)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "P3wxV9cqtgks",
+        "outputId": "4a090f42-a4c3-44e4-de73-e7aacef9b7b5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array(['ocean_proximity'], dtype=object)"
+            ]
+          },
+          "execution_count": 73,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "cat_encoder.feature_names_in_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PRtyvzrTtgk5",
+        "outputId": "7a0b2f0a-fde7-4a51-b278-a84bbdb7bdf7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array(['ocean_proximity_<1H OCEAN', 'ocean_proximity_INLAND',\n",
+              "       'ocean_proximity_ISLAND', 'ocean_proximity_NEAR BAY',\n",
+              "       'ocean_proximity_NEAR OCEAN'], dtype=object)"
+            ]
+          },
+          "execution_count": 74,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "cat_encoder.get_feature_names_out()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hB1mqMJutgk6"
+      },
+      "outputs": [],
+      "source": [
+        "df_output = pd.DataFrame(cat_encoder.transform(df_test_unknown),\n",
+        "                         columns=cat_encoder.get_feature_names_out(),\n",
+        "                         index=df_test_unknown.index)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kxCBVSZFtgk6",
+        "outputId": "cb97c3c5-7de5-4e3a-8f7e-9ce5e52d82ed"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_output\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"ocean_proximity_<1H OCEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_INLAND\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_ISLAND\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7071067811865476,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR BAY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR OCEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_output"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b48a6f0c-6e57-49b4-adb1-22d06d4366cf\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ocean_proximity_&lt;1H OCEAN</th>\n",
+              "      <th>ocean_proximity_INLAND</th>\n",
+              "      <th>ocean_proximity_ISLAND</th>\n",
+              "      <th>ocean_proximity_NEAR BAY</th>\n",
+              "      <th>ocean_proximity_NEAR OCEAN</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b48a6f0c-6e57-49b4-adb1-22d06d4366cf')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b48a6f0c-6e57-49b4-adb1-22d06d4366cf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b48a6f0c-6e57-49b4-adb1-22d06d4366cf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-89cbd3ac-51a0-45e6-9984-158208da42f2\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-89cbd3ac-51a0-45e6-9984-158208da42f2')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-89cbd3ac-51a0-45e6-9984-158208da42f2 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_d916aad7-0e99-4ec8-8543-46c62722f5e3\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_output')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_d916aad7-0e99-4ec8-8543-46c62722f5e3 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_output');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "   ocean_proximity_<1H OCEAN  ocean_proximity_INLAND  ocean_proximity_ISLAND  \\\n",
+              "0                        0.0                     0.0                     0.0   \n",
+              "1                        0.0                     0.0                     1.0   \n",
+              "\n",
+              "   ocean_proximity_NEAR BAY  ocean_proximity_NEAR OCEAN  \n",
+              "0                       0.0                         0.0  \n",
+              "1                       0.0                         0.0  "
+            ]
+          },
+          "execution_count": 76,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_output"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nOl01x_Btgk7"
+      },
+      "source": [
+        "## Feature Scaling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Fo1l9X1ktgk8"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "\n",
+        "min_max_scaler = MinMaxScaler(feature_range=(-1, 1))\n",
+        "housing_num_min_max_scaled = min_max_scaler.fit_transform(housing_num)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KfC3ALxItgk8"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import StandardScaler\n",
+        "\n",
+        "std_scaler = StandardScaler()\n",
+        "housing_num_std_scaled = std_scaler.fit_transform(housing_num)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bHX0soBhtglA",
+        "outputId": "33a9c606-1823-488b-c164-ff640a938212"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 800x300 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# extra code – this cell generates Figure 2–17\n",
+        "fig, axs = plt.subplots(1, 2, figsize=(8, 3), sharey=True)\n",
+        "housing[\"population\"].hist(ax=axs[0], bins=50)\n",
+        "housing[\"population\"].apply(np.log).hist(ax=axs[1], bins=50)\n",
+        "axs[0].set_xlabel(\"Population\")\n",
+        "axs[1].set_xlabel(\"Log of population\")\n",
+        "axs[0].set_ylabel(\"Number of districts\")\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3nJyA8KRtglA"
+      },
+      "source": [
+        "What if we replace each value with its percentile?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DzuML051tglB",
+        "outputId": "5be271fb-872d-4870-988a-80e40731cd06"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# extra code – just shows that we get a uniform distribution\n",
+        "percentiles = [np.percentile(housing[\"median_income\"], p)\n",
+        "               for p in range(1, 100)]\n",
+        "flattened_median_income = pd.cut(housing[\"median_income\"],\n",
+        "                                 bins=[-np.inf] + percentiles + [np.inf],\n",
+        "                                 labels=range(1, 100 + 1))\n",
+        "flattened_median_income.hist(bins=50)\n",
+        "plt.xlabel(\"Median income percentile\")\n",
+        "plt.ylabel(\"Number of districts\")\n",
+        "plt.show()\n",
+        "# Note: incomes below the 1st percentile are labeled 1, and incomes above the\n",
+        "# 99th percentile are labeled 100. This is why the distribution below ranges\n",
+        "# from 1 to 100 (not 0 to 100)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YEOFSOeVtglD"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics.pairwise import rbf_kernel\n",
+        "\n",
+        "age_simil_35 = rbf_kernel(housing[[\"housing_median_age\"]], [[35]], gamma=0.1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3BTt4go8tglD",
+        "outputId": "e2f5e6e5-4f8c-4fe3-fca2-0048b38ad6b3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# extra code – this cell generates Figure 2–18\n",
+        "\n",
+        "ages = np.linspace(housing[\"housing_median_age\"].min(),\n",
+        "                   housing[\"housing_median_age\"].max(),\n",
+        "                   500).reshape(-1, 1)\n",
+        "gamma1 = 0.1\n",
+        "gamma2 = 0.03\n",
+        "rbf1 = rbf_kernel(ages, [[35]], gamma=gamma1)\n",
+        "rbf2 = rbf_kernel(ages, [[35]], gamma=gamma2)\n",
+        "\n",
+        "fig, ax1 = plt.subplots()\n",
+        "\n",
+        "ax1.set_xlabel(\"Housing median age\")\n",
+        "ax1.set_ylabel(\"Number of districts\")\n",
+        "ax1.hist(housing[\"housing_median_age\"], bins=50)\n",
+        "\n",
+        "ax2 = ax1.twinx()  # create a twin axis that shares the same x-axis\n",
+        "color = \"blue\"\n",
+        "ax2.plot(ages, rbf1, color=color, label=\"gamma = 0.10\")\n",
+        "ax2.plot(ages, rbf2, color=color, label=\"gamma = 0.03\", linestyle=\"--\")\n",
+        "ax2.tick_params(axis='y', labelcolor=color)\n",
+        "ax2.set_ylabel(\"Age similarity\", color=color)\n",
+        "\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RIAe8xdPtglH"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "target_scaler = StandardScaler()\n",
+        "scaled_labels = target_scaler.fit_transform(housing_labels.to_frame())\n",
+        "\n",
+        "model = LinearRegression()\n",
+        "model.fit(housing[[\"median_income\"]], scaled_labels)\n",
+        "some_new_data = housing[[\"median_income\"]].iloc[:5]  # pretend this is new data\n",
+        "\n",
+        "scaled_predictions = model.predict(some_new_data)\n",
+        "predictions = target_scaler.inverse_transform(scaled_predictions)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YqHuFelTtglI",
+        "outputId": "f8e018fe-5513-4892-afe3-1d187c9b8d15"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[131997.15275877],\n",
+              "       [299359.35844434],\n",
+              "       [146023.37185694],\n",
+              "       [138840.33653057],\n",
+              "       [192016.61557639]])"
+            ]
+          },
+          "execution_count": 84,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "predictions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kCwB4f7TtglJ"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.compose import TransformedTargetRegressor\n",
+        "\n",
+        "model = TransformedTargetRegressor(LinearRegression(),\n",
+        "                                   transformer=StandardScaler())\n",
+        "model.fit(housing[[\"median_income\"]], housing_labels)\n",
+        "predictions = model.predict(some_new_data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TAyxJOpntglK",
+        "outputId": "1e46dc40-755b-4731-d9d0-6a800e403721"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([131997.15275877, 299359.35844434, 146023.37185694, 138840.33653057,\n",
+              "       192016.61557639])"
+            ]
+          },
+          "execution_count": 86,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "predictions"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "It6YXdOatglL"
+      },
+      "source": [
+        "## Custom Transformers"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H9dKfM1vtglL"
+      },
+      "source": [
+        "To create simple transformers:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7QqvK76XtglL"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import FunctionTransformer\n",
+        "\n",
+        "log_transformer = FunctionTransformer(np.log, inverse_func=np.exp)\n",
+        "log_pop = log_transformer.transform(housing[[\"population\"]])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "D4hIoiPXtglZ"
+      },
+      "outputs": [],
+      "source": [
+        "rbf_transformer = FunctionTransformer(rbf_kernel,\n",
+        "                                      kw_args=dict(Y=[[35.]], gamma=0.1))\n",
+        "age_simil_35 = rbf_transformer.transform(housing[[\"housing_median_age\"]])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "M-9V14-ctglb",
+        "outputId": "9bc5914f-901f-402c-e563-12c447d50c74"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[2.81118530e-13],\n",
+              "       [8.20849986e-02],\n",
+              "       [6.70320046e-01],\n",
+              "       ...,\n",
+              "       [9.55316054e-22],\n",
+              "       [6.70320046e-01],\n",
+              "       [3.03539138e-04]])"
+            ]
+          },
+          "execution_count": 89,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "age_simil_35"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3beGF03htgld"
+      },
+      "outputs": [],
+      "source": [
+        "sf_coords = 37.7749, -122.41\n",
+        "sf_transformer = FunctionTransformer(rbf_kernel,\n",
+        "                                     kw_args=dict(Y=[sf_coords], gamma=0.1))\n",
+        "sf_simil = sf_transformer.transform(housing[[\"latitude\", \"longitude\"]])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CcNOXX5Ctgle",
+        "outputId": "b14f57df-1a99-466d-ae1b-0eb17ec350dd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[0.999927  ],\n",
+              "       [0.05258419],\n",
+              "       [0.94864161],\n",
+              "       ...,\n",
+              "       [0.00388525],\n",
+              "       [0.05038518],\n",
+              "       [0.99868067]])"
+            ]
+          },
+          "execution_count": 91,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "sf_simil"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "P1ZKn4C1tgli",
+        "outputId": "caa89f5a-445f-4e80-b2d8-927121e94b91"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[0.5 ],\n",
+              "       [0.75]])"
+            ]
+          },
+          "execution_count": 92,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "ratio_transformer = FunctionTransformer(lambda X: X[:, [0]] / X[:, [1]])\n",
+        "ratio_transformer.transform(np.array([[1., 2.], [3., 4.]]))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AbXV8Oe5tglj"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.base import BaseEstimator, TransformerMixin\n",
+        "from sklearn.utils.validation import check_array, check_is_fitted\n",
+        "\n",
+        "class StandardScalerClone(BaseEstimator, TransformerMixin):\n",
+        "    def __init__(self, with_mean=True):  # no *args or **kwargs!\n",
+        "        self.with_mean = with_mean\n",
+        "\n",
+        "    def fit(self, X, y=None):  # y is required even though we don't use it\n",
+        "        X = check_array(X)  # checks that X is an array with finite float values\n",
+        "        self.mean_ = X.mean(axis=0)\n",
+        "        self.scale_ = X.std(axis=0)\n",
+        "        self.n_features_in_ = X.shape[1]  # every estimator stores this in fit()\n",
+        "        return self  # always return self!\n",
+        "\n",
+        "    def transform(self, X):\n",
+        "        check_is_fitted(self)  # looks for learned attributes (with trailing _)\n",
+        "        X = check_array(X)\n",
+        "        assert self.n_features_in_ == X.shape[1]\n",
+        "        if self.with_mean:\n",
+        "            X = X - self.mean_\n",
+        "        return X / self.scale_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pPsdVm9Ytglk"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.cluster import KMeans\n",
+        "\n",
+        "class ClusterSimilarity(BaseEstimator, TransformerMixin):\n",
+        "    def __init__(self, n_clusters=10, gamma=1.0, random_state=None):\n",
+        "        self.n_clusters = n_clusters\n",
+        "        self.gamma = gamma\n",
+        "        self.random_state = random_state\n",
+        "\n",
+        "    def fit(self, X, y=None, sample_weight=None):\n",
+        "        self.kmeans_ = KMeans(self.n_clusters, random_state=self.random_state)\n",
+        "        self.kmeans_.fit(X, sample_weight=sample_weight)\n",
+        "        return self  # always return self!\n",
+        "\n",
+        "    def transform(self, X):\n",
+        "        return rbf_kernel(X, self.kmeans_.cluster_centers_, gamma=self.gamma)\n",
+        "\n",
+        "    def get_feature_names_out(self, names=None):\n",
+        "        return [f\"Cluster {i} similarity\" for i in range(self.n_clusters)]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_mJYR6txtgll"
+      },
+      "source": [
+        "Let's cluster the districts based only on the latitude and longitude:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0mrdlyQxtgll"
+      },
+      "outputs": [],
+      "source": [
+        "cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\n",
+        "similarities = cluster_simil.fit_transform(housing[[\"latitude\", \"longitude\"]])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5bHEiXN5tglm",
+        "outputId": "d39066d7-391b-46f9-df72-68d83be873bf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[0.  , 0.98, 0.  , 0.  , 0.  , 0.  , 0.13, 0.55, 0.  , 0.56],\n",
+              "       [0.64, 0.  , 0.11, 0.04, 0.  , 0.  , 0.  , 0.  , 0.99, 0.  ],\n",
+              "       [0.  , 0.65, 0.  , 0.  , 0.01, 0.  , 0.49, 0.59, 0.  , 0.28]])"
+            ]
+          },
+          "execution_count": 96,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "similarities[:3].round(2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bUNRkg98tglm",
+        "outputId": "1e2302e1-0d55-48dc-9506-09c86c9faa8d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 1000x700 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# extra code – this cell generates Figure 2–19\n",
+        "\n",
+        "housing_renamed = housing.rename(columns={\n",
+        "    \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n",
+        "    \"population\": \"Population\",\n",
+        "    \"median_house_value\": \"Median house value (ᴜsᴅ)\"})\n",
+        "housing_renamed[\"Max cluster similarity\"] = similarities.max(axis=1)\n",
+        "\n",
+        "housing_renamed.plot(kind=\"scatter\", x=\"Longitude\", y=\"Latitude\", grid=True,\n",
+        "                     s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n",
+        "                     c=\"Max cluster similarity\",\n",
+        "                     cmap=\"jet\", colorbar=True,\n",
+        "                     legend=True, sharex=False, figsize=(10, 7))\n",
+        "plt.plot(cluster_simil.kmeans_.cluster_centers_[:, 1],\n",
+        "         cluster_simil.kmeans_.cluster_centers_[:, 0],\n",
+        "         linestyle=\"\", color=\"black\", marker=\"X\", markersize=20,\n",
+        "         label=\"Cluster centers\")\n",
+        "plt.legend(loc=\"upper right\")\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PBxtGMdntgln"
+      },
+      "source": [
+        "## Transformation Pipelines"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7XA3JEDJtglo"
+      },
+      "source": [
+        "Now let's build a pipeline to preprocess the numerical attributes:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-l2fx3zHtglr"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.pipeline import Pipeline\n",
+        "\n",
+        "num_pipeline = Pipeline([\n",
+        "    (\"impute\", SimpleImputer(strategy=\"median\")),\n",
+        "    (\"standardize\", StandardScaler()),\n",
+        "])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sjwoY8Rktglr"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.pipeline import make_pipeline\n",
+        "\n",
+        "num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Jhh5bEbZtgls",
+        "outputId": "92ed61e2-3592-4171-9ae4-bd13cbcf6195"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+              "                ('standardscaler', StandardScaler())])"
+            ]
+          },
+          "execution_count": 100,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn import set_config\n",
+        "\n",
+        "set_config(display='diagram')\n",
+        "\n",
+        "num_pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bDc7idTztglt",
+        "outputId": "e3dfe6ab-50a9-4ccf-c9e0-9fd3f457b369"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[-1.42,  1.01,  1.86,  0.31,  1.37,  0.14,  1.39, -0.94],\n",
+              "       [ 0.6 , -0.7 ,  0.91, -0.31, -0.44, -0.69, -0.37,  1.17]])"
+            ]
+          },
+          "execution_count": 101,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_num_prepared = num_pipeline.fit_transform(housing_num)\n",
+        "housing_num_prepared[:2].round(2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RnpV_3xLtglu"
+      },
+      "outputs": [],
+      "source": [
+        "df_housing_num_prepared = pd.DataFrame(\n",
+        "    housing_num_prepared, columns=num_pipeline.get_feature_names_out(),\n",
+        "    index=housing_num.index)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jHiW-_vhtglv",
+        "outputId": "b6fce632-cbd8-471c-87e7-5f1cb90f77af"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_housing_num_prepared\",\n  \"rows\": 16512,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.00003028238323,\n        \"min\": -2.3877646847945297,\n        \"max\": 2.5408465499717225,\n        \"num_unique_values\": 817,\n        \"samples\": [\n          -1.5280069440442305,\n          -0.6282604711660115,\n          1.361178952198056\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383244,\n        \"min\": -1.4474519456998403,\n        \"max\": 2.959013528420613,\n        \"num_unique_values\": 835,\n        \"samples\": [\n          2.424612396452814,\n          2.3496087288082084,\n          1.5808211354510233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383234,\n        \"min\": -2.1912096612263676,\n        \"max\": 1.8611187475164104,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          1.146001969502979,\n          1.3049168090615193,\n          1.7816613277371403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000302823832317,\n        \"min\": -1.2069271002738067,\n        \"max\": 16.785758645406965,\n        \"num_unique_values\": 5473,\n        \"samples\": [\n          0.4066394580269817,\n          0.45972332664946935,\n          -0.1278601846546181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000302823832243,\n        \"min\": -1.2727767935135679,\n        \"max\": 13.446810448110993,\n        \"num_unique_values\": 1827,\n        \"samples\": [\n          -0.25575264684632076,\n          1.2567448020434313,\n          4.115791703863245\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383224,\n        \"min\": -1.2993815273147138,\n        \"max\": 13.591521979866151,\n        \"num_unique_values\": 3640,\n        \"samples\": [\n          -0.21330102982740873,\n          0.8965289150397702,\n          0.026933764822853052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383228,\n        \"min\": -1.3033426245281265,\n        \"max\": 12.688929356290272,\n        \"num_unique_values\": 1712,\n        \"samples\": [\n          -0.031317898999181186,\n          0.8489127632251608,\n          1.6847400982749814\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000302823832292,\n        \"min\": -1.7815774618066682,\n        \"max\": 5.882868113005668,\n        \"num_unique_values\": 10930,\n        \"samples\": [\n          -0.11984286517386168,\n          -0.4482466137863569,\n          0.9520547073535472\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_housing_num_prepared"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b4a7d327-e438-49bc-bbe0-2295aaaf86ca\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>13096</th>\n",
+              "      <td>-1.423037</td>\n",
+              "      <td>1.013606</td>\n",
+              "      <td>1.861119</td>\n",
+              "      <td>0.311912</td>\n",
+              "      <td>1.368167</td>\n",
+              "      <td>0.137460</td>\n",
+              "      <td>1.394812</td>\n",
+              "      <td>-0.936491</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14973</th>\n",
+              "      <td>0.596394</td>\n",
+              "      <td>-0.702103</td>\n",
+              "      <td>0.907630</td>\n",
+              "      <td>-0.308620</td>\n",
+              "      <td>-0.435925</td>\n",
+              "      <td>-0.693771</td>\n",
+              "      <td>-0.373485</td>\n",
+              "      <td>1.171942</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4a7d327-e438-49bc-bbe0-2295aaaf86ca')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b4a7d327-e438-49bc-bbe0-2295aaaf86ca button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b4a7d327-e438-49bc-bbe0-2295aaaf86ca');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-691de7e9-26d8-4da9-b8ed-38663f3faacc\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-691de7e9-26d8-4da9-b8ed-38663f3faacc')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-691de7e9-26d8-4da9-b8ed-38663f3faacc button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "13096  -1.423037  1.013606            1.861119     0.311912        1.368167   \n",
+              "14973   0.596394 -0.702103            0.907630    -0.308620       -0.435925   \n",
+              "\n",
+              "       population  households  median_income  \n",
+              "13096    0.137460    1.394812      -0.936491  \n",
+              "14973   -0.693771   -0.373485       1.171942  "
+            ]
+          },
+          "execution_count": 103,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_housing_num_prepared.head(2)  # extra code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "srDEevGVtglv",
+        "outputId": "a9651784-ceb3-43ab-afee-7a82500cc7ae"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[('simpleimputer', SimpleImputer(strategy='median')),\n",
+              " ('standardscaler', StandardScaler())]"
+            ]
+          },
+          "execution_count": 104,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "num_pipeline.steps"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "L9zyunZ3tglw",
+        "outputId": "ce166f35-1165-4474-8f2e-4bcbadfea802"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "StandardScaler()"
+            ]
+          },
+          "execution_count": 105,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "num_pipeline[1]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "W5d5M73btglx",
+        "outputId": "94be59d6-d1dd-4db4-8905-12ef6c8b5b17"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median'))])"
+            ]
+          },
+          "execution_count": 106,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "num_pipeline[:-1]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "evIo3zDztglx",
+        "outputId": "42414dd6-9f85-4365-a0cc-1e60159f5dbb"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "SimpleImputer(strategy='median')"
+            ]
+          },
+          "execution_count": 107,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "num_pipeline.named_steps[\"simpleimputer\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ag14ENaItglz",
+        "outputId": "65eeab51-0676-419b-89ef-a2b60b0105eb"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+              "                ('standardscaler', StandardScaler())])"
+            ]
+          },
+          "execution_count": 108,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "num_pipeline.set_params(simpleimputer__strategy=\"median\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mGC5H8q6tgl0"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.compose import ColumnTransformer\n",
+        "\n",
+        "num_attribs = [\"longitude\", \"latitude\", \"housing_median_age\", \"total_rooms\",\n",
+        "               \"total_bedrooms\", \"population\", \"households\", \"median_income\"]\n",
+        "cat_attribs = [\"ocean_proximity\"]\n",
+        "\n",
+        "cat_pipeline = make_pipeline(\n",
+        "    SimpleImputer(strategy=\"most_frequent\"),\n",
+        "    OneHotEncoder(handle_unknown=\"ignore\"))\n",
+        "\n",
+        "preprocessing = ColumnTransformer([\n",
+        "    (\"num\", num_pipeline, num_attribs),\n",
+        "    (\"cat\", cat_pipeline, cat_attribs),\n",
+        "])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gvtnTQBvtgl1"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.compose import make_column_selector, make_column_transformer\n",
+        "\n",
+        "preprocessing = make_column_transformer(\n",
+        "    (num_pipeline, make_column_selector(dtype_include=np.number)),\n",
+        "    (cat_pipeline, make_column_selector(dtype_include=object)),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "R9cq9n5Gtgl2"
+      },
+      "outputs": [],
+      "source": [
+        "housing_prepared = preprocessing.fit_transform(housing)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KwqHJpIItgl2",
+        "outputId": "c77a6190-89dc-44f2-cb21-2145d06f0c67"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"housing_prepared_fr\",\n  \"rows\": 16512,\n  \"fields\": [\n    {\n      \"column\": \"pipeline-1__longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.00003028238323,\n        \"min\": -2.3877646847945297,\n        \"max\": 2.5408465499717225,\n        \"num_unique_values\": 817,\n        \"samples\": [\n          -1.5280069440442305,\n          -0.6282604711660115,\n          1.361178952198056\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383244,\n        \"min\": -1.4474519456998403,\n        \"max\": 2.959013528420613,\n        \"num_unique_values\": 835,\n        \"samples\": [\n          2.424612396452814,\n          2.3496087288082084,\n          1.5808211354510233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383234,\n        \"min\": -2.1912096612263676,\n        \"max\": 1.8611187475164104,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          1.146001969502979,\n          1.3049168090615193,\n          1.7816613277371403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000302823832317,\n        \"min\": -1.2069271002738067,\n        \"max\": 16.785758645406965,\n        \"num_unique_values\": 5473,\n        \"samples\": [\n          0.4066394580269817,\n          0.45972332664946935,\n          -0.1278601846546181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000302823832243,\n        \"min\": -1.2727767935135679,\n        \"max\": 13.446810448110993,\n        \"num_unique_values\": 1827,\n        \"samples\": [\n          -0.25575264684632076,\n          1.2567448020434313,\n          4.115791703863245\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383224,\n        \"min\": -1.2993815273147138,\n        \"max\": 13.591521979866151,\n        \"num_unique_values\": 3640,\n        \"samples\": [\n          -0.21330102982740873,\n          0.8965289150397702,\n          0.026933764822853052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000030282383228,\n        \"min\": -1.3033426245281265,\n        \"max\": 12.688929356290272,\n        \"num_unique_values\": 1712,\n        \"samples\": [\n          -0.031317898999181186,\n          0.8489127632251608,\n          1.6847400982749814\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-1__median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000302823832292,\n        \"min\": -1.7815774618066682,\n        \"max\": 5.882868113005668,\n        \"num_unique_values\": 10930,\n        \"samples\": [\n          -0.11984286517386168,\n          -0.4482466137863569,\n          0.9520547073535472\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-2__ocean_proximity_<1H OCEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.49646552791533155,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-2__ocean_proximity_INLAND\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.46688997586710157,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-2__ocean_proximity_ISLAND\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011005303042079697,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-2__ocean_proximity_NEAR BAY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3151266698441346,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pipeline-2__ocean_proximity_NEAR OCEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33243787156331894,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "housing_prepared_fr"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b9cf8dd7-5ed5-42d3-a085-f5d64acc5748\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pipeline-1__longitude</th>\n",
+              "      <th>pipeline-1__latitude</th>\n",
+              "      <th>pipeline-1__housing_median_age</th>\n",
+              "      <th>pipeline-1__total_rooms</th>\n",
+              "      <th>pipeline-1__total_bedrooms</th>\n",
+              "      <th>pipeline-1__population</th>\n",
+              "      <th>pipeline-1__households</th>\n",
+              "      <th>pipeline-1__median_income</th>\n",
+              "      <th>pipeline-2__ocean_proximity_&lt;1H OCEAN</th>\n",
+              "      <th>pipeline-2__ocean_proximity_INLAND</th>\n",
+              "      <th>pipeline-2__ocean_proximity_ISLAND</th>\n",
+              "      <th>pipeline-2__ocean_proximity_NEAR BAY</th>\n",
+              "      <th>pipeline-2__ocean_proximity_NEAR OCEAN</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>13096</th>\n",
+              "      <td>-1.423037</td>\n",
+              "      <td>1.013606</td>\n",
+              "      <td>1.861119</td>\n",
+              "      <td>0.311912</td>\n",
+              "      <td>1.368167</td>\n",
+              "      <td>0.137460</td>\n",
+              "      <td>1.394812</td>\n",
+              "      <td>-0.936491</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14973</th>\n",
+              "      <td>0.596394</td>\n",
+              "      <td>-0.702103</td>\n",
+              "      <td>0.907630</td>\n",
+              "      <td>-0.308620</td>\n",
+              "      <td>-0.435925</td>\n",
+              "      <td>-0.693771</td>\n",
+              "      <td>-0.373485</td>\n",
+              "      <td>1.171942</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b9cf8dd7-5ed5-42d3-a085-f5d64acc5748')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b9cf8dd7-5ed5-42d3-a085-f5d64acc5748 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b9cf8dd7-5ed5-42d3-a085-f5d64acc5748');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-58cad9b8-f58e-4e21-9bf3-92ee851f7074\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-58cad9b8-f58e-4e21-9bf3-92ee851f7074')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-58cad9b8-f58e-4e21-9bf3-92ee851f7074 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "       pipeline-1__longitude  pipeline-1__latitude  \\\n",
+              "13096              -1.423037              1.013606   \n",
+              "14973               0.596394             -0.702103   \n",
+              "\n",
+              "       pipeline-1__housing_median_age  pipeline-1__total_rooms  \\\n",
+              "13096                        1.861119                 0.311912   \n",
+              "14973                        0.907630                -0.308620   \n",
+              "\n",
+              "       pipeline-1__total_bedrooms  pipeline-1__population  \\\n",
+              "13096                    1.368167                0.137460   \n",
+              "14973                   -0.435925               -0.693771   \n",
+              "\n",
+              "       pipeline-1__households  pipeline-1__median_income  \\\n",
+              "13096                1.394812                  -0.936491   \n",
+              "14973               -0.373485                   1.171942   \n",
+              "\n",
+              "       pipeline-2__ocean_proximity_<1H OCEAN  \\\n",
+              "13096                                    0.0   \n",
+              "14973                                    1.0   \n",
+              "\n",
+              "       pipeline-2__ocean_proximity_INLAND  pipeline-2__ocean_proximity_ISLAND  \\\n",
+              "13096                                 0.0                                 0.0   \n",
+              "14973                                 0.0                                 0.0   \n",
+              "\n",
+              "       pipeline-2__ocean_proximity_NEAR BAY  \\\n",
+              "13096                                   1.0   \n",
+              "14973                                   0.0   \n",
+              "\n",
+              "       pipeline-2__ocean_proximity_NEAR OCEAN  \n",
+              "13096                                     0.0  \n",
+              "14973                                     0.0  "
+            ]
+          },
+          "execution_count": 112,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# extra code – shows that we can get a DataFrame out if we want\n",
+        "housing_prepared_fr = pd.DataFrame(\n",
+        "    housing_prepared,\n",
+        "    columns=preprocessing.get_feature_names_out(),\n",
+        "    index=housing.index)\n",
+        "housing_prepared_fr.head(2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KwZRHInhtgl2"
+      },
+      "outputs": [],
+      "source": [
+        "def column_ratio(X):\n",
+        "    return X[:, [0]] / X[:, [1]]\n",
+        "\n",
+        "def ratio_name(function_transformer, feature_names_in):\n",
+        "    return [\"ratio\"]  # feature names out\n",
+        "\n",
+        "def ratio_pipeline():\n",
+        "    return make_pipeline(\n",
+        "        SimpleImputer(strategy=\"median\"),\n",
+        "        FunctionTransformer(column_ratio, feature_names_out=ratio_name),\n",
+        "        StandardScaler())\n",
+        "\n",
+        "log_pipeline = make_pipeline(\n",
+        "    SimpleImputer(strategy=\"median\"),\n",
+        "    FunctionTransformer(np.log, feature_names_out=\"one-to-one\"),\n",
+        "    StandardScaler())\n",
+        "cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\n",
+        "default_num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n",
+        "                                     StandardScaler())\n",
+        "preprocessing = ColumnTransformer([\n",
+        "        (\"bedrooms\", ratio_pipeline(), [\"total_bedrooms\", \"total_rooms\"]),\n",
+        "        (\"rooms_per_house\", ratio_pipeline(), [\"total_rooms\", \"households\"]),\n",
+        "        (\"people_per_house\", ratio_pipeline(), [\"population\", \"households\"]),\n",
+        "        (\"log\", log_pipeline, [\"total_bedrooms\", \"total_rooms\", \"population\",\n",
+        "                               \"households\", \"median_income\"]),\n",
+        "        (\"geo\", cluster_simil, [\"latitude\", \"longitude\"]),\n",
+        "        (\"cat\", cat_pipeline, make_column_selector(dtype_include=object)),\n",
+        "    ],\n",
+        "    remainder=default_num_pipeline)  # one column remaining: housing_median_age"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QDz-pDoDtgl3",
+        "outputId": "37134850-8c7d-44ea-83a6-d9950122850f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(16512, 24)"
+            ]
+          },
+          "execution_count": 114,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_prepared = preprocessing.fit_transform(housing)\n",
+        "housing_prepared.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EvBV1I5-tgl6",
+        "outputId": "c3f0a44b-0fd2-45dd-cc9d-b79b46b2849b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array(['bedrooms__ratio', 'rooms_per_house__ratio',\n",
+              "       'people_per_house__ratio', 'log__total_bedrooms',\n",
+              "       'log__total_rooms', 'log__population', 'log__households',\n",
+              "       'log__median_income', 'geo__Cluster 0 similarity',\n",
+              "       'geo__Cluster 1 similarity', 'geo__Cluster 2 similarity',\n",
+              "       'geo__Cluster 3 similarity', 'geo__Cluster 4 similarity',\n",
+              "       'geo__Cluster 5 similarity', 'geo__Cluster 6 similarity',\n",
+              "       'geo__Cluster 7 similarity', 'geo__Cluster 8 similarity',\n",
+              "       'geo__Cluster 9 similarity', 'cat__ocean_proximity_<1H OCEAN',\n",
+              "       'cat__ocean_proximity_INLAND', 'cat__ocean_proximity_ISLAND',\n",
+              "       'cat__ocean_proximity_NEAR BAY', 'cat__ocean_proximity_NEAR OCEAN',\n",
+              "       'remainder__housing_median_age'], dtype=object)"
+            ]
+          },
+          "execution_count": 115,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "preprocessing.get_feature_names_out()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jz2Un_8Mtgl8"
+      },
+      "source": [
+        "# Select and Train a Model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "O5VUEk5ptgl9"
+      },
+      "source": [
+        "## Training and Evaluating on the Training Set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mG3iIPxptgl9",
+        "outputId": "617bed72-285d-49cd-841b-6ff80bf2f7c2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('columntransformer',\n",
+              "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+              "                                                              SimpleImputer(strategy='median')),\n",
+              "                                                             ('standardscaler',\n",
+              "                                                              StandardScaler())]),\n",
+              "                                   transformers=[('bedrooms',\n",
+              "                                                  Pipeline(steps=[('simpleimputer',\n",
+              "                                                                   SimpleImputer(strategy='median')),\n",
+              "                                                                  ('functiontransformer',\n",
+              "                                                                   FunctionTransformer(feature_names_out=<function ratio_name at 0x7ff...\n",
+              "                                                   'median_income']),\n",
+              "                                                 ('geo',\n",
+              "                                                  ClusterSimilarity(random_state=42),\n",
+              "                                                  ['latitude', 'longitude']),\n",
+              "                                                 ('cat',\n",
+              "                                                  Pipeline(steps=[('simpleimputer',\n",
+              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
+              "                                                                  ('onehotencoder',\n",
+              "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
+              "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7ffb95e12f90>)])),\n",
+              "                ('linearregression', LinearRegression())])"
+            ]
+          },
+          "execution_count": 116,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "lin_reg = make_pipeline(preprocessing, LinearRegression())\n",
+        "lin_reg.fit(housing, housing_labels)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PEkJX9xvtgl_"
+      },
+      "source": [
+        "Let's try the full preprocessing pipeline on a few training instances:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XAsZ_cyOtgl_",
+        "outputId": "b6f721cc-db06-401f-e873-91d6f229b941"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([246000., 372700., 135700.,  91400., 330900.])"
+            ]
+          },
+          "execution_count": 117,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_predictions = lin_reg.predict(housing)\n",
+        "housing_predictions[:5].round(-2)  # -2 = rounded to the nearest hundred"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LU_1BF7CtgmA"
+      },
+      "source": [
+        "Compare against the actual values:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oI4ePhWctgmA",
+        "outputId": "72fdf40f-d752-4c54-d4bf-e437e71c3959"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([458300., 483800., 101700.,  96100., 361800.])"
+            ]
+          },
+          "execution_count": 118,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_labels.iloc[:5].values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LdM4PE1EtgmA",
+        "outputId": "54943d1b-c0a2-4278-f43f-b9eaa21e49a3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "-46.3%, -23.0%, 33.4%, -4.9%, -8.5%\n"
+          ]
+        }
+      ],
+      "source": [
+        "# extra code – computes the error ratios discussed in the book\n",
+        "error_ratios = housing_predictions[:5].round(-2) / housing_labels.iloc[:5].values - 1\n",
+        "print(\", \".join([f\"{100 * ratio:.1f}%\" for ratio in error_ratios]))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Qs9K5YI6tgmB",
+        "outputId": "79cbc46a-6b01-4866-9f16-7f7ebc5e286d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "68972.88910758484"
+            ]
+          },
+          "execution_count": 120,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import root_mean_squared_error\n",
+        "\n",
+        "lin_rmse = root_mean_squared_error(housing_labels, housing_predictions)\n",
+        "lin_rmse"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "focOjqHrtgmB",
+        "outputId": "b712819e-9d05-405a-bd5a-2e7838d761f4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('columntransformer',\n",
+              "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+              "                                                              SimpleImputer(strategy='median')),\n",
+              "                                                             ('standardscaler',\n",
+              "                                                              StandardScaler())]),\n",
+              "                                   transformers=[('bedrooms',\n",
+              "                                                  Pipeline(steps=[('simpleimputer',\n",
+              "                                                                   SimpleImputer(strategy='median')),\n",
+              "                                                                  ('functiontransformer',\n",
+              "                                                                   FunctionTransformer(feature_names_out=<function ratio_name at 0x7ff...\n",
+              "                                                  ClusterSimilarity(random_state=42),\n",
+              "                                                  ['latitude', 'longitude']),\n",
+              "                                                 ('cat',\n",
+              "                                                  Pipeline(steps=[('simpleimputer',\n",
+              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
+              "                                                                  ('onehotencoder',\n",
+              "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
+              "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7ffb95e12f90>)])),\n",
+              "                ('decisiontreeregressor',\n",
+              "                 DecisionTreeRegressor(random_state=42))])"
+            ]
+          },
+          "execution_count": 121,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "\n",
+        "tree_reg = make_pipeline(preprocessing, DecisionTreeRegressor(random_state=42))\n",
+        "tree_reg.fit(housing, housing_labels)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "j5xNEnxHtgmB",
+        "outputId": "3c0068e0-9822-493a-c5dc-b6f31d4fc0c7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "0.0"
+            ]
+          },
+          "execution_count": 122,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "housing_predictions = tree_reg.predict(housing)\n",
+        "tree_rmse = root_mean_squared_error(housing_labels, housing_predictions)\n",
+        "tree_rmse"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2M2BIweMtgmD"
+      },
+      "source": [
+        "## Better Evaluation Using Cross-Validation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FbzMh2Ywtgm0"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import cross_val_score\n",
+        "\n",
+        "tree_rmses = -cross_val_score(tree_reg, housing, housing_labels,\n",
+        "                              scoring=\"neg_root_mean_squared_error\", cv=10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7gRxNvrjtgm1",
+        "outputId": "18aee7ec-3e06-4a84-bb60-8839a113d49b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>10.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>66573.734600</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1103.402323</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>64607.896046</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>66204.731788</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>66388.272499</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>66826.257468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>68532.210664</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ],
+            "text/plain": [
+              "count       10.000000\n",
+              "mean     66573.734600\n",
+              "std       1103.402323\n",
+              "min      64607.896046\n",
+              "25%      66204.731788\n",
+              "50%      66388.272499\n",
+              "75%      66826.257468\n",
+              "max      68532.210664\n",
+              "dtype: float64"
+            ]
+          },
+          "execution_count": 124,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pd.Series(tree_rmses).describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "p-9EKWoItgm3",
+        "outputId": "27002afc-28f7-4ca2-8d10-082d92af6af6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>10.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>70003.404818</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>4182.188328</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>65504.765753</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>68172.065831</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>68743.995249</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>70344.943988</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>81037.863741</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ],
+            "text/plain": [
+              "count       10.000000\n",
+              "mean     70003.404818\n",
+              "std       4182.188328\n",
+              "min      65504.765753\n",
+              "25%      68172.065831\n",
+              "50%      68743.995249\n",
+              "75%      70344.943988\n",
+              "max      81037.863741\n",
+              "dtype: float64"
+            ]
+          },
+          "execution_count": 125,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# extra code – computes the error stats for the linear model\n",
+        "lin_rmses = -cross_val_score(lin_reg, housing, housing_labels,\n",
+        "                              scoring=\"neg_root_mean_squared_error\", cv=10)\n",
+        "pd.Series(lin_rmses).describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RghYQ3votgm_"
+      },
+      "source": [
+        "**Warning:** the following cell may take a few minutes to run:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iDzJfvcWtgnA"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "\n",
+        "forest_reg = make_pipeline(preprocessing,\n",
+        "                           RandomForestRegressor(random_state=42))\n",
+        "forest_rmses = -cross_val_score(forest_reg, housing, housing_labels,\n",
+        "                                scoring=\"neg_root_mean_squared_error\", cv=10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Uh_n8cV3tgnA",
+        "outputId": "66b5fb1b-60b1-4666-d2a7-01a3a701955a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>10.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>47038.092799</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1021.491757</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>45495.976649</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>46510.418013</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>47118.719249</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>47480.519175</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>49140.832210</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ],
+            "text/plain": [
+              "count       10.000000\n",
+              "mean     47038.092799\n",
+              "std       1021.491757\n",
+              "min      45495.976649\n",
+              "25%      46510.418013\n",
+              "50%      47118.719249\n",
+              "75%      47480.519175\n",
+              "max      49140.832210\n",
+              "dtype: float64"
+            ]
+          },
+          "execution_count": 127,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pd.Series(forest_rmses).describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ERkmEaIEtgnB"
+      },
+      "source": [
+        "Let's compare this RMSE measured using cross-validation (the \"validation error\") with the RMSE measured on the training set (the \"training error\"):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "erPrHL7dtgnC",
+        "outputId": "108126a2-7022-4016-dac9-2e9fb3978e09"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "17551.2122500877"
+            ]
+          },
+          "execution_count": 128,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "forest_reg.fit(housing, housing_labels)\n",
+        "housing_predictions = forest_reg.predict(housing)\n",
+        "forest_rmse = root_mean_squared_error(housing_labels, housing_predictions)\n",
+        "forest_rmse"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FN7WloEHtgnC"
+      },
+      "source": [
+        "The training error is much lower than the validation error, which usually means that the model has overfit the training set. Another possible explanation may be that there's a mismatch between the training data and the validation data, but it's not the case here, since both came from the same dataset that we shuffled and split in two parts."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "O_pmlzFutgnC"
+      },
+      "source": [
+        "# Fine-Tune Your Model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vn_QftrStgnC"
+      },
+      "source": [
+        "## Grid Search"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IJQHn8DstgnD"
+      },
+      "source": [
+        "**Warning:** the following cell may take a few minutes to run:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lZEmCFkStgnD",
+        "outputId": "8e39071f-a461-4737-ba6f-8d50d877d357"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "GridSearchCV(cv=3,\n",
+              "             estimator=Pipeline(steps=[('preprocessing',\n",
+              "                                        ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+              "                                                                                     SimpleImputer(strategy='median')),\n",
+              "                                                                                    ('standardscaler',\n",
+              "                                                                                     StandardScaler())]),\n",
+              "                                                          transformers=[('bedrooms',\n",
+              "                                                                         Pipeline(steps=[('simpleimputer',\n",
+              "                                                                                          SimpleImputer(strategy='median')),\n",
+              "                                                                                         ('functiontransformer',\n",
+              "                                                                                          FunctionTransformer(feature_names_out=<f...\n",
+              "                                                                         <sklearn.compose._column_transformer.make_column_selector object at 0x7ffb95e12f90>)])),\n",
+              "                                       ('random_forest',\n",
+              "                                        RandomForestRegressor(random_state=42))]),\n",
+              "             param_grid=[{'preprocessing__geo__n_clusters': [5, 8, 10],\n",
+              "                          'random_forest__max_features': [4, 6, 8]},\n",
+              "                         {'preprocessing__geo__n_clusters': [10, 15],\n",
+              "                          'random_forest__max_features': [6, 8, 10]}],\n",
+              "             scoring='neg_root_mean_squared_error')"
+            ]
+          },
+          "execution_count": 129,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.model_selection import GridSearchCV\n",
+        "\n",
+        "full_pipeline = Pipeline([\n",
+        "    (\"preprocessing\", preprocessing),\n",
+        "    (\"random_forest\", RandomForestRegressor(random_state=42)),\n",
+        "])\n",
+        "param_grid = [\n",
+        "    {'preprocessing__geo__n_clusters': [5, 8, 10],\n",
+        "     'random_forest__max_features': [4, 6, 8]},\n",
+        "    {'preprocessing__geo__n_clusters': [10, 15],\n",
+        "     'random_forest__max_features': [6, 8, 10]},\n",
+        "]\n",
+        "grid_search = GridSearchCV(full_pipeline, param_grid, cv=3,\n",
+        "                           scoring='neg_root_mean_squared_error')\n",
+        "grid_search.fit(housing, housing_labels)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1QFD1VWJtgnD"
+      },
+      "source": [
+        "You can get the full list of hyperparameters available for tuning by looking at `full_pipeline.get_params().keys()`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "44G9fwoqtgnE",
+        "outputId": "a02c4ffd-89b0-49af-873b-6ac9828c70e3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "dict_keys(['memory', 'steps', 'transform_input', 'verbose', 'preprocessing', 'random_forest', 'preprocessing__force_int_remainder_cols', 'preprocessing__n_jobs', 'preprocessing__remainder__memory', 'preprocessing__remainder__steps', 'preprocessing__remainder__transform_input', 'preprocessing__remainder__verbose', 'preprocessing__remainder__simpleimputer', 'preprocessing__remainder__standardscaler', 'preprocessing__remainder__simpleimputer__add_indicator', 'preprocessing__remainder__simpleimputer__copy', 'preprocessing__remainder__simpleimputer__fill_value', 'preprocessing__remainder__simpleimputer__keep_empty_features', 'preprocessing__remainder__simpleimputer__missing_values', 'preprocessing__remainder__simpleimputer__strategy', 'preprocessing__remainder__standardscaler__copy', 'preprocessing__remainder__standardscaler__with_mean', 'preprocessing__remainder__standardscaler__with_std', 'preprocessing__remainder', 'preprocessing__sparse_threshold', 'preprocessing__transformer_weights', ...\n"
+          ]
+        }
+      ],
+      "source": [
+        "# extra code – shows part of the output of get_params().keys()\n",
+        "print(str(full_pipeline.get_params().keys())[:1000] + \"...\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E_QEIvC0tgnF"
+      },
+      "source": [
+        "The best hyperparameter combination found:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dcdVmYaYtgnG",
+        "outputId": "ca8d34c4-36fd-4f8f-cb2d-60b96e3fdabb"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'preprocessing__geo__n_clusters': 15, 'random_forest__max_features': 6}"
+            ]
+          },
+          "execution_count": 131,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "grid_search.best_params_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ESYtr0hItgnI",
+        "outputId": "00207010-5389-45f4-cc3b-f13d07fde859"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('preprocessing',\n",
+              "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+              "                                                              SimpleImputer(strategy='median')),\n",
+              "                                                             ('standardscaler',\n",
+              "                                                              StandardScaler())]),\n",
+              "                                   transformers=[('bedrooms',\n",
+              "                                                  Pipeline(steps=[('simpleimputer',\n",
+              "                                                                   SimpleImputer(strategy='median')),\n",
+              "                                                                  ('functiontransformer',\n",
+              "                                                                   FunctionTransformer(feature_names_out=<function ratio_name at 0x7ffb95e...\n",
+              "                                                  ClusterSimilarity(n_clusters=15,\n",
+              "                                                                    random_state=42),\n",
+              "                                                  ['latitude', 'longitude']),\n",
+              "                                                 ('cat',\n",
+              "                                                  Pipeline(steps=[('simpleimputer',\n",
+              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
+              "                                                                  ('onehotencoder',\n",
+              "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
+              "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7ffb9c38bb60>)])),\n",
+              "                ('random_forest',\n",
+              "                 RandomForestRegressor(max_features=6, random_state=42))])"
+            ]
+          },
+          "execution_count": 132,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "grid_search.best_estimator_"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U2ay7KRgtgnI"
+      },
+      "source": [
+        "Let's look at the score of each hyperparameter combination tested during the grid search:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "msXzFNBmtgnJ",
+        "outputId": "b4c3572f-a772-4723-c700-7c244380093e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"cv_res\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"n_clusters\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 5,\n        \"max\": 15,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10,\n          5,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_features\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 10,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8,\n          10,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1183,\n        \"min\": 42725,\n        \"max\": 46840,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          46473,\n          45568,\n          42725\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 965,\n        \"min\": 43708,\n        \"max\": 46905,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          46384,\n          45323,\n          43708\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1054,\n        \"min\": 44335,\n        \"max\": 47813,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          47621,\n          46134,\n          44335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_rmse\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1058,\n        \"min\": 43590,\n        \"max\": 47186,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          46826,\n          45675,\n          43590\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "cv_res"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-95709831-262a-4817-84d2-0c317b41d9be\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>n_clusters</th>\n",
+              "      <th>max_features</th>\n",
+              "      <th>split0</th>\n",
+              "      <th>split1</th>\n",
+              "      <th>split2</th>\n",
+              "      <th>mean_test_rmse</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>15</td>\n",
+              "      <td>6</td>\n",
+              "      <td>42725</td>\n",
+              "      <td>43708</td>\n",
+              "      <td>44335</td>\n",
+              "      <td>43590</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>15</td>\n",
+              "      <td>8</td>\n",
+              "      <td>43486</td>\n",
+              "      <td>43820</td>\n",
+              "      <td>44900</td>\n",
+              "      <td>44069</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>10</td>\n",
+              "      <td>4</td>\n",
+              "      <td>43798</td>\n",
+              "      <td>44036</td>\n",
+              "      <td>44961</td>\n",
+              "      <td>44265</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>10</td>\n",
+              "      <td>6</td>\n",
+              "      <td>43710</td>\n",
+              "      <td>44163</td>\n",
+              "      <td>44967</td>\n",
+              "      <td>44280</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>10</td>\n",
+              "      <td>6</td>\n",
+              "      <td>43710</td>\n",
+              "      <td>44163</td>\n",
+              "      <td>44967</td>\n",
+              "      <td>44280</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-95709831-262a-4817-84d2-0c317b41d9be')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-95709831-262a-4817-84d2-0c317b41d9be button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-95709831-262a-4817-84d2-0c317b41d9be');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-2e49b3f2-4f9a-46a1-82e4-eb3c51a91833\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2e49b3f2-4f9a-46a1-82e4-eb3c51a91833')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-2e49b3f2-4f9a-46a1-82e4-eb3c51a91833 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "    n_clusters  max_features  split0  split1  split2  mean_test_rmse\n",
+              "12          15             6   42725   43708   44335           43590\n",
+              "13          15             8   43486   43820   44900           44069\n",
+              "6           10             4   43798   44036   44961           44265\n",
+              "9           10             6   43710   44163   44967           44280\n",
+              "7           10             6   43710   44163   44967           44280"
+            ]
+          },
+          "execution_count": 133,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "cv_res = pd.DataFrame(grid_search.cv_results_)\n",
+        "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n",
+        "\n",
+        "# extra code – these few lines of code just make the DataFrame look nicer\n",
+        "cv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n",
+        "                 \"param_random_forest__max_features\", \"split0_test_score\",\n",
+        "                 \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n",
+        "score_cols = [\"split0\", \"split1\", \"split2\", \"mean_test_rmse\"]\n",
+        "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n",
+        "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n",
+        "\n",
+        "cv_res.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fvC6xI1HtgnK"
+      },
+      "source": [
+        "## Randomized Search"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yWo8aSqMtgnK"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.experimental import enable_halving_search_cv\n",
+        "from sklearn.model_selection import HalvingRandomSearchCV"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "T5OwQKtbtgnV"
+      },
+      "source": [
+        "Try 30 (`n_iter` × `cv`) random combinations of hyperparameters:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H8f8ZAd5tgnW"
+      },
+      "source": [
+        "**Warning:** the following cell may take a few minutes to run:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qpN9oPQPtgnY",
+        "outputId": "3f35ad61-4fdd-476c-e964-9c2faee75372"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "RandomizedSearchCV(cv=3,\n",
+              "                   estimator=Pipeline(steps=[('preprocessing',\n",
+              "                                              ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+              "                                                                                           SimpleImputer(strategy='median')),\n",
+              "                                                                                          ('standardscaler',\n",
+              "                                                                                           StandardScaler())]),\n",
+              "                                                                transformers=[('bedrooms',\n",
+              "                                                                               Pipeline(steps=[('simpleimputer',\n",
+              "                                                                                                SimpleImputer(strategy='median')),\n",
+              "                                                                                               ('functiontransformer',\n",
+              "                                                                                                FunctionTransformer(feature_names_...\n",
+              "                                             ('random_forest',\n",
+              "                                              RandomForestRegressor(random_state=42))]),\n",
+              "                   param_distributions={'preprocessing__geo__n_clusters': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7ffb95e65670>,\n",
+              "                                        'random_forest__max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7ffb9c355be0>},\n",
+              "                   random_state=42, scoring='neg_root_mean_squared_error')"
+            ]
+          },
+          "execution_count": 135,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.model_selection import RandomizedSearchCV\n",
+        "from scipy.stats import randint\n",
+        "\n",
+        "param_distribs = {'preprocessing__geo__n_clusters': randint(low=3, high=50),\n",
+        "                  'random_forest__max_features': randint(low=2, high=20)}\n",
+        "\n",
+        "rnd_search = RandomizedSearchCV(\n",
+        "    full_pipeline, param_distributions=param_distribs, n_iter=10, cv=3,\n",
+        "    scoring='neg_root_mean_squared_error', random_state=42)\n",
+        "\n",
+        "rnd_search.fit(housing, housing_labels)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RCZ6UGjotgna",
+        "outputId": "9f84d9df-0801-4d61-a05a-377446dbe432"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"cv_res\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"n_clusters\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 4,\n        \"max\": 45,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          21,\n          32,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_features\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 2,\n        \"max\": 16,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          12,\n          7,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2165,\n        \"min\": 41342,\n        \"max\": 48955,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          43481,\n          41825,\n          42502\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1881,\n        \"min\": 42242,\n        \"max\": 48833,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          43828,\n          42275,\n          43694\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1997,\n        \"min\": 43057,\n        \"max\": 49929,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          44501,\n          43241,\n          44234\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_rmse\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2007,\n        \"min\": 42214,\n        \"max\": 49239,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          43936,\n          42447,\n          43477\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "cv_res"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b19551b9-3ea6-4a3d-b852-261e02b9945b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>n_clusters</th>\n",
+              "      <th>max_features</th>\n",
+              "      <th>split0</th>\n",
+              "      <th>split1</th>\n",
+              "      <th>split2</th>\n",
+              "      <th>mean_test_rmse</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>45</td>\n",
+              "      <td>9</td>\n",
+              "      <td>41342</td>\n",
+              "      <td>42242</td>\n",
+              "      <td>43057</td>\n",
+              "      <td>42214</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>32</td>\n",
+              "      <td>7</td>\n",
+              "      <td>41825</td>\n",
+              "      <td>42275</td>\n",
+              "      <td>43241</td>\n",
+              "      <td>42447</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>41</td>\n",
+              "      <td>16</td>\n",
+              "      <td>42238</td>\n",
+              "      <td>42938</td>\n",
+              "      <td>43354</td>\n",
+              "      <td>42843</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>42</td>\n",
+              "      <td>4</td>\n",
+              "      <td>41869</td>\n",
+              "      <td>43362</td>\n",
+              "      <td>43664</td>\n",
+              "      <td>42965</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>23</td>\n",
+              "      <td>8</td>\n",
+              "      <td>42490</td>\n",
+              "      <td>42928</td>\n",
+              "      <td>43718</td>\n",
+              "      <td>43046</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b19551b9-3ea6-4a3d-b852-261e02b9945b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b19551b9-3ea6-4a3d-b852-261e02b9945b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b19551b9-3ea6-4a3d-b852-261e02b9945b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-dab67d90-91bb-47ae-8db6-77feef3236b4\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dab67d90-91bb-47ae-8db6-77feef3236b4')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-dab67d90-91bb-47ae-8db6-77feef3236b4 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "   n_clusters  max_features  split0  split1  split2  mean_test_rmse\n",
+              "1          45             9   41342   42242   43057           42214\n",
+              "8          32             7   41825   42275   43241           42447\n",
+              "0          41            16   42238   42938   43354           42843\n",
+              "5          42             4   41869   43362   43664           42965\n",
+              "2          23             8   42490   42928   43718           43046"
+            ]
+          },
+          "execution_count": 136,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# extra code – displays the random search results\n",
+        "cv_res = pd.DataFrame(rnd_search.cv_results_)\n",
+        "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n",
+        "cv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n",
+        "                 \"param_random_forest__max_features\", \"split0_test_score\",\n",
+        "                 \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n",
+        "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n",
+        "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n",
+        "cv_res.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V5DQ9-iutgnb"
+      },
+      "source": [
+        "**Bonus section: how to choose the sampling distribution for a hyperparameter**\n",
+        "\n",
+        "* `scipy.stats.randint(a, b+1)`: for hyperparameters with _discrete_ values that range from a to b, and all values in that range seem equally likely.\n",
+        "* `scipy.stats.uniform(a, b)`: this is very similar, but for _continuous_ hyperparameters.\n",
+        "* `scipy.stats.geom(1 / scale)`: for discrete values, when you want to sample roughly in a given scale. E.g., with scale=1000 most samples will be in this ballpark, but ~10% of all samples will be <100 and ~10% will be >2300.\n",
+        "* `scipy.stats.expon(scale)`: this is the continuous equivalent of `geom`. Just set `scale` to the most likely value.\n",
+        "* `scipy.stats.loguniform(a, b)`: when you have almost no idea what the optimal hyperparameter value's scale is. If you set a=0.01 and b=100, then you're just as likely to sample a value between 0.01 and 0.1 as a value between 10 and 100.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wptjR145tgnd"
+      },
+      "source": [
+        "Here are plots of the probability mass functions (for discrete variables), and probability density functions (for continuous variables) for `randint()`, `uniform()`, `geom()` and `expon()`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "y408-pTctgne",
+        "outputId": "fc5effe4-513e-42bb-fa02-5844b042f9b2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 1200x700 with 4 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# extra code – plots a few distributions you can use in randomized search\n",
+        "\n",
+        "from scipy.stats import randint, uniform, geom, expon\n",
+        "\n",
+        "xs1 = np.arange(0, 7 + 1)\n",
+        "randint_distrib = randint(0, 7 + 1).pmf(xs1)\n",
+        "\n",
+        "xs2 = np.linspace(0, 7, 500)\n",
+        "uniform_distrib = uniform(0, 7).pdf(xs2)\n",
+        "\n",
+        "xs3 = np.arange(0, 7 + 1)\n",
+        "geom_distrib = geom(0.5).pmf(xs3)\n",
+        "\n",
+        "xs4 = np.linspace(0, 7, 500)\n",
+        "expon_distrib = expon(scale=1).pdf(xs4)\n",
+        "\n",
+        "plt.figure(figsize=(12, 7))\n",
+        "\n",
+        "plt.subplot(2, 2, 1)\n",
+        "plt.bar(xs1, randint_distrib, label=\"scipy.randint(0, 7 + 1)\")\n",
+        "plt.ylabel(\"Probability\")\n",
+        "plt.legend()\n",
+        "plt.axis([-1, 8, 0, 0.2])\n",
+        "\n",
+        "plt.subplot(2, 2, 2)\n",
+        "plt.fill_between(xs2, uniform_distrib, label=\"scipy.uniform(0, 7)\")\n",
+        "plt.ylabel(\"PDF\")\n",
+        "plt.legend()\n",
+        "plt.axis([-1, 8, 0, 0.2])\n",
+        "\n",
+        "plt.subplot(2, 2, 3)\n",
+        "plt.bar(xs3, geom_distrib, label=\"scipy.geom(0.5)\")\n",
+        "plt.xlabel(\"Hyperparameter value\")\n",
+        "plt.ylabel(\"Probability\")\n",
+        "plt.legend()\n",
+        "plt.axis([0, 7, 0, 1])\n",
+        "\n",
+        "plt.subplot(2, 2, 4)\n",
+        "plt.fill_between(xs4, expon_distrib, label=\"scipy.expon(scale=1)\")\n",
+        "plt.xlabel(\"Hyperparameter value\")\n",
+        "plt.ylabel(\"PDF\")\n",
+        "plt.legend()\n",
+        "plt.axis([0, 7, 0, 1])\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gsI8iZ6Otgnr"
+      },
+      "source": [
+        "Here are the PDF for `expon()` and `loguniform()` (left column), as well as the PDF of log(X) (right column). The right column shows the distribution of hyperparameter _scales_. You can see that `expon()` favors hyperparameters with roughly the desired scale, with a longer tail towards the smaller scales. But `loguniform()` does not favor any scale, they are all equally likely:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hNLhA7pptgnt",
+        "outputId": "61b99559-3aca-4d4f-f05b-0001cfba07f4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 1200x700 with 4 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# extra code – shows the difference between expon and loguniform\n",
+        "\n",
+        "from scipy.stats import loguniform\n",
+        "\n",
+        "xs1 = np.linspace(0, 7, 500)\n",
+        "expon_distrib = expon(scale=1).pdf(xs1)\n",
+        "\n",
+        "log_xs2 = np.linspace(-5, 3, 500)\n",
+        "log_expon_distrib = np.exp(log_xs2 - np.exp(log_xs2))\n",
+        "\n",
+        "xs3 = np.linspace(0.001, 1000, 500)\n",
+        "loguniform_distrib = loguniform(0.001, 1000).pdf(xs3)\n",
+        "\n",
+        "log_xs4 = np.linspace(np.log(0.001), np.log(1000), 500)\n",
+        "log_loguniform_distrib = uniform(np.log(0.001), np.log(1000)).pdf(log_xs4)\n",
+        "\n",
+        "plt.figure(figsize=(12, 7))\n",
+        "\n",
+        "plt.subplot(2, 2, 1)\n",
+        "plt.fill_between(xs1, expon_distrib,\n",
+        "                 label=\"scipy.expon(scale=1)\")\n",
+        "plt.ylabel(\"PDF\")\n",
+        "plt.legend()\n",
+        "plt.axis([0, 7, 0, 1])\n",
+        "\n",
+        "plt.subplot(2, 2, 2)\n",
+        "plt.fill_between(log_xs2, log_expon_distrib,\n",
+        "                 label=\"log(X) with X ~ expon\")\n",
+        "plt.legend()\n",
+        "plt.axis([-5, 3, 0, 1])\n",
+        "\n",
+        "plt.subplot(2, 2, 3)\n",
+        "plt.fill_between(xs3, loguniform_distrib,\n",
+        "                 label=\"scipy.loguniform(0.001, 1000)\")\n",
+        "plt.xlabel(\"Hyperparameter value\")\n",
+        "plt.ylabel(\"PDF\")\n",
+        "plt.legend()\n",
+        "plt.axis([0.001, 1000, 0, 0.005])\n",
+        "\n",
+        "plt.subplot(2, 2, 4)\n",
+        "plt.fill_between(log_xs4, log_loguniform_distrib,\n",
+        "                 label=\"log(X) with X ~ loguniform\")\n",
+        "plt.xlabel(\"Log of hyperparameter value\")\n",
+        "plt.legend()\n",
+        "plt.axis([-8, 1, 0, 0.2])\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DivtXs9mtgnu"
+      },
+      "source": [
+        "## Analyze the Best Models and Their Errors"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jmjjZEM5tgnz",
+        "outputId": "64398862-05d0-4e47-e5b3-579598d80f94"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([0.07, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.19, 0.01, 0.02, 0.01,\n",
+              "       0.01, 0.01, 0.  , 0.01, 0.02, 0.01, 0.02, 0.01, 0.  , 0.01, 0.02,\n",
+              "       0.01, 0.01, 0.01, 0.  , 0.02, 0.01, 0.01, 0.  , 0.01, 0.01, 0.01,\n",
+              "       0.03, 0.01, 0.01, 0.01, 0.01, 0.04, 0.01, 0.02, 0.01, 0.02, 0.01,\n",
+              "       0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.  , 0.07,\n",
+              "       0.  , 0.  , 0.  , 0.01])"
+            ]
+          },
+          "execution_count": 139,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "final_model = rnd_search.best_estimator_  # includes preprocessing\n",
+        "feature_importances = final_model[\"random_forest\"].feature_importances_\n",
+        "feature_importances.round(2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aY7F-Z1Xtgn1",
+        "outputId": "29e0a898-bc95-44a2-8f12-5b4b28e2661f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[(np.float64(0.18599734460509476), 'log__median_income'),\n",
+              " (np.float64(0.07338850855844489), 'cat__ocean_proximity_INLAND'),\n",
+              " (np.float64(0.06556941990883976), 'bedrooms__ratio'),\n",
+              " (np.float64(0.053648710076725316), 'rooms_per_house__ratio'),\n",
+              " (np.float64(0.04598870861894749), 'people_per_house__ratio'),\n",
+              " (np.float64(0.04175269214442519), 'geo__Cluster 30 similarity'),\n",
+              " (np.float64(0.025976797232869678), 'geo__Cluster 25 similarity'),\n",
+              " (np.float64(0.023595895886342255), 'geo__Cluster 36 similarity'),\n",
+              " (np.float64(0.02021056221732893), 'geo__Cluster 9 similarity'),\n",
+              " (np.float64(0.01860691707666145), 'geo__Cluster 34 similarity'),\n",
+              " (np.float64(0.018137988374628867), 'geo__Cluster 37 similarity'),\n",
+              " (np.float64(0.01740435316632675), 'geo__Cluster 18 similarity'),\n",
+              " (np.float64(0.016778386143844894), 'geo__Cluster 1 similarity'),\n",
+              " (np.float64(0.015459009666188978), 'geo__Cluster 7 similarity'),\n",
+              " (np.float64(0.015325731028175924), 'geo__Cluster 32 similarity'),\n",
+              " (np.float64(0.015073772015038348), 'geo__Cluster 13 similarity'),\n",
+              " (np.float64(0.014272160962173805), 'geo__Cluster 35 similarity'),\n",
+              " (np.float64(0.014180636461860479), 'geo__Cluster 0 similarity'),\n",
+              " (np.float64(0.013746364498238989), 'geo__Cluster 3 similarity'),\n",
+              " (np.float64(0.01357230570846952), 'geo__Cluster 28 similarity'),\n",
+              " (np.float64(0.01294034969422872), 'geo__Cluster 26 similarity'),\n",
+              " (np.float64(0.012738123746761944), 'geo__Cluster 31 similarity'),\n",
+              " (np.float64(0.011654237215152624), 'geo__Cluster 19 similarity'),\n",
+              " (np.float64(0.011628003598059723), 'geo__Cluster 6 similarity'),\n",
+              " (np.float64(0.011134113333125398), 'geo__Cluster 24 similarity'),\n",
+              " (np.float64(0.011042979326385049), 'remainder__housing_median_age'),\n",
+              " (np.float64(0.010907388443940418), 'geo__Cluster 43 similarity'),\n",
+              " (np.float64(0.010847192663592166), 'geo__Cluster 44 similarity'),\n",
+              " (np.float64(0.010592244492858267), 'geo__Cluster 10 similarity'),\n",
+              " (np.float64(0.010512467290844922), 'geo__Cluster 23 similarity'),\n",
+              " (np.float64(0.01045866561538645), 'geo__Cluster 41 similarity'),\n",
+              " (np.float64(0.010261910692851673), 'geo__Cluster 40 similarity'),\n",
+              " (np.float64(0.009757306983097491), 'geo__Cluster 2 similarity'),\n",
+              " (np.float64(0.00965993322211448), 'geo__Cluster 12 similarity'),\n",
+              " (np.float64(0.009574969190852869), 'geo__Cluster 14 similarity'),\n",
+              " (np.float64(0.008199144719918425), 'geo__Cluster 20 similarity'),\n",
+              " (np.float64(0.008141941480860806), 'geo__Cluster 33 similarity'),\n",
+              " (np.float64(0.007596761219964691), 'geo__Cluster 8 similarity'),\n",
+              " (np.float64(0.0075762980128490295), 'geo__Cluster 22 similarity'),\n",
+              " (np.float64(0.007346290789504319), 'geo__Cluster 39 similarity'),\n",
+              " (np.float64(0.006898774333063982), 'geo__Cluster 4 similarity'),\n",
+              " (np.float64(0.0067947318450798395), 'log__total_rooms'),\n",
+              " (np.float64(0.006514889773323568), 'log__population'),\n",
+              " (np.float64(0.006350528211987125), 'geo__Cluster 27 similarity'),\n",
+              " (np.float64(0.006337558749902337), 'geo__Cluster 16 similarity'),\n",
+              " (np.float64(0.006231053672395539), 'geo__Cluster 38 similarity'),\n",
+              " (np.float64(0.0061213483458714855), 'log__households'),\n",
+              " (np.float64(0.005849842001582111), 'log__total_bedrooms'),\n",
+              " (np.float64(0.0056783104666850125), 'geo__Cluster 15 similarity'),\n",
+              " (np.float64(0.005479729990673467), 'geo__Cluster 29 similarity'),\n",
+              " (np.float64(0.005348325088535128), 'geo__Cluster 42 similarity'),\n",
+              " (np.float64(0.004866251452445486), 'geo__Cluster 17 similarity'),\n",
+              " (np.float64(0.004495340541933027), 'geo__Cluster 11 similarity'),\n",
+              " (np.float64(0.004418821635620684), 'geo__Cluster 5 similarity'),\n",
+              " (np.float64(0.0035344732505291285), 'geo__Cluster 21 similarity'),\n",
+              " (np.float64(0.001832424657341851), 'cat__ocean_proximity_<1H OCEAN'),\n",
+              " (np.float64(0.0015282226447271795), 'cat__ocean_proximity_NEAR OCEAN'),\n",
+              " (np.float64(0.0004325970342247361), 'cat__ocean_proximity_NEAR BAY'),\n",
+              " (np.float64(3.0190221102670295e-05), 'cat__ocean_proximity_ISLAND')]"
+            ]
+          },
+          "execution_count": 140,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "sorted(zip(feature_importances,\n",
+        "           final_model[\"preprocessing\"].get_feature_names_out()),\n",
+        "       reverse=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qicv-Vfztgn2"
+      },
+      "source": [
+        "## Evaluate Your System on the Test Set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e2X5InRwtgn3",
+        "outputId": "25e1c029-e558-4219-fb9f-ec65fc60b8b5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "41445.533268606625\n"
+          ]
+        }
+      ],
+      "source": [
+        "X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n",
+        "y_test = strat_test_set[\"median_house_value\"].copy()\n",
+        "\n",
+        "final_predictions = final_model.predict(X_test)\n",
+        "\n",
+        "final_rmse = root_mean_squared_error(y_test, final_predictions)\n",
+        "print(final_rmse)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "234O8QTdtgn4"
+      },
+      "source": [
+        "We can compute a 95% confidence interval for the test RMSE using SciPy's `bootstrap()` function:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MXgmxsE_tgn5"
+      },
+      "outputs": [],
+      "source": [
+        "from scipy.stats import bootstrap\n",
+        "\n",
+        "def rmse(squared_errors):\n",
+        "    return np.sqrt(np.mean(squared_errors))\n",
+        "\n",
+        "confidence = 0.95\n",
+        "squared_errors = (final_predictions - y_test) ** 2\n",
+        "boot_result = bootstrap([squared_errors], rmse, confidence_level=confidence,\n",
+        "                        random_state=42)\n",
+        "rmse_lower, rmse_upper = boot_result.confidence_interval"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "shRG1wS8tgn6",
+        "outputId": "4c715569-c921-4606-e058-dc97f0fce6b7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "95% CI for RMSE: (39520.9572, 43701.7681)\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"95% CI for RMSE: ({rmse_lower:.4f}, {rmse_upper:.4f})\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LlmMZdNrtgn7"
+      },
+      "source": [
+        "## Model persistence using joblib"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X852EQoctgn-"
+      },
+      "source": [
+        "Save the final model:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "M5-s2_h7tgn_",
+        "outputId": "f499d65e-e016-4f12-d86c-d179ed394099"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['my_california_housing_model.pkl']"
+            ]
+          },
+          "execution_count": 144,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import joblib\n",
+        "\n",
+        "joblib.dump(final_model, \"my_california_housing_model.pkl\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f3V3R7WstgoC"
+      },
+      "source": [
+        "Now you can deploy this model to production. For example, the following code could be a script that would run in production:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Vj_cH_IttgoD"
+      },
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "\n",
+        "# extra code – excluded for conciseness\n",
+        "from sklearn.cluster import KMeans\n",
+        "from sklearn.base import BaseEstimator, TransformerMixin\n",
+        "from sklearn.metrics.pairwise import rbf_kernel\n",
+        "\n",
+        "def column_ratio(X):\n",
+        "    return X[:, [0]] / X[:, [1]]\n",
+        "\n",
+        "#class ClusterSimilarity(BaseEstimator, TransformerMixin):\n",
+        "#    [...]\n",
+        "\n",
+        "final_model_reloaded = joblib.load(\"my_california_housing_model.pkl\")\n",
+        "\n",
+        "new_data = housing.iloc[:5]  # pretend these are new districts\n",
+        "predictions = final_model_reloaded.predict(new_data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e-jhr4_ltgoD",
+        "outputId": "236a478f-087e-46e1-ef6c-d50fc9f161b9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([441046.12, 454713.09, 104832.  , 101316.  , 336181.05])"
+            ]
+          },
+          "execution_count": 146,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "predictions"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FQ4g2l6jtgoG"
+      },
+      "source": [
+        "You could use pickle instead, but joblib is more efficient."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5jOdY5x5tgoG"
+      },
+      "source": [
+        "# Exercise solutions"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P8TRLXAMtgoH"
+      },
+      "source": [
+        "## 1."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fI7JqtsBtgoH"
+      },
+      "source": [
+        "Exercise: _Try a Support Vector Machine regressor (`sklearn.svm.SVR`) with various hyperparameters, such as `kernel=\"linear\"` (with various values for the `C` hyperparameter) or `kernel=\"rbf\"` (with various values for the `C` and `gamma` hyperparameters). Note that SVMs don't scale well to large datasets, so you should probably train your model on just the first 5,000 instances of the training set and use only 3-fold cross-validation, or else it will take hours. Don't worry about what the hyperparameters mean for now (see the SVM notebook if you're interested). How does the best `SVR` predictor perform?_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "h_HBO2OLtgoJ",
+        "outputId": "1f6dfee0-fad0-436f-cef0-2d89def9c080"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "GridSearchCV(cv=3,\n",
+              "             estimator=Pipeline(steps=[('preprocessing',\n",
+              "                                        ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+              "                                                                                     SimpleImputer(strategy='median')),\n",
+              "                                                                                    ('standardscaler',\n",
+              "                                                                                     StandardScaler())]),\n",
+              "                                                          transformers=[('bedrooms',\n",
+              "                                                                         Pipeline(steps=[('simpleimputer',\n",
+              "                                                                                          SimpleImputer(strategy='median')),\n",
+              "                                                                                         ('functiontransformer',\n",
+              "                                                                                          FunctionTransformer(feature_names_out=<f...\n",
+              "                                                                         <sklearn.compose._column_transformer.make_column_selector object at 0x7ffb95e12f90>)])),\n",
+              "                                       ('svr', SVR())]),\n",
+              "             param_grid=[{'svr__C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0,\n",
+              "                                     10000.0, 30000.0],\n",
+              "                          'svr__kernel': ['linear']},\n",
+              "                         {'svr__C': [1.0, 3.0, 10.0, 30.0, 100.0, 300.0,\n",
+              "                                     1000.0],\n",
+              "                          'svr__gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0],\n",
+              "                          'svr__kernel': ['rbf']}],\n",
+              "             scoring='neg_root_mean_squared_error')"
+            ]
+          },
+          "execution_count": 147,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.model_selection import GridSearchCV\n",
+        "from sklearn.svm import SVR\n",
+        "\n",
+        "param_grid = [\n",
+        "        {'svr__kernel': ['linear'], 'svr__C': [10., 30., 100., 300., 1000.,\n",
+        "                                               3000., 10000., 30000.0]},\n",
+        "        {'svr__kernel': ['rbf'], 'svr__C': [1.0, 3.0, 10., 30., 100., 300.,\n",
+        "                                            1000.0],\n",
+        "         'svr__gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]},\n",
+        "    ]\n",
+        "\n",
+        "svr_pipeline = Pipeline([(\"preprocessing\", preprocessing), (\"svr\", SVR())])\n",
+        "grid_search = GridSearchCV(svr_pipeline, param_grid, cv=3,\n",
+        "                           scoring='neg_root_mean_squared_error')\n",
+        "grid_search.fit(housing.iloc[:5000], housing_labels.iloc[:5000])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-fonhKqItgoR"
+      },
+      "source": [
+        "The best model achieves the following score (evaluated using 3-fold cross validation):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "I6kUjVdstgoT",
+        "outputId": "7a2b59c5-1449-487b-a5d9-2a242fb5cef6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "np.float64(70059.9277356289)"
+            ]
+          },
+          "execution_count": 148,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "svr_grid_search_rmse = -grid_search.best_score_\n",
+        "svr_grid_search_rmse"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_dN3u16Ntgod"
+      },
+      "source": [
+        "That's much worse than the `RandomForestRegressor` (but to be fair, we trained the model on much less data). Let's check the best hyperparameters found:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3sy4ybK_tgpI",
+        "outputId": "d2f33b73-eedb-44b0-de69-aeb18540855a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'svr__C': 10000.0, 'svr__kernel': 'linear'}"
+            ]
+          },
+          "execution_count": 149,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "grid_search.best_params_"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "73NB3McQtgpL"
+      },
+      "source": [
+        "The linear kernel seems better than the RBF kernel. Notice that the value of `C` is the maximum tested value. When this happens you definitely want to launch the grid search again with higher values for `C` (removing the smallest values), because it is likely that higher values of `C` will be better."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EmbQGwY0tgpM"
+      },
+      "source": [
+        "## 2."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rjgfWoMFtgpf"
+      },
+      "source": [
+        "Exercise: _Try replacing the `GridSearchCV` with a `RandomizedSearchCV`._"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lJ5b5zrVtgpp"
+      },
+      "source": [
+        "**Warning:** the following cell will take several minutes to run. You can specify `verbose=2` when creating the `RandomizedSearchCV` if you want to see the training details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6AWhph5XtgqH",
+        "outputId": "066c5f70-d8ee-43cb-9fa8-498a20d9e03c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "RandomizedSearchCV(cv=3,\n",
+              "                   estimator=Pipeline(steps=[('preprocessing',\n",
+              "                                              ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+              "                                                                                           SimpleImputer(strategy='median')),\n",
+              "                                                                                          ('standardscaler',\n",
+              "                                                                                           StandardScaler())]),\n",
+              "                                                                transformers=[('bedrooms',\n",
+              "                                                                               Pipeline(steps=[('simpleimputer',\n",
+              "                                                                                                SimpleImputer(strategy='median')),\n",
+              "                                                                                               ('functiontransformer',\n",
+              "                                                                                                FunctionTransformer(feature_names_...\n",
+              "                                                                               <sklearn.compose._column_transformer.make_column_selector object at 0x7ffb95e12f90>)])),\n",
+              "                                             ('svr', SVR())]),\n",
+              "                   n_iter=50,\n",
+              "                   param_distributions={'svr__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7ffb9fecb8f0>,\n",
+              "                                        'svr__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7ffb9c97e3f0>,\n",
+              "                                        'svr__kernel': ['linear', 'rbf']},\n",
+              "                   random_state=42, scoring='neg_root_mean_squared_error')"
+            ]
+          },
+          "execution_count": 150,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.model_selection import RandomizedSearchCV\n",
+        "from scipy.stats import expon, loguniform\n",
+        "\n",
+        "# see https://docs.scipy.org/doc/scipy/reference/stats.html\n",
+        "# for `expon()` and `loguniform()` documentation and more probability distribution functions.\n",
+        "\n",
+        "# Note: gamma is ignored when kernel is \"linear\"\n",
+        "param_distribs = {\n",
+        "        'svr__kernel': ['linear', 'rbf'],\n",
+        "        'svr__C': loguniform(20, 200_000),\n",
+        "        'svr__gamma': expon(scale=1.0),\n",
+        "    }\n",
+        "\n",
+        "rnd_search = RandomizedSearchCV(svr_pipeline,\n",
+        "                                param_distributions=param_distribs,\n",
+        "                                n_iter=50, cv=3,\n",
+        "                                scoring='neg_root_mean_squared_error',\n",
+        "                                random_state=42)\n",
+        "rnd_search.fit(housing.iloc[:5000], housing_labels.iloc[:5000])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-mg8v1-8tgqy"
+      },
+      "source": [
+        "The best model achieves the following score (evaluated using 3-fold cross validation):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ugUXNOXYtgqz",
+        "outputId": "dd7fd178-56f3-4a35-ebe6-3a79e45cd9c9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "np.float64(56152.053691161)"
+            ]
+          },
+          "execution_count": 151,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "svr_rnd_search_rmse = -rnd_search.best_score_\n",
+        "svr_rnd_search_rmse"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cRFUhYbCtgq2"
+      },
+      "source": [
+        "Now that's really much better, but still far from the `RandomForestRegressor`'s performance. Let's check the best hyperparameters found:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OPMJKdUntgq7",
+        "outputId": "902986a5-5658-48e3-d7ba-0bca2217f30c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'svr__C': np.float64(157055.10989448498),\n",
+              " 'svr__gamma': np.float64(0.26497040005002437),\n",
+              " 'svr__kernel': 'rbf'}"
+            ]
+          },
+          "execution_count": 152,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "rnd_search.best_params_"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ptxKUYP4tgr2"
+      },
+      "source": [
+        "This time the search found a good set of hyperparameters for the RBF kernel. Randomized search tends to find better hyperparameters than grid search in the same amount of time."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ofNH4ILGtgtG"
+      },
+      "source": [
+        "Note that we used the `expon()` distribution for `gamma`, with a scale of 1, so `RandomSearch` mostly searched for values roughly of that scale: about 80% of the samples were between 0.1 and 2.3 (roughly 10% were smaller and 10% were larger):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "T-hiDdyotgtI",
+        "outputId": "457889b0-8812-48da-e44c-2fdaa61240d7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "np.float64(0.80066)"
+            ]
+          },
+          "execution_count": 153,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "s = expon(scale=1).rvs(100_000, random_state=42)  # get 100,000 samples\n",
+        "((s > 0.105) & (s < 2.29)).sum() / 100_000"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WuVtqsCUtgtX"
+      },
+      "source": [
+        "We used the `loguniform()` distribution for `C`, meaning we did not have a clue what the optimal scale of `C` was before running the random search. It explored the range from 20 to 200 just as much as the range from 2,000 to 20,000 or from 20,000 to 200,000."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dwAZTI46tgth"
+      },
+      "source": [
+        "## 3."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NXoMDAUztguA"
+      },
+      "source": [
+        "Exercise: _Try adding a `SelectFromModel` transformer in the preparation pipeline to select only the most important attributes._"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uqNKUWD3tguQ"
+      },
+      "source": [
+        "Let's create a new pipeline that runs the previously defined preparation pipeline, and adds a `SelectFromModel` transformer based on a `RandomForestRegressor` before the final regressor:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "J_2pUV6otguS"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.feature_selection import SelectFromModel\n",
+        "\n",
+        "selector_pipeline = Pipeline([\n",
+        "    ('preprocessing', preprocessing),\n",
+        "    ('selector', SelectFromModel(RandomForestRegressor(random_state=42),\n",
+        "                                 threshold=0.005)),  # min feature importance\n",
+        "    ('svr', SVR(C=rnd_search.best_params_[\"svr__C\"],\n",
+        "                gamma=rnd_search.best_params_[\"svr__gamma\"],\n",
+        "                kernel=rnd_search.best_params_[\"svr__kernel\"])),\n",
+        "])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "U2IbsuvitguS",
+        "outputId": "92469fa0-53ae-41da-f9fa-35c840c93393"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>3.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>56622.643481</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2272.666703</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>54243.242290</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>55548.509493</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>56853.776696</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>57812.344076</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>58770.911457</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ],
+            "text/plain": [
+              "count        3.000000\n",
+              "mean     56622.643481\n",
+              "std       2272.666703\n",
+              "min      54243.242290\n",
+              "25%      55548.509493\n",
+              "50%      56853.776696\n",
+              "75%      57812.344076\n",
+              "max      58770.911457\n",
+              "dtype: float64"
+            ]
+          },
+          "execution_count": 155,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "selector_rmses = -cross_val_score(selector_pipeline,\n",
+        "                                  housing.iloc[:5000],\n",
+        "                                  housing_labels.iloc[:5000],\n",
+        "                                  scoring=\"neg_root_mean_squared_error\",\n",
+        "                                  cv=3)\n",
+        "pd.Series(selector_rmses).describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RsaBep0MtguU"
+      },
+      "source": [
+        "Oh well, feature selection does not seem to help. But maybe that's just because the threshold we used was not optimal. Perhaps try tuning it using random search or grid search?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zVaPkvDOtguU"
+      },
+      "source": [
+        "## 4."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5oqXmJz8tguV"
+      },
+      "source": [
+        "Exercise: _Try creating a custom transformer that trains a k-nearest neighbors regressor (`sklearn.neighbors.KNeighborsRegressor`) in its `fit()` method, and outputs the model's predictions in its `transform()` method. The KNN regressor should use only the latitude and longitude as input and predict the median income. Next, add this new transformer to the preprocessing pipeline. This will add a feature representing the smoothed median income over the nearby districts._"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e8tRzCQwtguY"
+      },
+      "source": [
+        "Rather than restrict ourselves to k-Nearest Neighbors regressors, let's create a transformer that accepts any regressor. For this, we can extend the `MetaEstimatorMixin` and have a required `estimator` argument in the constructor. The `fit()` method must work on a clone of this estimator, and it must also save <code>feature_names_in&#95;</code>. The `MetaEstimatorMixin` will ensure that `estimator` is listed as a required parameters, and it will update `get_params()` and `set_params()` to make the estimator's hyperparameters available for tuning. Lastly, we create a `get_feature_names_out()` method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tR9Zb79ZtguZ"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.base import MetaEstimatorMixin, clone\n",
+        "\n",
+        "class FeatureFromRegressor(BaseEstimator, TransformerMixin, MetaEstimatorMixin):\n",
+        "    def __init__(self, regressor, target_features):\n",
+        "        self.regressor = regressor\n",
+        "        self.target_features = target_features\n",
+        "\n",
+        "    def fit(self, X, y=None):\n",
+        "        if hasattr(X, \"columns\"):\n",
+        "            self.feature_names_in_ = list(X.columns)\n",
+        "            X_df = X\n",
+        "        else:\n",
+        "            X_df = pd.DataFrame(X)\n",
+        "\n",
+        "        self.input_features_ = [c for c in X_df.columns\n",
+        "                                if c not in self.target_features]\n",
+        "        self.regressor_ = clone(self.regressor)\n",
+        "        self.regressor_.fit(X_df[self.input_features_],\n",
+        "                            X_df[self.target_features])\n",
+        "        return self\n",
+        "\n",
+        "    def transform(self, X):\n",
+        "        columns = X.columns if hasattr(X, \"columns\") else None\n",
+        "        X_df = pd.DataFrame(X, columns=columns)\n",
+        "        preds = self.regressor_.predict(X_df[self.input_features_])\n",
+        "        if preds.ndim == 1:\n",
+        "            preds = preds.reshape(-1, 1)\n",
+        "        extra_columns = [f\"pred_{t}\" for t in self.target_features]\n",
+        "        preds_df = pd.DataFrame(preds, columns=extra_columns, index=X_df.index)\n",
+        "        return pd.concat([X_df, preds_df], axis=1)\n",
+        "\n",
+        "    def get_feature_names_out(self, input_features=None):\n",
+        "        extra_columns = [f\"pred_{t}\" for t in self.target_features]\n",
+        "        return self.feature_names_in_ + extra_columns"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6sKeS3Ontgub"
+      },
+      "source": [
+        "Good! Now let's test it:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DWObNaJrtgwK",
+        "outputId": "912610d6-4dca-4f0e-ce8e-a6bb424eaa25"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "       pred_median_income\n",
+            "13096            3.347233\n",
+            "14973            6.899400\n",
+            "3785             2.900900\n",
+            "14689            2.261800\n",
+            "20507            3.475633\n",
+            "...                   ...\n",
+            "14207            4.939100\n",
+            "13105            3.301550\n",
+            "19301            4.061600\n",
+            "19121            4.145500\n",
+            "19888            4.250667\n",
+            "\n",
+            "[16512 rows x 1 columns]\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "   n_clusters  max_features  split0  split1  split2  mean_test_rmse\n",
-       "1          45             9   41342   42242   43057           42214\n",
-       "8          32             7   41825   42275   43241           42447\n",
-       "0          41            16   42238   42938   43354           42843\n",
-       "5          42             4   41869   43362   43664           42965\n",
-       "2          23             8   42490   42928   43718           43046"
-      ]
-     },
-     "execution_count": 136,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# extra code – displays the random search results\n",
-    "cv_res = pd.DataFrame(rnd_search.cv_results_)\n",
-    "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n",
-    "cv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n",
-    "                 \"param_random_forest__max_features\", \"split0_test_score\",\n",
-    "                 \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n",
-    "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n",
-    "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n",
-    "cv_res.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Bonus section: how to choose the sampling distribution for a hyperparameter**\n",
-    "\n",
-    "* `scipy.stats.randint(a, b+1)`: for hyperparameters with _discrete_ values that range from a to b, and all values in that range seem equally likely.\n",
-    "* `scipy.stats.uniform(a, b)`: this is very similar, but for _continuous_ hyperparameters.\n",
-    "* `scipy.stats.geom(1 / scale)`: for discrete values, when you want to sample roughly in a given scale. E.g., with scale=1000 most samples will be in this ballpark, but ~10% of all samples will be <100 and ~10% will be >2300.\n",
-    "* `scipy.stats.expon(scale)`: this is the continuous equivalent of `geom`. Just set `scale` to the most likely value.\n",
-    "* `scipy.stats.loguniform(a, b)`: when you have almost no idea what the optimal hyperparameter value's scale is. If you set a=0.01 and b=100, then you're just as likely to sample a value between 0.01 and 0.1 as a value between 10 and 100.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are plots of the probability mass functions (for discrete variables), and probability density functions (for continuous variables) for `randint()`, `uniform()`, `geom()` and `expon()`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1200x700 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# extra code – plots a few distributions you can use in randomized search\n",
-    "\n",
-    "from scipy.stats import randint, uniform, geom, expon\n",
-    "\n",
-    "xs1 = np.arange(0, 7 + 1)\n",
-    "randint_distrib = randint(0, 7 + 1).pmf(xs1)\n",
-    "\n",
-    "xs2 = np.linspace(0, 7, 500)\n",
-    "uniform_distrib = uniform(0, 7).pdf(xs2)\n",
-    "\n",
-    "xs3 = np.arange(0, 7 + 1)\n",
-    "geom_distrib = geom(0.5).pmf(xs3)\n",
-    "\n",
-    "xs4 = np.linspace(0, 7, 500)\n",
-    "expon_distrib = expon(scale=1).pdf(xs4)\n",
-    "\n",
-    "plt.figure(figsize=(12, 7))\n",
-    "\n",
-    "plt.subplot(2, 2, 1)\n",
-    "plt.bar(xs1, randint_distrib, label=\"scipy.randint(0, 7 + 1)\")\n",
-    "plt.ylabel(\"Probability\")\n",
-    "plt.legend()\n",
-    "plt.axis([-1, 8, 0, 0.2])\n",
-    "\n",
-    "plt.subplot(2, 2, 2)\n",
-    "plt.fill_between(xs2, uniform_distrib, label=\"scipy.uniform(0, 7)\")\n",
-    "plt.ylabel(\"PDF\")\n",
-    "plt.legend()\n",
-    "plt.axis([-1, 8, 0, 0.2])\n",
-    "\n",
-    "plt.subplot(2, 2, 3)\n",
-    "plt.bar(xs3, geom_distrib, label=\"scipy.geom(0.5)\")\n",
-    "plt.xlabel(\"Hyperparameter value\")\n",
-    "plt.ylabel(\"Probability\")\n",
-    "plt.legend()\n",
-    "plt.axis([0, 7, 0, 1])\n",
-    "\n",
-    "plt.subplot(2, 2, 4)\n",
-    "plt.fill_between(xs4, expon_distrib, label=\"scipy.expon(scale=1)\")\n",
-    "plt.xlabel(\"Hyperparameter value\")\n",
-    "plt.ylabel(\"PDF\")\n",
-    "plt.legend()\n",
-    "plt.axis([0, 7, 0, 1])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the PDF for `expon()` and `loguniform()` (left column), as well as the PDF of log(X) (right column). The right column shows the distribution of hyperparameter _scales_. You can see that `expon()` favors hyperparameters with roughly the desired scale, with a longer tail towards the smaller scales. But `loguniform()` does not favor any scale, they are all equally likely:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1200x700 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# extra code – shows the difference between expon and loguniform\n",
-    "\n",
-    "from scipy.stats import loguniform\n",
-    "\n",
-    "xs1 = np.linspace(0, 7, 500)\n",
-    "expon_distrib = expon(scale=1).pdf(xs1)\n",
-    "\n",
-    "log_xs2 = np.linspace(-5, 3, 500)\n",
-    "log_expon_distrib = np.exp(log_xs2 - np.exp(log_xs2))\n",
-    "\n",
-    "xs3 = np.linspace(0.001, 1000, 500)\n",
-    "loguniform_distrib = loguniform(0.001, 1000).pdf(xs3)\n",
-    "\n",
-    "log_xs4 = np.linspace(np.log(0.001), np.log(1000), 500)\n",
-    "log_loguniform_distrib = uniform(np.log(0.001), np.log(1000)).pdf(log_xs4)\n",
-    "\n",
-    "plt.figure(figsize=(12, 7))\n",
-    "\n",
-    "plt.subplot(2, 2, 1)\n",
-    "plt.fill_between(xs1, expon_distrib,\n",
-    "                 label=\"scipy.expon(scale=1)\")\n",
-    "plt.ylabel(\"PDF\")\n",
-    "plt.legend()\n",
-    "plt.axis([0, 7, 0, 1])\n",
-    "\n",
-    "plt.subplot(2, 2, 2)\n",
-    "plt.fill_between(log_xs2, log_expon_distrib,\n",
-    "                 label=\"log(X) with X ~ expon\")\n",
-    "plt.legend()\n",
-    "plt.axis([-5, 3, 0, 1])\n",
-    "\n",
-    "plt.subplot(2, 2, 3)\n",
-    "plt.fill_between(xs3, loguniform_distrib,\n",
-    "                 label=\"scipy.loguniform(0.001, 1000)\")\n",
-    "plt.xlabel(\"Hyperparameter value\")\n",
-    "plt.ylabel(\"PDF\")\n",
-    "plt.legend()\n",
-    "plt.axis([0.001, 1000, 0, 0.005])\n",
-    "\n",
-    "plt.subplot(2, 2, 4)\n",
-    "plt.fill_between(log_xs4, log_loguniform_distrib,\n",
-    "                 label=\"log(X) with X ~ loguniform\")\n",
-    "plt.xlabel(\"Log of hyperparameter value\")\n",
-    "plt.legend()\n",
-    "plt.axis([-8, 1, 0, 0.2])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Analyze the Best Models and Their Errors"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.07, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.19, 0.01, 0.02, 0.01,\n",
-       "       0.01, 0.01, 0.  , 0.01, 0.02, 0.01, 0.02, 0.01, 0.  , 0.01, 0.02,\n",
-       "       0.01, 0.01, 0.01, 0.  , 0.02, 0.01, 0.01, 0.  , 0.01, 0.01, 0.01,\n",
-       "       0.03, 0.01, 0.01, 0.01, 0.01, 0.04, 0.01, 0.02, 0.01, 0.02, 0.01,\n",
-       "       0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.  , 0.07,\n",
-       "       0.  , 0.  , 0.  , 0.01])"
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "final_model = rnd_search.best_estimator_  # includes preprocessing\n",
-    "feature_importances = final_model[\"random_forest\"].feature_importances_\n",
-    "feature_importances.round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(np.float64(0.18599734460509476), 'log__median_income'),\n",
-       " (np.float64(0.07338850855844489), 'cat__ocean_proximity_INLAND'),\n",
-       " (np.float64(0.06556941990883976), 'bedrooms__ratio'),\n",
-       " (np.float64(0.053648710076725316), 'rooms_per_house__ratio'),\n",
-       " (np.float64(0.04598870861894749), 'people_per_house__ratio'),\n",
-       " (np.float64(0.04175269214442519), 'geo__Cluster 30 similarity'),\n",
-       " (np.float64(0.025976797232869678), 'geo__Cluster 25 similarity'),\n",
-       " (np.float64(0.023595895886342255), 'geo__Cluster 36 similarity'),\n",
-       " (np.float64(0.02021056221732893), 'geo__Cluster 9 similarity'),\n",
-       " (np.float64(0.01860691707666145), 'geo__Cluster 34 similarity'),\n",
-       " (np.float64(0.018137988374628867), 'geo__Cluster 37 similarity'),\n",
-       " (np.float64(0.01740435316632675), 'geo__Cluster 18 similarity'),\n",
-       " (np.float64(0.016778386143844894), 'geo__Cluster 1 similarity'),\n",
-       " (np.float64(0.015459009666188978), 'geo__Cluster 7 similarity'),\n",
-       " (np.float64(0.015325731028175924), 'geo__Cluster 32 similarity'),\n",
-       " (np.float64(0.015073772015038348), 'geo__Cluster 13 similarity'),\n",
-       " (np.float64(0.014272160962173805), 'geo__Cluster 35 similarity'),\n",
-       " (np.float64(0.014180636461860479), 'geo__Cluster 0 similarity'),\n",
-       " (np.float64(0.013746364498238989), 'geo__Cluster 3 similarity'),\n",
-       " (np.float64(0.01357230570846952), 'geo__Cluster 28 similarity'),\n",
-       " (np.float64(0.01294034969422872), 'geo__Cluster 26 similarity'),\n",
-       " (np.float64(0.012738123746761944), 'geo__Cluster 31 similarity'),\n",
-       " (np.float64(0.011654237215152624), 'geo__Cluster 19 similarity'),\n",
-       " (np.float64(0.011628003598059723), 'geo__Cluster 6 similarity'),\n",
-       " (np.float64(0.011134113333125398), 'geo__Cluster 24 similarity'),\n",
-       " (np.float64(0.011042979326385049), 'remainder__housing_median_age'),\n",
-       " (np.float64(0.010907388443940418), 'geo__Cluster 43 similarity'),\n",
-       " (np.float64(0.010847192663592166), 'geo__Cluster 44 similarity'),\n",
-       " (np.float64(0.010592244492858267), 'geo__Cluster 10 similarity'),\n",
-       " (np.float64(0.010512467290844922), 'geo__Cluster 23 similarity'),\n",
-       " (np.float64(0.01045866561538645), 'geo__Cluster 41 similarity'),\n",
-       " (np.float64(0.010261910692851673), 'geo__Cluster 40 similarity'),\n",
-       " (np.float64(0.009757306983097491), 'geo__Cluster 2 similarity'),\n",
-       " (np.float64(0.00965993322211448), 'geo__Cluster 12 similarity'),\n",
-       " (np.float64(0.009574969190852869), 'geo__Cluster 14 similarity'),\n",
-       " (np.float64(0.008199144719918425), 'geo__Cluster 20 similarity'),\n",
-       " (np.float64(0.008141941480860806), 'geo__Cluster 33 similarity'),\n",
-       " (np.float64(0.007596761219964691), 'geo__Cluster 8 similarity'),\n",
-       " (np.float64(0.0075762980128490295), 'geo__Cluster 22 similarity'),\n",
-       " (np.float64(0.007346290789504319), 'geo__Cluster 39 similarity'),\n",
-       " (np.float64(0.006898774333063982), 'geo__Cluster 4 similarity'),\n",
-       " (np.float64(0.0067947318450798395), 'log__total_rooms'),\n",
-       " (np.float64(0.006514889773323568), 'log__population'),\n",
-       " (np.float64(0.006350528211987125), 'geo__Cluster 27 similarity'),\n",
-       " (np.float64(0.006337558749902337), 'geo__Cluster 16 similarity'),\n",
-       " (np.float64(0.006231053672395539), 'geo__Cluster 38 similarity'),\n",
-       " (np.float64(0.0061213483458714855), 'log__households'),\n",
-       " (np.float64(0.005849842001582111), 'log__total_bedrooms'),\n",
-       " (np.float64(0.0056783104666850125), 'geo__Cluster 15 similarity'),\n",
-       " (np.float64(0.005479729990673467), 'geo__Cluster 29 similarity'),\n",
-       " (np.float64(0.005348325088535128), 'geo__Cluster 42 similarity'),\n",
-       " (np.float64(0.004866251452445486), 'geo__Cluster 17 similarity'),\n",
-       " (np.float64(0.004495340541933027), 'geo__Cluster 11 similarity'),\n",
-       " (np.float64(0.004418821635620684), 'geo__Cluster 5 similarity'),\n",
-       " (np.float64(0.0035344732505291285), 'geo__Cluster 21 similarity'),\n",
-       " (np.float64(0.001832424657341851), 'cat__ocean_proximity_<1H OCEAN'),\n",
-       " (np.float64(0.0015282226447271795), 'cat__ocean_proximity_NEAR OCEAN'),\n",
-       " (np.float64(0.0004325970342247361), 'cat__ocean_proximity_NEAR BAY'),\n",
-       " (np.float64(3.0190221102670295e-05), 'cat__ocean_proximity_ISLAND')]"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(zip(feature_importances,\n",
-    "           final_model[\"preprocessing\"].get_feature_names_out()),\n",
-    "       reverse=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluate Your System on the Test Set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "41445.533268606625\n"
-     ]
-    }
-   ],
-   "source": [
-    "X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n",
-    "y_test = strat_test_set[\"median_house_value\"].copy()\n",
-    "\n",
-    "final_predictions = final_model.predict(X_test)\n",
-    "\n",
-    "final_rmse = root_mean_squared_error(y_test, final_predictions)\n",
-    "print(final_rmse)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compute a 95% confidence interval for the test RMSE using SciPy's `bootstrap()` function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import bootstrap\n",
-    "\n",
-    "def rmse(squared_errors):\n",
-    "    return np.sqrt(np.mean(squared_errors))\n",
-    "\n",
-    "confidence = 0.95\n",
-    "squared_errors = (final_predictions - y_test) ** 2\n",
-    "boot_result = bootstrap([squared_errors], rmse, confidence_level=confidence,\n",
-    "                        random_state=42)\n",
-    "rmse_lower, rmse_upper = boot_result.confidence_interval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "95% CI for RMSE: (39520.9572, 43701.7681)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"95% CI for RMSE: ({rmse_lower:.4f}, {rmse_upper:.4f})\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model persistence using joblib"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Save the final model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['my_california_housing_model.pkl']"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import joblib\n",
-    "\n",
-    "joblib.dump(final_model, \"my_california_housing_model.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now you can deploy this model to production. For example, the following code could be a script that would run in production:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import joblib\n",
-    "\n",
-    "# extra code – excluded for conciseness\n",
-    "from sklearn.cluster import KMeans\n",
-    "from sklearn.base import BaseEstimator, TransformerMixin\n",
-    "from sklearn.metrics.pairwise import rbf_kernel\n",
-    "\n",
-    "def column_ratio(X):\n",
-    "    return X[:, [0]] / X[:, [1]]\n",
-    "\n",
-    "#class ClusterSimilarity(BaseEstimator, TransformerMixin):\n",
-    "#    [...]\n",
-    "\n",
-    "final_model_reloaded = joblib.load(\"my_california_housing_model.pkl\")\n",
-    "\n",
-    "new_data = housing.iloc[:5]  # pretend these are new districts\n",
-    "predictions = final_model_reloaded.predict(new_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([441046.12, 454713.09, 104832.  , 101316.  , 336181.05])"
-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You could use pickle instead, but joblib is more efficient."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exercise solutions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Exercise: _Try a Support Vector Machine regressor (`sklearn.svm.SVR`) with various hyperparameters, such as `kernel=\"linear\"` (with various values for the `C` hyperparameter) or `kernel=\"rbf\"` (with various values for the `C` and `gamma` hyperparameters). Note that SVMs don't scale well to large datasets, so you should probably train your model on just the first 5,000 instances of the training set and use only 3-fold cross-validation, or else it will take hours. Don't worry about what the hyperparameters mean for now (see the SVM notebook if you're interested). How does the best `SVR` predictor perform?_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "GridSearchCV(cv=3,\n",
-       "             estimator=Pipeline(steps=[('preprocessing',\n",
-       "                                        ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
-       "                                                                                     SimpleImputer(strategy='median')),\n",
-       "                                                                                    ('standardscaler',\n",
-       "                                                                                     StandardScaler())]),\n",
-       "                                                          transformers=[('bedrooms',\n",
-       "                                                                         Pipeline(steps=[('simpleimputer',\n",
-       "                                                                                          SimpleImputer(strategy='median')),\n",
-       "                                                                                         ('functiontransformer',\n",
-       "                                                                                          FunctionTransformer(feature_names_out=<f...\n",
-       "                                                                         <sklearn.compose._column_transformer.make_column_selector object at 0x7ffb95e12f90>)])),\n",
-       "                                       ('svr', SVR())]),\n",
-       "             param_grid=[{'svr__C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0,\n",
-       "                                     10000.0, 30000.0],\n",
-       "                          'svr__kernel': ['linear']},\n",
-       "                         {'svr__C': [1.0, 3.0, 10.0, 30.0, 100.0, 300.0,\n",
-       "                                     1000.0],\n",
-       "                          'svr__gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0],\n",
-       "                          'svr__kernel': ['rbf']}],\n",
-       "             scoring='neg_root_mean_squared_error')"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import GridSearchCV\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "param_grid = [\n",
-    "        {'svr__kernel': ['linear'], 'svr__C': [10., 30., 100., 300., 1000.,\n",
-    "                                               3000., 10000., 30000.0]},\n",
-    "        {'svr__kernel': ['rbf'], 'svr__C': [1.0, 3.0, 10., 30., 100., 300.,\n",
-    "                                            1000.0],\n",
-    "         'svr__gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]},\n",
-    "    ]\n",
-    "\n",
-    "svr_pipeline = Pipeline([(\"preprocessing\", preprocessing), (\"svr\", SVR())])\n",
-    "grid_search = GridSearchCV(svr_pipeline, param_grid, cv=3,\n",
-    "                           scoring='neg_root_mean_squared_error')\n",
-    "grid_search.fit(housing.iloc[:5000], housing_labels.iloc[:5000])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The best model achieves the following score (evaluated using 3-fold cross validation):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(70059.9277356289)"
-      ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svr_grid_search_rmse = -grid_search.best_score_\n",
-    "svr_grid_search_rmse"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's much worse than the `RandomForestRegressor` (but to be fair, we trained the model on much less data). Let's check the best hyperparameters found:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'svr__C': 10000.0, 'svr__kernel': 'linear'}"
-      ]
-     },
-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grid_search.best_params_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The linear kernel seems better than the RBF kernel. Notice that the value of `C` is the maximum tested value. When this happens you definitely want to launch the grid search again with higher values for `C` (removing the smallest values), because it is likely that higher values of `C` will be better."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Exercise: _Try replacing the `GridSearchCV` with a `RandomizedSearchCV`._"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Warning:** the following cell will take several minutes to run. You can specify `verbose=2` when creating the `RandomizedSearchCV` if you want to see the training details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RandomizedSearchCV(cv=3,\n",
-       "                   estimator=Pipeline(steps=[('preprocessing',\n",
-       "                                              ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
-       "                                                                                           SimpleImputer(strategy='median')),\n",
-       "                                                                                          ('standardscaler',\n",
-       "                                                                                           StandardScaler())]),\n",
-       "                                                                transformers=[('bedrooms',\n",
-       "                                                                               Pipeline(steps=[('simpleimputer',\n",
-       "                                                                                                SimpleImputer(strategy='median')),\n",
-       "                                                                                               ('functiontransformer',\n",
-       "                                                                                                FunctionTransformer(feature_names_...\n",
-       "                                                                               <sklearn.compose._column_transformer.make_column_selector object at 0x7ffb95e12f90>)])),\n",
-       "                                             ('svr', SVR())]),\n",
-       "                   n_iter=50,\n",
-       "                   param_distributions={'svr__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7ffb9fecb8f0>,\n",
-       "                                        'svr__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7ffb9c97e3f0>,\n",
-       "                                        'svr__kernel': ['linear', 'rbf']},\n",
-       "                   random_state=42, scoring='neg_root_mean_squared_error')"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import RandomizedSearchCV\n",
-    "from scipy.stats import expon, loguniform\n",
-    "\n",
-    "# see https://docs.scipy.org/doc/scipy/reference/stats.html\n",
-    "# for `expon()` and `loguniform()` documentation and more probability distribution functions.\n",
-    "\n",
-    "# Note: gamma is ignored when kernel is \"linear\"\n",
-    "param_distribs = {\n",
-    "        'svr__kernel': ['linear', 'rbf'],\n",
-    "        'svr__C': loguniform(20, 200_000),\n",
-    "        'svr__gamma': expon(scale=1.0),\n",
-    "    }\n",
-    "\n",
-    "rnd_search = RandomizedSearchCV(svr_pipeline,\n",
-    "                                param_distributions=param_distribs,\n",
-    "                                n_iter=50, cv=3,\n",
-    "                                scoring='neg_root_mean_squared_error',\n",
-    "                                random_state=42)\n",
-    "rnd_search.fit(housing.iloc[:5000], housing_labels.iloc[:5000])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The best model achieves the following score (evaluated using 3-fold cross validation):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(56152.053691161)"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "svr_rnd_search_rmse = -rnd_search.best_score_\n",
-    "svr_rnd_search_rmse"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that's really much better, but still far from the `RandomForestRegressor`'s performance. Let's check the best hyperparameters found:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'svr__C': np.float64(157055.10989448498),\n",
-       " 'svr__gamma': np.float64(0.26497040005002437),\n",
-       " 'svr__kernel': 'rbf'}"
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rnd_search.best_params_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This time the search found a good set of hyperparameters for the RBF kernel. Randomized search tends to find better hyperparameters than grid search in the same amount of time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that we used the `expon()` distribution for `gamma`, with a scale of 1, so `RandomSearch` mostly searched for values roughly of that scale: about 80% of the samples were between 0.1 and 2.3 (roughly 10% were smaller and 10% were larger):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(0.80066)"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s = expon(scale=1).rvs(100_000, random_state=42)  # get 100,000 samples\n",
-    "((s > 0.105) & (s < 2.29)).sum() / 100_000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We used the `loguniform()` distribution for `C`, meaning we did not have a clue what the optimal scale of `C` was before running the random search. It explored the range from 20 to 200 just as much as the range from 2,000 to 20,000 or from 20,000 to 200,000."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Exercise: _Try adding a `SelectFromModel` transformer in the preparation pipeline to select only the most important attributes._"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's create a new pipeline that runs the previously defined preparation pipeline, and adds a `SelectFromModel` transformer based on a `RandomForestRegressor` before the final regressor:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.feature_selection import SelectFromModel\n",
-    "\n",
-    "selector_pipeline = Pipeline([\n",
-    "    ('preprocessing', preprocessing),\n",
-    "    ('selector', SelectFromModel(RandomForestRegressor(random_state=42),\n",
-    "                                 threshold=0.005)),  # min feature importance\n",
-    "    ('svr', SVR(C=rnd_search.best_params_[\"svr__C\"],\n",
-    "                gamma=rnd_search.best_params_[\"svr__gamma\"],\n",
-    "                kernel=rnd_search.best_params_[\"svr__kernel\"])),\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>3.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>56622.643481</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>2272.666703</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>54243.242290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>55548.509493</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>56853.776696</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>57812.344076</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>58770.911457</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div><br><label><b>dtype:</b> float64</label>"
+      "source": [
+        "from sklearn.neighbors import KNeighborsRegressor\n",
+        "\n",
+        "knn_reg = KNeighborsRegressor(n_neighbors=3, weights=\"distance\")\n",
+        "knn_transformer = FeatureFromRegressor(knn_reg, [\"median_income\"])\n",
+        "geo_features = housing[[\"latitude\", \"longitude\", \"median_income\"]]\n",
+        "knn_transformer.fit_transform(geo_features, housing_labels)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zSSazDtLtgwN"
+      },
+      "source": [
+        "And what does its output feature name look like?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4kn-i4dRtgwO",
+        "outputId": "e0dbad44-8bab-4b3f-9756-891d09f6d191"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['pred_median_income']"
+            ]
+          },
+          "execution_count": 158,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "count        3.000000\n",
-       "mean     56622.643481\n",
-       "std       2272.666703\n",
-       "min      54243.242290\n",
-       "25%      55548.509493\n",
-       "50%      56853.776696\n",
-       "75%      57812.344076\n",
-       "max      58770.911457\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "selector_rmses = -cross_val_score(selector_pipeline,\n",
-    "                                  housing.iloc[:5000],\n",
-    "                                  housing_labels.iloc[:5000],\n",
-    "                                  scoring=\"neg_root_mean_squared_error\",\n",
-    "                                  cv=3)\n",
-    "pd.Series(selector_rmses).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Oh well, feature selection does not seem to help. But maybe that's just because the threshold we used was not optimal. Perhaps try tuning it using random search or grid search?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Exercise: _Try creating a custom transformer that trains a k-nearest neighbors regressor (`sklearn.neighbors.KNeighborsRegressor`) in its `fit()` method, and outputs the model's predictions in its `transform()` method. The KNN regressor should use only the latitude and longitude as input and predict the median income. Next, add this new transformer to the preprocessing pipeline. This will add a feature representing the smoothed median income over the nearby districts._"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Rather than restrict ourselves to k-Nearest Neighbors regressors, let's create a transformer that accepts any regressor. For this, we can extend the `MetaEstimatorMixin` and have a required `estimator` argument in the constructor. The `fit()` method must work on a clone of this estimator, and it must also save <code>feature_names_in&#95;</code>. The `MetaEstimatorMixin` will ensure that `estimator` is listed as a required parameters, and it will update `get_params()` and `set_params()` to make the estimator's hyperparameters available for tuning. Lastly, we create a `get_feature_names_out()` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.base import MetaEstimatorMixin, clone\n",
-    "\n",
-    "class FeatureFromRegressor(BaseEstimator, TransformerMixin, MetaEstimatorMixin):\n",
-    "    def __init__(self, regressor, target_features):\n",
-    "        self.regressor = regressor\n",
-    "        self.target_features = target_features\n",
-    "\n",
-    "    def fit(self, X, y=None):\n",
-    "        if hasattr(X, \"columns\"):\n",
-    "            self.feature_names_in_ = list(X.columns)\n",
-    "            X_df = X\n",
-    "        else:\n",
-    "            X_df = pd.DataFrame(X)\n",
-    "        \n",
-    "        self.input_features_ = [c for c in X_df.columns\n",
-    "                                if c not in self.target_features]\n",
-    "        self.regressor_ = clone(self.regressor)\n",
-    "        self.regressor_.fit(X_df[self.input_features_],\n",
-    "                            X_df[self.target_features])\n",
-    "        return self\n",
-    "\n",
-    "    def transform(self, X):\n",
-    "        columns = X.columns if hasattr(X, \"columns\") else None\n",
-    "        X_df = pd.DataFrame(X, columns=columns)\n",
-    "        preds = self.regressor_.predict(X_df[self.input_features_])\n",
-    "        if preds.ndim == 1:\n",
-    "            preds = preds.reshape(-1, 1)\n",
-    "        extra_columns = [f\"pred_{t}\" for t in self.target_features]\n",
-    "        preds_df = pd.DataFrame(preds, columns=extra_columns, index=X_df.index)\n",
-    "        return pd.concat([X_df, preds_df], axis=1)\n",
-    "    \n",
-    "    def get_feature_names_out(self, input_features=None):\n",
-    "        extra_columns = [f\"pred_{t}\" for t in self.target_features]\n",
-    "        return self.feature_names_in_ + extra_columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Good! Now let's test it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       pred_median_income\n",
-      "13096            3.347233\n",
-      "14973            6.899400\n",
-      "3785             2.900900\n",
-      "14689            2.261800\n",
-      "20507            3.475633\n",
-      "...                   ...\n",
-      "14207            4.939100\n",
-      "13105            3.301550\n",
-      "19301            4.061600\n",
-      "19121            4.145500\n",
-      "19888            4.250667\n",
-      "\n",
-      "[16512 rows x 1 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.neighbors import KNeighborsRegressor\n",
-    "\n",
-    "knn_reg = KNeighborsRegressor(n_neighbors=3, weights=\"distance\")\n",
-    "knn_transformer = FeatureFromRegressor(knn_reg, [\"median_income\"])\n",
-    "geo_features = housing[[\"latitude\", \"longitude\", \"median_income\"]]\n",
-    "knn_transformer.fit_transform(geo_features, housing_labels)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And what does its output feature name look like?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['pred_median_income']"
-      ]
-     },
-     "execution_count": 158,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "knn_transformer.get_feature_names_out()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Okay, now let's include this transformer in our preprocessing pipeline:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.base import clone\n",
-    "\n",
-    "transformers = [(name, clone(transformer), columns)\n",
-    "                for name, transformer, columns in preprocessing.transformers]\n",
-    "geo_index = [name for name, _, _ in transformers].index(\"geo\")\n",
-    "transformers[geo_index] = (\"geo\", knn_transformer,\n",
-    "                           [\"latitude\", \"longitude\", \"median_income\"])\n",
-    "new_geo_preprocessing = ColumnTransformer(transformers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_geo_pipeline = Pipeline([\n",
-    "    ('preprocessing', new_geo_preprocessing),\n",
-    "    ('svr', SVR(C=rnd_search.best_params_[\"svr__C\"],\n",
-    "                gamma=rnd_search.best_params_[\"svr__gamma\"],\n",
-    "                kernel=rnd_search.best_params_[\"svr__kernel\"])),\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count        3.000000\n",
-      "mean     70187.895052\n",
-      "std       1236.846898\n",
-      "min      68984.130231\n",
-      "25%      69554.161517\n",
-      "50%      70124.192803\n",
-      "75%      70789.777462\n",
-      "max      71455.362122\n",
-      "dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "new_pipe_rmses = -cross_val_score(new_geo_pipeline,\n",
-    "                                  housing.iloc[:5000],\n",
-    "                                  housing_labels.iloc[:5000],\n",
-    "                                  scoring=\"neg_root_mean_squared_error\",\n",
-    "                                  cv=3)\n",
-    "pd.Series(new_pipe_rmses).describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Yikes, that's terrible! Apparently the cluster similarity features were much better. But perhaps we should tune the `KNeighborsRegressor`'s hyperparameters? That's what the next exercise is about."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Exercise: _Automatically explore some preparation options using `RandomSearchCV`._"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "RandomizedSearchCV(cv=3,\n",
-      "                   estimator=Pipeline(steps=[('preprocessing',\n",
-      "                                              ColumnTransformer(transformers=[('bedrooms',\n",
-      "                                                                               Pipeline(steps=[('simpleimputer',\n",
-      "                                                                                                SimpleImputer(strategy='median')),\n",
-      "                                                                                               ('functiontransformer',\n",
-      "                                                                                                FunctionTransformer(feature_names_out=<function ratio_name at 0x798df1d84040>,\n",
-      "                                                                                                                    func=<function column_ratio at 0x798df1d84540>)),\n",
-      "                                                                                               ('standardscaler',\n",
-      "                                                                                                StandardSc...\n",
-      "                   param_distributions={'preprocessing__geo__regressor__n_neighbors': range(1, 30),\n",
-      "                                        'preprocessing__geo__regressor__weights': ['distance',\n",
-      "                                                                                   'uniform'],\n",
-      "                                        'svr__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x798dece01af0>,\n",
-      "                                        'svr__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x798dece026f0>},\n",
-      "                   random_state=42, scoring='neg_root_mean_squared_error')\n"
-     ]
-    }
-   ],
-   "source": [
-    "param_distribs = {\n",
-    "    \"preprocessing__geo__regressor__n_neighbors\": range(1, 30),\n",
-    "    \"preprocessing__geo__regressor__weights\": [\"distance\", \"uniform\"],\n",
-    "    \"svr__C\": loguniform(20, 200_000),\n",
-    "    \"svr__gamma\": expon(scale=1.0),\n",
-    "}\n",
-    "\n",
-    "new_geo_rnd_search = RandomizedSearchCV(new_geo_pipeline,\n",
-    "                                        param_distributions=param_distribs,\n",
-    "                                        n_iter=50,\n",
-    "                                        cv=3,\n",
-    "                                        scoring='neg_root_mean_squared_error',\n",
-    "                                        random_state=42)\n",
-    "new_geo_rnd_search.fit(housing.iloc[:5000], housing_labels.iloc[:5000])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(64573.262757363635)"
-      ]
-     },
-     "execution_count": 163,
-     "metadata": {},
-     "output_type": "execute_result"
+      "source": [
+        "knn_transformer.get_feature_names_out()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PeC3pBoQtgwP"
+      },
+      "source": [
+        "Okay, now let's include this transformer in our preprocessing pipeline:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2wz_hm_utgwP"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.base import clone\n",
+        "\n",
+        "transformers = [(name, clone(transformer), columns)\n",
+        "                for name, transformer, columns in preprocessing.transformers]\n",
+        "geo_index = [name for name, _, _ in transformers].index(\"geo\")\n",
+        "transformers[geo_index] = (\"geo\", knn_transformer,\n",
+        "                           [\"latitude\", \"longitude\", \"median_income\"])\n",
+        "new_geo_preprocessing = ColumnTransformer(transformers)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mJv8PMfvtgwR"
+      },
+      "outputs": [],
+      "source": [
+        "new_geo_pipeline = Pipeline([\n",
+        "    ('preprocessing', new_geo_preprocessing),\n",
+        "    ('svr', SVR(C=rnd_search.best_params_[\"svr__C\"],\n",
+        "                gamma=rnd_search.best_params_[\"svr__gamma\"],\n",
+        "                kernel=rnd_search.best_params_[\"svr__kernel\"])),\n",
+        "])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EpNK3hCitgwV",
+        "outputId": "c7470113-edd2-488d-ec2b-e1ab0c04396e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "count        3.000000\n",
+            "mean     70187.895052\n",
+            "std       1236.846898\n",
+            "min      68984.130231\n",
+            "25%      69554.161517\n",
+            "50%      70124.192803\n",
+            "75%      70789.777462\n",
+            "max      71455.362122\n",
+            "dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "new_pipe_rmses = -cross_val_score(new_geo_pipeline,\n",
+        "                                  housing.iloc[:5000],\n",
+        "                                  housing_labels.iloc[:5000],\n",
+        "                                  scoring=\"neg_root_mean_squared_error\",\n",
+        "                                  cv=3)\n",
+        "pd.Series(new_pipe_rmses).describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JyfymCLztgwX"
+      },
+      "source": [
+        "Yikes, that's terrible! Apparently the cluster similarity features were much better. But perhaps we should tune the `KNeighborsRegressor`'s hyperparameters? That's what the next exercise is about."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LRZhNaNOtgwa"
+      },
+      "source": [
+        "## 5."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_6oU1ULhtgwe"
+      },
+      "source": [
+        "Exercise: _Automatically explore some preparation options using `RandomSearchCV`._"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ydzmXb01tgwo",
+        "outputId": "c2968a2d-47d2-4185-e8ce-8c063d0ad8f1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RandomizedSearchCV(cv=3,\n",
+            "                   estimator=Pipeline(steps=[('preprocessing',\n",
+            "                                              ColumnTransformer(transformers=[('bedrooms',\n",
+            "                                                                               Pipeline(steps=[('simpleimputer',\n",
+            "                                                                                                SimpleImputer(strategy='median')),\n",
+            "                                                                                               ('functiontransformer',\n",
+            "                                                                                                FunctionTransformer(feature_names_out=<function ratio_name at 0x798df1d84040>,\n",
+            "                                                                                                                    func=<function column_ratio at 0x798df1d84540>)),\n",
+            "                                                                                               ('standardscaler',\n",
+            "                                                                                                StandardSc...\n",
+            "                   param_distributions={'preprocessing__geo__regressor__n_neighbors': range(1, 30),\n",
+            "                                        'preprocessing__geo__regressor__weights': ['distance',\n",
+            "                                                                                   'uniform'],\n",
+            "                                        'svr__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x798dece01af0>,\n",
+            "                                        'svr__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x798dece026f0>},\n",
+            "                   random_state=42, scoring='neg_root_mean_squared_error')\n"
+          ]
+        }
+      ],
+      "source": [
+        "param_distribs = {\n",
+        "    \"preprocessing__geo__regressor__n_neighbors\": range(1, 30),\n",
+        "    \"preprocessing__geo__regressor__weights\": [\"distance\", \"uniform\"],\n",
+        "    \"svr__C\": loguniform(20, 200_000),\n",
+        "    \"svr__gamma\": expon(scale=1.0),\n",
+        "}\n",
+        "\n",
+        "new_geo_rnd_search = RandomizedSearchCV(new_geo_pipeline,\n",
+        "                                        param_distributions=param_distribs,\n",
+        "                                        n_iter=50,\n",
+        "                                        cv=3,\n",
+        "                                        scoring='neg_root_mean_squared_error',\n",
+        "                                        random_state=42)\n",
+        "new_geo_rnd_search.fit(housing.iloc[:5000], housing_labels.iloc[:5000])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vyT_291atgwp",
+        "outputId": "6a7e9233-ecd4-473f-f0bf-8f53e1cafca3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "np.float64(64573.262757363635)"
+            ]
+          },
+          "execution_count": 163,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "new_geo_rnd_search_rmse = -new_geo_rnd_search.best_score_\n",
+        "new_geo_rnd_search_rmse"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HbGDmyujtgws"
+      },
+      "source": [
+        "Oh well... at least we tried! It looks like the cluster similarity features are definitely better than the KNN feature. But perhaps you could try having both? And maybe training on the full training set would help as well."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NqhJ695ltgwt"
+      },
+      "source": [
+        "## 6."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MfWML-Ovtgwt"
+      },
+      "source": [
+        "Exercise: _Try to implement the `StandardScalerClone` class again from scratch, then add support for the `inverse_transform()` method: executing `scaler.inverse_transform(scaler.fit_transform(X))` should return an array very close to `X`. Then add support for feature names: set <code>feature_names_in&#95;</code> in the `fit()` method if the input is a DataFrame. This attribute should be a NumPy array of column names. Lastly, implement the `get_feature_names_out()` method: it should have one optional `input_features=None` argument. If passed, the method should check that its length matches <code>n_features_in&#95;</code>, and it should match <code>feature_names_in&#95;</code> if it is defined, then `input_features` should be returned. If `input_features` is `None`, then the method should return <code>feature_names_in&#95;</code> if it is defined or `np.array([\"x0\", \"x1\", ...])` with length <code>n_features_in&#95;</code> otherwise._"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TgVPebxWtgwu"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.base import BaseEstimator, TransformerMixin\n",
+        "from sklearn.utils.validation import check_is_fitted, validate_data\n",
+        "\n",
+        "class StandardScalerClone(TransformerMixin, BaseEstimator):\n",
+        "    def __init__(self, with_mean=True):  # no *args or **kwargs!\n",
+        "        self.with_mean = with_mean\n",
+        "\n",
+        "    def fit(self, X, y=None):\n",
+        "        X = validate_data(self, X, ensure_2d=True)\n",
+        "        self.n_features_in_ = X.shape[1]\n",
+        "        if self.with_mean:\n",
+        "            self.mean_ = np.mean(X, axis=0)\n",
+        "        self.scale_ = np.std(X, axis=0, ddof=0)\n",
+        "        self.scale_[self.scale_ == 0] = 1  # Avoid division by zero\n",
+        "        return self\n",
+        "\n",
+        "    def transform(self, X):\n",
+        "        check_is_fitted(self)\n",
+        "        X = validate_data(self, X, ensure_2d=True, reset=False)\n",
+        "        if self.with_mean:\n",
+        "            X = X - self.mean_\n",
+        "        return X / self.scale_\n",
+        "\n",
+        "    def inverse_transform(self, X):\n",
+        "        check_is_fitted(self)\n",
+        "        X = validate_data(self, X, ensure_2d=True, reset=False)\n",
+        "        return X * self.scale_ + self.mean_\n",
+        "\n",
+        "    def get_feature_names_out(self, input_features=None):\n",
+        "        if input_features is None:\n",
+        "            return getattr(self, \"feature_names_in_\",\n",
+        "                           [f\"x{i}\" for i in range(self.n_features_in_)])\n",
+        "        else:\n",
+        "            if len(input_features) != self.n_features_in_:\n",
+        "                raise ValueError(\"Invalid number of features\")\n",
+        "            if hasattr(self, \"feature_names_in_\") and not np.all(\n",
+        "                self.feature_names_in_ == input_features\n",
+        "            ):\n",
+        "                raise ValueError(\"input_features ≠ feature_names_in_\")\n",
+        "            return input_features"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zc2fD86Dtgw5"
+      },
+      "source": [
+        "Let's test our custom transformer:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "J6semlbItgw8",
+        "outputId": "98ad2b25-f7ee-403e-f039-642834ae7a3f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[{'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimator_cloneable',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimator_cloneable',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimator_tags_renamed',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_valid_tag_types',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimator_repr',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_no_attributes_set_in_init',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_fit_score_takes_y',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimators_overwrite_params',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_dont_overwrite_parameters',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimators_fit_returns_self',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_readonly_memmap_input',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimators_unfitted',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_do_not_raise_errors_in_init_or_set_params',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_n_features_in_after_fitting',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_mixin_order',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_positive_only_tag_during_fit',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimators_dtypes',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_complex_data',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_dtype_object',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimators_empty_data_messages',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_pipeline_consistency',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimators_nan_inf',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimator_sparse_tag',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimator_sparse_array',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimator_sparse_matrix',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimators_pickle',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_estimators_pickle',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_f_contiguous_array_estimator',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_transformer_data_not_an_array',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_transformer_general',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_transformer_preserve_dtypes',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_transformer_general',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_transformers_unfitted',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_transformer_n_iter',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_parameters_default_constructible',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_methods_sample_order_invariance',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_methods_subset_invariance',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_fit2d_1sample',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_fit2d_1feature',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_get_params_invariance',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_set_params',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_dict_unchanged',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_fit_idempotent',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_fit_check_is_fitted',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_n_features_in',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_fit1d',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
+              " {'estimator': StandardScalerClone(),\n",
+              "  'check_name': 'check_fit2d_predict1d',\n",
+              "  'exception': None,\n",
+              "  'status': 'passed',\n",
+              "  'expected_to_fail': False,\n",
+              "  'expected_to_fail_reason': 'Check is not expected to fail'}]"
+            ]
+          },
+          "execution_count": 165,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.utils.estimator_checks import check_estimator\n",
+        "\n",
+        "check_estimator(StandardScalerClone())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JoNgldrKtgw9"
+      },
+      "source": [
+        "No errors, that's a great start, we respect the Scikit-Learn API."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "duqIcQUftgw_"
+      },
+      "source": [
+        "Now let's ensure the transformation works as expected:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9zVM17b9tgyN"
+      },
+      "outputs": [],
+      "source": [
+        "rng = np.random.default_rng(seed=42)\n",
+        "X = rng.random((1000, 3))\n",
+        "\n",
+        "scaler = StandardScalerClone()\n",
+        "X_scaled = scaler.fit_transform(X)\n",
+        "\n",
+        "assert np.allclose(X_scaled, (X - X.mean(axis=0)) / X.std(axis=0))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "llStfy73tgyS"
+      },
+      "source": [
+        "How about setting `with_mean=False`?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jYpzpvE6tgyl"
+      },
+      "outputs": [],
+      "source": [
+        "scaler = StandardScalerClone(with_mean=False)\n",
+        "X_scaled_uncentered = scaler.fit_transform(X)\n",
+        "\n",
+        "assert np.allclose(X_scaled_uncentered, X / X.std(axis=0))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "886SqizZtgyx"
+      },
+      "source": [
+        "And does the inverse work?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UxwsJSIStgy3"
+      },
+      "outputs": [],
+      "source": [
+        "scaler = StandardScalerClone()\n",
+        "X_back = scaler.inverse_transform(scaler.fit_transform(X))\n",
+        "\n",
+        "assert np.allclose(X, X_back)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zVD9RfqPtgy3"
+      },
+      "source": [
+        "How about the feature names out?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yp1TWMN7tgy9"
+      },
+      "outputs": [],
+      "source": [
+        "assert np.all(scaler.get_feature_names_out() == [\"x0\", \"x1\", \"x2\"])\n",
+        "assert np.all(scaler.get_feature_names_out([\"a\", \"b\", \"c\"]) == [\"a\", \"b\", \"c\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KbX90ur8tgy-"
+      },
+      "source": [
+        "And if we fit a DataFrame, are the feature in and out ok?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rKiSAbnOtgy_"
+      },
+      "outputs": [],
+      "source": [
+        "df = pd.DataFrame({\"a\": rng.random(100), \"b\": rng.random(100)})\n",
+        "scaler = StandardScalerClone()\n",
+        "X_scaled = scaler.fit_transform(df)\n",
+        "\n",
+        "assert np.all(scaler.feature_names_in_ == [\"a\", \"b\"])\n",
+        "assert np.all(scaler.get_feature_names_out() == [\"a\", \"b\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mcgkd0EvtgzF"
+      },
+      "source": [
+        "All good! That's all for today! 😀"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b0RsyO9mtgzG"
+      },
+      "source": [
+        "Congratulations! You already know quite a lot about Machine Learning. :)"
+      ]
     }
-   ],
-   "source": [
-    "new_geo_rnd_search_rmse = -new_geo_rnd_search.best_score_\n",
-    "new_geo_rnd_search_rmse"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Oh well... at least we tried! It looks like the cluster similarity features are definitely better than the KNN feature. But perhaps you could try having both? And maybe training on the full training set would help as well."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Exercise: _Try to implement the `StandardScalerClone` class again from scratch, then add support for the `inverse_transform()` method: executing `scaler.inverse_transform(scaler.fit_transform(X))` should return an array very close to `X`. Then add support for feature names: set <code>feature_names_in&#95;</code> in the `fit()` method if the input is a DataFrame. This attribute should be a NumPy array of column names. Lastly, implement the `get_feature_names_out()` method: it should have one optional `input_features=None` argument. If passed, the method should check that its length matches <code>n_features_in&#95;</code>, and it should match <code>feature_names_in&#95;</code> if it is defined, then `input_features` should be returned. If `input_features` is `None`, then the method should return <code>feature_names_in&#95;</code> if it is defined or `np.array([\"x0\", \"x1\", ...])` with length <code>n_features_in&#95;</code> otherwise._"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.base import BaseEstimator, TransformerMixin\n",
-    "from sklearn.utils.validation import check_is_fitted, validate_data\n",
-    "\n",
-    "class StandardScalerClone(TransformerMixin, BaseEstimator):\n",
-    "    def __init__(self, with_mean=True):  # no *args or **kwargs!\n",
-    "        self.with_mean = with_mean\n",
-    "\n",
-    "    def fit(self, X, y=None):\n",
-    "        X = validate_data(self, X, ensure_2d=True)\n",
-    "        self.n_features_in_ = X.shape[1]\n",
-    "        if self.with_mean:\n",
-    "            self.mean_ = np.mean(X, axis=0)\n",
-    "        self.scale_ = np.std(X, axis=0, ddof=0)\n",
-    "        self.scale_[self.scale_ == 0] = 1  # Avoid division by zero\n",
-    "        return self\n",
-    "\n",
-    "    def transform(self, X):\n",
-    "        check_is_fitted(self)\n",
-    "        X = validate_data(self, X, ensure_2d=True, reset=False)\n",
-    "        if self.with_mean:\n",
-    "            X = X - self.mean_\n",
-    "        return X / self.scale_\n",
-    "\n",
-    "    def inverse_transform(self, X):\n",
-    "        check_is_fitted(self)\n",
-    "        X = validate_data(self, X, ensure_2d=True, reset=False)\n",
-    "        return X * self.scale_ + self.mean_\n",
-    "\n",
-    "    def get_feature_names_out(self, input_features=None):\n",
-    "        if input_features is None:\n",
-    "            return getattr(self, \"feature_names_in_\",\n",
-    "                           [f\"x{i}\" for i in range(self.n_features_in_)])\n",
-    "        else:\n",
-    "            if len(input_features) != self.n_features_in_:\n",
-    "                raise ValueError(\"Invalid number of features\")\n",
-    "            if hasattr(self, \"feature_names_in_\") and not np.all(\n",
-    "                self.feature_names_in_ == input_features\n",
-    "            ):\n",
-    "                raise ValueError(\"input_features ≠ feature_names_in_\")\n",
-    "            return input_features"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's test our custom transformer:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimator_cloneable',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimator_cloneable',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimator_tags_renamed',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_valid_tag_types',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimator_repr',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_no_attributes_set_in_init',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_fit_score_takes_y',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimators_overwrite_params',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_dont_overwrite_parameters',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimators_fit_returns_self',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_readonly_memmap_input',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimators_unfitted',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_do_not_raise_errors_in_init_or_set_params',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_n_features_in_after_fitting',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_mixin_order',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_positive_only_tag_during_fit',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimators_dtypes',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_complex_data',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_dtype_object',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimators_empty_data_messages',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_pipeline_consistency',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimators_nan_inf',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimator_sparse_tag',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimator_sparse_array',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimator_sparse_matrix',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimators_pickle',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_estimators_pickle',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_f_contiguous_array_estimator',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_transformer_data_not_an_array',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_transformer_general',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_transformer_preserve_dtypes',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_transformer_general',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_transformers_unfitted',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_transformer_n_iter',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_parameters_default_constructible',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_methods_sample_order_invariance',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_methods_subset_invariance',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_fit2d_1sample',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_fit2d_1feature',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_get_params_invariance',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_set_params',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_dict_unchanged',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_fit_idempotent',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_fit_check_is_fitted',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_n_features_in',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_fit1d',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'},\n",
-       " {'estimator': StandardScalerClone(),\n",
-       "  'check_name': 'check_fit2d_predict1d',\n",
-       "  'exception': None,\n",
-       "  'status': 'passed',\n",
-       "  'expected_to_fail': False,\n",
-       "  'expected_to_fail_reason': 'Check is not expected to fail'}]"
-      ]
-     },
-     "execution_count": 165,
-     "metadata": {},
-     "output_type": "execute_result"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    },
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
     }
-   ],
-   "source": [
-    "from sklearn.utils.estimator_checks import check_estimator\n",
-    "\n",
-    "check_estimator(StandardScalerClone())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "No errors, that's a great start, we respect the Scikit-Learn API."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's ensure the transformation works as expected:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rng = np.random.default_rng(seed=42)\n",
-    "X = rng.random((1000, 3))\n",
-    "\n",
-    "scaler = StandardScalerClone()\n",
-    "X_scaled = scaler.fit_transform(X)\n",
-    "\n",
-    "assert np.allclose(X_scaled, (X - X.mean(axis=0)) / X.std(axis=0))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "How about setting `with_mean=False`?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scaler = StandardScalerClone(with_mean=False)\n",
-    "X_scaled_uncentered = scaler.fit_transform(X)\n",
-    "\n",
-    "assert np.allclose(X_scaled_uncentered, X / X.std(axis=0))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And does the inverse work?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scaler = StandardScalerClone()\n",
-    "X_back = scaler.inverse_transform(scaler.fit_transform(X))\n",
-    "\n",
-    "assert np.allclose(X, X_back)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "How about the feature names out?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "assert np.all(scaler.get_feature_names_out() == [\"x0\", \"x1\", \"x2\"])\n",
-    "assert np.all(scaler.get_feature_names_out([\"a\", \"b\", \"c\"]) == [\"a\", \"b\", \"c\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And if we fit a DataFrame, are the feature in and out ok?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame({\"a\": rng.random(100), \"b\": rng.random(100)})\n",
-    "scaler = StandardScalerClone()\n",
-    "X_scaled = scaler.fit_transform(df)\n",
-    "\n",
-    "assert np.all(scaler.feature_names_in_ == [\"a\", \"b\"])\n",
-    "assert np.all(scaler.get_feature_names_out() == [\"a\", \"b\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "All good! That's all for today! 😀"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Congratulations! You already know quite a lot about Machine Learning. :)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..3e4898b
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,7440 @@
+# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
+
+[[package]]
+name = "absl-py"
+version = "2.3.1"
+description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d"},
+    {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+]
+
+[[package]]
+name = "accelerate"
+version = "1.11.0"
+description = "Accelerate"
+optional = false
+python-versions = ">=3.10.0"
+groups = ["main"]
+files = [
+    {file = "accelerate-1.11.0-py3-none-any.whl", hash = "sha256:a628fa6beb069b8e549460fc449135d5bd8d73e7a11fd09f0bc9fc4ace7f06f1"},
+    {file = "accelerate-1.11.0.tar.gz", hash = "sha256:bb1caf2597b4cd632b917b5000c591d10730bb024a79746f1ee205bba80bd229"},
+]
+
+[package.dependencies]
+huggingface_hub = ">=0.21.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+psutil = "*"
+pyyaml = "*"
+safetensors = ">=0.4.3"
+torch = ">=2.0.0"
+
+[package.extras]
+deepspeed = ["deepspeed"]
+dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "rich", "ruff (==0.13.1)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+quality = ["ruff (==0.13.1)"]
+rich = ["rich"]
+sagemaker = ["sagemaker"]
+test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+test-fp8 = ["torchao"]
+test-prod = ["parameterized", "pytest (>=7.2.0)", "pytest-order", "pytest-subtests", "pytest-xdist"]
+test-trackers = ["comet-ml", "dvclive", "matplotlib", "swanlab[dashboard]", "tensorboard", "trackio", "wandb"]
+testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"},
+    {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"},
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.13.2"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "aiohttp-3.13.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:2372b15a5f62ed37789a6b383ff7344fc5b9f243999b0cd9b629d8bc5f5b4155"},
+    {file = "aiohttp-3.13.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e7f8659a48995edee7229522984bd1009c1213929c769c2daa80b40fe49a180c"},
+    {file = "aiohttp-3.13.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:939ced4a7add92296b0ad38892ce62b98c619288a081170695c6babe4f50e636"},
+    {file = "aiohttp-3.13.2-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6315fb6977f1d0dd41a107c527fee2ed5ab0550b7d885bc15fee20ccb17891da"},
+    {file = "aiohttp-3.13.2-cp310-cp310-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:6e7352512f763f760baaed2637055c49134fd1d35b37c2dedfac35bfe5cf8725"},
+    {file = "aiohttp-3.13.2-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e09a0a06348a2dd73e7213353c90d709502d9786219f69b731f6caa0efeb46f5"},
+    {file = "aiohttp-3.13.2-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a09a6d073fb5789456545bdee2474d14395792faa0527887f2f4ec1a486a59d3"},
+    {file = "aiohttp-3.13.2-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b59d13c443f8e049d9e94099c7e412e34610f1f49be0f230ec656a10692a5802"},
+    {file = "aiohttp-3.13.2-cp310-cp310-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:20db2d67985d71ca033443a1ba2001c4b5693fe09b0e29f6d9358a99d4d62a8a"},
+    {file = "aiohttp-3.13.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:960c2fc686ba27b535f9fd2b52d87ecd7e4fd1cf877f6a5cba8afb5b4a8bd204"},
+    {file = "aiohttp-3.13.2-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:6c00dbcf5f0d88796151e264a8eab23de2997c9303dd7c0bf622e23b24d3ce22"},
+    {file = "aiohttp-3.13.2-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:fed38a5edb7945f4d1bcabe2fcd05db4f6ec7e0e82560088b754f7e08d93772d"},
+    {file = "aiohttp-3.13.2-cp310-cp310-musllinux_1_2_riscv64.whl", hash = "sha256:b395bbca716c38bef3c764f187860e88c724b342c26275bc03e906142fc5964f"},
+    {file = "aiohttp-3.13.2-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:204ffff2426c25dfda401ba08da85f9c59525cdc42bda26660463dd1cbcfec6f"},
+    {file = "aiohttp-3.13.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:05c4dd3c48fb5f15db31f57eb35374cb0c09afdde532e7fb70a75aede0ed30f6"},
+    {file = "aiohttp-3.13.2-cp310-cp310-win32.whl", hash = "sha256:e574a7d61cf10351d734bcddabbe15ede0eaa8a02070d85446875dc11189a251"},
+    {file = "aiohttp-3.13.2-cp310-cp310-win_amd64.whl", hash = "sha256:364f55663085d658b8462a1c3f17b2b84a5c2e1ba858e1b79bff7b2e24ad1514"},
+    {file = "aiohttp-3.13.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:4647d02df098f6434bafd7f32ad14942f05a9caa06c7016fdcc816f343997dd0"},
+    {file = "aiohttp-3.13.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:e3403f24bcb9c3b29113611c3c16a2a447c3953ecf86b79775e7be06f7ae7ccb"},
+    {file = "aiohttp-3.13.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:43dff14e35aba17e3d6d5ba628858fb8cb51e30f44724a2d2f0c75be492c55e9"},
+    {file = "aiohttp-3.13.2-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e2a9ea08e8c58bb17655630198833109227dea914cd20be660f52215f6de5613"},
+    {file = "aiohttp-3.13.2-cp311-cp311-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:53b07472f235eb80e826ad038c9d106c2f653584753f3ddab907c83f49eedead"},
+    {file = "aiohttp-3.13.2-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e736c93e9c274fce6419af4aac199984d866e55f8a4cec9114671d0ea9688780"},
+    {file = "aiohttp-3.13.2-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ff5e771f5dcbc81c64898c597a434f7682f2259e0cd666932a913d53d1341d1a"},
+    {file = "aiohttp-3.13.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a3b6fb0c207cc661fa0bf8c66d8d9b657331ccc814f4719468af61034b478592"},
+    {file = "aiohttp-3.13.2-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:97a0895a8e840ab3520e2288db7cace3a1981300d48babeb50e7425609e2e0ab"},
+    {file = "aiohttp-3.13.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:9e8f8afb552297aca127c90cb840e9a1d4bfd6a10d7d8f2d9176e1acc69bad30"},
+    {file = "aiohttp-3.13.2-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:ed2f9c7216e53c3df02264f25d824b079cc5914f9e2deba94155190ef648ee40"},
+    {file = "aiohttp-3.13.2-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:99c5280a329d5fa18ef30fd10c793a190d996567667908bef8a7f81f8202b948"},
+    {file = "aiohttp-3.13.2-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:2ca6ffef405fc9c09a746cb5d019c1672cd7f402542e379afc66b370833170cf"},
+    {file = "aiohttp-3.13.2-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:47f438b1a28e926c37632bff3c44df7d27c9b57aaf4e34b1def3c07111fdb782"},
+    {file = "aiohttp-3.13.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:9acda8604a57bb60544e4646a4615c1866ee6c04a8edef9b8ee6fd1d8fa2ddc8"},
+    {file = "aiohttp-3.13.2-cp311-cp311-win32.whl", hash = "sha256:868e195e39b24aaa930b063c08bb0c17924899c16c672a28a65afded9c46c6ec"},
+    {file = "aiohttp-3.13.2-cp311-cp311-win_amd64.whl", hash = "sha256:7fd19df530c292542636c2a9a85854fab93474396a52f1695e799186bbd7f24c"},
+    {file = "aiohttp-3.13.2-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:b1e56bab2e12b2b9ed300218c351ee2a3d8c8fdab5b1ec6193e11a817767e47b"},
+    {file = "aiohttp-3.13.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:364e25edaabd3d37b1db1f0cbcee8c73c9a3727bfa262b83e5e4cf3489a2a9dc"},
+    {file = "aiohttp-3.13.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c5c94825f744694c4b8db20b71dba9a257cd2ba8e010a803042123f3a25d50d7"},
+    {file = "aiohttp-3.13.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ba2715d842ffa787be87cbfce150d5e88c87a98e0b62e0f5aa489169a393dbbb"},
+    {file = "aiohttp-3.13.2-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:585542825c4bc662221fb257889e011a5aa00f1ae4d75d1d246a5225289183e3"},
+    {file = "aiohttp-3.13.2-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:39d02cb6025fe1aabca329c5632f48c9532a3dabccd859e7e2f110668972331f"},
+    {file = "aiohttp-3.13.2-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:e67446b19e014d37342f7195f592a2a948141d15a312fe0e700c2fd2f03124f6"},
+    {file = "aiohttp-3.13.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4356474ad6333e41ccefd39eae869ba15a6c5299c9c01dfdcfdd5c107be4363e"},
+    {file = "aiohttp-3.13.2-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:eeacf451c99b4525f700f078becff32c32ec327b10dcf31306a8a52d78166de7"},
+    {file = "aiohttp-3.13.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:d8a9b889aeabd7a4e9af0b7f4ab5ad94d42e7ff679aaec6d0db21e3b639ad58d"},
+    {file = "aiohttp-3.13.2-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:fa89cb11bc71a63b69568d5b8a25c3ca25b6d54c15f907ca1c130d72f320b76b"},
+    {file = "aiohttp-3.13.2-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:8aa7c807df234f693fed0ecd507192fc97692e61fee5702cdc11155d2e5cadc8"},
+    {file = "aiohttp-3.13.2-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:9eb3e33fdbe43f88c3c75fa608c25e7c47bbd80f48d012763cb67c47f39a7e16"},
+    {file = "aiohttp-3.13.2-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:9434bc0d80076138ea986833156c5a48c9c7a8abb0c96039ddbb4afc93184169"},
+    {file = "aiohttp-3.13.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:ff15c147b2ad66da1f2cbb0622313f2242d8e6e8f9b79b5206c84523a4473248"},
+    {file = "aiohttp-3.13.2-cp312-cp312-win32.whl", hash = "sha256:27e569eb9d9e95dbd55c0fc3ec3a9335defbf1d8bc1d20171a49f3c4c607b93e"},
+    {file = "aiohttp-3.13.2-cp312-cp312-win_amd64.whl", hash = "sha256:8709a0f05d59a71f33fd05c17fc11fcb8c30140506e13c2f5e8ee1b8964e1b45"},
+    {file = "aiohttp-3.13.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:7519bdc7dfc1940d201651b52bf5e03f5503bda45ad6eacf64dda98be5b2b6be"},
+    {file = "aiohttp-3.13.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:088912a78b4d4f547a1f19c099d5a506df17eacec3c6f4375e2831ec1d995742"},
+    {file = "aiohttp-3.13.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:5276807b9de9092af38ed23ce120539ab0ac955547b38563a9ba4f5b07b95293"},
+    {file = "aiohttp-3.13.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1237c1375eaef0db4dcd7c2559f42e8af7b87ea7d295b118c60c36a6e61cb811"},
+    {file = "aiohttp-3.13.2-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:96581619c57419c3d7d78703d5b78c1e5e5fc0172d60f555bdebaced82ded19a"},
+    {file = "aiohttp-3.13.2-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a2713a95b47374169409d18103366de1050fe0ea73db358fc7a7acb2880422d4"},
+    {file = "aiohttp-3.13.2-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:228a1cd556b3caca590e9511a89444925da87d35219a49ab5da0c36d2d943a6a"},
+    {file = "aiohttp-3.13.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ac6cde5fba8d7d8c6ac963dbb0256a9854e9fafff52fbcc58fdf819357892c3e"},
+    {file = "aiohttp-3.13.2-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f2bef8237544f4e42878c61cef4e2839fee6346dc60f5739f876a9c50be7fcdb"},
+    {file = "aiohttp-3.13.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:16f15a4eac3bc2d76c45f7ebdd48a65d41b242eb6c31c2245463b40b34584ded"},
+    {file = "aiohttp-3.13.2-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:bb7fb776645af5cc58ab804c58d7eba545a97e047254a52ce89c157b5af6cd0b"},
+    {file = "aiohttp-3.13.2-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:e1b4951125ec10c70802f2cb09736c895861cd39fd9dcb35107b4dc8ae6220b8"},
+    {file = "aiohttp-3.13.2-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:550bf765101ae721ee1d37d8095f47b1f220650f85fe1af37a90ce75bab89d04"},
+    {file = "aiohttp-3.13.2-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:fe91b87fc295973096251e2d25a811388e7d8adf3bd2b97ef6ae78bc4ac6c476"},
+    {file = "aiohttp-3.13.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e0c8e31cfcc4592cb200160344b2fb6ae0f9e4effe06c644b5a125d4ae5ebe23"},
+    {file = "aiohttp-3.13.2-cp313-cp313-win32.whl", hash = "sha256:0740f31a60848d6edb296a0df827473eede90c689b8f9f2a4cdde74889eb2254"},
+    {file = "aiohttp-3.13.2-cp313-cp313-win_amd64.whl", hash = "sha256:a88d13e7ca367394908f8a276b89d04a3652044612b9a408a0bb22a5ed976a1a"},
+    {file = "aiohttp-3.13.2-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:2475391c29230e063ef53a66669b7b691c9bfc3f1426a0f7bcdf1216bdbac38b"},
+    {file = "aiohttp-3.13.2-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:f33c8748abef4d8717bb20e8fb1b3e07c6adacb7fd6beaae971a764cf5f30d61"},
+    {file = "aiohttp-3.13.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:ae32f24bbfb7dbb485a24b30b1149e2f200be94777232aeadba3eecece4d0aa4"},
+    {file = "aiohttp-3.13.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5d7f02042c1f009ffb70067326ef183a047425bb2ff3bc434ead4dd4a4a66a2b"},
+    {file = "aiohttp-3.13.2-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:93655083005d71cd6c072cdab54c886e6570ad2c4592139c3fb967bfc19e4694"},
+    {file = "aiohttp-3.13.2-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:0db1e24b852f5f664cd728db140cf11ea0e82450471232a394b3d1a540b0f906"},
+    {file = "aiohttp-3.13.2-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b009194665bcd128e23eaddef362e745601afa4641930848af4c8559e88f18f9"},
+    {file = "aiohttp-3.13.2-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c038a8fdc8103cd51dbd986ecdce141473ffd9775a7a8057a6ed9c3653478011"},
+    {file = "aiohttp-3.13.2-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:66bac29b95a00db411cd758fea0e4b9bdba6d549dfe333f9a945430f5f2cc5a6"},
+    {file = "aiohttp-3.13.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:4ebf9cfc9ba24a74cf0718f04aac2a3bbe745902cc7c5ebc55c0f3b5777ef213"},
+    {file = "aiohttp-3.13.2-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:a4b88ebe35ce54205c7074f7302bd08a4cb83256a3e0870c72d6f68a3aaf8e49"},
+    {file = "aiohttp-3.13.2-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:98c4fb90bb82b70a4ed79ca35f656f4281885be076f3f970ce315402b53099ae"},
+    {file = "aiohttp-3.13.2-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:ec7534e63ae0f3759df3a1ed4fa6bc8f75082a924b590619c0dd2f76d7043caa"},
+    {file = "aiohttp-3.13.2-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:5b927cf9b935a13e33644cbed6c8c4b2d0f25b713d838743f8fe7191b33829c4"},
+    {file = "aiohttp-3.13.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:88d6c017966a78c5265d996c19cdb79235be5e6412268d7e2ce7dee339471b7a"},
+    {file = "aiohttp-3.13.2-cp314-cp314-win32.whl", hash = "sha256:f7c183e786e299b5d6c49fb43a769f8eb8e04a2726a2bd5887b98b5cc2d67940"},
+    {file = "aiohttp-3.13.2-cp314-cp314-win_amd64.whl", hash = "sha256:fe242cd381e0fb65758faf5ad96c2e460df6ee5b2de1072fe97e4127927e00b4"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:f10d9c0b0188fe85398c61147bbd2a657d616c876863bfeff43376e0e3134673"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:e7c952aefdf2460f4ae55c5e9c3e80aa72f706a6317e06020f80e96253b1accd"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c20423ce14771d98353d2e25e83591fa75dfa90a3c1848f3d7c68243b4fbded3"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e96eb1a34396e9430c19d8338d2ec33015e4a87ef2b4449db94c22412e25ccdf"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:23fb0783bc1a33640036465019d3bba069942616a6a2353c6907d7fe1ccdaf4e"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2e1a9bea6244a1d05a4e57c295d69e159a5c50d8ef16aa390948ee873478d9a5"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0a3d54e822688b56e9f6b5816fb3de3a3a64660efac64e4c2dc435230ad23bad"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7a653d872afe9f33497215745da7a943d1dc15b728a9c8da1c3ac423af35178e"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:56d36e80d2003fa3fc0207fac644216d8532e9504a785ef9a8fd013f84a42c61"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:78cd586d8331fb8e241c2dd6b2f4061778cc69e150514b39a9e28dd050475661"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:20b10bbfbff766294fe99987f7bb3b74fdd2f1a2905f2562132641ad434dcf98"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:9ec49dff7e2b3c85cdeaa412e9d438f0ecd71676fde61ec57027dd392f00c693"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:94f05348c4406450f9d73d38efb41d669ad6cd90c7ee194810d0eefbfa875a7a"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:fa4dcb605c6f82a80c7f95713c2b11c3b8e9893b3ebd2bc9bde93165ed6107be"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:cf00e5db968c3f67eccd2778574cf64d8b27d95b237770aa32400bd7a1ca4f6c"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-win32.whl", hash = "sha256:d23b5fe492b0805a50d3371e8a728a9134d8de5447dce4c885f5587294750734"},
+    {file = "aiohttp-3.13.2-cp314-cp314t-win_amd64.whl", hash = "sha256:ff0a7b0a82a7ab905cbda74006318d1b12e37c797eb1b0d4eb3e316cf47f658f"},
+    {file = "aiohttp-3.13.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:7fbdf5ad6084f1940ce88933de34b62358d0f4a0b6ec097362dcd3e5a65a4989"},
+    {file = "aiohttp-3.13.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7c3a50345635a02db61792c85bb86daffac05330f6473d524f1a4e3ef9d0046d"},
+    {file = "aiohttp-3.13.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0e87dff73f46e969af38ab3f7cb75316a7c944e2e574ff7c933bc01b10def7f5"},
+    {file = "aiohttp-3.13.2-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2adebd4577724dcae085665f294cc57c8701ddd4d26140504db622b8d566d7aa"},
+    {file = "aiohttp-3.13.2-cp39-cp39-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e036a3a645fe92309ec34b918394bb377950cbb43039a97edae6c08db64b23e2"},
+    {file = "aiohttp-3.13.2-cp39-cp39-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:23ad365e30108c422d0b4428cf271156dd56790f6dd50d770b8e360e6c5ab2e6"},
+    {file = "aiohttp-3.13.2-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:1f9b2c2d4b9d958b1f9ae0c984ec1dd6b6689e15c75045be8ccb4011426268ca"},
+    {file = "aiohttp-3.13.2-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3a92cf4b9bea33e15ecbaa5c59921be0f23222608143d025c989924f7e3e0c07"},
+    {file = "aiohttp-3.13.2-cp39-cp39-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:070599407f4954021509193404c4ac53153525a19531051661440644728ba9a7"},
+    {file = "aiohttp-3.13.2-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:29562998ec66f988d49fb83c9b01694fa927186b781463f376c5845c121e4e0b"},
+    {file = "aiohttp-3.13.2-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:4dd3db9d0f4ebca1d887d76f7cdbcd1116ac0d05a9221b9dad82c64a62578c4d"},
+    {file = "aiohttp-3.13.2-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:d7bc4b7f9c4921eba72677cd9fedd2308f4a4ca3e12fab58935295ad9ea98700"},
+    {file = "aiohttp-3.13.2-cp39-cp39-musllinux_1_2_riscv64.whl", hash = "sha256:dacd50501cd017f8cccb328da0c90823511d70d24a323196826d923aad865901"},
+    {file = "aiohttp-3.13.2-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:8b2f1414f6a1e0683f212ec80e813f4abef94c739fd090b66c9adf9d2a05feac"},
+    {file = "aiohttp-3.13.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:04c3971421576ed24c191f610052bcb2f059e395bc2489dd99e397f9bc466329"},
+    {file = "aiohttp-3.13.2-cp39-cp39-win32.whl", hash = "sha256:9f377d0a924e5cc94dc620bc6366fc3e889586a7f18b748901cf016c916e2084"},
+    {file = "aiohttp-3.13.2-cp39-cp39-win_amd64.whl", hash = "sha256:9c705601e16c03466cb72011bd1af55d68fa65b045356d8f96c216e5f6db0fa5"},
+    {file = "aiohttp-3.13.2.tar.gz", hash = "sha256:40176a52c186aefef6eb3cad2cdd30cd06e3afbe88fe8ab2af9c0b90f228daca"},
+]
+
+[package.dependencies]
+aiohappyeyeballs = ">=2.5.0"
+aiosignal = ">=1.4.0"
+async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
+attrs = ">=17.3.0"
+frozenlist = ">=1.1.1"
+multidict = ">=4.5,<7.0"
+propcache = ">=0.2.0"
+yarl = ">=1.17.0,<2.0"
+
+[package.extras]
+speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.3.0)", "backports.zstd ; platform_python_implementation == \"CPython\" and python_version < \"3.14\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
+
+[[package]]
+name = "aiosignal"
+version = "1.4.0"
+description = "aiosignal: a list of registered asynchronous callbacks"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e"},
+    {file = "aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7"},
+]
+
+[package.dependencies]
+frozenlist = ">=1.1.0"
+typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
+
+[[package]]
+name = "ale-py"
+version = "0.11.2"
+description = "The Arcade Learning Environment (ALE) - a platform for AI research."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "ale_py-0.11.2-cp310-cp310-macosx_13_0_arm64.whl", hash = "sha256:58f18a60cdb6d48f7a4eb978327965c121674333a622a92ba60250776d8351c6"},
+    {file = "ale_py-0.11.2-cp310-cp310-macosx_13_0_x86_64.whl", hash = "sha256:c9730aa819fac17915fa72fe85feaeaa4c5181616af0783b3cb340930bfd285f"},
+    {file = "ale_py-0.11.2-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cdb8ce821c70bc60dfca1871b0b1608ba5d269e56370aad7aaae62a698d3746d"},
+    {file = "ale_py-0.11.2-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a8a2777db64e181faf69318aaf8098769ee48b84e377d6f8163c024a54967bf8"},
+    {file = "ale_py-0.11.2-cp310-cp310-win_amd64.whl", hash = "sha256:b70ab0eee7f5215dc2ab047b7c3e1d76a524d6764d496c2a6512c3a0beb96f70"},
+    {file = "ale_py-0.11.2-cp311-cp311-macosx_13_0_arm64.whl", hash = "sha256:808c98685a607cc5483238f73915c23426537259f9cece506f47f5213c370734"},
+    {file = "ale_py-0.11.2-cp311-cp311-macosx_13_0_x86_64.whl", hash = "sha256:d80311cf92ca6ca777dec363865891dbb5447e0c9f57774f72c8618851c9fd4b"},
+    {file = "ale_py-0.11.2-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:44838121ab5c2ef50033ebf0cc69aadf3954418d2f8812bdb76fced3797eb33f"},
+    {file = "ale_py-0.11.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f7a2082e00fc81b6706daf945bd4c97b69c5542739707638c65ddf65ad74db38"},
+    {file = "ale_py-0.11.2-cp311-cp311-win_amd64.whl", hash = "sha256:858a644ed92409cdef47a88d177d18421260b74d3f5cdb45963f21de870a6fd9"},
+    {file = "ale_py-0.11.2-cp312-cp312-macosx_13_0_arm64.whl", hash = "sha256:5ab4bfac7c17fdbd96e8068424f491a16c18584f7bbe2797cbb6c13cc4930e76"},
+    {file = "ale_py-0.11.2-cp312-cp312-macosx_13_0_x86_64.whl", hash = "sha256:db47d52e75ee0bc08899e32e3c2b05822c3a75f4e6f34e7896bd1133bec3dee7"},
+    {file = "ale_py-0.11.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7c42fa8a76caf04dd435bd3fc8682a9d25128102d1df96c35b7971ee31f137d0"},
+    {file = "ale_py-0.11.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:212927f98357390e651b830835e1d24690816416d0e0d2148ad2d4c679941e1c"},
+    {file = "ale_py-0.11.2-cp312-cp312-win_amd64.whl", hash = "sha256:bb8c4d6d8b6cbecfff2915c9f1787101f033719b66f8149dbc4685a2ff22514a"},
+    {file = "ale_py-0.11.2-cp313-cp313-macosx_13_0_arm64.whl", hash = "sha256:208c70a8a47d8ba5f0b6eb8bfd1c3adc6ec2718f66d7866646976f0853dbda4e"},
+    {file = "ale_py-0.11.2-cp313-cp313-macosx_13_0_x86_64.whl", hash = "sha256:f12a9ee789c3c851ea60afe91c6273e49b880dca510bae00496b0339c41cda81"},
+    {file = "ale_py-0.11.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d12e62ac6d57a02745ad8cbf72fbf11ffedbe12d14b48d08e33f22f5625c8ec8"},
+    {file = "ale_py-0.11.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:09e56970ae5f56f377d1c6d8d364d0c610f9c0bc4f88f7abce48174c83ea2882"},
+    {file = "ale_py-0.11.2-cp313-cp313-win_amd64.whl", hash = "sha256:868019090c66fc8c2c24fb19dd8e956a5a4211e594b78097ce1db11a5736684e"},
+    {file = "ale_py-0.11.2-cp39-cp39-macosx_13_0_arm64.whl", hash = "sha256:eb70b789ad03a2fe221185a07365f0b740f81ec378de87189a759efeeb4a8f6b"},
+    {file = "ale_py-0.11.2-cp39-cp39-macosx_13_0_x86_64.whl", hash = "sha256:6e4cc490a09495278a08355449ff445d46461fc2cb998fbb8fba7f9c0dc59deb"},
+    {file = "ale_py-0.11.2-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b89fb1a53ab57e1d8c9539f5004aa8afb405620d1fbab6e05fd6b341b7551110"},
+    {file = "ale_py-0.11.2-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8c09ce4980ccc6d7c94b4a6d8fd296bc5b6ff2538946a8cc648b7b9d95f9553b"},
+    {file = "ale_py-0.11.2-cp39-cp39-win_amd64.whl", hash = "sha256:c82eae022713a62b0fc580134108eb8895ebe5e4ff61ee3e9626c267ecf3fac7"},
+    {file = "ale_py-0.11.2.tar.gz", hash = "sha256:cc6fed9d994d796d76b0d042fbe0a7101834faa55b5e70b1e1f14367ed9e2a8a"},
+]
+
+[package.dependencies]
+numpy = ">1.20"
+typing-extensions = {version = "*", markers = "python_version < \"3.11\""}
+
+[package.extras]
+test = ["chex ; python_version > \"3.9\" and (sys_platform == \"win32\" or sys_platform == \"linux\")", "gymnasium (>=1.1.0)", "jax (>=0.4.31) ; python_version > \"3.9\" and (sys_platform == \"win32\" or sys_platform == \"linux\")", "opencv-python (>=3.0)", "pytest (>=7.0)"]
+vector = ["gymnasium (>=1.1.0)", "opencv-python (>=3.0)"]
+xla = ["gymnasium (>=1.1.0)", "jax (>=0.4.31) ; python_version > \"3.9\" and (sys_platform == \"win32\" or sys_platform == \"linux\")", "opencv-python (>=3.0)"]
+
+[[package]]
+name = "alembic"
+version = "1.17.1"
+description = "A database migration tool for SQLAlchemy."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "alembic-1.17.1-py3-none-any.whl", hash = "sha256:cbc2386e60f89608bb63f30d2d6cc66c7aaed1fe105bd862828600e5ad167023"},
+    {file = "alembic-1.17.1.tar.gz", hash = "sha256:8a289f6778262df31571d29cca4c7fbacd2f0f582ea0816f4c399b6da7528486"},
+]
+
+[package.dependencies]
+Mako = "*"
+SQLAlchemy = ">=1.4.0"
+tomli = {version = "*", markers = "python_version < \"3.11\""}
+typing-extensions = ">=4.12"
+
+[package.extras]
+tz = ["tzdata"]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
+[[package]]
+name = "anyio"
+version = "4.11.0"
+description = "High-level concurrency and networking framework on top of asyncio or Trio"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc"},
+    {file = "anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
+
+[package.extras]
+trio = ["trio (>=0.31.0)"]
+
+[[package]]
+name = "appnope"
+version = "0.1.4"
+description = "Disable App Nap on macOS >= 10.9"
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+markers = "platform_system == \"Darwin\""
+files = [
+    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
+    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
+]
+
+[[package]]
+name = "argon2-cffi"
+version = "25.1.0"
+description = "Argon2 for Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741"},
+    {file = "argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1"},
+]
+
+[package.dependencies]
+argon2-cffi-bindings = "*"
+
+[[package]]
+name = "argon2-cffi-bindings"
+version = "25.1.0"
+description = "Low-level CFFI bindings for Argon2"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:3d3f05610594151994ca9ccb3c771115bdb4daef161976a266f0dd8aa9996b8f"},
+    {file = "argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:8b8efee945193e667a396cbc7b4fb7d357297d6234d30a489905d96caabde56b"},
+    {file = "argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:3c6702abc36bf3ccba3f802b799505def420a1b7039862014a65db3205967f5a"},
+    {file = "argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a1c70058c6ab1e352304ac7e3b52554daadacd8d453c1752e547c76e9c99ac44"},
+    {file = "argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e2fd3bfbff3c5d74fef31a722f729bf93500910db650c925c2d6ef879a7e51cb"},
+    {file = "argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c4f9665de60b1b0e99bcd6be4f17d90339698ce954cfd8d9cf4f91c995165a92"},
+    {file = "argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ba92837e4a9aa6a508c8d2d7883ed5a8f6c308c89a4790e1e447a220deb79a85"},
+    {file = "argon2_cffi_bindings-25.1.0-cp314-cp314t-win32.whl", hash = "sha256:84a461d4d84ae1295871329b346a97f68eade8c53b6ed9a7ca2d7467f3c8ff6f"},
+    {file = "argon2_cffi_bindings-25.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b55aec3565b65f56455eebc9b9f34130440404f27fe21c3b375bf1ea4d8fbae6"},
+    {file = "argon2_cffi_bindings-25.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:87c33a52407e4c41f3b70a9c2d3f6056d88b10dad7695be708c5021673f55623"},
+    {file = "argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:aecba1723ae35330a008418a91ea6cfcedf6d31e5fbaa056a166462ff066d500"},
+    {file = "argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2630b6240b495dfab90aebe159ff784d08ea999aa4b0d17efa734055a07d2f44"},
+    {file = "argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:7aef0c91e2c0fbca6fc68e7555aa60ef7008a739cbe045541e438373bc54d2b0"},
+    {file = "argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e021e87faa76ae0d413b619fe2b65ab9a037f24c60a1e6cc43457ae20de6dc6"},
+    {file = "argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d3e924cfc503018a714f94a49a149fdc0b644eaead5d1f089330399134fa028a"},
+    {file = "argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:c87b72589133f0346a1cb8d5ecca4b933e3c9b64656c9d175270a000e73b288d"},
+    {file = "argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:1db89609c06afa1a214a69a462ea741cf735b29a57530478c06eb81dd403de99"},
+    {file = "argon2_cffi_bindings-25.1.0-cp39-abi3-win32.whl", hash = "sha256:473bcb5f82924b1becbb637b63303ec8d10e84c8d241119419897a26116515d2"},
+    {file = "argon2_cffi_bindings-25.1.0-cp39-abi3-win_amd64.whl", hash = "sha256:a98cd7d17e9f7ce244c0803cad3c23a7d379c301ba618a5fa76a67d116618b98"},
+    {file = "argon2_cffi_bindings-25.1.0-cp39-abi3-win_arm64.whl", hash = "sha256:b0fdbcf513833809c882823f98dc2f931cf659d9a1429616ac3adebb49f5db94"},
+    {file = "argon2_cffi_bindings-25.1.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:6dca33a9859abf613e22733131fc9194091c1fa7cb3e131c143056b4856aa47e"},
+    {file = "argon2_cffi_bindings-25.1.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:21378b40e1b8d1655dd5310c84a40fc19a9aa5e6366e835ceb8576bf0fea716d"},
+    {file = "argon2_cffi_bindings-25.1.0-pp310-pypy310_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5d588dec224e2a83edbdc785a5e6f3c6cd736f46bfd4b441bbb5aa1f5085e584"},
+    {file = "argon2_cffi_bindings-25.1.0-pp310-pypy310_pp73-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5acb4e41090d53f17ca1110c3427f0a130f944b896fc8c83973219c97f57b690"},
+    {file = "argon2_cffi_bindings-25.1.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:da0c79c23a63723aa5d782250fbf51b768abca630285262fb5144ba5ae01e520"},
+    {file = "argon2_cffi_bindings-25.1.0.tar.gz", hash = "sha256:b957f3e6ea4d55d820e40ff76f450952807013d361a65d7f28acc0acbf29229d"},
+]
+
+[package.dependencies]
+cffi = {version = ">=1.0.1", markers = "python_version < \"3.14\""}
+
+[[package]]
+name = "arrow"
+version = "1.4.0"
+description = "Better dates & times for Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "arrow-1.4.0-py3-none-any.whl", hash = "sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205"},
+    {file = "arrow-1.4.0.tar.gz", hash = "sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7"},
+]
+
+[package.dependencies]
+python-dateutil = ">=2.7.0"
+tzdata = {version = "*", markers = "python_version >= \"3.9\""}
+
+[package.extras]
+doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"]
+test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2025.2)", "simplejson (==3.*)"]
+
+[[package]]
+name = "asttokens"
+version = "3.0.0"
+description = "Annotate AST trees with source code positions"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"},
+    {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"},
+]
+
+[package.extras]
+astroid = ["astroid (>=2,<4)"]
+test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
+
+[[package]]
+name = "async-lru"
+version = "2.0.5"
+description = "Simple LRU cache for asyncio"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "async_lru-2.0.5-py3-none-any.whl", hash = "sha256:ab95404d8d2605310d345932697371a5f40def0487c03d6d0ad9138de52c9943"},
+    {file = "async_lru-2.0.5.tar.gz", hash = "sha256:481d52ccdd27275f42c43a928b4a50c3bfb2d67af4e78b170e3e0bb39c66e5bb"},
+]
+
+[package.dependencies]
+typing_extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
+
+[[package]]
+name = "async-timeout"
+version = "5.0.1"
+description = "Timeout context manager for asyncio programs"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version == \"3.10\""
+files = [
+    {file = "async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c"},
+    {file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"},
+]
+
+[[package]]
+name = "attrs"
+version = "25.4.0"
+description = "Classes Without Boilerplate"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373"},
+    {file = "attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11"},
+]
+
+[[package]]
+name = "babel"
+version = "2.17.0"
+description = "Internationalization utilities"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2"},
+    {file = "babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d"},
+]
+
+[package.extras]
+dev = ["backports.zoneinfo ; python_version < \"3.9\"", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata ; sys_platform == \"win32\""]
+
+[[package]]
+name = "beautifulsoup4"
+version = "4.14.2"
+description = "Screen-scraping library"
+optional = false
+python-versions = ">=3.7.0"
+groups = ["main"]
+files = [
+    {file = "beautifulsoup4-4.14.2-py3-none-any.whl", hash = "sha256:5ef6fa3a8cbece8488d66985560f97ed091e22bbc4e9c2338508a9d5de6d4515"},
+    {file = "beautifulsoup4-4.14.2.tar.gz", hash = "sha256:2a98ab9f944a11acee9cc848508ec28d9228abfd522ef0fad6a02a72e0ded69e"},
+]
+
+[package.dependencies]
+soupsieve = ">1.2"
+typing-extensions = ">=4.0.0"
+
+[package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
+html5lib = ["html5lib"]
+lxml = ["lxml"]
+
+[[package]]
+name = "bleach"
+version = "6.3.0"
+description = "An easy safelist-based HTML-sanitizing tool."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "bleach-6.3.0-py3-none-any.whl", hash = "sha256:fe10ec77c93ddf3d13a73b035abaac7a9f5e436513864ccdad516693213c65d6"},
+    {file = "bleach-6.3.0.tar.gz", hash = "sha256:6f3b91b1c0a02bb9a78b5a454c92506aa0fdf197e1d5e114d2e00c6f64306d22"},
+]
+
+[package.dependencies]
+tinycss2 = {version = ">=1.1.0,<1.5", optional = true, markers = "extra == \"css\""}
+webencodings = "*"
+
+[package.extras]
+css = ["tinycss2 (>=1.1.0,<1.5)"]
+
+[[package]]
+name = "cachetools"
+version = "6.2.1"
+description = "Extensible memoizing collections and decorators"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "cachetools-6.2.1-py3-none-any.whl", hash = "sha256:09868944b6dde876dfd44e1d47e18484541eaf12f26f29b7af91b26cc892d701"},
+    {file = "cachetools-6.2.1.tar.gz", hash = "sha256:3f391e4bd8f8bf0931169baf7456cc822705f4e2a31f840d218f445b9a854201"},
+]
+
+[[package]]
+name = "certifi"
+version = "2025.10.5"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "certifi-2025.10.5-py3-none-any.whl", hash = "sha256:0f212c2744a9bb6de0c56639a6f68afe01ecd92d91f14ae897c4fe7bbeeef0de"},
+    {file = "certifi-2025.10.5.tar.gz", hash = "sha256:47c09d31ccf2acf0be3f701ea53595ee7e0b8fa08801c6624be771df09ae7b43"},
+]
+
+[[package]]
+name = "cffi"
+version = "2.0.0"
+description = "Foreign Function Interface for Python calling C code."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "cffi-2.0.0-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:0cf2d91ecc3fcc0625c2c530fe004f82c110405f101548512cce44322fa8ac44"},
+    {file = "cffi-2.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f73b96c41e3b2adedc34a7356e64c8eb96e03a3782b535e043a986276ce12a49"},
+    {file = "cffi-2.0.0-cp310-cp310-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:53f77cbe57044e88bbd5ed26ac1d0514d2acf0591dd6bb02a3ae37f76811b80c"},
+    {file = "cffi-2.0.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:3e837e369566884707ddaf85fc1744b47575005c0a229de3327f8f9a20f4efeb"},
+    {file = "cffi-2.0.0-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:5eda85d6d1879e692d546a078b44251cdd08dd1cfb98dfb77b670c97cee49ea0"},
+    {file = "cffi-2.0.0-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:9332088d75dc3241c702d852d4671613136d90fa6881da7d770a483fd05248b4"},
+    {file = "cffi-2.0.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fc7de24befaeae77ba923797c7c87834c73648a05a4bde34b3b7e5588973a453"},
+    {file = "cffi-2.0.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:cf364028c016c03078a23b503f02058f1814320a56ad535686f90565636a9495"},
+    {file = "cffi-2.0.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:e11e82b744887154b182fd3e7e8512418446501191994dbf9c9fc1f32cc8efd5"},
+    {file = "cffi-2.0.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:8ea985900c5c95ce9db1745f7933eeef5d314f0565b27625d9a10ec9881e1bfb"},
+    {file = "cffi-2.0.0-cp310-cp310-win32.whl", hash = "sha256:1f72fb8906754ac8a2cc3f9f5aaa298070652a0ffae577e0ea9bd480dc3c931a"},
+    {file = "cffi-2.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:b18a3ed7d5b3bd8d9ef7a8cb226502c6bf8308df1525e1cc676c3680e7176739"},
+    {file = "cffi-2.0.0-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:b4c854ef3adc177950a8dfc81a86f5115d2abd545751a304c5bcf2c2c7283cfe"},
+    {file = "cffi-2.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2de9a304e27f7596cd03d16f1b7c72219bd944e99cc52b84d0145aefb07cbd3c"},
+    {file = "cffi-2.0.0-cp311-cp311-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:baf5215e0ab74c16e2dd324e8ec067ef59e41125d3eade2b863d294fd5035c92"},
+    {file = "cffi-2.0.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:730cacb21e1bdff3ce90babf007d0a0917cc3e6492f336c2f0134101e0944f93"},
+    {file = "cffi-2.0.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:6824f87845e3396029f3820c206e459ccc91760e8fa24422f8b0c3d1731cbec5"},
+    {file = "cffi-2.0.0-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:9de40a7b0323d889cf8d23d1ef214f565ab154443c42737dfe52ff82cf857664"},
+    {file = "cffi-2.0.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8941aaadaf67246224cee8c3803777eed332a19d909b47e29c9842ef1e79ac26"},
+    {file = "cffi-2.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:a05d0c237b3349096d3981b727493e22147f934b20f6f125a3eba8f994bec4a9"},
+    {file = "cffi-2.0.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:94698a9c5f91f9d138526b48fe26a199609544591f859c870d477351dc7b2414"},
+    {file = "cffi-2.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:5fed36fccc0612a53f1d4d9a816b50a36702c28a2aa880cb8a122b3466638743"},
+    {file = "cffi-2.0.0-cp311-cp311-win32.whl", hash = "sha256:c649e3a33450ec82378822b3dad03cc228b8f5963c0c12fc3b1e0ab940f768a5"},
+    {file = "cffi-2.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:66f011380d0e49ed280c789fbd08ff0d40968ee7b665575489afa95c98196ab5"},
+    {file = "cffi-2.0.0-cp311-cp311-win_arm64.whl", hash = "sha256:c6638687455baf640e37344fe26d37c404db8b80d037c3d29f58fe8d1c3b194d"},
+    {file = "cffi-2.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d"},
+    {file = "cffi-2.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c"},
+    {file = "cffi-2.0.0-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe"},
+    {file = "cffi-2.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062"},
+    {file = "cffi-2.0.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e"},
+    {file = "cffi-2.0.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037"},
+    {file = "cffi-2.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba"},
+    {file = "cffi-2.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94"},
+    {file = "cffi-2.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187"},
+    {file = "cffi-2.0.0-cp312-cp312-win32.whl", hash = "sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18"},
+    {file = "cffi-2.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5"},
+    {file = "cffi-2.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6"},
+    {file = "cffi-2.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb"},
+    {file = "cffi-2.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca"},
+    {file = "cffi-2.0.0-cp313-cp313-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b"},
+    {file = "cffi-2.0.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b"},
+    {file = "cffi-2.0.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2"},
+    {file = "cffi-2.0.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3"},
+    {file = "cffi-2.0.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26"},
+    {file = "cffi-2.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c"},
+    {file = "cffi-2.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b"},
+    {file = "cffi-2.0.0-cp313-cp313-win32.whl", hash = "sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27"},
+    {file = "cffi-2.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75"},
+    {file = "cffi-2.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91"},
+    {file = "cffi-2.0.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5"},
+    {file = "cffi-2.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13"},
+    {file = "cffi-2.0.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b"},
+    {file = "cffi-2.0.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c"},
+    {file = "cffi-2.0.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef"},
+    {file = "cffi-2.0.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775"},
+    {file = "cffi-2.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205"},
+    {file = "cffi-2.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1"},
+    {file = "cffi-2.0.0-cp314-cp314-win32.whl", hash = "sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f"},
+    {file = "cffi-2.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25"},
+    {file = "cffi-2.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad"},
+    {file = "cffi-2.0.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9"},
+    {file = "cffi-2.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d"},
+    {file = "cffi-2.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c"},
+    {file = "cffi-2.0.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8"},
+    {file = "cffi-2.0.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc"},
+    {file = "cffi-2.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592"},
+    {file = "cffi-2.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512"},
+    {file = "cffi-2.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4"},
+    {file = "cffi-2.0.0-cp314-cp314t-win32.whl", hash = "sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e"},
+    {file = "cffi-2.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6"},
+    {file = "cffi-2.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9"},
+    {file = "cffi-2.0.0-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:fe562eb1a64e67dd297ccc4f5addea2501664954f2692b69a76449ec7913ecbf"},
+    {file = "cffi-2.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:de8dad4425a6ca6e4e5e297b27b5c824ecc7581910bf9aee86cb6835e6812aa7"},
+    {file = "cffi-2.0.0-cp39-cp39-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:4647afc2f90d1ddd33441e5b0e85b16b12ddec4fca55f0d9671fef036ecca27c"},
+    {file = "cffi-2.0.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:3f4d46d8b35698056ec29bca21546e1551a205058ae1a181d871e278b0b28165"},
+    {file = "cffi-2.0.0-cp39-cp39-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:e6e73b9e02893c764e7e8d5bb5ce277f1a009cd5243f8228f75f842bf937c534"},
+    {file = "cffi-2.0.0-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:cb527a79772e5ef98fb1d700678fe031e353e765d1ca2d409c92263c6d43e09f"},
+    {file = "cffi-2.0.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:61d028e90346df14fedc3d1e5441df818d095f3b87d286825dfcbd6459b7ef63"},
+    {file = "cffi-2.0.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:0f6084a0ea23d05d20c3edcda20c3d006f9b6f3fefeac38f59262e10cef47ee2"},
+    {file = "cffi-2.0.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:1cd13c99ce269b3ed80b417dcd591415d3372bcac067009b6e0f59c7d4015e65"},
+    {file = "cffi-2.0.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:89472c9762729b5ae1ad974b777416bfda4ac5642423fa93bd57a09204712322"},
+    {file = "cffi-2.0.0-cp39-cp39-win32.whl", hash = "sha256:2081580ebb843f759b9f617314a24ed5738c51d2aee65d31e02f6f7a2b97707a"},
+    {file = "cffi-2.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:b882b3df248017dba09d6b16defe9b5c407fe32fc7c65a9c69798e6175601be9"},
+    {file = "cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529"},
+]
+
+[package.dependencies]
+pycparser = {version = "*", markers = "implementation_name != \"PyPy\""}
+
+[[package]]
+name = "charset-normalizer"
+version = "3.4.4"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "charset_normalizer-3.4.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e824f1492727fa856dd6eda4f7cee25f8518a12f3c4a56a74e8095695089cf6d"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4bd5d4137d500351a30687c2d3971758aac9a19208fc110ccb9d7188fbe709e8"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:027f6de494925c0ab2a55eab46ae5129951638a49a34d87f4c3eda90f696b4ad"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f820802628d2694cb7e56db99213f930856014862f3fd943d290ea8438d07ca8"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:798d75d81754988d2565bff1b97ba5a44411867c0cf32b77a7e8f8d84796b10d"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9d1bb833febdff5c8927f922386db610b49db6e0d4f4ee29601d71e7c2694313"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:9cd98cdc06614a2f768d2b7286d66805f94c48cde050acdbbb7db2600ab3197e"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:077fbb858e903c73f6c9db43374fd213b0b6a778106bc7032446a8e8b5b38b93"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:244bfb999c71b35de57821b8ea746b24e863398194a4014e4c76adc2bbdfeff0"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:64b55f9dce520635f018f907ff1b0df1fdc31f2795a922fb49dd14fbcdf48c84"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-musllinux_1_2_riscv64.whl", hash = "sha256:faa3a41b2b66b6e50f84ae4a68c64fcd0c44355741c6374813a800cd6695db9e"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:6515f3182dbe4ea06ced2d9e8666d97b46ef4c75e326b79bb624110f122551db"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:cc00f04ed596e9dc0da42ed17ac5e596c6ccba999ba6bd92b0e0aef2f170f2d6"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-win32.whl", hash = "sha256:f34be2938726fc13801220747472850852fe6b1ea75869a048d6f896838c896f"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-win_amd64.whl", hash = "sha256:a61900df84c667873b292c3de315a786dd8dac506704dea57bc957bd31e22c7d"},
+    {file = "charset_normalizer-3.4.4-cp310-cp310-win_arm64.whl", hash = "sha256:cead0978fc57397645f12578bfd2d5ea9138ea0fac82b2f63f7f7c6877986a69"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6e1fcf0720908f200cd21aa4e6750a48ff6ce4afe7ff5a79a90d5ed8a08296f8"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5f819d5fe9234f9f82d75bdfa9aef3a3d72c4d24a6e57aeaebba32a704553aa0"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:a59cb51917aa591b1c4e6a43c132f0cdc3c76dbad6155df4e28ee626cc77a0a3"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8ef3c867360f88ac904fd3f5e1f902f13307af9052646963ee08ff4f131adafc"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d9e45d7faa48ee908174d8fe84854479ef838fc6a705c9315372eacbc2f02897"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:840c25fb618a231545cbab0564a799f101b63b9901f2569faecd6b222ac72381"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:ca5862d5b3928c4940729dacc329aa9102900382fea192fc5e52eb69d6093815"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d9c7f57c3d666a53421049053eaacdd14bbd0a528e2186fcb2e672effd053bb0"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:277e970e750505ed74c832b4bf75dac7476262ee2a013f5574dd49075879e161"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:31fd66405eaf47bb62e8cd575dc621c56c668f27d46a61d975a249930dd5e2a4"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:0d3d8f15c07f86e9ff82319b3d9ef6f4bf907608f53fe9d92b28ea9ae3d1fd89"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:9f7fcd74d410a36883701fafa2482a6af2ff5ba96b9a620e9e0721e28ead5569"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:ebf3e58c7ec8a8bed6d66a75d7fb37b55e5015b03ceae72a8e7c74495551e224"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-win32.whl", hash = "sha256:eecbc200c7fd5ddb9a7f16c7decb07b566c29fa2161a16cf67b8d068bd21690a"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-win_amd64.whl", hash = "sha256:5ae497466c7901d54b639cf42d5b8c1b6a4fead55215500d2f486d34db48d016"},
+    {file = "charset_normalizer-3.4.4-cp311-cp311-win_arm64.whl", hash = "sha256:65e2befcd84bc6f37095f5961e68a6f077bf44946771354a28ad434c2cce0ae1"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:0a98e6759f854bd25a58a73fa88833fba3b7c491169f86ce1180c948ab3fd394"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b5b290ccc2a263e8d185130284f8501e3e36c5e02750fc6b6bdeb2e9e96f1e25"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74bb723680f9f7a6234dcf67aea57e708ec1fbdf5699fb91dfd6f511b0a320ef"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f1e34719c6ed0b92f418c7c780480b26b5d9c50349e9a9af7d76bf757530350d"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2437418e20515acec67d86e12bf70056a33abdacb5cb1655042f6538d6b085a8"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:11d694519d7f29d6cd09f6ac70028dba10f92f6cdd059096db198c283794ac86"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:ac1c4a689edcc530fc9d9aa11f5774b9e2f33f9a0c6a57864e90908f5208d30a"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:21d142cc6c0ec30d2efee5068ca36c128a30b0f2c53c1c07bd78cb6bc1d3be5f"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:5dbe56a36425d26d6cfb40ce79c314a2e4dd6211d51d6d2191c00bed34f354cc"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:5bfbb1b9acf3334612667b61bd3002196fe2a1eb4dd74d247e0f2a4d50ec9bbf"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:d055ec1e26e441f6187acf818b73564e6e6282709e9bcb5b63f5b23068356a15"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:af2d8c67d8e573d6de5bc30cdb27e9b95e49115cd9baad5ddbd1a6207aaa82a9"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:780236ac706e66881f3b7f2f32dfe90507a09e67d1d454c762cf642e6e1586e0"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-win32.whl", hash = "sha256:5833d2c39d8896e4e19b689ffc198f08ea58116bee26dea51e362ecc7cd3ed26"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-win_amd64.whl", hash = "sha256:a79cfe37875f822425b89a82333404539ae63dbdddf97f84dcbc3d339aae9525"},
+    {file = "charset_normalizer-3.4.4-cp312-cp312-win_arm64.whl", hash = "sha256:376bec83a63b8021bb5c8ea75e21c4ccb86e7e45ca4eb81146091b56599b80c3"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:e1f185f86a6f3403aa2420e815904c67b2f9ebc443f045edd0de921108345794"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b39f987ae8ccdf0d2642338faf2abb1862340facc796048b604ef14919e55ed"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3162d5d8ce1bb98dd51af660f2121c55d0fa541b46dff7bb9b9f86ea1d87de72"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:81d5eb2a312700f4ecaa977a8235b634ce853200e828fbadf3a9c50bab278328"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5bd2293095d766545ec1a8f612559f6b40abc0eb18bb2f5d1171872d34036ede"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a8a8b89589086a25749f471e6a900d3f662d1d3b6e2e59dcecf787b1cc3a1894"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc7637e2f80d8530ee4a78e878bce464f70087ce73cf7c1caf142416923b98f1"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f8bf04158c6b607d747e93949aa60618b61312fe647a6369f88ce2ff16043490"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:554af85e960429cf30784dd47447d5125aaa3b99a6f0683589dbd27e2f45da44"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:74018750915ee7ad843a774364e13a3db91682f26142baddf775342c3f5b1133"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:c0463276121fdee9c49b98908b3a89c39be45d86d1dbaa22957e38f6321d4ce3"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:362d61fd13843997c1c446760ef36f240cf81d3ebf74ac62652aebaf7838561e"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9a26f18905b8dd5d685d6d07b0cdf98a79f3c7a918906af7cc143ea2e164c8bc"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-win32.whl", hash = "sha256:9b35f4c90079ff2e2edc5b26c0c77925e5d2d255c42c74fdb70fb49b172726ac"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:b435cba5f4f750aa6c0a0d92c541fb79f69a387c91e61f1795227e4ed9cece14"},
+    {file = "charset_normalizer-3.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:542d2cee80be6f80247095cc36c418f7bddd14f4a6de45af91dfad36d817bba2"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:da3326d9e65ef63a817ecbcc0df6e94463713b754fe293eaa03da99befb9a5bd"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8af65f14dc14a79b924524b1e7fffe304517b2bff5a58bf64f30b98bbc5079eb"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74664978bb272435107de04e36db5a9735e78232b85b77d45cfb38f758efd33e"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:752944c7ffbfdd10c074dc58ec2d5a8a4cd9493b314d367c14d24c17684ddd14"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d1f13550535ad8cff21b8d757a3257963e951d96e20ec82ab44bc64aeb62a191"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ecaae4149d99b1c9e7b88bb03e3221956f68fd6d50be2ef061b2381b61d20838"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cb6254dc36b47a990e59e1068afacdcd02958bdcce30bb50cc1700a8b9d624a6"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c8ae8a0f02f57a6e61203a31428fa1d677cbe50c93622b4149d5c0f319c1d19e"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:47cc91b2f4dd2833fddaedd2893006b0106129d4b94fdb6af1f4ce5a9965577c"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:82004af6c302b5d3ab2cfc4cc5f29db16123b1a8417f2e25f9066f91d4411090"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:2b7d8f6c26245217bd2ad053761201e9f9680f8ce52f0fcd8d0755aeae5b2152"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:799a7a5e4fb2d5898c60b640fd4981d6a25f1c11790935a44ce38c54e985f828"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:99ae2cffebb06e6c22bdc25801d7b30f503cc87dbd283479e7b606f70aff57ec"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-win32.whl", hash = "sha256:f9d332f8c2a2fcbffe1378594431458ddbef721c1769d78e2cbc06280d8155f9"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-win_amd64.whl", hash = "sha256:8a6562c3700cce886c5be75ade4a5db4214fda19fede41d9792d100288d8f94c"},
+    {file = "charset_normalizer-3.4.4-cp314-cp314-win_arm64.whl", hash = "sha256:de00632ca48df9daf77a2c65a484531649261ec9f25489917f09e455cb09ddb2"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:ce8a0633f41a967713a59c4139d29110c07e826d131a316b50ce11b1d79b4f84"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:eaabd426fe94daf8fd157c32e571c85cb12e66692f15516a83a03264b08d06c3"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:c4ef880e27901b6cc782f1b95f82da9313c0eb95c3af699103088fa0ac3ce9ac"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2aaba3b0819274cc41757a1da876f810a3e4d7b6eb25699253a4effef9e8e4af"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:778d2e08eda00f4256d7f672ca9fef386071c9202f5e4607920b86d7803387f2"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f155a433c2ec037d4e8df17d18922c3a0d9b3232a396690f17175d2946f0218d"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a8bf8d0f749c5757af2142fe7903a9df1d2e8aa3841559b2bad34b08d0e2bcf3"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:194f08cbb32dc406d6e1aea671a68be0823673db2832b38405deba2fb0d88f63"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-musllinux_1_2_armv7l.whl", hash = "sha256:6aee717dcfead04c6eb1ce3bd29ac1e22663cdea57f943c87d1eab9a025438d7"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:cd4b7ca9984e5e7985c12bc60a6f173f3c958eae74f3ef6624bb6b26e2abbae4"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-musllinux_1_2_riscv64.whl", hash = "sha256:b7cf1017d601aa35e6bb650b6ad28652c9cd78ee6caff19f3c28d03e1c80acbf"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:e912091979546adf63357d7e2ccff9b44f026c075aeaf25a52d0e95ad2281074"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:5cb4d72eea50c8868f5288b7f7f33ed276118325c1dfd3957089f6b519e1382a"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-win32.whl", hash = "sha256:837c2ce8c5a65a2035be9b3569c684358dfbf109fd3b6969630a87535495ceaa"},
+    {file = "charset_normalizer-3.4.4-cp38-cp38-win_amd64.whl", hash = "sha256:44c2a8734b333e0578090c4cd6b16f275e07aa6614ca8715e6c038e865e70576"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:a9768c477b9d7bd54bc0c86dbaebdec6f03306675526c9927c0e8a04e8f94af9"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1bee1e43c28aa63cb16e5c14e582580546b08e535299b8b6158a7c9c768a1f3d"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:fd44c878ea55ba351104cb93cc85e74916eb8fa440ca7903e57575e97394f608"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:0f04b14ffe5fdc8c4933862d8306109a2c51e0704acfa35d51598eb45a1e89fc"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:cd09d08005f958f370f539f186d10aec3377d55b9eeb0d796025d4886119d76e"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4fe7859a4e3e8457458e2ff592f15ccb02f3da787fcd31e0183879c3ad4692a1"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:fa09f53c465e532f4d3db095e0c55b615f010ad81803d383195b6b5ca6cbf5f3"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:7fa17817dc5625de8a027cb8b26d9fefa3ea28c8253929b8d6649e705d2835b6"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:5947809c8a2417be3267efc979c47d76a079758166f7d43ef5ae8e9f92751f88"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:4902828217069c3c5c71094537a8e623f5d097858ac6ca8252f7b4d10b7560f1"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-musllinux_1_2_riscv64.whl", hash = "sha256:7c308f7e26e4363d79df40ca5b2be1c6ba9f02bdbccfed5abddb7859a6ce72cf"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:2c9d3c380143a1fedbff95a312aa798578371eb29da42106a29019368a475318"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:cb01158d8b88ee68f15949894ccc6712278243d95f344770fa7593fa2d94410c"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-win32.whl", hash = "sha256:2677acec1a2f8ef614c6888b5b4ae4060cc184174a938ed4e8ef690e15d3e505"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-win_amd64.whl", hash = "sha256:f8e160feb2aed042cd657a72acc0b481212ed28b1b9a95c0cee1621b524e1966"},
+    {file = "charset_normalizer-3.4.4-cp39-cp39-win_arm64.whl", hash = "sha256:b5d84d37db046c5ca74ee7bb47dd6cbc13f80665fdde3e8040bdd3fb015ecb50"},
+    {file = "charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f"},
+    {file = "charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a"},
+]
+
+[[package]]
+name = "click"
+version = "8.3.0"
+description = "Composable command line interface toolkit"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "click-8.3.0-py3-none-any.whl", hash = "sha256:9b9f285302c6e3064f4330c05f05b81945b2a39544279343e6e7c5f27a9baddc"},
+    {file = "click-8.3.0.tar.gz", hash = "sha256:e7b8232224eba16f4ebe410c25ced9f7875cb5f3263ffc93cc3e8da705e229c4"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[[package]]
+name = "cloudpickle"
+version = "3.1.2"
+description = "Pickler class to extend the standard pickle.Pickler functionality"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a"},
+    {file = "cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414"},
+]
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+description = "Cross-platform colored terminal text."
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["main"]
+files = [
+    {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
+    {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
+]
+
+[[package]]
+name = "colorlog"
+version = "6.10.1"
+description = "Add colours to the output of Python's logging module."
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+files = [
+    {file = "colorlog-6.10.1-py3-none-any.whl", hash = "sha256:2d7e8348291948af66122cff006c9f8da6255d224e7cf8e37d8de2df3bad8c9c"},
+    {file = "colorlog-6.10.1.tar.gz", hash = "sha256:eb4ae5cb65fe7fec7773c2306061a8e63e02efc2c72eba9d27b0fa23c94f1321"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+
+[package.extras]
+development = ["black", "flake8", "mypy", "pytest", "types-colorama"]
+
+[[package]]
+name = "comm"
+version = "0.2.3"
+description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417"},
+    {file = "comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971"},
+]
+
+[package.extras]
+test = ["pytest"]
+
+[[package]]
+name = "contourpy"
+version = "1.3.2"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "python_version == \"3.10\""
+files = [
+    {file = "contourpy-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ba38e3f9f330af820c4b27ceb4b9c7feee5fe0493ea53a8720f4792667465934"},
+    {file = "contourpy-1.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dc41ba0714aa2968d1f8674ec97504a8f7e334f48eeacebcaa6256213acb0989"},
+    {file = "contourpy-1.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9be002b31c558d1ddf1b9b415b162c603405414bacd6932d031c5b5a8b757f0d"},
+    {file = "contourpy-1.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8d2e74acbcba3bfdb6d9d8384cdc4f9260cae86ed9beee8bd5f54fee49a430b9"},
+    {file = "contourpy-1.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e259bced5549ac64410162adc973c5e2fb77f04df4a439d00b478e57a0e65512"},
+    {file = "contourpy-1.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad687a04bc802cbe8b9c399c07162a3c35e227e2daccf1668eb1f278cb698631"},
+    {file = "contourpy-1.3.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:cdd22595308f53ef2f891040ab2b93d79192513ffccbd7fe19be7aa773a5e09f"},
+    {file = "contourpy-1.3.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:b4f54d6a2defe9f257327b0f243612dd051cc43825587520b1bf74a31e2f6ef2"},
+    {file = "contourpy-1.3.2-cp310-cp310-win32.whl", hash = "sha256:f939a054192ddc596e031e50bb13b657ce318cf13d264f095ce9db7dc6ae81c0"},
+    {file = "contourpy-1.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:c440093bbc8fc21c637c03bafcbef95ccd963bc6e0514ad887932c18ca2a759a"},
+    {file = "contourpy-1.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6a37a2fb93d4df3fc4c0e363ea4d16f83195fc09c891bc8ce072b9d084853445"},
+    {file = "contourpy-1.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b7cd50c38f500bbcc9b6a46643a40e0913673f869315d8e70de0438817cb7773"},
+    {file = "contourpy-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d6658ccc7251a4433eebd89ed2672c2ed96fba367fd25ca9512aa92a4b46c4f1"},
+    {file = "contourpy-1.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:70771a461aaeb335df14deb6c97439973d253ae70660ca085eec25241137ef43"},
+    {file = "contourpy-1.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65a887a6e8c4cd0897507d814b14c54a8c2e2aa4ac9f7686292f9769fcf9a6ab"},
+    {file = "contourpy-1.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3859783aefa2b8355697f16642695a5b9792e7a46ab86da1118a4a23a51a33d7"},
+    {file = "contourpy-1.3.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:eab0f6db315fa4d70f1d8ab514e527f0366ec021ff853d7ed6a2d33605cf4b83"},
+    {file = "contourpy-1.3.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:d91a3ccc7fea94ca0acab82ceb77f396d50a1f67412efe4c526f5d20264e6ecd"},
+    {file = "contourpy-1.3.2-cp311-cp311-win32.whl", hash = "sha256:1c48188778d4d2f3d48e4643fb15d8608b1d01e4b4d6b0548d9b336c28fc9b6f"},
+    {file = "contourpy-1.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:5ebac872ba09cb8f2131c46b8739a7ff71de28a24c869bcad554477eb089a878"},
+    {file = "contourpy-1.3.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4caf2bcd2969402bf77edc4cb6034c7dd7c0803213b3523f111eb7460a51b8d2"},
+    {file = "contourpy-1.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:82199cb78276249796419fe36b7386bd8d2cc3f28b3bc19fe2454fe2e26c4c15"},
+    {file = "contourpy-1.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:106fab697af11456fcba3e352ad50effe493a90f893fca6c2ca5c033820cea92"},
+    {file = "contourpy-1.3.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d14f12932a8d620e307f715857107b1d1845cc44fdb5da2bc8e850f5ceba9f87"},
+    {file = "contourpy-1.3.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:532fd26e715560721bb0d5fc7610fce279b3699b018600ab999d1be895b09415"},
+    {file = "contourpy-1.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f26b383144cf2d2c29f01a1e8170f50dacf0eac02d64139dcd709a8ac4eb3cfe"},
+    {file = "contourpy-1.3.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c49f73e61f1f774650a55d221803b101d966ca0c5a2d6d5e4320ec3997489441"},
+    {file = "contourpy-1.3.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3d80b2c0300583228ac98d0a927a1ba6a2ba6b8a742463c564f1d419ee5b211e"},
+    {file = "contourpy-1.3.2-cp312-cp312-win32.whl", hash = "sha256:90df94c89a91b7362e1142cbee7568f86514412ab8a2c0d0fca72d7e91b62912"},
+    {file = "contourpy-1.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:8c942a01d9163e2e5cfb05cb66110121b8d07ad438a17f9e766317bcb62abf73"},
+    {file = "contourpy-1.3.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:de39db2604ae755316cb5967728f4bea92685884b1e767b7c24e983ef5f771cb"},
+    {file = "contourpy-1.3.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3f9e896f447c5c8618f1edb2bafa9a4030f22a575ec418ad70611450720b5b08"},
+    {file = "contourpy-1.3.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:71e2bd4a1c4188f5c2b8d274da78faab884b59df20df63c34f74aa1813c4427c"},
+    {file = "contourpy-1.3.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de425af81b6cea33101ae95ece1f696af39446db9682a0b56daaa48cfc29f38f"},
+    {file = "contourpy-1.3.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:977e98a0e0480d3fe292246417239d2d45435904afd6d7332d8455981c408b85"},
+    {file = "contourpy-1.3.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:434f0adf84911c924519d2b08fc10491dd282b20bdd3fa8f60fd816ea0b48841"},
+    {file = "contourpy-1.3.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c66c4906cdbc50e9cba65978823e6e00b45682eb09adbb78c9775b74eb222422"},
+    {file = "contourpy-1.3.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8b7fc0cd78ba2f4695fd0a6ad81a19e7e3ab825c31b577f384aa9d7817dc3bef"},
+    {file = "contourpy-1.3.2-cp313-cp313-win32.whl", hash = "sha256:15ce6ab60957ca74cff444fe66d9045c1fd3e92c8936894ebd1f3eef2fff075f"},
+    {file = "contourpy-1.3.2-cp313-cp313-win_amd64.whl", hash = "sha256:e1578f7eafce927b168752ed7e22646dad6cd9bca673c60bff55889fa236ebf9"},
+    {file = "contourpy-1.3.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0475b1f6604896bc7c53bb070e355e9321e1bc0d381735421a2d2068ec56531f"},
+    {file = "contourpy-1.3.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:c85bb486e9be652314bb5b9e2e3b0d1b2e643d5eec4992c0fbe8ac71775da739"},
+    {file = "contourpy-1.3.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:745b57db7758f3ffc05a10254edd3182a2a83402a89c00957a8e8a22f5582823"},
+    {file = "contourpy-1.3.2-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:970e9173dbd7eba9b4e01aab19215a48ee5dd3f43cef736eebde064a171f89a5"},
+    {file = "contourpy-1.3.2-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c6c4639a9c22230276b7bffb6a850dfc8258a2521305e1faefe804d006b2e532"},
+    {file = "contourpy-1.3.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc829960f34ba36aad4302e78eabf3ef16a3a100863f0d4eeddf30e8a485a03b"},
+    {file = "contourpy-1.3.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:d32530b534e986374fc19eaa77fcb87e8a99e5431499949b828312bdcd20ac52"},
+    {file = "contourpy-1.3.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e298e7e70cf4eb179cc1077be1c725b5fd131ebc81181bf0c03525c8abc297fd"},
+    {file = "contourpy-1.3.2-cp313-cp313t-win32.whl", hash = "sha256:d0e589ae0d55204991450bb5c23f571c64fe43adaa53f93fc902a84c96f52fe1"},
+    {file = "contourpy-1.3.2-cp313-cp313t-win_amd64.whl", hash = "sha256:78e9253c3de756b3f6a5174d024c4835acd59eb3f8e2ca13e775dbffe1558f69"},
+    {file = "contourpy-1.3.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:fd93cc7f3139b6dd7aab2f26a90dde0aa9fc264dbf70f6740d498a70b860b82c"},
+    {file = "contourpy-1.3.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:107ba8a6a7eec58bb475329e6d3b95deba9440667c4d62b9b6063942b61d7f16"},
+    {file = "contourpy-1.3.2-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:ded1706ed0c1049224531b81128efbd5084598f18d8a2d9efae833edbd2b40ad"},
+    {file = "contourpy-1.3.2-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:5f5964cdad279256c084b69c3f412b7801e15356b16efa9d78aa974041903da0"},
+    {file = "contourpy-1.3.2-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:49b65a95d642d4efa8f64ba12558fcb83407e58a2dfba9d796d77b63ccfcaff5"},
+    {file = "contourpy-1.3.2-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:8c5acb8dddb0752bf252e01a3035b21443158910ac16a3b0d20e7fed7d534ce5"},
+    {file = "contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54"},
+]
+
+[package.dependencies]
+numpy = ">=1.23"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
+mypy = ["bokeh", "contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.15.0)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
+
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.11"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
+files = [
+    {file = "contourpy-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1"},
+    {file = "contourpy-1.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381"},
+    {file = "contourpy-1.3.3-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:929ddf8c4c7f348e4c0a5a3a714b5c8542ffaa8c22954862a46ca1813b667ee7"},
+    {file = "contourpy-1.3.3-cp311-cp311-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9e999574eddae35f1312c2b4b717b7885d4edd6cb46700e04f7f02db454e67c1"},
+    {file = "contourpy-1.3.3-cp311-cp311-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0bf67e0e3f482cb69779dd3061b534eb35ac9b17f163d851e2a547d56dba0a3a"},
+    {file = "contourpy-1.3.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:51e79c1f7470158e838808d4a996fa9bac72c498e93d8ebe5119bc1e6becb0db"},
+    {file = "contourpy-1.3.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:598c3aaece21c503615fd59c92a3598b428b2f01bfb4b8ca9c4edeecc2438620"},
+    {file = "contourpy-1.3.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:322ab1c99b008dad206d406bb61d014cf0174df491ae9d9d0fac6a6fda4f977f"},
+    {file = "contourpy-1.3.3-cp311-cp311-win32.whl", hash = "sha256:fd907ae12cd483cd83e414b12941c632a969171bf90fc937d0c9f268a31cafff"},
+    {file = "contourpy-1.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:3519428f6be58431c56581f1694ba8e50626f2dd550af225f82fb5f5814d2a42"},
+    {file = "contourpy-1.3.3-cp311-cp311-win_arm64.whl", hash = "sha256:15ff10bfada4bf92ec8b31c62bf7c1834c244019b4a33095a68000d7075df470"},
+    {file = "contourpy-1.3.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb"},
+    {file = "contourpy-1.3.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6"},
+    {file = "contourpy-1.3.3-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7"},
+    {file = "contourpy-1.3.3-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8"},
+    {file = "contourpy-1.3.3-cp312-cp312-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea"},
+    {file = "contourpy-1.3.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1"},
+    {file = "contourpy-1.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7"},
+    {file = "contourpy-1.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411"},
+    {file = "contourpy-1.3.3-cp312-cp312-win32.whl", hash = "sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69"},
+    {file = "contourpy-1.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b"},
+    {file = "contourpy-1.3.3-cp312-cp312-win_arm64.whl", hash = "sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc"},
+    {file = "contourpy-1.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5"},
+    {file = "contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1"},
+    {file = "contourpy-1.3.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286"},
+    {file = "contourpy-1.3.3-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5"},
+    {file = "contourpy-1.3.3-cp313-cp313-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67"},
+    {file = "contourpy-1.3.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9"},
+    {file = "contourpy-1.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659"},
+    {file = "contourpy-1.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7"},
+    {file = "contourpy-1.3.3-cp313-cp313-win32.whl", hash = "sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d"},
+    {file = "contourpy-1.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263"},
+    {file = "contourpy-1.3.3-cp313-cp313-win_arm64.whl", hash = "sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9"},
+    {file = "contourpy-1.3.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d"},
+    {file = "contourpy-1.3.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216"},
+    {file = "contourpy-1.3.3-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae"},
+    {file = "contourpy-1.3.3-cp313-cp313t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20"},
+    {file = "contourpy-1.3.3-cp313-cp313t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99"},
+    {file = "contourpy-1.3.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b"},
+    {file = "contourpy-1.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a"},
+    {file = "contourpy-1.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e"},
+    {file = "contourpy-1.3.3-cp313-cp313t-win32.whl", hash = "sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3"},
+    {file = "contourpy-1.3.3-cp313-cp313t-win_amd64.whl", hash = "sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8"},
+    {file = "contourpy-1.3.3-cp313-cp313t-win_arm64.whl", hash = "sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301"},
+    {file = "contourpy-1.3.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a"},
+    {file = "contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77"},
+    {file = "contourpy-1.3.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5"},
+    {file = "contourpy-1.3.3-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4"},
+    {file = "contourpy-1.3.3-cp314-cp314-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36"},
+    {file = "contourpy-1.3.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3"},
+    {file = "contourpy-1.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b"},
+    {file = "contourpy-1.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36"},
+    {file = "contourpy-1.3.3-cp314-cp314-win32.whl", hash = "sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d"},
+    {file = "contourpy-1.3.3-cp314-cp314-win_amd64.whl", hash = "sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd"},
+    {file = "contourpy-1.3.3-cp314-cp314-win_arm64.whl", hash = "sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339"},
+    {file = "contourpy-1.3.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772"},
+    {file = "contourpy-1.3.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77"},
+    {file = "contourpy-1.3.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13"},
+    {file = "contourpy-1.3.3-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe"},
+    {file = "contourpy-1.3.3-cp314-cp314t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f"},
+    {file = "contourpy-1.3.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0"},
+    {file = "contourpy-1.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4"},
+    {file = "contourpy-1.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f"},
+    {file = "contourpy-1.3.3-cp314-cp314t-win32.whl", hash = "sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae"},
+    {file = "contourpy-1.3.3-cp314-cp314t-win_amd64.whl", hash = "sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc"},
+    {file = "contourpy-1.3.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b"},
+    {file = "contourpy-1.3.3-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:cd5dfcaeb10f7b7f9dc8941717c6c2ade08f587be2226222c12b25f0483ed497"},
+    {file = "contourpy-1.3.3-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:0c1fc238306b35f246d61a1d416a627348b5cf0648648a031e14bb8705fcdfe8"},
+    {file = "contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:70f9aad7de812d6541d29d2bbf8feb22ff7e1c299523db288004e3157ff4674e"},
+    {file = "contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ed3657edf08512fc3fe81b510e35c2012fbd3081d2e26160f27ca28affec989"},
+    {file = "contourpy-1.3.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77"},
+    {file = "contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880"},
+]
+
+[package.dependencies]
+numpy = ">=1.25"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
+mypy = ["bokeh", "contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.17.0)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
+
+[[package]]
+name = "cycler"
+version = "0.12.1"
+description = "Composable style cycles"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"},
+    {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"},
+]
+
+[package.extras]
+docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
+tests = ["pytest", "pytest-cov", "pytest-xdist"]
+
+[[package]]
+name = "datasets"
+version = "4.4.1"
+description = "HuggingFace community-driven open-source library of datasets"
+optional = false
+python-versions = ">=3.9.0"
+groups = ["main"]
+files = [
+    {file = "datasets-4.4.1-py3-none-any.whl", hash = "sha256:c1163de5211e42546079ab355cc0250c7e6db16eb209ac5ac6252f801f596c44"},
+    {file = "datasets-4.4.1.tar.gz", hash = "sha256:80322699aa8c0bbbdb7caa87906da689c3c2e29523cff698775c67f28fdab1fc"},
+]
+
+[package.dependencies]
+dill = ">=0.3.0,<0.4.1"
+filelock = "*"
+fsspec = {version = ">=2023.1.0,<=2025.10.0", extras = ["http"]}
+httpx = "<1.0.0"
+huggingface-hub = ">=0.25.0,<2.0"
+multiprocess = "<0.70.19"
+numpy = ">=1.17"
+packaging = "*"
+pandas = "*"
+pyarrow = ">=21.0.0"
+pyyaml = ">=5.1"
+requests = ">=2.32.2"
+tqdm = ">=4.66.3"
+xxhash = "*"
+
+[package.extras]
+audio = ["torch (>=2.8.0)", "torchcodec (>=0.6.0)"]
+benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
+dev = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "h5py", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark ; python_version < \"3.14\"", "lz4 ; python_version < \"3.14\"", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4) ; python_version < \"3.14\"", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "sqlalchemy", "tensorflow (>=2.16.0) ; python_version >= \"3.10\" and sys_platform != \"win32\" and python_version < \"3.14\"", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0) ; python_version < \"3.10\" and sys_platform != \"win32\"", "tiktoken", "torch", "torch (>=2.8.0)", "torchcodec (>=0.7.0) ; python_version < \"3.14\"", "torchdata", "transformers", "transformers (>=4.42.0)", "zstandard"]
+docs = ["tensorflow (>=2.6.0)", "torch", "transformers"]
+jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
+nibabel = ["nibabel (>=5.3.2)"]
+pdfs = ["pdfplumber (>=0.11.4)"]
+quality = ["ruff (>=0.3.0)"]
+tensorflow = ["tensorflow (>=2.6.0)"]
+tensorflow-gpu = ["tensorflow (>=2.6.0)"]
+tests = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "h5py", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark ; python_version < \"3.14\"", "lz4 ; python_version < \"3.14\"", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4) ; python_version < \"3.14\"", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "sqlalchemy", "tensorflow (>=2.16.0) ; python_version >= \"3.10\" and sys_platform != \"win32\" and python_version < \"3.14\"", "tensorflow (>=2.6.0) ; python_version < \"3.10\" and sys_platform != \"win32\"", "tiktoken", "torch (>=2.8.0)", "torchcodec (>=0.7.0) ; python_version < \"3.14\"", "torchdata", "transformers (>=4.42.0)", "zstandard"]
+tests-numpy2 = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "h5py", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark ; python_version < \"3.14\"", "lz4 ; python_version < \"3.14\"", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4) ; python_version < \"3.14\"", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "sqlalchemy", "tiktoken", "torch (>=2.8.0)", "torchcodec (>=0.7.0) ; python_version < \"3.14\"", "torchdata", "transformers (>=4.42.0)", "zstandard"]
+torch = ["torch"]
+vision = ["Pillow (>=9.4.0)"]
+
+[[package]]
+name = "debugpy"
+version = "1.8.17"
+description = "An implementation of the Debug Adapter Protocol for Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "debugpy-1.8.17-cp310-cp310-macosx_15_0_x86_64.whl", hash = "sha256:c41d2ce8bbaddcc0009cc73f65318eedfa3dbc88a8298081deb05389f1ab5542"},
+    {file = "debugpy-1.8.17-cp310-cp310-manylinux_2_34_x86_64.whl", hash = "sha256:1440fd514e1b815edd5861ca394786f90eb24960eb26d6f7200994333b1d79e3"},
+    {file = "debugpy-1.8.17-cp310-cp310-win32.whl", hash = "sha256:3a32c0af575749083d7492dc79f6ab69f21b2d2ad4cd977a958a07d5865316e4"},
+    {file = "debugpy-1.8.17-cp310-cp310-win_amd64.whl", hash = "sha256:a3aad0537cf4d9c1996434be68c6c9a6d233ac6f76c2a482c7803295b4e4f99a"},
+    {file = "debugpy-1.8.17-cp311-cp311-macosx_15_0_universal2.whl", hash = "sha256:d3fce3f0e3de262a3b67e69916d001f3e767661c6e1ee42553009d445d1cd840"},
+    {file = "debugpy-1.8.17-cp311-cp311-manylinux_2_34_x86_64.whl", hash = "sha256:c6bdf134457ae0cac6fb68205776be635d31174eeac9541e1d0c062165c6461f"},
+    {file = "debugpy-1.8.17-cp311-cp311-win32.whl", hash = "sha256:e79a195f9e059edfe5d8bf6f3749b2599452d3e9380484cd261f6b7cd2c7c4da"},
+    {file = "debugpy-1.8.17-cp311-cp311-win_amd64.whl", hash = "sha256:b532282ad4eca958b1b2d7dbcb2b7218e02cb934165859b918e3b6ba7772d3f4"},
+    {file = "debugpy-1.8.17-cp312-cp312-macosx_15_0_universal2.whl", hash = "sha256:f14467edef672195c6f6b8e27ce5005313cb5d03c9239059bc7182b60c176e2d"},
+    {file = "debugpy-1.8.17-cp312-cp312-manylinux_2_34_x86_64.whl", hash = "sha256:24693179ef9dfa20dca8605905a42b392be56d410c333af82f1c5dff807a64cc"},
+    {file = "debugpy-1.8.17-cp312-cp312-win32.whl", hash = "sha256:6a4e9dacf2cbb60d2514ff7b04b4534b0139facbf2abdffe0639ddb6088e59cf"},
+    {file = "debugpy-1.8.17-cp312-cp312-win_amd64.whl", hash = "sha256:e8f8f61c518952fb15f74a302e068b48d9c4691768ade433e4adeea961993464"},
+    {file = "debugpy-1.8.17-cp313-cp313-macosx_15_0_universal2.whl", hash = "sha256:857c1dd5d70042502aef1c6d1c2801211f3ea7e56f75e9c335f434afb403e464"},
+    {file = "debugpy-1.8.17-cp313-cp313-manylinux_2_34_x86_64.whl", hash = "sha256:3bea3b0b12f3946e098cce9b43c3c46e317b567f79570c3f43f0b96d00788088"},
+    {file = "debugpy-1.8.17-cp313-cp313-win32.whl", hash = "sha256:e34ee844c2f17b18556b5bbe59e1e2ff4e86a00282d2a46edab73fd7f18f4a83"},
+    {file = "debugpy-1.8.17-cp313-cp313-win_amd64.whl", hash = "sha256:6c5cd6f009ad4fca8e33e5238210dc1e5f42db07d4b6ab21ac7ffa904a196420"},
+    {file = "debugpy-1.8.17-cp314-cp314-macosx_15_0_universal2.whl", hash = "sha256:045290c010bcd2d82bc97aa2daf6837443cd52f6328592698809b4549babcee1"},
+    {file = "debugpy-1.8.17-cp314-cp314-manylinux_2_34_x86_64.whl", hash = "sha256:b69b6bd9dba6a03632534cdf67c760625760a215ae289f7489a452af1031fe1f"},
+    {file = "debugpy-1.8.17-cp314-cp314-win32.whl", hash = "sha256:5c59b74aa5630f3a5194467100c3b3d1c77898f9ab27e3f7dc5d40fc2f122670"},
+    {file = "debugpy-1.8.17-cp314-cp314-win_amd64.whl", hash = "sha256:893cba7bb0f55161de4365584b025f7064e1f88913551bcd23be3260b231429c"},
+    {file = "debugpy-1.8.17-cp38-cp38-macosx_15_0_x86_64.whl", hash = "sha256:8deb4e31cd575c9f9370042876e078ca118117c1b5e1f22c32befcfbb6955f0c"},
+    {file = "debugpy-1.8.17-cp38-cp38-manylinux_2_34_x86_64.whl", hash = "sha256:b75868b675949a96ab51abc114c7163f40ff0d8f7d6d5fd63f8932fd38e9c6d7"},
+    {file = "debugpy-1.8.17-cp38-cp38-win32.whl", hash = "sha256:17e456da14848d618662354e1dccfd5e5fb75deec3d1d48dc0aa0baacda55860"},
+    {file = "debugpy-1.8.17-cp38-cp38-win_amd64.whl", hash = "sha256:e851beb536a427b5df8aa7d0c7835b29a13812f41e46292ff80b2ef77327355a"},
+    {file = "debugpy-1.8.17-cp39-cp39-macosx_15_0_x86_64.whl", hash = "sha256:f2ac8055a0c4a09b30b931100996ba49ef334c6947e7ae365cdd870416d7513e"},
+    {file = "debugpy-1.8.17-cp39-cp39-manylinux_2_34_x86_64.whl", hash = "sha256:eaa85bce251feca8e4c87ce3b954aba84b8c645b90f0e6a515c00394a9f5c0e7"},
+    {file = "debugpy-1.8.17-cp39-cp39-win32.whl", hash = "sha256:b13eea5587e44f27f6c48588b5ad56dcb74a4f3a5f89250443c94587f3eb2ea1"},
+    {file = "debugpy-1.8.17-cp39-cp39-win_amd64.whl", hash = "sha256:bb1bbf92317e1f35afcf3ef0450219efb3afe00be79d8664b250ac0933b9015f"},
+    {file = "debugpy-1.8.17-py2.py3-none-any.whl", hash = "sha256:60c7dca6571efe660ccb7a9508d73ca14b8796c4ed484c2002abba714226cfef"},
+    {file = "debugpy-1.8.17.tar.gz", hash = "sha256:fd723b47a8c08892b1a16b2c6239a8b96637c62a59b94bb5dab4bac592a58a8e"},
+]
+
+[[package]]
+name = "decorator"
+version = "5.2.1"
+description = "Decorators for Humans"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a"},
+    {file = "decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360"},
+]
+
+[[package]]
+name = "defusedxml"
+version = "0.7.1"
+description = "XML bomb protection for Python stdlib modules"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["main"]
+files = [
+    {file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"},
+    {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
+]
+
+[[package]]
+name = "diffusers"
+version = "0.35.2"
+description = "State-of-the-art diffusion in PyTorch and JAX."
+optional = false
+python-versions = ">=3.8.0"
+groups = ["main"]
+files = [
+    {file = "diffusers-0.35.2-py3-none-any.whl", hash = "sha256:d50d5e74fdd6dcf55e5c1d304bc52cc7c2659abd1752740d736d7b54078b4db5"},
+    {file = "diffusers-0.35.2.tar.gz", hash = "sha256:30ecd552303edfcfe1724573c3918a8462ee3ab4d529bdbd4c0045f763affded"},
+]
+
+[package.dependencies]
+filelock = "*"
+huggingface-hub = ">=0.34.0"
+importlib_metadata = "*"
+numpy = "*"
+Pillow = "*"
+regex = "!=2019.12.17"
+requests = "*"
+safetensors = ">=0.3.1"
+
+[package.extras]
+bitsandbytes = ["accelerate (>=0.31.0)", "bitsandbytes (>=0.43.3)"]
+dev = ["GitPython (<3.1.19)", "Jinja2", "Jinja2", "accelerate (>=0.31.0)", "accelerate (>=0.31.0)", "compel (==0.1.8)", "datasets", "datasets", "flax (>=0.4.1)", "hf-doc-builder (>=0.3.0)", "hf-doc-builder (>=0.3.0)", "invisible-watermark (>=0.2.0)", "isort (>=5.5.4)", "jax (>=0.4.1)", "jaxlib (>=0.4.1)", "k-diffusion (==0.0.12)", "librosa", "parameterized", "peft (>=0.17.0)", "phonemizer", "protobuf (>=3.20.3,<4)", "pytest", "pytest-timeout", "pytest-xdist", "requests-mock (==1.10.0)", "ruff (==0.9.10)", "safetensors (>=0.3.1)", "scipy", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tiktoken (>=0.7.0)", "torch (>=1.4)", "torchvision", "transformers (>=4.41.2)", "urllib3 (<=2.0.0)"]
+docs = ["hf-doc-builder (>=0.3.0)"]
+flax = ["flax (>=0.4.1)", "jax (>=0.4.1)", "jaxlib (>=0.4.1)"]
+gguf = ["accelerate (>=0.31.0)", "gguf (>=0.10.0)"]
+optimum-quanto = ["accelerate (>=0.31.0)", "optimum_quanto (>=0.2.6)"]
+quality = ["hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.9.10)", "urllib3 (<=2.0.0)"]
+test = ["GitPython (<3.1.19)", "Jinja2", "compel (==0.1.8)", "datasets", "invisible-watermark (>=0.2.0)", "k-diffusion (==0.0.12)", "librosa", "parameterized", "phonemizer", "pytest", "pytest-timeout", "pytest-xdist", "requests-mock (==1.10.0)", "safetensors (>=0.3.1)", "scipy", "sentencepiece (>=0.1.91,!=0.1.92)", "tiktoken (>=0.7.0)", "torchvision", "transformers (>=4.41.2)"]
+torch = ["accelerate (>=0.31.0)", "torch (>=1.4)"]
+torchao = ["accelerate (>=0.31.0)", "torchao (>=0.7.0)"]
+training = ["Jinja2", "accelerate (>=0.31.0)", "datasets", "peft (>=0.17.0)", "protobuf (>=3.20.3,<4)", "tensorboard"]
+
+[[package]]
+name = "dill"
+version = "0.4.0"
+description = "serialize all of Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "dill-0.4.0-py3-none-any.whl", hash = "sha256:44f54bf6412c2c8464c14e8243eb163690a9800dbe2c367330883b19c7561049"},
+    {file = "dill-0.4.0.tar.gz", hash = "sha256:0633f1d2df477324f53a895b02c901fb961bdbf65a17122586ea7019292cbcf0"},
+]
+
+[package.extras]
+graph = ["objgraph (>=1.7.2)"]
+profile = ["gprof2dot (>=2022.7.29)"]
+
+[[package]]
+name = "einops"
+version = "0.8.1"
+description = "A new flavour of deep learning operations"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "einops-0.8.1-py3-none-any.whl", hash = "sha256:919387eb55330f5757c6bea9165c5ff5cfe63a642682ea788a6d472576d81737"},
+    {file = "einops-0.8.1.tar.gz", hash = "sha256:de5d960a7a761225532e0f1959e5315ebeafc0cd43394732f103ca44b9837e84"},
+]
+
+[[package]]
+name = "exceptiongroup"
+version = "1.3.0"
+description = "Backport of PEP 654 (exception groups)"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+markers = "python_version == \"3.10\""
+files = [
+    {file = "exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10"},
+    {file = "exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.6.0", markers = "python_version < \"3.13\""}
+
+[package.extras]
+test = ["pytest (>=6)"]
+
+[[package]]
+name = "executing"
+version = "2.2.1"
+description = "Get the currently executing AST node of a frame, and other information"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "executing-2.2.1-py2.py3-none-any.whl", hash = "sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017"},
+    {file = "executing-2.2.1.tar.gz", hash = "sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4"},
+]
+
+[package.extras]
+tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich ; python_version >= \"3.11\""]
+
+[[package]]
+name = "farama-notifications"
+version = "0.0.4"
+description = "Notifications for all Farama Foundation maintained libraries."
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "Farama-Notifications-0.0.4.tar.gz", hash = "sha256:13fceff2d14314cf80703c8266462ebf3733c7d165336eee998fc58e545efd18"},
+    {file = "Farama_Notifications-0.0.4-py3-none-any.whl", hash = "sha256:14de931035a41961f7c056361dc7f980762a143d05791ef5794a751a2caf05ae"},
+]
+
+[[package]]
+name = "fastjsonschema"
+version = "2.21.2"
+description = "Fastest Python implementation of JSON schema"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "fastjsonschema-2.21.2-py3-none-any.whl", hash = "sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463"},
+    {file = "fastjsonschema-2.21.2.tar.gz", hash = "sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de"},
+]
+
+[package.extras]
+devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
+
+[[package]]
+name = "filelock"
+version = "3.20.0"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "filelock-3.20.0-py3-none-any.whl", hash = "sha256:339b4732ffda5cd79b13f4e2711a31b0365ce445d95d243bb996273d072546a2"},
+    {file = "filelock-3.20.0.tar.gz", hash = "sha256:711e943b4ec6be42e1d4e6690b48dc175c822967466bb31c0c293f34334c13f4"},
+]
+
+[[package]]
+name = "fonttools"
+version = "4.60.1"
+description = "Tools to manipulate font files"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "fonttools-4.60.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:9a52f254ce051e196b8fe2af4634c2d2f02c981756c6464dc192f1b6050b4e28"},
+    {file = "fonttools-4.60.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c7420a2696a44650120cdd269a5d2e56a477e2bfa9d95e86229059beb1c19e15"},
+    {file = "fonttools-4.60.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee0c0b3b35b34f782afc673d503167157094a16f442ace7c6c5e0ca80b08f50c"},
+    {file = "fonttools-4.60.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:282dafa55f9659e8999110bd8ed422ebe1c8aecd0dc396550b038e6c9a08b8ea"},
+    {file = "fonttools-4.60.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:4ba4bd646e86de16160f0fb72e31c3b9b7d0721c3e5b26b9fa2fc931dfdb2652"},
+    {file = "fonttools-4.60.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:0b0835ed15dd5b40d726bb61c846a688f5b4ce2208ec68779bc81860adb5851a"},
+    {file = "fonttools-4.60.1-cp310-cp310-win32.whl", hash = "sha256:1525796c3ffe27bb6268ed2a1bb0dcf214d561dfaf04728abf01489eb5339dce"},
+    {file = "fonttools-4.60.1-cp310-cp310-win_amd64.whl", hash = "sha256:268ecda8ca6cb5c4f044b1fb9b3b376e8cd1b361cef275082429dc4174907038"},
+    {file = "fonttools-4.60.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:7b4c32e232a71f63a5d00259ca3d88345ce2a43295bb049d21061f338124246f"},
+    {file = "fonttools-4.60.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3630e86c484263eaac71d117085d509cbcf7b18f677906824e4bace598fb70d2"},
+    {file = "fonttools-4.60.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5c1015318e4fec75dd4943ad5f6a206d9727adf97410d58b7e32ab644a807914"},
+    {file = "fonttools-4.60.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e6c58beb17380f7c2ea181ea11e7db8c0ceb474c9dd45f48e71e2cb577d146a1"},
+    {file = "fonttools-4.60.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:ec3681a0cb34c255d76dd9d865a55f260164adb9fa02628415cdc2d43ee2c05d"},
+    {file = "fonttools-4.60.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f4b5c37a5f40e4d733d3bbaaef082149bee5a5ea3156a785ff64d949bd1353fa"},
+    {file = "fonttools-4.60.1-cp311-cp311-win32.whl", hash = "sha256:398447f3d8c0c786cbf1209711e79080a40761eb44b27cdafffb48f52bcec258"},
+    {file = "fonttools-4.60.1-cp311-cp311-win_amd64.whl", hash = "sha256:d066ea419f719ed87bc2c99a4a4bfd77c2e5949cb724588b9dd58f3fd90b92bf"},
+    {file = "fonttools-4.60.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:7b0c6d57ab00dae9529f3faf187f2254ea0aa1e04215cf2f1a8ec277c96661bc"},
+    {file = "fonttools-4.60.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:839565cbf14645952d933853e8ade66a463684ed6ed6c9345d0faf1f0e868877"},
+    {file = "fonttools-4.60.1-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:8177ec9676ea6e1793c8a084a90b65a9f778771998eb919d05db6d4b1c0b114c"},
+    {file = "fonttools-4.60.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:996a4d1834524adbb423385d5a629b868ef9d774670856c63c9a0408a3063401"},
+    {file = "fonttools-4.60.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a46b2f450bc79e06ef3b6394f0c68660529ed51692606ad7f953fc2e448bc903"},
+    {file = "fonttools-4.60.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6ec722ee589e89a89f5b7574f5c45604030aa6ae24cb2c751e2707193b466fed"},
+    {file = "fonttools-4.60.1-cp312-cp312-win32.whl", hash = "sha256:b2cf105cee600d2de04ca3cfa1f74f1127f8455b71dbad02b9da6ec266e116d6"},
+    {file = "fonttools-4.60.1-cp312-cp312-win_amd64.whl", hash = "sha256:992775c9fbe2cf794786fa0ffca7f09f564ba3499b8fe9f2f80bd7197db60383"},
+    {file = "fonttools-4.60.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:6f68576bb4bbf6060c7ab047b1574a1ebe5c50a17de62830079967b211059ebb"},
+    {file = "fonttools-4.60.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:eedacb5c5d22b7097482fa834bda0dafa3d914a4e829ec83cdea2a01f8c813c4"},
+    {file = "fonttools-4.60.1-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b33a7884fabd72bdf5f910d0cf46be50dce86a0362a65cfc746a4168c67eb96c"},
+    {file = "fonttools-4.60.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2409d5fb7b55fd70f715e6d34e7a6e4f7511b8ad29a49d6df225ee76da76dd77"},
+    {file = "fonttools-4.60.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c8651e0d4b3bdeda6602b85fdc2abbefc1b41e573ecb37b6779c4ca50753a199"},
+    {file = "fonttools-4.60.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:145daa14bf24824b677b9357c5e44fd8895c2a8f53596e1b9ea3496081dc692c"},
+    {file = "fonttools-4.60.1-cp313-cp313-win32.whl", hash = "sha256:2299df884c11162617a66b7c316957d74a18e3758c0274762d2cc87df7bc0272"},
+    {file = "fonttools-4.60.1-cp313-cp313-win_amd64.whl", hash = "sha256:a3db56f153bd4c5c2b619ab02c5db5192e222150ce5a1bc10f16164714bc39ac"},
+    {file = "fonttools-4.60.1-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:a884aef09d45ba1206712c7dbda5829562d3fea7726935d3289d343232ecb0d3"},
+    {file = "fonttools-4.60.1-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8a44788d9d91df72d1a5eac49b31aeb887a5f4aab761b4cffc4196c74907ea85"},
+    {file = "fonttools-4.60.1-cp314-cp314-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:e852d9dda9f93ad3651ae1e3bb770eac544ec93c3807888798eccddf84596537"},
+    {file = "fonttools-4.60.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:154cb6ee417e417bf5f7c42fe25858c9140c26f647c7347c06f0cc2d47eff003"},
+    {file = "fonttools-4.60.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:5664fd1a9ea7f244487ac8f10340c4e37664675e8667d6fee420766e0fb3cf08"},
+    {file = "fonttools-4.60.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:583b7f8e3c49486e4d489ad1deacfb8d5be54a8ef34d6df824f6a171f8511d99"},
+    {file = "fonttools-4.60.1-cp314-cp314-win32.whl", hash = "sha256:66929e2ea2810c6533a5184f938502cfdaea4bc3efb7130d8cc02e1c1b4108d6"},
+    {file = "fonttools-4.60.1-cp314-cp314-win_amd64.whl", hash = "sha256:f3d5be054c461d6a2268831f04091dc82753176f6ea06dc6047a5e168265a987"},
+    {file = "fonttools-4.60.1-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:b6379e7546ba4ae4b18f8ae2b9bc5960936007a1c0e30b342f662577e8bc3299"},
+    {file = "fonttools-4.60.1-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9d0ced62b59e0430b3690dbc5373df1c2aa7585e9a8ce38eff87f0fd993c5b01"},
+    {file = "fonttools-4.60.1-cp314-cp314t-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:875cb7764708b3132637f6c5fb385b16eeba0f7ac9fa45a69d35e09b47045801"},
+    {file = "fonttools-4.60.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a184b2ea57b13680ab6d5fbde99ccef152c95c06746cb7718c583abd8f945ccc"},
+    {file = "fonttools-4.60.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:026290e4ec76583881763fac284aca67365e0be9f13a7fb137257096114cb3bc"},
+    {file = "fonttools-4.60.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f0e8817c7d1a0c2eedebf57ef9a9896f3ea23324769a9a2061a80fe8852705ed"},
+    {file = "fonttools-4.60.1-cp314-cp314t-win32.whl", hash = "sha256:1410155d0e764a4615774e5c2c6fc516259fe3eca5882f034eb9bfdbee056259"},
+    {file = "fonttools-4.60.1-cp314-cp314t-win_amd64.whl", hash = "sha256:022beaea4b73a70295b688f817ddc24ed3e3418b5036ffcd5658141184ef0d0c"},
+    {file = "fonttools-4.60.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:122e1a8ada290423c493491d002f622b1992b1ab0b488c68e31c413390dc7eb2"},
+    {file = "fonttools-4.60.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a140761c4ff63d0cb9256ac752f230460ee225ccef4ad8f68affc723c88e2036"},
+    {file = "fonttools-4.60.1-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0eae96373e4b7c9e45d099d7a523444e3554360927225c1cdae221a58a45b856"},
+    {file = "fonttools-4.60.1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:596ecaca36367027d525b3b426d8a8208169d09edcf8c7506aceb3a38bfb55c7"},
+    {file = "fonttools-4.60.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:2ee06fc57512144d8b0445194c2da9f190f61ad51e230f14836286470c99f854"},
+    {file = "fonttools-4.60.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:b42d86938e8dda1cd9a1a87a6d82f1818eaf933348429653559a458d027446da"},
+    {file = "fonttools-4.60.1-cp39-cp39-win32.whl", hash = "sha256:8b4eb332f9501cb1cd3d4d099374a1e1306783ff95489a1026bde9eb02ccc34a"},
+    {file = "fonttools-4.60.1-cp39-cp39-win_amd64.whl", hash = "sha256:7473a8ed9ed09aeaa191301244a5a9dbe46fe0bf54f9d6cd21d83044c3321217"},
+    {file = "fonttools-4.60.1-py3-none-any.whl", hash = "sha256:906306ac7afe2156fcf0042173d6ebbb05416af70f6b370967b47f8f00103bbb"},
+    {file = "fonttools-4.60.1.tar.gz", hash = "sha256:ef00af0439ebfee806b25f24c8f92109157ff3fac5731dc7867957812e87b8d9"},
+]
+
+[package.extras]
+all = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres ; platform_python_implementation == \"PyPy\"", "pycairo", "scipy ; platform_python_implementation != \"PyPy\"", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0) ; python_version <= \"3.12\"", "xattr ; sys_platform == \"darwin\"", "zopfli (>=0.1.4)"]
+graphite = ["lz4 (>=1.7.4.2)"]
+interpolatable = ["munkres ; platform_python_implementation == \"PyPy\"", "pycairo", "scipy ; platform_python_implementation != \"PyPy\""]
+lxml = ["lxml (>=4.0)"]
+pathops = ["skia-pathops (>=0.5.0)"]
+plot = ["matplotlib"]
+repacker = ["uharfbuzz (>=0.23.0)"]
+symfont = ["sympy"]
+type1 = ["xattr ; sys_platform == \"darwin\""]
+unicode = ["unicodedata2 (>=15.1.0) ; python_version <= \"3.12\""]
+woff = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "zopfli (>=0.1.4)"]
+
+[[package]]
+name = "fqdn"
+version = "1.5.1"
+description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
+optional = false
+python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
+groups = ["main"]
+files = [
+    {file = "fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014"},
+    {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"},
+]
+
+[[package]]
+name = "frozenlist"
+version = "1.8.0"
+description = "A list-like structure which implements collections.abc.MutableSequence"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "frozenlist-1.8.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b37f6d31b3dcea7deb5e9696e529a6aa4a898adc33db82da12e4c60a7c4d2011"},
+    {file = "frozenlist-1.8.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ef2b7b394f208233e471abc541cc6991f907ffd47dc72584acee3147899d6565"},
+    {file = "frozenlist-1.8.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a88f062f072d1589b7b46e951698950e7da00442fc1cacbe17e19e025dc327ad"},
+    {file = "frozenlist-1.8.0-cp310-cp310-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:f57fb59d9f385710aa7060e89410aeb5058b99e62f4d16b08b91986b9a2140c2"},
+    {file = "frozenlist-1.8.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:799345ab092bee59f01a915620b5d014698547afd011e691a208637312db9186"},
+    {file = "frozenlist-1.8.0-cp310-cp310-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:c23c3ff005322a6e16f71bf8692fcf4d5a304aaafe1e262c98c6d4adc7be863e"},
+    {file = "frozenlist-1.8.0-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8a76ea0f0b9dfa06f254ee06053d93a600865b3274358ca48a352ce4f0798450"},
+    {file = "frozenlist-1.8.0-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:c7366fe1418a6133d5aa824ee53d406550110984de7637d65a178010f759c6ef"},
+    {file = "frozenlist-1.8.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:13d23a45c4cebade99340c4165bd90eeb4a56c6d8a9d8aa49568cac19a6d0dc4"},
+    {file = "frozenlist-1.8.0-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:e4a3408834f65da56c83528fb52ce7911484f0d1eaf7b761fc66001db1646eff"},
+    {file = "frozenlist-1.8.0-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:42145cd2748ca39f32801dad54aeea10039da6f86e303659db90db1c4b614c8c"},
+    {file = "frozenlist-1.8.0-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:e2de870d16a7a53901e41b64ffdf26f2fbb8917b3e6ebf398098d72c5b20bd7f"},
+    {file = "frozenlist-1.8.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:20e63c9493d33ee48536600d1a5c95eefc870cd71e7ab037763d1fbb89cc51e7"},
+    {file = "frozenlist-1.8.0-cp310-cp310-win32.whl", hash = "sha256:adbeebaebae3526afc3c96fad434367cafbfd1b25d72369a9e5858453b1bb71a"},
+    {file = "frozenlist-1.8.0-cp310-cp310-win_amd64.whl", hash = "sha256:667c3777ca571e5dbeb76f331562ff98b957431df140b54c85fd4d52eea8d8f6"},
+    {file = "frozenlist-1.8.0-cp310-cp310-win_arm64.whl", hash = "sha256:80f85f0a7cc86e7a54c46d99c9e1318ff01f4687c172ede30fd52d19d1da1c8e"},
+    {file = "frozenlist-1.8.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:09474e9831bc2b2199fad6da3c14c7b0fbdd377cce9d3d77131be28906cb7d84"},
+    {file = "frozenlist-1.8.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:17c883ab0ab67200b5f964d2b9ed6b00971917d5d8a92df149dc2c9779208ee9"},
+    {file = "frozenlist-1.8.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:fa47e444b8ba08fffd1c18e8cdb9a75db1b6a27f17507522834ad13ed5922b93"},
+    {file = "frozenlist-1.8.0-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:2552f44204b744fba866e573be4c1f9048d6a324dfe14475103fd51613eb1d1f"},
+    {file = "frozenlist-1.8.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:957e7c38f250991e48a9a73e6423db1bb9dd14e722a10f6b8bb8e16a0f55f695"},
+    {file = "frozenlist-1.8.0-cp311-cp311-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:8585e3bb2cdea02fc88ffa245069c36555557ad3609e83be0ec71f54fd4abb52"},
+    {file = "frozenlist-1.8.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:edee74874ce20a373d62dc28b0b18b93f645633c2943fd90ee9d898550770581"},
+    {file = "frozenlist-1.8.0-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:c9a63152fe95756b85f31186bddf42e4c02c6321207fd6601a1c89ebac4fe567"},
+    {file = "frozenlist-1.8.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:b6db2185db9be0a04fecf2f241c70b63b1a242e2805be291855078f2b404dd6b"},
+    {file = "frozenlist-1.8.0-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:f4be2e3d8bc8aabd566f8d5b8ba7ecc09249d74ba3c9ed52e54dc23a293f0b92"},
+    {file = "frozenlist-1.8.0-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:c8d1634419f39ea6f5c427ea2f90ca85126b54b50837f31497f3bf38266e853d"},
+    {file = "frozenlist-1.8.0-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:1a7fa382a4a223773ed64242dbe1c9c326ec09457e6b8428efb4118c685c3dfd"},
+    {file = "frozenlist-1.8.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:11847b53d722050808926e785df837353bd4d75f1d494377e59b23594d834967"},
+    {file = "frozenlist-1.8.0-cp311-cp311-win32.whl", hash = "sha256:27c6e8077956cf73eadd514be8fb04d77fc946a7fe9f7fe167648b0b9085cc25"},
+    {file = "frozenlist-1.8.0-cp311-cp311-win_amd64.whl", hash = "sha256:ac913f8403b36a2c8610bbfd25b8013488533e71e62b4b4adce9c86c8cea905b"},
+    {file = "frozenlist-1.8.0-cp311-cp311-win_arm64.whl", hash = "sha256:d4d3214a0f8394edfa3e303136d0575eece0745ff2b47bd2cb2e66dd92d4351a"},
+    {file = "frozenlist-1.8.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:78f7b9e5d6f2fdb88cdde9440dc147259b62b9d3b019924def9f6478be254ac1"},
+    {file = "frozenlist-1.8.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:229bf37d2e4acdaf808fd3f06e854a4a7a3661e871b10dc1f8f1896a3b05f18b"},
+    {file = "frozenlist-1.8.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f833670942247a14eafbb675458b4e61c82e002a148f49e68257b79296e865c4"},
+    {file = "frozenlist-1.8.0-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:494a5952b1c597ba44e0e78113a7266e656b9794eec897b19ead706bd7074383"},
+    {file = "frozenlist-1.8.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:96f423a119f4777a4a056b66ce11527366a8bb92f54e541ade21f2374433f6d4"},
+    {file = "frozenlist-1.8.0-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3462dd9475af2025c31cc61be6652dfa25cbfb56cbbf52f4ccfe029f38decaf8"},
+    {file = "frozenlist-1.8.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c4c800524c9cd9bac5166cd6f55285957fcfc907db323e193f2afcd4d9abd69b"},
+    {file = "frozenlist-1.8.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d6a5df73acd3399d893dafc71663ad22534b5aa4f94e8a2fabfe856c3c1b6a52"},
+    {file = "frozenlist-1.8.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:405e8fe955c2280ce66428b3ca55e12b3c4e9c336fb2103a4937e891c69a4a29"},
+    {file = "frozenlist-1.8.0-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:908bd3f6439f2fef9e85031b59fd4f1297af54415fb60e4254a95f75b3cab3f3"},
+    {file = "frozenlist-1.8.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:294e487f9ec720bd8ffcebc99d575f7eff3568a08a253d1ee1a0378754b74143"},
+    {file = "frozenlist-1.8.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:74c51543498289c0c43656701be6b077f4b265868fa7f8a8859c197006efb608"},
+    {file = "frozenlist-1.8.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:776f352e8329135506a1d6bf16ac3f87bc25b28e765949282dcc627af36123aa"},
+    {file = "frozenlist-1.8.0-cp312-cp312-win32.whl", hash = "sha256:433403ae80709741ce34038da08511d4a77062aa924baf411ef73d1146e74faf"},
+    {file = "frozenlist-1.8.0-cp312-cp312-win_amd64.whl", hash = "sha256:34187385b08f866104f0c0617404c8eb08165ab1272e884abc89c112e9c00746"},
+    {file = "frozenlist-1.8.0-cp312-cp312-win_arm64.whl", hash = "sha256:fe3c58d2f5db5fbd18c2987cba06d51b0529f52bc3a6cdc33d3f4eab725104bd"},
+    {file = "frozenlist-1.8.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:8d92f1a84bb12d9e56f818b3a746f3efba93c1b63c8387a73dde655e1e42282a"},
+    {file = "frozenlist-1.8.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:96153e77a591c8adc2ee805756c61f59fef4cf4073a9275ee86fe8cba41241f7"},
+    {file = "frozenlist-1.8.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f21f00a91358803399890ab167098c131ec2ddd5f8f5fd5fe9c9f2c6fcd91e40"},
+    {file = "frozenlist-1.8.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:fb30f9626572a76dfe4293c7194a09fb1fe93ba94c7d4f720dfae3b646b45027"},
+    {file = "frozenlist-1.8.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:eaa352d7047a31d87dafcacbabe89df0aa506abb5b1b85a2fb91bc3faa02d822"},
+    {file = "frozenlist-1.8.0-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:03ae967b4e297f58f8c774c7eabcce57fe3c2434817d4385c50661845a058121"},
+    {file = "frozenlist-1.8.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f6292f1de555ffcc675941d65fffffb0a5bcd992905015f85d0592201793e0e5"},
+    {file = "frozenlist-1.8.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:29548f9b5b5e3460ce7378144c3010363d8035cea44bc0bf02d57f5a685e084e"},
+    {file = "frozenlist-1.8.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ec3cc8c5d4084591b4237c0a272cc4f50a5b03396a47d9caaf76f5d7b38a4f11"},
+    {file = "frozenlist-1.8.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:517279f58009d0b1f2e7c1b130b377a349405da3f7621ed6bfae50b10adf20c1"},
+    {file = "frozenlist-1.8.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:db1e72ede2d0d7ccb213f218df6a078a9c09a7de257c2fe8fcef16d5925230b1"},
+    {file = "frozenlist-1.8.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:b4dec9482a65c54a5044486847b8a66bf10c9cb4926d42927ec4e8fd5db7fed8"},
+    {file = "frozenlist-1.8.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:21900c48ae04d13d416f0e1e0c4d81f7931f73a9dfa0b7a8746fb2fe7dd970ed"},
+    {file = "frozenlist-1.8.0-cp313-cp313-win32.whl", hash = "sha256:8b7b94a067d1c504ee0b16def57ad5738701e4ba10cec90529f13fa03c833496"},
+    {file = "frozenlist-1.8.0-cp313-cp313-win_amd64.whl", hash = "sha256:878be833caa6a3821caf85eb39c5ba92d28e85df26d57afb06b35b2efd937231"},
+    {file = "frozenlist-1.8.0-cp313-cp313-win_arm64.whl", hash = "sha256:44389d135b3ff43ba8cc89ff7f51f5a0bb6b63d829c8300f79a2fe4fe61bcc62"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:e25ac20a2ef37e91c1b39938b591457666a0fa835c7783c3a8f33ea42870db94"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:07cdca25a91a4386d2e76ad992916a85038a9b97561bf7a3fd12d5d9ce31870c"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:4e0c11f2cc6717e0a741f84a527c52616140741cd812a50422f83dc31749fb52"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b3210649ee28062ea6099cfda39e147fa1bc039583c8ee4481cb7811e2448c51"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:581ef5194c48035a7de2aefc72ac6539823bb71508189e5de01d60c9dcd5fa65"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3ef2d026f16a2b1866e1d86fc4e1291e1ed8a387b2c333809419a2f8b3a77b82"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5500ef82073f599ac84d888e3a8c1f77ac831183244bfd7f11eaa0289fb30714"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:50066c3997d0091c411a66e710f4e11752251e6d2d73d70d8d5d4c76442a199d"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:5c1c8e78426e59b3f8005e9b19f6ff46e5845895adbde20ece9218319eca6506"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:eefdba20de0d938cec6a89bd4d70f346a03108a19b9df4248d3cf0d88f1b0f51"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:cf253e0e1c3ceb4aaff6df637ce033ff6535fb8c70a764a8f46aafd3d6ab798e"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:032efa2674356903cd0261c4317a561a6850f3ac864a63fc1583147fb05a79b0"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6da155091429aeba16851ecb10a9104a108bcd32f6c1642867eadaee401c1c41"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-win32.whl", hash = "sha256:0f96534f8bfebc1a394209427d0f8a63d343c9779cda6fc25e8e121b5fd8555b"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-win_amd64.whl", hash = "sha256:5d63a068f978fc69421fb0e6eb91a9603187527c86b7cd3f534a5b77a592b888"},
+    {file = "frozenlist-1.8.0-cp313-cp313t-win_arm64.whl", hash = "sha256:bf0a7e10b077bf5fb9380ad3ae8ce20ef919a6ad93b4552896419ac7e1d8e042"},
+    {file = "frozenlist-1.8.0-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:cee686f1f4cadeb2136007ddedd0aaf928ab95216e7691c63e50a8ec066336d0"},
+    {file = "frozenlist-1.8.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:119fb2a1bd47307e899c2fac7f28e85b9a543864df47aa7ec9d3c1b4545f096f"},
+    {file = "frozenlist-1.8.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:4970ece02dbc8c3a92fcc5228e36a3e933a01a999f7094ff7c23fbd2beeaa67c"},
+    {file = "frozenlist-1.8.0-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:cba69cb73723c3f329622e34bdbf5ce1f80c21c290ff04256cff1cd3c2036ed2"},
+    {file = "frozenlist-1.8.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:778a11b15673f6f1df23d9586f83c4846c471a8af693a22e066508b77d201ec8"},
+    {file = "frozenlist-1.8.0-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:0325024fe97f94c41c08872db482cf8ac4800d80e79222c6b0b7b162d5b13686"},
+    {file = "frozenlist-1.8.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:97260ff46b207a82a7567b581ab4190bd4dfa09f4db8a8b49d1a958f6aa4940e"},
+    {file = "frozenlist-1.8.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:54b2077180eb7f83dd52c40b2750d0a9f175e06a42e3213ce047219de902717a"},
+    {file = "frozenlist-1.8.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:2f05983daecab868a31e1da44462873306d3cbfd76d1f0b5b69c473d21dbb128"},
+    {file = "frozenlist-1.8.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:33f48f51a446114bc5d251fb2954ab0164d5be02ad3382abcbfe07e2531d650f"},
+    {file = "frozenlist-1.8.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:154e55ec0655291b5dd1b8731c637ecdb50975a2ae70c606d100750a540082f7"},
+    {file = "frozenlist-1.8.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:4314debad13beb564b708b4a496020e5306c7333fa9a3ab90374169a20ffab30"},
+    {file = "frozenlist-1.8.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:073f8bf8becba60aa931eb3bc420b217bb7d5b8f4750e6f8b3be7f3da85d38b7"},
+    {file = "frozenlist-1.8.0-cp314-cp314-win32.whl", hash = "sha256:bac9c42ba2ac65ddc115d930c78d24ab8d4f465fd3fc473cdedfccadb9429806"},
+    {file = "frozenlist-1.8.0-cp314-cp314-win_amd64.whl", hash = "sha256:3e0761f4d1a44f1d1a47996511752cf3dcec5bbdd9cc2b4fe595caf97754b7a0"},
+    {file = "frozenlist-1.8.0-cp314-cp314-win_arm64.whl", hash = "sha256:d1eaff1d00c7751b7c6662e9c5ba6eb2c17a2306ba5e2a37f24ddf3cc953402b"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:d3bb933317c52d7ea5004a1c442eef86f426886fba134ef8cf4226ea6ee1821d"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:8009897cdef112072f93a0efdce29cd819e717fd2f649ee3016efd3cd885a7ed"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2c5dcbbc55383e5883246d11fd179782a9d07a986c40f49abe89ddf865913930"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:39ecbc32f1390387d2aa4f5a995e465e9e2f79ba3adcac92d68e3e0afae6657c"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92db2bf818d5cc8d9c1f1fc56b897662e24ea5adb36ad1f1d82875bd64e03c24"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:2dc43a022e555de94c3b68a4ef0b11c4f747d12c024a520c7101709a2144fb37"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:cb89a7f2de3602cfed448095bab3f178399646ab7c61454315089787df07733a"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:33139dc858c580ea50e7e60a1b0ea003efa1fd42e6ec7fdbad78fff65fad2fd2"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:168c0969a329b416119507ba30b9ea13688fafffac1b7822802537569a1cb0ef"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:28bd570e8e189d7f7b001966435f9dac6718324b5be2990ac496cf1ea9ddb7fe"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:b2a095d45c5d46e5e79ba1e5b9cb787f541a8dee0433836cea4b96a2c439dcd8"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:eab8145831a0d56ec9c4139b6c3e594c7a83c2c8be25d5bcf2d86136a532287a"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:974b28cf63cc99dfb2188d8d222bc6843656188164848c4f679e63dae4b0708e"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-win32.whl", hash = "sha256:342c97bf697ac5480c0a7ec73cd700ecfa5a8a40ac923bd035484616efecc2df"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-win_amd64.whl", hash = "sha256:06be8f67f39c8b1dc671f5d83aaefd3358ae5cdcf8314552c57e7ed3e6475bdd"},
+    {file = "frozenlist-1.8.0-cp314-cp314t-win_arm64.whl", hash = "sha256:102e6314ca4da683dca92e3b1355490fed5f313b768500084fbe6371fddfdb79"},
+    {file = "frozenlist-1.8.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:d8b7138e5cd0647e4523d6685b0eac5d4be9a184ae9634492f25c6eb38c12a47"},
+    {file = "frozenlist-1.8.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a6483e309ca809f1efd154b4d37dc6d9f61037d6c6a81c2dc7a15cb22c8c5dca"},
+    {file = "frozenlist-1.8.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:1b9290cf81e95e93fdf90548ce9d3c1211cf574b8e3f4b3b7cb0537cf2227068"},
+    {file = "frozenlist-1.8.0-cp39-cp39-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:59a6a5876ca59d1b63af8cd5e7ffffb024c3dc1e9cf9301b21a2e76286505c95"},
+    {file = "frozenlist-1.8.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6dc4126390929823e2d2d9dc79ab4046ed74680360fc5f38b585c12c66cdf459"},
+    {file = "frozenlist-1.8.0-cp39-cp39-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:332db6b2563333c5671fecacd085141b5800cb866be16d5e3eb15a2086476675"},
+    {file = "frozenlist-1.8.0-cp39-cp39-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9ff15928d62a0b80bb875655c39bf517938c7d589554cbd2669be42d97c2cb61"},
+    {file = "frozenlist-1.8.0-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7bf6cdf8e07c8151fba6fe85735441240ec7f619f935a5205953d58009aef8c6"},
+    {file = "frozenlist-1.8.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:48e6d3f4ec5c7273dfe83ff27c91083c6c9065af655dc2684d2c200c94308bb5"},
+    {file = "frozenlist-1.8.0-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:1a7607e17ad33361677adcd1443edf6f5da0ce5e5377b798fba20fae194825f3"},
+    {file = "frozenlist-1.8.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:5a3a935c3a4e89c733303a2d5a7c257ea44af3a56c8202df486b7f5de40f37e1"},
+    {file = "frozenlist-1.8.0-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:940d4a017dbfed9daf46a3b086e1d2167e7012ee297fef9e1c545c4d022f5178"},
+    {file = "frozenlist-1.8.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:b9be22a69a014bc47e78072d0ecae716f5eb56c15238acca0f43d6eb8e4a5bda"},
+    {file = "frozenlist-1.8.0-cp39-cp39-win32.whl", hash = "sha256:1aa77cb5697069af47472e39612976ed05343ff2e84a3dcf15437b232cbfd087"},
+    {file = "frozenlist-1.8.0-cp39-cp39-win_amd64.whl", hash = "sha256:7398c222d1d405e796970320036b1b563892b65809d9e5261487bb2c7f7b5c6a"},
+    {file = "frozenlist-1.8.0-cp39-cp39-win_arm64.whl", hash = "sha256:b4f3b365f31c6cd4af24545ca0a244a53688cad8834e32f56831c4923b50a103"},
+    {file = "frozenlist-1.8.0-py3-none-any.whl", hash = "sha256:0c18a16eab41e82c295618a77502e17b195883241c563b00f0aa5106fc4eaa0d"},
+    {file = "frozenlist-1.8.0.tar.gz", hash = "sha256:3ede829ed8d842f6cd48fc7081d7a41001a56f1f38603f9d49bf3020d59a31ad"},
+]
+
+[[package]]
+name = "fsspec"
+version = "2025.10.0"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "fsspec-2025.10.0-py3-none-any.whl", hash = "sha256:7c7712353ae7d875407f97715f0e1ffcc21e33d5b24556cb1e090ae9409ec61d"},
+    {file = "fsspec-2025.10.0.tar.gz", hash = "sha256:b6789427626f068f9a83ca4e8a3cc050850b6c0f71f99ddb4f542b8266a26a59"},
+]
+
+[package.dependencies]
+aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+dev = ["pre-commit", "ruff (>=0.5)"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
+fuse = ["fusepy"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+gui = ["panel"]
+hdfs = ["pyarrow (>=1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
+test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
+test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard ; python_version < \"3.14\""]
+tqdm = ["tqdm"]
+
+[[package]]
+name = "gitdb"
+version = "4.0.12"
+description = "Git Object Database"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "gitdb-4.0.12-py3-none-any.whl", hash = "sha256:67073e15955400952c6565cc3e707c554a4eea2e428946f7a4c162fab9bd9bcf"},
+    {file = "gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571"},
+]
+
+[package.dependencies]
+smmap = ">=3.0.1,<6"
+
+[[package]]
+name = "gitpython"
+version = "3.1.45"
+description = "GitPython is a Python library used to interact with Git repositories"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "gitpython-3.1.45-py3-none-any.whl", hash = "sha256:8908cb2e02fb3b93b7eb0f2827125cb699869470432cc885f019b8fd0fccff77"},
+    {file = "gitpython-3.1.45.tar.gz", hash = "sha256:85b0ee964ceddf211c41b9f27a49086010a190fd8132a24e21f362a4b36a791c"},
+]
+
+[package.dependencies]
+gitdb = ">=4.0.1,<5"
+
+[package.extras]
+doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
+test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock ; python_version < \"3.8\"", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions ; python_version < \"3.11\""]
+
+[[package]]
+name = "google-auth"
+version = "2.43.0"
+description = "Google Authentication Library"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "google_auth-2.43.0-py2.py3-none-any.whl", hash = "sha256:af628ba6fa493f75c7e9dbe9373d148ca9f4399b5ea29976519e0a3848eddd16"},
+    {file = "google_auth-2.43.0.tar.gz", hash = "sha256:88228eee5fc21b62a1b5fe773ca15e67778cb07dc8363adcb4a8827b52d81483"},
+]
+
+[package.dependencies]
+cachetools = ">=2.0.0,<7.0"
+pyasn1-modules = ">=0.2.1"
+rsa = ">=3.1.4,<5"
+
+[package.extras]
+aiohttp = ["aiohttp (>=3.6.2,<4.0.0)", "requests (>=2.20.0,<3.0.0)"]
+enterprise-cert = ["cryptography", "pyopenssl"]
+pyjwt = ["cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "pyjwt (>=2.0)"]
+pyopenssl = ["cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
+reauth = ["pyu2f (>=0.1.5)"]
+requests = ["requests (>=2.20.0,<3.0.0)"]
+testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "cryptography (>=38.0.3)", "flask", "freezegun", "grpcio", "mock", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"]
+urllib3 = ["packaging", "urllib3"]
+
+[[package]]
+name = "google-genai"
+version = "1.49.0"
+description = "GenAI Python SDK"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "google_genai-1.49.0-py3-none-any.whl", hash = "sha256:ad49cd5be5b63397069e7aef9a4fe0a84cbdf25fcd93408e795292308db4ef32"},
+    {file = "google_genai-1.49.0.tar.gz", hash = "sha256:35eb16023b72e298571ae30e919c810694f258f2ba68fc77a2185c7c8829ad5a"},
+]
+
+[package.dependencies]
+anyio = ">=4.8.0,<5.0.0"
+google-auth = ">=2.14.1,<3.0.0"
+httpx = ">=0.28.1,<1.0.0"
+pydantic = ">=2.9.0,<3.0.0"
+requests = ">=2.28.1,<3.0.0"
+tenacity = ">=8.2.3,<9.2.0"
+typing-extensions = ">=4.11.0,<5.0.0"
+websockets = ">=13.0.0,<15.1.0"
+
+[package.extras]
+aiohttp = ["aiohttp (<4.0.0)"]
+local-tokenizer = ["protobuf", "sentencepiece (>=0.2.0)"]
+
+[[package]]
+name = "graphviz"
+version = "0.21"
+description = "Simple Python interface for Graphviz"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "graphviz-0.21-py3-none-any.whl", hash = "sha256:54f33de9f4f911d7e84e4191749cac8cc5653f815b06738c54db9a15ab8b1e42"},
+    {file = "graphviz-0.21.tar.gz", hash = "sha256:20743e7183be82aaaa8ad6c93f8893c923bd6658a04c32ee115edb3c8a835f78"},
+]
+
+[package.extras]
+dev = ["Flake8-pyproject", "build", "flake8", "pep8-naming", "tox (>=3)", "twine", "wheel"]
+docs = ["sphinx (>=5,<7)", "sphinx-autodoc-typehints", "sphinx-rtd-theme (>=0.2.5)"]
+test = ["coverage", "pytest (>=7,<8.1)", "pytest-cov", "pytest-mock (>=3)"]
+
+[[package]]
+name = "greenlet"
+version = "3.2.4"
+description = "Lightweight in-process concurrent programming"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""
+files = [
+    {file = "greenlet-3.2.4-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:8c68325b0d0acf8d91dde4e6f930967dd52a5302cd4062932a6b2e7c2969f47c"},
+    {file = "greenlet-3.2.4-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:94385f101946790ae13da500603491f04a76b6e4c059dab271b3ce2e283b2590"},
+    {file = "greenlet-3.2.4-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f10fd42b5ee276335863712fa3da6608e93f70629c631bf77145021600abc23c"},
+    {file = "greenlet-3.2.4-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:c8c9e331e58180d0d83c5b7999255721b725913ff6bc6cf39fa2a45841a4fd4b"},
+    {file = "greenlet-3.2.4-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:58b97143c9cc7b86fc458f215bd0932f1757ce649e05b640fea2e79b54cedb31"},
+    {file = "greenlet-3.2.4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c2ca18a03a8cfb5b25bc1cbe20f3d9a4c80d8c3b13ba3df49ac3961af0b1018d"},
+    {file = "greenlet-3.2.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:9fe0a28a7b952a21e2c062cd5756d34354117796c6d9215a87f55e38d15402c5"},
+    {file = "greenlet-3.2.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:8854167e06950ca75b898b104b63cc646573aa5fef1353d4508ecdd1ee76254f"},
+    {file = "greenlet-3.2.4-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:f47617f698838ba98f4ff4189aef02e7343952df3a615f847bb575c3feb177a7"},
+    {file = "greenlet-3.2.4-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:af41be48a4f60429d5cad9d22175217805098a9ef7c40bfef44f7669fb9d74d8"},
+    {file = "greenlet-3.2.4-cp310-cp310-win_amd64.whl", hash = "sha256:73f49b5368b5359d04e18d15828eecc1806033db5233397748f4ca813ff1056c"},
+    {file = "greenlet-3.2.4-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:96378df1de302bc38e99c3a9aa311967b7dc80ced1dcc6f171e99842987882a2"},
+    {file = "greenlet-3.2.4-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:1ee8fae0519a337f2329cb78bd7a8e128ec0f881073d43f023c7b8d4831d5246"},
+    {file = "greenlet-3.2.4-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:94abf90142c2a18151632371140b3dba4dee031633fe614cb592dbb6c9e17bc3"},
+    {file = "greenlet-3.2.4-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:4d1378601b85e2e5171b99be8d2dc85f594c79967599328f95c1dc1a40f1c633"},
+    {file = "greenlet-3.2.4-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0db5594dce18db94f7d1650d7489909b57afde4c580806b8d9203b6e79cdc079"},
+    {file = "greenlet-3.2.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2523e5246274f54fdadbce8494458a2ebdcdbc7b802318466ac5606d3cded1f8"},
+    {file = "greenlet-3.2.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:1987de92fec508535687fb807a5cea1560f6196285a4cde35c100b8cd632cc52"},
+    {file = "greenlet-3.2.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:55e9c5affaa6775e2c6b67659f3a71684de4c549b3dd9afca3bc773533d284fa"},
+    {file = "greenlet-3.2.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c9c6de1940a7d828635fbd254d69db79e54619f165ee7ce32fda763a9cb6a58c"},
+    {file = "greenlet-3.2.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:03c5136e7be905045160b1b9fdca93dd6727b180feeafda6818e6496434ed8c5"},
+    {file = "greenlet-3.2.4-cp311-cp311-win_amd64.whl", hash = "sha256:9c40adce87eaa9ddb593ccb0fa6a07caf34015a29bf8d344811665b573138db9"},
+    {file = "greenlet-3.2.4-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:3b67ca49f54cede0186854a008109d6ee71f66bd57bb36abd6d0a0267b540cdd"},
+    {file = "greenlet-3.2.4-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ddf9164e7a5b08e9d22511526865780a576f19ddd00d62f8a665949327fde8bb"},
+    {file = "greenlet-3.2.4-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f28588772bb5fb869a8eb331374ec06f24a83a9c25bfa1f38b6993afe9c1e968"},
+    {file = "greenlet-3.2.4-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:5c9320971821a7cb77cfab8d956fa8e39cd07ca44b6070db358ceb7f8797c8c9"},
+    {file = "greenlet-3.2.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c60a6d84229b271d44b70fb6e5fa23781abb5d742af7b808ae3f6efd7c9c60f6"},
+    {file = "greenlet-3.2.4-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3b3812d8d0c9579967815af437d96623f45c0f2ae5f04e366de62a12d83a8fb0"},
+    {file = "greenlet-3.2.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:abbf57b5a870d30c4675928c37278493044d7c14378350b3aa5d484fa65575f0"},
+    {file = "greenlet-3.2.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:20fb936b4652b6e307b8f347665e2c615540d4b42b3b4c8a321d8286da7e520f"},
+    {file = "greenlet-3.2.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ee7a6ec486883397d70eec05059353b8e83eca9168b9f3f9a361971e77e0bcd0"},
+    {file = "greenlet-3.2.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:326d234cbf337c9c3def0676412eb7040a35a768efc92504b947b3e9cfc7543d"},
+    {file = "greenlet-3.2.4-cp312-cp312-win_amd64.whl", hash = "sha256:a7d4e128405eea3814a12cc2605e0e6aedb4035bf32697f72deca74de4105e02"},
+    {file = "greenlet-3.2.4-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:1a921e542453fe531144e91e1feedf12e07351b1cf6c9e8a3325ea600a715a31"},
+    {file = "greenlet-3.2.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cd3c8e693bff0fff6ba55f140bf390fa92c994083f838fece0f63be121334945"},
+    {file = "greenlet-3.2.4-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:710638eb93b1fa52823aa91bf75326f9ecdfd5e0466f00789246a5280f4ba0fc"},
+    {file = "greenlet-3.2.4-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:c5111ccdc9c88f423426df3fd1811bfc40ed66264d35aa373420a34377efc98a"},
+    {file = "greenlet-3.2.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d76383238584e9711e20ebe14db6c88ddcedc1829a9ad31a584389463b5aa504"},
+    {file = "greenlet-3.2.4-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:23768528f2911bcd7e475210822ffb5254ed10d71f4028387e5a99b4c6699671"},
+    {file = "greenlet-3.2.4-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:00fadb3fedccc447f517ee0d3fd8fe49eae949e1cd0f6a611818f4f6fb7dc83b"},
+    {file = "greenlet-3.2.4-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:d25c5091190f2dc0eaa3f950252122edbbadbb682aa7b1ef2f8af0f8c0afefae"},
+    {file = "greenlet-3.2.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6e343822feb58ac4d0a1211bd9399de2b3a04963ddeec21530fc426cc121f19b"},
+    {file = "greenlet-3.2.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca7f6f1f2649b89ce02f6f229d7c19f680a6238af656f61e0115b24857917929"},
+    {file = "greenlet-3.2.4-cp313-cp313-win_amd64.whl", hash = "sha256:554b03b6e73aaabec3745364d6239e9e012d64c68ccd0b8430c64ccc14939a8b"},
+    {file = "greenlet-3.2.4-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:49a30d5fda2507ae77be16479bdb62a660fa51b1eb4928b524975b3bde77b3c0"},
+    {file = "greenlet-3.2.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:299fd615cd8fc86267b47597123e3f43ad79c9d8a22bebdce535e53550763e2f"},
+    {file = "greenlet-3.2.4-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:c17b6b34111ea72fc5a4e4beec9711d2226285f0386ea83477cbb97c30a3f3a5"},
+    {file = "greenlet-3.2.4-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:b4a1870c51720687af7fa3e7cda6d08d801dae660f75a76f3845b642b4da6ee1"},
+    {file = "greenlet-3.2.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:061dc4cf2c34852b052a8620d40f36324554bc192be474b9e9770e8c042fd735"},
+    {file = "greenlet-3.2.4-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:44358b9bf66c8576a9f57a590d5f5d6e72fa4228b763d0e43fee6d3b06d3a337"},
+    {file = "greenlet-3.2.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:2917bdf657f5859fbf3386b12d68ede4cf1f04c90c3a6bc1f013dd68a22e2269"},
+    {file = "greenlet-3.2.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:015d48959d4add5d6c9f6c5210ee3803a830dce46356e3bc326d6776bde54681"},
+    {file = "greenlet-3.2.4-cp314-cp314-win_amd64.whl", hash = "sha256:e37ab26028f12dbb0ff65f29a8d3d44a765c61e729647bf2ddfbbed621726f01"},
+    {file = "greenlet-3.2.4-cp39-cp39-macosx_11_0_universal2.whl", hash = "sha256:b6a7c19cf0d2742d0809a4c05975db036fdff50cd294a93632d6a310bf9ac02c"},
+    {file = "greenlet-3.2.4-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:27890167f55d2387576d1f41d9487ef171849ea0359ce1510ca6e06c8bece11d"},
+    {file = "greenlet-3.2.4-cp39-cp39-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:18d9260df2b5fbf41ae5139e1be4e796d99655f023a636cd0e11e6406cca7d58"},
+    {file = "greenlet-3.2.4-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:671df96c1f23c4a0d4077a325483c1503c96a1b7d9db26592ae770daa41233d4"},
+    {file = "greenlet-3.2.4-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:16458c245a38991aa19676900d48bd1a6f2ce3e16595051a4db9d012154e8433"},
+    {file = "greenlet-3.2.4-cp39-cp39-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c9913f1a30e4526f432991f89ae263459b1c64d1608c0d22a5c79c287b3c70df"},
+    {file = "greenlet-3.2.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:b90654e092f928f110e0007f572007c9727b5265f7632c2fa7415b4689351594"},
+    {file = "greenlet-3.2.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:81701fd84f26330f0d5f4944d4e92e61afe6319dcd9775e39396e39d7c3e5f98"},
+    {file = "greenlet-3.2.4-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:28a3c6b7cd72a96f61b0e4b2a36f681025b60ae4779cc73c1535eb5f29560b10"},
+    {file = "greenlet-3.2.4-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:52206cd642670b0b320a1fd1cbfd95bca0e043179c1d8a045f2c6109dfe973be"},
+    {file = "greenlet-3.2.4-cp39-cp39-win32.whl", hash = "sha256:65458b409c1ed459ea899e939f0e1cdb14f58dbc803f2f93c5eab5694d32671b"},
+    {file = "greenlet-3.2.4-cp39-cp39-win_amd64.whl", hash = "sha256:d2e685ade4dafd447ede19c31277a224a239a0a1a4eca4e6390efedf20260cfb"},
+    {file = "greenlet-3.2.4.tar.gz", hash = "sha256:0dca0d95ff849f9a364385f36ab49f50065d76964944638be9691e1832e9f86d"},
+]
+
+[package.extras]
+docs = ["Sphinx", "furo"]
+test = ["objgraph", "psutil", "setuptools"]
+
+[[package]]
+name = "grpcio"
+version = "1.76.0"
+description = "HTTP/2-based RPC framework"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "grpcio-1.76.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:65a20de41e85648e00305c1bb09a3598f840422e522277641145a32d42dcefcc"},
+    {file = "grpcio-1.76.0-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:40ad3afe81676fd9ec6d9d406eda00933f218038433980aa19d401490e46ecde"},
+    {file = "grpcio-1.76.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:035d90bc79eaa4bed83f524331d55e35820725c9fbb00ffa1904d5550ed7ede3"},
+    {file = "grpcio-1.76.0-cp310-cp310-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:4215d3a102bd95e2e11b5395c78562967959824156af11fa93d18fdd18050990"},
+    {file = "grpcio-1.76.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:49ce47231818806067aea3324d4bf13825b658ad662d3b25fada0bdad9b8a6af"},
+    {file = "grpcio-1.76.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:8cc3309d8e08fd79089e13ed4819d0af72aa935dd8f435a195fd152796752ff2"},
+    {file = "grpcio-1.76.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:971fd5a1d6e62e00d945423a567e42eb1fa678ba89072832185ca836a94daaa6"},
+    {file = "grpcio-1.76.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:9d9adda641db7207e800a7f089068f6f645959f2df27e870ee81d44701dd9db3"},
+    {file = "grpcio-1.76.0-cp310-cp310-win32.whl", hash = "sha256:063065249d9e7e0782d03d2bca50787f53bd0fb89a67de9a7b521c4a01f1989b"},
+    {file = "grpcio-1.76.0-cp310-cp310-win_amd64.whl", hash = "sha256:a6ae758eb08088d36812dd5d9af7a9859c05b1e0f714470ea243694b49278e7b"},
+    {file = "grpcio-1.76.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:2e1743fbd7f5fa713a1b0a8ac8ebabf0ec980b5d8809ec358d488e273b9cf02a"},
+    {file = "grpcio-1.76.0-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:a8c2cf1209497cf659a667d7dea88985e834c24b7c3b605e6254cbb5076d985c"},
+    {file = "grpcio-1.76.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:08caea849a9d3c71a542827d6df9d5a69067b0a1efbea8a855633ff5d9571465"},
+    {file = "grpcio-1.76.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:f0e34c2079d47ae9f6188211db9e777c619a21d4faba6977774e8fa43b085e48"},
+    {file = "grpcio-1.76.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8843114c0cfce61b40ad48df65abcfc00d4dba82eae8718fab5352390848c5da"},
+    {file = "grpcio-1.76.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8eddfb4d203a237da6f3cc8a540dad0517d274b5a1e9e636fd8d2c79b5c1d397"},
+    {file = "grpcio-1.76.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:32483fe2aab2c3794101c2a159070584e5db11d0aa091b2c0ea9c4fc43d0d749"},
+    {file = "grpcio-1.76.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:dcfe41187da8992c5f40aa8c5ec086fa3672834d2be57a32384c08d5a05b4c00"},
+    {file = "grpcio-1.76.0-cp311-cp311-win32.whl", hash = "sha256:2107b0c024d1b35f4083f11245c0e23846ae64d02f40b2b226684840260ed054"},
+    {file = "grpcio-1.76.0-cp311-cp311-win_amd64.whl", hash = "sha256:522175aba7af9113c48ec10cc471b9b9bd4f6ceb36aeb4544a8e2c80ed9d252d"},
+    {file = "grpcio-1.76.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:81fd9652b37b36f16138611c7e884eb82e0cec137c40d3ef7c3f9b3ed00f6ed8"},
+    {file = "grpcio-1.76.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:04bbe1bfe3a68bbfd4e52402ab7d4eb59d72d02647ae2042204326cf4bbad280"},
+    {file = "grpcio-1.76.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d388087771c837cdb6515539f43b9d4bf0b0f23593a24054ac16f7a960be16f4"},
+    {file = "grpcio-1.76.0-cp312-cp312-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:9f8f757bebaaea112c00dba718fc0d3260052ce714e25804a03f93f5d1c6cc11"},
+    {file = "grpcio-1.76.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:980a846182ce88c4f2f7e2c22c56aefd515daeb36149d1c897f83cf57999e0b6"},
+    {file = "grpcio-1.76.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:f92f88e6c033db65a5ae3d97905c8fea9c725b63e28d5a75cb73b49bda5024d8"},
+    {file = "grpcio-1.76.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:4baf3cbe2f0be3289eb68ac8ae771156971848bb8aaff60bad42005539431980"},
+    {file = "grpcio-1.76.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:615ba64c208aaceb5ec83bfdce7728b80bfeb8be97562944836a7a0a9647d882"},
+    {file = "grpcio-1.76.0-cp312-cp312-win32.whl", hash = "sha256:45d59a649a82df5718fd9527ce775fd66d1af35e6d31abdcdc906a49c6822958"},
+    {file = "grpcio-1.76.0-cp312-cp312-win_amd64.whl", hash = "sha256:c088e7a90b6017307f423efbb9d1ba97a22aa2170876223f9709e9d1de0b5347"},
+    {file = "grpcio-1.76.0-cp313-cp313-linux_armv7l.whl", hash = "sha256:26ef06c73eb53267c2b319f43e6634c7556ea37672029241a056629af27c10e2"},
+    {file = "grpcio-1.76.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:45e0111e73f43f735d70786557dc38141185072d7ff8dc1829d6a77ac1471468"},
+    {file = "grpcio-1.76.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:83d57312a58dcfe2a3a0f9d1389b299438909a02db60e2f2ea2ae2d8034909d3"},
+    {file = "grpcio-1.76.0-cp313-cp313-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:3e2a27c89eb9ac3d81ec8835e12414d73536c6e620355d65102503064a4ed6eb"},
+    {file = "grpcio-1.76.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:61f69297cba3950a524f61c7c8ee12e55c486cb5f7db47ff9dcee33da6f0d3ae"},
+    {file = "grpcio-1.76.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6a15c17af8839b6801d554263c546c69c4d7718ad4321e3166175b37eaacca77"},
+    {file = "grpcio-1.76.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:25a18e9810fbc7e7f03ec2516addc116a957f8cbb8cbc95ccc80faa072743d03"},
+    {file = "grpcio-1.76.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:931091142fd8cc14edccc0845a79248bc155425eee9a98b2db2ea4f00a235a42"},
+    {file = "grpcio-1.76.0-cp313-cp313-win32.whl", hash = "sha256:5e8571632780e08526f118f74170ad8d50fb0a48c23a746bef2a6ebade3abd6f"},
+    {file = "grpcio-1.76.0-cp313-cp313-win_amd64.whl", hash = "sha256:f9f7bd5faab55f47231ad8dba7787866b69f5e93bc306e3915606779bbfb4ba8"},
+    {file = "grpcio-1.76.0-cp314-cp314-linux_armv7l.whl", hash = "sha256:ff8a59ea85a1f2191a0ffcc61298c571bc566332f82e5f5be1b83c9d8e668a62"},
+    {file = "grpcio-1.76.0-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:06c3d6b076e7b593905d04fdba6a0525711b3466f43b3400266f04ff735de0cd"},
+    {file = "grpcio-1.76.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fd5ef5932f6475c436c4a55e4336ebbe47bd3272be04964a03d316bbf4afbcbc"},
+    {file = "grpcio-1.76.0-cp314-cp314-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:b331680e46239e090f5b3cead313cc772f6caa7d0fc8de349337563125361a4a"},
+    {file = "grpcio-1.76.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2229ae655ec4e8999599469559e97630185fdd53ae1e8997d147b7c9b2b72cba"},
+    {file = "grpcio-1.76.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:490fa6d203992c47c7b9e4a9d39003a0c2bcc1c9aa3c058730884bbbb0ee9f09"},
+    {file = "grpcio-1.76.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:479496325ce554792dba6548fae3df31a72cef7bad71ca2e12b0e58f9b336bfc"},
+    {file = "grpcio-1.76.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:1c9b93f79f48b03ada57ea24725d83a30284a012ec27eab2cf7e50a550cbbbcc"},
+    {file = "grpcio-1.76.0-cp314-cp314-win32.whl", hash = "sha256:747fa73efa9b8b1488a95d0ba1039c8e2dca0f741612d80415b1e1c560febf4e"},
+    {file = "grpcio-1.76.0-cp314-cp314-win_amd64.whl", hash = "sha256:922fa70ba549fce362d2e2871ab542082d66e2aaf0c19480ea453905b01f384e"},
+    {file = "grpcio-1.76.0-cp39-cp39-linux_armv7l.whl", hash = "sha256:8ebe63ee5f8fa4296b1b8cfc743f870d10e902ca18afc65c68cf46fd39bb0783"},
+    {file = "grpcio-1.76.0-cp39-cp39-macosx_11_0_universal2.whl", hash = "sha256:3bf0f392c0b806905ed174dcd8bdd5e418a40d5567a05615a030a5aeddea692d"},
+    {file = "grpcio-1.76.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0b7604868b38c1bfd5cf72d768aedd7db41d78cb6a4a18585e33fb0f9f2363fd"},
+    {file = "grpcio-1.76.0-cp39-cp39-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:e6d1db20594d9daba22f90da738b1a0441a7427552cc6e2e3d1297aeddc00378"},
+    {file = "grpcio-1.76.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d099566accf23d21037f18a2a63d323075bebace807742e4b0ac210971d4dd70"},
+    {file = "grpcio-1.76.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:ebea5cc3aa8ea72e04df9913492f9a96d9348db876f9dda3ad729cfedf7ac416"},
+    {file = "grpcio-1.76.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:0c37db8606c258e2ee0c56b78c62fc9dee0e901b5dbdcf816c2dd4ad652b8b0c"},
+    {file = "grpcio-1.76.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:ebebf83299b0cb1721a8859ea98f3a77811e35dce7609c5c963b9ad90728f886"},
+    {file = "grpcio-1.76.0-cp39-cp39-win32.whl", hash = "sha256:0aaa82d0813fd4c8e589fac9b65d7dd88702555f702fb10417f96e2a2a6d4c0f"},
+    {file = "grpcio-1.76.0-cp39-cp39-win_amd64.whl", hash = "sha256:acab0277c40eff7143c2323190ea57b9ee5fd353d8190ee9652369fae735668a"},
+    {file = "grpcio-1.76.0.tar.gz", hash = "sha256:7be78388d6da1a25c0d5ec506523db58b18be22d9c37d8d3a32c08be4987bd73"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.12,<5.0"
+
+[package.extras]
+protobuf = ["grpcio-tools (>=1.76.0)"]
+
+[[package]]
+name = "gymnasium"
+version = "1.2.2"
+description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "gymnasium-1.2.2-py3-none-any.whl", hash = "sha256:f04ec362b1fdf73a8b327db5ef89384a3f2ba411e05d3521513414fbbb2199c8"},
+    {file = "gymnasium-1.2.2.tar.gz", hash = "sha256:46d927328f8518bb5a689dbe270d228c1da2b08bcb71ae0152c10aa66f48d530"},
+]
+
+[package.dependencies]
+ale_py = {version = ">=0.9", optional = true, markers = "extra == \"atari\""}
+cloudpickle = ">=1.2.0"
+farama-notifications = ">=0.0.1"
+numpy = ">=1.21.0"
+typing-extensions = ">=4.3.0"
+
+[package.extras]
+all = ["ale_py (>=0.9)", "array-api-compat (>=1.11.0)", "array-api-compat (>=1.11.0)", "array-api-compat (>=1.11.0)", "box2d-py (==2.3.5)", "flax (>=0.5.0)", "imageio (>=2.14.1)", "jax (>=0.4.16)", "jaxlib (>=0.4.16)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (>=2.1.5)", "numpy (>=2.1)", "numpy (>=2.1)", "numpy (>=2.1)", "opencv-python (>=3.0)", "packaging (>=23.0)", "pygame (>=2.1.3)", "pygame (>=2.1.3)", "pygame (>=2.1.3)", "swig (==4.*)", "torch (>=1.13.0)"]
+array-api = ["array-api-compat (>=1.11.0)", "numpy (>=2.1)", "packaging (>=23.0)"]
+atari = ["ale_py (>=0.9)"]
+box2d = ["box2d-py (==2.3.5)", "pygame (>=2.1.3)", "swig (==4.*)"]
+classic-control = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+jax = ["array-api-compat (>=1.11.0)", "flax (>=0.5.0)", "jax (>=0.4.16)", "jaxlib (>=0.4.16)", "numpy (>=2.1)"]
+mujoco = ["imageio (>=2.14.1)", "mujoco (>=2.1.5)", "packaging (>=23.0)"]
+other = ["matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)", "seaborn (>=0.13)"]
+testing = ["array_api_extra (>=0.7.0)", "dill (>=0.3.7)", "pytest (>=7.1.3)", "scipy (>=1.7.3)"]
+torch = ["array-api-compat (>=1.11.0)", "numpy (>=2.1)", "torch (>=1.13.0)"]
+toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+
+[[package]]
+name = "h11"
+version = "0.16.0"
+description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"},
+    {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
+]
+
+[[package]]
+name = "hf-xet"
+version = "1.2.0"
+description = "Fast transfer of large files with the Hugging Face Hub."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""
+files = [
+    {file = "hf_xet-1.2.0-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:ceeefcd1b7aed4956ae8499e2199607765fbd1c60510752003b6cc0b8413b649"},
+    {file = "hf_xet-1.2.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:b70218dd548e9840224df5638fdc94bd033552963cfa97f9170829381179c813"},
+    {file = "hf_xet-1.2.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7d40b18769bb9a8bc82a9ede575ce1a44c75eb80e7375a01d76259089529b5dc"},
+    {file = "hf_xet-1.2.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:cd3a6027d59cfb60177c12d6424e31f4b5ff13d8e3a1247b3a584bf8977e6df5"},
+    {file = "hf_xet-1.2.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:6de1fc44f58f6dd937956c8d304d8c2dea264c80680bcfa61ca4a15e7b76780f"},
+    {file = "hf_xet-1.2.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f182f264ed2acd566c514e45da9f2119110e48a87a327ca271027904c70c5832"},
+    {file = "hf_xet-1.2.0-cp313-cp313t-win_amd64.whl", hash = "sha256:293a7a3787e5c95d7be1857358a9130694a9c6021de3f27fa233f37267174382"},
+    {file = "hf_xet-1.2.0-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:10bfab528b968c70e062607f663e21e34e2bba349e8038db546646875495179e"},
+    {file = "hf_xet-1.2.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2a212e842647b02eb6a911187dc878e79c4aa0aa397e88dd3b26761676e8c1f8"},
+    {file = "hf_xet-1.2.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:30e06daccb3a7d4c065f34fc26c14c74f4653069bb2b194e7f18f17cbe9939c0"},
+    {file = "hf_xet-1.2.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:29c8fc913a529ec0a91867ce3d119ac1aac966e098cf49501800c870328cc090"},
+    {file = "hf_xet-1.2.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e159cbfcfbb29f920db2c09ed8b660eb894640d284f102ada929b6e3dc410a"},
+    {file = "hf_xet-1.2.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:9c91d5ae931510107f148874e9e2de8a16052b6f1b3ca3c1b12f15ccb491390f"},
+    {file = "hf_xet-1.2.0-cp314-cp314t-win_amd64.whl", hash = "sha256:210d577732b519ac6ede149d2f2f34049d44e8622bf14eb3d63bbcd2d4b332dc"},
+    {file = "hf_xet-1.2.0-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:46740d4ac024a7ca9b22bebf77460ff43332868b661186a8e46c227fdae01848"},
+    {file = "hf_xet-1.2.0-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:27df617a076420d8845bea087f59303da8be17ed7ec0cd7ee3b9b9f579dff0e4"},
+    {file = "hf_xet-1.2.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3651fd5bfe0281951b988c0facbe726aa5e347b103a675f49a3fa8144c7968fd"},
+    {file = "hf_xet-1.2.0-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:d06fa97c8562fb3ee7a378dd9b51e343bc5bc8190254202c9771029152f5e08c"},
+    {file = "hf_xet-1.2.0-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:4c1428c9ae73ec0939410ec73023c4f842927f39db09b063b9482dac5a3bb737"},
+    {file = "hf_xet-1.2.0-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a55558084c16b09b5ed32ab9ed38421e2d87cf3f1f89815764d1177081b99865"},
+    {file = "hf_xet-1.2.0-cp37-abi3-win_amd64.whl", hash = "sha256:e6584a52253f72c9f52f9e549d5895ca7a471608495c4ecaa6cc73dba2b24d69"},
+    {file = "hf_xet-1.2.0.tar.gz", hash = "sha256:a8c27070ca547293b6890c4bf389f713f80e8c478631432962bb7f4bc0bd7d7f"},
+]
+
+[package.extras]
+tests = ["pytest"]
+
+[[package]]
+name = "httpcore"
+version = "1.0.9"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55"},
+    {file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"},
+]
+
+[package.dependencies]
+certifi = "*"
+h11 = ">=0.16"
+
+[package.extras]
+asyncio = ["anyio (>=4.0,<5.0)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+trio = ["trio (>=0.22.0,<1.0)"]
+
+[[package]]
+name = "httpx"
+version = "0.28.1"
+description = "The next generation HTTP client."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"},
+    {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"},
+]
+
+[package.dependencies]
+anyio = "*"
+certifi = "*"
+httpcore = "==1.*"
+idna = "*"
+
+[package.extras]
+brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
+cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "huggingface-hub"
+version = "0.36.0"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+optional = false
+python-versions = ">=3.8.0"
+groups = ["main"]
+files = [
+    {file = "huggingface_hub-0.36.0-py3-none-any.whl", hash = "sha256:7bcc9ad17d5b3f07b57c78e79d527102d08313caa278a641993acddcb894548d"},
+    {file = "huggingface_hub-0.36.0.tar.gz", hash = "sha256:47b3f0e2539c39bf5cde015d63b72ec49baff67b6931c3d97f3f84532e2b8d25"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = ">=2023.5.0"
+hf-xet = {version = ">=1.1.3,<2.0.0", markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""}
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures (<16.0)", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "ty", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures (<16.0)", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "ty", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+hf-xet = ["hf-xet (>=1.1.2,<2.0.0)"]
+inference = ["aiohttp"]
+mcp = ["aiohttp", "mcp (>=1.8.0)", "typer"]
+oauth = ["authlib (>=1.3.2)", "fastapi", "httpx", "itsdangerous"]
+quality = ["libcst (>=1.4.0)", "mypy (==1.15.0) ; python_version >= \"3.9\"", "mypy (>=1.14.1,<1.15.0) ; python_version == \"3.8\"", "ruff (>=0.9.0)", "ty"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures (<16.0)", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors[torch]", "torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
+
+[[package]]
+name = "idna"
+version = "3.11"
+description = "Internationalized Domain Names in Applications (IDNA)"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea"},
+    {file = "idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902"},
+]
+
+[package.extras]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+
+[[package]]
+name = "importlib-metadata"
+version = "8.7.0"
+description = "Read metadata from Python packages"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd"},
+    {file = "importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000"},
+]
+
+[package.dependencies]
+zipp = ">=3.20"
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+perf = ["ipython"]
+test = ["flufl.flake8", "importlib_resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+type = ["pytest-mypy"]
+
+[[package]]
+name = "ipykernel"
+version = "7.1.0"
+description = "IPython Kernel for Jupyter"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "ipykernel-7.1.0-py3-none-any.whl", hash = "sha256:763b5ec6c5b7776f6a8d7ce09b267693b4e5ce75cb50ae696aaefb3c85e1ea4c"},
+    {file = "ipykernel-7.1.0.tar.gz", hash = "sha256:58a3fc88533d5930c3546dc7eac66c6d288acde4f801e2001e65edc5dc9cf0db"},
+]
+
+[package.dependencies]
+appnope = {version = ">=0.1.2", markers = "platform_system == \"Darwin\""}
+comm = ">=0.1.1"
+debugpy = ">=1.6.5"
+ipython = ">=7.23.1"
+jupyter-client = ">=8.0.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+matplotlib-inline = ">=0.1"
+nest-asyncio = ">=1.4"
+packaging = ">=22"
+psutil = ">=5.7"
+pyzmq = ">=25"
+tornado = ">=6.2"
+traitlets = ">=5.4.0"
+
+[package.extras]
+cov = ["coverage[toml]", "matplotlib", "pytest-cov", "trio"]
+docs = ["intersphinx-registry", "myst-parser", "pydata-sphinx-theme", "sphinx (<8.2.0)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
+pyqt5 = ["pyqt5"]
+pyside6 = ["pyside6"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0,<9)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "ipython"
+version = "8.37.0"
+description = "IPython: Productive Interactive Computing"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "python_version == \"3.10\""
+files = [
+    {file = "ipython-8.37.0-py3-none-any.whl", hash = "sha256:ed87326596b878932dbcb171e3e698845434d8c61b8d8cd474bf663041a9dcf2"},
+    {file = "ipython-8.37.0.tar.gz", hash = "sha256:ca815841e1a41a1e6b73a0b08f3038af9b2252564d01fc405356d34033012216"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+decorator = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
+jedi = ">=0.16"
+matplotlib-inline = "*"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
+prompt_toolkit = ">=3.0.41,<3.1.0"
+pygments = ">=2.4.0"
+stack_data = "*"
+traitlets = ">=5.13.0"
+typing_extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
+
+[package.extras]
+all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
+black = ["black"]
+doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli ; python_version < \"3.11\"", "typing_extensions"]
+kernel = ["ipykernel"]
+matplotlib = ["matplotlib"]
+nbconvert = ["nbconvert"]
+nbformat = ["nbformat"]
+notebook = ["ipywidgets", "notebook"]
+parallel = ["ipyparallel"]
+qtconsole = ["qtconsole"]
+test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "ipython[test]", "jupyter_ai", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
+
+[[package]]
+name = "ipython"
+version = "9.7.0"
+description = "IPython: Productive Interactive Computing"
+optional = false
+python-versions = ">=3.11"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
+files = [
+    {file = "ipython-9.7.0-py3-none-any.whl", hash = "sha256:bce8ac85eb9521adc94e1845b4c03d88365fd6ac2f4908ec4ed1eb1b0a065f9f"},
+    {file = "ipython-9.7.0.tar.gz", hash = "sha256:5f6de88c905a566c6a9d6c400a8fed54a638e1f7543d17aae2551133216b1e4e"},
+]
+
+[package.dependencies]
+colorama = {version = ">=0.4.4", markers = "sys_platform == \"win32\""}
+decorator = ">=4.3.2"
+ipython-pygments-lexers = ">=1.0.0"
+jedi = ">=0.18.1"
+matplotlib-inline = ">=0.1.5"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
+prompt_toolkit = ">=3.0.41,<3.1.0"
+pygments = ">=2.11.0"
+stack_data = ">=0.6.0"
+traitlets = ">=5.13.0"
+typing_extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
+
+[package.extras]
+all = ["ipython[doc,matplotlib,test,test-extra]"]
+black = ["black"]
+doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[matplotlib,test]", "setuptools (>=70.0)", "sphinx (>=8.0)", "sphinx-rtd-theme (>=0.1.8)", "sphinx_toml (==0.0.4)", "typing_extensions"]
+matplotlib = ["matplotlib (>3.9)"]
+test = ["packaging (>=20.1.0)", "pytest (>=7.0.0)", "pytest-asyncio (>=1.0.0)", "setuptools (>=61.2)", "testpath (>=0.2)"]
+test-extra = ["curio", "ipykernel (>6.30)", "ipython[matplotlib]", "ipython[test]", "jupyter_ai", "nbclient", "nbformat", "numpy (>=1.27)", "pandas (>2.1)", "trio (>=0.1.0)"]
+
+[[package]]
+name = "ipython-pygments-lexers"
+version = "1.1.1"
+description = "Defines a variety of Pygments lexers for highlighting IPython code."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
+files = [
+    {file = "ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c"},
+    {file = "ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81"},
+]
+
+[package.dependencies]
+pygments = "*"
+
+[[package]]
+name = "ipywidgets"
+version = "8.1.8"
+description = "Jupyter interactive widgets"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "ipywidgets-8.1.8-py3-none-any.whl", hash = "sha256:ecaca67aed704a338f88f67b1181b58f821ab5dc89c1f0f5ef99db43c1c2921e"},
+    {file = "ipywidgets-8.1.8.tar.gz", hash = "sha256:61f969306b95f85fba6b6986b7fe45d73124d1d9e3023a8068710d47a22ea668"},
+]
+
+[package.dependencies]
+comm = ">=0.1.3"
+ipython = ">=6.1.0"
+jupyterlab_widgets = ">=3.0.15,<3.1.0"
+traitlets = ">=4.3.1"
+widgetsnbextension = ">=4.0.14,<4.1.0"
+
+[package.extras]
+test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+
+[[package]]
+name = "isoduration"
+version = "20.11.0"
+description = "Operations with ISO 8601 durations"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"},
+    {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"},
+]
+
+[package.dependencies]
+arrow = ">=0.15.0"
+
+[[package]]
+name = "jedi"
+version = "0.19.2"
+description = "An autocompletion tool for Python that can be used for text editors."
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+files = [
+    {file = "jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9"},
+    {file = "jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0"},
+]
+
+[package.dependencies]
+parso = ">=0.8.4,<0.9.0"
+
+[package.extras]
+docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
+
+[[package]]
+name = "jinja2"
+version = "3.1.6"
+description = "A very fast and expressive template engine."
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67"},
+    {file = "jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d"},
+]
+
+[package.dependencies]
+MarkupSafe = ">=2.0"
+
+[package.extras]
+i18n = ["Babel (>=2.7)"]
+
+[[package]]
+name = "joblib"
+version = "1.5.2"
+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "joblib-1.5.2-py3-none-any.whl", hash = "sha256:4e1f0bdbb987e6d843c70cf43714cb276623def372df3c22fe5266b2670bc241"},
+    {file = "joblib-1.5.2.tar.gz", hash = "sha256:3faa5c39054b2f03ca547da9b2f52fde67c06240c31853f306aea97f13647b55"},
+]
+
+[[package]]
+name = "json5"
+version = "0.12.1"
+description = "A Python implementation of the JSON5 data format."
+optional = false
+python-versions = ">=3.8.0"
+groups = ["main"]
+files = [
+    {file = "json5-0.12.1-py3-none-any.whl", hash = "sha256:d9c9b3bc34a5f54d43c35e11ef7cb87d8bdd098c6ace87117a7b7e83e705c1d5"},
+    {file = "json5-0.12.1.tar.gz", hash = "sha256:b2743e77b3242f8d03c143dd975a6ec7c52e2f2afe76ed934e53503dd4ad4990"},
+]
+
+[package.extras]
+dev = ["build (==1.2.2.post1)", "coverage (==7.5.4) ; python_version < \"3.9\"", "coverage (==7.8.0) ; python_version >= \"3.9\"", "mypy (==1.14.1) ; python_version < \"3.9\"", "mypy (==1.15.0) ; python_version >= \"3.9\"", "pip (==25.0.1)", "pylint (==3.2.7) ; python_version < \"3.9\"", "pylint (==3.3.6) ; python_version >= \"3.9\"", "ruff (==0.11.2)", "twine (==6.1.0)", "uv (==0.6.11)"]
+
+[[package]]
+name = "jsonpointer"
+version = "3.0.0"
+description = "Identify specific nodes in a JSON document (RFC 6901)"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
+    {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
+]
+
+[[package]]
+name = "jsonschema"
+version = "4.25.1"
+description = "An implementation of JSON Schema validation for Python"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "jsonschema-4.25.1-py3-none-any.whl", hash = "sha256:3fba0169e345c7175110351d456342c364814cfcf3b964ba4587f22915230a63"},
+    {file = "jsonschema-4.25.1.tar.gz", hash = "sha256:e4a9655ce0da0c0b67a085847e00a3a51449e1157f4f75e9fb5aa545e122eb85"},
+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
+jsonschema-specifications = ">=2023.03.6"
+referencing = ">=0.28.4"
+rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3987-syntax = {version = ">=1.1.0", optional = true, markers = "extra == \"format-nongpl\""}
+rpds-py = ">=0.7.1"
+uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+webcolors = {version = ">=24.6.0", optional = true, markers = "extra == \"format-nongpl\""}
+
+[package.extras]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "rfc3987-syntax (>=1.1.0)", "uri-template", "webcolors (>=24.6.0)"]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2025.9.1"
+description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe"},
+    {file = "jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d"},
+]
+
+[package.dependencies]
+referencing = ">=0.31.0"
+
+[[package]]
+name = "jupyter"
+version = "1.1.1"
+description = "Jupyter metapackage. Install all the Jupyter components in one go."
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "jupyter-1.1.1-py2.py3-none-any.whl", hash = "sha256:7a59533c22af65439b24bbe60373a4e95af8f16ac65a6c00820ad378e3f7cc83"},
+    {file = "jupyter-1.1.1.tar.gz", hash = "sha256:d55467bceabdea49d7e3624af7e33d59c37fff53ed3a350e1ac957bed731de7a"},
+]
+
+[package.dependencies]
+ipykernel = "*"
+ipywidgets = "*"
+jupyter-console = "*"
+jupyterlab = "*"
+nbconvert = "*"
+notebook = "*"
+
+[[package]]
+name = "jupyter-client"
+version = "8.6.3"
+description = "Jupyter protocol implementation and client libraries"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f"},
+    {file = "jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419"},
+]
+
+[package.dependencies]
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+python-dateutil = ">=2.8.2"
+pyzmq = ">=23.0"
+tornado = ">=6.2"
+traitlets = ">=5.3"
+
+[package.extras]
+docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko ; sys_platform == \"win32\"", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
+
+[[package]]
+name = "jupyter-console"
+version = "6.6.3"
+description = "Jupyter terminal console"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "jupyter_console-6.6.3-py3-none-any.whl", hash = "sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485"},
+    {file = "jupyter_console-6.6.3.tar.gz", hash = "sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539"},
+]
+
+[package.dependencies]
+ipykernel = ">=6.14"
+ipython = "*"
+jupyter-client = ">=7.0.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+prompt-toolkit = ">=3.0.30"
+pygments = "*"
+pyzmq = ">=17"
+traitlets = ">=5.4"
+
+[package.extras]
+test = ["flaky", "pexpect", "pytest"]
+
+[[package]]
+name = "jupyter-core"
+version = "5.9.1"
+description = "Jupyter core package. A base package on which Jupyter projects rely."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "jupyter_core-5.9.1-py3-none-any.whl", hash = "sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407"},
+    {file = "jupyter_core-5.9.1.tar.gz", hash = "sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508"},
+]
+
+[package.dependencies]
+platformdirs = ">=2.5"
+traitlets = ">=5.3"
+
+[package.extras]
+docs = ["intersphinx-registry", "myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-spelling", "traitlets"]
+test = ["ipykernel", "pre-commit", "pytest (<9)", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "jupyter-events"
+version = "0.12.0"
+description = "Jupyter Event System library"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "jupyter_events-0.12.0-py3-none-any.whl", hash = "sha256:6464b2fa5ad10451c3d35fabc75eab39556ae1e2853ad0c0cc31b656731a97fb"},
+    {file = "jupyter_events-0.12.0.tar.gz", hash = "sha256:fc3fce98865f6784c9cd0a56a20644fc6098f21c8c33834a8d9fe383c17e554b"},
+]
+
+[package.dependencies]
+jsonschema = {version = ">=4.18.0", extras = ["format-nongpl"]}
+packaging = "*"
+python-json-logger = ">=2.0.4"
+pyyaml = ">=5.3"
+referencing = "*"
+rfc3339-validator = "*"
+rfc3986-validator = ">=0.1.1"
+traitlets = ">=5.3"
+
+[package.extras]
+cli = ["click", "rich"]
+docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme (>=0.16)", "sphinx (>=8)", "sphinxcontrib-spelling"]
+test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
+
+[[package]]
+name = "jupyter-lsp"
+version = "2.3.0"
+description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "jupyter_lsp-2.3.0-py3-none-any.whl", hash = "sha256:e914a3cb2addf48b1c7710914771aaf1819d46b2e5a79b0f917b5478ec93f34f"},
+    {file = "jupyter_lsp-2.3.0.tar.gz", hash = "sha256:458aa59339dc868fb784d73364f17dbce8836e906cd75fd471a325cba02e0245"},
+]
+
+[package.dependencies]
+jupyter_server = ">=1.1.2"
+
+[[package]]
+name = "jupyter-server"
+version = "2.17.0"
+description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "jupyter_server-2.17.0-py3-none-any.whl", hash = "sha256:e8cb9c7db4251f51ed307e329b81b72ccf2056ff82d50524debde1ee1870e13f"},
+    {file = "jupyter_server-2.17.0.tar.gz", hash = "sha256:c38ea898566964c888b4772ae1ed58eca84592e88251d2cfc4d171f81f7e99d5"},
+]
+
+[package.dependencies]
+anyio = ">=3.1.0"
+argon2-cffi = ">=21.1"
+jinja2 = ">=3.0.3"
+jupyter-client = ">=7.4.4"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+jupyter-events = ">=0.11.0"
+jupyter-server-terminals = ">=0.4.4"
+nbconvert = ">=6.4.4"
+nbformat = ">=5.3.0"
+overrides = {version = ">=5.0", markers = "python_version < \"3.12\""}
+packaging = ">=22.0"
+prometheus-client = ">=0.9"
+pywinpty = {version = ">=2.0.1", markers = "os_name == \"nt\""}
+pyzmq = ">=24"
+send2trash = ">=1.8.2"
+terminado = ">=0.8.3"
+tornado = ">=6.2.0"
+traitlets = ">=5.6.0"
+websocket-client = ">=1.7"
+
+[package.extras]
+docs = ["ipykernel", "jinja2", "jupyter-client", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
+test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0,<9)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"]
+
+[[package]]
+name = "jupyter-server-mathjax"
+version = "0.2.6"
+description = "MathJax resources as a Jupyter Server Extension."
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "jupyter_server_mathjax-0.2.6-py3-none-any.whl", hash = "sha256:416389dde2010df46d5fbbb7adb087a5607111070af65a1445391040f2babb5e"},
+    {file = "jupyter_server_mathjax-0.2.6.tar.gz", hash = "sha256:bb1e6b6dc0686c1fe386a22b5886163db548893a99c2810c36399e9c4ca23943"},
+]
+
+[package.dependencies]
+jupyter-server = ">=1.1"
+
+[package.extras]
+test = ["jupyter-server[test]", "pytest"]
+
+[[package]]
+name = "jupyter-server-terminals"
+version = "0.5.3"
+description = "A Jupyter Server Extension Providing Terminals."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa"},
+    {file = "jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269"},
+]
+
+[package.dependencies]
+pywinpty = {version = ">=2.0.3", markers = "os_name == \"nt\""}
+terminado = ">=0.8.3"
+
+[package.extras]
+docs = ["jinja2", "jupyter-server", "mistune (<4.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"]
+test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"]
+
+[[package]]
+name = "jupyterlab"
+version = "4.4.10"
+description = "JupyterLab computational environment"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "jupyterlab-4.4.10-py3-none-any.whl", hash = "sha256:65939ab4c8dcd0c42185c2d0d1a9d60b254dc8c46fc4fdb286b63c51e9358e07"},
+    {file = "jupyterlab-4.4.10.tar.gz", hash = "sha256:521c017508af4e1d6d9d8a9d90f47a11c61197ad63b2178342489de42540a615"},
+]
+
+[package.dependencies]
+async-lru = ">=1.0.0"
+httpx = ">=0.25.0,<1"
+ipykernel = ">=6.5.0,<6.30.0 || >6.30.0"
+jinja2 = ">=3.0.3"
+jupyter-core = "*"
+jupyter-lsp = ">=2.0.0"
+jupyter-server = ">=2.4.0,<3"
+jupyterlab-server = ">=2.27.1,<3"
+notebook-shim = ">=0.2"
+packaging = "*"
+setuptools = ">=41.1.0"
+tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
+tornado = ">=6.2.0"
+traitlets = "*"
+
+[package.extras]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.11.4)"]
+docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<8.2.0)", "sphinx-copybutton"]
+docs-screenshots = ["altair (==5.5.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.5)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.3.post1)", "matplotlib (==3.10.0)", "nbconvert (>=7.0.0)", "pandas (==2.2.3)", "scipy (==1.15.1)", "vega-datasets (==0.9.0)"]
+test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
+upgrade-extension = ["copier (>=9,<10)", "jinja2-time (<0.3)", "pydantic (<3.0)", "pyyaml-include (<3.0)", "tomli-w (<2.0)"]
+
+[[package]]
+name = "jupyterlab-pygments"
+version = "0.3.0"
+description = "Pygments theme using JupyterLab CSS variables"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780"},
+    {file = "jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d"},
+]
+
+[[package]]
+name = "jupyterlab-server"
+version = "2.28.0"
+description = "A set of server components for JupyterLab and JupyterLab like applications."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "jupyterlab_server-2.28.0-py3-none-any.whl", hash = "sha256:e4355b148fdcf34d312bbbc80f22467d6d20460e8b8736bf235577dd18506968"},
+    {file = "jupyterlab_server-2.28.0.tar.gz", hash = "sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c"},
+]
+
+[package.dependencies]
+babel = ">=2.10"
+jinja2 = ">=3.0.3"
+json5 = ">=0.9.0"
+jsonschema = ">=4.18.0"
+jupyter-server = ">=1.21,<3"
+packaging = ">=21.3"
+requests = ">=2.31"
+
+[package.extras]
+docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
+openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0,<8)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+
+[[package]]
+name = "jupyterlab-widgets"
+version = "3.0.16"
+description = "Jupyter interactive widgets for JupyterLab"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "jupyterlab_widgets-3.0.16-py3-none-any.whl", hash = "sha256:45fa36d9c6422cf2559198e4db481aa243c7a32d9926b500781c830c80f7ecf8"},
+    {file = "jupyterlab_widgets-3.0.16.tar.gz", hash = "sha256:423da05071d55cf27a9e602216d35a3a65a3e41cdf9c5d3b643b814ce38c19e0"},
+]
+
+[[package]]
+name = "kiwisolver"
+version = "1.4.9"
+description = "A fast implementation of the Cassowary constraint solver"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "kiwisolver-1.4.9-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b4b4d74bda2b8ebf4da5bd42af11d02d04428b2c32846e4c2c93219df8a7987b"},
+    {file = "kiwisolver-1.4.9-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:fb3b8132019ea572f4611d770991000d7f58127560c4889729248eb5852a102f"},
+    {file = "kiwisolver-1.4.9-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:84fd60810829c27ae375114cd379da1fa65e6918e1da405f356a775d49a62bcf"},
+    {file = "kiwisolver-1.4.9-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:b78efa4c6e804ecdf727e580dbb9cba85624d2e1c6b5cb059c66290063bd99a9"},
+    {file = "kiwisolver-1.4.9-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d4efec7bcf21671db6a3294ff301d2fc861c31faa3c8740d1a94689234d1b415"},
+    {file = "kiwisolver-1.4.9-cp310-cp310-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:90f47e70293fc3688b71271100a1a5453aa9944a81d27ff779c108372cf5567b"},
+    {file = "kiwisolver-1.4.9-cp310-cp310-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:8fdca1def57a2e88ef339de1737a1449d6dbf5fab184c54a1fca01d541317154"},
+    {file = "kiwisolver-1.4.9-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:9cf554f21be770f5111a1690d42313e140355e687e05cf82cb23d0a721a64a48"},
+    {file = "kiwisolver-1.4.9-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:fc1795ac5cd0510207482c3d1d3ed781143383b8cfd36f5c645f3897ce066220"},
+    {file = "kiwisolver-1.4.9-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:ccd09f20ccdbbd341b21a67ab50a119b64a403b09288c27481575105283c1586"},
+    {file = "kiwisolver-1.4.9-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:540c7c72324d864406a009d72f5d6856f49693db95d1fbb46cf86febef873634"},
+    {file = "kiwisolver-1.4.9-cp310-cp310-win_amd64.whl", hash = "sha256:ede8c6d533bc6601a47ad4046080d36b8fc99f81e6f1c17b0ac3c2dc91ac7611"},
+    {file = "kiwisolver-1.4.9-cp310-cp310-win_arm64.whl", hash = "sha256:7b4da0d01ac866a57dd61ac258c5607b4cd677f63abaec7b148354d2b2cdd536"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:eb14a5da6dc7642b0f3a18f13654847cd8b7a2550e2645a5bda677862b03ba16"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:39a219e1c81ae3b103643d2aedb90f1ef22650deb266ff12a19e7773f3e5f089"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2405a7d98604b87f3fc28b1716783534b1b4b8510d8142adca34ee0bc3c87543"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:dc1ae486f9abcef254b5618dfb4113dd49f94c68e3e027d03cf0143f3f772b61"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a1f570ce4d62d718dce3f179ee78dac3b545ac16c0c04bb363b7607a949c0d1"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:cb27e7b78d716c591e88e0a09a2139c6577865d7f2e152488c2cc6257f460872"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:15163165efc2f627eb9687ea5f3a28137217d217ac4024893d753f46bce9de26"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:bdee92c56a71d2b24c33a7d4c2856bd6419d017e08caa7802d2963870e315028"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:412f287c55a6f54b0650bd9b6dce5aceddb95864a1a90c87af16979d37c89771"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:2c93f00dcba2eea70af2be5f11a830a742fe6b579a1d4e00f47760ef13be247a"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f117e1a089d9411663a3207ba874f31be9ac8eaa5b533787024dc07aeb74f464"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-win_amd64.whl", hash = "sha256:be6a04e6c79819c9a8c2373317d19a96048e5a3f90bec587787e86a1153883c2"},
+    {file = "kiwisolver-1.4.9-cp311-cp311-win_arm64.whl", hash = "sha256:0ae37737256ba2de764ddc12aed4956460277f00c4996d51a197e72f62f5eec7"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:ac5a486ac389dddcc5bef4f365b6ae3ffff2c433324fb38dd35e3fab7c957999"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f2ba92255faa7309d06fe44c3a4a97efe1c8d640c2a79a5ef728b685762a6fd2"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4a2899935e724dd1074cb568ce7ac0dce28b2cd6ab539c8e001a8578eb106d14"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f6008a4919fdbc0b0097089f67a1eb55d950ed7e90ce2cc3e640abadd2757a04"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:67bb8b474b4181770f926f7b7d2f8c0248cbcb78b660fdd41a47054b28d2a752"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2327a4a30d3ee07d2fbe2e7933e8a37c591663b96ce42a00bc67461a87d7df77"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7a08b491ec91b1d5053ac177afe5290adacf1f0f6307d771ccac5de30592d198"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:d8fc5c867c22b828001b6a38d2eaeb88160bf5783c6cb4a5e440efc981ce286d"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:3b3115b2581ea35bb6d1f24a4c90af37e5d9b49dcff267eeed14c3893c5b86ab"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:858e4c22fb075920b96a291928cb7dea5644e94c0ee4fcd5af7e865655e4ccf2"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:ed0fecd28cc62c54b262e3736f8bb2512d8dcfdc2bcf08be5f47f96bf405b145"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-win_amd64.whl", hash = "sha256:f68208a520c3d86ea51acf688a3e3002615a7f0238002cccc17affecc86a8a54"},
+    {file = "kiwisolver-1.4.9-cp312-cp312-win_arm64.whl", hash = "sha256:2c1a4f57df73965f3f14df20b80ee29e6a7930a57d2d9e8491a25f676e197c60"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a5d0432ccf1c7ab14f9949eec60c5d1f924f17c037e9f8b33352fa05799359b8"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:efb3a45b35622bb6c16dbfab491a8f5a391fe0e9d45ef32f4df85658232ca0e2"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1a12cf6398e8a0a001a059747a1cbf24705e18fe413bc22de7b3d15c67cffe3f"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b67e6efbf68e077dd71d1a6b37e43e1a99d0bff1a3d51867d45ee8908b931098"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5656aa670507437af0207645273ccdfee4f14bacd7f7c67a4306d0dcaeaf6eed"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:bfc08add558155345129c7803b3671cf195e6a56e7a12f3dde7c57d9b417f525"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:40092754720b174e6ccf9e845d0d8c7d8e12c3d71e7fc35f55f3813e96376f78"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:497d05f29a1300d14e02e6441cf0f5ee81c1ff5a304b0d9fb77423974684e08b"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:bdd1a81a1860476eb41ac4bc1e07b3f07259e6d55bbf739b79c8aaedcf512799"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:e6b93f13371d341afee3be9f7c5964e3fe61d5fa30f6a30eb49856935dfe4fc3"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d75aa530ccfaa593da12834b86a0724f58bff12706659baa9227c2ccaa06264c"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-win_amd64.whl", hash = "sha256:dd0a578400839256df88c16abddf9ba14813ec5f21362e1fe65022e00c883d4d"},
+    {file = "kiwisolver-1.4.9-cp313-cp313-win_arm64.whl", hash = "sha256:d4188e73af84ca82468f09cadc5ac4db578109e52acb4518d8154698d3a87ca2"},
+    {file = "kiwisolver-1.4.9-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:5a0f2724dfd4e3b3ac5a82436a8e6fd16baa7d507117e4279b660fe8ca38a3a1"},
+    {file = "kiwisolver-1.4.9-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:1b11d6a633e4ed84fc0ddafd4ebfd8ea49b3f25082c04ad12b8315c11d504dc1"},
+    {file = "kiwisolver-1.4.9-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:61874cdb0a36016354853593cffc38e56fc9ca5aa97d2c05d3dcf6922cd55a11"},
+    {file = "kiwisolver-1.4.9-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:60c439763a969a6af93b4881db0eed8fadf93ee98e18cbc35bc8da868d0c4f0c"},
+    {file = "kiwisolver-1.4.9-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92a2f997387a1b79a75e7803aa7ded2cfbe2823852ccf1ba3bcf613b62ae3197"},
+    {file = "kiwisolver-1.4.9-cp313-cp313t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a31d512c812daea6d8b3be3b2bfcbeb091dbb09177706569bcfc6240dcf8b41c"},
+    {file = "kiwisolver-1.4.9-cp313-cp313t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:52a15b0f35dad39862d376df10c5230155243a2c1a436e39eb55623ccbd68185"},
+    {file = "kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:a30fd6fdef1430fd9e1ba7b3398b5ee4e2887783917a687d86ba69985fb08748"},
+    {file = "kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:cc9617b46837c6468197b5945e196ee9ca43057bb7d9d1ae688101e4e1dddf64"},
+    {file = "kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:0ab74e19f6a2b027ea4f845a78827969af45ce790e6cb3e1ebab71bdf9f215ff"},
+    {file = "kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dba5ee5d3981160c28d5490f0d1b7ed730c22470ff7f6cc26cfcfaacb9896a07"},
+    {file = "kiwisolver-1.4.9-cp313-cp313t-win_arm64.whl", hash = "sha256:0749fd8f4218ad2e851e11cc4dc05c7cbc0cbc4267bdfdb31782e65aace4ee9c"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:9928fe1eb816d11ae170885a74d074f57af3a0d65777ca47e9aeb854a1fba386"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:d0005b053977e7b43388ddec89fa567f43d4f6d5c2c0affe57de5ebf290dc552"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:2635d352d67458b66fd0667c14cb1d4145e9560d503219034a18a87e971ce4f3"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:767c23ad1c58c9e827b649a9ab7809fd5fd9db266a9cf02b0e926ddc2c680d58"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:72d0eb9fba308b8311685c2268cf7d0a0639a6cd027d8128659f72bdd8a024b4"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f68e4f3eeca8fb22cc3d731f9715a13b652795ef657a13df1ad0c7dc0e9731df"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d84cd4061ae292d8ac367b2c3fa3aad11cb8625a95d135fe93f286f914f3f5a6"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:a60ea74330b91bd22a29638940d115df9dc00af5035a9a2a6ad9399ffb4ceca5"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:ce6a3a4e106cf35c2d9c4fa17c05ce0b180db622736845d4315519397a77beaf"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:77937e5e2a38a7b48eef0585114fe7930346993a88060d0bf886086d2aa49ef5"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:24c175051354f4a28c5d6a31c93906dc653e2bf234e8a4bbfb964892078898ce"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-win_amd64.whl", hash = "sha256:0763515d4df10edf6d06a3c19734e2566368980d21ebec439f33f9eb936c07b7"},
+    {file = "kiwisolver-1.4.9-cp314-cp314-win_arm64.whl", hash = "sha256:0e4e2bf29574a6a7b7f6cb5fa69293b9f96c928949ac4a53ba3f525dffb87f9c"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:d976bbb382b202f71c67f77b0ac11244021cfa3f7dfd9e562eefcea2df711548"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:2489e4e5d7ef9a1c300a5e0196e43d9c739f066ef23270607d45aba368b91f2d"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:e2ea9f7ab7fbf18fffb1b5434ce7c69a07582f7acc7717720f1d69f3e806f90c"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b34e51affded8faee0dfdb705416153819d8ea9250bbbf7ea1b249bdeb5f1122"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d8aacd3d4b33b772542b2e01beb50187536967b514b00003bdda7589722d2a64"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7cf974dd4e35fa315563ac99d6287a1024e4dc2077b8a7d7cd3d2fb65d283134"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:85bd218b5ecfbee8c8a82e121802dcb519a86044c9c3b2e4aef02fa05c6da370"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:0856e241c2d3df4efef7c04a1e46b1936b6120c9bcf36dd216e3acd84bc4fb21"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:9af39d6551f97d31a4deebeac6f45b156f9755ddc59c07b402c148f5dbb6482a"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:bb4ae2b57fc1d8cbd1cf7b1d9913803681ffa903e7488012be5b76dedf49297f"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:aedff62918805fb62d43a4aa2ecd4482c380dc76cd31bd7c8878588a61bd0369"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-win_amd64.whl", hash = "sha256:1fa333e8b2ce4d9660f2cda9c0e1b6bafcfb2457a9d259faa82289e73ec24891"},
+    {file = "kiwisolver-1.4.9-cp314-cp314t-win_arm64.whl", hash = "sha256:4a48a2ce79d65d363597ef7b567ce3d14d68783d2b2263d98db3d9477805ba32"},
+    {file = "kiwisolver-1.4.9-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:4d1d9e582ad4d63062d34077a9a1e9f3c34088a2ec5135b1f7190c07cf366527"},
+    {file = "kiwisolver-1.4.9-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:deed0c7258ceb4c44ad5ec7d9918f9f14fd05b2be86378d86cf50e63d1e7b771"},
+    {file = "kiwisolver-1.4.9-pp310-pypy310_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0a590506f303f512dff6b7f75fd2fd18e16943efee932008fe7140e5fa91d80e"},
+    {file = "kiwisolver-1.4.9-pp310-pypy310_pp73-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e09c2279a4d01f099f52d5c4b3d9e208e91edcbd1a175c9662a8b16e000fece9"},
+    {file = "kiwisolver-1.4.9-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:c9e7cdf45d594ee04d5be1b24dd9d49f3d1590959b2271fb30b5ca2b262c00fb"},
+    {file = "kiwisolver-1.4.9-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:720e05574713db64c356e86732c0f3c5252818d05f9df320f0ad8380641acea5"},
+    {file = "kiwisolver-1.4.9-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:17680d737d5335b552994a2008fab4c851bcd7de33094a82067ef3a576ff02fa"},
+    {file = "kiwisolver-1.4.9-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:85b5352f94e490c028926ea567fc569c52ec79ce131dadb968d3853e809518c2"},
+    {file = "kiwisolver-1.4.9-pp311-pypy311_pp73-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:464415881e4801295659462c49461a24fb107c140de781d55518c4b80cb6790f"},
+    {file = "kiwisolver-1.4.9-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:fb940820c63a9590d31d88b815e7a3aa5915cad3ce735ab45f0c730b39547de1"},
+    {file = "kiwisolver-1.4.9.tar.gz", hash = "sha256:c3b22c26c6fd6811b0ae8363b95ca8ce4ea3c202d3d0975b2914310ceb1bcc4d"},
+]
+
+[[package]]
+name = "lark"
+version = "1.3.1"
+description = "a modern parsing library"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "lark-1.3.1-py3-none-any.whl", hash = "sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12"},
+    {file = "lark-1.3.1.tar.gz", hash = "sha256:b426a7a6d6d53189d318f2b6236ab5d6429eaf09259f1ca33eb716eed10d2905"},
+]
+
+[package.extras]
+atomic-cache = ["atomicwrites"]
+interegular = ["interegular (>=0.3.1,<0.4.0)"]
+nearley = ["js2py"]
+regex = ["regex"]
+
+[[package]]
+name = "lightning-utilities"
+version = "0.15.2"
+description = "Lightning toolbox for across the our ecosystem."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "lightning_utilities-0.15.2-py3-none-any.whl", hash = "sha256:ad3ab1703775044bbf880dbf7ddaaac899396c96315f3aa1779cec9d618a9841"},
+    {file = "lightning_utilities-0.15.2.tar.gz", hash = "sha256:cdf12f530214a63dacefd713f180d1ecf5d165338101617b4742e8f22c032e24"},
+]
+
+[package.dependencies]
+packaging = ">=17.1"
+setuptools = "*"
+typing_extensions = "*"
+
+[package.extras]
+cli = ["jsonargparse[signatures] (>=4.38.0)", "tomlkit"]
+docs = ["requests (>=2.0.0)"]
+typing = ["mypy (>=1.0.0)", "types-setuptools"]
+
+[[package]]
+name = "lion-pytorch"
+version = "0.2.3"
+description = "Lion Optimizer - Pytorch"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "lion_pytorch-0.2.3-py3-none-any.whl", hash = "sha256:a1f0cb6ddb46c1f5e130b985d2759c33c178195ef88b216621cb4177c6284f81"},
+    {file = "lion_pytorch-0.2.3.tar.gz", hash = "sha256:42ba117ce857e9dd6c67c727e22e575671fd72e441900af137b05e7ee5c8fd88"},
+]
+
+[package.dependencies]
+torch = ">=1.6"
+
+[[package]]
+name = "mako"
+version = "1.3.10"
+description = "A super-fast templating language that borrows the best ideas from the existing templating languages."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "mako-1.3.10-py3-none-any.whl", hash = "sha256:baef24a52fc4fc514a0887ac600f9f1cff3d82c61d4d700a1fa84d597b88db59"},
+    {file = "mako-1.3.10.tar.gz", hash = "sha256:99579a6f39583fa7e5630a28c3c1f440e4e97a414b80372649c0ce338da2ea28"},
+]
+
+[package.dependencies]
+MarkupSafe = ">=0.9.2"
+
+[package.extras]
+babel = ["Babel"]
+lingua = ["lingua"]
+testing = ["pytest"]
+
+[[package]]
+name = "markdown"
+version = "3.10"
+description = "Python implementation of John Gruber's Markdown."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "markdown-3.10-py3-none-any.whl", hash = "sha256:b5b99d6951e2e4948d939255596523444c0e677c669700b1d17aa4a8a464cb7c"},
+    {file = "markdown-3.10.tar.gz", hash = "sha256:37062d4f2aa4b2b6b32aefb80faa300f82cc790cb949a35b8caede34f2b68c0e"},
+]
+
+[package.extras]
+docs = ["mdx_gh_links (>=0.2)", "mkdocs (>=1.6)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
+testing = ["coverage", "pyyaml"]
+
+[[package]]
+name = "markupsafe"
+version = "3.0.3"
+description = "Safely add untrusted strings to HTML/XML markup."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "markupsafe-3.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2f981d352f04553a7171b8e44369f2af4055f888dfb147d55e42d29e29e74559"},
+    {file = "markupsafe-3.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e1c1493fb6e50ab01d20a22826e57520f1284df32f2d8601fdd90b6304601419"},
+    {file = "markupsafe-3.0.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1ba88449deb3de88bd40044603fafffb7bc2b055d626a330323a9ed736661695"},
+    {file = "markupsafe-3.0.3-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f42d0984e947b8adf7dd6dde396e720934d12c506ce84eea8476409563607591"},
+    {file = "markupsafe-3.0.3-cp310-cp310-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c0c0b3ade1c0b13b936d7970b1d37a57acde9199dc2aecc4c336773e1d86049c"},
+    {file = "markupsafe-3.0.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:0303439a41979d9e74d18ff5e2dd8c43ed6c6001fd40e5bf2e43f7bd9bbc523f"},
+    {file = "markupsafe-3.0.3-cp310-cp310-musllinux_1_2_riscv64.whl", hash = "sha256:d2ee202e79d8ed691ceebae8e0486bd9a2cd4794cec4824e1c99b6f5009502f6"},
+    {file = "markupsafe-3.0.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:177b5253b2834fe3678cb4a5f0059808258584c559193998be2601324fdeafb1"},
+    {file = "markupsafe-3.0.3-cp310-cp310-win32.whl", hash = "sha256:2a15a08b17dd94c53a1da0438822d70ebcd13f8c3a95abe3a9ef9f11a94830aa"},
+    {file = "markupsafe-3.0.3-cp310-cp310-win_amd64.whl", hash = "sha256:c4ffb7ebf07cfe8931028e3e4c85f0357459a3f9f9490886198848f4fa002ec8"},
+    {file = "markupsafe-3.0.3-cp310-cp310-win_arm64.whl", hash = "sha256:e2103a929dfa2fcaf9bb4e7c091983a49c9ac3b19c9061b6d5427dd7d14d81a1"},
+    {file = "markupsafe-3.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad"},
+    {file = "markupsafe-3.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4bd4cd07944443f5a265608cc6aab442e4f74dff8088b0dfc8238647b8f6ae9a"},
+    {file = "markupsafe-3.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50"},
+    {file = "markupsafe-3.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf"},
+    {file = "markupsafe-3.0.3-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc51efed119bc9cfdf792cdeaa4d67e8f6fcccab66ed4bfdd6bde3e59bfcbb2f"},
+    {file = "markupsafe-3.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a"},
+    {file = "markupsafe-3.0.3-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115"},
+    {file = "markupsafe-3.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a"},
+    {file = "markupsafe-3.0.3-cp311-cp311-win32.whl", hash = "sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19"},
+    {file = "markupsafe-3.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01"},
+    {file = "markupsafe-3.0.3-cp311-cp311-win_arm64.whl", hash = "sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c"},
+    {file = "markupsafe-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e"},
+    {file = "markupsafe-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce"},
+    {file = "markupsafe-3.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d"},
+    {file = "markupsafe-3.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d"},
+    {file = "markupsafe-3.0.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a"},
+    {file = "markupsafe-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b"},
+    {file = "markupsafe-3.0.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f"},
+    {file = "markupsafe-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b"},
+    {file = "markupsafe-3.0.3-cp312-cp312-win32.whl", hash = "sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d"},
+    {file = "markupsafe-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c"},
+    {file = "markupsafe-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f"},
+    {file = "markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795"},
+    {file = "markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219"},
+    {file = "markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6"},
+    {file = "markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676"},
+    {file = "markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9"},
+    {file = "markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1"},
+    {file = "markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc"},
+    {file = "markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12"},
+    {file = "markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed"},
+    {file = "markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5"},
+    {file = "markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485"},
+    {file = "markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73"},
+    {file = "markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37"},
+    {file = "markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19"},
+    {file = "markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025"},
+    {file = "markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6"},
+    {file = "markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f"},
+    {file = "markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb"},
+    {file = "markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009"},
+    {file = "markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354"},
+    {file = "markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218"},
+    {file = "markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287"},
+    {file = "markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe"},
+    {file = "markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026"},
+    {file = "markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737"},
+    {file = "markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97"},
+    {file = "markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d"},
+    {file = "markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda"},
+    {file = "markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf"},
+    {file = "markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe"},
+    {file = "markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9"},
+    {file = "markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581"},
+    {file = "markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4"},
+    {file = "markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab"},
+    {file = "markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175"},
+    {file = "markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634"},
+    {file = "markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50"},
+    {file = "markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e"},
+    {file = "markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5"},
+    {file = "markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523"},
+    {file = "markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc"},
+    {file = "markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d"},
+    {file = "markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9"},
+    {file = "markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa"},
+    {file = "markupsafe-3.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:15d939a21d546304880945ca1ecb8a039db6b4dc49b2c5a400387cdae6a62e26"},
+    {file = "markupsafe-3.0.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f71a396b3bf33ecaa1626c255855702aca4d3d9fea5e051b41ac59a9c1c41edc"},
+    {file = "markupsafe-3.0.3-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0f4b68347f8c5eab4a13419215bdfd7f8c9b19f2b25520968adfad23eb0ce60c"},
+    {file = "markupsafe-3.0.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e8fc20152abba6b83724d7ff268c249fa196d8259ff481f3b1476383f8f24e42"},
+    {file = "markupsafe-3.0.3-cp39-cp39-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:949b8d66bc381ee8b007cd945914c721d9aba8e27f71959d750a46f7c282b20b"},
+    {file = "markupsafe-3.0.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:3537e01efc9d4dccdf77221fb1cb3b8e1a38d5428920e0657ce299b20324d758"},
+    {file = "markupsafe-3.0.3-cp39-cp39-musllinux_1_2_riscv64.whl", hash = "sha256:591ae9f2a647529ca990bc681daebdd52c8791ff06c2bfa05b65163e28102ef2"},
+    {file = "markupsafe-3.0.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:a320721ab5a1aba0a233739394eb907f8c8da5c98c9181d1161e77a0c8e36f2d"},
+    {file = "markupsafe-3.0.3-cp39-cp39-win32.whl", hash = "sha256:df2449253ef108a379b8b5d6b43f4b1a8e81a061d6537becd5582fba5f9196d7"},
+    {file = "markupsafe-3.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:7c3fb7d25180895632e5d3148dbdc29ea38ccb7fd210aa27acbd1201a1902c6e"},
+    {file = "markupsafe-3.0.3-cp39-cp39-win_arm64.whl", hash = "sha256:38664109c14ffc9e7437e86b4dceb442b0096dfe3541d7864d9cbe1da4cf36c8"},
+    {file = "markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698"},
+]
+
+[[package]]
+name = "matplotlib"
+version = "3.10.7"
+description = "Python plotting package"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "matplotlib-3.10.7-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:7ac81eee3b7c266dd92cee1cd658407b16c57eed08c7421fa354ed68234de380"},
+    {file = "matplotlib-3.10.7-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:667ecd5d8d37813a845053d8f5bf110b534c3c9f30e69ebd25d4701385935a6d"},
+    {file = "matplotlib-3.10.7-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cc1c51b846aca49a5a8b44fbba6a92d583a35c64590ad9e1e950dc88940a4297"},
+    {file = "matplotlib-3.10.7-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4a11c2e9e72e7de09b7b72e62f3df23317c888299c875e2b778abf1eda8c0a42"},
+    {file = "matplotlib-3.10.7-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:f19410b486fdd139885ace124e57f938c1e6a3210ea13dd29cab58f5d4bc12c7"},
+    {file = "matplotlib-3.10.7-cp310-cp310-win_amd64.whl", hash = "sha256:b498e9e4022f93de2d5a37615200ca01297ceebbb56fe4c833f46862a490f9e3"},
+    {file = "matplotlib-3.10.7-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:53b492410a6cd66c7a471de6c924f6ede976e963c0f3097a3b7abfadddc67d0a"},
+    {file = "matplotlib-3.10.7-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d9749313deb729f08207718d29c86246beb2ea3fdba753595b55901dee5d2fd6"},
+    {file = "matplotlib-3.10.7-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2222c7ba2cbde7fe63032769f6eb7e83ab3227f47d997a8453377709b7fe3a5a"},
+    {file = "matplotlib-3.10.7-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e91f61a064c92c307c5a9dc8c05dc9f8a68f0a3be199d9a002a0622e13f874a1"},
+    {file = "matplotlib-3.10.7-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:6f1851eab59ca082c95df5a500106bad73672645625e04538b3ad0f69471ffcc"},
+    {file = "matplotlib-3.10.7-cp311-cp311-win_amd64.whl", hash = "sha256:6516ce375109c60ceec579e699524e9d504cd7578506f01150f7a6bc174a775e"},
+    {file = "matplotlib-3.10.7-cp311-cp311-win_arm64.whl", hash = "sha256:b172db79759f5f9bc13ef1c3ef8b9ee7b37b0247f987fbbbdaa15e4f87fd46a9"},
+    {file = "matplotlib-3.10.7-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7a0edb7209e21840e8361e91ea84ea676658aa93edd5f8762793dec77a4a6748"},
+    {file = "matplotlib-3.10.7-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c380371d3c23e0eadf8ebff114445b9f970aff2010198d498d4ab4c3b41eea4f"},
+    {file = "matplotlib-3.10.7-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d5f256d49fea31f40f166a5e3131235a5d2f4b7f44520b1cf0baf1ce568ccff0"},
+    {file = "matplotlib-3.10.7-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:11ae579ac83cdf3fb72573bb89f70e0534de05266728740d478f0f818983c695"},
+    {file = "matplotlib-3.10.7-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:4c14b6acd16cddc3569a2d515cfdd81c7a68ac5639b76548cfc1a9e48b20eb65"},
+    {file = "matplotlib-3.10.7-cp312-cp312-win_amd64.whl", hash = "sha256:0d8c32b7ea6fb80b1aeff5a2ceb3fb9778e2759e899d9beff75584714afcc5ee"},
+    {file = "matplotlib-3.10.7-cp312-cp312-win_arm64.whl", hash = "sha256:5f3f6d315dcc176ba7ca6e74c7768fb7e4cf566c49cb143f6bc257b62e634ed8"},
+    {file = "matplotlib-3.10.7-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:1d9d3713a237970569156cfb4de7533b7c4eacdd61789726f444f96a0d28f57f"},
+    {file = "matplotlib-3.10.7-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:37a1fea41153dd6ee061d21ab69c9cf2cf543160b1b85d89cd3d2e2a7902ca4c"},
+    {file = "matplotlib-3.10.7-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b3c4ea4948d93c9c29dc01c0c23eef66f2101bf75158c291b88de6525c55c3d1"},
+    {file = "matplotlib-3.10.7-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22df30ffaa89f6643206cf13877191c63a50e8f800b038bc39bee9d2d4957632"},
+    {file = "matplotlib-3.10.7-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b69676845a0a66f9da30e87f48be36734d6748024b525ec4710be40194282c84"},
+    {file = "matplotlib-3.10.7-cp313-cp313-win_amd64.whl", hash = "sha256:744991e0cc863dd669c8dc9136ca4e6e0082be2070b9d793cbd64bec872a6815"},
+    {file = "matplotlib-3.10.7-cp313-cp313-win_arm64.whl", hash = "sha256:fba2974df0bf8ce3c995fa84b79cde38326e0f7b5409e7a3a481c1141340bcf7"},
+    {file = "matplotlib-3.10.7-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:932c55d1fa7af4423422cb6a492a31cbcbdbe68fd1a9a3f545aa5e7a143b5355"},
+    {file = "matplotlib-3.10.7-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5e38c2d581d62ee729a6e144c47a71b3f42fb4187508dbbf4fe71d5612c3433b"},
+    {file = "matplotlib-3.10.7-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:786656bb13c237bbcebcd402f65f44dd61ead60ee3deb045af429d889c8dbc67"},
+    {file = "matplotlib-3.10.7-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:09d7945a70ea43bf9248f4b6582734c2fe726723204a76eca233f24cffc7ef67"},
+    {file = "matplotlib-3.10.7-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:d0b181e9fa8daf1d9f2d4c547527b167cb8838fc587deabca7b5c01f97199e84"},
+    {file = "matplotlib-3.10.7-cp313-cp313t-win_amd64.whl", hash = "sha256:31963603041634ce1a96053047b40961f7a29eb8f9a62e80cc2c0427aa1d22a2"},
+    {file = "matplotlib-3.10.7-cp313-cp313t-win_arm64.whl", hash = "sha256:aebed7b50aa6ac698c90f60f854b47e48cd2252b30510e7a1feddaf5a3f72cbf"},
+    {file = "matplotlib-3.10.7-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:d883460c43e8c6b173fef244a2341f7f7c0e9725c7fe68306e8e44ed9c8fb100"},
+    {file = "matplotlib-3.10.7-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:07124afcf7a6504eafcb8ce94091c5898bbdd351519a1beb5c45f7a38c67e77f"},
+    {file = "matplotlib-3.10.7-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c17398b709a6cce3d9fdb1595c33e356d91c098cd9486cb2cc21ea2ea418e715"},
+    {file = "matplotlib-3.10.7-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7146d64f561498764561e9cd0ed64fcf582e570fc519e6f521e2d0cfd43365e1"},
+    {file = "matplotlib-3.10.7-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:90ad854c0a435da3104c01e2c6f0028d7e719b690998a2333d7218db80950722"},
+    {file = "matplotlib-3.10.7-cp314-cp314-win_amd64.whl", hash = "sha256:4645fc5d9d20ffa3a39361fcdbcec731382763b623b72627806bf251b6388866"},
+    {file = "matplotlib-3.10.7-cp314-cp314-win_arm64.whl", hash = "sha256:9257be2f2a03415f9105c486d304a321168e61ad450f6153d77c69504ad764bb"},
+    {file = "matplotlib-3.10.7-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1e4bbad66c177a8fdfa53972e5ef8be72a5f27e6a607cec0d8579abd0f3102b1"},
+    {file = "matplotlib-3.10.7-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:d8eb7194b084b12feb19142262165832fc6ee879b945491d1c3d4660748020c4"},
+    {file = "matplotlib-3.10.7-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b4d41379b05528091f00e1728004f9a8d7191260f3862178b88e8fd770206318"},
+    {file = "matplotlib-3.10.7-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4a74f79fafb2e177f240579bc83f0b60f82cc47d2f1d260f422a0627207008ca"},
+    {file = "matplotlib-3.10.7-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:702590829c30aada1e8cef0568ddbffa77ca747b4d6e36c6d173f66e301f89cc"},
+    {file = "matplotlib-3.10.7-cp314-cp314t-win_amd64.whl", hash = "sha256:f79d5de970fc90cd5591f60053aecfce1fcd736e0303d9f0bf86be649fa68fb8"},
+    {file = "matplotlib-3.10.7-cp314-cp314t-win_arm64.whl", hash = "sha256:cb783436e47fcf82064baca52ce748af71725d0352e1d31564cbe9c95df92b9c"},
+    {file = "matplotlib-3.10.7-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:5c09cf8f2793f81368f49f118b6f9f937456362bee282eac575cca7f84cda537"},
+    {file = "matplotlib-3.10.7-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:de66744b2bb88d5cd27e80dfc2ec9f0517d0a46d204ff98fe9e5f2864eb67657"},
+    {file = "matplotlib-3.10.7-pp310-pypy310_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:53cc80662dd197ece414dd5b66e07370201515a3eaf52e7c518c68c16814773b"},
+    {file = "matplotlib-3.10.7-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:15112bcbaef211bd663fa935ec33313b948e214454d949b723998a43357b17b0"},
+    {file = "matplotlib-3.10.7-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:d2a959c640cdeecdd2ec3136e8ea0441da59bcaf58d67e9c590740addba2cb68"},
+    {file = "matplotlib-3.10.7-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3886e47f64611046bc1db523a09dd0a0a6bed6081e6f90e13806dd1d1d1b5e91"},
+    {file = "matplotlib-3.10.7.tar.gz", hash = "sha256:a06ba7e2a2ef9131c79c49e63dad355d2d878413a0376c1727c8b9335ff731c7"},
+]
+
+[package.dependencies]
+contourpy = ">=1.0.1"
+cycler = ">=0.10"
+fonttools = ">=4.22.0"
+kiwisolver = ">=1.3.1"
+numpy = ">=1.23"
+packaging = ">=20.0"
+pillow = ">=8"
+pyparsing = ">=3"
+python-dateutil = ">=2.7"
+
+[package.extras]
+dev = ["meson-python (>=0.13.1,<0.17.0)", "pybind11 (>=2.13.2,!=2.13.3)", "setuptools (>=64)", "setuptools_scm (>=7)"]
+
+[[package]]
+name = "matplotlib-inline"
+version = "0.2.1"
+description = "Inline Matplotlib backend for Jupyter"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "matplotlib_inline-0.2.1-py3-none-any.whl", hash = "sha256:d56ce5156ba6085e00a9d54fead6ed29a9c47e215cd1bba2e976ef39f5710a76"},
+    {file = "matplotlib_inline-0.2.1.tar.gz", hash = "sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe"},
+]
+
+[package.dependencies]
+traitlets = "*"
+
+[package.extras]
+test = ["flake8", "nbdime", "nbval", "notebook", "pytest"]
+
+[[package]]
+name = "mistune"
+version = "3.1.4"
+description = "A sane and fast Markdown parser with useful plugins and renderers"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "mistune-3.1.4-py3-none-any.whl", hash = "sha256:93691da911e5d9d2e23bc54472892aff676df27a75274962ff9edc210364266d"},
+    {file = "mistune-3.1.4.tar.gz", hash = "sha256:b5a7f801d389f724ec702840c11d8fc48f2b33519102fc7ee739e8177b672164"},
+]
+
+[package.dependencies]
+typing-extensions = {version = "*", markers = "python_version < \"3.11\""}
+
+[[package]]
+name = "ml-dtypes"
+version = "0.5.3"
+description = "ml_dtypes is a stand-alone implementation of several NumPy dtype extensions used in machine learning."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "ml_dtypes-0.5.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0a1d68a7cb53e3f640b2b6a34d12c0542da3dd935e560fdf463c0c77f339fc20"},
+    {file = "ml_dtypes-0.5.3-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0cd5a6c711b5350f3cbc2ac28def81cd1c580075ccb7955e61e9d8f4bfd40d24"},
+    {file = "ml_dtypes-0.5.3-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bdcf26c2dbc926b8a35ec8cbfad7eff1a8bd8239e12478caca83a1fc2c400dc2"},
+    {file = "ml_dtypes-0.5.3-cp310-cp310-win_amd64.whl", hash = "sha256:aecbd7c5272c82e54d5b99d8435fd10915d1bc704b7df15e4d9ca8dc3902be61"},
+    {file = "ml_dtypes-0.5.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:4a177b882667c69422402df6ed5c3428ce07ac2c1f844d8a1314944651439458"},
+    {file = "ml_dtypes-0.5.3-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9849ce7267444c0a717c80c6900997de4f36e2815ce34ac560a3edb2d9a64cd2"},
+    {file = "ml_dtypes-0.5.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c3f5ae0309d9f888fd825c2e9d0241102fadaca81d888f26f845bc8c13c1e4ee"},
+    {file = "ml_dtypes-0.5.3-cp311-cp311-win_amd64.whl", hash = "sha256:58e39349d820b5702bb6f94ea0cb2dc8ec62ee81c0267d9622067d8333596a46"},
+    {file = "ml_dtypes-0.5.3-cp311-cp311-win_arm64.whl", hash = "sha256:66c2756ae6cfd7f5224e355c893cfd617fa2f747b8bbd8996152cbdebad9a184"},
+    {file = "ml_dtypes-0.5.3-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:156418abeeda48ea4797db6776db3c5bdab9ac7be197c1233771e0880c304057"},
+    {file = "ml_dtypes-0.5.3-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1db60c154989af253f6c4a34e8a540c2c9dce4d770784d426945e09908fbb177"},
+    {file = "ml_dtypes-0.5.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1b255acada256d1fa8c35ed07b5f6d18bc21d1556f842fbc2d5718aea2cd9e55"},
+    {file = "ml_dtypes-0.5.3-cp312-cp312-win_amd64.whl", hash = "sha256:da65e5fd3eea434ccb8984c3624bc234ddcc0d9f4c81864af611aaebcc08a50e"},
+    {file = "ml_dtypes-0.5.3-cp312-cp312-win_arm64.whl", hash = "sha256:8bb9cd1ce63096567f5f42851f5843b5a0ea11511e50039a7649619abfb4ba6d"},
+    {file = "ml_dtypes-0.5.3-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:5103856a225465371fe119f2fef737402b705b810bd95ad5f348e6e1a6ae21af"},
+    {file = "ml_dtypes-0.5.3-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4cae435a68861660af81fa3c5af16b70ca11a17275c5b662d9c6f58294e0f113"},
+    {file = "ml_dtypes-0.5.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6936283b56d74fbec431ca57ce58a90a908fdbd14d4e2d22eea6d72bb208a7b7"},
+    {file = "ml_dtypes-0.5.3-cp313-cp313-win_amd64.whl", hash = "sha256:d0f730a17cf4f343b2c7ad50cee3bd19e969e793d2be6ed911f43086460096e4"},
+    {file = "ml_dtypes-0.5.3-cp313-cp313-win_arm64.whl", hash = "sha256:2db74788fc01914a3c7f7da0763427280adfc9cd377e9604b6b64eb8097284bd"},
+    {file = "ml_dtypes-0.5.3-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:93c36a08a6d158db44f2eb9ce3258e53f24a9a4a695325a689494f0fdbc71770"},
+    {file = "ml_dtypes-0.5.3-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0e44a3761f64bc009d71ddb6d6c71008ba21b53ab6ee588dadab65e2fa79eafc"},
+    {file = "ml_dtypes-0.5.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bdf40d2aaabd3913dec11840f0d0ebb1b93134f99af6a0a4fd88ffe924928ab4"},
+    {file = "ml_dtypes-0.5.3-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:aec640bd94c4c85c0d11e2733bd13cbb10438fb004852996ec0efbc6cacdaf70"},
+    {file = "ml_dtypes-0.5.3-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bda32ce212baa724e03c68771e5c69f39e584ea426bfe1a701cb01508ffc7035"},
+    {file = "ml_dtypes-0.5.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c205cac07d24a29840c163d6469f61069ce4b065518519216297fc2f261f8db9"},
+    {file = "ml_dtypes-0.5.3-cp314-cp314-win_amd64.whl", hash = "sha256:cd7c0bb22d4ff86d65ad61b5dd246812e8993fbc95b558553624c33e8b6903ea"},
+    {file = "ml_dtypes-0.5.3-cp314-cp314-win_arm64.whl", hash = "sha256:9d55ea7f7baf2aed61bf1872116cefc9d0c3693b45cae3916897ee27ef4b835e"},
+    {file = "ml_dtypes-0.5.3-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:e12e29764a0e66a7a31e9b8bf1de5cc0423ea72979f45909acd4292de834ccd3"},
+    {file = "ml_dtypes-0.5.3-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:19f6c3a4f635c2fc9e2aa7d91416bd7a3d649b48350c51f7f715a09370a90d93"},
+    {file = "ml_dtypes-0.5.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ab039ffb40f3dc0aeeeba84fd6c3452781b5e15bef72e2d10bcb33e4bbffc39"},
+    {file = "ml_dtypes-0.5.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:5ee72568d46b9533ad54f78b1e1f3067c0534c5065120ea8ecc6f210d22748b3"},
+    {file = "ml_dtypes-0.5.3-cp39-cp39-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:01de48de4537dc3c46e684b969a40ec36594e7eeb7c69e9a093e7239f030a28a"},
+    {file = "ml_dtypes-0.5.3-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8b1a6e231b0770f2894910f1dce6d2f31d65884dbf7668f9b08d73623cdca909"},
+    {file = "ml_dtypes-0.5.3-cp39-cp39-win_amd64.whl", hash = "sha256:a4f39b9bf6555fab9bfb536cf5fdd1c1c727e8d22312078702e9ff005354b37f"},
+    {file = "ml_dtypes-0.5.3.tar.gz", hash = "sha256:95ce33057ba4d05df50b1f3cfefab22e351868a843b3b15a46c65836283670c9"},
+]
+
+[package.dependencies]
+numpy = [
+    {version = ">=1.21.2", markers = "python_version >= \"3.10\""},
+    {version = ">=1.23.3", markers = "python_version >= \"3.11\""},
+    {version = ">=2.1.0", markers = "python_version >= \"3.13\""},
+    {version = ">=1.26.0", markers = "python_version == \"3.12\""},
+]
+
+[package.extras]
+dev = ["absl-py", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-xdist"]
+
+[[package]]
+name = "mpmath"
+version = "1.3.0"
+description = "Python library for arbitrary-precision floating-point arithmetic"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
+    {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
+]
+
+[package.extras]
+develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
+docs = ["sphinx"]
+gmpy = ["gmpy2 (>=2.1.0a4) ; platform_python_implementation != \"PyPy\""]
+tests = ["pytest (>=4.6)"]
+
+[[package]]
+name = "multidict"
+version = "6.7.0"
+description = "multidict implementation"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "multidict-6.7.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:9f474ad5acda359c8758c8accc22032c6abe6dc87a8be2440d097785e27a9349"},
+    {file = "multidict-6.7.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:4b7a9db5a870f780220e931d0002bbfd88fb53aceb6293251e2c839415c1b20e"},
+    {file = "multidict-6.7.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:03ca744319864e92721195fa28c7a3b2bc7b686246b35e4078c1e4d0eb5466d3"},
+    {file = "multidict-6.7.0-cp310-cp310-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:f0e77e3c0008bc9316e662624535b88d360c3a5d3f81e15cf12c139a75250046"},
+    {file = "multidict-6.7.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:08325c9e5367aa379a3496aa9a022fe8837ff22e00b94db256d3a1378c76ab32"},
+    {file = "multidict-6.7.0-cp310-cp310-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e2862408c99f84aa571ab462d25236ef9cb12a602ea959ba9c9009a54902fc73"},
+    {file = "multidict-6.7.0-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4d72a9a2d885f5c208b0cb91ff2ed43636bb7e345ec839ff64708e04f69a13cc"},
+    {file = "multidict-6.7.0-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:478cc36476687bac1514d651cbbaa94b86b0732fb6855c60c673794c7dd2da62"},
+    {file = "multidict-6.7.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6843b28b0364dc605f21481c90fadb5f60d9123b442eb8a726bb74feef588a84"},
+    {file = "multidict-6.7.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:23bfeee5316266e5ee2d625df2d2c602b829435fc3a235c2ba2131495706e4a0"},
+    {file = "multidict-6.7.0-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:680878b9f3d45c31e1f730eef731f9b0bc1da456155688c6745ee84eb818e90e"},
+    {file = "multidict-6.7.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:eb866162ef2f45063acc7a53a88ef6fe8bf121d45c30ea3c9cd87ce7e191a8d4"},
+    {file = "multidict-6.7.0-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:df0e3bf7993bdbeca5ac25aa859cf40d39019e015c9c91809ba7093967f7a648"},
+    {file = "multidict-6.7.0-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:661709cdcd919a2ece2234f9bae7174e5220c80b034585d7d8a755632d3e2111"},
+    {file = "multidict-6.7.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:096f52730c3fb8ed419db2d44391932b63891b2c5ed14850a7e215c0ba9ade36"},
+    {file = "multidict-6.7.0-cp310-cp310-win32.whl", hash = "sha256:afa8a2978ec65d2336305550535c9c4ff50ee527914328c8677b3973ade52b85"},
+    {file = "multidict-6.7.0-cp310-cp310-win_amd64.whl", hash = "sha256:b15b3afff74f707b9275d5ba6a91ae8f6429c3ffb29bbfd216b0b375a56f13d7"},
+    {file = "multidict-6.7.0-cp310-cp310-win_arm64.whl", hash = "sha256:4b73189894398d59131a66ff157837b1fafea9974be486d036bb3d32331fdbf0"},
+    {file = "multidict-6.7.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:4d409aa42a94c0b3fa617708ef5276dfe81012ba6753a0370fcc9d0195d0a1fc"},
+    {file = "multidict-6.7.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:14c9e076eede3b54c636f8ce1c9c252b5f057c62131211f0ceeec273810c9721"},
+    {file = "multidict-6.7.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4c09703000a9d0fa3c3404b27041e574cc7f4df4c6563873246d0e11812a94b6"},
+    {file = "multidict-6.7.0-cp311-cp311-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:a265acbb7bb33a3a2d626afbe756371dce0279e7b17f4f4eda406459c2b5ff1c"},
+    {file = "multidict-6.7.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:51cb455de290ae462593e5b1cb1118c5c22ea7f0d3620d9940bf695cea5a4bd7"},
+    {file = "multidict-6.7.0-cp311-cp311-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:db99677b4457c7a5c5a949353e125ba72d62b35f74e26da141530fbb012218a7"},
+    {file = "multidict-6.7.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f470f68adc395e0183b92a2f4689264d1ea4b40504a24d9882c27375e6662bb9"},
+    {file = "multidict-6.7.0-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0db4956f82723cc1c270de9c6e799b4c341d327762ec78ef82bb962f79cc07d8"},
+    {file = "multidict-6.7.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3e56d780c238f9e1ae66a22d2adf8d16f485381878250db8d496623cd38b22bd"},
+    {file = "multidict-6.7.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:9d14baca2ee12c1a64740d4531356ba50b82543017f3ad6de0deb943c5979abb"},
+    {file = "multidict-6.7.0-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:295a92a76188917c7f99cda95858c822f9e4aae5824246bba9b6b44004ddd0a6"},
+    {file = "multidict-6.7.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:39f1719f57adbb767ef592a50ae5ebb794220d1188f9ca93de471336401c34d2"},
+    {file = "multidict-6.7.0-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:0a13fb8e748dfc94749f622de065dd5c1def7e0d2216dba72b1d8069a389c6ff"},
+    {file = "multidict-6.7.0-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:e3aa16de190d29a0ea1b48253c57d99a68492c8dd8948638073ab9e74dc9410b"},
+    {file = "multidict-6.7.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a048ce45dcdaaf1defb76b2e684f997fb5abf74437b6cb7b22ddad934a964e34"},
+    {file = "multidict-6.7.0-cp311-cp311-win32.whl", hash = "sha256:a90af66facec4cebe4181b9e62a68be65e45ac9b52b67de9eec118701856e7ff"},
+    {file = "multidict-6.7.0-cp311-cp311-win_amd64.whl", hash = "sha256:95b5ffa4349df2887518bb839409bcf22caa72d82beec453216802f475b23c81"},
+    {file = "multidict-6.7.0-cp311-cp311-win_arm64.whl", hash = "sha256:329aa225b085b6f004a4955271a7ba9f1087e39dcb7e65f6284a988264a63912"},
+    {file = "multidict-6.7.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:8a3862568a36d26e650a19bb5cbbba14b71789032aebc0423f8cc5f150730184"},
+    {file = "multidict-6.7.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:960c60b5849b9b4f9dcc9bea6e3626143c252c74113df2c1540aebce70209b45"},
+    {file = "multidict-6.7.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2049be98fb57a31b4ccf870bf377af2504d4ae35646a19037ec271e4c07998aa"},
+    {file = "multidict-6.7.0-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:0934f3843a1860dd465d38895c17fce1f1cb37295149ab05cd1b9a03afacb2a7"},
+    {file = "multidict-6.7.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b3e34f3a1b8131ba06f1a73adab24f30934d148afcd5f5de9a73565a4404384e"},
+    {file = "multidict-6.7.0-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:efbb54e98446892590dc2458c19c10344ee9a883a79b5cec4bc34d6656e8d546"},
+    {file = "multidict-6.7.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a35c5fc61d4f51eb045061e7967cfe3123d622cd500e8868e7c0c592a09fedc4"},
+    {file = "multidict-6.7.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:29fe6740ebccba4175af1b9b87bf553e9c15cd5868ee967e010efcf94e4fd0f1"},
+    {file = "multidict-6.7.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:123e2a72e20537add2f33a79e605f6191fba2afda4cbb876e35c1a7074298a7d"},
+    {file = "multidict-6.7.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:b284e319754366c1aee2267a2036248b24eeb17ecd5dc16022095e747f2f4304"},
+    {file = "multidict-6.7.0-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:803d685de7be4303b5a657b76e2f6d1240e7e0a8aa2968ad5811fa2285553a12"},
+    {file = "multidict-6.7.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:c04a328260dfd5db8c39538f999f02779012268f54614902d0afc775d44e0a62"},
+    {file = "multidict-6.7.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:8a19cdb57cd3df4cd865849d93ee14920fb97224300c88501f16ecfa2604b4e0"},
+    {file = "multidict-6.7.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:9b2fd74c52accced7e75de26023b7dccee62511a600e62311b918ec5c168fc2a"},
+    {file = "multidict-6.7.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3e8bfdd0e487acf992407a140d2589fe598238eaeffa3da8448d63a63cd363f8"},
+    {file = "multidict-6.7.0-cp312-cp312-win32.whl", hash = "sha256:dd32a49400a2c3d52088e120ee00c1e3576cbff7e10b98467962c74fdb762ed4"},
+    {file = "multidict-6.7.0-cp312-cp312-win_amd64.whl", hash = "sha256:92abb658ef2d7ef22ac9f8bb88e8b6c3e571671534e029359b6d9e845923eb1b"},
+    {file = "multidict-6.7.0-cp312-cp312-win_arm64.whl", hash = "sha256:490dab541a6a642ce1a9d61a4781656b346a55c13038f0b1244653828e3a83ec"},
+    {file = "multidict-6.7.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:bee7c0588aa0076ce77c0ea5d19a68d76ad81fcd9fe8501003b9a24f9d4000f6"},
+    {file = "multidict-6.7.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:7ef6b61cad77091056ce0e7ce69814ef72afacb150b7ac6a3e9470def2198159"},
+    {file = "multidict-6.7.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:9c0359b1ec12b1d6849c59f9d319610b7f20ef990a6d454ab151aa0e3b9f78ca"},
+    {file = "multidict-6.7.0-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:cd240939f71c64bd658f186330603aac1a9a81bf6273f523fca63673cb7378a8"},
+    {file = "multidict-6.7.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a60a4d75718a5efa473ebd5ab685786ba0c67b8381f781d1be14da49f1a2dc60"},
+    {file = "multidict-6.7.0-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:53a42d364f323275126aff81fb67c5ca1b7a04fda0546245730a55c8c5f24bc4"},
+    {file = "multidict-6.7.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3b29b980d0ddbecb736735ee5bef69bb2ddca56eff603c86f3f29a1128299b4f"},
+    {file = "multidict-6.7.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f8a93b1c0ed2d04b97a5e9336fd2d33371b9a6e29ab7dd6503d63407c20ffbaf"},
+    {file = "multidict-6.7.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9ff96e8815eecacc6645da76c413eb3b3d34cfca256c70b16b286a687d013c32"},
+    {file = "multidict-6.7.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:7516c579652f6a6be0e266aec0acd0db80829ca305c3d771ed898538804c2036"},
+    {file = "multidict-6.7.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:040f393368e63fb0f3330e70c26bfd336656bed925e5cbe17c9da839a6ab13ec"},
+    {file = "multidict-6.7.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:b3bc26a951007b1057a1c543af845f1c7e3e71cc240ed1ace7bf4484aa99196e"},
+    {file = "multidict-6.7.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:7b022717c748dd1992a83e219587aabe45980d88969f01b316e78683e6285f64"},
+    {file = "multidict-6.7.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:9600082733859f00d79dee64effc7aef1beb26adb297416a4ad2116fd61374bd"},
+    {file = "multidict-6.7.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:94218fcec4d72bc61df51c198d098ce2b378e0ccbac41ddbed5ef44092913288"},
+    {file = "multidict-6.7.0-cp313-cp313-win32.whl", hash = "sha256:a37bd74c3fa9d00be2d7b8eca074dc56bd8077ddd2917a839bd989612671ed17"},
+    {file = "multidict-6.7.0-cp313-cp313-win_amd64.whl", hash = "sha256:30d193c6cc6d559db42b6bcec8a5d395d34d60c9877a0b71ecd7c204fcf15390"},
+    {file = "multidict-6.7.0-cp313-cp313-win_arm64.whl", hash = "sha256:ea3334cabe4d41b7ccd01e4d349828678794edbc2d3ae97fc162a3312095092e"},
+    {file = "multidict-6.7.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:ad9ce259f50abd98a1ca0aa6e490b58c316a0fce0617f609723e40804add2c00"},
+    {file = "multidict-6.7.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:07f5594ac6d084cbb5de2df218d78baf55ef150b91f0ff8a21cc7a2e3a5a58eb"},
+    {file = "multidict-6.7.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:0591b48acf279821a579282444814a2d8d0af624ae0bc600aa4d1b920b6e924b"},
+    {file = "multidict-6.7.0-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:749a72584761531d2b9467cfbdfd29487ee21124c304c4b6cb760d8777b27f9c"},
+    {file = "multidict-6.7.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b4c3d199f953acd5b446bf7c0de1fe25d94e09e79086f8dc2f48a11a129cdf1"},
+    {file = "multidict-6.7.0-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:9fb0211dfc3b51efea2f349ec92c114d7754dd62c01f81c3e32b765b70c45c9b"},
+    {file = "multidict-6.7.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a027ec240fe73a8d6281872690b988eed307cd7d91b23998ff35ff577ca688b5"},
+    {file = "multidict-6.7.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d1d964afecdf3a8288789df2f5751dc0a8261138c3768d9af117ed384e538fad"},
+    {file = "multidict-6.7.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:caf53b15b1b7df9fbd0709aa01409000a2b4dd03a5f6f5cc548183c7c8f8b63c"},
+    {file = "multidict-6.7.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:654030da3197d927f05a536a66186070e98765aa5142794c9904555d3a9d8fb5"},
+    {file = "multidict-6.7.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:2090d3718829d1e484706a2f525e50c892237b2bf9b17a79b059cb98cddc2f10"},
+    {file = "multidict-6.7.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:2d2cfeec3f6f45651b3d408c4acec0ebf3daa9bc8a112a084206f5db5d05b754"},
+    {file = "multidict-6.7.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:4ef089f985b8c194d341eb2c24ae6e7408c9a0e2e5658699c92f497437d88c3c"},
+    {file = "multidict-6.7.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:e93a0617cd16998784bf4414c7e40f17a35d2350e5c6f0bd900d3a8e02bd3762"},
+    {file = "multidict-6.7.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f0feece2ef8ebc42ed9e2e8c78fc4aa3cf455733b507c09ef7406364c94376c6"},
+    {file = "multidict-6.7.0-cp313-cp313t-win32.whl", hash = "sha256:19a1d55338ec1be74ef62440ca9e04a2f001a04d0cc49a4983dc320ff0f3212d"},
+    {file = "multidict-6.7.0-cp313-cp313t-win_amd64.whl", hash = "sha256:3da4fb467498df97e986af166b12d01f05d2e04f978a9c1c680ea1988e0bc4b6"},
+    {file = "multidict-6.7.0-cp313-cp313t-win_arm64.whl", hash = "sha256:b4121773c49a0776461f4a904cdf6264c88e42218aaa8407e803ca8025872792"},
+    {file = "multidict-6.7.0-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:3bab1e4aff7adaa34410f93b1f8e57c4b36b9af0426a76003f441ee1d3c7e842"},
+    {file = "multidict-6.7.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:b8512bac933afc3e45fb2b18da8e59b78d4f408399a960339598374d4ae3b56b"},
+    {file = "multidict-6.7.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:79dcf9e477bc65414ebfea98ffd013cb39552b5ecd62908752e0e413d6d06e38"},
+    {file = "multidict-6.7.0-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:31bae522710064b5cbeddaf2e9f32b1abab70ac6ac91d42572502299e9953128"},
+    {file = "multidict-6.7.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4a0df7ff02397bb63e2fd22af2c87dfa39e8c7f12947bc524dbdc528282c7e34"},
+    {file = "multidict-6.7.0-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:7a0222514e8e4c514660e182d5156a415c13ef0aabbd71682fc714e327b95e99"},
+    {file = "multidict-6.7.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2397ab4daaf2698eb51a76721e98db21ce4f52339e535725de03ea962b5a3202"},
+    {file = "multidict-6.7.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:8891681594162635948a636c9fe0ff21746aeb3dd5463f6e25d9bea3a8a39ca1"},
+    {file = "multidict-6.7.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:18706cc31dbf402a7945916dd5cddf160251b6dab8a2c5f3d6d5a55949f676b3"},
+    {file = "multidict-6.7.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:f844a1bbf1d207dd311a56f383f7eda2d0e134921d45751842d8235e7778965d"},
+    {file = "multidict-6.7.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:d4393e3581e84e5645506923816b9cc81f5609a778c7e7534054091acc64d1c6"},
+    {file = "multidict-6.7.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:fbd18dc82d7bf274b37aa48d664534330af744e03bccf696d6f4c6042e7d19e7"},
+    {file = "multidict-6.7.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:b6234e14f9314731ec45c42fc4554b88133ad53a09092cc48a88e771c125dadb"},
+    {file = "multidict-6.7.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:08d4379f9744d8f78d98c8673c06e202ffa88296f009c71bbafe8a6bf847d01f"},
+    {file = "multidict-6.7.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:9fe04da3f79387f450fd0061d4dd2e45a72749d31bf634aecc9e27f24fdc4b3f"},
+    {file = "multidict-6.7.0-cp314-cp314-win32.whl", hash = "sha256:fbafe31d191dfa7c4c51f7a6149c9fb7e914dcf9ffead27dcfd9f1ae382b3885"},
+    {file = "multidict-6.7.0-cp314-cp314-win_amd64.whl", hash = "sha256:2f67396ec0310764b9222a1728ced1ab638f61aadc6226f17a71dd9324f9a99c"},
+    {file = "multidict-6.7.0-cp314-cp314-win_arm64.whl", hash = "sha256:ba672b26069957ee369cfa7fc180dde1fc6f176eaf1e6beaf61fbebbd3d9c000"},
+    {file = "multidict-6.7.0-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:c1dcc7524066fa918c6a27d61444d4ee7900ec635779058571f70d042d86ed63"},
+    {file = "multidict-6.7.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:27e0b36c2d388dc7b6ced3406671b401e84ad7eb0656b8f3a2f46ed0ce483718"},
+    {file = "multidict-6.7.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2a7baa46a22e77f0988e3b23d4ede5513ebec1929e34ee9495be535662c0dfe2"},
+    {file = "multidict-6.7.0-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:7bf77f54997a9166a2f5675d1201520586439424c2511723a7312bdb4bcc034e"},
+    {file = "multidict-6.7.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e011555abada53f1578d63389610ac8a5400fc70ce71156b0aa30d326f1a5064"},
+    {file = "multidict-6.7.0-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:28b37063541b897fd6a318007373930a75ca6d6ac7c940dbe14731ffdd8d498e"},
+    {file = "multidict-6.7.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:05047ada7a2fde2631a0ed706f1fd68b169a681dfe5e4cf0f8e4cb6618bbc2cd"},
+    {file = "multidict-6.7.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:716133f7d1d946a4e1b91b1756b23c088881e70ff180c24e864c26192ad7534a"},
+    {file = "multidict-6.7.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d1bed1b467ef657f2a0ae62844a607909ef1c6889562de5e1d505f74457d0b96"},
+    {file = "multidict-6.7.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:ca43bdfa5d37bd6aee89d85e1d0831fb86e25541be7e9d376ead1b28974f8e5e"},
+    {file = "multidict-6.7.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:44b546bd3eb645fd26fb949e43c02a25a2e632e2ca21a35e2e132c8105dc8599"},
+    {file = "multidict-6.7.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:a6ef16328011d3f468e7ebc326f24c1445f001ca1dec335b2f8e66bed3006394"},
+    {file = "multidict-6.7.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:5aa873cbc8e593d361ae65c68f85faadd755c3295ea2c12040ee146802f23b38"},
+    {file = "multidict-6.7.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:3d7b6ccce016e29df4b7ca819659f516f0bc7a4b3efa3bb2012ba06431b044f9"},
+    {file = "multidict-6.7.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:171b73bd4ee683d307599b66793ac80981b06f069b62eea1c9e29c9241aa66b0"},
+    {file = "multidict-6.7.0-cp314-cp314t-win32.whl", hash = "sha256:b2d7f80c4e1fd010b07cb26820aae86b7e73b681ee4889684fb8d2d4537aab13"},
+    {file = "multidict-6.7.0-cp314-cp314t-win_amd64.whl", hash = "sha256:09929cab6fcb68122776d575e03c6cc64ee0b8fca48d17e135474b042ce515cd"},
+    {file = "multidict-6.7.0-cp314-cp314t-win_arm64.whl", hash = "sha256:cc41db090ed742f32bd2d2c721861725e6109681eddf835d0a82bd3a5c382827"},
+    {file = "multidict-6.7.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:363eb68a0a59bd2303216d2346e6c441ba10d36d1f9969fcb6f1ba700de7bb5c"},
+    {file = "multidict-6.7.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:d874eb056410ca05fed180b6642e680373688efafc7f077b2a2f61811e873a40"},
+    {file = "multidict-6.7.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:8b55d5497b51afdfde55925e04a022f1de14d4f4f25cdfd4f5d9b0aa96166851"},
+    {file = "multidict-6.7.0-cp39-cp39-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:f8e5c0031b90ca9ce555e2e8fd5c3b02a25f14989cbc310701823832c99eb687"},
+    {file = "multidict-6.7.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9cf41880c991716f3c7cec48e2f19ae4045fc9db5fc9cff27347ada24d710bb5"},
+    {file = "multidict-6.7.0-cp39-cp39-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:8cfc12a8630a29d601f48d47787bd7eb730e475e83edb5d6c5084317463373eb"},
+    {file = "multidict-6.7.0-cp39-cp39-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3996b50c3237c4aec17459217c1e7bbdead9a22a0fcd3c365564fbd16439dde6"},
+    {file = "multidict-6.7.0-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7f5170993a0dd3ab871c74f45c0a21a4e2c37a2f2b01b5f722a2ad9c6650469e"},
+    {file = "multidict-6.7.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ec81878ddf0e98817def1e77d4f50dae5ef5b0e4fe796fae3bd674304172416e"},
+    {file = "multidict-6.7.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:9281bf5b34f59afbc6b1e477a372e9526b66ca446f4bf62592839c195a718b32"},
+    {file = "multidict-6.7.0-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:68af405971779d8b37198726f2b6fe3955db846fee42db7a4286fc542203934c"},
+    {file = "multidict-6.7.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:3ba3ef510467abb0667421a286dc906e30eb08569365f5cdb131d7aff7c2dd84"},
+    {file = "multidict-6.7.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:b61189b29081a20c7e4e0b49b44d5d44bb0dc92be3c6d06a11cc043f81bf9329"},
+    {file = "multidict-6.7.0-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:fb287618b9c7aa3bf8d825f02d9201b2f13078a5ed3b293c8f4d953917d84d5e"},
+    {file = "multidict-6.7.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:521f33e377ff64b96c4c556b81c55d0cfffb96a11c194fd0c3f1e56f3d8dd5a4"},
+    {file = "multidict-6.7.0-cp39-cp39-win32.whl", hash = "sha256:ce8fdc2dca699f8dbf055a61d73eaa10482569ad20ee3c36ef9641f69afa8c91"},
+    {file = "multidict-6.7.0-cp39-cp39-win_amd64.whl", hash = "sha256:7e73299c99939f089dd9b2120a04a516b95cdf8c1cd2b18c53ebf0de80b1f18f"},
+    {file = "multidict-6.7.0-cp39-cp39-win_arm64.whl", hash = "sha256:6bdce131e14b04fd34a809b6380dbfd826065c3e2fe8a50dbae659fa0c390546"},
+    {file = "multidict-6.7.0-py3-none-any.whl", hash = "sha256:394fc5c42a333c9ffc3e421a4c85e08580d990e08b99f6bf35b4132114c5dcb3"},
+    {file = "multidict-6.7.0.tar.gz", hash = "sha256:c6e99d9a65ca282e578dfea819cfa9c0a62b2499d8677392e09feaf305e9e6f5"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
+
+[[package]]
+name = "multiprocess"
+version = "0.70.18"
+description = "better multiprocessing and multithreading in Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "multiprocess-0.70.18-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:25d4012dcaaf66b9e8e955f58482b42910c2ee526d532844d8bcf661bbc604df"},
+    {file = "multiprocess-0.70.18-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:06b19433de0d02afe5869aec8931dd5c01d99074664f806c73896b0d9e527213"},
+    {file = "multiprocess-0.70.18-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:6fa1366f994373aaf2d4738b0f56e707caeaa05486e97a7f71ee0853823180c2"},
+    {file = "multiprocess-0.70.18-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:8b8940ae30139e04b076da6c5b83e9398585ebdf0f2ad3250673fef5b2ff06d6"},
+    {file = "multiprocess-0.70.18-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:0929ba95831adb938edbd5fb801ac45e705ecad9d100b3e653946b7716cb6bd3"},
+    {file = "multiprocess-0.70.18-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:4d77f8e4bfe6c6e2e661925bbf9aed4d5ade9a1c6502d5dfc10129b9d1141797"},
+    {file = "multiprocess-0.70.18-pp38-pypy38_pp73-macosx_10_9_arm64.whl", hash = "sha256:2dbaae9bffa1fb2d58077c0044ffe87a8c8974e90fcf778cdf90e139c970d42a"},
+    {file = "multiprocess-0.70.18-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:bcac5a4e81f1554d98d1bba963eeb1bd24966432f04fcbd29b6e1a16251ad712"},
+    {file = "multiprocess-0.70.18-pp38-pypy38_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:c0c7cd75d0987ab6166d64e654787c781dbacbcbcaaede4c1ffe664720b3e14b"},
+    {file = "multiprocess-0.70.18-pp39-pypy39_pp73-macosx_10_13_arm64.whl", hash = "sha256:9fd8d662f7524a95a1be7cbea271f0b33089fe792baabec17d93103d368907da"},
+    {file = "multiprocess-0.70.18-pp39-pypy39_pp73-macosx_10_13_x86_64.whl", hash = "sha256:3fbba48bfcd932747c33f0b152b26207c4e0840c35cab359afaff7a8672b1031"},
+    {file = "multiprocess-0.70.18-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:5f9be0342e597dde86152c10442c5fb6c07994b1c29de441b7a3a08b0e6be2a0"},
+    {file = "multiprocess-0.70.18-py310-none-any.whl", hash = "sha256:60c194974c31784019c1f459d984e8f33ee48f10fcf42c309ba97b30d9bd53ea"},
+    {file = "multiprocess-0.70.18-py311-none-any.whl", hash = "sha256:5aa6eef98e691281b3ad923be2832bf1c55dd2c859acd73e5ec53a66aae06a1d"},
+    {file = "multiprocess-0.70.18-py312-none-any.whl", hash = "sha256:9b78f8e5024b573730bfb654783a13800c2c0f2dfc0c25e70b40d184d64adaa2"},
+    {file = "multiprocess-0.70.18-py313-none-any.whl", hash = "sha256:871743755f43ef57d7910a38433cfe41319e72be1bbd90b79c7a5ac523eb9334"},
+    {file = "multiprocess-0.70.18-py38-none-any.whl", hash = "sha256:dbf705e52a154fe5e90fb17b38f02556169557c2dd8bb084f2e06c2784d8279b"},
+    {file = "multiprocess-0.70.18-py39-none-any.whl", hash = "sha256:e78ca805a72b1b810c690b6b4cc32579eba34f403094bbbae962b7b5bf9dfcb8"},
+    {file = "multiprocess-0.70.18.tar.gz", hash = "sha256:f9597128e6b3e67b23956da07cf3d2e5cba79e2f4e0fba8d7903636663ec6d0d"},
+]
+
+[package.dependencies]
+dill = ">=0.4.0"
+
+[[package]]
+name = "nbclient"
+version = "0.10.2"
+description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
+optional = false
+python-versions = ">=3.9.0"
+groups = ["main"]
+files = [
+    {file = "nbclient-0.10.2-py3-none-any.whl", hash = "sha256:4ffee11e788b4a27fabeb7955547e4318a5298f34342a4bfd01f2e1faaeadc3d"},
+    {file = "nbclient-0.10.2.tar.gz", hash = "sha256:90b7fc6b810630db87a6d0c2250b1f0ab4cf4d3c27a299b0cde78a4ed3fd9193"},
+]
+
+[package.dependencies]
+jupyter-client = ">=6.1.12"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+nbformat = ">=5.1"
+traitlets = ">=5.4"
+
+[package.extras]
+dev = ["pre-commit"]
+docs = ["autodoc-traits", "flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "mock", "moto", "myst-parser", "nbconvert (>=7.1.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling", "testpath", "xmltodict"]
+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.1.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
+
+[[package]]
+name = "nbconvert"
+version = "7.16.6"
+description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b"},
+    {file = "nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582"},
+]
+
+[package.dependencies]
+beautifulsoup4 = "*"
+bleach = {version = "!=5.0.0", extras = ["css"]}
+defusedxml = "*"
+jinja2 = ">=3.0"
+jupyter-core = ">=4.7"
+jupyterlab-pygments = "*"
+markupsafe = ">=2.0"
+mistune = ">=2.0.3,<4"
+nbclient = ">=0.5.0"
+nbformat = ">=5.7"
+packaging = "*"
+pandocfilters = ">=1.4.1"
+pygments = ">=2.4.1"
+traitlets = ">=5.1"
+
+[package.extras]
+all = ["flaky", "ipykernel", "ipython", "ipywidgets (>=7.5)", "myst-parser", "nbsphinx (>=0.2.12)", "playwright", "pydata-sphinx-theme", "pyqtwebengine (>=5.15)", "pytest (>=7)", "sphinx (==5.0.2)", "sphinxcontrib-spelling", "tornado (>=6.1)"]
+docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)", "sphinxcontrib-spelling"]
+qtpdf = ["pyqtwebengine (>=5.15)"]
+qtpng = ["pyqtwebengine (>=5.15)"]
+serve = ["tornado (>=6.1)"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
+webpdf = ["playwright"]
+
+[[package]]
+name = "nbdime"
+version = "4.0.2"
+description = "Diff and merge of Jupyter Notebooks"
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+files = [
+    {file = "nbdime-4.0.2-py3-none-any.whl", hash = "sha256:e5a43aca669c576c66e757071c0e882de05ac305311d79aded99bfb5a3e9419e"},
+    {file = "nbdime-4.0.2.tar.gz", hash = "sha256:d8279f8f4b236c0b253b20d60c4831bb67843ed8dbd6e09f234eb011d36f1bf2"},
+]
+
+[package.dependencies]
+colorama = "*"
+gitpython = "<2.1.4 || >2.1.4,<2.1.5 || >2.1.5,<2.1.6 || >2.1.6"
+jinja2 = ">=2.9"
+jupyter-server = "*"
+jupyter-server-mathjax = ">=0.2.2"
+nbformat = "*"
+pygments = "*"
+requests = "*"
+tornado = "*"
+
+[package.extras]
+docs = ["recommonmark", "sphinx", "sphinx-rtd-theme"]
+test = ["jsonschema", "jupyter-server[test]", "mock", "notebook", "pytest (>=6.0)", "pytest-cov", "pytest-timeout", "pytest-tornado", "requests", "tabulate"]
+
+[[package]]
+name = "nbformat"
+version = "5.10.4"
+description = "The Jupyter Notebook format"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b"},
+    {file = "nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a"},
+]
+
+[package.dependencies]
+fastjsonschema = ">=2.15"
+jsonschema = ">=2.6"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+traitlets = ">=5.1"
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["pep440", "pre-commit", "pytest", "testpath"]
+
+[[package]]
+name = "nest-asyncio"
+version = "1.6.0"
+description = "Patch asyncio to allow nested event loops"
+optional = false
+python-versions = ">=3.5"
+groups = ["main"]
+files = [
+    {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"},
+    {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
+]
+
+[[package]]
+name = "networkx"
+version = "3.4.2"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "python_version == \"3.10\""
+files = [
+    {file = "networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f"},
+    {file = "networkx-3.4.2.tar.gz", hash = "sha256:307c3669428c5362aab27c8a1260aa8f47c4e91d3891f48be0141738d8d053e1"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.7)", "numpy (>=1.24)", "pandas (>=2.0)", "scipy (>=1.10,!=1.11.0,!=1.11.1)"]
+developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
+doc = ["intersphinx-registry", "myst-nb (>=1.1)", "numpydoc (>=1.8.0)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.15)", "sphinx (>=7.3)", "sphinx-gallery (>=0.16)", "texext (>=0.6.7)"]
+example = ["cairocffi (>=1.7)", "contextily (>=1.6)", "igraph (>=0.11)", "momepy (>=0.7.2)", "osmnx (>=1.9)", "scikit-learn (>=1.5)", "seaborn (>=0.13)"]
+extra = ["lxml (>=4.6)", "pydot (>=3.0.1)", "pygraphviz (>=1.14)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
+[[package]]
+name = "networkx"
+version = "3.5"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.11"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
+files = [
+    {file = "networkx-3.5-py3-none-any.whl", hash = "sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec"},
+    {file = "networkx-3.5.tar.gz", hash = "sha256:d4c6f9cf81f52d69230866796b82afbccdec3db7ae4fbd1b65ea750feed50037"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.8)", "numpy (>=1.25)", "pandas (>=2.0)", "scipy (>=1.11.2)"]
+developer = ["mypy (>=1.15)", "pre-commit (>=4.1)"]
+doc = ["intersphinx-registry", "myst-nb (>=1.1)", "numpydoc (>=1.8.0)", "pillow (>=10)", "pydata-sphinx-theme (>=0.16)", "sphinx (>=8.0)", "sphinx-gallery (>=0.18)", "texext (>=0.6.7)"]
+example = ["cairocffi (>=1.7)", "contextily (>=1.6)", "igraph (>=0.11)", "momepy (>=0.7.2)", "osmnx (>=2.0.0)", "scikit-learn (>=1.5)", "seaborn (>=0.13)"]
+extra = ["lxml (>=4.6)", "pydot (>=3.0.1)", "pygraphviz (>=1.14)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)", "pytest-xdist (>=3.0)"]
+test-extras = ["pytest-mpl", "pytest-randomly"]
+
+[[package]]
+name = "nltk"
+version = "3.9.2"
+description = "Natural Language Toolkit"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "nltk-3.9.2-py3-none-any.whl", hash = "sha256:1e209d2b3009110635ed9709a67a1a3e33a10f799490fa71cf4bec218c11c88a"},
+    {file = "nltk-3.9.2.tar.gz", hash = "sha256:0f409e9b069ca4177c1903c3e843eef90c7e92992fa4931ae607da6de49e1419"},
+]
+
+[package.dependencies]
+click = "*"
+joblib = "*"
+regex = ">=2021.8.3"
+tqdm = "*"
+
+[package.extras]
+all = ["matplotlib", "numpy", "pyparsing", "python-crfsuite", "requests", "scikit-learn", "scipy", "twython"]
+corenlp = ["requests"]
+machine-learning = ["numpy", "python-crfsuite", "scikit-learn", "scipy"]
+plot = ["matplotlib"]
+tgrep = ["pyparsing"]
+twitter = ["twython"]
+
+[[package]]
+name = "notebook"
+version = "7.4.7"
+description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "notebook-7.4.7-py3-none-any.whl", hash = "sha256:362b7c95527f7dd3c4c84d410b782872fd9c734fb2524c11dd92758527b6eda6"},
+    {file = "notebook-7.4.7.tar.gz", hash = "sha256:3f0a04027dfcee8a876de48fba13ab77ec8c12f72f848a222ed7f5081b9e342a"},
+]
+
+[package.dependencies]
+jupyter-server = ">=2.4.0,<3"
+jupyterlab = ">=4.4.9,<4.5"
+jupyterlab-server = ">=2.27.1,<3"
+notebook-shim = ">=0.2,<0.3"
+tornado = ">=6.2.0"
+
+[package.extras]
+dev = ["hatch", "pre-commit"]
+docs = ["myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["importlib-resources (>=5.0) ; python_version < \"3.10\"", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.27.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
+
+[[package]]
+name = "notebook-shim"
+version = "0.2.4"
+description = "A shim layer for notebook traits and config"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef"},
+    {file = "notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb"},
+]
+
+[package.dependencies]
+jupyter-server = ">=1.8,<3"
+
+[package.extras]
+test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"]
+
+[[package]]
+name = "numexpr"
+version = "2.14.1"
+description = "Fast numerical expression evaluator for NumPy"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "numexpr-2.14.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d0fab3fd06a04f6b86102552b26aa5d85e20ac7d8296c15764c726eeabae6cc8"},
+    {file = "numexpr-2.14.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:64ae5dfd62d74a3ef82fe0b37f80527247f3626171ad82025900f46ffca4b39a"},
+    {file = "numexpr-2.14.1-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:955c92b064f9074d2970cf3138f5e3b965be673b82024962ed526f39bc25a920"},
+    {file = "numexpr-2.14.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:75440c54fc01e130396650fdf307aa9d41a67dc06ddbfb288971b591c13a395b"},
+    {file = "numexpr-2.14.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:dde9fa47ed319e1e1728940a539df3cb78326b7754bc7c6ab3152afc91808f9b"},
+    {file = "numexpr-2.14.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:76db0bc6267e591ab9c4df405ffb533598e4c88239db7338d11ae9e4b368a85a"},
+    {file = "numexpr-2.14.1-cp310-cp310-win32.whl", hash = "sha256:0d1dcbdc4d0374c0d523cee2f94f06b001623cbc1fd163612841017a3495427c"},
+    {file = "numexpr-2.14.1-cp310-cp310-win_amd64.whl", hash = "sha256:823cd82c8e7937981339f634e7a9c6a92cb2d0b9d0a5cf627a5e394fffc05377"},
+    {file = "numexpr-2.14.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:2d03fcb4644a12f70a14d74006f72662824da5b6128bf1bcd10cc3ed80e64c34"},
+    {file = "numexpr-2.14.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2773ee1133f77009a1fc2f34fe236f3d9823779f5f75450e183137d49f00499f"},
+    {file = "numexpr-2.14.1-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ebe4980f9494b9f94d10d2e526edc29e72516698d3bf95670ba79415492212a4"},
+    {file = "numexpr-2.14.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2a381e5e919a745c9503bcefffc1c7f98c972c04ec58fc8e999ed1a929e01ba6"},
+    {file = "numexpr-2.14.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d08856cfc1b440eb1caaa60515235369654321995dd68eb9377577392020f6cb"},
+    {file = "numexpr-2.14.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:03130afa04edf83a7b590d207444f05a00363c9b9ea5d81c0f53b1ea13fad55a"},
+    {file = "numexpr-2.14.1-cp311-cp311-win32.whl", hash = "sha256:db78fa0c9fcbaded3ae7453faf060bd7a18b0dc10299d7fcd02d9362be1213ed"},
+    {file = "numexpr-2.14.1-cp311-cp311-win_amd64.whl", hash = "sha256:e9b2f957798c67a2428be96b04bce85439bed05efe78eb78e4c2ca43737578e7"},
+    {file = "numexpr-2.14.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:91ebae0ab18c799b0e6b8c5a8d11e1fa3848eb4011271d99848b297468a39430"},
+    {file = "numexpr-2.14.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:47041f2f7b9e69498fb311af672ba914a60e6e6d804011caacb17d66f639e659"},
+    {file = "numexpr-2.14.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d686dfb2c1382d9e6e0ee0b7647f943c1886dba3adbf606c625479f35f1956c1"},
+    {file = "numexpr-2.14.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:eee6d4fbbbc368e6cdd0772734d6249128d957b3b8ad47a100789009f4de7083"},
+    {file = "numexpr-2.14.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3a2839efa25f3c8d4133252ea7342d8f81226c7c4dda81f97a57e090b9d87a48"},
+    {file = "numexpr-2.14.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:9f9137f1351b310436662b5dc6f4082a245efa8950c3b0d9008028df92fefb9b"},
+    {file = "numexpr-2.14.1-cp312-cp312-win32.whl", hash = "sha256:36f8d5c1bd1355df93b43d766790f9046cccfc1e32b7c6163f75bcde682cda07"},
+    {file = "numexpr-2.14.1-cp312-cp312-win_amd64.whl", hash = "sha256:fdd886f4b7dbaf167633ee396478f0d0aa58ea2f9e7ccc3c6431019623e8d68f"},
+    {file = "numexpr-2.14.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:09078ba73cffe94745abfbcc2d81ab8b4b4e9d7bfbbde6cac2ee5dbf38eee222"},
+    {file = "numexpr-2.14.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:dce0b5a0447baa7b44bc218ec2d7dcd175b8eee6083605293349c0c1d9b82fb6"},
+    {file = "numexpr-2.14.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:06855053de7a3a8425429bd996e8ae3c50b57637ad3e757e0fa0602a7874be30"},
+    {file = "numexpr-2.14.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:05f9366d23a2e991fd5a8b5e61a17558f028ba86158a4552f8f239b005cdf83c"},
+    {file = "numexpr-2.14.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c5f1b1605695778896534dfc6e130d54a65cd52be7ed2cd0cfee3981fd676bf5"},
+    {file = "numexpr-2.14.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a4ba71db47ea99c659d88ee6233fa77b6dc83392f1d324e0c90ddf617ae3f421"},
+    {file = "numexpr-2.14.1-cp313-cp313-win32.whl", hash = "sha256:638dce8320f4a1483d5ca4fda69f60a70ed7e66be6e68bc23fb9f1a6b78a9e3b"},
+    {file = "numexpr-2.14.1-cp313-cp313-win_amd64.whl", hash = "sha256:9fdcd4735121658a313f878fd31136d1bfc6a5b913219e7274e9fca9f8dac3bb"},
+    {file = "numexpr-2.14.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:557887ad7f5d3c2a40fd7310e50597045a68e66b20a77b3f44d7bc7608523b4b"},
+    {file = "numexpr-2.14.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:af111c8fe6fc55d15e4c7cab11920fc50740d913636d486545b080192cd0ad73"},
+    {file = "numexpr-2.14.1-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:33265294376e7e2ae4d264d75b798a915d2acf37b9dd2b9405e8b04f84d05cfc"},
+    {file = "numexpr-2.14.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:83647d846d3eeeb9a9255311236135286728b398d0d41d35dedb532dca807fe9"},
+    {file = "numexpr-2.14.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:6e575fd3ad41ddf3355d0c7ef6bd0168619dc1779a98fe46693cad5e95d25e6e"},
+    {file = "numexpr-2.14.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:67ea4771029ce818573b1998f5ca416bd255156feea017841b86176a938f7d19"},
+    {file = "numexpr-2.14.1-cp313-cp313t-win32.whl", hash = "sha256:15015d47d3d1487072d58c0e7682ef2eb608321e14099c39d52e2dd689483611"},
+    {file = "numexpr-2.14.1-cp313-cp313t-win_amd64.whl", hash = "sha256:94c711f6d8f17dfb4606842b403699603aa591ab9f6bf23038b488ea9cfb0f09"},
+    {file = "numexpr-2.14.1-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:ede79f7ff06629f599081de644546ce7324f1581c09b0ac174da88a470d39c21"},
+    {file = "numexpr-2.14.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:2eac7a5a2f70b3768c67056445d1ceb4ecd9b853c8eda9563823b551aeaa5082"},
+    {file = "numexpr-2.14.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5aedf38d4c0c19d3cecfe0334c3f4099fb496f54c146223d30fa930084bc8574"},
+    {file = "numexpr-2.14.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:439ec4d57b853792ebe5456e3160312281c3a7071ecac5532ded3278ede614de"},
+    {file = "numexpr-2.14.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e23b87f744e04e302d82ac5e2189ae20a533566aec76a46885376e20b0645bf8"},
+    {file = "numexpr-2.14.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:44f84e0e5af219dbb62a081606156420815890e041b87252fbcea5df55214c4c"},
+    {file = "numexpr-2.14.1-cp314-cp314-win32.whl", hash = "sha256:1f1a5e817c534539351aa75d26088e9e1e0ef1b3a6ab484047618a652ccc4fc3"},
+    {file = "numexpr-2.14.1-cp314-cp314-win_amd64.whl", hash = "sha256:587c41509bc373dfb1fe6086ba55a73147297247bedb6d588cda69169fc412f2"},
+    {file = "numexpr-2.14.1-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:ec368819502b64f190c3f71be14a304780b5935c42aae5bf22c27cc2cbba70b5"},
+    {file = "numexpr-2.14.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7e87f6d203ac57239de32261c941e9748f9309cbc0da6295eabd0c438b920d3a"},
+    {file = "numexpr-2.14.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dd72d8c2a165fe45ea7650b16eb8cc1792a94a722022006bb97c86fe51fd2091"},
+    {file = "numexpr-2.14.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:70d80fcb418a54ca208e9a38e58ddc425c07f66485176b261d9a67c7f2864f73"},
+    {file = "numexpr-2.14.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:edea2f20c2040df8b54ee8ca8ebda63de9545b2112872466118e9df4d0ae99f3"},
+    {file = "numexpr-2.14.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:790447be6879a6c51b9545f79612d24c9ea0a41d537a84e15e6a8ddef0b6268e"},
+    {file = "numexpr-2.14.1-cp314-cp314t-win32.whl", hash = "sha256:538961096c2300ea44240209181e31fae82759d26b51713b589332b9f2a4117e"},
+    {file = "numexpr-2.14.1-cp314-cp314t-win_amd64.whl", hash = "sha256:a40b350cd45b4446076fa11843fa32bbe07024747aeddf6d467290bf9011b392"},
+    {file = "numexpr-2.14.1.tar.gz", hash = "sha256:4be00b1086c7b7a5c32e31558122b7b80243fe098579b170967da83f3152b48b"},
+]
+
+[package.dependencies]
+numpy = ">=1.23.0"
+
+[[package]]
+name = "numpy"
+version = "2.2.6"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "numpy-2.2.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b412caa66f72040e6d268491a59f2c43bf03eb6c96dd8f0307829feb7fa2b6fb"},
+    {file = "numpy-2.2.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8e41fd67c52b86603a91c1a505ebaef50b3314de0213461c7a6e99c9a3beff90"},
+    {file = "numpy-2.2.6-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:37e990a01ae6ec7fe7fa1c26c55ecb672dd98b19c3d0e1d1f326fa13cb38d163"},
+    {file = "numpy-2.2.6-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:5a6429d4be8ca66d889b7cf70f536a397dc45ba6faeb5f8c5427935d9592e9cf"},
+    {file = "numpy-2.2.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:efd28d4e9cd7d7a8d39074a4d44c63eda73401580c5c76acda2ce969e0a38e83"},
+    {file = "numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc7b73d02efb0e18c000e9ad8b83480dfcd5dfd11065997ed4c6747470ae8915"},
+    {file = "numpy-2.2.6-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:74d4531beb257d2c3f4b261bfb0fc09e0f9ebb8842d82a7b4209415896adc680"},
+    {file = "numpy-2.2.6-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:8fc377d995680230e83241d8a96def29f204b5782f371c532579b4f20607a289"},
+    {file = "numpy-2.2.6-cp310-cp310-win32.whl", hash = "sha256:b093dd74e50a8cba3e873868d9e93a85b78e0daf2e98c6797566ad8044e8363d"},
+    {file = "numpy-2.2.6-cp310-cp310-win_amd64.whl", hash = "sha256:f0fd6321b839904e15c46e0d257fdd101dd7f530fe03fd6359c1ea63738703f3"},
+    {file = "numpy-2.2.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f9f1adb22318e121c5c69a09142811a201ef17ab257a1e66ca3025065b7f53ae"},
+    {file = "numpy-2.2.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c820a93b0255bc360f53eca31a0e676fd1101f673dda8da93454a12e23fc5f7a"},
+    {file = "numpy-2.2.6-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:3d70692235e759f260c3d837193090014aebdf026dfd167834bcba43e30c2a42"},
+    {file = "numpy-2.2.6-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:481b49095335f8eed42e39e8041327c05b0f6f4780488f61286ed3c01368d491"},
+    {file = "numpy-2.2.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b64d8d4d17135e00c8e346e0a738deb17e754230d7e0810ac5012750bbd85a5a"},
+    {file = "numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba10f8411898fc418a521833e014a77d3ca01c15b0c6cdcce6a0d2897e6dbbdf"},
+    {file = "numpy-2.2.6-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:bd48227a919f1bafbdda0583705e547892342c26fb127219d60a5c36882609d1"},
+    {file = "numpy-2.2.6-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:9551a499bf125c1d4f9e250377c1ee2eddd02e01eac6644c080162c0c51778ab"},
+    {file = "numpy-2.2.6-cp311-cp311-win32.whl", hash = "sha256:0678000bb9ac1475cd454c6b8c799206af8107e310843532b04d49649c717a47"},
+    {file = "numpy-2.2.6-cp311-cp311-win_amd64.whl", hash = "sha256:e8213002e427c69c45a52bbd94163084025f533a55a59d6f9c5b820774ef3303"},
+    {file = "numpy-2.2.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:41c5a21f4a04fa86436124d388f6ed60a9343a6f767fced1a8a71c3fbca038ff"},
+    {file = "numpy-2.2.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:de749064336d37e340f640b05f24e9e3dd678c57318c7289d222a8a2f543e90c"},
+    {file = "numpy-2.2.6-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:894b3a42502226a1cac872f840030665f33326fc3dac8e57c607905773cdcde3"},
+    {file = "numpy-2.2.6-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:71594f7c51a18e728451bb50cc60a3ce4e6538822731b2933209a1f3614e9282"},
+    {file = "numpy-2.2.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f2618db89be1b4e05f7a1a847a9c1c0abd63e63a1607d892dd54668dd92faf87"},
+    {file = "numpy-2.2.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd83c01228a688733f1ded5201c678f0c53ecc1006ffbc404db9f7a899ac6249"},
+    {file = "numpy-2.2.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:37c0ca431f82cd5fa716eca9506aefcabc247fb27ba69c5062a6d3ade8cf8f49"},
+    {file = "numpy-2.2.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fe27749d33bb772c80dcd84ae7e8df2adc920ae8297400dabec45f0dedb3f6de"},
+    {file = "numpy-2.2.6-cp312-cp312-win32.whl", hash = "sha256:4eeaae00d789f66c7a25ac5f34b71a7035bb474e679f410e5e1a94deb24cf2d4"},
+    {file = "numpy-2.2.6-cp312-cp312-win_amd64.whl", hash = "sha256:c1f9540be57940698ed329904db803cf7a402f3fc200bfe599334c9bd84a40b2"},
+    {file = "numpy-2.2.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0811bb762109d9708cca4d0b13c4f67146e3c3b7cf8d34018c722adb2d957c84"},
+    {file = "numpy-2.2.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:287cc3162b6f01463ccd86be154f284d0893d2b3ed7292439ea97eafa8170e0b"},
+    {file = "numpy-2.2.6-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:f1372f041402e37e5e633e586f62aa53de2eac8d98cbfb822806ce4bbefcb74d"},
+    {file = "numpy-2.2.6-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:55a4d33fa519660d69614a9fad433be87e5252f4b03850642f88993f7b2ca566"},
+    {file = "numpy-2.2.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f92729c95468a2f4f15e9bb94c432a9229d0d50de67304399627a943201baa2f"},
+    {file = "numpy-2.2.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1bc23a79bfabc5d056d106f9befb8d50c31ced2fbc70eedb8155aec74a45798f"},
+    {file = "numpy-2.2.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:e3143e4451880bed956e706a3220b4e5cf6172ef05fcc397f6f36a550b1dd868"},
+    {file = "numpy-2.2.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b4f13750ce79751586ae2eb824ba7e1e8dba64784086c98cdbbcc6a42112ce0d"},
+    {file = "numpy-2.2.6-cp313-cp313-win32.whl", hash = "sha256:5beb72339d9d4fa36522fc63802f469b13cdbe4fdab4a288f0c441b74272ebfd"},
+    {file = "numpy-2.2.6-cp313-cp313-win_amd64.whl", hash = "sha256:b0544343a702fa80c95ad5d3d608ea3599dd54d4632df855e4c8d24eb6ecfa1c"},
+    {file = "numpy-2.2.6-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0bca768cd85ae743b2affdc762d617eddf3bcf8724435498a1e80132d04879e6"},
+    {file = "numpy-2.2.6-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:fc0c5673685c508a142ca65209b4e79ed6740a4ed6b2267dbba90f34b0b3cfda"},
+    {file = "numpy-2.2.6-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:5bd4fc3ac8926b3819797a7c0e2631eb889b4118a9898c84f585a54d475b7e40"},
+    {file = "numpy-2.2.6-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:fee4236c876c4e8369388054d02d0e9bb84821feb1a64dd59e137e6511a551f8"},
+    {file = "numpy-2.2.6-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e1dda9c7e08dc141e0247a5b8f49cf05984955246a327d4c48bda16821947b2f"},
+    {file = "numpy-2.2.6-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f447e6acb680fd307f40d3da4852208af94afdfab89cf850986c3ca00562f4fa"},
+    {file = "numpy-2.2.6-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:389d771b1623ec92636b0786bc4ae56abafad4a4c513d36a55dce14bd9ce8571"},
+    {file = "numpy-2.2.6-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8e9ace4a37db23421249ed236fdcdd457d671e25146786dfc96835cd951aa7c1"},
+    {file = "numpy-2.2.6-cp313-cp313t-win32.whl", hash = "sha256:038613e9fb8c72b0a41f025a7e4c3f0b7a1b5d768ece4796b674c8f3fe13efff"},
+    {file = "numpy-2.2.6-cp313-cp313t-win_amd64.whl", hash = "sha256:6031dd6dfecc0cf9f668681a37648373bddd6421fff6c66ec1624eed0180ee06"},
+    {file = "numpy-2.2.6-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:0b605b275d7bd0c640cad4e5d30fa701a8d59302e127e5f79138ad62762c3e3d"},
+    {file = "numpy-2.2.6-pp310-pypy310_pp73-macosx_14_0_x86_64.whl", hash = "sha256:7befc596a7dc9da8a337f79802ee8adb30a552a94f792b9c9d18c840055907db"},
+    {file = "numpy-2.2.6-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ce47521a4754c8f4593837384bd3424880629f718d87c5d44f8ed763edd63543"},
+    {file = "numpy-2.2.6-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:d042d24c90c41b54fd506da306759e06e568864df8ec17ccc17e9e884634fd00"},
+    {file = "numpy-2.2.6.tar.gz", hash = "sha256:e29554e2bef54a90aa5cc07da6ce955accb83f21ab5de01a62c8478897b264fd"},
+]
+
+[[package]]
+name = "nvidia-cublas-cu12"
+version = "12.8.4.1"
+description = "CUBLAS native runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:b86f6dd8935884615a0683b663891d43781b819ac4f2ba2b0c9604676af346d0"},
+    {file = "nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:8ac4e771d5a348c551b2a426eda6193c19aa630236b418086020df5ba9667142"},
+    {file = "nvidia_cublas_cu12-12.8.4.1-py3-none-win_amd64.whl", hash = "sha256:47e9b82132fa8d2b4944e708049229601448aaad7e6f296f630f2d1a32de35af"},
+]
+
+[[package]]
+name = "nvidia-cuda-cupti-cu12"
+version = "12.8.90"
+description = "CUDA profiling tools runtime libs."
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:4412396548808ddfed3f17a467b104ba7751e6b58678a4b840675c56d21cf7ed"},
+    {file = "nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ea0cb07ebda26bb9b29ba82cda34849e73c166c18162d3913575b0c9db9a6182"},
+    {file = "nvidia_cuda_cupti_cu12-12.8.90-py3-none-win_amd64.whl", hash = "sha256:bb479dcdf7e6d4f8b0b01b115260399bf34154a1a2e9fe11c85c517d87efd98e"},
+]
+
+[[package]]
+name = "nvidia-cuda-nvrtc-cu12"
+version = "12.8.93"
+description = "NVRTC native runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994"},
+    {file = "nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fc1fec1e1637854b4c0a65fb9a8346b51dd9ee69e61ebaccc82058441f15bce8"},
+    {file = "nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-win_amd64.whl", hash = "sha256:7a4b6b2904850fe78e0bd179c4b655c404d4bb799ef03ddc60804247099ae909"},
+]
+
+[[package]]
+name = "nvidia-cuda-runtime-cu12"
+version = "12.8.90"
+description = "CUDA Runtime native Libraries"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:52bf7bbee900262ffefe5e9d5a2a69a30d97e2bc5bb6cc866688caa976966e3d"},
+    {file = "nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adade8dcbd0edf427b7204d480d6066d33902cab2a4707dcfc48a2d0fd44ab90"},
+    {file = "nvidia_cuda_runtime_cu12-12.8.90-py3-none-win_amd64.whl", hash = "sha256:c0c6027f01505bfed6c3b21ec546f69c687689aad5f1a377554bc6ca4aa993a8"},
+]
+
+[[package]]
+name = "nvidia-cudnn-cu12"
+version = "9.10.2.21"
+description = "cuDNN runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:c9132cc3f8958447b4910a1720036d9eff5928cc3179b0a51fb6d167c6cc87d8"},
+    {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8"},
+    {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-win_amd64.whl", hash = "sha256:c6288de7d63e6cf62988f0923f96dc339cea362decb1bf5b3141883392a7d65e"},
+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+
+[[package]]
+name = "nvidia-cufft-cu12"
+version = "11.3.3.83"
+description = "CUFFT native runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:848ef7224d6305cdb2a4df928759dca7b1201874787083b6e7550dd6765ce69a"},
+    {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74"},
+    {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-win_amd64.whl", hash = "sha256:7a64a98ef2a7c47f905aaf8931b69a3a43f27c55530c698bb2ed7c75c0b42cb7"},
+]
+
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cufile-cu12"
+version = "1.13.1.3"
+description = "cuFile GPUDirect libraries"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc"},
+    {file = "nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:4beb6d4cce47c1a0f1013d72e02b0994730359e17801d395bdcbf20cfb3bb00a"},
+]
+
+[[package]]
+name = "nvidia-curand-cu12"
+version = "10.3.9.90"
+description = "CURAND native runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:dfab99248034673b779bc6decafdc3404a8a6f502462201f2f31f11354204acd"},
+    {file = "nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:b32331d4f4df5d6eefa0554c565b626c7216f87a06a4f56fab27c3b68a830ec9"},
+    {file = "nvidia_curand_cu12-10.3.9.90-py3-none-win_amd64.whl", hash = "sha256:f149a8ca457277da854f89cf282d6ef43176861926c7ac85b2a0fbd237c587ec"},
+]
+
+[[package]]
+name = "nvidia-cusolver-cu12"
+version = "11.7.3.90"
+description = "CUDA solver native runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:db9ed69dbef9715071232caa9b69c52ac7de3a95773c2db65bdba85916e4e5c0"},
+    {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450"},
+    {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-win_amd64.whl", hash = "sha256:4a550db115fcabc4d495eb7d39ac8b58d4ab5d8e63274d3754df1c0ad6a22d34"},
+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+nvidia-cusparse-cu12 = "*"
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cusparse-cu12"
+version = "12.5.8.93"
+description = "CUSPARSE native runtime libraries"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9b6c161cb130be1a07a27ea6923df8141f3c295852f4b260c65f18f3e0a091dc"},
+    {file = "nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b"},
+    {file = "nvidia_cusparse_cu12-12.5.8.93-py3-none-win_amd64.whl", hash = "sha256:9a33604331cb2cac199f2e7f5104dfbb8a5a898c367a53dfda9ff2acb6b6b4dd"},
+]
+
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cusparselt-cu12"
+version = "0.7.1"
+description = "NVIDIA cuSPARSELt"
+optional = false
+python-versions = "*"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8878dce784d0fac90131b6817b607e803c36e629ba34dc5b433471382196b6a5"},
+    {file = "nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f1bb701d6b930d5a7cea44c19ceb973311500847f81b634d802b7b539dc55623"},
+    {file = "nvidia_cusparselt_cu12-0.7.1-py3-none-win_amd64.whl", hash = "sha256:f67fbb5831940ec829c9117b7f33807db9f9678dc2a617fbe781cac17b4e1075"},
+]
+
+[[package]]
+name = "nvidia-nccl-cu12"
+version = "2.27.5"
+description = "NVIDIA Collective Communication Library (NCCL) Runtime"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine != \"aarch64\""
+files = [
+    {file = "nvidia_nccl_cu12-2.27.5-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:31432ad4d1fb1004eb0c56203dc9bc2178a1ba69d1d9e02d64a6938ab5e40e7a"},
+    {file = "nvidia_nccl_cu12-2.27.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ad730cf15cb5d25fe849c6e6ca9eb5b76db16a80f13f425ac68d8e2e55624457"},
+]
+
+[[package]]
+name = "nvidia-nvjitlink-cu12"
+version = "12.8.93"
+description = "Nvidia JIT LTO Library"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88"},
+    {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:adccd7161ace7261e01bb91e44e88da350895c270d23f744f0820c818b7229e7"},
+    {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-win_amd64.whl", hash = "sha256:bd93fbeeee850917903583587f4fc3a4eafa022e34572251368238ab5e6bd67f"},
+]
+
+[[package]]
+name = "nvidia-nvshmem-cu12"
+version = "3.3.20"
+description = "NVSHMEM creates a global address space that provides efficient and scalable communication for NVIDIA GPU clusters."
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_nvshmem_cu12-3.3.20-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0b0b960da3842212758e4fa4696b94f129090b30e5122fea3c5345916545cff0"},
+    {file = "nvidia_nvshmem_cu12-3.3.20-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d00f26d3f9b2e3c3065be895e3059d6479ea5c638a3f38c9fec49b1b9dd7c1e5"},
+]
+
+[[package]]
+name = "nvidia-nvtx-cu12"
+version = "12.8.90"
+description = "NVIDIA Tools Extension"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d7ad891da111ebafbf7e015d34879f7112832fc239ff0d7d776b6cb685274615"},
+    {file = "nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5b17e2001cc0d751a5bc2c6ec6d26ad95913324a4adb86788c944f8ce9ba441f"},
+    {file = "nvidia_nvtx_cu12-12.8.90-py3-none-win_amd64.whl", hash = "sha256:619c8304aedc69f02ea82dd244541a83c3d9d40993381b3b590f1adaed3db41e"},
+]
+
+[[package]]
+name = "onnx"
+version = "1.19.1"
+description = "Open Neural Network Exchange"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "onnx-1.19.1-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:7343250cc5276cf439fe623b8f92e11cf0d1eebc733ae4a8b2e86903bb72ae68"},
+    {file = "onnx-1.19.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:1fb8f79de7f3920bb82b537f3c6ac70c0ce59f600471d9c3eed2b5f8b079b748"},
+    {file = "onnx-1.19.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:92b9d2dece41cc84213dbbfd1acbc2a28c27108c53bd28ddb6d1043fbfcbd2d5"},
+    {file = "onnx-1.19.1-cp310-cp310-win32.whl", hash = "sha256:c0b1a2b6bb19a0fc9f5de7661a547136d082c03c169a5215e18ff3ececd2a82f"},
+    {file = "onnx-1.19.1-cp310-cp310-win_amd64.whl", hash = "sha256:1c0498c00db05fcdb3426697d330dcecc3f60020015065e2c76fa795f2c9a605"},
+    {file = "onnx-1.19.1-cp311-cp311-macosx_12_0_universal2.whl", hash = "sha256:17aaf5832126de0a5197a5864e4f09a764dd7681d3035135547959b4b6b77a09"},
+    {file = "onnx-1.19.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:01b292a4d0b197c45d8184545bbc8ae1df83466341b604187c1b05902cb9c920"},
+    {file = "onnx-1.19.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1839af08ab4a909e4af936b8149c27f8c64b96138981024e251906e0539d8bf9"},
+    {file = "onnx-1.19.1-cp311-cp311-win32.whl", hash = "sha256:0bdbb676e3722bd32f9227c465d552689f49086f986a696419d865cb4e70b989"},
+    {file = "onnx-1.19.1-cp311-cp311-win_amd64.whl", hash = "sha256:1346853df5c1e3ebedb2e794cf2a51e0f33759affd655524864ccbcddad7035b"},
+    {file = "onnx-1.19.1-cp311-cp311-win_arm64.whl", hash = "sha256:2d69c280c0e665b7f923f499243b9bb84fe97970b7a4668afa0032045de602c8"},
+    {file = "onnx-1.19.1-cp312-cp312-macosx_12_0_universal2.whl", hash = "sha256:3612193a89ddbce5c4e86150869b9258780a82fb8c4ca197723a4460178a6ce9"},
+    {file = "onnx-1.19.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6c2fd2f744e7a3880ad0c262efa2edf6d965d0bd02b8f327ec516ad4cb0f2f15"},
+    {file = "onnx-1.19.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:485d3674d50d789e0ee72fa6f6e174ab81cb14c772d594f992141bd744729d8a"},
+    {file = "onnx-1.19.1-cp312-cp312-win32.whl", hash = "sha256:638bc56ff1a5718f7441e887aeb4e450f37a81c6eac482040381b140bd9ba601"},
+    {file = "onnx-1.19.1-cp312-cp312-win_amd64.whl", hash = "sha256:bc7e2e4e163e679721e547958b5a7db875bf822cad371b7c1304aa4401a7c7a4"},
+    {file = "onnx-1.19.1-cp312-cp312-win_arm64.whl", hash = "sha256:17c215b1c0f20fe93b4cbe62668247c1d2294b9bc7f6be0ca9ced28e980c07b7"},
+    {file = "onnx-1.19.1-cp313-cp313-macosx_12_0_universal2.whl", hash = "sha256:4e5f938c68c4dffd3e19e4fd76eb98d298174eb5ebc09319cdd0ec5fe50050dc"},
+    {file = "onnx-1.19.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:86e20a5984b017feeef2dbf4ceff1c7c161ab9423254968dd77d3696c38691d0"},
+    {file = "onnx-1.19.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c8d9c467f0f29993c12f330736af87972f30adb8329b515f39d63a0db929cb2c"},
+    {file = "onnx-1.19.1-cp313-cp313-win32.whl", hash = "sha256:65eee353a51b4e4ca3e797784661e5376e2b209f17557e04921eac9166a8752e"},
+    {file = "onnx-1.19.1-cp313-cp313-win_amd64.whl", hash = "sha256:c3bc87e38b53554b1fc9ef7b275c81c6f5c93c90a91935bb0aa8d4d498a6d48e"},
+    {file = "onnx-1.19.1-cp313-cp313-win_arm64.whl", hash = "sha256:e41496f400afb980ec643d80d5164753a88a85234fa5c06afdeebc8b7d1ec252"},
+    {file = "onnx-1.19.1-cp313-cp313t-macosx_12_0_universal2.whl", hash = "sha256:5f6274abf0fd74e80e78ecbb44bd44509409634525c89a9b38276c8af47dc0a2"},
+    {file = "onnx-1.19.1-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:07dcd4d83584eb4bf8f21ac04c82643712e5e93ac2a0ed10121ec123cb127e1e"},
+    {file = "onnx-1.19.1-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1975860c3e720db25d37f1619976582828264bdcc64fa7511c321ac4fc01add3"},
+    {file = "onnx-1.19.1-cp313-cp313t-win_amd64.whl", hash = "sha256:9807d0e181f6070ee3a6276166acdc571575d1bd522fc7e89dba16fd6e7ffed9"},
+    {file = "onnx-1.19.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b6ee83e6929d75005482d9f304c502ac7c9b8d6db153aa6b484dae74d0f28570"},
+    {file = "onnx-1.19.1-cp39-cp39-macosx_12_0_universal2.whl", hash = "sha256:2980de39df1f5afd005a8aeb0b35703dbbab8e4012bcec1634febbdfb8654da8"},
+    {file = "onnx-1.19.1-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:bf35f7abc7096df2bb0171102fa7d89ba4a5f5407e3b352ee27bb5e1867e0f19"},
+    {file = "onnx-1.19.1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cc81f200ed98bd0ced53c3f0fdb8164a42e2b8582a1fa9cb8aeb01b64367c7f4"},
+    {file = "onnx-1.19.1-cp39-cp39-win32.whl", hash = "sha256:a2e51118c3db00b169cac8170d94d832c2ffe80935563ced596182d4baa6fcb4"},
+    {file = "onnx-1.19.1-cp39-cp39-win_amd64.whl", hash = "sha256:4650d053c7c26e40a080b7378d61446958d6da4e217e1d0d422eb9264f8064ae"},
+    {file = "onnx-1.19.1.tar.gz", hash = "sha256:737524d6eb3907d3499ea459c6f01c5a96278bb3a0f2ff8ae04786fb5d7f1ed5"},
+]
+
+[package.dependencies]
+ml_dtypes = ">=0.5.0"
+numpy = ">=1.22"
+protobuf = ">=4.25.1"
+typing_extensions = ">=4.7.1"
+
+[package.extras]
+reference = ["Pillow"]
+
+[[package]]
+name = "opencv-python"
+version = "4.12.0.88"
+description = "Wrapper package for OpenCV python bindings."
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+files = [
+    {file = "opencv-python-4.12.0.88.tar.gz", hash = "sha256:8b738389cede219405f6f3880b851efa3415ccd674752219377353f017d2994d"},
+    {file = "opencv_python-4.12.0.88-cp37-abi3-macosx_13_0_arm64.whl", hash = "sha256:f9a1f08883257b95a5764bf517a32d75aec325319c8ed0f89739a57fae9e92a5"},
+    {file = "opencv_python-4.12.0.88-cp37-abi3-macosx_13_0_x86_64.whl", hash = "sha256:812eb116ad2b4de43ee116fcd8991c3a687f099ada0b04e68f64899c09448e81"},
+    {file = "opencv_python-4.12.0.88-cp37-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:51fd981c7df6af3e8f70b1556696b05224c4e6b6777bdd2a46b3d4fb09de1a92"},
+    {file = "opencv_python-4.12.0.88-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:092c16da4c5a163a818f120c22c5e4a2f96e0db4f24e659c701f1fe629a690f9"},
+    {file = "opencv_python-4.12.0.88-cp37-abi3-win32.whl", hash = "sha256:ff554d3f725b39878ac6a2e1fa232ec509c36130927afc18a1719ebf4fbf4357"},
+    {file = "opencv_python-4.12.0.88-cp37-abi3-win_amd64.whl", hash = "sha256:d98edb20aa932fd8ebd276a72627dad9dc097695b3d435a4257557bbb49a79d2"},
+]
+
+[package.dependencies]
+numpy = {version = ">=2,<2.3.0", markers = "python_version >= \"3.9\""}
+
+[[package]]
+name = "optuna"
+version = "4.5.0"
+description = "A hyperparameter optimization framework"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "optuna-4.5.0-py3-none-any.whl", hash = "sha256:5b8a783e84e448b0742501bc27195344a28d2c77bd2feef5b558544d954851b0"},
+    {file = "optuna-4.5.0.tar.gz", hash = "sha256:264844da16dad744dea295057d8bc218646129c47567d52c35a201d9f99942ba"},
+]
+
+[package.dependencies]
+alembic = ">=1.5.0"
+colorlog = "*"
+numpy = "*"
+packaging = ">=20.0"
+PyYAML = "*"
+sqlalchemy = ">=1.4.2"
+tqdm = "*"
+
+[package.extras]
+benchmark = ["asv (>=0.5.0)", "cma", "virtualenv"]
+checking = ["black", "blackdoc", "flake8", "isort", "mypy", "mypy_boto3_s3", "scipy-stubs ; python_version >= \"3.10\"", "types-PyYAML", "types-redis", "types-setuptools", "types-tqdm", "typing_extensions (>=3.10.0.0)"]
+document = ["ase", "cmaes (>=0.12.0)", "fvcore", "kaleido (<0.4)", "lightgbm", "matplotlib (!=3.6.0)", "pandas", "pillow", "plotly (>=4.9.0)", "scikit-learn", "sphinx", "sphinx-copybutton", "sphinx-gallery", "sphinx-notfound-page", "sphinx_rtd_theme (>=1.2.0)", "torch", "torchvision"]
+optional = ["boto3", "cmaes (>=0.12.0)", "google-cloud-storage", "grpcio", "matplotlib (!=3.6.0)", "pandas", "plotly (>=4.9.0)", "protobuf (>=5.28.1)", "redis", "scikit-learn (>=0.24.2)", "scipy", "torch"]
+test = ["coverage", "fakeredis[lua]", "grpcio", "kaleido (<0.4)", "moto", "protobuf (>=5.28.1)", "pytest", "pytest-xdist", "scipy (>=1.9.2)", "torch"]
+
+[[package]]
+name = "overrides"
+version = "7.7.0"
+description = "A decorator to automatically detect mismatch when overriding a method."
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+markers = "python_version < \"3.12\""
+files = [
+    {file = "overrides-7.7.0-py3-none-any.whl", hash = "sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49"},
+    {file = "overrides-7.7.0.tar.gz", hash = "sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a"},
+]
+
+[[package]]
+name = "packaging"
+version = "25.0"
+description = "Core utilities for Python packages"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484"},
+    {file = "packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f"},
+]
+
+[[package]]
+name = "pandas"
+version = "2.3.3"
+description = "Powerful data structures for data analysis, time series, and statistics"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "pandas-2.3.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:376c6446ae31770764215a6c937f72d917f214b43560603cd60da6408f183b6c"},
+    {file = "pandas-2.3.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e19d192383eab2f4ceb30b412b22ea30690c9e618f78870357ae1d682912015a"},
+    {file = "pandas-2.3.3-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5caf26f64126b6c7aec964f74266f435afef1c1b13da3b0636c7518a1fa3e2b1"},
+    {file = "pandas-2.3.3-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:dd7478f1463441ae4ca7308a70e90b33470fa593429f9d4c578dd00d1fa78838"},
+    {file = "pandas-2.3.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:4793891684806ae50d1288c9bae9330293ab4e083ccd1c5e383c34549c6e4250"},
+    {file = "pandas-2.3.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:28083c648d9a99a5dd035ec125d42439c6c1c525098c58af0fc38dd1a7a1b3d4"},
+    {file = "pandas-2.3.3-cp310-cp310-win_amd64.whl", hash = "sha256:503cf027cf9940d2ceaa1a93cfb5f8c8c7e6e90720a2850378f0b3f3b1e06826"},
+    {file = "pandas-2.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:602b8615ebcc4a0c1751e71840428ddebeb142ec02c786e8ad6b1ce3c8dec523"},
+    {file = "pandas-2.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8fe25fc7b623b0ef6b5009149627e34d2a4657e880948ec3c840e9402e5c1b45"},
+    {file = "pandas-2.3.3-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b468d3dad6ff947df92dcb32ede5b7bd41a9b3cceef0a30ed925f6d01fb8fa66"},
+    {file = "pandas-2.3.3-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b98560e98cb334799c0b07ca7967ac361a47326e9b4e5a7dfb5ab2b1c9d35a1b"},
+    {file = "pandas-2.3.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1d37b5848ba49824e5c30bedb9c830ab9b7751fd049bc7914533e01c65f79791"},
+    {file = "pandas-2.3.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:db4301b2d1f926ae677a751eb2bd0e8c5f5319c9cb3f88b0becbbb0b07b34151"},
+    {file = "pandas-2.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:f086f6fe114e19d92014a1966f43a3e62285109afe874f067f5abbdcbb10e59c"},
+    {file = "pandas-2.3.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6d21f6d74eb1725c2efaa71a2bfc661a0689579b58e9c0ca58a739ff0b002b53"},
+    {file = "pandas-2.3.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:3fd2f887589c7aa868e02632612ba39acb0b8948faf5cc58f0850e165bd46f35"},
+    {file = "pandas-2.3.3-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ecaf1e12bdc03c86ad4a7ea848d66c685cb6851d807a26aa245ca3d2017a1908"},
+    {file = "pandas-2.3.3-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b3d11d2fda7eb164ef27ffc14b4fcab16a80e1ce67e9f57e19ec0afaf715ba89"},
+    {file = "pandas-2.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a68e15f780eddf2b07d242e17a04aa187a7ee12b40b930bfdd78070556550e98"},
+    {file = "pandas-2.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:371a4ab48e950033bcf52b6527eccb564f52dc826c02afd9a1bc0ab731bba084"},
+    {file = "pandas-2.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:a16dcec078a01eeef8ee61bf64074b4e524a2a3f4b3be9326420cabe59c4778b"},
+    {file = "pandas-2.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:56851a737e3470de7fa88e6131f41281ed440d29a9268dcbf0002da5ac366713"},
+    {file = "pandas-2.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:bdcd9d1167f4885211e401b3036c0c8d9e274eee67ea8d0758a256d60704cfe8"},
+    {file = "pandas-2.3.3-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e32e7cc9af0f1cc15548288a51a3b681cc2a219faa838e995f7dc53dbab1062d"},
+    {file = "pandas-2.3.3-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:318d77e0e42a628c04dc56bcef4b40de67918f7041c2b061af1da41dcff670ac"},
+    {file = "pandas-2.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:4e0a175408804d566144e170d0476b15d78458795bb18f1304fb94160cabf40c"},
+    {file = "pandas-2.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:93c2d9ab0fc11822b5eece72ec9587e172f63cff87c00b062f6e37448ced4493"},
+    {file = "pandas-2.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:f8bfc0e12dc78f777f323f55c58649591b2cd0c43534e8355c51d3fede5f4dee"},
+    {file = "pandas-2.3.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:75ea25f9529fdec2d2e93a42c523962261e567d250b0013b16210e1d40d7c2e5"},
+    {file = "pandas-2.3.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:74ecdf1d301e812db96a465a525952f4dde225fdb6d8e5a521d47e1f42041e21"},
+    {file = "pandas-2.3.3-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6435cb949cb34ec11cc9860246ccb2fdc9ecd742c12d3304989017d53f039a78"},
+    {file = "pandas-2.3.3-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:900f47d8f20860de523a1ac881c4c36d65efcb2eb850e6948140fa781736e110"},
+    {file = "pandas-2.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:a45c765238e2ed7d7c608fc5bc4a6f88b642f2f01e70c0c23d2224dd21829d86"},
+    {file = "pandas-2.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:c4fc4c21971a1a9f4bdb4c73978c7f7256caa3e62b323f70d6cb80db583350bc"},
+    {file = "pandas-2.3.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:ee15f284898e7b246df8087fc82b87b01686f98ee67d85a17b7ab44143a3a9a0"},
+    {file = "pandas-2.3.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:1611aedd912e1ff81ff41c745822980c49ce4a7907537be8692c8dbc31924593"},
+    {file = "pandas-2.3.3-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6d2cefc361461662ac48810cb14365a365ce864afe85ef1f447ff5a1e99ea81c"},
+    {file = "pandas-2.3.3-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ee67acbbf05014ea6c763beb097e03cd629961c8a632075eeb34247120abcb4b"},
+    {file = "pandas-2.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c46467899aaa4da076d5abc11084634e2d197e9460643dd455ac3db5856b24d6"},
+    {file = "pandas-2.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6253c72c6a1d990a410bc7de641d34053364ef8bcd3126f7e7450125887dffe3"},
+    {file = "pandas-2.3.3-cp314-cp314-win_amd64.whl", hash = "sha256:1b07204a219b3b7350abaae088f451860223a52cfb8a6c53358e7948735158e5"},
+    {file = "pandas-2.3.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:2462b1a365b6109d275250baaae7b760fd25c726aaca0054649286bcfbb3e8ec"},
+    {file = "pandas-2.3.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:0242fe9a49aa8b4d78a4fa03acb397a58833ef6199e9aa40a95f027bb3a1b6e7"},
+    {file = "pandas-2.3.3-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a21d830e78df0a515db2b3d2f5570610f5e6bd2e27749770e8bb7b524b89b450"},
+    {file = "pandas-2.3.3-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2e3ebdb170b5ef78f19bfb71b0dc5dc58775032361fa188e814959b74d726dd5"},
+    {file = "pandas-2.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:d051c0e065b94b7a3cea50eb1ec32e912cd96dba41647eb24104b6c6c14c5788"},
+    {file = "pandas-2.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3869faf4bd07b3b66a9f462417d0ca3a9df29a9f6abd5d0d0dbab15dac7abe87"},
+    {file = "pandas-2.3.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c503ba5216814e295f40711470446bc3fd00f0faea8a086cbc688808e26f92a2"},
+    {file = "pandas-2.3.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a637c5cdfa04b6d6e2ecedcb81fc52ffb0fd78ce2ebccc9ea964df9f658de8c8"},
+    {file = "pandas-2.3.3-cp39-cp39-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:854d00d556406bffe66a4c0802f334c9ad5a96b4f1f868adf036a21b11ef13ff"},
+    {file = "pandas-2.3.3-cp39-cp39-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bf1f8a81d04ca90e32a0aceb819d34dbd378a98bf923b6398b9a3ec0bf44de29"},
+    {file = "pandas-2.3.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:23ebd657a4d38268c7dfbdf089fbc31ea709d82e4923c5ffd4fbd5747133ce73"},
+    {file = "pandas-2.3.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:5554c929ccc317d41a5e3d1234f3be588248e61f08a74dd17c9eabb535777dc9"},
+    {file = "pandas-2.3.3-cp39-cp39-win_amd64.whl", hash = "sha256:d3e28b3e83862ccf4d85ff19cf8c20b2ae7e503881711ff2d534dc8f761131aa"},
+    {file = "pandas-2.3.3.tar.gz", hash = "sha256:e05e1af93b977f7eafa636d043f9f94c7ee3ac81af99c13508215942e64c993b"},
+]
+
+[package.dependencies]
+numpy = [
+    {version = ">=1.22.4", markers = "python_version < \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
+]
+python-dateutil = ">=2.8.2"
+pytz = ">=2020.1"
+tzdata = ">=2022.7"
+
+[package.extras]
+all = ["PyQt5 (>=5.15.9)", "SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)", "beautifulsoup4 (>=4.11.2)", "bottleneck (>=1.3.6)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=2022.12.0)", "fsspec (>=2022.11.0)", "gcsfs (>=2022.11.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.9.2)", "matplotlib (>=3.6.3)", "numba (>=0.56.4)", "numexpr (>=2.8.4)", "odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "pandas-gbq (>=0.19.0)", "psycopg2 (>=2.9.6)", "pyarrow (>=10.0.1)", "pymysql (>=1.0.2)", "pyreadstat (>=1.2.0)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "qtpy (>=2.3.0)", "s3fs (>=2022.11.0)", "scipy (>=1.10.0)", "tables (>=3.8.0)", "tabulate (>=0.9.0)", "xarray (>=2022.12.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)", "zstandard (>=0.19.0)"]
+aws = ["s3fs (>=2022.11.0)"]
+clipboard = ["PyQt5 (>=5.15.9)", "qtpy (>=2.3.0)"]
+compression = ["zstandard (>=0.19.0)"]
+computation = ["scipy (>=1.10.0)", "xarray (>=2022.12.0)"]
+consortium-standard = ["dataframe-api-compat (>=0.1.7)"]
+excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)"]
+feather = ["pyarrow (>=10.0.1)"]
+fss = ["fsspec (>=2022.11.0)"]
+gcp = ["gcsfs (>=2022.11.0)", "pandas-gbq (>=0.19.0)"]
+hdf5 = ["tables (>=3.8.0)"]
+html = ["beautifulsoup4 (>=4.11.2)", "html5lib (>=1.1)", "lxml (>=4.9.2)"]
+mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"]
+output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.9.0)"]
+parquet = ["pyarrow (>=10.0.1)"]
+performance = ["bottleneck (>=1.3.6)", "numba (>=0.56.4)", "numexpr (>=2.8.4)"]
+plot = ["matplotlib (>=3.6.3)"]
+postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"]
+pyarrow = ["pyarrow (>=10.0.1)"]
+spss = ["pyreadstat (>=1.2.0)"]
+sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"]
+test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
+xml = ["lxml (>=4.9.2)"]
+
+[[package]]
+name = "pandocfilters"
+version = "1.5.1"
+description = "Utilities for writing pandoc filters in python"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["main"]
+files = [
+    {file = "pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc"},
+    {file = "pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e"},
+]
+
+[[package]]
+name = "parso"
+version = "0.8.5"
+description = "A Python Parser"
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+files = [
+    {file = "parso-0.8.5-py2.py3-none-any.whl", hash = "sha256:646204b5ee239c396d040b90f9e272e9a8017c630092bf59980beb62fd033887"},
+    {file = "parso-0.8.5.tar.gz", hash = "sha256:034d7354a9a018bdce352f48b2a8a450f05e9d6ee85db84764e9b6bd96dafe5a"},
+]
+
+[package.extras]
+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["docopt", "pytest"]
+
+[[package]]
+name = "patsy"
+version = "1.0.2"
+description = "A Python package for describing statistical models and for building design matrices."
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+files = [
+    {file = "patsy-1.0.2-py2.py3-none-any.whl", hash = "sha256:37bfddbc58fcf0362febb5f54f10743f8b21dd2aa73dec7e7ef59d1b02ae668a"},
+    {file = "patsy-1.0.2.tar.gz", hash = "sha256:cdc995455f6233e90e22de72c37fcadb344e7586fb83f06696f54d92f8ce74c0"},
+]
+
+[package.dependencies]
+numpy = ">=1.4"
+
+[package.extras]
+test = ["pytest", "pytest-cov", "scipy"]
+
+[[package]]
+name = "peft"
+version = "0.17.1"
+description = "Parameter-Efficient Fine-Tuning (PEFT)"
+optional = false
+python-versions = ">=3.9.0"
+groups = ["main"]
+files = [
+    {file = "peft-0.17.1-py3-none-any.whl", hash = "sha256:3d129d64def3d74779c32a080d2567e5f7b674e77d546e3585138216d903f99e"},
+    {file = "peft-0.17.1.tar.gz", hash = "sha256:e6002b42517976c290b3b8bbb9829a33dd5d470676b2dec7cb4df8501b77eb9f"},
+]
+
+[package.dependencies]
+accelerate = ">=0.21.0"
+huggingface_hub = ">=0.25.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+psutil = "*"
+pyyaml = "*"
+safetensors = "*"
+torch = ">=1.13.0"
+tqdm = "*"
+transformers = "*"
+
+[package.extras]
+dev = ["black", "black", "hf-doc-builder", "hf-doc-builder", "ruff (>=0.9.2,<0.10.0)"]
+docs-specific = ["black", "hf-doc-builder"]
+quality = ["black", "hf-doc-builder", "ruff (>=0.9.2,<0.10.0)"]
+test = ["black", "black", "datasets", "diffusers", "hf-doc-builder", "hf-doc-builder", "parameterized", "protobuf", "pytest", "pytest-cov", "pytest-xdist", "ruff (>=0.9.2,<0.10.0)", "scipy", "sentencepiece"]
+
+[[package]]
+name = "pexpect"
+version = "4.9.0"
+description = "Pexpect allows easy control of interactive console applications."
+optional = false
+python-versions = "*"
+groups = ["main"]
+markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""
+files = [
+    {file = "pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523"},
+    {file = "pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f"},
+]
+
+[package.dependencies]
+ptyprocess = ">=0.5"
+
+[[package]]
+name = "pillow"
+version = "12.0.0"
+description = "Python Imaging Library (fork)"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "pillow-12.0.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:3adfb466bbc544b926d50fe8f4a4e6abd8c6bffd28a26177594e6e9b2b76572b"},
+    {file = "pillow-12.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1ac11e8ea4f611c3c0147424eae514028b5e9077dd99ab91e1bd7bc33ff145e1"},
+    {file = "pillow-12.0.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d49e2314c373f4c2b39446fb1a45ed333c850e09d0c59ac79b72eb3b95397363"},
+    {file = "pillow-12.0.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c7b2a63fd6d5246349f3d3f37b14430d73ee7e8173154461785e43036ffa96ca"},
+    {file = "pillow-12.0.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d64317d2587c70324b79861babb9c09f71fbb780bad212018874b2c013d8600e"},
+    {file = "pillow-12.0.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d77153e14b709fd8b8af6f66a3afbb9ed6e9fc5ccf0b6b7e1ced7b036a228782"},
+    {file = "pillow-12.0.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:32ed80ea8a90ee3e6fa08c21e2e091bba6eda8eccc83dbc34c95169507a91f10"},
+    {file = "pillow-12.0.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:c828a1ae702fc712978bda0320ba1b9893d99be0badf2647f693cc01cf0f04fa"},
+    {file = "pillow-12.0.0-cp310-cp310-win32.whl", hash = "sha256:bd87e140e45399c818fac4247880b9ce719e4783d767e030a883a970be632275"},
+    {file = "pillow-12.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:455247ac8a4cfb7b9bc45b7e432d10421aea9fc2e74d285ba4072688a74c2e9d"},
+    {file = "pillow-12.0.0-cp310-cp310-win_arm64.whl", hash = "sha256:6ace95230bfb7cd79ef66caa064bbe2f2a1e63d93471c3a2e1f1348d9f22d6b7"},
+    {file = "pillow-12.0.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:0fd00cac9c03256c8b2ff58f162ebcd2587ad3e1f2e397eab718c47e24d231cc"},
+    {file = "pillow-12.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a3475b96f5908b3b16c47533daaa87380c491357d197564e0ba34ae75c0f3257"},
+    {file = "pillow-12.0.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:110486b79f2d112cf6add83b28b627e369219388f64ef2f960fef9ebaf54c642"},
+    {file = "pillow-12.0.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5269cc1caeedb67e6f7269a42014f381f45e2e7cd42d834ede3c703a1d915fe3"},
+    {file = "pillow-12.0.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:aa5129de4e174daccbc59d0a3b6d20eaf24417d59851c07ebb37aeb02947987c"},
+    {file = "pillow-12.0.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bee2a6db3a7242ea309aa7ee8e2780726fed67ff4e5b40169f2c940e7eb09227"},
+    {file = "pillow-12.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:90387104ee8400a7b4598253b4c406f8958f59fcf983a6cea2b50d59f7d63d0b"},
+    {file = "pillow-12.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:bc91a56697869546d1b8f0a3ff35224557ae7f881050e99f615e0119bf934b4e"},
+    {file = "pillow-12.0.0-cp311-cp311-win32.whl", hash = "sha256:27f95b12453d165099c84f8a8bfdfd46b9e4bda9e0e4b65f0635430027f55739"},
+    {file = "pillow-12.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:b583dc9070312190192631373c6c8ed277254aa6e6084b74bdd0a6d3b221608e"},
+    {file = "pillow-12.0.0-cp311-cp311-win_arm64.whl", hash = "sha256:759de84a33be3b178a64c8ba28ad5c135900359e85fb662bc6e403ad4407791d"},
+    {file = "pillow-12.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:53561a4ddc36facb432fae7a9d8afbfaf94795414f5cdc5fc52f28c1dca90371"},
+    {file = "pillow-12.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:71db6b4c1653045dacc1585c1b0d184004f0d7e694c7b34ac165ca70c0838082"},
+    {file = "pillow-12.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2fa5f0b6716fc88f11380b88b31fe591a06c6315e955c096c35715788b339e3f"},
+    {file = "pillow-12.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:82240051c6ca513c616f7f9da06e871f61bfd7805f566275841af15015b8f98d"},
+    {file = "pillow-12.0.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:55f818bd74fe2f11d4d7cbc65880a843c4075e0ac7226bc1a23261dbea531953"},
+    {file = "pillow-12.0.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b87843e225e74576437fd5b6a4c2205d422754f84a06942cfaf1dc32243e45a8"},
+    {file = "pillow-12.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c607c90ba67533e1b2355b821fef6764d1dd2cbe26b8c1005ae84f7aea25ff79"},
+    {file = "pillow-12.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:21f241bdd5080a15bc86d3466a9f6074a9c2c2b314100dd896ac81ee6db2f1ba"},
+    {file = "pillow-12.0.0-cp312-cp312-win32.whl", hash = "sha256:dd333073e0cacdc3089525c7df7d39b211bcdf31fc2824e49d01c6b6187b07d0"},
+    {file = "pillow-12.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:9fe611163f6303d1619bbcb653540a4d60f9e55e622d60a3108be0d5b441017a"},
+    {file = "pillow-12.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:7dfb439562f234f7d57b1ac6bc8fe7f838a4bd49c79230e0f6a1da93e82f1fad"},
+    {file = "pillow-12.0.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:0869154a2d0546545cde61d1789a6524319fc1897d9ee31218eae7a60ccc5643"},
+    {file = "pillow-12.0.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:a7921c5a6d31b3d756ec980f2f47c0cfdbce0fc48c22a39347a895f41f4a6ea4"},
+    {file = "pillow-12.0.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:1ee80a59f6ce048ae13cda1abf7fbd2a34ab9ee7d401c46be3ca685d1999a399"},
+    {file = "pillow-12.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c50f36a62a22d350c96e49ad02d0da41dbd17ddc2e29750dbdba4323f85eb4a5"},
+    {file = "pillow-12.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:5193fde9a5f23c331ea26d0cf171fbf67e3f247585f50c08b3e205c7aeb4589b"},
+    {file = "pillow-12.0.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:bde737cff1a975b70652b62d626f7785e0480918dece11e8fef3c0cf057351c3"},
+    {file = "pillow-12.0.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a6597ff2b61d121172f5844b53f21467f7082f5fb385a9a29c01414463f93b07"},
+    {file = "pillow-12.0.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0b817e7035ea7f6b942c13aa03bb554fc44fea70838ea21f8eb31c638326584e"},
+    {file = "pillow-12.0.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f4f1231b7dec408e8670264ce63e9c71409d9583dd21d32c163e25213ee2a344"},
+    {file = "pillow-12.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6e51b71417049ad6ab14c49608b4a24d8fb3fe605e5dfabfe523b58064dc3d27"},
+    {file = "pillow-12.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d120c38a42c234dc9a8c5de7ceaaf899cf33561956acb4941653f8bdc657aa79"},
+    {file = "pillow-12.0.0-cp313-cp313-win32.whl", hash = "sha256:4cc6b3b2efff105c6a1656cfe59da4fdde2cda9af1c5e0b58529b24525d0a098"},
+    {file = "pillow-12.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:4cf7fed4b4580601c4345ceb5d4cbf5a980d030fd5ad07c4d2ec589f95f09905"},
+    {file = "pillow-12.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:9f0b04c6b8584c2c193babcccc908b38ed29524b29dd464bc8801bf10d746a3a"},
+    {file = "pillow-12.0.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:7fa22993bac7b77b78cae22bad1e2a987ddf0d9015c63358032f84a53f23cdc3"},
+    {file = "pillow-12.0.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:f135c702ac42262573fe9714dfe99c944b4ba307af5eb507abef1667e2cbbced"},
+    {file = "pillow-12.0.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:c85de1136429c524e55cfa4e033b4a7940ac5c8ee4d9401cc2d1bf48154bbc7b"},
+    {file = "pillow-12.0.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:38df9b4bfd3db902c9c2bd369bcacaf9d935b2fff73709429d95cc41554f7b3d"},
+    {file = "pillow-12.0.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7d87ef5795da03d742bf49439f9ca4d027cde49c82c5371ba52464aee266699a"},
+    {file = "pillow-12.0.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:aff9e4d82d082ff9513bdd6acd4f5bd359f5b2c870907d2b0a9c5e10d40c88fe"},
+    {file = "pillow-12.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:8d8ca2b210ada074d57fcee40c30446c9562e542fc46aedc19baf758a93532ee"},
+    {file = "pillow-12.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:99a7f72fb6249302aa62245680754862a44179b545ded638cf1fef59befb57ef"},
+    {file = "pillow-12.0.0-cp313-cp313t-win32.whl", hash = "sha256:4078242472387600b2ce8d93ade8899c12bf33fa89e55ec89fe126e9d6d5d9e9"},
+    {file = "pillow-12.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:2c54c1a783d6d60595d3514f0efe9b37c8808746a66920315bfd34a938d7994b"},
+    {file = "pillow-12.0.0-cp313-cp313t-win_arm64.whl", hash = "sha256:26d9f7d2b604cd23aba3e9faf795787456ac25634d82cd060556998e39c6fa47"},
+    {file = "pillow-12.0.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:beeae3f27f62308f1ddbcfb0690bf44b10732f2ef43758f169d5e9303165d3f9"},
+    {file = "pillow-12.0.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:d4827615da15cd59784ce39d3388275ec093ae3ee8d7f0c089b76fa87af756c2"},
+    {file = "pillow-12.0.0-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:3e42edad50b6909089750e65c91aa09aaf1e0a71310d383f11321b27c224ed8a"},
+    {file = "pillow-12.0.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:e5d8efac84c9afcb40914ab49ba063d94f5dbdf5066db4482c66a992f47a3a3b"},
+    {file = "pillow-12.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:266cd5f2b63ff316d5a1bba46268e603c9caf5606d44f38c2873c380950576ad"},
+    {file = "pillow-12.0.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:58eea5ebe51504057dd95c5b77d21700b77615ab0243d8152793dc00eb4faf01"},
+    {file = "pillow-12.0.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f13711b1a5ba512d647a0e4ba79280d3a9a045aaf7e0cc6fbe96b91d4cdf6b0c"},
+    {file = "pillow-12.0.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6846bd2d116ff42cba6b646edf5bf61d37e5cbd256425fa089fee4ff5c07a99e"},
+    {file = "pillow-12.0.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c98fa880d695de164b4135a52fd2e9cd7b7c90a9d8ac5e9e443a24a95ef9248e"},
+    {file = "pillow-12.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fa3ed2a29a9e9d2d488b4da81dcb54720ac3104a20bf0bd273f1e4648aff5af9"},
+    {file = "pillow-12.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d034140032870024e6b9892c692fe2968493790dd57208b2c37e3fb35f6df3ab"},
+    {file = "pillow-12.0.0-cp314-cp314-win32.whl", hash = "sha256:1b1b133e6e16105f524a8dec491e0586d072948ce15c9b914e41cdadd209052b"},
+    {file = "pillow-12.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:8dc232e39d409036af549c86f24aed8273a40ffa459981146829a324e0848b4b"},
+    {file = "pillow-12.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:d52610d51e265a51518692045e372a4c363056130d922a7351429ac9f27e70b0"},
+    {file = "pillow-12.0.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:1979f4566bb96c1e50a62d9831e2ea2d1211761e5662afc545fa766f996632f6"},
+    {file = "pillow-12.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:b2e4b27a6e15b04832fe9bf292b94b5ca156016bbc1ea9c2c20098a0320d6cf6"},
+    {file = "pillow-12.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fb3096c30df99fd01c7bf8e544f392103d0795b9f98ba71a8054bcbf56b255f1"},
+    {file = "pillow-12.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7438839e9e053ef79f7112c881cef684013855016f928b168b81ed5835f3e75e"},
+    {file = "pillow-12.0.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5d5c411a8eaa2299322b647cd932586b1427367fd3184ffbb8f7a219ea2041ca"},
+    {file = "pillow-12.0.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d7e091d464ac59d2c7ad8e7e08105eaf9dafbc3883fd7265ffccc2baad6ac925"},
+    {file = "pillow-12.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:792a2c0be4dcc18af9d4a2dfd8a11a17d5e25274a1062b0ec1c2d79c76f3e7f8"},
+    {file = "pillow-12.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:afbefa430092f71a9593a99ab6a4e7538bc9eabbf7bf94f91510d3503943edc4"},
+    {file = "pillow-12.0.0-cp314-cp314t-win32.whl", hash = "sha256:3830c769decf88f1289680a59d4f4c46c72573446352e2befec9a8512104fa52"},
+    {file = "pillow-12.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:905b0365b210c73afb0ebe9101a32572152dfd1c144c7e28968a331b9217b94a"},
+    {file = "pillow-12.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:99353a06902c2e43b43e8ff74ee65a7d90307d82370604746738a1e0661ccca7"},
+    {file = "pillow-12.0.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:b22bd8c974942477156be55a768f7aa37c46904c175be4e158b6a86e3a6b7ca8"},
+    {file = "pillow-12.0.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:805ebf596939e48dbb2e4922a1d3852cfc25c38160751ce02da93058b48d252a"},
+    {file = "pillow-12.0.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cae81479f77420d217def5f54b5b9d279804d17e982e0f2fa19b1d1e14ab5197"},
+    {file = "pillow-12.0.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:aeaefa96c768fc66818730b952a862235d68825c178f1b3ffd4efd7ad2edcb7c"},
+    {file = "pillow-12.0.0-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:09f2d0abef9e4e2f349305a4f8cc784a8a6c2f58a8c4892eea13b10a943bd26e"},
+    {file = "pillow-12.0.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bdee52571a343d721fb2eb3b090a82d959ff37fc631e3f70422e0c2e029f3e76"},
+    {file = "pillow-12.0.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:b290fd8aa38422444d4b50d579de197557f182ef1068b75f5aa8558638b8d0a5"},
+    {file = "pillow-12.0.0.tar.gz", hash = "sha256:87d4f8125c9988bfbed67af47dd7a953e2fc7b0cc1e7800ec6d2080d490bb353"},
+]
+
+[package.extras]
+docs = ["furo", "olefile", "sphinx (>=8.2)", "sphinx-autobuild", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
+fpx = ["olefile"]
+mic = ["olefile"]
+test-arrow = ["arro3-compute", "arro3-core", "nanoarrow", "pyarrow"]
+tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "olefile", "packaging", "pyroma (>=5)", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "trove-classifiers (>=2024.10.12)"]
+xmp = ["defusedxml"]
+
+[[package]]
+name = "platformdirs"
+version = "4.5.0"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "platformdirs-4.5.0-py3-none-any.whl", hash = "sha256:e578a81bb873cbb89a41fcc904c7ef523cc18284b7e3b3ccf06aca1403b7ebd3"},
+    {file = "platformdirs-4.5.0.tar.gz", hash = "sha256:70ddccdd7c99fc5942e9fc25636a8b34d04c24b335100223152c2803e4063312"},
+]
+
+[package.extras]
+docs = ["furo (>=2025.9.25)", "proselint (>=0.14)", "sphinx (>=8.2.3)", "sphinx-autodoc-typehints (>=3.2)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.4.2)", "pytest-cov (>=7)", "pytest-mock (>=3.15.1)"]
+type = ["mypy (>=1.18.2)"]
+
+[[package]]
+name = "polars"
+version = "1.35.1"
+description = "Blazingly fast DataFrame library"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "polars-1.35.1-py3-none-any.whl", hash = "sha256:c29a933f28aa330d96a633adbd79aa5e6a6247a802a720eead9933f4613bdbf4"},
+    {file = "polars-1.35.1.tar.gz", hash = "sha256:06548e6d554580151d6ca7452d74bceeec4640b5b9261836889b8e68cfd7a62e"},
+]
+
+[package.dependencies]
+polars-runtime-32 = "1.35.1"
+
+[package.extras]
+adbc = ["adbc-driver-manager[dbapi]", "adbc-driver-sqlite[dbapi]"]
+all = ["polars[async,cloudpickle,database,deltalake,excel,fsspec,graph,iceberg,numpy,pandas,plot,pyarrow,pydantic,style,timezone]"]
+async = ["gevent"]
+calamine = ["fastexcel (>=0.9)"]
+cloudpickle = ["cloudpickle"]
+connectorx = ["connectorx (>=0.3.2)"]
+database = ["polars[adbc,connectorx,sqlalchemy]"]
+deltalake = ["deltalake (>=1.0.0)"]
+excel = ["polars[calamine,openpyxl,xlsx2csv,xlsxwriter]"]
+fsspec = ["fsspec"]
+gpu = ["cudf-polars-cu12"]
+graph = ["matplotlib"]
+iceberg = ["pyiceberg (>=0.7.1)"]
+numpy = ["numpy (>=1.16.0)"]
+openpyxl = ["openpyxl (>=3.0.0)"]
+pandas = ["pandas", "polars[pyarrow]"]
+plot = ["altair (>=5.4.0)"]
+polars-cloud = ["polars_cloud (>=0.0.1a1)"]
+pyarrow = ["pyarrow (>=7.0.0)"]
+pydantic = ["pydantic"]
+rt64 = ["polars-runtime-64 (==1.35.1)"]
+rtcompat = ["polars-runtime-compat (==1.35.1)"]
+sqlalchemy = ["polars[pandas]", "sqlalchemy"]
+style = ["great-tables (>=0.8.0)"]
+timezone = ["tzdata ; platform_system == \"Windows\""]
+xlsx2csv = ["xlsx2csv (>=0.8.0)"]
+xlsxwriter = ["xlsxwriter"]
+
+[[package]]
+name = "polars-runtime-32"
+version = "1.35.1"
+description = "Blazingly fast DataFrame library"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "polars_runtime_32-1.35.1-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:6f051a42f6ae2f26e3bc2cf1f170f2120602976e2a3ffb6cfba742eecc7cc620"},
+    {file = "polars_runtime_32-1.35.1-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:c2232f9cf05ba59efc72d940b86c033d41fd2d70bf2742e8115ed7112a766aa9"},
+    {file = "polars_runtime_32-1.35.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:42f9837348557fd674477ea40a6ac8a7e839674f6dd0a199df24be91b026024c"},
+    {file = "polars_runtime_32-1.35.1-cp39-abi3-manylinux_2_24_aarch64.whl", hash = "sha256:c873aeb36fed182d5ebc35ca17c7eb193fe83ae2ea551ee8523ec34776731390"},
+    {file = "polars_runtime_32-1.35.1-cp39-abi3-win_amd64.whl", hash = "sha256:35cde9453ca7032933f0e58e9ed4388f5a1e415dd0db2dd1e442c81d815e630c"},
+    {file = "polars_runtime_32-1.35.1-cp39-abi3-win_arm64.whl", hash = "sha256:fd77757a6c9eb9865c4bfb7b07e22225207c6b7da382bd0b9bd47732f637105d"},
+    {file = "polars_runtime_32-1.35.1.tar.gz", hash = "sha256:f6b4ec9cd58b31c87af1b8c110c9c986d82345f1d50d7f7595b5d447a19dc365"},
+]
+
+[[package]]
+name = "prometheus-client"
+version = "0.23.1"
+description = "Python client for the Prometheus monitoring system."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "prometheus_client-0.23.1-py3-none-any.whl", hash = "sha256:dd1913e6e76b59cfe44e7a4b83e01afc9873c1bdfd2ed8739f1e76aeca115f99"},
+    {file = "prometheus_client-0.23.1.tar.gz", hash = "sha256:6ae8f9081eaaaf153a2e959d2e6c4f4fb57b12ef76c8c7980202f1e57b48b2ce"},
+]
+
+[package.extras]
+twisted = ["twisted"]
+
+[[package]]
+name = "prompt-toolkit"
+version = "3.0.52"
+description = "Library for building powerful interactive command lines in Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955"},
+    {file = "prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855"},
+]
+
+[package.dependencies]
+wcwidth = "*"
+
+[[package]]
+name = "propcache"
+version = "0.4.1"
+description = "Accelerated property cache"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "propcache-0.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7c2d1fa3201efaf55d730400d945b5b3ab6e672e100ba0f9a409d950ab25d7db"},
+    {file = "propcache-0.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1eb2994229cc8ce7fe9b3db88f5465f5fd8651672840b2e426b88cdb1a30aac8"},
+    {file = "propcache-0.4.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:66c1f011f45a3b33d7bcb22daed4b29c0c9e2224758b6be00686731e1b46f925"},
+    {file = "propcache-0.4.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9a52009f2adffe195d0b605c25ec929d26b36ef986ba85244891dee3b294df21"},
+    {file = "propcache-0.4.1-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5d4e2366a9c7b837555cf02fb9be2e3167d333aff716332ef1b7c3a142ec40c5"},
+    {file = "propcache-0.4.1-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:9d2b6caef873b4f09e26ea7e33d65f42b944837563a47a94719cc3544319a0db"},
+    {file = "propcache-0.4.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2b16ec437a8c8a965ecf95739448dd938b5c7f56e67ea009f4300d8df05f32b7"},
+    {file = "propcache-0.4.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:296f4c8ed03ca7476813fe666c9ea97869a8d7aec972618671b33a38a5182ef4"},
+    {file = "propcache-0.4.1-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:1f0978529a418ebd1f49dad413a2b68af33f85d5c5ca5c6ca2a3bed375a7ac60"},
+    {file = "propcache-0.4.1-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:fd138803047fb4c062b1c1dd95462f5209456bfab55c734458f15d11da288f8f"},
+    {file = "propcache-0.4.1-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:8c9b3cbe4584636d72ff556d9036e0c9317fa27b3ac1f0f558e7e84d1c9c5900"},
+    {file = "propcache-0.4.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:f93243fdc5657247533273ac4f86ae106cc6445a0efacb9a1bfe982fcfefd90c"},
+    {file = "propcache-0.4.1-cp310-cp310-win32.whl", hash = "sha256:a0ee98db9c5f80785b266eb805016e36058ac72c51a064040f2bc43b61101cdb"},
+    {file = "propcache-0.4.1-cp310-cp310-win_amd64.whl", hash = "sha256:1cdb7988c4e5ac7f6d175a28a9aa0c94cb6f2ebe52756a3c0cda98d2809a9e37"},
+    {file = "propcache-0.4.1-cp310-cp310-win_arm64.whl", hash = "sha256:d82ad62b19645419fe79dd63b3f9253e15b30e955c0170e5cebc350c1844e581"},
+    {file = "propcache-0.4.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:60a8fda9644b7dfd5dece8c61d8a85e271cb958075bfc4e01083c148b61a7caf"},
+    {file = "propcache-0.4.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c30b53e7e6bda1d547cabb47c825f3843a0a1a42b0496087bb58d8fedf9f41b5"},
+    {file = "propcache-0.4.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:6918ecbd897443087a3b7cd978d56546a812517dcaaca51b49526720571fa93e"},
+    {file = "propcache-0.4.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3d902a36df4e5989763425a8ab9e98cd8ad5c52c823b34ee7ef307fd50582566"},
+    {file = "propcache-0.4.1-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a9695397f85973bb40427dedddf70d8dc4a44b22f1650dd4af9eedf443d45165"},
+    {file = "propcache-0.4.1-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2bb07ffd7eaad486576430c89f9b215f9e4be68c4866a96e97db9e97fead85dc"},
+    {file = "propcache-0.4.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fd6f30fdcf9ae2a70abd34da54f18da086160e4d7d9251f81f3da0ff84fc5a48"},
+    {file = "propcache-0.4.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:fc38cba02d1acba4e2869eef1a57a43dfbd3d49a59bf90dda7444ec2be6a5570"},
+    {file = "propcache-0.4.1-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:67fad6162281e80e882fb3ec355398cf72864a54069d060321f6cd0ade95fe85"},
+    {file = "propcache-0.4.1-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:f10207adf04d08bec185bae14d9606a1444715bc99180f9331c9c02093e1959e"},
+    {file = "propcache-0.4.1-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:e9b0d8d0845bbc4cfcdcbcdbf5086886bc8157aa963c31c777ceff7846c77757"},
+    {file = "propcache-0.4.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:981333cb2f4c1896a12f4ab92a9cc8f09ea664e9b7dbdc4eff74627af3a11c0f"},
+    {file = "propcache-0.4.1-cp311-cp311-win32.whl", hash = "sha256:f1d2f90aeec838a52f1c1a32fe9a619fefd5e411721a9117fbf82aea638fe8a1"},
+    {file = "propcache-0.4.1-cp311-cp311-win_amd64.whl", hash = "sha256:364426a62660f3f699949ac8c621aad6977be7126c5807ce48c0aeb8e7333ea6"},
+    {file = "propcache-0.4.1-cp311-cp311-win_arm64.whl", hash = "sha256:e53f3a38d3510c11953f3e6a33f205c6d1b001129f972805ca9b42fc308bc239"},
+    {file = "propcache-0.4.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:e153e9cd40cc8945138822807139367f256f89c6810c2634a4f6902b52d3b4e2"},
+    {file = "propcache-0.4.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:cd547953428f7abb73c5ad82cbb32109566204260d98e41e5dfdc682eb7f8403"},
+    {file = "propcache-0.4.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f048da1b4f243fc44f205dfd320933a951b8d89e0afd4c7cacc762a8b9165207"},
+    {file = "propcache-0.4.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ec17c65562a827bba85e3872ead335f95405ea1674860d96483a02f5c698fa72"},
+    {file = "propcache-0.4.1-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:405aac25c6394ef275dee4c709be43745d36674b223ba4eb7144bf4d691b7367"},
+    {file = "propcache-0.4.1-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0013cb6f8dde4b2a2f66903b8ba740bdfe378c943c4377a200551ceb27f379e4"},
+    {file = "propcache-0.4.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:15932ab57837c3368b024473a525e25d316d8353016e7cc0e5ba9eb343fbb1cf"},
+    {file = "propcache-0.4.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:031dce78b9dc099f4c29785d9cf5577a3faf9ebf74ecbd3c856a7b92768c3df3"},
+    {file = "propcache-0.4.1-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:ab08df6c9a035bee56e31af99be621526bd237bea9f32def431c656b29e41778"},
+    {file = "propcache-0.4.1-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:4d7af63f9f93fe593afbf104c21b3b15868efb2c21d07d8732c0c4287e66b6a6"},
+    {file = "propcache-0.4.1-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:cfc27c945f422e8b5071b6e93169679e4eb5bf73bbcbf1ba3ae3a83d2f78ebd9"},
+    {file = "propcache-0.4.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:35c3277624a080cc6ec6f847cbbbb5b49affa3598c4535a0a4682a697aaa5c75"},
+    {file = "propcache-0.4.1-cp312-cp312-win32.whl", hash = "sha256:671538c2262dadb5ba6395e26c1731e1d52534bfe9ae56d0b5573ce539266aa8"},
+    {file = "propcache-0.4.1-cp312-cp312-win_amd64.whl", hash = "sha256:cb2d222e72399fcf5890d1d5cc1060857b9b236adff2792ff48ca2dfd46c81db"},
+    {file = "propcache-0.4.1-cp312-cp312-win_arm64.whl", hash = "sha256:204483131fb222bdaaeeea9f9e6c6ed0cac32731f75dfc1d4a567fc1926477c1"},
+    {file = "propcache-0.4.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:43eedf29202c08550aac1d14e0ee619b0430aaef78f85864c1a892294fbc28cf"},
+    {file = "propcache-0.4.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:d62cdfcfd89ccb8de04e0eda998535c406bf5e060ffd56be6c586cbcc05b3311"},
+    {file = "propcache-0.4.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:cae65ad55793da34db5f54e4029b89d3b9b9490d8abe1b4c7ab5d4b8ec7ebf74"},
+    {file = "propcache-0.4.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:333ddb9031d2704a301ee3e506dc46b1fe5f294ec198ed6435ad5b6a085facfe"},
+    {file = "propcache-0.4.1-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:fd0858c20f078a32cf55f7e81473d96dcf3b93fd2ccdb3d40fdf54b8573df3af"},
+    {file = "propcache-0.4.1-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:678ae89ebc632c5c204c794f8dab2837c5f159aeb59e6ed0539500400577298c"},
+    {file = "propcache-0.4.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d472aeb4fbf9865e0c6d622d7f4d54a4e101a89715d8904282bb5f9a2f476c3f"},
+    {file = "propcache-0.4.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:4d3df5fa7e36b3225954fba85589da77a0fe6a53e3976de39caf04a0db4c36f1"},
+    {file = "propcache-0.4.1-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:ee17f18d2498f2673e432faaa71698032b0127ebf23ae5974eeaf806c279df24"},
+    {file = "propcache-0.4.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:580e97762b950f993ae618e167e7be9256b8353c2dcd8b99ec100eb50f5286aa"},
+    {file = "propcache-0.4.1-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:501d20b891688eb8e7aa903021f0b72d5a55db40ffaab27edefd1027caaafa61"},
+    {file = "propcache-0.4.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9a0bd56e5b100aef69bd8562b74b46254e7c8812918d3baa700c8a8009b0af66"},
+    {file = "propcache-0.4.1-cp313-cp313-win32.whl", hash = "sha256:bcc9aaa5d80322bc2fb24bb7accb4a30f81e90ab8d6ba187aec0744bc302ad81"},
+    {file = "propcache-0.4.1-cp313-cp313-win_amd64.whl", hash = "sha256:381914df18634f5494334d201e98245c0596067504b9372d8cf93f4bb23e025e"},
+    {file = "propcache-0.4.1-cp313-cp313-win_arm64.whl", hash = "sha256:8873eb4460fd55333ea49b7d189749ecf6e55bf85080f11b1c4530ed3034cba1"},
+    {file = "propcache-0.4.1-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:92d1935ee1f8d7442da9c0c4fa7ac20d07e94064184811b685f5c4fada64553b"},
+    {file = "propcache-0.4.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:473c61b39e1460d386479b9b2f337da492042447c9b685f28be4f74d3529e566"},
+    {file = "propcache-0.4.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:c0ef0aaafc66fbd87842a3fe3902fd889825646bc21149eafe47be6072725835"},
+    {file = "propcache-0.4.1-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f95393b4d66bfae908c3ca8d169d5f79cd65636ae15b5e7a4f6e67af675adb0e"},
+    {file = "propcache-0.4.1-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c07fda85708bc48578467e85099645167a955ba093be0a2dcba962195676e859"},
+    {file = "propcache-0.4.1-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:af223b406d6d000830c6f65f1e6431783fc3f713ba3e6cc8c024d5ee96170a4b"},
+    {file = "propcache-0.4.1-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a78372c932c90ee474559c5ddfffd718238e8673c340dc21fe45c5b8b54559a0"},
+    {file = "propcache-0.4.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:564d9f0d4d9509e1a870c920a89b2fec951b44bf5ba7d537a9e7c1ccec2c18af"},
+    {file = "propcache-0.4.1-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:17612831fda0138059cc5546f4d12a2aacfb9e47068c06af35c400ba58ba7393"},
+    {file = "propcache-0.4.1-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:41a89040cb10bd345b3c1a873b2bf36413d48da1def52f268a055f7398514874"},
+    {file = "propcache-0.4.1-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:e35b88984e7fa64aacecea39236cee32dd9bd8c55f57ba8a75cf2399553f9bd7"},
+    {file = "propcache-0.4.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6f8b465489f927b0df505cbe26ffbeed4d6d8a2bbc61ce90eb074ff129ef0ab1"},
+    {file = "propcache-0.4.1-cp313-cp313t-win32.whl", hash = "sha256:2ad890caa1d928c7c2965b48f3a3815c853180831d0e5503d35cf00c472f4717"},
+    {file = "propcache-0.4.1-cp313-cp313t-win_amd64.whl", hash = "sha256:f7ee0e597f495cf415bcbd3da3caa3bd7e816b74d0d52b8145954c5e6fd3ff37"},
+    {file = "propcache-0.4.1-cp313-cp313t-win_arm64.whl", hash = "sha256:929d7cbe1f01bb7baffb33dc14eb5691c95831450a26354cd210a8155170c93a"},
+    {file = "propcache-0.4.1-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:3f7124c9d820ba5548d431afb4632301acf965db49e666aa21c305cbe8c6de12"},
+    {file = "propcache-0.4.1-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:c0d4b719b7da33599dfe3b22d3db1ef789210a0597bc650b7cee9c77c2be8c5c"},
+    {file = "propcache-0.4.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:9f302f4783709a78240ebc311b793f123328716a60911d667e0c036bc5dcbded"},
+    {file = "propcache-0.4.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c80ee5802e3fb9ea37938e7eecc307fb984837091d5fd262bb37238b1ae97641"},
+    {file = "propcache-0.4.1-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ed5a841e8bb29a55fb8159ed526b26adc5bdd7e8bd7bf793ce647cb08656cdf4"},
+    {file = "propcache-0.4.1-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:55c72fd6ea2da4c318e74ffdf93c4fe4e926051133657459131a95c846d16d44"},
+    {file = "propcache-0.4.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8326e144341460402713f91df60ade3c999d601e7eb5ff8f6f7862d54de0610d"},
+    {file = "propcache-0.4.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:060b16ae65bc098da7f6d25bf359f1f31f688384858204fe5d652979e0015e5b"},
+    {file = "propcache-0.4.1-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:89eb3fa9524f7bec9de6e83cf3faed9d79bffa560672c118a96a171a6f55831e"},
+    {file = "propcache-0.4.1-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:dee69d7015dc235f526fe80a9c90d65eb0039103fe565776250881731f06349f"},
+    {file = "propcache-0.4.1-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:5558992a00dfd54ccbc64a32726a3357ec93825a418a401f5cc67df0ac5d9e49"},
+    {file = "propcache-0.4.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:c9b822a577f560fbd9554812526831712c1436d2c046cedee4c3796d3543b144"},
+    {file = "propcache-0.4.1-cp314-cp314-win32.whl", hash = "sha256:ab4c29b49d560fe48b696cdcb127dd36e0bc2472548f3bf56cc5cb3da2b2984f"},
+    {file = "propcache-0.4.1-cp314-cp314-win_amd64.whl", hash = "sha256:5a103c3eb905fcea0ab98be99c3a9a5ab2de60228aa5aceedc614c0281cf6153"},
+    {file = "propcache-0.4.1-cp314-cp314-win_arm64.whl", hash = "sha256:74c1fb26515153e482e00177a1ad654721bf9207da8a494a0c05e797ad27b992"},
+    {file = "propcache-0.4.1-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:824e908bce90fb2743bd6b59db36eb4f45cd350a39637c9f73b1c1ea66f5b75f"},
+    {file = "propcache-0.4.1-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:c2b5e7db5328427c57c8e8831abda175421b709672f6cfc3d630c3b7e2146393"},
+    {file = "propcache-0.4.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:6f6ff873ed40292cd4969ef5310179afd5db59fdf055897e282485043fc80ad0"},
+    {file = "propcache-0.4.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:49a2dc67c154db2c1463013594c458881a069fcf98940e61a0569016a583020a"},
+    {file = "propcache-0.4.1-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:005f08e6a0529984491e37d8dbc3dd86f84bd78a8ceb5fa9a021f4c48d4984be"},
+    {file = "propcache-0.4.1-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5c3310452e0d31390da9035c348633b43d7e7feb2e37be252be6da45abd1abcc"},
+    {file = "propcache-0.4.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4c3c70630930447f9ef1caac7728c8ad1c56bc5015338b20fed0d08ea2480b3a"},
+    {file = "propcache-0.4.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8e57061305815dfc910a3634dcf584f08168a8836e6999983569f51a8544cd89"},
+    {file = "propcache-0.4.1-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:521a463429ef54143092c11a77e04056dd00636f72e8c45b70aaa3140d639726"},
+    {file = "propcache-0.4.1-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:120c964da3fdc75e3731aa392527136d4ad35868cc556fd09bb6d09172d9a367"},
+    {file = "propcache-0.4.1-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:d8f353eb14ee3441ee844ade4277d560cdd68288838673273b978e3d6d2c8f36"},
+    {file = "propcache-0.4.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ab2943be7c652f09638800905ee1bab2c544e537edb57d527997a24c13dc1455"},
+    {file = "propcache-0.4.1-cp314-cp314t-win32.whl", hash = "sha256:05674a162469f31358c30bcaa8883cb7829fa3110bf9c0991fe27d7896c42d85"},
+    {file = "propcache-0.4.1-cp314-cp314t-win_amd64.whl", hash = "sha256:990f6b3e2a27d683cb7602ed6c86f15ee6b43b1194736f9baaeb93d0016633b1"},
+    {file = "propcache-0.4.1-cp314-cp314t-win_arm64.whl", hash = "sha256:ecef2343af4cc68e05131e45024ba34f6095821988a9d0a02aa7c73fcc448aa9"},
+    {file = "propcache-0.4.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:3d233076ccf9e450c8b3bc6720af226b898ef5d051a2d145f7d765e6e9f9bcff"},
+    {file = "propcache-0.4.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:357f5bb5c377a82e105e44bd3d52ba22b616f7b9773714bff93573988ef0a5fb"},
+    {file = "propcache-0.4.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:cbc3b6dfc728105b2a57c06791eb07a94229202ea75c59db644d7d496b698cac"},
+    {file = "propcache-0.4.1-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:182b51b421f0501952d938dc0b0eb45246a5b5153c50d42b495ad5fb7517c888"},
+    {file = "propcache-0.4.1-cp39-cp39-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4b536b39c5199b96fc6245eb5fb796c497381d3942f169e44e8e392b29c9ebcc"},
+    {file = "propcache-0.4.1-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:db65d2af507bbfbdcedb254a11149f894169d90488dd3e7190f7cdcb2d6cd57a"},
+    {file = "propcache-0.4.1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fd2dbc472da1f772a4dae4fa24be938a6c544671a912e30529984dd80400cd88"},
+    {file = "propcache-0.4.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:daede9cd44e0f8bdd9e6cc9a607fc81feb80fae7a5fc6cecaff0e0bb32e42d00"},
+    {file = "propcache-0.4.1-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:71b749281b816793678ae7f3d0d84bd36e694953822eaad408d682efc5ca18e0"},
+    {file = "propcache-0.4.1-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:0002004213ee1f36cfb3f9a42b5066100c44276b9b72b4e1504cddd3d692e86e"},
+    {file = "propcache-0.4.1-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:fe49d0a85038f36ba9e3ffafa1103e61170b28e95b16622e11be0a0ea07c6781"},
+    {file = "propcache-0.4.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:99d43339c83aaf4d32bda60928231848eee470c6bda8d02599cc4cebe872d183"},
+    {file = "propcache-0.4.1-cp39-cp39-win32.whl", hash = "sha256:a129e76735bc792794d5177069691c3217898b9f5cee2b2661471e52ffe13f19"},
+    {file = "propcache-0.4.1-cp39-cp39-win_amd64.whl", hash = "sha256:948dab269721ae9a87fd16c514a0a2c2a1bdb23a9a61b969b0f9d9ee2968546f"},
+    {file = "propcache-0.4.1-cp39-cp39-win_arm64.whl", hash = "sha256:5fd37c406dd6dc85aa743e214cef35dc54bbdd1419baac4f6ae5e5b1a2976938"},
+    {file = "propcache-0.4.1-py3-none-any.whl", hash = "sha256:af2a6052aeb6cf17d3e46ee169099044fd8224cbaf75c76a2ef596e8163e2237"},
+    {file = "propcache-0.4.1.tar.gz", hash = "sha256:f48107a8c637e80362555f37ecf49abe20370e557cc4ab374f04ec4423c97c3d"},
+]
+
+[[package]]
+name = "protobuf"
+version = "6.33.0"
+description = ""
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "protobuf-6.33.0-cp310-abi3-win32.whl", hash = "sha256:d6101ded078042a8f17959eccd9236fb7a9ca20d3b0098bbcb91533a5680d035"},
+    {file = "protobuf-6.33.0-cp310-abi3-win_amd64.whl", hash = "sha256:9a031d10f703f03768f2743a1c403af050b6ae1f3480e9c140f39c45f81b13ee"},
+    {file = "protobuf-6.33.0-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:905b07a65f1a4b72412314082c7dbfae91a9e8b68a0cc1577515f8df58ecf455"},
+    {file = "protobuf-6.33.0-cp39-abi3-manylinux2014_aarch64.whl", hash = "sha256:e0697ece353e6239b90ee43a9231318302ad8353c70e6e45499fa52396debf90"},
+    {file = "protobuf-6.33.0-cp39-abi3-manylinux2014_s390x.whl", hash = "sha256:e0a1715e4f27355afd9570f3ea369735afc853a6c3951a6afe1f80d8569ad298"},
+    {file = "protobuf-6.33.0-cp39-abi3-manylinux2014_x86_64.whl", hash = "sha256:35be49fd3f4fefa4e6e2aacc35e8b837d6703c37a2168a55ac21e9b1bc7559ef"},
+    {file = "protobuf-6.33.0-cp39-cp39-win32.whl", hash = "sha256:cd33a8e38ea3e39df66e1bbc462b076d6e5ba3a4ebbde58219d777223a7873d3"},
+    {file = "protobuf-6.33.0-cp39-cp39-win_amd64.whl", hash = "sha256:c963e86c3655af3a917962c9619e1a6b9670540351d7af9439d06064e3317cc9"},
+    {file = "protobuf-6.33.0-py3-none-any.whl", hash = "sha256:25c9e1963c6734448ea2d308cfa610e692b801304ba0908d7bfa564ac5132995"},
+    {file = "protobuf-6.33.0.tar.gz", hash = "sha256:140303d5c8d2037730c548f8c7b93b20bb1dc301be280c378b82b8894589c954"},
+]
+
+[[package]]
+name = "psutil"
+version = "7.1.3"
+description = "Cross-platform lib for process and system monitoring."
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+files = [
+    {file = "psutil-7.1.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0005da714eee687b4b8decd3d6cc7c6db36215c9e74e5ad2264b90c3df7d92dc"},
+    {file = "psutil-7.1.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:19644c85dcb987e35eeeaefdc3915d059dac7bd1167cdcdbf27e0ce2df0c08c0"},
+    {file = "psutil-7.1.3-cp313-cp313t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:95ef04cf2e5ba0ab9eaafc4a11eaae91b44f4ef5541acd2ee91d9108d00d59a7"},
+    {file = "psutil-7.1.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1068c303be3a72f8e18e412c5b2a8f6d31750fb152f9cb106b54090296c9d251"},
+    {file = "psutil-7.1.3-cp313-cp313t-win_amd64.whl", hash = "sha256:18349c5c24b06ac5612c0428ec2a0331c26443d259e2a0144a9b24b4395b58fa"},
+    {file = "psutil-7.1.3-cp313-cp313t-win_arm64.whl", hash = "sha256:c525ffa774fe4496282fb0b1187725793de3e7c6b29e41562733cae9ada151ee"},
+    {file = "psutil-7.1.3-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:b403da1df4d6d43973dc004d19cee3b848e998ae3154cc8097d139b77156c353"},
+    {file = "psutil-7.1.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ad81425efc5e75da3f39b3e636293360ad8d0b49bed7df824c79764fb4ba9b8b"},
+    {file = "psutil-7.1.3-cp314-cp314t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8f33a3702e167783a9213db10ad29650ebf383946e91bc77f28a5eb083496bc9"},
+    {file = "psutil-7.1.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fac9cd332c67f4422504297889da5ab7e05fd11e3c4392140f7370f4208ded1f"},
+    {file = "psutil-7.1.3-cp314-cp314t-win_amd64.whl", hash = "sha256:3792983e23b69843aea49c8f5b8f115572c5ab64c153bada5270086a2123c7e7"},
+    {file = "psutil-7.1.3-cp314-cp314t-win_arm64.whl", hash = "sha256:31d77fcedb7529f27bb3a0472bea9334349f9a04160e8e6e5020f22c59893264"},
+    {file = "psutil-7.1.3-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2bdbcd0e58ca14996a42adf3621a6244f1bb2e2e528886959c72cf1e326677ab"},
+    {file = "psutil-7.1.3-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:bc31fa00f1fbc3c3802141eede66f3a2d51d89716a194bf2cd6fc68310a19880"},
+    {file = "psutil-7.1.3-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3bb428f9f05c1225a558f53e30ccbad9930b11c3fc206836242de1091d3e7dd3"},
+    {file = "psutil-7.1.3-cp36-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:56d974e02ca2c8eb4812c3f76c30e28836fffc311d55d979f1465c1feeb2b68b"},
+    {file = "psutil-7.1.3-cp37-abi3-win_amd64.whl", hash = "sha256:f39c2c19fe824b47484b96f9692932248a54c43799a84282cfe58d05a6449efd"},
+    {file = "psutil-7.1.3-cp37-abi3-win_arm64.whl", hash = "sha256:bd0d69cee829226a761e92f28140bec9a5ee9d5b4fb4b0cc589068dbfff559b1"},
+    {file = "psutil-7.1.3.tar.gz", hash = "sha256:6c86281738d77335af7aec228328e944b30930899ea760ecf33a4dba66be5e74"},
+]
+
+[package.extras]
+dev = ["abi3audit", "black", "check-manifest", "colorama ; os_name == \"nt\"", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pyreadline ; os_name == \"nt\"", "pytest", "pytest-cov", "pytest-instafail", "pytest-subtests", "pytest-xdist", "pywin32 ; os_name == \"nt\" and platform_python_implementation != \"PyPy\"", "requests", "rstcheck", "ruff", "setuptools", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "validate-pyproject[all]", "virtualenv", "vulture", "wheel", "wheel ; os_name == \"nt\" and platform_python_implementation != \"PyPy\"", "wmi ; os_name == \"nt\" and platform_python_implementation != \"PyPy\""]
+test = ["pytest", "pytest-instafail", "pytest-subtests", "pytest-xdist", "pywin32 ; os_name == \"nt\" and platform_python_implementation != \"PyPy\"", "setuptools", "wheel ; os_name == \"nt\" and platform_python_implementation != \"PyPy\"", "wmi ; os_name == \"nt\" and platform_python_implementation != \"PyPy\""]
+
+[[package]]
+name = "ptyprocess"
+version = "0.7.0"
+description = "Run a subprocess in a pseudo terminal"
+optional = false
+python-versions = "*"
+groups = ["main"]
+markers = "os_name != \"nt\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
+files = [
+    {file = "ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35"},
+    {file = "ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"},
+]
+
+[[package]]
+name = "pure-eval"
+version = "0.2.3"
+description = "Safely evaluate AST nodes without side effects"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0"},
+    {file = "pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42"},
+]
+
+[package.extras]
+tests = ["pytest"]
+
+[[package]]
+name = "pyarrow"
+version = "22.0.0"
+description = "Python library for Apache Arrow"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "pyarrow-22.0.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:77718810bd3066158db1e95a63c160ad7ce08c6b0710bc656055033e39cdad88"},
+    {file = "pyarrow-22.0.0-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:44d2d26cda26d18f7af7db71453b7b783788322d756e81730acb98f24eb90ace"},
+    {file = "pyarrow-22.0.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:b9d71701ce97c95480fecb0039ec5bb889e75f110da72005743451339262f4ce"},
+    {file = "pyarrow-22.0.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:710624ab925dc2b05a6229d47f6f0dac1c1155e6ed559be7109f684eba048a48"},
+    {file = "pyarrow-22.0.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:f963ba8c3b0199f9d6b794c90ec77545e05eadc83973897a4523c9e8d84e9340"},
+    {file = "pyarrow-22.0.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:bd0d42297ace400d8febe55f13fdf46e86754842b860c978dfec16f081e5c653"},
+    {file = "pyarrow-22.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:00626d9dc0f5ef3a75fe63fd68b9c7c8302d2b5bbc7f74ecaedba83447a24f84"},
+    {file = "pyarrow-22.0.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:3e294c5eadfb93d78b0763e859a0c16d4051fc1c5231ae8956d61cb0b5666f5a"},
+    {file = "pyarrow-22.0.0-cp311-cp311-macosx_12_0_x86_64.whl", hash = "sha256:69763ab2445f632d90b504a815a2a033f74332997052b721002298ed6de40f2e"},
+    {file = "pyarrow-22.0.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:b41f37cabfe2463232684de44bad753d6be08a7a072f6a83447eeaf0e4d2a215"},
+    {file = "pyarrow-22.0.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:35ad0f0378c9359b3f297299c3309778bb03b8612f987399a0333a560b43862d"},
+    {file = "pyarrow-22.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8382ad21458075c2e66a82a29d650f963ce51c7708c7c0ff313a8c206c4fd5e8"},
+    {file = "pyarrow-22.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:1a812a5b727bc09c3d7ea072c4eebf657c2f7066155506ba31ebf4792f88f016"},
+    {file = "pyarrow-22.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:ec5d40dd494882704fb876c16fa7261a69791e784ae34e6b5992e977bd2e238c"},
+    {file = "pyarrow-22.0.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:bea79263d55c24a32b0d79c00a1c58bb2ee5f0757ed95656b01c0fb310c5af3d"},
+    {file = "pyarrow-22.0.0-cp312-cp312-macosx_12_0_x86_64.whl", hash = "sha256:12fe549c9b10ac98c91cf791d2945e878875d95508e1a5d14091a7aaa66d9cf8"},
+    {file = "pyarrow-22.0.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:334f900ff08ce0423407af97e6c26ad5d4e3b0763645559ece6fbf3747d6a8f5"},
+    {file = "pyarrow-22.0.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:c6c791b09c57ed76a18b03f2631753a4960eefbbca80f846da8baefc6491fcfe"},
+    {file = "pyarrow-22.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c3200cb41cdbc65156e5f8c908d739b0dfed57e890329413da2748d1a2cd1a4e"},
+    {file = "pyarrow-22.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:ac93252226cf288753d8b46280f4edf3433bf9508b6977f8dd8526b521a1bbb9"},
+    {file = "pyarrow-22.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:44729980b6c50a5f2bfcc2668d36c569ce17f8b17bccaf470c4313dcbbf13c9d"},
+    {file = "pyarrow-22.0.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:e6e95176209257803a8b3d0394f21604e796dadb643d2f7ca21b66c9c0b30c9a"},
+    {file = "pyarrow-22.0.0-cp313-cp313-macosx_12_0_x86_64.whl", hash = "sha256:001ea83a58024818826a9e3f89bf9310a114f7e26dfe404a4c32686f97bd7901"},
+    {file = "pyarrow-22.0.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:ce20fe000754f477c8a9125543f1936ea5b8867c5406757c224d745ed033e691"},
+    {file = "pyarrow-22.0.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:e0a15757fccb38c410947df156f9749ae4a3c89b2393741a50521f39a8cf202a"},
+    {file = "pyarrow-22.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:cedb9dd9358e4ea1d9bce3665ce0797f6adf97ff142c8e25b46ba9cdd508e9b6"},
+    {file = "pyarrow-22.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:252be4a05f9d9185bb8c18e83764ebcfea7185076c07a7a662253af3a8c07941"},
+    {file = "pyarrow-22.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:a4893d31e5ef780b6edcaf63122df0f8d321088bb0dee4c8c06eccb1ca28d145"},
+    {file = "pyarrow-22.0.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:f7fe3dbe871294ba70d789be16b6e7e52b418311e166e0e3cba9522f0f437fb1"},
+    {file = "pyarrow-22.0.0-cp313-cp313t-macosx_12_0_x86_64.whl", hash = "sha256:ba95112d15fd4f1105fb2402c4eab9068f0554435e9b7085924bcfaac2cc306f"},
+    {file = "pyarrow-22.0.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:c064e28361c05d72eed8e744c9605cbd6d2bb7481a511c74071fd9b24bc65d7d"},
+    {file = "pyarrow-22.0.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:6f9762274496c244d951c819348afbcf212714902742225f649cf02823a6a10f"},
+    {file = "pyarrow-22.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:a9d9ffdc2ab696f6b15b4d1f7cec6658e1d788124418cb30030afbae31c64746"},
+    {file = "pyarrow-22.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ec1a15968a9d80da01e1d30349b2b0d7cc91e96588ee324ce1b5228175043e95"},
+    {file = "pyarrow-22.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:bba208d9c7decf9961998edf5c65e3ea4355d5818dd6cd0f6809bec1afb951cc"},
+    {file = "pyarrow-22.0.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:9bddc2cade6561f6820d4cd73f99a0243532ad506bc510a75a5a65a522b2d74d"},
+    {file = "pyarrow-22.0.0-cp314-cp314-macosx_12_0_x86_64.whl", hash = "sha256:e70ff90c64419709d38c8932ea9fe1cc98415c4f87ea8da81719e43f02534bc9"},
+    {file = "pyarrow-22.0.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:92843c305330aa94a36e706c16209cd4df274693e777ca47112617db7d0ef3d7"},
+    {file = "pyarrow-22.0.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:6dda1ddac033d27421c20d7a7943eec60be44e0db4e079f33cc5af3b8280ccde"},
+    {file = "pyarrow-22.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:84378110dd9a6c06323b41b56e129c504d157d1a983ce8f5443761eb5256bafc"},
+    {file = "pyarrow-22.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:854794239111d2b88b40b6ef92aa478024d1e5074f364033e73e21e3f76b25e0"},
+    {file = "pyarrow-22.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:b883fe6fd85adad7932b3271c38ac289c65b7337c2c132e9569f9d3940620730"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:7a820d8ae11facf32585507c11f04e3f38343c1e784c9b5a8b1da5c930547fe2"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-macosx_12_0_x86_64.whl", hash = "sha256:c6ec3675d98915bf1ec8b3c7986422682f7232ea76cad276f4c8abd5b7319b70"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:3e739edd001b04f654b166204fc7a9de896cf6007eaff33409ee9e50ceaff754"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:7388ac685cab5b279a41dfe0a6ccd99e4dbf322edfb63e02fc0443bf24134e91"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:f633074f36dbc33d5c05b5dc75371e5660f1dbf9c8b1d95669def05e5425989c"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:4c19236ae2402a8663a2c8f21f1870a03cc57f0bef7e4b6eb3238cc82944de80"},
+    {file = "pyarrow-22.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:0c34fe18094686194f204a3b1787a27456897d8a2d62caf84b61e8dfbc0252ae"},
+    {file = "pyarrow-22.0.0.tar.gz", hash = "sha256:3d600dc583260d845c7d8a6db540339dd883081925da2bd1c5cb808f720b3cd9"},
+]
+
+[[package]]
+name = "pyasn1"
+version = "0.6.1"
+description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
+    {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
+]
+
+[[package]]
+name = "pyasn1-modules"
+version = "0.4.2"
+description = "A collection of ASN.1-based protocols modules"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "pyasn1_modules-0.4.2-py3-none-any.whl", hash = "sha256:29253a9207ce32b64c3ac6600edc75368f98473906e8fd1043bd6b5b1de2c14a"},
+    {file = "pyasn1_modules-0.4.2.tar.gz", hash = "sha256:677091de870a80aae844b1ca6134f54652fa2c8c5a52aa396440ac3106e941e6"},
+]
+
+[package.dependencies]
+pyasn1 = ">=0.6.1,<0.7.0"
+
+[[package]]
+name = "pycparser"
+version = "2.23"
+description = "C parser in Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "implementation_name != \"PyPy\""
+files = [
+    {file = "pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934"},
+    {file = "pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2"},
+]
+
+[[package]]
+name = "pydantic"
+version = "2.12.4"
+description = "Data validation using Python type hints"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "pydantic-2.12.4-py3-none-any.whl", hash = "sha256:92d3d202a745d46f9be6df459ac5a064fdaa3c1c4cd8adcfa332ccf3c05f871e"},
+    {file = "pydantic-2.12.4.tar.gz", hash = "sha256:0f8cb9555000a4b5b617f66bfd2566264c4984b27589d3b845685983e8ea85ac"},
+]
+
+[package.dependencies]
+annotated-types = ">=0.6.0"
+pydantic-core = "2.41.5"
+typing-extensions = ">=4.14.1"
+typing-inspection = ">=0.4.2"
+
+[package.extras]
+email = ["email-validator (>=2.0.0)"]
+timezone = ["tzdata ; python_version >= \"3.9\" and platform_system == \"Windows\""]
+
+[[package]]
+name = "pydantic-core"
+version = "2.41.5"
+description = "Core functionality for Pydantic validation and serialization"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "pydantic_core-2.41.5-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:77b63866ca88d804225eaa4af3e664c5faf3568cea95360d21f4725ab6e07146"},
+    {file = "pydantic_core-2.41.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dfa8a0c812ac681395907e71e1274819dec685fec28273a28905df579ef137e2"},
+    {file = "pydantic_core-2.41.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5921a4d3ca3aee735d9fd163808f5e8dd6c6972101e4adbda9a4667908849b97"},
+    {file = "pydantic_core-2.41.5-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e25c479382d26a2a41b7ebea1043564a937db462816ea07afa8a44c0866d52f9"},
+    {file = "pydantic_core-2.41.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f547144f2966e1e16ae626d8ce72b4cfa0caedc7fa28052001c94fb2fcaa1c52"},
+    {file = "pydantic_core-2.41.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6f52298fbd394f9ed112d56f3d11aabd0d5bd27beb3084cc3d8ad069483b8941"},
+    {file = "pydantic_core-2.41.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:100baa204bb412b74fe285fb0f3a385256dad1d1879f0a5cb1499ed2e83d132a"},
+    {file = "pydantic_core-2.41.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:05a2c8852530ad2812cb7914dc61a1125dc4e06252ee98e5638a12da6cc6fb6c"},
+    {file = "pydantic_core-2.41.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:29452c56df2ed968d18d7e21f4ab0ac55e71dc59524872f6fc57dcf4a3249ed2"},
+    {file = "pydantic_core-2.41.5-cp310-cp310-musllinux_1_1_armv7l.whl", hash = "sha256:d5160812ea7a8a2ffbe233d8da666880cad0cbaf5d4de74ae15c313213d62556"},
+    {file = "pydantic_core-2.41.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:df3959765b553b9440adfd3c795617c352154e497a4eaf3752555cfb5da8fc49"},
+    {file = "pydantic_core-2.41.5-cp310-cp310-win32.whl", hash = "sha256:1f8d33a7f4d5a7889e60dc39856d76d09333d8a6ed0f5f1190635cbec70ec4ba"},
+    {file = "pydantic_core-2.41.5-cp310-cp310-win_amd64.whl", hash = "sha256:62de39db01b8d593e45871af2af9e497295db8d73b085f6bfd0b18c83c70a8f9"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:a3a52f6156e73e7ccb0f8cced536adccb7042be67cb45f9562e12b319c119da6"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7f3bf998340c6d4b0c9a2f02d6a400e51f123b59565d74dc60d252ce888c260b"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:378bec5c66998815d224c9ca994f1e14c0c21cb95d2f52b6021cc0b2a58f2a5a"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e7b576130c69225432866fe2f4a469a85a54ade141d96fd396dffcf607b558f8"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6cb58b9c66f7e4179a2d5e0f849c48eff5c1fca560994d6eb6543abf955a149e"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:88942d3a3dff3afc8288c21e565e476fc278902ae4d6d134f1eeda118cc830b1"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f31d95a179f8d64d90f6831d71fa93290893a33148d890ba15de25642c5d075b"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c1df3d34aced70add6f867a8cf413e299177e0c22660cc767218373d0779487b"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:4009935984bd36bd2c774e13f9a09563ce8de4abaa7226f5108262fa3e637284"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-musllinux_1_1_armv7l.whl", hash = "sha256:34a64bc3441dc1213096a20fe27e8e128bd3ff89921706e83c0b1ac971276594"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:c9e19dd6e28fdcaa5a1de679aec4141f691023916427ef9bae8584f9c2fb3b0e"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-win32.whl", hash = "sha256:2c010c6ded393148374c0f6f0bf89d206bf3217f201faa0635dcd56bd1520f6b"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-win_amd64.whl", hash = "sha256:76ee27c6e9c7f16f47db7a94157112a2f3a00e958bc626e2f4ee8bec5c328fbe"},
+    {file = "pydantic_core-2.41.5-cp311-cp311-win_arm64.whl", hash = "sha256:4bc36bbc0b7584de96561184ad7f012478987882ebf9f9c389b23f432ea3d90f"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:f41a7489d32336dbf2199c8c0a215390a751c5b014c2c1c5366e817202e9cdf7"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:070259a8818988b9a84a449a2a7337c7f430a22acc0859c6b110aa7212a6d9c0"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e96cea19e34778f8d59fe40775a7a574d95816eb150850a85a7a4c8f4b94ac69"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ed2e99c456e3fadd05c991f8f437ef902e00eedf34320ba2b0842bd1c3ca3a75"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:65840751b72fbfd82c3c640cff9284545342a4f1eb1586ad0636955b261b0b05"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e536c98a7626a98feb2d3eaf75944ef6f3dbee447e1f841eae16f2f0a72d8ddc"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eceb81a8d74f9267ef4081e246ffd6d129da5d87e37a77c9bde550cb04870c1c"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d38548150c39b74aeeb0ce8ee1d8e82696f4a4e16ddc6de7b1d8823f7de4b9b5"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:c23e27686783f60290e36827f9c626e63154b82b116d7fe9adba1fda36da706c"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-musllinux_1_1_armv7l.whl", hash = "sha256:482c982f814460eabe1d3bb0adfdc583387bd4691ef00b90575ca0d2b6fe2294"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:bfea2a5f0b4d8d43adf9d7b8bf019fb46fdd10a2e5cde477fbcb9d1fa08c68e1"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-win32.whl", hash = "sha256:b74557b16e390ec12dca509bce9264c3bbd128f8a2c376eaa68003d7f327276d"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-win_amd64.whl", hash = "sha256:1962293292865bca8e54702b08a4f26da73adc83dd1fcf26fbc875b35d81c815"},
+    {file = "pydantic_core-2.41.5-cp312-cp312-win_arm64.whl", hash = "sha256:1746d4a3d9a794cacae06a5eaaccb4b8643a131d45fbc9af23e353dc0a5ba5c3"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:941103c9be18ac8daf7b7adca8228f8ed6bb7a1849020f643b3a14d15b1924d9"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:112e305c3314f40c93998e567879e887a3160bb8689ef3d2c04b6cc62c33ac34"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0cbaad15cb0c90aa221d43c00e77bb33c93e8d36e0bf74760cd00e732d10a6a0"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:03ca43e12fab6023fc79d28ca6b39b05f794ad08ec2feccc59a339b02f2b3d33"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:dc799088c08fa04e43144b164feb0c13f9a0bc40503f8df3e9fde58a3c0c101e"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:97aeba56665b4c3235a0e52b2c2f5ae9cd071b8a8310ad27bddb3f7fb30e9aa2"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:406bf18d345822d6c21366031003612b9c77b3e29ffdb0f612367352aab7d586"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:b93590ae81f7010dbe380cdeab6f515902ebcbefe0b9327cc4804d74e93ae69d"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:01a3d0ab748ee531f4ea6c3e48ad9dac84ddba4b0d82291f87248f2f9de8d740"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-musllinux_1_1_armv7l.whl", hash = "sha256:6561e94ba9dacc9c61bce40e2d6bdc3bfaa0259d3ff36ace3b1e6901936d2e3e"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:915c3d10f81bec3a74fbd4faebe8391013ba61e5a1a8d48c4455b923bdda7858"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-win32.whl", hash = "sha256:650ae77860b45cfa6e2cdafc42618ceafab3a2d9a3811fcfbd3bbf8ac3c40d36"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-win_amd64.whl", hash = "sha256:79ec52ec461e99e13791ec6508c722742ad745571f234ea6255bed38c6480f11"},
+    {file = "pydantic_core-2.41.5-cp313-cp313-win_arm64.whl", hash = "sha256:3f84d5c1b4ab906093bdc1ff10484838aca54ef08de4afa9de0f5f14d69639cd"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:3f37a19d7ebcdd20b96485056ba9e8b304e27d9904d233d7b1015db320e51f0a"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:1d1d9764366c73f996edd17abb6d9d7649a7eb690006ab6adbda117717099b14"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:25e1c2af0fce638d5f1988b686f3b3ea8cd7de5f244ca147c777769e798a9cd1"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:506d766a8727beef16b7adaeb8ee6217c64fc813646b424d0804d67c16eddb66"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4819fa52133c9aa3c387b3328f25c1facc356491e6135b459f1de698ff64d869"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2b761d210c9ea91feda40d25b4efe82a1707da2ef62901466a42492c028553a2"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:22f0fb8c1c583a3b6f24df2470833b40207e907b90c928cc8d3594b76f874375"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2782c870e99878c634505236d81e5443092fba820f0373997ff75f90f68cd553"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-musllinux_1_1_aarch64.whl", hash = "sha256:0177272f88ab8312479336e1d777f6b124537d47f2123f89cb37e0accea97f90"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-musllinux_1_1_armv7l.whl", hash = "sha256:63510af5e38f8955b8ee5687740d6ebf7c2a0886d15a6d65c32814613681bc07"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-musllinux_1_1_x86_64.whl", hash = "sha256:e56ba91f47764cc14f1daacd723e3e82d1a89d783f0f5afe9c364b8bb491ccdb"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-win32.whl", hash = "sha256:aec5cf2fd867b4ff45b9959f8b20ea3993fc93e63c7363fe6851424c8a7e7c23"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-win_amd64.whl", hash = "sha256:8e7c86f27c585ef37c35e56a96363ab8de4e549a95512445b85c96d3e2f7c1bf"},
+    {file = "pydantic_core-2.41.5-cp314-cp314-win_arm64.whl", hash = "sha256:e672ba74fbc2dc8eea59fb6d4aed6845e6905fc2a8afe93175d94a83ba2a01a0"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:8566def80554c3faa0e65ac30ab0932b9e3a5cd7f8323764303d468e5c37595a"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:b80aa5095cd3109962a298ce14110ae16b8c1aece8b72f9dafe81cf597ad80b3"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3006c3dd9ba34b0c094c544c6006cc79e87d8612999f1a5d43b769b89181f23c"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:72f6c8b11857a856bcfa48c86f5368439f74453563f951e473514579d44aa612"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5cb1b2f9742240e4bb26b652a5aeb840aa4b417c7748b6f8387927bc6e45e40d"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bd3d54f38609ff308209bd43acea66061494157703364ae40c951f83ba99a1a9"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2ff4321e56e879ee8d2a879501c8e469414d948f4aba74a2d4593184eb326660"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d0d2568a8c11bf8225044aa94409e21da0cb09dcdafe9ecd10250b2baad531a9"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-musllinux_1_1_aarch64.whl", hash = "sha256:a39455728aabd58ceabb03c90e12f71fd30fa69615760a075b9fec596456ccc3"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-musllinux_1_1_armv7l.whl", hash = "sha256:239edca560d05757817c13dc17c50766136d21f7cd0fac50295499ae24f90fdf"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-musllinux_1_1_x86_64.whl", hash = "sha256:2a5e06546e19f24c6a96a129142a75cee553cc018ffee48a460059b1185f4470"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-win32.whl", hash = "sha256:b4ececa40ac28afa90871c2cc2b9ffd2ff0bf749380fbdf57d165fd23da353aa"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-win_amd64.whl", hash = "sha256:80aa89cad80b32a912a65332f64a4450ed00966111b6615ca6816153d3585a8c"},
+    {file = "pydantic_core-2.41.5-cp314-cp314t-win_arm64.whl", hash = "sha256:35b44f37a3199f771c3eaa53051bc8a70cd7b54f333531c59e29fd4db5d15008"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:8bfeaf8735be79f225f3fefab7f941c712aaca36f1128c9d7e2352ee1aa87bdf"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:346285d28e4c8017da95144c7f3acd42740d637ff41946af5ce6e5e420502dd5"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a75dafbf87d6276ddc5b2bf6fae5254e3d0876b626eb24969a574fff9149ee5d"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:7b93a4d08587e2b7e7882de461e82b6ed76d9026ce91ca7915e740ecc7855f60"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e8465ab91a4bd96d36dde3263f06caa6a8a6019e4113f24dc753d79a8b3a3f82"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:299e0a22e7ae2b85c1a57f104538b2656e8ab1873511fd718a1c1c6f149b77b5"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:707625ef0983fcfb461acfaf14de2067c5942c6bb0f3b4c99158bed6fedd3cf3"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f41eb9797986d6ebac5e8edff36d5cef9de40def462311b3eb3eeded1431e425"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:0384e2e1021894b1ff5a786dbf94771e2986ebe2869533874d7e43bc79c6f504"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-musllinux_1_1_armv7l.whl", hash = "sha256:f0cd744688278965817fd0839c4a4116add48d23890d468bc436f78beb28abf5"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:753e230374206729bf0a807954bcc6c150d3743928a73faffee51ac6557a03c3"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-win32.whl", hash = "sha256:873e0d5b4fb9b89ef7c2d2a963ea7d02879d9da0da8d9d4933dee8ee86a8b460"},
+    {file = "pydantic_core-2.41.5-cp39-cp39-win_amd64.whl", hash = "sha256:e4f4a984405e91527a0d62649ee21138f8e3d0ef103be488c1dc11a80d7f184b"},
+    {file = "pydantic_core-2.41.5-graalpy311-graalpy242_311_native-macosx_10_12_x86_64.whl", hash = "sha256:b96d5f26b05d03cc60f11a7761a5ded1741da411e7fe0909e27a5e6a0cb7b034"},
+    {file = "pydantic_core-2.41.5-graalpy311-graalpy242_311_native-macosx_11_0_arm64.whl", hash = "sha256:634e8609e89ceecea15e2d61bc9ac3718caaaa71963717bf3c8f38bfde64242c"},
+    {file = "pydantic_core-2.41.5-graalpy311-graalpy242_311_native-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:93e8740d7503eb008aa2df04d3b9735f845d43ae845e6dcd2be0b55a2da43cd2"},
+    {file = "pydantic_core-2.41.5-graalpy311-graalpy242_311_native-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f15489ba13d61f670dcc96772e733aad1a6f9c429cc27574c6cdaed82d0146ad"},
+    {file = "pydantic_core-2.41.5-graalpy312-graalpy250_312_native-macosx_10_12_x86_64.whl", hash = "sha256:7da7087d756b19037bc2c06edc6c170eeef3c3bafcb8f532ff17d64dc427adfd"},
+    {file = "pydantic_core-2.41.5-graalpy312-graalpy250_312_native-macosx_11_0_arm64.whl", hash = "sha256:aabf5777b5c8ca26f7824cb4a120a740c9588ed58df9b2d196ce92fba42ff8dc"},
+    {file = "pydantic_core-2.41.5-graalpy312-graalpy250_312_native-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c007fe8a43d43b3969e8469004e9845944f1a80e6acd47c150856bb87f230c56"},
+    {file = "pydantic_core-2.41.5-graalpy312-graalpy250_312_native-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:76d0819de158cd855d1cbb8fcafdf6f5cf1eb8e470abe056d5d161106e38062b"},
+    {file = "pydantic_core-2.41.5-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:b5819cd790dbf0c5eb9f82c73c16b39a65dd6dd4d1439dcdea7816ec9adddab8"},
+    {file = "pydantic_core-2.41.5-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:5a4e67afbc95fa5c34cf27d9089bca7fcab4e51e57278d710320a70b956d1b9a"},
+    {file = "pydantic_core-2.41.5-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ece5c59f0ce7d001e017643d8d24da587ea1f74f6993467d85ae8a5ef9d4f42b"},
+    {file = "pydantic_core-2.41.5-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:16f80f7abe3351f8ea6858914ddc8c77e02578544a0ebc15b4c2e1a0e813b0b2"},
+    {file = "pydantic_core-2.41.5-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:33cb885e759a705b426baada1fe68cbb0a2e68e34c5d0d0289a364cf01709093"},
+    {file = "pydantic_core-2.41.5-pp310-pypy310_pp73-musllinux_1_1_armv7l.whl", hash = "sha256:c8d8b4eb992936023be7dee581270af5c6e0697a8559895f527f5b7105ecd36a"},
+    {file = "pydantic_core-2.41.5-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:242a206cd0318f95cd21bdacff3fcc3aab23e79bba5cac3db5a841c9ef9c6963"},
+    {file = "pydantic_core-2.41.5-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:d3a978c4f57a597908b7e697229d996d77a6d3c94901e9edee593adada95ce1a"},
+    {file = "pydantic_core-2.41.5-pp311-pypy311_pp73-macosx_10_12_x86_64.whl", hash = "sha256:b2379fa7ed44ddecb5bfe4e48577d752db9fc10be00a6b7446e9663ba143de26"},
+    {file = "pydantic_core-2.41.5-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:266fb4cbf5e3cbd0b53669a6d1b039c45e3ce651fd5442eff4d07c2cc8d66808"},
+    {file = "pydantic_core-2.41.5-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:58133647260ea01e4d0500089a8c4f07bd7aa6ce109682b1426394988d8aaacc"},
+    {file = "pydantic_core-2.41.5-pp311-pypy311_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:287dad91cfb551c363dc62899a80e9e14da1f0e2b6ebde82c806612ca2a13ef1"},
+    {file = "pydantic_core-2.41.5-pp311-pypy311_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:03b77d184b9eb40240ae9fd676ca364ce1085f203e1b1256f8ab9984dca80a84"},
+    {file = "pydantic_core-2.41.5-pp311-pypy311_pp73-musllinux_1_1_armv7l.whl", hash = "sha256:a668ce24de96165bb239160b3d854943128f4334822900534f2fe947930e5770"},
+    {file = "pydantic_core-2.41.5-pp311-pypy311_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:f14f8f046c14563f8eb3f45f499cc658ab8d10072961e07225e507adb700e93f"},
+    {file = "pydantic_core-2.41.5-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:56121965f7a4dc965bff783d70b907ddf3d57f6eba29b6d2e5dabfaf07799c51"},
+    {file = "pydantic_core-2.41.5.tar.gz", hash = "sha256:08daa51ea16ad373ffd5e7606252cc32f07bc72b28284b6bc9c6df804816476e"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.14.1"
+
+[[package]]
+name = "pygame"
+version = "2.6.1"
+description = "Python Game Development"
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+files = [
+    {file = "pygame-2.6.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9beeb647e555afb5657111fa83acb74b99ad88761108eaea66472e8b8547b55b"},
+    {file = "pygame-2.6.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:10e3d2a55f001f6c0a6eb44aa79ea7607091c9352b946692acedb2ac1482f1c9"},
+    {file = "pygame-2.6.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:816e85000c5d8b02a42b9834f761a5925ef3377d2924e3a7c4c143d2990ce5b8"},
+    {file = "pygame-2.6.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8a78fd030d98faab4a8e27878536fdff7518d3e062a72761c552f624ebba5a5f"},
+    {file = "pygame-2.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da3ad64d685f84a34ebe5daacb39fff14f1251acb34c098d760d63fee768f50c"},
+    {file = "pygame-2.6.1-cp310-cp310-win32.whl", hash = "sha256:9dd5c054d4bd875a8caf978b82672f02bec332f52a833a76899220c460bb4b58"},
+    {file = "pygame-2.6.1-cp310-cp310-win_amd64.whl", hash = "sha256:00827aba089355925902d533f9c41e79a799641f03746c50a374dc5c3362e43d"},
+    {file = "pygame-2.6.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:20349195326a5e82a16e351ed93465a7845a7e2a9af55b7bc1b2110ea3e344e1"},
+    {file = "pygame-2.6.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f3935459109da4bb0b3901da9904f0a3e52028a3332a355d298b1673a334cf21"},
+    {file = "pygame-2.6.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c31dbdb5d0217f32764797d21c2752e258e5fb7e895326538d82b5f75a0cd856"},
+    {file = "pygame-2.6.1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:173badf82fa198e6888017bea40f511cb28e69ecdd5a72b214e81e4dcd66c3b1"},
+    {file = "pygame-2.6.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ce8cc108b92de9b149b344ad2e25eedbe773af0dc41dfb24d1f07f679b558c60"},
+    {file = "pygame-2.6.1-cp311-cp311-win32.whl", hash = "sha256:811e7b925146d8149d79193652cbb83e0eca0aae66476b1cb310f0f4226b8b5c"},
+    {file = "pygame-2.6.1-cp311-cp311-win_amd64.whl", hash = "sha256:91476902426facd4bb0dad4dc3b2573bc82c95c71b135e0daaea072ed528d299"},
+    {file = "pygame-2.6.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:4ee7f2771f588c966fa2fa8b829be26698c9b4836f82ede5e4edc1a68594942e"},
+    {file = "pygame-2.6.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c8040ea2ab18c6b255af706ec01355c8a6b08dc48d77fd4ee783f8fc46a843bf"},
+    {file = "pygame-2.6.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c47a6938de93fa610accd4969e638c2aebcb29b2fca518a84c3a39d91ab47116"},
+    {file = "pygame-2.6.1-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:33006f784e1c7d7e466fcb61d5489da59cc5f7eb098712f792a225df1d4e229d"},
+    {file = "pygame-2.6.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1206125f14cae22c44565c9d333607f1d9f59487b1f1432945dfc809aeaa3e88"},
+    {file = "pygame-2.6.1-cp312-cp312-win32.whl", hash = "sha256:84fc4054e25262140d09d39e094f6880d730199710829902f0d8ceae0213379e"},
+    {file = "pygame-2.6.1-cp312-cp312-win_amd64.whl", hash = "sha256:3a9e7396be0d9633831c3f8d5d82dd63ba373ad65599628294b7a4f8a5a01a65"},
+    {file = "pygame-2.6.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ae6039f3a55d800db80e8010f387557b528d34d534435e0871326804df2a62f2"},
+    {file = "pygame-2.6.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2a3a1288e2e9b1e5834e425bedd5ba01a3cd4902b5c2bff8ed4a740ccfe98171"},
+    {file = "pygame-2.6.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:27eb17e3dc9640e4b4683074f1890e2e879827447770470c2aba9f125f74510b"},
+    {file = "pygame-2.6.1-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4c1623180e70a03c4a734deb9bac50fc9c82942ae84a3a220779062128e75f3b"},
+    {file = "pygame-2.6.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ef07c0103d79492c21fced9ad68c11c32efa6801ca1920ebfd0f15fb46c78b1c"},
+    {file = "pygame-2.6.1-cp313-cp313-win32.whl", hash = "sha256:3acd8c009317190c2bfd81db681ecef47d5eb108c2151d09596d9c7ea9df5c0e"},
+    {file = "pygame-2.6.1-cp313-cp313-win_amd64.whl", hash = "sha256:813af4fba5d0b2cb8e58f5d95f7910295c34067dcc290d34f1be59c48bd1ea6a"},
+    {file = "pygame-2.6.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:56ffca6059b165bbf64f4b4be23b8068f6a0e220780e4f96ec0bb5ac3c63ec39"},
+    {file = "pygame-2.6.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3bede70ec708057e305815d6546012669226d1d80566785feca9b044216062e7"},
+    {file = "pygame-2.6.1-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f84f15d146d6aa93254008a626c56ef96fed276006202881a47b29757f0cd65a"},
+    {file = "pygame-2.6.1-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:14f9dda45469b254c0f15edaaeaa85d2cc072ff6a83584a265f5d684c7f7efd8"},
+    {file = "pygame-2.6.1-cp36-cp36m-win32.whl", hash = "sha256:28b43190436037e428a5be28fc80cf6615304fd528009f2c688cc828f4ff104b"},
+    {file = "pygame-2.6.1-cp36-cp36m-win_amd64.whl", hash = "sha256:a4b8f04fceddd9a3ac30778d11f0254f59efcd1c382d5801271113cea8b4f2f3"},
+    {file = "pygame-2.6.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:a620883d589926f157b8f1d1f543183ac52e5c30507dea445e3927ae0bee1c54"},
+    {file = "pygame-2.6.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b46e68cd168f44d0224c670bb72186688fc692d7079715f79d04096757d703d0"},
+    {file = "pygame-2.6.1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c0b11356ac96261162d54a2c2b41a41978f00525631b01ec9c4fe26b01c66595"},
+    {file = "pygame-2.6.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:325a84d072d52e3c2921eff02f87c6a74b7e77d71db3bdf53801c6c975f1b6c4"},
+    {file = "pygame-2.6.1-cp37-cp37m-win32.whl", hash = "sha256:2a615d78b2364e86f541458ff41c2a46181b9a1e9eabd97b389282fdf04efbb3"},
+    {file = "pygame-2.6.1-cp37-cp37m-win_amd64.whl", hash = "sha256:94afd1177680d92f9214c54966ad3517d18210c4fbc5d84a0192d218e93647e0"},
+    {file = "pygame-2.6.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:97ac4e13847b6b293ecaffa5ffce9886c98d09c03309406931cc592f0cea6366"},
+    {file = "pygame-2.6.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:d1a7f2b66ac2e4c9583b6d4c6d6f346fb10a3392c04163f537061f86a448ed5c"},
+    {file = "pygame-2.6.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ac3f033d2be4a9e23660a96afe2986df3a6916227538a6a0061bc218c5088507"},
+    {file = "pygame-2.6.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a1bf7ab5311bbced70320f1a56701650b4c18231343ae5af42111eea91e0949a"},
+    {file = "pygame-2.6.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:21160d9093533eb831f1b708e630706e5ac16b30750571ec27bc3b8364814f38"},
+    {file = "pygame-2.6.1-cp38-cp38-win32.whl", hash = "sha256:7bffdd3eaf394d9645331d1c3a5df9d782ebcc3c5a78f3b657c7879a828dd111"},
+    {file = "pygame-2.6.1-cp38-cp38-win_amd64.whl", hash = "sha256:818b4eaec9c4acb6ac64805d4ca8edd4062bebca77bd815c18739fe2842c97e9"},
+    {file = "pygame-2.6.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:15efaa11a80a65dd589a95bebe812fa5bfc7e14946b638a424c5bd9ac6cca1a4"},
+    {file = "pygame-2.6.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:481cfe1bdbb7fe00acc5950c494c26f00240888619bdc396fc8c39a734797432"},
+    {file = "pygame-2.6.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5d09fd950725d187aa5207c0cb8eb9ab0d2f8ce9ab8d189c30eeb470e71b617e"},
+    {file = "pygame-2.6.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:163e66de169bd5670c86e27d0b74aad0d2d745e3b63cf4e7eb5b2bff1231ca8d"},
+    {file = "pygame-2.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fb6e8d0547f30ddc845f4fd1e33070ef548233ad0dbf21f7ecea768883d1bbdc"},
+    {file = "pygame-2.6.1-cp39-cp39-win32.whl", hash = "sha256:d29eb9a93f12aa3d997b6e3c447ac85b2a4b142ab2548441523a8fcf5e216042"},
+    {file = "pygame-2.6.1-cp39-cp39-win_amd64.whl", hash = "sha256:6582aa71a681e02e55d43150a9ab41394e6bf4d783d2962a10aea58f424be060"},
+    {file = "pygame-2.6.1-pp36-pypy36_pp73-win32.whl", hash = "sha256:4a8ea113b1bf627322a025a1a5a87e3818a7f55ab3a4077ff1ae5c8c60576614"},
+    {file = "pygame-2.6.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:b7f9f8e6f76de36f4725175d686601214af362a4f30614b4dae2240198e72e6f"},
+    {file = "pygame-2.6.1-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:bbb7167c92103a2091366e9af26d4914ba3776666e8677d3c93551353fffa626"},
+    {file = "pygame-2.6.1-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:17498a2b043bc0e795faedef1b081199c688890200aef34991c1941caa2d2c89"},
+    {file = "pygame-2.6.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7103c60939bbc1e05cfc7ba3f1d2ad3bbf103b7828b82a7166a9ab6f51950146"},
+    {file = "pygame-2.6.1.tar.gz", hash = "sha256:56fb02ead529cee00d415c3e007f75e0780c655909aaa8e8bf616ee09c9feb1f"},
+]
+
+[[package]]
+name = "pygments"
+version = "2.19.2"
+description = "Pygments is a syntax highlighting package written in Python."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b"},
+    {file = "pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887"},
+]
+
+[package.extras]
+windows-terminal = ["colorama (>=0.4.6)"]
+
+[[package]]
+name = "pyparsing"
+version = "3.2.5"
+description = "pyparsing - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "pyparsing-3.2.5-py3-none-any.whl", hash = "sha256:e38a4f02064cf41fe6593d328d0512495ad1f3d8a91c4f73fc401b3079a59a5e"},
+    {file = "pyparsing-3.2.5.tar.gz", hash = "sha256:2df8d5b7b2802ef88e8d016a2eb9c7aeaa923529cd251ed0fe4608275d4105b6"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+description = "Extensions to the standard Python datetime module"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
+files = [
+    {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
+    {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
+]
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+name = "python-json-logger"
+version = "4.0.0"
+description = "JSON Log Formatter for the Python Logging Package"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "python_json_logger-4.0.0-py3-none-any.whl", hash = "sha256:af09c9daf6a813aa4cc7180395f50f2a9e5fa056034c9953aec92e381c5ba1e2"},
+    {file = "python_json_logger-4.0.0.tar.gz", hash = "sha256:f58e68eb46e1faed27e0f574a55a0455eecd7b8a5b88b85a784519ba3cff047f"},
+]
+
+[package.extras]
+dev = ["backports.zoneinfo ; python_version < \"3.9\"", "black", "build", "freezegun", "mdx_truly_sane_lists", "mike", "mkdocs", "mkdocs-awesome-pages-plugin", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-material (>=8.5)", "mkdocstrings[python]", "msgspec ; implementation_name != \"pypy\"", "mypy", "orjson ; implementation_name != \"pypy\"", "pylint", "pytest", "tzdata", "validate-pyproject[all]"]
+
+[[package]]
+name = "pytz"
+version = "2025.2"
+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00"},
+    {file = "pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3"},
+]
+
+[[package]]
+name = "pywinpty"
+version = "3.0.2"
+description = "Pseudo terminal support for Windows from Python."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "os_name == \"nt\""
+files = [
+    {file = "pywinpty-3.0.2-cp310-cp310-win_amd64.whl", hash = "sha256:65db57fd3387d71e8372b6a54269cbcd0f6dfa6d4616a29e0af749ec19f5c558"},
+    {file = "pywinpty-3.0.2-cp311-cp311-win_amd64.whl", hash = "sha256:327790d70e4c841ebd9d0f295a780177149aeb405bca44c7115a3de5c2054b23"},
+    {file = "pywinpty-3.0.2-cp312-cp312-win_amd64.whl", hash = "sha256:99fdd9b455f0ad6419aba6731a7a0d2f88ced83c3c94a80ff9533d95fa8d8a9e"},
+    {file = "pywinpty-3.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:18f78b81e4cfee6aabe7ea8688441d30247b73e52cd9657138015c5f4ee13a51"},
+    {file = "pywinpty-3.0.2-cp313-cp313t-win_amd64.whl", hash = "sha256:663383ecfab7fc382cc97ea5c4f7f0bb32c2f889259855df6ea34e5df42d305b"},
+    {file = "pywinpty-3.0.2-cp314-cp314-win_amd64.whl", hash = "sha256:28297cecc37bee9f24d8889e47231972d6e9e84f7b668909de54f36ca785029a"},
+    {file = "pywinpty-3.0.2-cp314-cp314t-win_amd64.whl", hash = "sha256:34b55ae9a1b671fe3eae071d86618110538e8eaad18fcb1531c0830b91a82767"},
+    {file = "pywinpty-3.0.2-cp39-cp39-win_amd64.whl", hash = "sha256:3962daf801bc38dd4de872108c424b5338c9a46c6efca5761854cd66370a9022"},
+    {file = "pywinpty-3.0.2.tar.gz", hash = "sha256:1505cc4cb248af42cb6285a65c9c2086ee9e7e574078ee60933d5d7fa86fb004"},
+]
+
+[[package]]
+name = "pyyaml"
+version = "6.0.3"
+description = "YAML parser and emitter for Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "PyYAML-6.0.3-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:c2514fceb77bc5e7a2f7adfaa1feb2fb311607c9cb518dbc378688ec73d8292f"},
+    {file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c57bb8c96f6d1808c030b1687b9b5fb476abaa47f0db9c0101f5e9f394e97f4"},
+    {file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:efd7b85f94a6f21e4932043973a7ba2613b059c4a000551892ac9f1d11f5baf3"},
+    {file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:22ba7cfcad58ef3ecddc7ed1db3409af68d023b7f940da23c6c2a1890976eda6"},
+    {file = "PyYAML-6.0.3-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:6344df0d5755a2c9a276d4473ae6b90647e216ab4757f8426893b5dd2ac3f369"},
+    {file = "PyYAML-6.0.3-cp38-cp38-win32.whl", hash = "sha256:3ff07ec89bae51176c0549bc4c63aa6202991da2d9a6129d7aef7f1407d3f295"},
+    {file = "PyYAML-6.0.3-cp38-cp38-win_amd64.whl", hash = "sha256:5cf4e27da7e3fbed4d6c3d8e797387aaad68102272f8f9752883bc32d61cb87b"},
+    {file = "pyyaml-6.0.3-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:214ed4befebe12df36bcc8bc2b64b396ca31be9304b8f59e25c11cf94a4c033b"},
+    {file = "pyyaml-6.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:02ea2dfa234451bbb8772601d7b8e426c2bfa197136796224e50e35a78777956"},
+    {file = "pyyaml-6.0.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b30236e45cf30d2b8e7b3e85881719e98507abed1011bf463a8fa23e9c3e98a8"},
+    {file = "pyyaml-6.0.3-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:66291b10affd76d76f54fad28e22e51719ef9ba22b29e1d7d03d6777a9174198"},
+    {file = "pyyaml-6.0.3-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9c7708761fccb9397fe64bbc0395abcae8c4bf7b0eac081e12b809bf47700d0b"},
+    {file = "pyyaml-6.0.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:418cf3f2111bc80e0933b2cd8cd04f286338bb88bdc7bc8e6dd775ebde60b5e0"},
+    {file = "pyyaml-6.0.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:5e0b74767e5f8c593e8c9b5912019159ed0533c70051e9cce3e8b6aa699fcd69"},
+    {file = "pyyaml-6.0.3-cp310-cp310-win32.whl", hash = "sha256:28c8d926f98f432f88adc23edf2e6d4921ac26fb084b028c733d01868d19007e"},
+    {file = "pyyaml-6.0.3-cp310-cp310-win_amd64.whl", hash = "sha256:bdb2c67c6c1390b63c6ff89f210c8fd09d9a1217a465701eac7316313c915e4c"},
+    {file = "pyyaml-6.0.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:44edc647873928551a01e7a563d7452ccdebee747728c1080d881d68af7b997e"},
+    {file = "pyyaml-6.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:652cb6edd41e718550aad172851962662ff2681490a8a711af6a4d288dd96824"},
+    {file = "pyyaml-6.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:10892704fc220243f5305762e276552a0395f7beb4dbf9b14ec8fd43b57f126c"},
+    {file = "pyyaml-6.0.3-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:850774a7879607d3a6f50d36d04f00ee69e7fc816450e5f7e58d7f17f1ae5c00"},
+    {file = "pyyaml-6.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b8bb0864c5a28024fac8a632c443c87c5aa6f215c0b126c449ae1a150412f31d"},
+    {file = "pyyaml-6.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1d37d57ad971609cf3c53ba6a7e365e40660e3be0e5175fa9f2365a379d6095a"},
+    {file = "pyyaml-6.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:37503bfbfc9d2c40b344d06b2199cf0e96e97957ab1c1b546fd4f87e53e5d3e4"},
+    {file = "pyyaml-6.0.3-cp311-cp311-win32.whl", hash = "sha256:8098f252adfa6c80ab48096053f512f2321f0b998f98150cea9bd23d83e1467b"},
+    {file = "pyyaml-6.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:9f3bfb4965eb874431221a3ff3fdcddc7e74e3b07799e0e84ca4a0f867d449bf"},
+    {file = "pyyaml-6.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196"},
+    {file = "pyyaml-6.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0"},
+    {file = "pyyaml-6.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28"},
+    {file = "pyyaml-6.0.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c"},
+    {file = "pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc"},
+    {file = "pyyaml-6.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e"},
+    {file = "pyyaml-6.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea"},
+    {file = "pyyaml-6.0.3-cp312-cp312-win32.whl", hash = "sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5"},
+    {file = "pyyaml-6.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b"},
+    {file = "pyyaml-6.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd"},
+    {file = "pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8"},
+    {file = "pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1"},
+    {file = "pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c"},
+    {file = "pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5"},
+    {file = "pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6"},
+    {file = "pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6"},
+    {file = "pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be"},
+    {file = "pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26"},
+    {file = "pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c"},
+    {file = "pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb"},
+    {file = "pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac"},
+    {file = "pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310"},
+    {file = "pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7"},
+    {file = "pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788"},
+    {file = "pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5"},
+    {file = "pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764"},
+    {file = "pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35"},
+    {file = "pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac"},
+    {file = "pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3"},
+    {file = "pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3"},
+    {file = "pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba"},
+    {file = "pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c"},
+    {file = "pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702"},
+    {file = "pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c"},
+    {file = "pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065"},
+    {file = "pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65"},
+    {file = "pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9"},
+    {file = "pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b"},
+    {file = "pyyaml-6.0.3-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:b865addae83924361678b652338317d1bd7e79b1f4596f96b96c77a5a34b34da"},
+    {file = "pyyaml-6.0.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c3355370a2c156cffb25e876646f149d5d68f5e0a3ce86a5084dd0b64a994917"},
+    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3c5677e12444c15717b902a5798264fa7909e41153cdf9ef7ad571b704a63dd9"},
+    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5ed875a24292240029e4483f9d4a4b8a1ae08843b9c54f43fcc11e404532a8a5"},
+    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0150219816b6a1fa26fb4699fb7daa9caf09eb1999f3b70fb6e786805e80375a"},
+    {file = "pyyaml-6.0.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:fa160448684b4e94d80416c0fa4aac48967a969efe22931448d853ada8baf926"},
+    {file = "pyyaml-6.0.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:27c0abcb4a5dac13684a37f76e701e054692a9b2d3064b70f5e4eb54810553d7"},
+    {file = "pyyaml-6.0.3-cp39-cp39-win32.whl", hash = "sha256:1ebe39cb5fc479422b83de611d14e2c0d3bb2a18bbcb01f229ab3cfbd8fee7a0"},
+    {file = "pyyaml-6.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:2e71d11abed7344e42a8849600193d15b6def118602c4c176f748e4583246007"},
+    {file = "pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f"},
+]
+
+[[package]]
+name = "pyzmq"
+version = "27.1.0"
+description = "Python bindings for 0MQ"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "pyzmq-27.1.0-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:508e23ec9bc44c0005c4946ea013d9317ae00ac67778bd47519fdf5a0e930ff4"},
+    {file = "pyzmq-27.1.0-cp310-cp310-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:507b6f430bdcf0ee48c0d30e734ea89ce5567fd7b8a0f0044a369c176aa44556"},
+    {file = "pyzmq-27.1.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bf7b38f9fd7b81cb6d9391b2946382c8237fd814075c6aa9c3b746d53076023b"},
+    {file = "pyzmq-27.1.0-cp310-cp310-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:03ff0b279b40d687691a6217c12242ee71f0fba28bf8626ff50e3ef0f4410e1e"},
+    {file = "pyzmq-27.1.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:677e744fee605753eac48198b15a2124016c009a11056f93807000ab11ce6526"},
+    {file = "pyzmq-27.1.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:dd2fec2b13137416a1c5648b7009499bcc8fea78154cd888855fa32514f3dad1"},
+    {file = "pyzmq-27.1.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:08e90bb4b57603b84eab1d0ca05b3bbb10f60c1839dc471fc1c9e1507bef3386"},
+    {file = "pyzmq-27.1.0-cp310-cp310-win32.whl", hash = "sha256:a5b42d7a0658b515319148875fcb782bbf118dd41c671b62dae33666c2213bda"},
+    {file = "pyzmq-27.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:c0bb87227430ee3aefcc0ade2088100e528d5d3298a0a715a64f3d04c60ba02f"},
+    {file = "pyzmq-27.1.0-cp310-cp310-win_arm64.whl", hash = "sha256:9a916f76c2ab8d045b19f2286851a38e9ac94ea91faf65bd64735924522a8b32"},
+    {file = "pyzmq-27.1.0-cp311-cp311-macosx_10_15_universal2.whl", hash = "sha256:226b091818d461a3bef763805e75685e478ac17e9008f49fce2d3e52b3d58b86"},
+    {file = "pyzmq-27.1.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:0790a0161c281ca9723f804871b4027f2e8b5a528d357c8952d08cd1a9c15581"},
+    {file = "pyzmq-27.1.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c895a6f35476b0c3a54e3eb6ccf41bf3018de937016e6e18748317f25d4e925f"},
+    {file = "pyzmq-27.1.0-cp311-cp311-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5bbf8d3630bf96550b3be8e1fc0fea5cbdc8d5466c1192887bd94869da17a63e"},
+    {file = "pyzmq-27.1.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:15c8bd0fe0dabf808e2d7a681398c4e5ded70a551ab47482067a572c054c8e2e"},
+    {file = "pyzmq-27.1.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:bafcb3dd171b4ae9f19ee6380dfc71ce0390fefaf26b504c0e5f628d7c8c54f2"},
+    {file = "pyzmq-27.1.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:e829529fcaa09937189178115c49c504e69289abd39967cd8a4c215761373394"},
+    {file = "pyzmq-27.1.0-cp311-cp311-win32.whl", hash = "sha256:6df079c47d5902af6db298ec92151db82ecb557af663098b92f2508c398bb54f"},
+    {file = "pyzmq-27.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:190cbf120fbc0fc4957b56866830def56628934a9d112aec0e2507aa6a032b97"},
+    {file = "pyzmq-27.1.0-cp311-cp311-win_arm64.whl", hash = "sha256:eca6b47df11a132d1745eb3b5b5e557a7dae2c303277aa0e69c6ba91b8736e07"},
+    {file = "pyzmq-27.1.0-cp312-abi3-macosx_10_15_universal2.whl", hash = "sha256:452631b640340c928fa343801b0d07eb0c3789a5ffa843f6e1a9cee0ba4eb4fc"},
+    {file = "pyzmq-27.1.0-cp312-abi3-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1c179799b118e554b66da67d88ed66cd37a169f1f23b5d9f0a231b4e8d44a113"},
+    {file = "pyzmq-27.1.0-cp312-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3837439b7f99e60312f0c926a6ad437b067356dc2bc2ec96eb395fd0fe804233"},
+    {file = "pyzmq-27.1.0-cp312-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:43ad9a73e3da1fab5b0e7e13402f0b2fb934ae1c876c51d0afff0e7c052eca31"},
+    {file = "pyzmq-27.1.0-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:0de3028d69d4cdc475bfe47a6128eb38d8bc0e8f4d69646adfbcd840facbac28"},
+    {file = "pyzmq-27.1.0-cp312-abi3-musllinux_1_2_i686.whl", hash = "sha256:cf44a7763aea9298c0aa7dbf859f87ed7012de8bda0f3977b6fb1d96745df856"},
+    {file = "pyzmq-27.1.0-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:f30f395a9e6fbca195400ce833c731e7b64c3919aa481af4d88c3759e0cb7496"},
+    {file = "pyzmq-27.1.0-cp312-abi3-win32.whl", hash = "sha256:250e5436a4ba13885494412b3da5d518cd0d3a278a1ae640e113c073a5f88edd"},
+    {file = "pyzmq-27.1.0-cp312-abi3-win_amd64.whl", hash = "sha256:9ce490cf1d2ca2ad84733aa1d69ce6855372cb5ce9223802450c9b2a7cba0ccf"},
+    {file = "pyzmq-27.1.0-cp312-abi3-win_arm64.whl", hash = "sha256:75a2f36223f0d535a0c919e23615fc85a1e23b71f40c7eb43d7b1dedb4d8f15f"},
+    {file = "pyzmq-27.1.0-cp313-cp313-android_24_arm64_v8a.whl", hash = "sha256:93ad4b0855a664229559e45c8d23797ceac03183c7b6f5b4428152a6b06684a5"},
+    {file = "pyzmq-27.1.0-cp313-cp313-android_24_x86_64.whl", hash = "sha256:fbb4f2400bfda24f12f009cba62ad5734148569ff4949b1b6ec3b519444342e6"},
+    {file = "pyzmq-27.1.0-cp313-cp313t-macosx_10_15_universal2.whl", hash = "sha256:e343d067f7b151cfe4eb3bb796a7752c9d369eed007b91231e817071d2c2fec7"},
+    {file = "pyzmq-27.1.0-cp313-cp313t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:08363b2011dec81c354d694bdecaef4770e0ae96b9afea70b3f47b973655cc05"},
+    {file = "pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d54530c8c8b5b8ddb3318f481297441af102517602b569146185fa10b63f4fa9"},
+    {file = "pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6f3afa12c392f0a44a2414056d730eebc33ec0926aae92b5ad5cf26ebb6cc128"},
+    {file = "pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c65047adafe573ff023b3187bb93faa583151627bc9c51fc4fb2c561ed689d39"},
+    {file = "pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:90e6e9441c946a8b0a667356f7078d96411391a3b8f80980315455574177ec97"},
+    {file = "pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:add071b2d25f84e8189aaf0882d39a285b42fa3853016ebab234a5e78c7a43db"},
+    {file = "pyzmq-27.1.0-cp313-cp313t-win32.whl", hash = "sha256:7ccc0700cfdf7bd487bea8d850ec38f204478681ea02a582a8da8171b7f90a1c"},
+    {file = "pyzmq-27.1.0-cp313-cp313t-win_amd64.whl", hash = "sha256:8085a9fba668216b9b4323be338ee5437a235fe275b9d1610e422ccc279733e2"},
+    {file = "pyzmq-27.1.0-cp313-cp313t-win_arm64.whl", hash = "sha256:6bb54ca21bcfe361e445256c15eedf083f153811c37be87e0514934d6913061e"},
+    {file = "pyzmq-27.1.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:ce980af330231615756acd5154f29813d553ea555485ae712c491cd483df6b7a"},
+    {file = "pyzmq-27.1.0-cp314-cp314t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1779be8c549e54a1c38f805e56d2a2e5c009d26de10921d7d51cfd1c8d4632ea"},
+    {file = "pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7200bb0f03345515df50d99d3db206a0a6bee1955fbb8c453c76f5bf0e08fb96"},
+    {file = "pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:01c0e07d558b06a60773744ea6251f769cd79a41a97d11b8bf4ab8f034b0424d"},
+    {file = "pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:80d834abee71f65253c91540445d37c4c561e293ba6e741b992f20a105d69146"},
+    {file = "pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:544b4e3b7198dde4a62b8ff6685e9802a9a1ebf47e77478a5eb88eca2a82f2fd"},
+    {file = "pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:cedc4c68178e59a4046f97eca31b148ddcf51e88677de1ef4e78cf06c5376c9a"},
+    {file = "pyzmq-27.1.0-cp314-cp314t-win32.whl", hash = "sha256:1f0b2a577fd770aa6f053211a55d1c47901f4d537389a034c690291485e5fe92"},
+    {file = "pyzmq-27.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:19c9468ae0437f8074af379e986c5d3d7d7bfe033506af442e8c879732bedbe0"},
+    {file = "pyzmq-27.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:dc5dbf68a7857b59473f7df42650c621d7e8923fb03fa74a526890f4d33cc4d7"},
+    {file = "pyzmq-27.1.0-cp38-cp38-macosx_10_15_universal2.whl", hash = "sha256:18339186c0ed0ce5835f2656cdfb32203125917711af64da64dbaa3d949e5a1b"},
+    {file = "pyzmq-27.1.0-cp38-cp38-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:753d56fba8f70962cd8295fb3edb40b9b16deaa882dd2b5a3a2039f9ff7625aa"},
+    {file = "pyzmq-27.1.0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b721c05d932e5ad9ff9344f708c96b9e1a485418c6618d765fca95d4daacfbef"},
+    {file = "pyzmq-27.1.0-cp38-cp38-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7be883ff3d722e6085ee3f4afc057a50f7f2e0c72d289fd54df5706b4e3d3a50"},
+    {file = "pyzmq-27.1.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:b2e592db3a93128daf567de9650a2f3859017b3f7a66bc4ed6e4779d6034976f"},
+    {file = "pyzmq-27.1.0-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:ad68808a61cbfbbae7ba26d6233f2a4aa3b221de379ce9ee468aa7a83b9c36b0"},
+    {file = "pyzmq-27.1.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:e2687c2d230e8d8584fbea433c24382edfeda0c60627aca3446aa5e58d5d1831"},
+    {file = "pyzmq-27.1.0-cp38-cp38-win32.whl", hash = "sha256:a1aa0ee920fb3825d6c825ae3f6c508403b905b698b6460408ebd5bb04bbb312"},
+    {file = "pyzmq-27.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:df7cd397ece96cf20a76fae705d40efbab217d217897a5053267cd88a700c266"},
+    {file = "pyzmq-27.1.0-cp39-cp39-macosx_10_15_universal2.whl", hash = "sha256:96c71c32fff75957db6ae33cd961439f386505c6e6b377370af9b24a1ef9eafb"},
+    {file = "pyzmq-27.1.0-cp39-cp39-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:49d3980544447f6bd2968b6ac913ab963a49dcaa2d4a2990041f16057b04c429"},
+    {file = "pyzmq-27.1.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:849ca054d81aa1c175c49484afaaa5db0622092b5eccb2055f9f3bb8f703782d"},
+    {file = "pyzmq-27.1.0-cp39-cp39-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3970778e74cb7f85934d2b926b9900e92bfe597e62267d7499acc39c9c28e345"},
+    {file = "pyzmq-27.1.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:da96ecdcf7d3919c3be2de91a8c513c186f6762aa6cf7c01087ed74fad7f0968"},
+    {file = "pyzmq-27.1.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:9541c444cfe1b1c0156c5c86ece2bb926c7079a18e7b47b0b1b3b1b875e5d098"},
+    {file = "pyzmq-27.1.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:e30a74a39b93e2e1591b58eb1acef4902be27c957a8720b0e368f579b82dc22f"},
+    {file = "pyzmq-27.1.0-cp39-cp39-win32.whl", hash = "sha256:b1267823d72d1e40701dcba7edc45fd17f71be1285557b7fe668887150a14b78"},
+    {file = "pyzmq-27.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:0c996ded912812a2fcd7ab6574f4ad3edc27cb6510349431e4930d4196ade7db"},
+    {file = "pyzmq-27.1.0-cp39-cp39-win_arm64.whl", hash = "sha256:346e9ba4198177a07e7706050f35d733e08c1c1f8ceacd5eb6389d653579ffbc"},
+    {file = "pyzmq-27.1.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:c17e03cbc9312bee223864f1a2b13a99522e0dc9f7c5df0177cd45210ac286e6"},
+    {file = "pyzmq-27.1.0-pp310-pypy310_pp73-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:f328d01128373cb6763823b2b4e7f73bdf767834268c565151eacb3b7a392f90"},
+    {file = "pyzmq-27.1.0-pp310-pypy310_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c1790386614232e1b3a40a958454bdd42c6d1811837b15ddbb052a032a43f62"},
+    {file = "pyzmq-27.1.0-pp310-pypy310_pp73-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:448f9cb54eb0cee4732b46584f2710c8bc178b0e5371d9e4fc8125201e413a74"},
+    {file = "pyzmq-27.1.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:05b12f2d32112bf8c95ef2e74ec4f1d4beb01f8b5e703b38537f8849f92cb9ba"},
+    {file = "pyzmq-27.1.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:18770c8d3563715387139060d37859c02ce40718d1faf299abddcdcc6a649066"},
+    {file = "pyzmq-27.1.0-pp311-pypy311_pp73-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:ac25465d42f92e990f8d8b0546b01c391ad431c3bf447683fdc40565941d0604"},
+    {file = "pyzmq-27.1.0-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:53b40f8ae006f2734ee7608d59ed661419f087521edbfc2149c3932e9c14808c"},
+    {file = "pyzmq-27.1.0-pp311-pypy311_pp73-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f605d884e7c8be8fe1aa94e0a783bf3f591b84c24e4bc4f3e7564c82ac25e271"},
+    {file = "pyzmq-27.1.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:c9f7f6e13dff2e44a6afeaf2cf54cee5929ad64afaf4d40b50f93c58fc687355"},
+    {file = "pyzmq-27.1.0-pp38-pypy38_pp73-macosx_10_15_x86_64.whl", hash = "sha256:50081a4e98472ba9f5a02850014b4c9b629da6710f8f14f3b15897c666a28f1b"},
+    {file = "pyzmq-27.1.0-pp38-pypy38_pp73-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:510869f9df36ab97f89f4cff9d002a89ac554c7ac9cadd87d444aa4cf66abd27"},
+    {file = "pyzmq-27.1.0-pp38-pypy38_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1f8426a01b1c4098a750973c37131cf585f61c7911d735f729935a0c701b68d3"},
+    {file = "pyzmq-27.1.0-pp38-pypy38_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:726b6a502f2e34c6d2ada5e702929586d3ac948a4dbbb7fed9854ec8c0466027"},
+    {file = "pyzmq-27.1.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:bd67e7c8f4654bef471c0b1ca6614af0b5202a790723a58b79d9584dc8022a78"},
+    {file = "pyzmq-27.1.0-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:722ea791aa233ac0a819fc2c475e1292c76930b31f1d828cb61073e2fe5e208f"},
+    {file = "pyzmq-27.1.0-pp39-pypy39_pp73-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:01f9437501886d3a1dd4b02ef59fb8cc384fa718ce066d52f175ee49dd5b7ed8"},
+    {file = "pyzmq-27.1.0-pp39-pypy39_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4a19387a3dddcc762bfd2f570d14e2395b2c9701329b266f83dd87a2b3cbd381"},
+    {file = "pyzmq-27.1.0-pp39-pypy39_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4c618fbcd069e3a29dcd221739cacde52edcc681f041907867e0f5cc7e85f172"},
+    {file = "pyzmq-27.1.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:ff8d114d14ac671d88c89b9224c63d6c4e5a613fe8acd5594ce53d752a3aafe9"},
+    {file = "pyzmq-27.1.0.tar.gz", hash = "sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540"},
+]
+
+[package.dependencies]
+cffi = {version = "*", markers = "implementation_name == \"pypy\""}
+
+[[package]]
+name = "referencing"
+version = "0.37.0"
+description = "JSON Referencing + Python"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "referencing-0.37.0-py3-none-any.whl", hash = "sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231"},
+    {file = "referencing-0.37.0.tar.gz", hash = "sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8"},
+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+rpds-py = ">=0.7.0"
+typing-extensions = {version = ">=4.4.0", markers = "python_version < \"3.13\""}
+
+[[package]]
+name = "regex"
+version = "2025.11.3"
+description = "Alternative regular expression module, to replace re."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "regex-2025.11.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:2b441a4ae2c8049106e8b39973bfbddfb25a179dda2bdb99b0eeb60c40a6a3af"},
+    {file = "regex-2025.11.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2fa2eed3f76677777345d2f81ee89f5de2f5745910e805f7af7386a920fa7313"},
+    {file = "regex-2025.11.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d8b4a27eebd684319bdf473d39f1d79eed36bf2cd34bd4465cdb4618d82b3d56"},
+    {file = "regex-2025.11.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5cf77eac15bd264986c4a2c63353212c095b40f3affb2bc6b4ef80c4776c1a28"},
+    {file = "regex-2025.11.3-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b7f9ee819f94c6abfa56ec7b1dbab586f41ebbdc0a57e6524bd5e7f487a878c7"},
+    {file = "regex-2025.11.3-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:838441333bc90b829406d4a03cb4b8bf7656231b84358628b0406d803931ef32"},
+    {file = "regex-2025.11.3-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cfe6d3f0c9e3b7e8c0c694b24d25e677776f5ca26dce46fd6b0489f9c8339391"},
+    {file = "regex-2025.11.3-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2ab815eb8a96379a27c3b6157fcb127c8f59c36f043c1678110cea492868f1d5"},
+    {file = "regex-2025.11.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:728a9d2d173a65b62bdc380b7932dd8e74ed4295279a8fe1021204ce210803e7"},
+    {file = "regex-2025.11.3-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:509dc827f89c15c66a0c216331260d777dd6c81e9a4e4f830e662b0bb296c313"},
+    {file = "regex-2025.11.3-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:849202cd789e5f3cf5dcc7822c34b502181b4824a65ff20ce82da5524e45e8e9"},
+    {file = "regex-2025.11.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:b6f78f98741dcc89607c16b1e9426ee46ce4bf31ac5e6b0d40e81c89f3481ea5"},
+    {file = "regex-2025.11.3-cp310-cp310-win32.whl", hash = "sha256:149eb0bba95231fb4f6d37c8f760ec9fa6fabf65bab555e128dde5f2475193ec"},
+    {file = "regex-2025.11.3-cp310-cp310-win_amd64.whl", hash = "sha256:ee3a83ce492074c35a74cc76cf8235d49e77b757193a5365ff86e3f2f93db9fd"},
+    {file = "regex-2025.11.3-cp310-cp310-win_arm64.whl", hash = "sha256:38af559ad934a7b35147716655d4a2f79fcef2d695ddfe06a06ba40ae631fa7e"},
+    {file = "regex-2025.11.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:eadade04221641516fa25139273505a1c19f9bf97589a05bc4cfcd8b4a618031"},
+    {file = "regex-2025.11.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:feff9e54ec0dd3833d659257f5c3f5322a12eee58ffa360984b716f8b92983f4"},
+    {file = "regex-2025.11.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:3b30bc921d50365775c09a7ed446359e5c0179e9e2512beec4a60cbcef6ddd50"},
+    {file = "regex-2025.11.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f99be08cfead2020c7ca6e396c13543baea32343b7a9a5780c462e323bd8872f"},
+    {file = "regex-2025.11.3-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6dd329a1b61c0ee95ba95385fb0c07ea0d3fe1a21e1349fa2bec272636217118"},
+    {file = "regex-2025.11.3-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:4c5238d32f3c5269d9e87be0cf096437b7622b6920f5eac4fd202468aaeb34d2"},
+    {file = "regex-2025.11.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:10483eefbfb0adb18ee9474498c9a32fcf4e594fbca0543bb94c48bac6183e2e"},
+    {file = "regex-2025.11.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:78c2d02bb6e1da0720eedc0bad578049cad3f71050ef8cd065ecc87691bed2b0"},
+    {file = "regex-2025.11.3-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:e6b49cd2aad93a1790ce9cffb18964f6d3a4b0b3dbdbd5de094b65296fce6e58"},
+    {file = "regex-2025.11.3-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:885b26aa3ee56433b630502dc3d36ba78d186a00cc535d3806e6bfd9ed3c70ab"},
+    {file = "regex-2025.11.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:ddd76a9f58e6a00f8772e72cff8ebcff78e022be95edf018766707c730593e1e"},
+    {file = "regex-2025.11.3-cp311-cp311-win32.whl", hash = "sha256:3e816cc9aac1cd3cc9a4ec4d860f06d40f994b5c7b4d03b93345f44e08cc68bf"},
+    {file = "regex-2025.11.3-cp311-cp311-win_amd64.whl", hash = "sha256:087511f5c8b7dfbe3a03f5d5ad0c2a33861b1fc387f21f6f60825a44865a385a"},
+    {file = "regex-2025.11.3-cp311-cp311-win_arm64.whl", hash = "sha256:1ff0d190c7f68ae7769cd0313fe45820ba07ffebfddfaa89cc1eb70827ba0ddc"},
+    {file = "regex-2025.11.3-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:bc8ab71e2e31b16e40868a40a69007bc305e1109bd4658eb6cad007e0bf67c41"},
+    {file = "regex-2025.11.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:22b29dda7e1f7062a52359fca6e58e548e28c6686f205e780b02ad8ef710de36"},
+    {file = "regex-2025.11.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:3a91e4a29938bc1a082cc28fdea44be420bf2bebe2665343029723892eb073e1"},
+    {file = "regex-2025.11.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:08b884f4226602ad40c5d55f52bf91a9df30f513864e0054bad40c0e9cf1afb7"},
+    {file = "regex-2025.11.3-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3e0b11b2b2433d1c39c7c7a30e3f3d0aeeea44c2a8d0bae28f6b95f639927a69"},
+    {file = "regex-2025.11.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:87eb52a81ef58c7ba4d45c3ca74e12aa4b4e77816f72ca25258a85b3ea96cb48"},
+    {file = "regex-2025.11.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a12ab1f5c29b4e93db518f5e3872116b7e9b1646c9f9f426f777b50d44a09e8c"},
+    {file = "regex-2025.11.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:7521684c8c7c4f6e88e35ec89680ee1aa8358d3f09d27dfbdf62c446f5d4c695"},
+    {file = "regex-2025.11.3-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:7fe6e5440584e94cc4b3f5f4d98a25e29ca12dccf8873679a635638349831b98"},
+    {file = "regex-2025.11.3-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:8e026094aa12b43f4fd74576714e987803a315c76edb6b098b9809db5de58f74"},
+    {file = "regex-2025.11.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:435bbad13e57eb5606a68443af62bed3556de2f46deb9f7d4237bc2f1c9fb3a0"},
+    {file = "regex-2025.11.3-cp312-cp312-win32.whl", hash = "sha256:3839967cf4dc4b985e1570fd8d91078f0c519f30491c60f9ac42a8db039be204"},
+    {file = "regex-2025.11.3-cp312-cp312-win_amd64.whl", hash = "sha256:e721d1b46e25c481dc5ded6f4b3f66c897c58d2e8cfdf77bbced84339108b0b9"},
+    {file = "regex-2025.11.3-cp312-cp312-win_arm64.whl", hash = "sha256:64350685ff08b1d3a6fff33f45a9ca183dc1d58bbfe4981604e70ec9801bbc26"},
+    {file = "regex-2025.11.3-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:c1e448051717a334891f2b9a620fe36776ebf3dd8ec46a0b877c8ae69575feb4"},
+    {file = "regex-2025.11.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:9b5aca4d5dfd7fbfbfbdaf44850fcc7709a01146a797536a8f84952e940cca76"},
+    {file = "regex-2025.11.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:04d2765516395cf7dda331a244a3282c0f5ae96075f728629287dfa6f76ba70a"},
+    {file = "regex-2025.11.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5d9903ca42bfeec4cebedba8022a7c97ad2aab22e09573ce9976ba01b65e4361"},
+    {file = "regex-2025.11.3-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:639431bdc89d6429f6721625e8129413980ccd62e9d3f496be618a41d205f160"},
+    {file = "regex-2025.11.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f117efad42068f9715677c8523ed2be1518116d1c49b1dd17987716695181efe"},
+    {file = "regex-2025.11.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4aecb6f461316adf9f1f0f6a4a1a3d79e045f9b71ec76055a791affa3b285850"},
+    {file = "regex-2025.11.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:3b3a5f320136873cc5561098dfab677eea139521cb9a9e8db98b7e64aef44cbc"},
+    {file = "regex-2025.11.3-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:75fa6f0056e7efb1f42a1c34e58be24072cb9e61a601340cc1196ae92326a4f9"},
+    {file = "regex-2025.11.3-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:dbe6095001465294f13f1adcd3311e50dd84e5a71525f20a10bd16689c61ce0b"},
+    {file = "regex-2025.11.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:454d9b4ae7881afbc25015b8627c16d88a597479b9dea82b8c6e7e2e07240dc7"},
+    {file = "regex-2025.11.3-cp313-cp313-win32.whl", hash = "sha256:28ba4d69171fc6e9896337d4fc63a43660002b7da53fc15ac992abcf3410917c"},
+    {file = "regex-2025.11.3-cp313-cp313-win_amd64.whl", hash = "sha256:bac4200befe50c670c405dc33af26dad5a3b6b255dd6c000d92fe4629f9ed6a5"},
+    {file = "regex-2025.11.3-cp313-cp313-win_arm64.whl", hash = "sha256:2292cd5a90dab247f9abe892ac584cb24f0f54680c73fcb4a7493c66c2bf2467"},
+    {file = "regex-2025.11.3-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:1eb1ebf6822b756c723e09f5186473d93236c06c579d2cc0671a722d2ab14281"},
+    {file = "regex-2025.11.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:1e00ec2970aab10dc5db34af535f21fcf32b4a31d99e34963419636e2f85ae39"},
+    {file = "regex-2025.11.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:a4cb042b615245d5ff9b3794f56be4138b5adc35a4166014d31d1814744148c7"},
+    {file = "regex-2025.11.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:44f264d4bf02f3176467d90b294d59bf1db9fe53c141ff772f27a8b456b2a9ed"},
+    {file = "regex-2025.11.3-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7be0277469bf3bd7a34a9c57c1b6a724532a0d235cd0dc4e7f4316f982c28b19"},
+    {file = "regex-2025.11.3-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0d31e08426ff4b5b650f68839f5af51a92a5b51abd8554a60c2fbc7c71f25d0b"},
+    {file = "regex-2025.11.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e43586ce5bd28f9f285a6e729466841368c4a0353f6fd08d4ce4630843d3648a"},
+    {file = "regex-2025.11.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:0f9397d561a4c16829d4e6ff75202c1c08b68a3bdbfe29dbfcdb31c9830907c6"},
+    {file = "regex-2025.11.3-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:dd16e78eb18ffdb25ee33a0682d17912e8cc8a770e885aeee95020046128f1ce"},
+    {file = "regex-2025.11.3-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:ffcca5b9efe948ba0661e9df0fa50d2bc4b097c70b9810212d6b62f05d83b2dd"},
+    {file = "regex-2025.11.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:c56b4d162ca2b43318ac671c65bd4d563e841a694ac70e1a976ac38fcf4ca1d2"},
+    {file = "regex-2025.11.3-cp313-cp313t-win32.whl", hash = "sha256:9ddc42e68114e161e51e272f667d640f97e84a2b9ef14b7477c53aac20c2d59a"},
+    {file = "regex-2025.11.3-cp313-cp313t-win_amd64.whl", hash = "sha256:7a7c7fdf755032ffdd72c77e3d8096bdcb0eb92e89e17571a196f03d88b11b3c"},
+    {file = "regex-2025.11.3-cp313-cp313t-win_arm64.whl", hash = "sha256:df9eb838c44f570283712e7cff14c16329a9f0fb19ca492d21d4b7528ee6821e"},
+    {file = "regex-2025.11.3-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:9697a52e57576c83139d7c6f213d64485d3df5bf84807c35fa409e6c970801c6"},
+    {file = "regex-2025.11.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:e18bc3f73bd41243c9b38a6d9f2366cd0e0137a9aebe2d8ff76c5b67d4c0a3f4"},
+    {file = "regex-2025.11.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:61a08bcb0ec14ff4e0ed2044aad948d0659604f824cbd50b55e30b0ec6f09c73"},
+    {file = "regex-2025.11.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c9c30003b9347c24bcc210958c5d167b9e4f9be786cb380a7d32f14f9b84674f"},
+    {file = "regex-2025.11.3-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4e1e592789704459900728d88d41a46fe3969b82ab62945560a31732ffc19a6d"},
+    {file = "regex-2025.11.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6538241f45eb5a25aa575dbba1069ad786f68a4f2773a29a2bd3dd1f9de787be"},
+    {file = "regex-2025.11.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bce22519c989bb72a7e6b36a199384c53db7722fe669ba891da75907fe3587db"},
+    {file = "regex-2025.11.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:66d559b21d3640203ab9075797a55165d79017520685fb407b9234d72ab63c62"},
+    {file = "regex-2025.11.3-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:669dcfb2e38f9e8c69507bace46f4889e3abbfd9b0c29719202883c0a603598f"},
+    {file = "regex-2025.11.3-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:32f74f35ff0f25a5021373ac61442edcb150731fbaa28286bbc8bb1582c89d02"},
+    {file = "regex-2025.11.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e6c7a21dffba883234baefe91bc3388e629779582038f75d2a5be918e250f0ed"},
+    {file = "regex-2025.11.3-cp314-cp314-win32.whl", hash = "sha256:795ea137b1d809eb6836b43748b12634291c0ed55ad50a7d72d21edf1cd565c4"},
+    {file = "regex-2025.11.3-cp314-cp314-win_amd64.whl", hash = "sha256:9f95fbaa0ee1610ec0fc6b26668e9917a582ba80c52cc6d9ada15e30aa9ab9ad"},
+    {file = "regex-2025.11.3-cp314-cp314-win_arm64.whl", hash = "sha256:dfec44d532be4c07088c3de2876130ff0fbeeacaa89a137decbbb5f665855a0f"},
+    {file = "regex-2025.11.3-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:ba0d8a5d7f04f73ee7d01d974d47c5834f8a1b0224390e4fe7c12a3a92a78ecc"},
+    {file = "regex-2025.11.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:442d86cf1cfe4faabf97db7d901ef58347efd004934da045c745e7b5bd57ac49"},
+    {file = "regex-2025.11.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:fd0a5e563c756de210bb964789b5abe4f114dacae9104a47e1a649b910361536"},
+    {file = "regex-2025.11.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bf3490bcbb985a1ae97b2ce9ad1c0f06a852d5b19dde9b07bdf25bf224248c95"},
+    {file = "regex-2025.11.3-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3809988f0a8b8c9dcc0f92478d6501fac7200b9ec56aecf0ec21f4a2ec4b6009"},
+    {file = "regex-2025.11.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f4ff94e58e84aedb9c9fce66d4ef9f27a190285b451420f297c9a09f2b9abee9"},
+    {file = "regex-2025.11.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7eb542fd347ce61e1321b0a6b945d5701528dca0cd9759c2e3bb8bd57e47964d"},
+    {file = "regex-2025.11.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:d6c2d5919075a1f2e413c00b056ea0c2f065b3f5fe83c3d07d325ab92dce51d6"},
+    {file = "regex-2025.11.3-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:3f8bf11a4827cc7ce5a53d4ef6cddd5ad25595d3c1435ef08f76825851343154"},
+    {file = "regex-2025.11.3-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:22c12d837298651e5550ac1d964e4ff57c3f56965fc1812c90c9fb2028eaf267"},
+    {file = "regex-2025.11.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:62ba394a3dda9ad41c7c780f60f6e4a70988741415ae96f6d1bf6c239cf01379"},
+    {file = "regex-2025.11.3-cp314-cp314t-win32.whl", hash = "sha256:4bf146dca15cdd53224a1bf46d628bd7590e4a07fbb69e720d561aea43a32b38"},
+    {file = "regex-2025.11.3-cp314-cp314t-win_amd64.whl", hash = "sha256:adad1a1bcf1c9e76346e091d22d23ac54ef28e1365117d99521631078dfec9de"},
+    {file = "regex-2025.11.3-cp314-cp314t-win_arm64.whl", hash = "sha256:c54f768482cef41e219720013cd05933b6f971d9562544d691c68699bf2b6801"},
+    {file = "regex-2025.11.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:81519e25707fc076978c6143b81ea3dc853f176895af05bf7ec51effe818aeec"},
+    {file = "regex-2025.11.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3bf28b1873a8af8bbb58c26cc56ea6e534d80053b41fb511a35795b6de507e6a"},
+    {file = "regex-2025.11.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:856a25c73b697f2ce2a24e7968285579e62577a048526161a2c0f53090bea9f9"},
+    {file = "regex-2025.11.3-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a3d571bd95fade53c86c0517f859477ff3a93c3fde10c9e669086f038e0f207"},
+    {file = "regex-2025.11.3-cp39-cp39-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:732aea6de26051af97b94bc98ed86448821f839d058e5d259c72bf6d73ad0fc0"},
+    {file = "regex-2025.11.3-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:51c1c1847128238f54930edb8805b660305dca164645a9fd29243f5610beea34"},
+    {file = "regex-2025.11.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:22dd622a402aad4558277305350699b2be14bc59f64d64ae1d928ce7d072dced"},
+    {file = "regex-2025.11.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f3b5a391c7597ffa96b41bd5cbd2ed0305f515fcbb367dfa72735679d5502364"},
+    {file = "regex-2025.11.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:cc4076a5b4f36d849fd709284b4a3b112326652f3b0466f04002a6c15a0c96c1"},
+    {file = "regex-2025.11.3-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:a295ca2bba5c1c885826ce3125fa0b9f702a1be547d821c01d65f199e10c01e2"},
+    {file = "regex-2025.11.3-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:b4774ff32f18e0504bfc4e59a3e71e18d83bc1e171a3c8ed75013958a03b2f14"},
+    {file = "regex-2025.11.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:22e7d1cdfa88ef33a2ae6aa0d707f9255eb286ffbd90045f1088246833223aee"},
+    {file = "regex-2025.11.3-cp39-cp39-win32.whl", hash = "sha256:74d04244852ff73b32eeede4f76f51c5bcf44bc3c207bc3e6cf1c5c45b890708"},
+    {file = "regex-2025.11.3-cp39-cp39-win_amd64.whl", hash = "sha256:7a50cd39f73faa34ec18d6720ee25ef10c4c1839514186fcda658a06c06057a2"},
+    {file = "regex-2025.11.3-cp39-cp39-win_arm64.whl", hash = "sha256:43b4fb020e779ca81c1b5255015fe2b82816c76ec982354534ad9ec09ad7c9e3"},
+    {file = "regex-2025.11.3.tar.gz", hash = "sha256:1fedc720f9bb2494ce31a58a1631f9c82df6a09b49c19517ea5cc280b4541e01"},
+]
+
+[[package]]
+name = "requests"
+version = "2.32.5"
+description = "Python HTTP for Humans."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6"},
+    {file = "requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf"},
+]
+
+[package.dependencies]
+certifi = ">=2017.4.17"
+charset_normalizer = ">=2,<4"
+idna = ">=2.5,<4"
+urllib3 = ">=1.21.1,<3"
+
+[package.extras]
+socks = ["PySocks (>=1.5.6,!=1.5.7)"]
+use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
+
+[[package]]
+name = "rfc3339-validator"
+version = "0.1.4"
+description = "A pure python RFC3339 validator"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["main"]
+files = [
+    {file = "rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa"},
+    {file = "rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b"},
+]
+
+[package.dependencies]
+six = "*"
+
+[[package]]
+name = "rfc3986-validator"
+version = "0.1.1"
+description = "Pure python rfc3986 validator"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["main"]
+files = [
+    {file = "rfc3986_validator-0.1.1-py2.py3-none-any.whl", hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9"},
+    {file = "rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055"},
+]
+
+[[package]]
+name = "rfc3987-syntax"
+version = "1.1.0"
+description = "Helper functions to syntactically validate strings according to RFC 3987."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "rfc3987_syntax-1.1.0-py3-none-any.whl", hash = "sha256:6c3d97604e4c5ce9f714898e05401a0445a641cfa276432b0a648c80856f6a3f"},
+    {file = "rfc3987_syntax-1.1.0.tar.gz", hash = "sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d"},
+]
+
+[package.dependencies]
+lark = ">=1.2.2"
+
+[package.extras]
+testing = ["pytest (>=8.3.5)"]
+
+[[package]]
+name = "rpds-py"
+version = "0.28.0"
+description = "Python bindings to Rust's persistent data structures (rpds)"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "rpds_py-0.28.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:7b6013db815417eeb56b2d9d7324e64fcd4fa289caeee6e7a78b2e11fc9b438a"},
+    {file = "rpds_py-0.28.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1a4c6b05c685c0c03f80dabaeb73e74218c49deea965ca63f76a752807397207"},
+    {file = "rpds_py-0.28.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f4794c6c3fbe8f9ac87699b131a1f26e7b4abcf6d828da46a3a52648c7930eba"},
+    {file = "rpds_py-0.28.0-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2e8456b6ee5527112ff2354dd9087b030e3429e43a74f480d4a5ca79d269fd85"},
+    {file = "rpds_py-0.28.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:beb880a9ca0a117415f241f66d56025c02037f7c4efc6fe59b5b8454f1eaa50d"},
+    {file = "rpds_py-0.28.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6897bebb118c44b38c9cb62a178e09f1593c949391b9a1a6fe777ccab5934ee7"},
+    {file = "rpds_py-0.28.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b1b553dd06e875249fd43efd727785efb57a53180e0fde321468222eabbeaafa"},
+    {file = "rpds_py-0.28.0-cp310-cp310-manylinux_2_31_riscv64.whl", hash = "sha256:f0b2044fdddeea5b05df832e50d2a06fe61023acb44d76978e1b060206a8a476"},
+    {file = "rpds_py-0.28.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:05cf1e74900e8da73fa08cc76c74a03345e5a3e37691d07cfe2092d7d8e27b04"},
+    {file = "rpds_py-0.28.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:efd489fec7c311dae25e94fe7eeda4b3d06be71c68f2cf2e8ef990ffcd2cd7e8"},
+    {file = "rpds_py-0.28.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:ada7754a10faacd4f26067e62de52d6af93b6d9542f0df73c57b9771eb3ba9c4"},
+    {file = "rpds_py-0.28.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:c2a34fd26588949e1e7977cfcbb17a9a42c948c100cab890c6d8d823f0586457"},
+    {file = "rpds_py-0.28.0-cp310-cp310-win32.whl", hash = "sha256:f9174471d6920cbc5e82a7822de8dfd4dcea86eb828b04fc8c6519a77b0ee51e"},
+    {file = "rpds_py-0.28.0-cp310-cp310-win_amd64.whl", hash = "sha256:6e32dd207e2c4f8475257a3540ab8a93eff997abfa0a3fdb287cae0d6cd874b8"},
+    {file = "rpds_py-0.28.0-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:03065002fd2e287725d95fbc69688e0c6daf6c6314ba38bdbaa3895418e09296"},
+    {file = "rpds_py-0.28.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:28ea02215f262b6d078daec0b45344c89e161eab9526b0d898221d96fdda5f27"},
+    {file = "rpds_py-0.28.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:25dbade8fbf30bcc551cb352376c0ad64b067e4fc56f90e22ba70c3ce205988c"},
+    {file = "rpds_py-0.28.0-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3c03002f54cc855860bfdc3442928ffdca9081e73b5b382ed0b9e8efe6e5e205"},
+    {file = "rpds_py-0.28.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b9699fa7990368b22032baf2b2dce1f634388e4ffc03dfefaaac79f4695edc95"},
+    {file = "rpds_py-0.28.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b9b06fe1a75e05e0713f06ea0c89ecb6452210fd60e2f1b6ddc1067b990e08d9"},
+    {file = "rpds_py-0.28.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ac9f83e7b326a3f9ec3ef84cda98fb0a74c7159f33e692032233046e7fd15da2"},
+    {file = "rpds_py-0.28.0-cp311-cp311-manylinux_2_31_riscv64.whl", hash = "sha256:0d3259ea9ad8743a75a43eb7819324cdab393263c91be86e2d1901ee65c314e0"},
+    {file = "rpds_py-0.28.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:9a7548b345f66f6695943b4ef6afe33ccd3f1b638bd9afd0f730dd255c249c9e"},
+    {file = "rpds_py-0.28.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c9a40040aa388b037eb39416710fbcce9443498d2eaab0b9b45ae988b53f5c67"},
+    {file = "rpds_py-0.28.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:8f60c7ea34e78c199acd0d3cda37a99be2c861dd2b8cf67399784f70c9f8e57d"},
+    {file = "rpds_py-0.28.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:1571ae4292649100d743b26d5f9c63503bb1fedf538a8f29a98dce2d5ba6b4e6"},
+    {file = "rpds_py-0.28.0-cp311-cp311-win32.whl", hash = "sha256:5cfa9af45e7c1140af7321fa0bef25b386ee9faa8928c80dc3a5360971a29e8c"},
+    {file = "rpds_py-0.28.0-cp311-cp311-win_amd64.whl", hash = "sha256:dd8d86b5d29d1b74100982424ba53e56033dc47720a6de9ba0259cf81d7cecaa"},
+    {file = "rpds_py-0.28.0-cp311-cp311-win_arm64.whl", hash = "sha256:4e27d3a5709cc2b3e013bf93679a849213c79ae0573f9b894b284b55e729e120"},
+    {file = "rpds_py-0.28.0-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:6b4f28583a4f247ff60cd7bdda83db8c3f5b05a7a82ff20dd4b078571747708f"},
+    {file = "rpds_py-0.28.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d678e91b610c29c4b3d52a2c148b641df2b4676ffe47c59f6388d58b99cdc424"},
+    {file = "rpds_py-0.28.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e819e0e37a44a78e1383bf1970076e2ccc4dc8c2bbaa2f9bd1dc987e9afff628"},
+    {file = "rpds_py-0.28.0-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5ee514e0f0523db5d3fb171f397c54875dbbd69760a414dccf9d4d7ad628b5bd"},
+    {file = "rpds_py-0.28.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5f3fa06d27fdcee47f07a39e02862da0100cb4982508f5ead53ec533cd5fe55e"},
+    {file = "rpds_py-0.28.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:46959ef2e64f9e4a41fc89aa20dbca2b85531f9a72c21099a3360f35d10b0d5a"},
+    {file = "rpds_py-0.28.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8455933b4bcd6e83fde3fefc987a023389c4b13f9a58c8d23e4b3f6d13f78c84"},
+    {file = "rpds_py-0.28.0-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:ad50614a02c8c2962feebe6012b52f9802deec4263946cddea37aaf28dd25a66"},
+    {file = "rpds_py-0.28.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:e5deca01b271492553fdb6c7fd974659dce736a15bae5dad7ab8b93555bceb28"},
+    {file = "rpds_py-0.28.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:735f8495a13159ce6a0d533f01e8674cec0c57038c920495f87dcb20b3ddb48a"},
+    {file = "rpds_py-0.28.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:961ca621ff10d198bbe6ba4957decca61aa2a0c56695384c1d6b79bf61436df5"},
+    {file = "rpds_py-0.28.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2374e16cc9131022e7d9a8f8d65d261d9ba55048c78f3b6e017971a4f5e6353c"},
+    {file = "rpds_py-0.28.0-cp312-cp312-win32.whl", hash = "sha256:d15431e334fba488b081d47f30f091e5d03c18527c325386091f31718952fe08"},
+    {file = "rpds_py-0.28.0-cp312-cp312-win_amd64.whl", hash = "sha256:a410542d61fc54710f750d3764380b53bf09e8c4edbf2f9141a82aa774a04f7c"},
+    {file = "rpds_py-0.28.0-cp312-cp312-win_arm64.whl", hash = "sha256:1f0cfd1c69e2d14f8c892b893997fa9a60d890a0c8a603e88dca4955f26d1edd"},
+    {file = "rpds_py-0.28.0-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:e9e184408a0297086f880556b6168fa927d677716f83d3472ea333b42171ee3b"},
+    {file = "rpds_py-0.28.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:edd267266a9b0448f33dc465a97cfc5d467594b600fe28e7fa2f36450e03053a"},
+    {file = "rpds_py-0.28.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:85beb8b3f45e4e32f6802fb6cd6b17f615ef6c6a52f265371fb916fae02814aa"},
+    {file = "rpds_py-0.28.0-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:d2412be8d00a1b895f8ad827cc2116455196e20ed994bb704bf138fe91a42724"},
+    {file = "rpds_py-0.28.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:cf128350d384b777da0e68796afdcebc2e9f63f0e9f242217754e647f6d32491"},
+    {file = "rpds_py-0.28.0-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a2036d09b363aa36695d1cc1a97b36865597f4478470b0697b5ee9403f4fe399"},
+    {file = "rpds_py-0.28.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8e1e9be4fa6305a16be628959188e4fd5cd6f1b0e724d63c6d8b2a8adf74ea6"},
+    {file = "rpds_py-0.28.0-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:0a403460c9dd91a7f23fc3188de6d8977f1d9603a351d5db6cf20aaea95b538d"},
+    {file = "rpds_py-0.28.0-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d7366b6553cdc805abcc512b849a519167db8f5e5c3472010cd1228b224265cb"},
+    {file = "rpds_py-0.28.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5b43c6a3726efd50f18d8120ec0551241c38785b68952d240c45ea553912ac41"},
+    {file = "rpds_py-0.28.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:0cb7203c7bc69d7c1585ebb33a2e6074492d2fc21ad28a7b9d40457ac2a51ab7"},
+    {file = "rpds_py-0.28.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7a52a5169c664dfb495882adc75c304ae1d50df552fbd68e100fdc719dee4ff9"},
+    {file = "rpds_py-0.28.0-cp313-cp313-win32.whl", hash = "sha256:2e42456917b6687215b3e606ab46aa6bca040c77af7df9a08a6dcfe8a4d10ca5"},
+    {file = "rpds_py-0.28.0-cp313-cp313-win_amd64.whl", hash = "sha256:e0a0311caedc8069d68fc2bf4c9019b58a2d5ce3cd7cb656c845f1615b577e1e"},
+    {file = "rpds_py-0.28.0-cp313-cp313-win_arm64.whl", hash = "sha256:04c1b207ab8b581108801528d59ad80aa83bb170b35b0ddffb29c20e411acdc1"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:f296ea3054e11fc58ad42e850e8b75c62d9a93a9f981ad04b2e5ae7d2186ff9c"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5a7306c19b19005ad98468fcefeb7100b19c79fc23a5f24a12e06d91181193fa"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e5d9b86aa501fed9862a443c5c3116f6ead8bc9296185f369277c42542bd646b"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e5bbc701eff140ba0e872691d573b3d5d30059ea26e5785acba9132d10c8c31d"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9a5690671cd672a45aa8616d7374fdf334a1b9c04a0cac3c854b1136e92374fe"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9f1d92ecea4fa12f978a367c32a5375a1982834649cdb96539dcdc12e609ab1a"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8d252db6b1a78d0a3928b6190156042d54c93660ce4d98290d7b16b5296fb7cc"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-manylinux_2_31_riscv64.whl", hash = "sha256:d61b355c3275acb825f8777d6c4505f42b5007e357af500939d4a35b19177259"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:acbe5e8b1026c0c580d0321c8aae4b0a1e1676861d48d6e8c6586625055b606a"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:8aa23b6f0fc59b85b4c7d89ba2965af274346f738e8d9fc2455763602e62fd5f"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:7b14b0c680286958817c22d76fcbca4800ddacef6f678f3a7c79a1fe7067fe37"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:bcf1d210dfee61a6c86551d67ee1031899c0fdbae88b2d44a569995d43797712"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-win32.whl", hash = "sha256:3aa4dc0fdab4a7029ac63959a3ccf4ed605fee048ba67ce89ca3168da34a1342"},
+    {file = "rpds_py-0.28.0-cp313-cp313t-win_amd64.whl", hash = "sha256:7b7d9d83c942855e4fdcfa75d4f96f6b9e272d42fffcb72cd4bb2577db2e2907"},
+    {file = "rpds_py-0.28.0-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:dcdcb890b3ada98a03f9f2bb108489cdc7580176cb73b4f2d789e9a1dac1d472"},
+    {file = "rpds_py-0.28.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:f274f56a926ba2dc02976ca5b11c32855cbd5925534e57cfe1fda64e04d1add2"},
+    {file = "rpds_py-0.28.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4fe0438ac4a29a520ea94c8c7f1754cdd8feb1bc490dfda1bfd990072363d527"},
+    {file = "rpds_py-0.28.0-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:8a358a32dd3ae50e933347889b6af9a1bdf207ba5d1a3f34e1a38cd3540e6733"},
+    {file = "rpds_py-0.28.0-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e80848a71c78aa328fefaba9c244d588a342c8e03bda518447b624ea64d1ff56"},
+    {file = "rpds_py-0.28.0-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f586db2e209d54fe177e58e0bc4946bea5fb0102f150b1b2f13de03e1f0976f8"},
+    {file = "rpds_py-0.28.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ae8ee156d6b586e4292491e885d41483136ab994e719a13458055bec14cf370"},
+    {file = "rpds_py-0.28.0-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:a805e9b3973f7e27f7cab63a6b4f61d90f2e5557cff73b6e97cd5b8540276d3d"},
+    {file = "rpds_py-0.28.0-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:5d3fd16b6dc89c73a4da0b4ac8b12a7ecc75b2864b95c9e5afed8003cb50a728"},
+    {file = "rpds_py-0.28.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:6796079e5d24fdaba6d49bda28e2c47347e89834678f2bc2c1b4fc1489c0fb01"},
+    {file = "rpds_py-0.28.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:76500820c2af232435cbe215e3324c75b950a027134e044423f59f5b9a1ba515"},
+    {file = "rpds_py-0.28.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:bbdc5640900a7dbf9dd707fe6388972f5bbd883633eb68b76591044cfe346f7e"},
+    {file = "rpds_py-0.28.0-cp314-cp314-win32.whl", hash = "sha256:adc8aa88486857d2b35d75f0640b949759f79dc105f50aa2c27816b2e0dd749f"},
+    {file = "rpds_py-0.28.0-cp314-cp314-win_amd64.whl", hash = "sha256:66e6fa8e075b58946e76a78e69e1a124a21d9a48a5b4766d15ba5b06869d1fa1"},
+    {file = "rpds_py-0.28.0-cp314-cp314-win_arm64.whl", hash = "sha256:a6fe887c2c5c59413353b7c0caff25d0e566623501ccfff88957fa438a69377d"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:7a69df082db13c7070f7b8b1f155fa9e687f1d6aefb7b0e3f7231653b79a067b"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:b1cde22f2c30ebb049a9e74c5374994157b9b70a16147d332f89c99c5960737a"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5338742f6ba7a51012ea470bd4dc600a8c713c0c72adaa0977a1b1f4327d6592"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e1460ebde1bcf6d496d80b191d854adedcc619f84ff17dc1c6d550f58c9efbba"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e3eb248f2feba84c692579257a043a7699e28a77d86c77b032c1d9fbb3f0219c"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bd3bbba5def70b16cd1c1d7255666aad3b290fbf8d0fe7f9f91abafb73611a91"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3114f4db69ac5a1f32e7e4d1cbbe7c8f9cf8217f78e6e002cedf2d54c2a548ed"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:4b0cb8a906b1a0196b863d460c0222fb8ad0f34041568da5620f9799b83ccf0b"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:cf681ac76a60b667106141e11a92a3330890257e6f559ca995fbb5265160b56e"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:1e8ee6413cfc677ce8898d9cde18cc3a60fc2ba756b0dec5b71eb6eb21c49fa1"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:b3072b16904d0b5572a15eb9d31c1954e0d3227a585fc1351aa9878729099d6c"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:b670c30fd87a6aec281c3c9896d3bae4b205fd75d79d06dc87c2503717e46092"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-win32.whl", hash = "sha256:8014045a15b4d2b3476f0a287fcc93d4f823472d7d1308d47884ecac9e612be3"},
+    {file = "rpds_py-0.28.0-cp314-cp314t-win_amd64.whl", hash = "sha256:7a4e59c90d9c27c561eb3160323634a9ff50b04e4f7820600a2beb0ac90db578"},
+    {file = "rpds_py-0.28.0-pp311-pypy311_pp73-macosx_10_12_x86_64.whl", hash = "sha256:f5e7101145427087e493b9c9b959da68d357c28c562792300dd21a095118ed16"},
+    {file = "rpds_py-0.28.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:31eb671150b9c62409a888850aaa8e6533635704fe2b78335f9aaf7ff81eec4d"},
+    {file = "rpds_py-0.28.0-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:48b55c1f64482f7d8bd39942f376bfdf2f6aec637ee8c805b5041e14eeb771db"},
+    {file = "rpds_py-0.28.0-pp311-pypy311_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:24743a7b372e9a76171f6b69c01aedf927e8ac3e16c474d9fe20d552a8cb45c7"},
+    {file = "rpds_py-0.28.0-pp311-pypy311_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:389c29045ee8bbb1627ea190b4976a310a295559eaf9f1464a1a6f2bf84dde78"},
+    {file = "rpds_py-0.28.0-pp311-pypy311_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:23690b5827e643150cf7b49569679ec13fe9a610a15949ed48b85eb7f98f34ec"},
+    {file = "rpds_py-0.28.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6f0c9266c26580e7243ad0d72fc3e01d6b33866cfab5084a6da7576bcf1c4f72"},
+    {file = "rpds_py-0.28.0-pp311-pypy311_pp73-manylinux_2_31_riscv64.whl", hash = "sha256:4c6c4db5d73d179746951486df97fd25e92396be07fc29ee8ff9a8f5afbdfb27"},
+    {file = "rpds_py-0.28.0-pp311-pypy311_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a3b695a8fa799dd2cfdb4804b37096c5f6dba1ac7f48a7fbf6d0485bcd060316"},
+    {file = "rpds_py-0.28.0-pp311-pypy311_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:6aa1bfce3f83baf00d9c5fcdbba93a3ab79958b4c7d7d1f55e7fe68c20e63912"},
+    {file = "rpds_py-0.28.0-pp311-pypy311_pp73-musllinux_1_2_i686.whl", hash = "sha256:7b0f9dceb221792b3ee6acb5438eb1f02b0cb2c247796a72b016dcc92c6de829"},
+    {file = "rpds_py-0.28.0-pp311-pypy311_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:5d0145edba8abd3db0ab22b5300c99dc152f5c9021fab861be0f0544dc3cbc5f"},
+    {file = "rpds_py-0.28.0.tar.gz", hash = "sha256:abd4df20485a0983e2ca334a216249b6186d6e3c1627e106651943dbdb791aea"},
+]
+
+[[package]]
+name = "rsa"
+version = "4.9.1"
+description = "Pure-Python RSA implementation"
+optional = false
+python-versions = "<4,>=3.6"
+groups = ["main"]
+files = [
+    {file = "rsa-4.9.1-py3-none-any.whl", hash = "sha256:68635866661c6836b8d39430f97a996acbd61bfa49406748ea243539fe239762"},
+    {file = "rsa-4.9.1.tar.gz", hash = "sha256:e7bdbfdb5497da4c07dfd35530e1a902659db6ff241e39d9953cad06ebd0ae75"},
+]
+
+[package.dependencies]
+pyasn1 = ">=0.1.3"
+
+[[package]]
+name = "safetensors"
+version = "0.6.2"
+description = ""
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "safetensors-0.6.2-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:9c85ede8ec58f120bad982ec47746981e210492a6db876882aa021446af8ffba"},
+    {file = "safetensors-0.6.2-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:d6675cf4b39c98dbd7d940598028f3742e0375a6b4d4277e76beb0c35f4b843b"},
+    {file = "safetensors-0.6.2-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1d2d2b3ce1e2509c68932ca03ab8f20570920cd9754b05063d4368ee52833ecd"},
+    {file = "safetensors-0.6.2-cp38-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:93de35a18f46b0f5a6a1f9e26d91b442094f2df02e9fd7acf224cfec4238821a"},
+    {file = "safetensors-0.6.2-cp38-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:89a89b505f335640f9120fac65ddeb83e40f1fd081cb8ed88b505bdccec8d0a1"},
+    {file = "safetensors-0.6.2-cp38-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fc4d0d0b937e04bdf2ae6f70cd3ad51328635fe0e6214aa1fc811f3b576b3bda"},
+    {file = "safetensors-0.6.2-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8045db2c872db8f4cbe3faa0495932d89c38c899c603f21e9b6486951a5ecb8f"},
+    {file = "safetensors-0.6.2-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:81e67e8bab9878bb568cffbc5f5e655adb38d2418351dc0859ccac158f753e19"},
+    {file = "safetensors-0.6.2-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:b0e4d029ab0a0e0e4fdf142b194514695b1d7d3735503ba700cf36d0fc7136ce"},
+    {file = "safetensors-0.6.2-cp38-abi3-musllinux_1_2_armv7l.whl", hash = "sha256:fa48268185c52bfe8771e46325a1e21d317207bcabcb72e65c6e28e9ffeb29c7"},
+    {file = "safetensors-0.6.2-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:d83c20c12c2d2f465997c51b7ecb00e407e5f94d7dec3ea0cc11d86f60d3fde5"},
+    {file = "safetensors-0.6.2-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:d944cea65fad0ead848b6ec2c37cc0b197194bec228f8020054742190e9312ac"},
+    {file = "safetensors-0.6.2-cp38-abi3-win32.whl", hash = "sha256:cab75ca7c064d3911411461151cb69380c9225798a20e712b102edda2542ddb1"},
+    {file = "safetensors-0.6.2-cp38-abi3-win_amd64.whl", hash = "sha256:c7b214870df923cbc1593c3faee16bec59ea462758699bd3fee399d00aac072c"},
+    {file = "safetensors-0.6.2.tar.gz", hash = "sha256:43ff2aa0e6fa2dc3ea5524ac7ad93a9839256b8703761e76e2d0b2a3fa4f15d9"},
+]
+
+[package.extras]
+all = ["safetensors[jax]", "safetensors[numpy]", "safetensors[paddlepaddle]", "safetensors[pinned-tf]", "safetensors[quality]", "safetensors[testing]", "safetensors[torch]"]
+dev = ["safetensors[all]"]
+jax = ["flax (>=0.6.3)", "jax (>=0.3.25)", "jaxlib (>=0.3.25)", "safetensors[numpy]"]
+mlx = ["mlx (>=0.0.9)"]
+numpy = ["numpy (>=1.21.6)"]
+paddlepaddle = ["paddlepaddle (>=2.4.1)", "safetensors[numpy]"]
+pinned-tf = ["safetensors[numpy]", "tensorflow (==2.18.0)"]
+quality = ["ruff"]
+tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
+testing = ["h5py (>=3.7.0)", "huggingface-hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools-rust (>=1.5.2)"]
+testingfree = ["huggingface-hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools-rust (>=1.5.2)"]
+torch = ["safetensors[numpy]", "torch (>=1.10)"]
+
+[[package]]
+name = "scikit-learn"
+version = "1.7.2"
+description = "A set of python modules for machine learning and data mining"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "scikit_learn-1.7.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6b33579c10a3081d076ab403df4a4190da4f4432d443521674637677dc91e61f"},
+    {file = "scikit_learn-1.7.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:36749fb62b3d961b1ce4fedf08fa57a1986cd409eff2d783bca5d4b9b5fce51c"},
+    {file = "scikit_learn-1.7.2-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7a58814265dfc52b3295b1900cfb5701589d30a8bb026c7540f1e9d3499d5ec8"},
+    {file = "scikit_learn-1.7.2-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4a847fea807e278f821a0406ca01e387f97653e284ecbd9750e3ee7c90347f18"},
+    {file = "scikit_learn-1.7.2-cp310-cp310-win_amd64.whl", hash = "sha256:ca250e6836d10e6f402436d6463d6c0e4d8e0234cfb6a9a47835bd392b852ce5"},
+    {file = "scikit_learn-1.7.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c7509693451651cd7361d30ce4e86a1347493554f172b1c72a39300fa2aea79e"},
+    {file = "scikit_learn-1.7.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:0486c8f827c2e7b64837c731c8feff72c0bd2b998067a8a9cbc10643c31f0fe1"},
+    {file = "scikit_learn-1.7.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:89877e19a80c7b11a2891a27c21c4894fb18e2c2e077815bcade10d34287b20d"},
+    {file = "scikit_learn-1.7.2-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8da8bf89d4d79aaec192d2bda62f9b56ae4e5b4ef93b6a56b5de4977e375c1f1"},
+    {file = "scikit_learn-1.7.2-cp311-cp311-win_amd64.whl", hash = "sha256:9b7ed8d58725030568523e937c43e56bc01cadb478fc43c042a9aca1dacb3ba1"},
+    {file = "scikit_learn-1.7.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:8d91a97fa2b706943822398ab943cde71858a50245e31bc71dba62aab1d60a96"},
+    {file = "scikit_learn-1.7.2-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:acbc0f5fd2edd3432a22c69bed78e837c70cf896cd7993d71d51ba6708507476"},
+    {file = "scikit_learn-1.7.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e5bf3d930aee75a65478df91ac1225ff89cd28e9ac7bd1196853a9229b6adb0b"},
+    {file = "scikit_learn-1.7.2-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b4d6e9deed1a47aca9fe2f267ab8e8fe82ee20b4526b2c0cd9e135cea10feb44"},
+    {file = "scikit_learn-1.7.2-cp312-cp312-win_amd64.whl", hash = "sha256:6088aa475f0785e01bcf8529f55280a3d7d298679f50c0bb70a2364a82d0b290"},
+    {file = "scikit_learn-1.7.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0b7dacaa05e5d76759fb071558a8b5130f4845166d88654a0f9bdf3eb57851b7"},
+    {file = "scikit_learn-1.7.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:abebbd61ad9e1deed54cca45caea8ad5f79e1b93173dece40bb8e0c658dbe6fe"},
+    {file = "scikit_learn-1.7.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:502c18e39849c0ea1a5d681af1dbcf15f6cce601aebb657aabbfe84133c1907f"},
+    {file = "scikit_learn-1.7.2-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7a4c328a71785382fe3fe676a9ecf2c86189249beff90bf85e22bdb7efaf9ae0"},
+    {file = "scikit_learn-1.7.2-cp313-cp313-win_amd64.whl", hash = "sha256:63a9afd6f7b229aad94618c01c252ce9e6fa97918c5ca19c9a17a087d819440c"},
+    {file = "scikit_learn-1.7.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:9acb6c5e867447b4e1390930e3944a005e2cb115922e693c08a323421a6966e8"},
+    {file = "scikit_learn-1.7.2-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:2a41e2a0ef45063e654152ec9d8bcfc39f7afce35b08902bfe290c2498a67a6a"},
+    {file = "scikit_learn-1.7.2-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:98335fb98509b73385b3ab2bd0639b1f610541d3988ee675c670371d6a87aa7c"},
+    {file = "scikit_learn-1.7.2-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:191e5550980d45449126e23ed1d5e9e24b2c68329ee1f691a3987476e115e09c"},
+    {file = "scikit_learn-1.7.2-cp313-cp313t-win_amd64.whl", hash = "sha256:57dc4deb1d3762c75d685507fbd0bc17160144b2f2ba4ccea5dc285ab0d0e973"},
+    {file = "scikit_learn-1.7.2-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fa8f63940e29c82d1e67a45d5297bdebbcb585f5a5a50c4914cc2e852ab77f33"},
+    {file = "scikit_learn-1.7.2-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:f95dc55b7902b91331fa4e5845dd5bde0580c9cd9612b1b2791b7e80c3d32615"},
+    {file = "scikit_learn-1.7.2-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9656e4a53e54578ad10a434dc1f993330568cfee176dff07112b8785fb413106"},
+    {file = "scikit_learn-1.7.2-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:96dc05a854add0e50d3f47a1ef21a10a595016da5b007c7d9cd9d0bffd1fcc61"},
+    {file = "scikit_learn-1.7.2-cp314-cp314-win_amd64.whl", hash = "sha256:bb24510ed3f9f61476181e4db51ce801e2ba37541def12dc9333b946fc7a9cf8"},
+    {file = "scikit_learn-1.7.2.tar.gz", hash = "sha256:20e9e49ecd130598f1ca38a1d85090e1a600147b9c02fa6f15d69cb53d968fda"},
+]
+
+[package.dependencies]
+joblib = ">=1.2.0"
+numpy = ">=1.22.0"
+scipy = ">=1.8.0"
+threadpoolctl = ">=3.1.0"
+
+[package.extras]
+benchmark = ["matplotlib (>=3.5.0)", "memory_profiler (>=0.57.0)", "pandas (>=1.4.0)"]
+build = ["cython (>=3.0.10)", "meson-python (>=0.17.1)", "numpy (>=1.22.0)", "scipy (>=1.8.0)"]
+docs = ["Pillow (>=8.4.0)", "matplotlib (>=3.5.0)", "memory_profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.4.0)", "plotly (>=5.14.0)", "polars (>=0.20.30)", "pooch (>=1.6.0)", "pydata-sphinx-theme (>=0.15.3)", "scikit-image (>=0.19.0)", "seaborn (>=0.9.0)", "sphinx (>=7.3.7)", "sphinx-copybutton (>=0.5.2)", "sphinx-design (>=0.5.0)", "sphinx-design (>=0.6.0)", "sphinx-gallery (>=0.17.1)", "sphinx-prompt (>=1.4.0)", "sphinx-remove-toctrees (>=1.0.0.post1)", "sphinxcontrib-sass (>=0.3.4)", "sphinxext-opengraph (>=0.9.1)", "towncrier (>=24.8.0)"]
+examples = ["matplotlib (>=3.5.0)", "pandas (>=1.4.0)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.19.0)", "seaborn (>=0.9.0)"]
+install = ["joblib (>=1.2.0)", "numpy (>=1.22.0)", "scipy (>=1.8.0)", "threadpoolctl (>=3.1.0)"]
+maintenance = ["conda-lock (==3.0.1)"]
+tests = ["matplotlib (>=3.5.0)", "mypy (>=1.15)", "numpydoc (>=1.2.0)", "pandas (>=1.4.0)", "polars (>=0.20.30)", "pooch (>=1.6.0)", "pyamg (>=4.2.1)", "pyarrow (>=12.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.11.7)", "scikit-image (>=0.19.0)"]
+
+[[package]]
+name = "scipy"
+version = "1.15.3"
+description = "Fundamental algorithms for scientific computing in Python"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "python_version == \"3.10\""
+files = [
+    {file = "scipy-1.15.3-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:a345928c86d535060c9c2b25e71e87c39ab2f22fc96e9636bd74d1dbf9de448c"},
+    {file = "scipy-1.15.3-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:ad3432cb0f9ed87477a8d97f03b763fd1d57709f1bbde3c9369b1dff5503b253"},
+    {file = "scipy-1.15.3-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:aef683a9ae6eb00728a542b796f52a5477b78252edede72b8327a886ab63293f"},
+    {file = "scipy-1.15.3-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:1c832e1bd78dea67d5c16f786681b28dd695a8cb1fb90af2e27580d3d0967e92"},
+    {file = "scipy-1.15.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:263961f658ce2165bbd7b99fa5135195c3a12d9bef045345016b8b50c315cb82"},
+    {file = "scipy-1.15.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e2abc762b0811e09a0d3258abee2d98e0c703eee49464ce0069590846f31d40"},
+    {file = "scipy-1.15.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:ed7284b21a7a0c8f1b6e5977ac05396c0d008b89e05498c8b7e8f4a1423bba0e"},
+    {file = "scipy-1.15.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:5380741e53df2c566f4d234b100a484b420af85deb39ea35a1cc1be84ff53a5c"},
+    {file = "scipy-1.15.3-cp310-cp310-win_amd64.whl", hash = "sha256:9d61e97b186a57350f6d6fd72640f9e99d5a4a2b8fbf4b9ee9a841eab327dc13"},
+    {file = "scipy-1.15.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:993439ce220d25e3696d1b23b233dd010169b62f6456488567e830654ee37a6b"},
+    {file = "scipy-1.15.3-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:34716e281f181a02341ddeaad584205bd2fd3c242063bd3423d61ac259ca7eba"},
+    {file = "scipy-1.15.3-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:3b0334816afb8b91dab859281b1b9786934392aa3d527cd847e41bb6f45bee65"},
+    {file = "scipy-1.15.3-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:6db907c7368e3092e24919b5e31c76998b0ce1684d51a90943cb0ed1b4ffd6c1"},
+    {file = "scipy-1.15.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:721d6b4ef5dc82ca8968c25b111e307083d7ca9091bc38163fb89243e85e3889"},
+    {file = "scipy-1.15.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39cb9c62e471b1bb3750066ecc3a3f3052b37751c7c3dfd0fd7e48900ed52982"},
+    {file = "scipy-1.15.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:795c46999bae845966368a3c013e0e00947932d68e235702b5c3f6ea799aa8c9"},
+    {file = "scipy-1.15.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:18aaacb735ab38b38db42cb01f6b92a2d0d4b6aabefeb07f02849e47f8fb3594"},
+    {file = "scipy-1.15.3-cp311-cp311-win_amd64.whl", hash = "sha256:ae48a786a28412d744c62fd7816a4118ef97e5be0bee968ce8f0a2fba7acf3bb"},
+    {file = "scipy-1.15.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6ac6310fdbfb7aa6612408bd2f07295bcbd3fda00d2d702178434751fe48e019"},
+    {file = "scipy-1.15.3-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:185cd3d6d05ca4b44a8f1595af87f9c372bb6acf9c808e99aa3e9aa03bd98cf6"},
+    {file = "scipy-1.15.3-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:05dc6abcd105e1a29f95eada46d4a3f251743cfd7d3ae8ddb4088047f24ea477"},
+    {file = "scipy-1.15.3-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:06efcba926324df1696931a57a176c80848ccd67ce6ad020c810736bfd58eb1c"},
+    {file = "scipy-1.15.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c05045d8b9bfd807ee1b9f38761993297b10b245f012b11b13b91ba8945f7e45"},
+    {file = "scipy-1.15.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:271e3713e645149ea5ea3e97b57fdab61ce61333f97cfae392c28ba786f9bb49"},
+    {file = "scipy-1.15.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6cfd56fc1a8e53f6e89ba3a7a7251f7396412d655bca2aa5611c8ec9a6784a1e"},
+    {file = "scipy-1.15.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:0ff17c0bb1cb32952c09217d8d1eed9b53d1463e5f1dd6052c7857f83127d539"},
+    {file = "scipy-1.15.3-cp312-cp312-win_amd64.whl", hash = "sha256:52092bc0472cfd17df49ff17e70624345efece4e1a12b23783a1ac59a1b728ed"},
+    {file = "scipy-1.15.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:2c620736bcc334782e24d173c0fdbb7590a0a436d2fdf39310a8902505008759"},
+    {file = "scipy-1.15.3-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:7e11270a000969409d37ed399585ee530b9ef6aa99d50c019de4cb01e8e54e62"},
+    {file = "scipy-1.15.3-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:8c9ed3ba2c8a2ce098163a9bdb26f891746d02136995df25227a20e71c396ebb"},
+    {file = "scipy-1.15.3-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:0bdd905264c0c9cfa74a4772cdb2070171790381a5c4d312c973382fc6eaf730"},
+    {file = "scipy-1.15.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79167bba085c31f38603e11a267d862957cbb3ce018d8b38f79ac043bc92d825"},
+    {file = "scipy-1.15.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9deabd6d547aee2c9a81dee6cc96c6d7e9a9b1953f74850c179f91fdc729cb7"},
+    {file = "scipy-1.15.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:dde4fc32993071ac0c7dd2d82569e544f0bdaff66269cb475e0f369adad13f11"},
+    {file = "scipy-1.15.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f77f853d584e72e874d87357ad70f44b437331507d1c311457bed8ed2b956126"},
+    {file = "scipy-1.15.3-cp313-cp313-win_amd64.whl", hash = "sha256:b90ab29d0c37ec9bf55424c064312930ca5f4bde15ee8619ee44e69319aab163"},
+    {file = "scipy-1.15.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:3ac07623267feb3ae308487c260ac684b32ea35fd81e12845039952f558047b8"},
+    {file = "scipy-1.15.3-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:6487aa99c2a3d509a5227d9a5e889ff05830a06b2ce08ec30df6d79db5fcd5c5"},
+    {file = "scipy-1.15.3-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:50f9e62461c95d933d5c5ef4a1f2ebf9a2b4e83b0db374cb3f1de104d935922e"},
+    {file = "scipy-1.15.3-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:14ed70039d182f411ffc74789a16df3835e05dc469b898233a245cdfd7f162cb"},
+    {file = "scipy-1.15.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0a769105537aa07a69468a0eefcd121be52006db61cdd8cac8a0e68980bbb723"},
+    {file = "scipy-1.15.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9db984639887e3dffb3928d118145ffe40eff2fa40cb241a306ec57c219ebbbb"},
+    {file = "scipy-1.15.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:40e54d5c7e7ebf1aa596c374c49fa3135f04648a0caabcb66c52884b943f02b4"},
+    {file = "scipy-1.15.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:5e721fed53187e71d0ccf382b6bf977644c533e506c4d33c3fb24de89f5c3ed5"},
+    {file = "scipy-1.15.3-cp313-cp313t-win_amd64.whl", hash = "sha256:76ad1fb5f8752eabf0fa02e4cc0336b4e8f021e2d5f061ed37d6d264db35e3ca"},
+    {file = "scipy-1.15.3.tar.gz", hash = "sha256:eae3cf522bc7df64b42cad3925c876e1b0b6c35c1337c93e12c0f366f55b0eaf"},
+]
+
+[package.dependencies]
+numpy = ">=1.23.5,<2.5"
+
+[package.extras]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
+doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.19.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.0.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict (>=2.0,<2.1.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
+[[package]]
+name = "scipy"
+version = "1.16.3"
+description = "Fundamental algorithms for scientific computing in Python"
+optional = false
+python-versions = ">=3.11"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
+files = [
+    {file = "scipy-1.16.3-cp311-cp311-macosx_10_14_x86_64.whl", hash = "sha256:40be6cf99e68b6c4321e9f8782e7d5ff8265af28ef2cd56e9c9b2638fa08ad97"},
+    {file = "scipy-1.16.3-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:8be1ca9170fcb6223cc7c27f4305d680ded114a1567c0bd2bfcbf947d1b17511"},
+    {file = "scipy-1.16.3-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:bea0a62734d20d67608660f69dcda23e7f90fb4ca20974ab80b6ed40df87a005"},
+    {file = "scipy-1.16.3-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:2a207a6ce9c24f1951241f4693ede2d393f59c07abc159b2cb2be980820e01fb"},
+    {file = "scipy-1.16.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:532fb5ad6a87e9e9cd9c959b106b73145a03f04c7d57ea3e6f6bb60b86ab0876"},
+    {file = "scipy-1.16.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0151a0749efeaaab78711c78422d413c583b8cdd2011a3c1d6c794938ee9fdb2"},
+    {file = "scipy-1.16.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:b7180967113560cca57418a7bc719e30366b47959dd845a93206fbed693c867e"},
+    {file = "scipy-1.16.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:deb3841c925eeddb6afc1e4e4a45e418d19ec7b87c5df177695224078e8ec733"},
+    {file = "scipy-1.16.3-cp311-cp311-win_amd64.whl", hash = "sha256:53c3844d527213631e886621df5695d35e4f6a75f620dca412bcd292f6b87d78"},
+    {file = "scipy-1.16.3-cp311-cp311-win_arm64.whl", hash = "sha256:9452781bd879b14b6f055b26643703551320aa8d79ae064a71df55c00286a184"},
+    {file = "scipy-1.16.3-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:81fc5827606858cf71446a5e98715ba0e11f0dbc83d71c7409d05486592a45d6"},
+    {file = "scipy-1.16.3-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:c97176013d404c7346bf57874eaac5187d969293bf40497140b0a2b2b7482e07"},
+    {file = "scipy-1.16.3-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:2b71d93c8a9936046866acebc915e2af2e292b883ed6e2cbe5c34beb094b82d9"},
+    {file = "scipy-1.16.3-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:3d4a07a8e785d80289dfe66b7c27d8634a773020742ec7187b85ccc4b0e7b686"},
+    {file = "scipy-1.16.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0553371015692a898e1aa858fed67a3576c34edefa6b7ebdb4e9dde49ce5c203"},
+    {file = "scipy-1.16.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:72d1717fd3b5e6ec747327ce9bda32d5463f472c9dce9f54499e81fbd50245a1"},
+    {file = "scipy-1.16.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1fb2472e72e24d1530debe6ae078db70fb1605350c88a3d14bc401d6306dbffe"},
+    {file = "scipy-1.16.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c5192722cffe15f9329a3948c4b1db789fbb1f05c97899187dcf009b283aea70"},
+    {file = "scipy-1.16.3-cp312-cp312-win_amd64.whl", hash = "sha256:56edc65510d1331dae01ef9b658d428e33ed48b4f77b1d51caf479a0253f96dc"},
+    {file = "scipy-1.16.3-cp312-cp312-win_arm64.whl", hash = "sha256:a8a26c78ef223d3e30920ef759e25625a0ecdd0d60e5a8818b7513c3e5384cf2"},
+    {file = "scipy-1.16.3-cp313-cp313-macosx_10_14_x86_64.whl", hash = "sha256:d2ec56337675e61b312179a1ad124f5f570c00f920cc75e1000025451b88241c"},
+    {file = "scipy-1.16.3-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:16b8bc35a4cc24db80a0ec836a9286d0e31b2503cb2fd7ff7fb0e0374a97081d"},
+    {file = "scipy-1.16.3-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:5803c5fadd29de0cf27fa08ccbfe7a9e5d741bf63e4ab1085437266f12460ff9"},
+    {file = "scipy-1.16.3-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:b81c27fc41954319a943d43b20e07c40bdcd3ff7cf013f4fb86286faefe546c4"},
+    {file = "scipy-1.16.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0c3b4dd3d9b08dbce0f3440032c52e9e2ab9f96ade2d3943313dfe51a7056959"},
+    {file = "scipy-1.16.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7dc1360c06535ea6116a2220f760ae572db9f661aba2d88074fe30ec2aa1ff88"},
+    {file = "scipy-1.16.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:663b8d66a8748051c3ee9c96465fb417509315b99c71550fda2591d7dd634234"},
+    {file = "scipy-1.16.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eab43fae33a0c39006a88096cd7b4f4ef545ea0447d250d5ac18202d40b6611d"},
+    {file = "scipy-1.16.3-cp313-cp313-win_amd64.whl", hash = "sha256:062246acacbe9f8210de8e751b16fc37458213f124bef161a5a02c7a39284304"},
+    {file = "scipy-1.16.3-cp313-cp313-win_arm64.whl", hash = "sha256:50a3dbf286dbc7d84f176f9a1574c705f277cb6565069f88f60db9eafdbe3ee2"},
+    {file = "scipy-1.16.3-cp313-cp313t-macosx_10_14_x86_64.whl", hash = "sha256:fb4b29f4cf8cc5a8d628bc8d8e26d12d7278cd1f219f22698a378c3d67db5e4b"},
+    {file = "scipy-1.16.3-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:8d09d72dc92742988b0e7750bddb8060b0c7079606c0d24a8cc8e9c9c11f9079"},
+    {file = "scipy-1.16.3-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:03192a35e661470197556de24e7cb1330d84b35b94ead65c46ad6f16f6b28f2a"},
+    {file = "scipy-1.16.3-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:57d01cb6f85e34f0946b33caa66e892aae072b64b034183f3d87c4025802a119"},
+    {file = "scipy-1.16.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:96491a6a54e995f00a28a3c3badfff58fd093bf26cd5fb34a2188c8c756a3a2c"},
+    {file = "scipy-1.16.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cd13e354df9938598af2be05822c323e97132d5e6306b83a3b4ee6724c6e522e"},
+    {file = "scipy-1.16.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:63d3cdacb8a824a295191a723ee5e4ea7768ca5ca5f2838532d9f2e2b3ce2135"},
+    {file = "scipy-1.16.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e7efa2681ea410b10dde31a52b18b0154d66f2485328830e45fdf183af5aefc6"},
+    {file = "scipy-1.16.3-cp313-cp313t-win_amd64.whl", hash = "sha256:2d1ae2cf0c350e7705168ff2429962a89ad90c2d49d1dd300686d8b2a5af22fc"},
+    {file = "scipy-1.16.3-cp313-cp313t-win_arm64.whl", hash = "sha256:0c623a54f7b79dd88ef56da19bc2873afec9673a48f3b85b18e4d402bdd29a5a"},
+    {file = "scipy-1.16.3-cp314-cp314-macosx_10_14_x86_64.whl", hash = "sha256:875555ce62743e1d54f06cdf22c1e0bc47b91130ac40fe5d783b6dfa114beeb6"},
+    {file = "scipy-1.16.3-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:bb61878c18a470021fb515a843dc7a76961a8daceaaaa8bad1332f1bf4b54657"},
+    {file = "scipy-1.16.3-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:f2622206f5559784fa5c4b53a950c3c7c1cf3e84ca1b9c4b6c03f062f289ca26"},
+    {file = "scipy-1.16.3-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:7f68154688c515cdb541a31ef8eb66d8cd1050605be9dcd74199cbd22ac739bc"},
+    {file = "scipy-1.16.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8b3c820ddb80029fe9f43d61b81d8b488d3ef8ca010d15122b152db77dc94c22"},
+    {file = "scipy-1.16.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d3837938ae715fc0fe3c39c0202de3a8853aff22ca66781ddc2ade7554b7e2cc"},
+    {file = "scipy-1.16.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:aadd23f98f9cb069b3bd64ddc900c4d277778242e961751f77a8cb5c4b946fb0"},
+    {file = "scipy-1.16.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:b7c5f1bda1354d6a19bc6af73a649f8285ca63ac6b52e64e658a5a11d4d69800"},
+    {file = "scipy-1.16.3-cp314-cp314-win_amd64.whl", hash = "sha256:e5d42a9472e7579e473879a1990327830493a7047506d58d73fc429b84c1d49d"},
+    {file = "scipy-1.16.3-cp314-cp314-win_arm64.whl", hash = "sha256:6020470b9d00245926f2d5bb93b119ca0340f0d564eb6fbaad843eaebf9d690f"},
+    {file = "scipy-1.16.3-cp314-cp314t-macosx_10_14_x86_64.whl", hash = "sha256:e1d27cbcb4602680a49d787d90664fa4974063ac9d4134813332a8c53dbe667c"},
+    {file = "scipy-1.16.3-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:9b9c9c07b6d56a35777a1b4cc8966118fb16cfd8daf6743867d17d36cfad2d40"},
+    {file = "scipy-1.16.3-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:3a4c460301fb2cffb7f88528f30b3127742cff583603aa7dc964a52c463b385d"},
+    {file = "scipy-1.16.3-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:f667a4542cc8917af1db06366d3f78a5c8e83badd56409f94d1eac8d8d9133fa"},
+    {file = "scipy-1.16.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f379b54b77a597aa7ee5e697df0d66903e41b9c85a6dd7946159e356319158e8"},
+    {file = "scipy-1.16.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4aff59800a3b7f786b70bfd6ab551001cb553244988d7d6b8299cb1ea653b353"},
+    {file = "scipy-1.16.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:da7763f55885045036fabcebd80144b757d3db06ab0861415d1c3b7c69042146"},
+    {file = "scipy-1.16.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ffa6eea95283b2b8079b821dc11f50a17d0571c92b43e2b5b12764dc5f9b285d"},
+    {file = "scipy-1.16.3-cp314-cp314t-win_amd64.whl", hash = "sha256:d9f48cafc7ce94cf9b15c6bffdc443a81a27bf7075cf2dcd5c8b40f85d10c4e7"},
+    {file = "scipy-1.16.3-cp314-cp314t-win_arm64.whl", hash = "sha256:21d9d6b197227a12dcbf9633320a4e34c6b0e51c57268df255a0942983bac562"},
+    {file = "scipy-1.16.3.tar.gz", hash = "sha256:01e87659402762f43bd2fee13370553a17ada367d42e7487800bf2916535aecb"},
+]
+
+[package.dependencies]
+numpy = ">=1.25.2,<2.6"
+
+[package.extras]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
+doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.19.1)", "jupytext", "linkify-it-py", "matplotlib (>=3.5)", "myst-nb (>=1.2.0)", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.2.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict (>=2.3.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest (>=8.0.0)", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
+[[package]]
+name = "send2trash"
+version = "1.8.3"
+description = "Send file to trash natively under Mac OS X, Windows and Linux"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+groups = ["main"]
+files = [
+    {file = "Send2Trash-1.8.3-py3-none-any.whl", hash = "sha256:0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9"},
+    {file = "Send2Trash-1.8.3.tar.gz", hash = "sha256:b18e7a3966d99871aefeb00cfbcfdced55ce4871194810fc71f4aa484b953abf"},
+]
+
+[package.extras]
+nativelib = ["pyobjc-framework-Cocoa ; sys_platform == \"darwin\"", "pywin32 ; sys_platform == \"win32\""]
+objc = ["pyobjc-framework-Cocoa ; sys_platform == \"darwin\""]
+win32 = ["pywin32 ; sys_platform == \"win32\""]
+
+[[package]]
+name = "sentence-transformers"
+version = "5.1.2"
+description = "Embeddings, Retrieval, and Reranking"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "sentence_transformers-5.1.2-py3-none-any.whl", hash = "sha256:724ce0ea62200f413f1a5059712aff66495bc4e815a1493f7f9bca242414c333"},
+    {file = "sentence_transformers-5.1.2.tar.gz", hash = "sha256:0f6c8bd916a78dc65b366feb8d22fd885efdb37432e7630020d113233af2b856"},
+]
+
+[package.dependencies]
+huggingface-hub = ">=0.20.0"
+Pillow = "*"
+scikit-learn = "*"
+scipy = "*"
+torch = ">=1.11.0"
+tqdm = "*"
+transformers = ">=4.41.0,<5.0.0"
+typing_extensions = ">=4.5.0"
+
+[package.extras]
+dev = ["accelerate (>=0.20.3)", "datasets", "peft", "pre-commit", "pytest", "pytest-cov"]
+onnx = ["optimum[onnxruntime] (>=1.23.1)"]
+onnx-gpu = ["optimum[onnxruntime-gpu] (>=1.23.1)"]
+openvino = ["optimum-intel[openvino] (>=1.20.0)"]
+train = ["accelerate (>=0.20.3)", "datasets"]
+
+[[package]]
+name = "sentencepiece"
+version = "0.2.1"
+description = "Unsupervised text tokenizer and detokenizer."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "sentencepiece-0.2.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e10fa50bdbaa5e2445dbd387979980d391760faf0ec99a09bd7780ff37eaec44"},
+    {file = "sentencepiece-0.2.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2f27ae6deea72efdb6f361750c92f6c21fd0ad087445082770cc34015213c526"},
+    {file = "sentencepiece-0.2.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:60937c959e6f44159fdd9f56fbdd302501f96114a5ba436829496d5f32d8de3f"},
+    {file = "sentencepiece-0.2.1-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d8b1d91545578852f128650b8cce4ec20f93d39b378ff554ebe66290f2dabb92"},
+    {file = "sentencepiece-0.2.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:27e38eee653abc3d387862e67bc5c8b6f428cd604e688b85d29170b7e725c26c"},
+    {file = "sentencepiece-0.2.1-cp310-cp310-win32.whl", hash = "sha256:251874d720ac7f28024a168501f3c7bb15d1802245f6e66de565f18bbb9b5eaa"},
+    {file = "sentencepiece-0.2.1-cp310-cp310-win_amd64.whl", hash = "sha256:e52144670738b4b477fade6c2a9b6af71a8d0094514c9853ac9f6fc1fcfabae7"},
+    {file = "sentencepiece-0.2.1-cp310-cp310-win_arm64.whl", hash = "sha256:9076430ac25dfa7147d9d05751dbc66a04bc1aaac371c07f84952979ea59f0d0"},
+    {file = "sentencepiece-0.2.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6356d0986b8b8dc351b943150fcd81a1c6e6e4d439772e8584c64230e58ca987"},
+    {file = "sentencepiece-0.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:8f8ba89a3acb3dc1ae90f65ec1894b0b9596fdb98ab003ff38e058f898b39bc7"},
+    {file = "sentencepiece-0.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:02593eca45440ef39247cee8c47322a34bdcc1d8ae83ad28ba5a899a2cf8d79a"},
+    {file = "sentencepiece-0.2.1-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0a0d15781a171d188b661ae4bde1d998c303f6bd8621498c50c671bd45a4798e"},
+    {file = "sentencepiece-0.2.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4f5a3e0d9f445ed9d66c0fec47d4b23d12cfc858b407a03c194c1b26c2ac2a63"},
+    {file = "sentencepiece-0.2.1-cp311-cp311-win32.whl", hash = "sha256:6d297a1748d429ba8534eebe5535448d78b8acc32d00a29b49acf28102eeb094"},
+    {file = "sentencepiece-0.2.1-cp311-cp311-win_amd64.whl", hash = "sha256:82d9ead6591015f009cb1be1cb1c015d5e6f04046dbb8c9588b931e869a29728"},
+    {file = "sentencepiece-0.2.1-cp311-cp311-win_arm64.whl", hash = "sha256:39f8651bd10974eafb9834ce30d9bcf5b73e1fc798a7f7d2528f9820ca86e119"},
+    {file = "sentencepiece-0.2.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:57cae326c8727de58c85977b175af132a7138d84c764635d7e71bbee7e774133"},
+    {file = "sentencepiece-0.2.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:56dd39a3c4d6493db3cdca7e8cc68c6b633f0d4195495cbadfcf5af8a22d05a6"},
+    {file = "sentencepiece-0.2.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d9381351182ff9888cc80e41c632e7e274b106f450de33d67a9e8f6043da6f76"},
+    {file = "sentencepiece-0.2.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:99f955df238021bf11f0fc37cdb54fd5e5b5f7fd30ecc3d93fb48b6815437167"},
+    {file = "sentencepiece-0.2.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0cdfecef430d985f1c2bcbfff3defd1d95dae876fbd0173376012d2d7d24044b"},
+    {file = "sentencepiece-0.2.1-cp312-cp312-win32.whl", hash = "sha256:a483fd29a34c3e34c39ac5556b0a90942bec253d260235729e50976f5dba1068"},
+    {file = "sentencepiece-0.2.1-cp312-cp312-win_amd64.whl", hash = "sha256:4cdc7c36234fda305e85c32949c5211faaf8dd886096c7cea289ddc12a2d02de"},
+    {file = "sentencepiece-0.2.1-cp312-cp312-win_arm64.whl", hash = "sha256:daeb5e9e9fcad012324807856113708614d534f596d5008638eb9b40112cd9e4"},
+    {file = "sentencepiece-0.2.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:dcd8161eee7b41aae57ded06272905dbd680a0a04b91edd0f64790c796b2f706"},
+    {file = "sentencepiece-0.2.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c6c8f42949f419ff8c7e9960dbadcfbc982d7b5efc2f6748210d3dd53a7de062"},
+    {file = "sentencepiece-0.2.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:097f3394e99456e9e4efba1737c3749d7e23563dd1588ce71a3d007f25475fff"},
+    {file = "sentencepiece-0.2.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d7b670879c370d350557edabadbad1f6561a9e6968126e6debca4029e5547820"},
+    {file = "sentencepiece-0.2.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c7f0fd2f2693309e6628aeeb2e2faf6edd221134dfccac3308ca0de01f8dab47"},
+    {file = "sentencepiece-0.2.1-cp313-cp313-win32.whl", hash = "sha256:92b3816aa2339355fda2c8c4e021a5de92180b00aaccaf5e2808972e77a4b22f"},
+    {file = "sentencepiece-0.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:10ed3dab2044c47f7a2e7b4969b0c430420cdd45735d78c8f853191fa0e3148b"},
+    {file = "sentencepiece-0.2.1-cp313-cp313-win_arm64.whl", hash = "sha256:ac650534e2251083c5f75dde4ff28896ce7c8904133dc8fef42780f4d5588fcd"},
+    {file = "sentencepiece-0.2.1-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:8dd4b477a7b069648d19363aad0cab9bad2f4e83b2d179be668efa672500dc94"},
+    {file = "sentencepiece-0.2.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0c0f672da370cc490e4c59d89e12289778310a0e71d176c541e4834759e1ae07"},
+    {file = "sentencepiece-0.2.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:ad8493bea8432dae8d6830365352350f3b4144415a1d09c4c8cb8d30cf3b6c3c"},
+    {file = "sentencepiece-0.2.1-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b81a24733726e3678d2db63619acc5a8dccd074f7aa7a54ecd5ca33ca6d2d596"},
+    {file = "sentencepiece-0.2.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0a81799d0a68d618e89063fb423c3001a034c893069135ffe51fee439ae474d6"},
+    {file = "sentencepiece-0.2.1-cp313-cp313t-win32.whl", hash = "sha256:89a3ea015517c42c0341d0d962f3e6aaf2cf10d71b1932d475c44ba48d00aa2b"},
+    {file = "sentencepiece-0.2.1-cp313-cp313t-win_amd64.whl", hash = "sha256:33f068c9382dc2e7c228eedfd8163b52baa86bb92f50d0488bf2b7da7032e484"},
+    {file = "sentencepiece-0.2.1-cp313-cp313t-win_arm64.whl", hash = "sha256:b3616ad246f360e52c85781e47682d31abfb6554c779e42b65333d4b5f44ecc0"},
+    {file = "sentencepiece-0.2.1-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:5d0350b686c320068702116276cfb26c066dc7e65cfef173980b11bb4d606719"},
+    {file = "sentencepiece-0.2.1-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:c7f54a31cde6fa5cb030370566f68152a742f433f8d2be458463d06c208aef33"},
+    {file = "sentencepiece-0.2.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c83b85ab2d6576607f31df77ff86f28182be4a8de6d175d2c33ca609925f5da1"},
+    {file = "sentencepiece-0.2.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1855f57db07b51fb51ed6c9c452f570624d2b169b36f0f79ef71a6e6c618cd8b"},
+    {file = "sentencepiece-0.2.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:01e6912125cb45d3792f530a4d38f8e21bf884d6b4d4ade1b2de5cf7a8d2a52b"},
+    {file = "sentencepiece-0.2.1-cp314-cp314-win32.whl", hash = "sha256:c415c9de1447e0a74ae3fdb2e52f967cb544113a3a5ce3a194df185cbc1f962f"},
+    {file = "sentencepiece-0.2.1-cp314-cp314-win_amd64.whl", hash = "sha256:881b2e44b14fc19feade3cbed314be37de639fc415375cefaa5bc81a4be137fd"},
+    {file = "sentencepiece-0.2.1-cp314-cp314-win_arm64.whl", hash = "sha256:2005242a16d2dc3ac5fe18aa7667549134d37854823df4c4db244752453b78a8"},
+    {file = "sentencepiece-0.2.1-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:a19adcec27c524cb7069a1c741060add95f942d1cbf7ad0d104dffa0a7d28a2b"},
+    {file = "sentencepiece-0.2.1-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:e37e4b4c4a11662b5db521def4e44d4d30ae69a1743241412a93ae40fdcab4bb"},
+    {file = "sentencepiece-0.2.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:477c81505db072b3ab627e7eab972ea1025331bd3a92bacbf798df2b75ea86ec"},
+    {file = "sentencepiece-0.2.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:010f025a544ef770bb395091d57cb94deb9652d8972e0d09f71d85d5a0816c8c"},
+    {file = "sentencepiece-0.2.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:733e59ff1794d26db706cd41fc2d7ca5f6c64a820709cb801dc0ea31780d64ab"},
+    {file = "sentencepiece-0.2.1-cp314-cp314t-win32.whl", hash = "sha256:d3233770f78e637dc8b1fda2cd7c3b99ec77e7505041934188a4e7fe751de3b0"},
+    {file = "sentencepiece-0.2.1-cp314-cp314t-win_amd64.whl", hash = "sha256:5e4366c97b68218fd30ea72d70c525e6e78a6c0a88650f57ac4c43c63b234a9d"},
+    {file = "sentencepiece-0.2.1-cp314-cp314t-win_arm64.whl", hash = "sha256:105e36e75cbac1292642045458e8da677b2342dcd33df503e640f0b457cb6751"},
+    {file = "sentencepiece-0.2.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:afefe50a0cdcb4f2fd9733cb52001a2c164181ee2d82c32d38f5b1b326a8528c"},
+    {file = "sentencepiece-0.2.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:891ade6503dd93d418c03993f7d6a8aa20260c422cefff5096b9068185e67642"},
+    {file = "sentencepiece-0.2.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:814978ac05130dd5812b4b03215c766bc6abaef13e7bd72bc534e4d1e12e9a4c"},
+    {file = "sentencepiece-0.2.1-cp39-cp39-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:017f97b274d4b0baa84b2dc743bf4517be81156f413bb24f12aacacde378e5ab"},
+    {file = "sentencepiece-0.2.1-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:22c4ebcb3c6ab1496ab1c37c79ef7bb563b8726f29548c30773b7a4cb152df1a"},
+    {file = "sentencepiece-0.2.1-cp39-cp39-win32.whl", hash = "sha256:caa4e560c72c151da80036aecc2159e51a7fd8ae9efebefd96860460ce6bd025"},
+    {file = "sentencepiece-0.2.1-cp39-cp39-win_amd64.whl", hash = "sha256:2af5a1fb05013332ad94343b8b5f3973e006a2dde2dfba55a819549e054e2f0f"},
+    {file = "sentencepiece-0.2.1-cp39-cp39-win_arm64.whl", hash = "sha256:3d165fbb9bf8fba35f1946ba2617c3f9995679f07438325f07c026d53f33e746"},
+    {file = "sentencepiece-0.2.1.tar.gz", hash = "sha256:8138cec27c2f2282f4a34d9a016e3374cd40e5c6e9cb335063db66a0a3b71fad"},
+]
+
+[package.extras]
+test = ["pytest"]
+testpaths = ["test"]
+
+[[package]]
+name = "setuptools"
+version = "80.9.0"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "setuptools-80.9.0-py3-none-any.whl", hash = "sha256:062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922"},
+    {file = "setuptools-80.9.0.tar.gz", hash = "sha256:f36b47402ecde768dbfafc46e8e4207b4360c654f1f3bb84475f0a28628fb19c"},
+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\"", "ruff (>=0.8.0) ; sys_platform != \"cygwin\""]
+core = ["importlib_metadata (>=6) ; python_version < \"3.10\"", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1) ; python_version < \"3.11\"", "wheel (>=0.43.0)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21) ; python_version >= \"3.9\" and sys_platform != \"cygwin\"", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf ; sys_platform != \"cygwin\"", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib_metadata (>=7.0.2) ; python_version < \"3.10\"", "jaraco.develop (>=7.21) ; sys_platform != \"cygwin\"", "mypy (==1.14.*)", "pytest-mypy"]
+
+[[package]]
+name = "six"
+version = "1.17.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
+files = [
+    {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"},
+    {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"},
+]
+
+[[package]]
+name = "smmap"
+version = "5.0.2"
+description = "A pure Python implementation of a sliding window memory map manager"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "smmap-5.0.2-py3-none-any.whl", hash = "sha256:b30115f0def7d7531d22a0fb6502488d879e75b260a9db4d0819cfb25403af5e"},
+    {file = "smmap-5.0.2.tar.gz", hash = "sha256:26ea65a03958fa0c8a1c7e8c7a58fdc77221b8910f6be2131affade476898ad5"},
+]
+
+[[package]]
+name = "sniffio"
+version = "1.3.1"
+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
+    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
+]
+
+[[package]]
+name = "soupsieve"
+version = "2.8"
+description = "A modern CSS selector implementation for Beautiful Soup."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "soupsieve-2.8-py3-none-any.whl", hash = "sha256:0cc76456a30e20f5d7f2e14a98a4ae2ee4e5abdc7c5ea0aafe795f344bc7984c"},
+    {file = "soupsieve-2.8.tar.gz", hash = "sha256:e2dd4a40a628cb5f28f6d4b0db8800b8f581b65bb380b97de22ba5ca8d72572f"},
+]
+
+[[package]]
+name = "sqlalchemy"
+version = "2.0.44"
+description = "Database Abstraction Library"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "SQLAlchemy-2.0.44-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:471733aabb2e4848d609141a9e9d56a427c0a038f4abf65dd19d7a21fd563632"},
+    {file = "SQLAlchemy-2.0.44-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:48bf7d383a35e668b984c805470518b635d48b95a3c57cb03f37eaa3551b5f9f"},
+    {file = "SQLAlchemy-2.0.44-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2bf4bb6b3d6228fcf3a71b50231199fb94d2dd2611b66d33be0578ea3e6c2726"},
+    {file = "SQLAlchemy-2.0.44-cp37-cp37m-musllinux_1_2_aarch64.whl", hash = "sha256:e998cf7c29473bd077704cea3577d23123094311f59bdc4af551923b168332b1"},
+    {file = "SQLAlchemy-2.0.44-cp37-cp37m-musllinux_1_2_x86_64.whl", hash = "sha256:ebac3f0b5732014a126b43c2b7567f2f0e0afea7d9119a3378bde46d3dcad88e"},
+    {file = "SQLAlchemy-2.0.44-cp37-cp37m-win32.whl", hash = "sha256:3255d821ee91bdf824795e936642bbf43a4c7cedf5d1aed8d24524e66843aa74"},
+    {file = "SQLAlchemy-2.0.44-cp37-cp37m-win_amd64.whl", hash = "sha256:78e6c137ba35476adb5432103ae1534f2f5295605201d946a4198a0dea4b38e7"},
+    {file = "sqlalchemy-2.0.44-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7c77f3080674fc529b1bd99489378c7f63fcb4ba7f8322b79732e0258f0ea3ce"},
+    {file = "sqlalchemy-2.0.44-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4c26ef74ba842d61635b0152763d057c8d48215d5be9bb8b7604116a059e9985"},
+    {file = "sqlalchemy-2.0.44-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f4a172b31785e2f00780eccab00bc240ccdbfdb8345f1e6063175b3ff12ad1b0"},
+    {file = "sqlalchemy-2.0.44-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f9480c0740aabd8cb29c329b422fb65358049840b34aba0adf63162371d2a96e"},
+    {file = "sqlalchemy-2.0.44-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:17835885016b9e4d0135720160db3095dc78c583e7b902b6be799fb21035e749"},
+    {file = "sqlalchemy-2.0.44-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:cbe4f85f50c656d753890f39468fcd8190c5f08282caf19219f684225bfd5fd2"},
+    {file = "sqlalchemy-2.0.44-cp310-cp310-win32.whl", hash = "sha256:2fcc4901a86ed81dc76703f3b93ff881e08761c63263c46991081fd7f034b165"},
+    {file = "sqlalchemy-2.0.44-cp310-cp310-win_amd64.whl", hash = "sha256:9919e77403a483ab81e3423151e8ffc9dd992c20d2603bf17e4a8161111e55f5"},
+    {file = "sqlalchemy-2.0.44-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0fe3917059c7ab2ee3f35e77757062b1bea10a0b6ca633c58391e3f3c6c488dd"},
+    {file = "sqlalchemy-2.0.44-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:de4387a354ff230bc979b46b2207af841dc8bf29847b6c7dbe60af186d97aefa"},
+    {file = "sqlalchemy-2.0.44-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c3678a0fb72c8a6a29422b2732fe423db3ce119c34421b5f9955873eb9b62c1e"},
+    {file = "sqlalchemy-2.0.44-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3cf6872a23601672d61a68f390e44703442639a12ee9dd5a88bbce52a695e46e"},
+    {file = "sqlalchemy-2.0.44-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:329aa42d1be9929603f406186630135be1e7a42569540577ba2c69952b7cf399"},
+    {file = "sqlalchemy-2.0.44-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:70e03833faca7166e6a9927fbee7c27e6ecde436774cd0b24bbcc96353bce06b"},
+    {file = "sqlalchemy-2.0.44-cp311-cp311-win32.whl", hash = "sha256:253e2f29843fb303eca6b2fc645aca91fa7aa0aa70b38b6950da92d44ff267f3"},
+    {file = "sqlalchemy-2.0.44-cp311-cp311-win_amd64.whl", hash = "sha256:7a8694107eb4308a13b425ca8c0e67112f8134c846b6e1f722698708741215d5"},
+    {file = "sqlalchemy-2.0.44-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:72fea91746b5890f9e5e0997f16cbf3d53550580d76355ba2d998311b17b2250"},
+    {file = "sqlalchemy-2.0.44-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:585c0c852a891450edbb1eaca8648408a3cc125f18cf433941fa6babcc359e29"},
+    {file = "sqlalchemy-2.0.44-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9b94843a102efa9ac68a7a30cd46df3ff1ed9c658100d30a725d10d9c60a2f44"},
+    {file = "sqlalchemy-2.0.44-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:119dc41e7a7defcefc57189cfa0e61b1bf9c228211aba432b53fb71ef367fda1"},
+    {file = "sqlalchemy-2.0.44-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0765e318ee9179b3718c4fd7ba35c434f4dd20332fbc6857a5e8df17719c24d7"},
+    {file = "sqlalchemy-2.0.44-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2e7b5b079055e02d06a4308d0481658e4f06bc7ef211567edc8f7d5dce52018d"},
+    {file = "sqlalchemy-2.0.44-cp312-cp312-win32.whl", hash = "sha256:846541e58b9a81cce7dee8329f352c318de25aa2f2bbe1e31587eb1f057448b4"},
+    {file = "sqlalchemy-2.0.44-cp312-cp312-win_amd64.whl", hash = "sha256:7cbcb47fd66ab294703e1644f78971f6f2f1126424d2b300678f419aa73c7b6e"},
+    {file = "sqlalchemy-2.0.44-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ff486e183d151e51b1d694c7aa1695747599bb00b9f5f604092b54b74c64a8e1"},
+    {file = "sqlalchemy-2.0.44-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0b1af8392eb27b372ddb783b317dea0f650241cea5bd29199b22235299ca2e45"},
+    {file = "sqlalchemy-2.0.44-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2b61188657e3a2b9ac4e8f04d6cf8e51046e28175f79464c67f2fd35bceb0976"},
+    {file = "sqlalchemy-2.0.44-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b87e7b91a5d5973dda5f00cd61ef72ad75a1db73a386b62877d4875a8840959c"},
+    {file = "sqlalchemy-2.0.44-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:15f3326f7f0b2bfe406ee562e17f43f36e16167af99c4c0df61db668de20002d"},
+    {file = "sqlalchemy-2.0.44-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:1e77faf6ff919aa8cd63f1c4e561cac1d9a454a191bb864d5dd5e545935e5a40"},
+    {file = "sqlalchemy-2.0.44-cp313-cp313-win32.whl", hash = "sha256:ee51625c2d51f8baadf2829fae817ad0b66b140573939dd69284d2ba3553ae73"},
+    {file = "sqlalchemy-2.0.44-cp313-cp313-win_amd64.whl", hash = "sha256:c1c80faaee1a6c3428cecf40d16a2365bcf56c424c92c2b6f0f9ad204b899e9e"},
+    {file = "sqlalchemy-2.0.44-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:2fc44e5965ea46909a416fff0af48a219faefd5773ab79e5f8a5fcd5d62b2667"},
+    {file = "sqlalchemy-2.0.44-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:dc8b3850d2a601ca2320d081874033684e246d28e1c5e89db0864077cfc8f5a9"},
+    {file = "sqlalchemy-2.0.44-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d733dec0614bb8f4bcb7c8af88172b974f685a31dc3a65cca0527e3120de5606"},
+    {file = "sqlalchemy-2.0.44-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:22be14009339b8bc16d6b9dc8780bacaba3402aa7581658e246114abbd2236e3"},
+    {file = "sqlalchemy-2.0.44-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:357bade0e46064f88f2c3a99808233e67b0051cdddf82992379559322dfeb183"},
+    {file = "sqlalchemy-2.0.44-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:4848395d932e93c1595e59a8672aa7400e8922c39bb9b0668ed99ac6fa867822"},
+    {file = "sqlalchemy-2.0.44-cp38-cp38-win32.whl", hash = "sha256:2f19644f27c76f07e10603580a47278abb2a70311136a7f8fd27dc2e096b9013"},
+    {file = "sqlalchemy-2.0.44-cp38-cp38-win_amd64.whl", hash = "sha256:1df4763760d1de0dfc8192cc96d8aa293eb1a44f8f7a5fbe74caf1b551905c5e"},
+    {file = "sqlalchemy-2.0.44-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:f7027414f2b88992877573ab780c19ecb54d3a536bef3397933573d6b5068be4"},
+    {file = "sqlalchemy-2.0.44-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3fe166c7d00912e8c10d3a9a0ce105569a31a3d0db1a6e82c4e0f4bf16d5eca9"},
+    {file = "sqlalchemy-2.0.44-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3caef1ff89b1caefc28f0368b3bde21a7e3e630c2eddac16abd9e47bd27cc36a"},
+    {file = "sqlalchemy-2.0.44-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc2856d24afa44295735e72f3c75d6ee7fdd4336d8d3a8f3d44de7aa6b766df2"},
+    {file = "sqlalchemy-2.0.44-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:11bac86b0deada30b6b5f93382712ff0e911fe8d31cb9bf46e6b149ae175eff0"},
+    {file = "sqlalchemy-2.0.44-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:4d18cd0e9a0f37c9f4088e50e3839fcb69a380a0ec957408e0b57cff08ee0a26"},
+    {file = "sqlalchemy-2.0.44-cp39-cp39-win32.whl", hash = "sha256:9e9018544ab07614d591a26c1bd4293ddf40752cc435caf69196740516af7100"},
+    {file = "sqlalchemy-2.0.44-cp39-cp39-win_amd64.whl", hash = "sha256:8e0e4e66fd80f277a8c3de016a81a554e76ccf6b8d881ee0b53200305a8433f6"},
+    {file = "sqlalchemy-2.0.44-py3-none-any.whl", hash = "sha256:19de7ca1246fbef9f9d1bff8f1ab25641569df226364a0e40457dc5457c54b05"},
+    {file = "sqlalchemy-2.0.44.tar.gz", hash = "sha256:0ae7454e1ab1d780aee69fd2aae7d6b8670a581d8847f2d1e0f7ddfbf47e5a22"},
+]
+
+[package.dependencies]
+greenlet = {version = ">=1", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""}
+typing-extensions = ">=4.6.0"
+
+[package.extras]
+aiomysql = ["aiomysql (>=0.2.0)", "greenlet (>=1)"]
+aioodbc = ["aioodbc", "greenlet (>=1)"]
+aiosqlite = ["aiosqlite", "greenlet (>=1)", "typing_extensions (!=3.10.0.1)"]
+asyncio = ["greenlet (>=1)"]
+asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (>=1)"]
+mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5,!=1.1.10)"]
+mssql = ["pyodbc"]
+mssql-pymssql = ["pymssql"]
+mssql-pyodbc = ["pyodbc"]
+mypy = ["mypy (>=0.910)"]
+mysql = ["mysqlclient (>=1.4.0)"]
+mysql-connector = ["mysql-connector-python"]
+oracle = ["cx_oracle (>=8)"]
+oracle-oracledb = ["oracledb (>=1.0.1)"]
+postgresql = ["psycopg2 (>=2.7)"]
+postgresql-asyncpg = ["asyncpg", "greenlet (>=1)"]
+postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
+postgresql-psycopg = ["psycopg (>=3.0.7)"]
+postgresql-psycopg2binary = ["psycopg2-binary"]
+postgresql-psycopg2cffi = ["psycopg2cffi"]
+postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
+pymysql = ["pymysql"]
+sqlcipher = ["sqlcipher3_binary"]
+
+[[package]]
+name = "stable-baselines3"
+version = "2.7.0"
+description = "Pytorch version of Stable Baselines, implementations of reinforcement learning algorithms."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "stable_baselines3-2.7.0-py3-none-any.whl", hash = "sha256:3de94fab840b3eb379a352c8d9b390998686d2fcb41de36298066935eef94bea"},
+    {file = "stable_baselines3-2.7.0.tar.gz", hash = "sha256:5258561e5becd15234274262cf09fcb9a082a73c2c67a85322f5652a05195ec4"},
+]
+
+[package.dependencies]
+cloudpickle = "*"
+gymnasium = ">=0.29.1,<1.3.0"
+matplotlib = "*"
+numpy = ">=1.20,<3.0"
+pandas = "*"
+torch = ">=2.3,<3.0"
+
+[package.extras]
+docs = ["sphinx (>=5,<9)", "sphinx-autobuild", "sphinx-rtd-theme (>=1.3.0)", "sphinx_copybutton", "sphinxcontrib.spelling"]
+extra = ["ale-py (>=0.9.0)", "opencv-python", "pillow", "psutil", "pygame", "rich", "tensorboard (>=2.9.1)", "tqdm"]
+tests = ["black (>=25.1.0,<26)", "mypy", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "ruff (>=0.3.1)"]
+
+[[package]]
+name = "stack-data"
+version = "0.6.3"
+description = "Extract data from python stack frames and tracebacks for informative displays"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"},
+    {file = "stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9"},
+]
+
+[package.dependencies]
+asttokens = ">=2.1.0"
+executing = ">=1.2.0"
+pure-eval = "*"
+
+[package.extras]
+tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
+
+[[package]]
+name = "statsmodels"
+version = "0.14.5"
+description = "Statistical computations and models for Python"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "statsmodels-0.14.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9fc2b5cdc0c95cba894849651fec1fa1511d365e3eb72b0cc75caac44077cd48"},
+    {file = "statsmodels-0.14.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b8d96b0bbaeabd3a557c35cc7249baa9cfbc6dd305c32a9f2cbdd7f46c037e7f"},
+    {file = "statsmodels-0.14.5-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:145bc39b2cb201efb6c83cc3f2163c269e63b0d4809801853dec6f440bd3bc37"},
+    {file = "statsmodels-0.14.5-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d7c14fb2617bb819fb2532e1424e1da2b98a3419a80e95f33365a72d437d474e"},
+    {file = "statsmodels-0.14.5-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:1e9742d8a5ac38a3bfc4b7f4b0681903920f20cbbf466d72b1fd642033846108"},
+    {file = "statsmodels-0.14.5-cp310-cp310-win_amd64.whl", hash = "sha256:1cab9e6fce97caf4239cdb2df375806937da5d0b7ba2699b13af33a07f438464"},
+    {file = "statsmodels-0.14.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4b7091a8442076c708c926de3603653a160955e80a2b6d931475b7bb8ddc02e5"},
+    {file = "statsmodels-0.14.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:128872be8f3208f4446d91ea9e4261823902fc7997fee7e1a983eb62fd3b7c6e"},
+    {file = "statsmodels-0.14.5-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f2ad5aee04ae7196c429df2174df232c057e478c5fa63193d01c8ec9aae04d31"},
+    {file = "statsmodels-0.14.5-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f402fc793458dd6d96e099acb44cd1de1428565bf7ef3030878a8daff091f08a"},
+    {file = "statsmodels-0.14.5-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:26c028832730aebfbfd4e7501694e1f9ad31ec8536e776716673f4e7afd4059a"},
+    {file = "statsmodels-0.14.5-cp311-cp311-win_amd64.whl", hash = "sha256:ec56f771d9529cdc17ed2fb2a950d100b6e83a7c5372aae8ac5bb065c474b856"},
+    {file = "statsmodels-0.14.5-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:37e7364a39f9aa3b51d15a208c2868b90aadb8412f868530f5cba9197cb00eaa"},
+    {file = "statsmodels-0.14.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4263d7f4d0f1d5ac6eb4db22e1ee34264a14d634b9332c975c9d9109b6b46e12"},
+    {file = "statsmodels-0.14.5-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:86224f6e36f38486e471e75759d241fe2912d8bc25ab157d54ee074c6aedbf45"},
+    {file = "statsmodels-0.14.5-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c3dd760a6fa80cd5e0371685c697bb9c2c0e6e1f394d975e596a1e6d0bbb9372"},
+    {file = "statsmodels-0.14.5-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6264fb00e02f858b86bd01ef2dc05055a71d4a0cc7551b9976b07b0f0e6cf24f"},
+    {file = "statsmodels-0.14.5-cp312-cp312-win_amd64.whl", hash = "sha256:b2ed065bfbaf8bb214c7201656df840457c2c8c65e1689e3eb09dc7440f9c61c"},
+    {file = "statsmodels-0.14.5-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:906263134dd1a640e55ecb01fda4a9be7b9e08558dba9e4c4943a486fdb0c9c8"},
+    {file = "statsmodels-0.14.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:9118f76344f77cffbb3a9cbcff8682b325be5eed54a4b3253e09da77a74263d3"},
+    {file = "statsmodels-0.14.5-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9dc4ee159070557c9a6c000625d85f653de437772fe7086857cff68f501afe45"},
+    {file = "statsmodels-0.14.5-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5a085d47c8ef5387279a991633883d0e700de2b0acc812d7032d165888627bef"},
+    {file = "statsmodels-0.14.5-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9f866b2ebb2904b47c342d00def83c526ef2eb1df6a9a3c94ba5fe63d0005aec"},
+    {file = "statsmodels-0.14.5-cp313-cp313-win_amd64.whl", hash = "sha256:2a06bca03b7a492f88c8106103ab75f1a5ced25de90103a89f3a287518017939"},
+    {file = "statsmodels-0.14.5-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:07c4dad25bbb15864a31b4917a820f6d104bdc24e5ddadcda59027390c3bed9e"},
+    {file = "statsmodels-0.14.5-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:babb067c852e966c2c933b79dbb5d0240919d861941a2ef6c0e13321c255528d"},
+    {file = "statsmodels-0.14.5-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:110194b137286173cc676d7bad0119a197778de6478fc6cbdc3b33571165ac1e"},
+    {file = "statsmodels-0.14.5-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9c8a9c384a60c80731b278e7fd18764364c8817f4995b13a175d636f967823d1"},
+    {file = "statsmodels-0.14.5-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:557df3a870a57248df744fdfcc444ecbc5bdbf1c042b8a8b5d8e3e797830dc2a"},
+    {file = "statsmodels-0.14.5-cp314-cp314-win_amd64.whl", hash = "sha256:95af7a9c4689d514f4341478b891f867766f3da297f514b8c4adf08f4fa61d03"},
+    {file = "statsmodels-0.14.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b23b8f646dd78ef5e8d775d879208f8dc0a73418b41c16acac37361ff9ab7738"},
+    {file = "statsmodels-0.14.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4e5e26b21d2920905764fb0860957d08b5ba2fae4466ef41b1f7c53ecf9fc7fa"},
+    {file = "statsmodels-0.14.5-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4a060c7e0841c549c8ce2825fd6687e6757e305d9c11c9a73f6c5a0ce849bb69"},
+    {file = "statsmodels-0.14.5-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:56da20def5350d676388213a330fd40ed15d0e8dd0bb1b92c0e4b0f2a65d3ad2"},
+    {file = "statsmodels-0.14.5-cp39-cp39-win_amd64.whl", hash = "sha256:afb37ca1d70d99b5fd876e8574ea46372298ae0f0a8b17e4cf0a9afd2373ae62"},
+    {file = "statsmodels-0.14.5.tar.gz", hash = "sha256:de260e58cccfd2ceddf835b55a357233d6ca853a1aa4f90f7553a52cc71c6ddf"},
+]
+
+[package.dependencies]
+numpy = ">=1.22.3,<3"
+packaging = ">=21.3"
+pandas = ">=1.4,<2.1.0 || >2.1.0"
+patsy = ">=0.5.6"
+scipy = ">=1.8,<1.9.2 || >1.9.2"
+
+[package.extras]
+build = ["cython (>=3.0.10)"]
+develop = ["colorama", "cython (>=3.0.10)", "cython (>=3.0.10,<4)", "flake8", "isort", "jinja2", "joblib", "matplotlib (>=3)", "pytest (>=7.3.0,<8)", "pytest-cov", "pytest-randomly", "pytest-xdist", "pywinpty ; os_name == \"nt\"", "setuptools_scm[toml] (>=8.0,<9.0)"]
+docs = ["ipykernel", "jupyter_client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
+
+[[package]]
+name = "sympy"
+version = "1.14.0"
+description = "Computer algebra system (CAS) in Python"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5"},
+    {file = "sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517"},
+]
+
+[package.dependencies]
+mpmath = ">=1.1.0,<1.4"
+
+[package.extras]
+dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
+
+[[package]]
+name = "tenacity"
+version = "9.1.2"
+description = "Retry code until it succeeds"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138"},
+    {file = "tenacity-9.1.2.tar.gz", hash = "sha256:1169d376c297e7de388d18b4481760d478b0e99a777cad3a9c86e556f4b697cb"},
+]
+
+[package.extras]
+doc = ["reno", "sphinx"]
+test = ["pytest", "tornado (>=4.5)", "typeguard"]
+
+[[package]]
+name = "tensorboard"
+version = "2.20.0"
+description = "TensorBoard lets you watch Tensors Flow"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "tensorboard-2.20.0-py3-none-any.whl", hash = "sha256:9dc9f978cb84c0723acf9a345d96c184f0293d18f166bb8d59ee098e6cfaaba6"},
+]
+
+[package.dependencies]
+absl-py = ">=0.4"
+grpcio = ">=1.48.2"
+markdown = ">=2.6.8"
+numpy = ">=1.12.0"
+packaging = "*"
+pillow = "*"
+protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
+setuptools = ">=41.0.0"
+tensorboard-data-server = ">=0.7.0,<0.8.0"
+werkzeug = ">=1.0.1"
+
+[[package]]
+name = "tensorboard-data-server"
+version = "0.7.2"
+description = "Fast data loading for TensorBoard"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"},
+    {file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"},
+    {file = "tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530"},
+]
+
+[[package]]
+name = "terminado"
+version = "0.18.1"
+description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0"},
+    {file = "terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e"},
+]
+
+[package.dependencies]
+ptyprocess = {version = "*", markers = "os_name != \"nt\""}
+pywinpty = {version = ">=1.1.0", markers = "os_name == \"nt\""}
+tornado = ">=6.1.0"
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
+typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
+
+[[package]]
+name = "threadpoolctl"
+version = "3.6.0"
+description = "threadpoolctl"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb"},
+    {file = "threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e"},
+]
+
+[[package]]
+name = "tinycss2"
+version = "1.4.0"
+description = "A tiny CSS parser"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289"},
+    {file = "tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7"},
+]
+
+[package.dependencies]
+webencodings = ">=0.4"
+
+[package.extras]
+doc = ["sphinx", "sphinx_rtd_theme"]
+test = ["pytest", "ruff"]
+
+[[package]]
+name = "tokenizers"
+version = "0.22.1"
+description = ""
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "tokenizers-0.22.1-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:59fdb013df17455e5f950b4b834a7b3ee2e0271e6378ccb33aa74d178b513c73"},
+    {file = "tokenizers-0.22.1-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:8d4e484f7b0827021ac5f9f71d4794aaef62b979ab7608593da22b1d2e3c4edc"},
+    {file = "tokenizers-0.22.1-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:19d2962dd28bc67c1f205ab180578a78eef89ac60ca7ef7cbe9635a46a56422a"},
+    {file = "tokenizers-0.22.1-cp39-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:38201f15cdb1f8a6843e6563e6e79f4abd053394992b9bbdf5213ea3469b4ae7"},
+    {file = "tokenizers-0.22.1-cp39-abi3-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d1cbe5454c9a15df1b3443c726063d930c16f047a3cc724b9e6e1a91140e5a21"},
+    {file = "tokenizers-0.22.1-cp39-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e7d094ae6312d69cc2a872b54b91b309f4f6fbce871ef28eb27b52a98e4d0214"},
+    {file = "tokenizers-0.22.1-cp39-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:afd7594a56656ace95cdd6df4cca2e4059d294c5cfb1679c57824b605556cb2f"},
+    {file = "tokenizers-0.22.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e2ef6063d7a84994129732b47e7915e8710f27f99f3a3260b8a38fc7ccd083f4"},
+    {file = "tokenizers-0.22.1-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:ba0a64f450b9ef412c98f6bcd2a50c6df6e2443b560024a09fa6a03189726879"},
+    {file = "tokenizers-0.22.1-cp39-abi3-musllinux_1_2_armv7l.whl", hash = "sha256:331d6d149fa9c7d632cde4490fb8bbb12337fa3a0232e77892be656464f4b446"},
+    {file = "tokenizers-0.22.1-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:607989f2ea68a46cb1dfbaf3e3aabdf3f21d8748312dbeb6263d1b3b66c5010a"},
+    {file = "tokenizers-0.22.1-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a0f307d490295717726598ef6fa4f24af9d484809223bbc253b201c740a06390"},
+    {file = "tokenizers-0.22.1-cp39-abi3-win32.whl", hash = "sha256:b5120eed1442765cd90b903bb6cfef781fd8fe64e34ccaecbae4c619b7b12a82"},
+    {file = "tokenizers-0.22.1-cp39-abi3-win_amd64.whl", hash = "sha256:65fd6e3fb11ca1e78a6a93602490f134d1fdeb13bcef99389d5102ea318ed138"},
+    {file = "tokenizers-0.22.1.tar.gz", hash = "sha256:61de6522785310a309b3407bac22d99c4db5dba349935e99e4d15ea2226af2d9"},
+]
+
+[package.dependencies]
+huggingface-hub = ">=0.16.4,<2.0"
+
+[package.extras]
+dev = ["tokenizers[testing]"]
+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "pytest-asyncio", "requests", "ruff"]
+
+[[package]]
+name = "tomli"
+version = "2.3.0"
+description = "A lil' TOML parser"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version == \"3.10\""
+files = [
+    {file = "tomli-2.3.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:88bd15eb972f3664f5ed4b57c1634a97153b4bac4479dcb6a495f41921eb7f45"},
+    {file = "tomli-2.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:883b1c0d6398a6a9d29b508c331fa56adbcdff647f6ace4dfca0f50e90dfd0ba"},
+    {file = "tomli-2.3.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d1381caf13ab9f300e30dd8feadb3de072aeb86f1d34a8569453ff32a7dea4bf"},
+    {file = "tomli-2.3.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a0e285d2649b78c0d9027570d4da3425bdb49830a6156121360b3f8511ea3441"},
+    {file = "tomli-2.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:0a154a9ae14bfcf5d8917a59b51ffd5a3ac1fd149b71b47a3a104ca4edcfa845"},
+    {file = "tomli-2.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:74bf8464ff93e413514fefd2be591c3b0b23231a77f901db1eb30d6f712fc42c"},
+    {file = "tomli-2.3.0-cp311-cp311-win32.whl", hash = "sha256:00b5f5d95bbfc7d12f91ad8c593a1659b6387b43f054104cda404be6bda62456"},
+    {file = "tomli-2.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:4dc4ce8483a5d429ab602f111a93a6ab1ed425eae3122032db7e9acf449451be"},
+    {file = "tomli-2.3.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d7d86942e56ded512a594786a5ba0a5e521d02529b3826e7761a05138341a2ac"},
+    {file = "tomli-2.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:73ee0b47d4dad1c5e996e3cd33b8a76a50167ae5f96a2607cbe8cc773506ab22"},
+    {file = "tomli-2.3.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:792262b94d5d0a466afb5bc63c7daa9d75520110971ee269152083270998316f"},
+    {file = "tomli-2.3.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4f195fe57ecceac95a66a75ac24d9d5fbc98ef0962e09b2eddec5d39375aae52"},
+    {file = "tomli-2.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e31d432427dcbf4d86958c184b9bfd1e96b5b71f8eb17e6d02531f434fd335b8"},
+    {file = "tomli-2.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:7b0882799624980785240ab732537fcfc372601015c00f7fc367c55308c186f6"},
+    {file = "tomli-2.3.0-cp312-cp312-win32.whl", hash = "sha256:ff72b71b5d10d22ecb084d345fc26f42b5143c5533db5e2eaba7d2d335358876"},
+    {file = "tomli-2.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:1cb4ed918939151a03f33d4242ccd0aa5f11b3547d0cf30f7c74a408a5b99878"},
+    {file = "tomli-2.3.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:5192f562738228945d7b13d4930baffda67b69425a7f0da96d360b0a3888136b"},
+    {file = "tomli-2.3.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:be71c93a63d738597996be9528f4abe628d1adf5e6eb11607bc8fe1a510b5dae"},
+    {file = "tomli-2.3.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c4665508bcbac83a31ff8ab08f424b665200c0e1e645d2bd9ab3d3e557b6185b"},
+    {file = "tomli-2.3.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4021923f97266babc6ccab9f5068642a0095faa0a51a246a6a02fccbb3514eaf"},
+    {file = "tomli-2.3.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4ea38c40145a357d513bffad0ed869f13c1773716cf71ccaa83b0fa0cc4e42f"},
+    {file = "tomli-2.3.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ad805ea85eda330dbad64c7ea7a4556259665bdf9d2672f5dccc740eb9d3ca05"},
+    {file = "tomli-2.3.0-cp313-cp313-win32.whl", hash = "sha256:97d5eec30149fd3294270e889b4234023f2c69747e555a27bd708828353ab606"},
+    {file = "tomli-2.3.0-cp313-cp313-win_amd64.whl", hash = "sha256:0c95ca56fbe89e065c6ead5b593ee64b84a26fca063b5d71a1122bf26e533999"},
+    {file = "tomli-2.3.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:cebc6fe843e0733ee827a282aca4999b596241195f43b4cc371d64fc6639da9e"},
+    {file = "tomli-2.3.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:4c2ef0244c75aba9355561272009d934953817c49f47d768070c3c94355c2aa3"},
+    {file = "tomli-2.3.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c22a8bf253bacc0cf11f35ad9808b6cb75ada2631c2d97c971122583b129afbc"},
+    {file = "tomli-2.3.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0eea8cc5c5e9f89c9b90c4896a8deefc74f518db5927d0e0e8d4a80953d774d0"},
+    {file = "tomli-2.3.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:b74a0e59ec5d15127acdabd75ea17726ac4c5178ae51b85bfe39c4f8a278e879"},
+    {file = "tomli-2.3.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:b5870b50c9db823c595983571d1296a6ff3e1b88f734a4c8f6fc6188397de005"},
+    {file = "tomli-2.3.0-cp314-cp314-win32.whl", hash = "sha256:feb0dacc61170ed7ab602d3d972a58f14ee3ee60494292d384649a3dc38ef463"},
+    {file = "tomli-2.3.0-cp314-cp314-win_amd64.whl", hash = "sha256:b273fcbd7fc64dc3600c098e39136522650c49bca95df2d11cf3b626422392c8"},
+    {file = "tomli-2.3.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:940d56ee0410fa17ee1f12b817b37a4d4e4dc4d27340863cc67236c74f582e77"},
+    {file = "tomli-2.3.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:f85209946d1fe94416debbb88d00eb92ce9cd5266775424ff81bc959e001acaf"},
+    {file = "tomli-2.3.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a56212bdcce682e56b0aaf79e869ba5d15a6163f88d5451cbde388d48b13f530"},
+    {file = "tomli-2.3.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c5f3ffd1e098dfc032d4d3af5c0ac64f6d286d98bc148698356847b80fa4de1b"},
+    {file = "tomli-2.3.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:5e01decd096b1530d97d5d85cb4dff4af2d8347bd35686654a004f8dea20fc67"},
+    {file = "tomli-2.3.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:8a35dd0e643bb2610f156cca8db95d213a90015c11fee76c946aa62b7ae7e02f"},
+    {file = "tomli-2.3.0-cp314-cp314t-win32.whl", hash = "sha256:a1f7f282fe248311650081faafa5f4732bdbfef5d45fe3f2e702fbc6f2d496e0"},
+    {file = "tomli-2.3.0-cp314-cp314t-win_amd64.whl", hash = "sha256:70a251f8d4ba2d9ac2542eecf008b3c8a9fc5c3f9f02c56a9d7952612be2fdba"},
+    {file = "tomli-2.3.0-py3-none-any.whl", hash = "sha256:e95b1af3c5b07d9e643909b5abbec77cd9f1217e6d0bca72b0234736b9fb1f1b"},
+    {file = "tomli-2.3.0.tar.gz", hash = "sha256:64be704a875d2a59753d80ee8a533c3fe183e3f06807ff7dc2232938ccb01549"},
+]
+
+[[package]]
+name = "torch"
+version = "2.9.0"
+description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "torch-2.9.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:030bbfe367379ae6a4ae4042b6c44da25383343b8b3c68abaa9c7231efbaf2dd"},
+    {file = "torch-2.9.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:51cb63902182a78e90886e8068befd8ea102af4b00e420263591a3d70c7d3c6c"},
+    {file = "torch-2.9.0-cp310-cp310-win_amd64.whl", hash = "sha256:3f6aad4d2f0ee2248bac25339d74858ff846c3969b27d14ac235821f055af83d"},
+    {file = "torch-2.9.0-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:413e1654c9203733138858780e184d9fc59442f0b3b209e16f39354eb893db9b"},
+    {file = "torch-2.9.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:c596708b5105d0b199215acf0c9be7c1db5f1680d88eddadf4b75a299259a677"},
+    {file = "torch-2.9.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:51de31219c97c51cf4bf2be94d622e3deb5dcc526c6dc00e97c17eaec0fc1d67"},
+    {file = "torch-2.9.0-cp311-cp311-win_amd64.whl", hash = "sha256:dd515c70059afd95f48b8192733764c08ca37a1d19803af6401b5ecad7c8676e"},
+    {file = "torch-2.9.0-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:614a185e4986326d526a91210c8fc1397e76e8cfafa78baf6296a790e53a9eec"},
+    {file = "torch-2.9.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:e5f7af1dc4c0a7c4a260c2534f41ddaf209714f7c89145e644c44712fbd6b642"},
+    {file = "torch-2.9.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:01cff95ecd9a212ea2f141db28acccdceb6a4c54f64e6c51091146f5e2a772c6"},
+    {file = "torch-2.9.0-cp312-cp312-win_amd64.whl", hash = "sha256:4582b162f541651f0cb184d3e291c05c2f556c7117c64a9873e2ee158d40062b"},
+    {file = "torch-2.9.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:33f58e9a102a91259af289d50525c30323b5c9ae1d31322b6447c0814da68695"},
+    {file = "torch-2.9.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:c30a17fc83eeab346913e237c64b15b5ba6407fff812f6c541e322e19bc9ea0e"},
+    {file = "torch-2.9.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:8f25033b8667b57857dfd01458fbf2a9e6a6df1f8def23aef0dc46292f6aa642"},
+    {file = "torch-2.9.0-cp313-cp313-win_amd64.whl", hash = "sha256:d037f1b4ffd25013be4a7bf3651a0a910c68554956c7b2c92ebe87c76475dece"},
+    {file = "torch-2.9.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e4e5b5cba837a2a8d1a497ba9a58dae46fa392593eaa13b871c42f71847503a5"},
+    {file = "torch-2.9.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:64693568f5dc4dbd5f880a478b1cea0201cc6b510d91d1bc54fea86ac5d1a637"},
+    {file = "torch-2.9.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:f8ed31ddd7d10bfb3fbe0b9fe01b1243577f13d75e6f4a0839a283915ce3791e"},
+    {file = "torch-2.9.0-cp313-cp313t-win_amd64.whl", hash = "sha256:eff527d4e4846e6f70d2afd8058b73825761203d66576a7e04ea2ecfebcb4ab8"},
+    {file = "torch-2.9.0-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:f8877779cf56d1ce431a7636703bdb13307f5960bb1af49716d8b179225e0e6a"},
+    {file = "torch-2.9.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:7e614fae699838038d888729f82b687c03413c5989ce2a9481f9a7e7a396e0bb"},
+    {file = "torch-2.9.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:dfb5b8cd310ba3436c7e14e8b7833ef658cf3045e50d2bdaed23c8fc517065eb"},
+    {file = "torch-2.9.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:b3d29524993a478e46f5d598b249cd824b7ed98d7fba538bd9c4cde6c803948f"},
+    {file = "torch-2.9.0-cp314-cp314-win_amd64.whl", hash = "sha256:71c7578984f5ec0eb645eb4816ac8435fcf3e3e2ae1901bcd2f519a9cafb5125"},
+    {file = "torch-2.9.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:71d9309aee457bbe0b164bce2111cd911c4ed4e847e65d5077dbbcd3aba6befc"},
+    {file = "torch-2.9.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:c08fb654d783899e204a32cca758a7ce8a45b2d78eeb89517cc937088316f78e"},
+    {file = "torch-2.9.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:ec8feb0099b2daa5728fbc7abb0b05730fd97e0f359ff8bda09865aaa7bd7d4b"},
+    {file = "torch-2.9.0-cp314-cp314t-win_amd64.whl", hash = "sha256:695ba920f234ad4170c9c50e28d56c848432f8f530e6bc7f88fcb15ddf338e75"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = ">=0.8.5"
+jinja2 = "*"
+networkx = ">=2.5.1"
+nvidia-cublas-cu12 = {version = "12.8.4.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-cupti-cu12 = {version = "12.8.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-nvrtc-cu12 = {version = "12.8.93", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-runtime-cu12 = {version = "12.8.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cudnn-cu12 = {version = "9.10.2.21", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufft-cu12 = {version = "11.3.3.83", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufile-cu12 = {version = "1.13.1.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-curand-cu12 = {version = "10.3.9.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusolver-cu12 = {version = "11.7.3.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparse-cu12 = {version = "12.5.8.93", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparselt-cu12 = {version = "0.7.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.27.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvjitlink-cu12 = {version = "12.8.93", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvshmem-cu12 = {version = "3.3.20", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvtx-cu12 = {version = "12.8.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+setuptools = {version = "*", markers = "python_version >= \"3.12\""}
+sympy = ">=1.13.3"
+triton = {version = "3.5.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+typing-extensions = ">=4.10.0"
+
+[package.extras]
+opt-einsum = ["opt-einsum (>=3.3)"]
+optree = ["optree (>=0.13.0)"]
+pyyaml = ["pyyaml"]
+
+[[package]]
+name = "torchaudio"
+version = "2.9.0"
+description = "An audio package for PyTorch"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "torchaudio-2.9.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:214d2e8bec2b204ac3f552f3dceae51550e06a91c5863d5dc341d81691ef655e"},
+    {file = "torchaudio-2.9.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:1e84e45f74bf5b208b5ce59b36f26ec1e5f63596542c3ebee6edeadf85e73563"},
+    {file = "torchaudio-2.9.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:905f2c916e392b6dde375c002abe98f6fc64705fdf1192c90a6df2de235305f3"},
+    {file = "torchaudio-2.9.0-cp310-cp310-win_amd64.whl", hash = "sha256:4ed556da9de16f69ccbe804df510ae8fefdf995cbdc2fcf26ea7532d25463326"},
+    {file = "torchaudio-2.9.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:662eb49ab25e1a2b7367bb072a8ad05c8a4b650ebbe7090a5af1a1eb1d40767c"},
+    {file = "torchaudio-2.9.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:914f1408142bdeda1ca9f834dd04967625fccc75893bd1504a018a13a04f1b66"},
+    {file = "torchaudio-2.9.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:86b15ce1d74814d5ca14bfac0d3b33f325c8cac4a6f09dcc5b82748133a96792"},
+    {file = "torchaudio-2.9.0-cp311-cp311-win_amd64.whl", hash = "sha256:840487d748128ded45bd65b213b55db701ad047544e77ae3c57ea48f55623a77"},
+    {file = "torchaudio-2.9.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ab4cbcccfd873b0fb41fcb39c9869e59ef84bb95b093f6f58e2d05172a7500d2"},
+    {file = "torchaudio-2.9.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:7f93388b6e536c14d6015b6f75277a8b45efc532f61b35adc1ed06c98a86003e"},
+    {file = "torchaudio-2.9.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:508318a2130b40ad51378f90caf8727a4bd3ac2b296f2b90c900b44e6068a940"},
+    {file = "torchaudio-2.9.0-cp312-cp312-win_amd64.whl", hash = "sha256:82117e3a605f2959dc09b4cd8a11178d6e92727d5f85e5d4f9fe47502f84ee96"},
+    {file = "torchaudio-2.9.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:5549c25db4c2da306e179e9aa99980e7f5b1826a8d2d7de08125f3943a5620b2"},
+    {file = "torchaudio-2.9.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:1eb0d1dac8cefbc4a54afb21aac72a1c25a91f73e9c3bd85f6684930a4a1be5d"},
+    {file = "torchaudio-2.9.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:266d304dd4ed738a10148b020e3d066e81272ee851f6f92193fe549df96af868"},
+    {file = "torchaudio-2.9.0-cp313-cp313-win_amd64.whl", hash = "sha256:7d3926129389d934aa048bd6c6f68fbf3ef26828ebbbbeac99794ea00e90dc1c"},
+    {file = "torchaudio-2.9.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:967d664477fb91dffad82ef64ea3695801c0cc35304baec71be875b569440872"},
+    {file = "torchaudio-2.9.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:276871d6f5fed5268a87c5da303a13ca2e06b9d29a4c44663b960f0a2e2f46d7"},
+    {file = "torchaudio-2.9.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:3d5657d929d6ca07b08cfa005988f2ea8caacf9af42f20bc7eff10f88812ce30"},
+    {file = "torchaudio-2.9.0-cp313-cp313t-win_amd64.whl", hash = "sha256:3fe9cac0c2ee713e07f8c88d09528d55e0fa74987b0122e27911dfb720f39054"},
+    {file = "torchaudio-2.9.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:3fa41447a21103fcde930b4ad2bd2634565a0becff1a5425535b4f0116c0d5df"},
+    {file = "torchaudio-2.9.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:69f46f21bd67e90ade33a7d0f0cf98270cd61b98f5f8249d3893be0a16b3e31f"},
+    {file = "torchaudio-2.9.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:631b0f43564a25e27e615b217454c334f52162679f39ae10b9fa7562ed587dfc"},
+    {file = "torchaudio-2.9.0-cp314-cp314-win_amd64.whl", hash = "sha256:ed6df9f14431e13498b984dc87df1aabb2156b9ce0ce7268ce4a61650197310a"},
+    {file = "torchaudio-2.9.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:93358d8f2f24969ba3f368f4eec33295df830af54836c7fd3336740228f9af16"},
+    {file = "torchaudio-2.9.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:742143d9d62769bc4b9a2977ca4f4720e0a5e922bdc5df585c155e0a1f545461"},
+    {file = "torchaudio-2.9.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:0a234634e1142fb2652c49e935a98b4d9656fd0af9e4aa14b1b05a80c3cf8e78"},
+    {file = "torchaudio-2.9.0-cp314-cp314t-win_amd64.whl", hash = "sha256:cbf5d6da8fd2ed545c78218b39fd6aacaa4dd5e265c5f85b248a2fac223f0bd6"},
+]
+
+[package.dependencies]
+torch = "2.9.0"
+
+[[package]]
+name = "torchmetrics"
+version = "1.8.2"
+description = "PyTorch native Metrics"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "torchmetrics-1.8.2-py3-none-any.whl", hash = "sha256:08382fd96b923e39e904c4d570f3d49e2cc71ccabd2a94e0f895d1f0dac86242"},
+    {file = "torchmetrics-1.8.2.tar.gz", hash = "sha256:cf64a901036bf107f17a524009eea7781c9c5315d130713aeca5747a686fe7a5"},
+]
+
+[package.dependencies]
+lightning-utilities = ">=0.8.0"
+numpy = ">1.20.0"
+packaging = ">17.1"
+torch = ">=2.0.0"
+
+[package.extras]
+all = ["SciencePlots (>=2.0.0)", "einops (>=0.7.0)", "einops (>=0.7.0)", "gammatone (>=1.0.0)", "ipadic (>=1.0.0)", "librosa (>=0.10.0)", "matplotlib (>=3.6.0)", "mecab-python3 (>=1.0.6)", "mypy (==1.17.1)", "nltk (>3.8.1)", "onnxruntime (>=1.12.0)", "pesq (>=0.0.4)", "piq (<=0.8.0)", "pycocotools (>2.0.0)", "pystoi (>=0.4.0)", "regex (>=2021.9.24)", "requests (>=2.19.0)", "scipy (>1.0.0)", "sentencepiece (>=0.2.0)", "timm (>=0.9.0)", "torch (==2.8.0)", "torch-fidelity (<=0.4.0)", "torch_linear_assignment (>=0.0.2)", "torchaudio (>=2.0.1)", "torchvision (>=0.15.1)", "torchvision (>=0.15.1)", "tqdm (<4.68.0)", "transformers (>=4.43.0)", "transformers (>=4.43.0)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate", "vmaf-torch (>=1.1.0)"]
+audio = ["gammatone (>=1.0.0)", "librosa (>=0.10.0)", "onnxruntime (>=1.12.0)", "pesq (>=0.0.4)", "pystoi (>=0.4.0)", "requests (>=2.19.0)", "torchaudio (>=2.0.1)"]
+clustering = ["torch_linear_assignment (>=0.0.2)"]
+detection = ["pycocotools (>2.0.0)", "torchvision (>=0.15.1)"]
+dev = ["PyTDC (==0.4.1) ; python_version < \"3.10\" or platform_system == \"Windows\" and python_version < \"3.12\"", "SciencePlots (>=2.0.0)", "aeon (>=1.0.0) ; python_version > \"3.10\"", "bert_score (==0.3.13)", "dists-pytorch (==0.1)", "dython (==0.7.9)", "einops (>=0.7.0)", "einops (>=0.7.0)", "fairlearn", "fast-bss-eval (>=0.1.0)", "faster-coco-eval (>=1.6.3)", "gammatone (>=1.0.0)", "huggingface-hub (<0.35)", "ipadic (>=1.0.0)", "jiwer (>=2.3.0)", "kornia (>=0.6.7)", "librosa (>=0.10.0)", "lpips (<=0.1.4)", "matplotlib (>=3.6.0)", "mecab-ko (>=1.0.0,<1.1.0) ; python_version < \"3.12\"", "mecab-ko-dic (>=1.0.0) ; python_version < \"3.12\"", "mecab-python3 (>=1.0.6)", "mir-eval (>=0.6)", "monai (==1.4.0)", "mypy (==1.17.1)", "netcal (>1.0.0)", "nltk (>3.8.1)", "numpy (<2.4.0)", "onnxruntime (>=1.12.0)", "pandas (>1.4.0)", "permetrics (==2.0.0)", "pesq (>=0.0.4)", "piq (<=0.8.0)", "properscoring (==0.1)", "pycocotools (>2.0.0)", "pystoi (>=0.4.0)", "pytorch-msssim (==1.0.0)", "regex (>=2021.9.24)", "requests (>=2.19.0)", "rouge-score (>0.1.0)", "sacrebleu (>=2.3.0)", "scikit-image (>=0.19.0)", "scipy (>1.0.0)", "scipy (>1.0.0)", "sentencepiece (>=0.2.0)", "sewar (>=0.4.4)", "statsmodels (>0.13.5)", "timm (>=0.9.0)", "torch (==2.8.0)", "torch-fidelity (<=0.4.0)", "torch_complex (<0.5.0)", "torch_linear_assignment (>=0.0.2)", "torchaudio (>=2.0.1)", "torchvision (>=0.15.1)", "torchvision (>=0.15.1)", "tqdm (<4.68.0)", "transformers (>=4.43.0)", "transformers (>=4.43.0)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate", "vmaf-torch (>=1.1.0)"]
+image = ["scipy (>1.0.0)", "torch-fidelity (<=0.4.0)", "torchvision (>=0.15.1)"]
+multimodal = ["einops (>=0.7.0)", "piq (<=0.8.0)", "timm (>=0.9.0)", "transformers (>=4.43.0)"]
+text = ["ipadic (>=1.0.0)", "mecab-python3 (>=1.0.6)", "nltk (>3.8.1)", "regex (>=2021.9.24)", "sentencepiece (>=0.2.0)", "tqdm (<4.68.0)", "transformers (>=4.43.0)"]
+typing = ["mypy (==1.17.1)", "torch (==2.8.0)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
+video = ["einops (>=0.7.0)", "vmaf-torch (>=1.1.0)"]
+visual = ["SciencePlots (>=2.0.0)", "matplotlib (>=3.6.0)"]
+
+[[package]]
+name = "torchvision"
+version = "0.24.0"
+description = "image and video datasets and models for torch deep learning"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "torchvision-0.24.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5e8d5e667deff87bd66d26df6d225f46224bb0782d4f3f8f5d2f3068b5fd4492"},
+    {file = "torchvision-0.24.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:a110a51c75e89807a8382b0d8034f5e180fb9319570be3389ffd3d4ac4fd57a9"},
+    {file = "torchvision-0.24.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:81d5b12a6df1bb2cc8bdbad837b637d6ea446f2866e6d94f1b5d478856331be3"},
+    {file = "torchvision-0.24.0-cp310-cp310-win_amd64.whl", hash = "sha256:0839dbb305d34671f5a64f558782095134b04bbeff8b90f11eb80515d7d50092"},
+    {file = "torchvision-0.24.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f771cf918351ad509a28488be475f3e9cc71a750d6b1467842bfb64863a5e986"},
+    {file = "torchvision-0.24.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:bbd63bf4ebff84c48c50123eba90526cc9f794fe45bc9f5dd07cec19e8c62bce"},
+    {file = "torchvision-0.24.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:78fe414b3bb6dbf7e6f6da6f733ba96881f6b29a9b997228de7c5f603e5ed940"},
+    {file = "torchvision-0.24.0-cp311-cp311-win_amd64.whl", hash = "sha256:629584b94e52f32a6278f2a35d85eeaae95fcc38730fcb765064f26c3c96df5d"},
+    {file = "torchvision-0.24.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c61d40bcd2e2451e932902a702ad495ba1ec6f279e90b1e15cef2bb55dc911e2"},
+    {file = "torchvision-0.24.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:b0531d1483fc322d7da0d83be52f0df860a75114ab87dbeeb9de765feaeda843"},
+    {file = "torchvision-0.24.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:26b9dd9c083f8e5f7ac827de6d5b88c615d9c582dc87666770fbdf16887e4c25"},
+    {file = "torchvision-0.24.0-cp312-cp312-win_amd64.whl", hash = "sha256:060b7c50ed4b3fb0316b08e2e31bfd874ec2f63ef5ae02f81e54341ca4e88703"},
+    {file = "torchvision-0.24.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:84d79cfc6457310107ce4d712de7a3d388b24484bc9aeded4a76d8f8e3a2813d"},
+    {file = "torchvision-0.24.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:fec12a269cf80f6b0b71471c8d498cd3bdd9d8e892c425bf39fecb604852c3b0"},
+    {file = "torchvision-0.24.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:7323a9be5e3da695605753f501cdc87824888c5655d27735cdeaa9986b45884c"},
+    {file = "torchvision-0.24.0-cp313-cp313-win_amd64.whl", hash = "sha256:08cad8b204196e945f0b2d73adee952d433db1c03645851d52b22a45f1015b13"},
+    {file = "torchvision-0.24.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:6ab956a6e588623353e0f20d4b03eb1656cb4a3c75ca4dd8b4e32e01bc43271a"},
+    {file = "torchvision-0.24.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:b1b3db80609c32a088554e8e94b4fc31f1033fe5bb4ac0673ec49c3eb03fb4da"},
+    {file = "torchvision-0.24.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:e6635f100d455c80b43f297df4b8585a76c6a2e114802f6567ddd28d7b5479b0"},
+    {file = "torchvision-0.24.0-cp313-cp313t-win_amd64.whl", hash = "sha256:4ce158bbdc3a9086034bced0b5212888bd5b251fee6d08a9eff151d30b4b228a"},
+    {file = "torchvision-0.24.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:4bdfc85a5ed706421555f32cdc5e3ddb6d40bf65ef03a274ce3c176393e2904b"},
+    {file = "torchvision-0.24.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:73576a9c4a593223fbae85a64e8bbd77049abd1101893ecf3c5e981284fd58b4"},
+    {file = "torchvision-0.24.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:dd565b1b06666ff399d0801d4d1824fa570c0167a179ca700a5be232527b3c62"},
+    {file = "torchvision-0.24.0-cp314-cp314-win_amd64.whl", hash = "sha256:eb45d12ac48d757738788fd3fb8e88e647d6b2ab2424134ca87556efc72d81b5"},
+    {file = "torchvision-0.24.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:68120e7e03c31900e499a10bb7fdd63cfd67f0054c9fa108e7e27f9cd372f315"},
+    {file = "torchvision-0.24.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:64e54494043eecf9f57a9881c6fdea49c62282782e737c002ae8b1639e6ea80e"},
+    {file = "torchvision-0.24.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:75ef9546323b321a451239d886f0cb528f7e98bb294da47a3200effd4e572064"},
+    {file = "torchvision-0.24.0-cp314-cp314t-win_amd64.whl", hash = "sha256:2efb617667950814fc8bb9437e5893861b3616e214285be33cbc364a3f42c599"},
+]
+
+[package.dependencies]
+numpy = "*"
+pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
+torch = "2.9.0"
+
+[package.extras]
+gdown = ["gdown (>=4.7.3)"]
+scipy = ["scipy"]
+
+[[package]]
+name = "tornado"
+version = "6.5.2"
+description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "tornado-6.5.2-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:2436822940d37cde62771cff8774f4f00b3c8024fe482e16ca8387b8a2724db6"},
+    {file = "tornado-6.5.2-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:583a52c7aa94ee046854ba81d9ebb6c81ec0fd30386d96f7640c96dad45a03ef"},
+    {file = "tornado-6.5.2-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b0fe179f28d597deab2842b86ed4060deec7388f1fd9c1b4a41adf8af058907e"},
+    {file = "tornado-6.5.2-cp39-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b186e85d1e3536d69583d2298423744740986018e393d0321df7340e71898882"},
+    {file = "tornado-6.5.2-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e792706668c87709709c18b353da1f7662317b563ff69f00bab83595940c7108"},
+    {file = "tornado-6.5.2-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:06ceb1300fd70cb20e43b1ad8aaee0266e69e7ced38fa910ad2e03285009ce7c"},
+    {file = "tornado-6.5.2-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:74db443e0f5251be86cbf37929f84d8c20c27a355dd452a5cfa2aada0d001ec4"},
+    {file = "tornado-6.5.2-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:b5e735ab2889d7ed33b32a459cac490eda71a1ba6857b0118de476ab6c366c04"},
+    {file = "tornado-6.5.2-cp39-abi3-win32.whl", hash = "sha256:c6f29e94d9b37a95013bb669616352ddb82e3bfe8326fccee50583caebc8a5f0"},
+    {file = "tornado-6.5.2-cp39-abi3-win_amd64.whl", hash = "sha256:e56a5af51cc30dd2cae649429af65ca2f6571da29504a07995175df14c18f35f"},
+    {file = "tornado-6.5.2-cp39-abi3-win_arm64.whl", hash = "sha256:d6c33dc3672e3a1f3618eb63b7ef4683a7688e7b9e6e8f0d9aa5726360a004af"},
+    {file = "tornado-6.5.2.tar.gz", hash = "sha256:ab53c8f9a0fa351e2c0741284e06c7a45da86afb544133201c5cc8578eb076a0"},
+]
+
+[[package]]
+name = "tqdm"
+version = "4.67.1"
+description = "Fast, Extensible Progress Meter"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2"},
+    {file = "tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"]
+discord = ["requests"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
+[[package]]
+name = "traitlets"
+version = "5.14.3"
+description = "Traitlets Python configuration system"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
+    {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
+]
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
+
+[[package]]
+name = "transformers"
+version = "4.57.1"
+description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
+optional = false
+python-versions = ">=3.9.0"
+groups = ["main"]
+files = [
+    {file = "transformers-4.57.1-py3-none-any.whl", hash = "sha256:b10d05da8fa67dc41644dbbf9bc45a44cb86ae33da6f9295f5fbf5b7890bd267"},
+    {file = "transformers-4.57.1.tar.gz", hash = "sha256:f06c837959196c75039809636cd964b959f6604b75b8eeec6fdfc0440b89cc55"},
+]
+
+[package.dependencies]
+filelock = "*"
+huggingface-hub = ">=0.34.0,<1.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+pyyaml = ">=5.1"
+regex = "!=2019.12.17"
+requests = "*"
+safetensors = ">=0.4.3"
+tokenizers = ">=0.22.0,<=0.23.0"
+tqdm = ">=4.27"
+
+[package.extras]
+accelerate = ["accelerate (>=0.26.0)"]
+all = ["Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "av", "codecarbon (>=2.8.1)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "jinja2 (>=3.1.0)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "kernels (>=0.6.1,<=0.9)", "librosa", "mistral-common[opencv] (>=1.6.3)", "num2words", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "torchaudio", "torchvision"]
+audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+benchmark = ["optimum-benchmark (>=0.3.0)"]
+chat-template = ["jinja2 (>=3.1.0)"]
+codecarbon = ["codecarbon (>=2.8.1)"]
+deepspeed = ["accelerate (>=0.26.0)", "deepspeed (>=0.9.3)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "libcst", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "openai (>=1.98.0)", "optuna", "parameterized (>=0.9)", "protobuf", "psutil", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "tensorboard", "timeout-decorator", "torch (>=2.2)", "uvicorn"]
+dev = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "av", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "jinja2 (>=3.1.0)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "kernels (>=0.6.1,<=0.9)", "libcst", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "num2words", "onnxconverter-common", "openai (>=1.98.0)", "optax (>=0.0.8,<=0.1.4)", "optuna", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "torch (>=2.2)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)", "uvicorn"]
+dev-tensorflow = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "onnxconverter-common", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "openai (>=1.98.0)", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "tf2onnx", "timeout-decorator", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "urllib3 (<2.0.0)", "uvicorn"]
+dev-torch = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "kenlm", "kernels (>=0.6.1,<=0.9)", "libcst", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "num2words", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "openai (>=1.98.0)", "optuna", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "torch (>=2.2)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)", "uvicorn"]
+flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+hf-xet = ["hf_xet"]
+hub-kernels = ["kernels (>=0.6.1,<=0.9)"]
+integrations = ["kernels (>=0.6.1,<=0.9)", "optuna", "ray[tune] (>=2.7.0)"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)"]
+mistral-common = ["mistral-common[opencv] (>=1.6.3)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.6,<0.15.0)"]
+num2words = ["num2words"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+open-telemetry = ["opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "datasets (>=2.15.0)", "libcst", "pandas (<2.3.0)", "rich", "ruff (==0.13.1)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune] (>=2.7.0)"]
+retrieval = ["datasets (>=2.15.0)", "faiss-cpu"]
+ruff = ["ruff (==0.13.1)"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["accelerate (>=0.26.0)", "fastapi", "openai (>=1.98.0)", "pydantic (>=2)", "starlette", "torch (>=2.2)", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fastapi", "libcst", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "openai (>=1.98.0)", "parameterized (>=0.9)", "psutil", "pydantic (>=2)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures (<16.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.13.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "starlette", "tensorboard", "timeout-decorator", "torch (>=2.2)", "uvicorn"]
+tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+tiktoken = ["blobfile", "tiktoken"]
+timm = ["timm (!=1.0.18,<=1.0.19)"]
+tokenizers = ["tokenizers (>=0.22.0,<=0.23.0)"]
+torch = ["accelerate (>=0.26.0)", "torch (>=2.2)"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.34.0,<1.0)", "importlib_metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "tqdm (>=4.27)"]
+video = ["av"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
+[[package]]
+name = "triton"
+version = "3.5.0"
+description = "A language and compiler for custom Deep Learning operations"
+optional = false
+python-versions = "<3.15,>=3.10"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "triton-3.5.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6f90de6a6566bb619b4c0adc9855729e1b1b5e26533fca1bf6206e96b6d277a3"},
+    {file = "triton-3.5.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d5d3b3d480debf24eaa739623c9a42446b0b77f95593d30eb1f64cd2278cc1f0"},
+    {file = "triton-3.5.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8457b22148defefdcb7fa8144b05ce211b9faefad650a1ce85b23df488d5549c"},
+    {file = "triton-3.5.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f34bfa21c5b3a203c0f0eab28dcc1e49bd1f67d22724e77fb6665a659200a4ec"},
+    {file = "triton-3.5.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7da21fccceafc163e3a5e857abe34351ef76345af06cabf9637a914742671f0b"},
+    {file = "triton-3.5.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c9e71db82261c4ffa3921cd050cd5faa18322d2d405c30eb56084afaff3b0833"},
+    {file = "triton-3.5.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:188da5b81fa2f8322c27fec1627703eac24cb9bb7ab0dfbe9925973bc1b070d3"},
+    {file = "triton-3.5.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e6bb9aa5519c084a333acdba443789e50012a4b851cd486c54f0b8dc2a8d3a12"},
+    {file = "triton-3.5.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:03127d9b33aaf979c856676b394bc059ec1d68cb6da68ae03f62dd8ad77a04ae"},
+    {file = "triton-3.5.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c83f2343e1a220a716c7b3ab9fccfcbe3ad4020d189549200e2d2e8d5868bed9"},
+    {file = "triton-3.5.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:468936651d383f4a6d10068d34a627505e13af55be5d002b9f27b987e7a5f0ac"},
+    {file = "triton-3.5.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:da0fa67ccd76c3dcfb0bffe1b1c57c685136a6bd33d141c24d9655d4185b1289"},
+    {file = "triton-3.5.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c7ceef21410229ac23173a28eee5cfc0e37c1dfdb8b4bc11ecda2e3ecec7c686"},
+    {file = "triton-3.5.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:317fe477ea8fd4524a6a8c499fb0a36984a56d0b75bf9c9cb6133a1c56d5a6e7"},
+]
+
+[package.extras]
+build = ["cmake (>=3.20,<4.0)", "lit"]
+tests = ["autopep8", "isort", "llnl-hatchet", "numpy", "pytest", "pytest-forked", "pytest-xdist", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
+
+[[package]]
+name = "trl"
+version = "0.25.0"
+description = "Train transformer language models with reinforcement learning."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "trl-0.25.0-py3-none-any.whl", hash = "sha256:89ca23469736d8d94fdbc09bc3df691694aa841f82d96a955af858233d02ea31"},
+    {file = "trl-0.25.0.tar.gz", hash = "sha256:9637dc52d89c2374783dc6d8949ba724b30fc0ef22dec5c82d72b84bf94d2e08"},
+]
+
+[package.dependencies]
+accelerate = ">=1.4.0"
+datasets = ">=3.0.0"
+transformers = ">=4.56.1"
+
+[package.extras]
+bco = ["joblib", "scikit-learn"]
+deepspeed = ["deepspeed (>=0.14.4)"]
+dev = ["Pillow", "bitsandbytes", "deepspeed (>=0.14.4)", "hf-doc-builder", "joblib", "liger-kernel (>=0.6.2)", "llm-blender (>=0.0.2)", "num2words (==0.5.14)", "openai (>=1.23.2)", "peft (>=0.8.0)", "pre-commit", "pytest", "pytest-cov", "pytest-rerunfailures (==15.1)", "pytest-xdist", "scikit-learn", "torchvision"]
+judges = ["llm-blender (>=0.0.2)", "openai (>=1.23.2)"]
+liger = ["liger-kernel (>=0.6.2)"]
+math-verify = ["math-verify (>=0.5.2)"]
+peft = ["peft (>=0.8.0)"]
+quality = ["hf-doc-builder", "pre-commit"]
+quantization = ["bitsandbytes"]
+scikit = ["scikit-learn"]
+test = ["pytest", "pytest-cov", "pytest-rerunfailures (==15.1)", "pytest-xdist"]
+vllm = ["fastapi", "pydantic", "requests", "uvicorn", "vllm (==0.10.2)"]
+vlm = ["Pillow", "num2words (==0.5.14)", "torchvision"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.15.0"
+description = "Backported and Experimental Type Hints for Python 3.9+"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548"},
+    {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+]
+
+[[package]]
+name = "typing-inspection"
+version = "0.4.2"
+description = "Runtime typing introspection tools"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
+    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.12.0"
+
+[[package]]
+name = "tzdata"
+version = "2025.2"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+groups = ["main"]
+files = [
+    {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"},
+    {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"},
+]
+
+[[package]]
+name = "ultralytics"
+version = "8.3.225"
+description = "Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "ultralytics-8.3.225-py3-none-any.whl", hash = "sha256:3a486fc3662038fb9488b67dfe0ff980cb03fb4dcc79ab8b61507084df1b5ba6"},
+    {file = "ultralytics-8.3.225.tar.gz", hash = "sha256:d757ac2d040e6195c187eb65bb58d36b03d99ac9dac47ec48f0bc55d027b91d2"},
+]
+
+[package.dependencies]
+matplotlib = ">=3.3.0"
+numpy = ">=1.23.0"
+opencv-python = ">=4.6.0"
+pillow = ">=7.1.2"
+polars = "*"
+psutil = "*"
+pyyaml = ">=5.3.1"
+requests = ">=2.23.0"
+scipy = ">=1.4.1"
+torch = [
+    {version = ">=1.8.0,<2.4.0 || >2.4.0", markers = "sys_platform == \"win32\""},
+    {version = ">=1.8.0", markers = "sys_platform != \"win32\""},
+]
+torchvision = ">=0.9.0"
+ultralytics-thop = ">=2.0.18"
+
+[package.extras]
+dev = ["coverage[toml]", "ipython", "mkdocs (>=1.6.0)", "mkdocs-macros-plugin (>=1.0.5)", "mkdocs-material (>=9.5.9)", "mkdocs-ultralytics-plugin (>=0.1.29)", "mkdocstrings[python]", "pytest", "pytest-cov"]
+export = ["coremltools (>=8.0) ; platform_system != \"Windows\" and python_version <= \"3.13\"", "h5py (!=3.11.0) ; platform_machine == \"aarch64\"", "numpy (<2.0.0)", "onnx (>=1.12.0) ; platform_system != \"Darwin\"", "onnx (>=1.12.0,<1.18.0) ; platform_system == \"Darwin\"", "openvino (>=2024.0.0)", "scikit-learn (>=1.3.2) ; platform_system != \"Windows\" and python_version <= \"3.13\"", "tensorflow (>=2.0.0,<=2.19.0)", "tensorflowjs (>=2.0.0)", "tensorstore (>=0.1.63) ; platform_machine == \"aarch64\" and python_version >= \"3.9\""]
+extra = ["albumentations (>=1.4.6)", "faster-coco-eval (>=1.6.7)", "hub-sdk (>=0.0.12)", "ipython"]
+logging = ["mlflow", "tensorboard", "wandb"]
+solutions = ["flask (>=3.0.1)", "shapely (>=2.0.0)", "streamlit (>=1.29.0)"]
+typing = ["scipy-stubs", "types-pillow", "types-psutil", "types-pyyaml", "types-requests", "types-shapely"]
+
+[[package]]
+name = "ultralytics-thop"
+version = "2.0.18"
+description = "Ultralytics THOP package for fast computation of PyTorch model FLOPs and parameters."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "ultralytics_thop-2.0.18-py3-none-any.whl", hash = "sha256:2bb44851ad224b116c3995b02dd5e474a5ccf00acf237fe0edb9e1506ede04ec"},
+    {file = "ultralytics_thop-2.0.18.tar.gz", hash = "sha256:21103bcd39cc9928477dc3d9374561749b66a1781b35f46256c8d8c4ac01d9cf"},
+]
+
+[package.dependencies]
+numpy = "*"
+torch = "*"
+
+[[package]]
+name = "uri-template"
+version = "1.3.0"
+description = "RFC 6570 URI Template Processor"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"},
+    {file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"},
+]
+
+[package.extras]
+dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
+
+[[package]]
+name = "uritools"
+version = "5.0.0"
+description = "URI parsing, classification and composition"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "uritools-5.0.0-py3-none-any.whl", hash = "sha256:cead3a49ba8fbca3f91857343849d506d8639718f4a2e51b62e87393b493bd6f"},
+    {file = "uritools-5.0.0.tar.gz", hash = "sha256:68180cad154062bd5b5d9ffcdd464f8de6934414b25462ae807b00b8df9345de"},
+]
+
+[[package]]
+name = "urlextract"
+version = "1.9.0"
+description = "Collects and extracts URLs from given text."
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "urlextract-1.9.0-py3-none-any.whl", hash = "sha256:f88963532488b1c7c405e21bd162ae97871754ea04b60e18d33ee075b19b82fd"},
+    {file = "urlextract-1.9.0.tar.gz", hash = "sha256:70508e02ba9df372e25cf0642db367cece273e8712cd0ce78178fc5dd7ea00db"},
+]
+
+[package.dependencies]
+filelock = "*"
+idna = "*"
+platformdirs = "*"
+uritools = "*"
+
+[[package]]
+name = "urllib3"
+version = "2.5.0"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc"},
+    {file = "urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "wcwidth"
+version = "0.2.14"
+description = "Measures the displayed width of unicode strings in a terminal"
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+files = [
+    {file = "wcwidth-0.2.14-py2.py3-none-any.whl", hash = "sha256:a7bb560c8aee30f9957e5f9895805edd20602f2d7f720186dfd906e82b4982e1"},
+    {file = "wcwidth-0.2.14.tar.gz", hash = "sha256:4d478375d31bc5395a3c55c40ccdf3354688364cd61c4f6adacaa9215d0b3605"},
+]
+
+[[package]]
+name = "webcolors"
+version = "25.10.0"
+description = "A library for working with the color formats defined by HTML and CSS."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "webcolors-25.10.0-py3-none-any.whl", hash = "sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d"},
+    {file = "webcolors-25.10.0.tar.gz", hash = "sha256:62abae86504f66d0f6364c2a8520de4a0c47b80c03fc3a5f1815fedbef7c19bf"},
+]
+
+[[package]]
+name = "webencodings"
+version = "0.5.1"
+description = "Character encoding aliases for legacy web content"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
+    {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
+]
+
+[[package]]
+name = "websocket-client"
+version = "1.9.0"
+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "websocket_client-1.9.0-py3-none-any.whl", hash = "sha256:af248a825037ef591efbf6ed20cc5faa03d3b47b9e5a2230a529eeee1c1fc3ef"},
+    {file = "websocket_client-1.9.0.tar.gz", hash = "sha256:9e813624b6eb619999a97dc7958469217c3176312b3a16a4bd1bc7e08a46ec98"},
+]
+
+[package.extras]
+docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx_rtd_theme (>=1.1.0)"]
+optional = ["python-socks", "wsaccel"]
+test = ["pytest", "websockets"]
+
+[[package]]
+name = "websockets"
+version = "15.0.1"
+description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "websockets-15.0.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d63efaa0cd96cf0c5fe4d581521d9fa87744540d4bc999ae6e08595a1014b45b"},
+    {file = "websockets-15.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ac60e3b188ec7574cb761b08d50fcedf9d77f1530352db4eef1707fe9dee7205"},
+    {file = "websockets-15.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5756779642579d902eed757b21b0164cd6fe338506a8083eb58af5c372e39d9a"},
+    {file = "websockets-15.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0fdfe3e2a29e4db3659dbd5bbf04560cea53dd9610273917799f1cde46aa725e"},
+    {file = "websockets-15.0.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4c2529b320eb9e35af0fa3016c187dffb84a3ecc572bcee7c3ce302bfeba52bf"},
+    {file = "websockets-15.0.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ac1e5c9054fe23226fb11e05a6e630837f074174c4c2f0fe442996112a6de4fb"},
+    {file = "websockets-15.0.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:5df592cd503496351d6dc14f7cdad49f268d8e618f80dce0cd5a36b93c3fc08d"},
+    {file = "websockets-15.0.1-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:0a34631031a8f05657e8e90903e656959234f3a04552259458aac0b0f9ae6fd9"},
+    {file = "websockets-15.0.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:3d00075aa65772e7ce9e990cab3ff1de702aa09be3940d1dc88d5abf1ab8a09c"},
+    {file = "websockets-15.0.1-cp310-cp310-win32.whl", hash = "sha256:1234d4ef35db82f5446dca8e35a7da7964d02c127b095e172e54397fb6a6c256"},
+    {file = "websockets-15.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:39c1fec2c11dc8d89bba6b2bf1556af381611a173ac2b511cf7231622058af41"},
+    {file = "websockets-15.0.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:823c248b690b2fd9303ba00c4f66cd5e2d8c3ba4aa968b2779be9532a4dad431"},
+    {file = "websockets-15.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678999709e68425ae2593acf2e3ebcbcf2e69885a5ee78f9eb80e6e371f1bf57"},
+    {file = "websockets-15.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d50fd1ee42388dcfb2b3676132c78116490976f1300da28eb629272d5d93e905"},
+    {file = "websockets-15.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d99e5546bf73dbad5bf3547174cd6cb8ba7273062a23808ffea025ecb1cf8562"},
+    {file = "websockets-15.0.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:66dd88c918e3287efc22409d426c8f729688d89a0c587c88971a0faa2c2f3792"},
+    {file = "websockets-15.0.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8dd8327c795b3e3f219760fa603dcae1dcc148172290a8ab15158cf85a953413"},
+    {file = "websockets-15.0.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8fdc51055e6ff4adeb88d58a11042ec9a5eae317a0a53d12c062c8a8865909e8"},
+    {file = "websockets-15.0.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:693f0192126df6c2327cce3baa7c06f2a117575e32ab2308f7f8216c29d9e2e3"},
+    {file = "websockets-15.0.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:54479983bd5fb469c38f2f5c7e3a24f9a4e70594cd68cd1fa6b9340dadaff7cf"},
+    {file = "websockets-15.0.1-cp311-cp311-win32.whl", hash = "sha256:16b6c1b3e57799b9d38427dda63edcbe4926352c47cf88588c0be4ace18dac85"},
+    {file = "websockets-15.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:27ccee0071a0e75d22cb35849b1db43f2ecd3e161041ac1ee9d2352ddf72f065"},
+    {file = "websockets-15.0.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:3e90baa811a5d73f3ca0bcbf32064d663ed81318ab225ee4f427ad4e26e5aff3"},
+    {file = "websockets-15.0.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:592f1a9fe869c778694f0aa806ba0374e97648ab57936f092fd9d87f8bc03665"},
+    {file = "websockets-15.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0701bc3cfcb9164d04a14b149fd74be7347a530ad3bbf15ab2c678a2cd3dd9a2"},
+    {file = "websockets-15.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e8b56bdcdb4505c8078cb6c7157d9811a85790f2f2b3632c7d1462ab5783d215"},
+    {file = "websockets-15.0.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0af68c55afbd5f07986df82831c7bff04846928ea8d1fd7f30052638788bc9b5"},
+    {file = "websockets-15.0.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:64dee438fed052b52e4f98f76c5790513235efaa1ef7f3f2192c392cd7c91b65"},
+    {file = "websockets-15.0.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:d5f6b181bb38171a8ad1d6aa58a67a6aa9d4b38d0f8c5f496b9e42561dfc62fe"},
+    {file = "websockets-15.0.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:5d54b09eba2bada6011aea5375542a157637b91029687eb4fdb2dab11059c1b4"},
+    {file = "websockets-15.0.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3be571a8b5afed347da347bfcf27ba12b069d9d7f42cb8c7028b5e98bbb12597"},
+    {file = "websockets-15.0.1-cp312-cp312-win32.whl", hash = "sha256:c338ffa0520bdb12fbc527265235639fb76e7bc7faafbb93f6ba80d9c06578a9"},
+    {file = "websockets-15.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:fcd5cf9e305d7b8338754470cf69cf81f420459dbae8a3b40cee57417f4614a7"},
+    {file = "websockets-15.0.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:ee443ef070bb3b6ed74514f5efaa37a252af57c90eb33b956d35c8e9c10a1931"},
+    {file = "websockets-15.0.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:5a939de6b7b4e18ca683218320fc67ea886038265fd1ed30173f5ce3f8e85675"},
+    {file = "websockets-15.0.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:746ee8dba912cd6fc889a8147168991d50ed70447bf18bcda7039f7d2e3d9151"},
+    {file = "websockets-15.0.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:595b6c3969023ecf9041b2936ac3827e4623bfa3ccf007575f04c5a6aa318c22"},
+    {file = "websockets-15.0.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3c714d2fc58b5ca3e285461a4cc0c9a66bd0e24c5da9911e30158286c9b5be7f"},
+    {file = "websockets-15.0.1-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0f3c1e2ab208db911594ae5b4f79addeb3501604a165019dd221c0bdcabe4db8"},
+    {file = "websockets-15.0.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:229cf1d3ca6c1804400b0a9790dc66528e08a6a1feec0d5040e8b9eb14422375"},
+    {file = "websockets-15.0.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:756c56e867a90fb00177d530dca4b097dd753cde348448a1012ed6c5131f8b7d"},
+    {file = "websockets-15.0.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:558d023b3df0bffe50a04e710bc87742de35060580a293c2a984299ed83bc4e4"},
+    {file = "websockets-15.0.1-cp313-cp313-win32.whl", hash = "sha256:ba9e56e8ceeeedb2e080147ba85ffcd5cd0711b89576b83784d8605a7df455fa"},
+    {file = "websockets-15.0.1-cp313-cp313-win_amd64.whl", hash = "sha256:e09473f095a819042ecb2ab9465aee615bd9c2028e4ef7d933600a8401c79561"},
+    {file = "websockets-15.0.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:5f4c04ead5aed67c8a1a20491d54cdfba5884507a48dd798ecaf13c74c4489f5"},
+    {file = "websockets-15.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:abdc0c6c8c648b4805c5eacd131910d2a7f6455dfd3becab248ef108e89ab16a"},
+    {file = "websockets-15.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a625e06551975f4b7ea7102bc43895b90742746797e2e14b70ed61c43a90f09b"},
+    {file = "websockets-15.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d591f8de75824cbb7acad4e05d2d710484f15f29d4a915092675ad3456f11770"},
+    {file = "websockets-15.0.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:47819cea040f31d670cc8d324bb6435c6f133b8c7a19ec3d61634e62f8d8f9eb"},
+    {file = "websockets-15.0.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ac017dd64572e5c3bd01939121e4d16cf30e5d7e110a119399cf3133b63ad054"},
+    {file = "websockets-15.0.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:4a9fac8e469d04ce6c25bb2610dc535235bd4aa14996b4e6dbebf5e007eba5ee"},
+    {file = "websockets-15.0.1-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:363c6f671b761efcb30608d24925a382497c12c506b51661883c3e22337265ed"},
+    {file = "websockets-15.0.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:2034693ad3097d5355bfdacfffcbd3ef5694f9718ab7f29c29689a9eae841880"},
+    {file = "websockets-15.0.1-cp39-cp39-win32.whl", hash = "sha256:3b1ac0d3e594bf121308112697cf4b32be538fb1444468fb0a6ae4feebc83411"},
+    {file = "websockets-15.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:b7643a03db5c95c799b89b31c036d5f27eeb4d259c798e878d6937d71832b1e4"},
+    {file = "websockets-15.0.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:0c9e74d766f2818bb95f84c25be4dea09841ac0f734d1966f415e4edfc4ef1c3"},
+    {file = "websockets-15.0.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:1009ee0c7739c08a0cd59de430d6de452a55e42d6b522de7aa15e6f67db0b8e1"},
+    {file = "websockets-15.0.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76d1f20b1c7a2fa82367e04982e708723ba0e7b8d43aa643d3dcd404d74f1475"},
+    {file = "websockets-15.0.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f29d80eb9a9263b8d109135351caf568cc3f80b9928bccde535c235de55c22d9"},
+    {file = "websockets-15.0.1-pp310-pypy310_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b359ed09954d7c18bbc1680f380c7301f92c60bf924171629c5db97febb12f04"},
+    {file = "websockets-15.0.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:cad21560da69f4ce7658ca2cb83138fb4cf695a2ba3e475e0559e05991aa8122"},
+    {file = "websockets-15.0.1-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:7f493881579c90fc262d9cdbaa05a6b54b3811c2f300766748db79f098db9940"},
+    {file = "websockets-15.0.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:47b099e1f4fbc95b701b6e85768e1fcdaf1630f3cbe4765fa216596f12310e2e"},
+    {file = "websockets-15.0.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:67f2b6de947f8c757db2db9c71527933ad0019737ec374a8a6be9a956786aaf9"},
+    {file = "websockets-15.0.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d08eb4c2b7d6c41da6ca0600c077e93f5adcfd979cd777d747e9ee624556da4b"},
+    {file = "websockets-15.0.1-pp39-pypy39_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b826973a4a2ae47ba357e4e82fa44a463b8f168e1ca775ac64521442b19e87f"},
+    {file = "websockets-15.0.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:21c1fa28a6a7e3cbdc171c694398b6df4744613ce9b36b1a498e816787e28123"},
+    {file = "websockets-15.0.1-py3-none-any.whl", hash = "sha256:f7a866fbc1e97b5c617ee4116daaa09b722101d4a3c170c787450ba409f9736f"},
+    {file = "websockets-15.0.1.tar.gz", hash = "sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee"},
+]
+
+[[package]]
+name = "werkzeug"
+version = "3.1.3"
+description = "The comprehensive WSGI web application library."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "werkzeug-3.1.3-py3-none-any.whl", hash = "sha256:54b78bf3716d19a65be4fceccc0d1d7b89e608834989dfae50ea87564639213e"},
+    {file = "werkzeug-3.1.3.tar.gz", hash = "sha256:60723ce945c19328679790e3282cc758aa4a6040e4bb330f53d30fa546d44746"},
+]
+
+[package.dependencies]
+MarkupSafe = ">=2.1.1"
+
+[package.extras]
+watchdog = ["watchdog (>=2.3)"]
+
+[[package]]
+name = "widgetsnbextension"
+version = "4.0.15"
+description = "Jupyter interactive widgets for Jupyter Notebook"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "widgetsnbextension-4.0.15-py3-none-any.whl", hash = "sha256:8156704e4346a571d9ce73b84bee86a29906c9abfd7223b7228a28899ccf3366"},
+    {file = "widgetsnbextension-4.0.15.tar.gz", hash = "sha256:de8610639996f1567952d763a5a41af8af37f2575a41f9852a38f947eb82a3b9"},
+]
+
+[[package]]
+name = "xgboost"
+version = "3.1.1"
+description = "XGBoost Python Package"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "xgboost-3.1.1-py3-none-macosx_10_15_x86_64.whl", hash = "sha256:a51a2e488102a007b8c222d58bf855415002e8cdf06d104eea24b08dbf4eec4f"},
+    {file = "xgboost-3.1.1-py3-none-macosx_12_0_arm64.whl", hash = "sha256:fac06c989f2cf11af7aa546b3bb78e7fa87595891e5dfde28edf3e7492e5440a"},
+    {file = "xgboost-3.1.1-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:4347671aa8a495595f17135171aeae5f6d9ab4b4e7b02f191864cf2202e3c902"},
+    {file = "xgboost-3.1.1-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:405e48a201495fe9474f7aa27419f937794726a1bc7d2c2f3208b351c816580a"},
+    {file = "xgboost-3.1.1-py3-none-win_amd64.whl", hash = "sha256:2e1067489688ad99a410e8f2acdfe9d21a299c2f3b4b25dc8f094eae709c7447"},
+    {file = "xgboost-3.1.1.tar.gz", hash = "sha256:47fbf190a3804d5a8c25188781f8f5412a5724ea3a0604d29d4af4b3120ffa6b"},
+]
+
+[package.dependencies]
+numpy = "*"
+nvidia-nccl-cu12 = {version = "*", markers = "platform_system == \"Linux\" and platform_machine != \"aarch64\""}
+scipy = "*"
+
+[package.extras]
+dask = ["dask", "distributed", "pandas"]
+pandas = ["pandas (>=1.2)"]
+plotting = ["graphviz", "matplotlib"]
+pyspark = ["cloudpickle", "pyspark (>=3.4)", "scikit-learn"]
+scikit-learn = ["scikit-learn"]
+
+[[package]]
+name = "xxhash"
+version = "3.6.0"
+description = "Python binding for xxHash"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "xxhash-3.6.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:87ff03d7e35c61435976554477a7f4cd1704c3596a89a8300d5ce7fc83874a71"},
+    {file = "xxhash-3.6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f572dfd3d0e2eb1a57511831cf6341242f5a9f8298a45862d085f5b93394a27d"},
+    {file = "xxhash-3.6.0-cp310-cp310-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:89952ea539566b9fed2bbd94e589672794b4286f342254fad28b149f9615fef8"},
+    {file = "xxhash-3.6.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:48e6f2ffb07a50b52465a1032c3cf1f4a5683f944acaca8a134a2f23674c2058"},
+    {file = "xxhash-3.6.0-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b5b848ad6c16d308c3ac7ad4ba6bede80ed5df2ba8ed382f8932df63158dd4b2"},
+    {file = "xxhash-3.6.0-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a034590a727b44dd8ac5914236a7b8504144447a9682586c3327e935f33ec8cc"},
+    {file = "xxhash-3.6.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8a8f1972e75ebdd161d7896743122834fe87378160c20e97f8b09166213bf8cc"},
+    {file = "xxhash-3.6.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:ee34327b187f002a596d7b167ebc59a1b729e963ce645964bbc050d2f1b73d07"},
+    {file = "xxhash-3.6.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:339f518c3c7a850dd033ab416ea25a692759dc7478a71131fe8869010d2b75e4"},
+    {file = "xxhash-3.6.0-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:bf48889c9630542d4709192578aebbd836177c9f7a4a2778a7d6340107c65f06"},
+    {file = "xxhash-3.6.0-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:5576b002a56207f640636056b4160a378fe36a58db73ae5c27a7ec8db35f71d4"},
+    {file = "xxhash-3.6.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:af1f3278bd02814d6dedc5dec397993b549d6f16c19379721e5a1d31e132c49b"},
+    {file = "xxhash-3.6.0-cp310-cp310-win32.whl", hash = "sha256:aed058764db109dc9052720da65fafe84873b05eb8b07e5e653597951af57c3b"},
+    {file = "xxhash-3.6.0-cp310-cp310-win_amd64.whl", hash = "sha256:e82da5670f2d0d98950317f82a0e4a0197150ff19a6df2ba40399c2a3b9ae5fb"},
+    {file = "xxhash-3.6.0-cp310-cp310-win_arm64.whl", hash = "sha256:4a082ffff8c6ac07707fb6b671caf7c6e020c75226c561830b73d862060f281d"},
+    {file = "xxhash-3.6.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b47bbd8cf2d72797f3c2772eaaac0ded3d3af26481a26d7d7d41dc2d3c46b04a"},
+    {file = "xxhash-3.6.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2b6821e94346f96db75abaa6e255706fb06ebd530899ed76d32cd99f20dc52fa"},
+    {file = "xxhash-3.6.0-cp311-cp311-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:d0a9751f71a1a65ce3584e9cae4467651c7e70c9d31017fa57574583a4540248"},
+    {file = "xxhash-3.6.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8b29ee68625ab37b04c0b40c3fafdf24d2f75ccd778333cfb698f65f6c463f62"},
+    {file = "xxhash-3.6.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6812c25fe0d6c36a46ccb002f40f27ac903bf18af9f6dd8f9669cb4d176ab18f"},
+    {file = "xxhash-3.6.0-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:4ccbff013972390b51a18ef1255ef5ac125c92dc9143b2d1909f59abc765540e"},
+    {file = "xxhash-3.6.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:297b7fbf86c82c550e12e8fb71968b3f033d27b874276ba3624ea868c11165a8"},
+    {file = "xxhash-3.6.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:dea26ae1eb293db089798d3973a5fc928a18fdd97cc8801226fae705b02b14b0"},
+    {file = "xxhash-3.6.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:7a0b169aafb98f4284f73635a8e93f0735f9cbde17bd5ec332480484241aaa77"},
+    {file = "xxhash-3.6.0-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:08d45aef063a4531b785cd72de4887766d01dc8f362a515693df349fdb825e0c"},
+    {file = "xxhash-3.6.0-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:929142361a48ee07f09121fe9e96a84950e8d4df3bb298ca5d88061969f34d7b"},
+    {file = "xxhash-3.6.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:51312c768403d8540487dbbfb557454cfc55589bbde6424456951f7fcd4facb3"},
+    {file = "xxhash-3.6.0-cp311-cp311-win32.whl", hash = "sha256:d1927a69feddc24c987b337ce81ac15c4720955b667fe9b588e02254b80446fd"},
+    {file = "xxhash-3.6.0-cp311-cp311-win_amd64.whl", hash = "sha256:26734cdc2d4ffe449b41d186bbeac416f704a482ed835d375a5c0cb02bc63fef"},
+    {file = "xxhash-3.6.0-cp311-cp311-win_arm64.whl", hash = "sha256:d72f67ef8bf36e05f5b6c65e8524f265bd61071471cd4cf1d36743ebeeeb06b7"},
+    {file = "xxhash-3.6.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:01362c4331775398e7bb34e3ab403bc9ee9f7c497bc7dee6272114055277dd3c"},
+    {file = "xxhash-3.6.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b7b2df81a23f8cb99656378e72501b2cb41b1827c0f5a86f87d6b06b69f9f204"},
+    {file = "xxhash-3.6.0-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:dc94790144e66b14f67b10ac8ed75b39ca47536bf8800eb7c24b50271ea0c490"},
+    {file = "xxhash-3.6.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:93f107c673bccf0d592cdba077dedaf52fe7f42dcd7676eba1f6d6f0c3efffd2"},
+    {file = "xxhash-3.6.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2aa5ee3444c25b69813663c9f8067dcfaa2e126dc55e8dddf40f4d1c25d7effa"},
+    {file = "xxhash-3.6.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f7f99123f0e1194fa59cc69ad46dbae2e07becec5df50a0509a808f90a0f03f0"},
+    {file = "xxhash-3.6.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:49e03e6fe2cac4a1bc64952dd250cf0dbc5ef4ebb7b8d96bce82e2de163c82a2"},
+    {file = "xxhash-3.6.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:bd17fede52a17a4f9a7bc4472a5867cb0b160deeb431795c0e4abe158bc784e9"},
+    {file = "xxhash-3.6.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:6fb5f5476bef678f69db04f2bd1efbed3030d2aba305b0fc1773645f187d6a4e"},
+    {file = "xxhash-3.6.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:843b52f6d88071f87eba1631b684fcb4b2068cd2180a0224122fe4ef011a9374"},
+    {file = "xxhash-3.6.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:7d14a6cfaf03b1b6f5f9790f76880601ccc7896aff7ab9cd8978a939c1eb7e0d"},
+    {file = "xxhash-3.6.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:418daf3db71e1413cfe211c2f9a528456936645c17f46b5204705581a45390ae"},
+    {file = "xxhash-3.6.0-cp312-cp312-win32.whl", hash = "sha256:50fc255f39428a27299c20e280d6193d8b63b8ef8028995323bf834a026b4fbb"},
+    {file = "xxhash-3.6.0-cp312-cp312-win_amd64.whl", hash = "sha256:c0f2ab8c715630565ab8991b536ecded9416d615538be8ecddce43ccf26cbc7c"},
+    {file = "xxhash-3.6.0-cp312-cp312-win_arm64.whl", hash = "sha256:eae5c13f3bc455a3bbb68bdc513912dc7356de7e2280363ea235f71f54064829"},
+    {file = "xxhash-3.6.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:599e64ba7f67472481ceb6ee80fa3bd828fd61ba59fb11475572cc5ee52b89ec"},
+    {file = "xxhash-3.6.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7d8b8aaa30fca4f16f0c84a5c8d7ddee0e25250ec2796c973775373257dde8f1"},
+    {file = "xxhash-3.6.0-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:d597acf8506d6e7101a4a44a5e428977a51c0fadbbfd3c39650cca9253f6e5a6"},
+    {file = "xxhash-3.6.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:858dc935963a33bc33490128edc1c12b0c14d9c7ebaa4e387a7869ecc4f3e263"},
+    {file = "xxhash-3.6.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ba284920194615cb8edf73bf52236ce2e1664ccd4a38fdb543506413529cc546"},
+    {file = "xxhash-3.6.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:4b54219177f6c6674d5378bd862c6aedf64725f70dd29c472eaae154df1a2e89"},
+    {file = "xxhash-3.6.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:42c36dd7dbad2f5238950c377fcbf6811b1cdb1c444fab447960030cea60504d"},
+    {file = "xxhash-3.6.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f22927652cba98c44639ffdc7aaf35828dccf679b10b31c4ad72a5b530a18eb7"},
+    {file = "xxhash-3.6.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:b45fad44d9c5c119e9c6fbf2e1c656a46dc68e280275007bbfd3d572b21426db"},
+    {file = "xxhash-3.6.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:6f2580ffab1a8b68ef2b901cde7e55fa8da5e4be0977c68f78fc80f3c143de42"},
+    {file = "xxhash-3.6.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:40c391dd3cd041ebc3ffe6f2c862f402e306eb571422e0aa918d8070ba31da11"},
+    {file = "xxhash-3.6.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f205badabde7aafd1a31e8ca2a3e5a763107a71c397c4481d6a804eb5063d8bd"},
+    {file = "xxhash-3.6.0-cp313-cp313-win32.whl", hash = "sha256:2577b276e060b73b73a53042ea5bd5203d3e6347ce0d09f98500f418a9fcf799"},
+    {file = "xxhash-3.6.0-cp313-cp313-win_amd64.whl", hash = "sha256:757320d45d2fbcce8f30c42a6b2f47862967aea7bf458b9625b4bbe7ee390392"},
+    {file = "xxhash-3.6.0-cp313-cp313-win_arm64.whl", hash = "sha256:457b8f85dec5825eed7b69c11ae86834a018b8e3df5e77783c999663da2f96d6"},
+    {file = "xxhash-3.6.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a42e633d75cdad6d625434e3468126c73f13f7584545a9cf34e883aa1710e702"},
+    {file = "xxhash-3.6.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:568a6d743219e717b07b4e03b0a828ce593833e498c3b64752e0f5df6bfe84db"},
+    {file = "xxhash-3.6.0-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:bec91b562d8012dae276af8025a55811b875baace6af510412a5e58e3121bc54"},
+    {file = "xxhash-3.6.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:78e7f2f4c521c30ad5e786fdd6bae89d47a32672a80195467b5de0480aa97b1f"},
+    {file = "xxhash-3.6.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3ed0df1b11a79856df5ffcab572cbd6b9627034c1c748c5566fa79df9048a7c5"},
+    {file = "xxhash-3.6.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0e4edbfc7d420925b0dd5e792478ed393d6e75ff8fc219a6546fb446b6a417b1"},
+    {file = "xxhash-3.6.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fba27a198363a7ef87f8c0f6b171ec36b674fe9053742c58dd7e3201c1ab30ee"},
+    {file = "xxhash-3.6.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:794fe9145fe60191c6532fa95063765529770edcdd67b3d537793e8004cabbfd"},
+    {file = "xxhash-3.6.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:6105ef7e62b5ac73a837778efc331a591d8442f8ef5c7e102376506cb4ae2729"},
+    {file = "xxhash-3.6.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:f01375c0e55395b814a679b3eea205db7919ac2af213f4a6682e01220e5fe292"},
+    {file = "xxhash-3.6.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:d706dca2d24d834a4661619dcacf51a75c16d65985718d6a7d73c1eeeb903ddf"},
+    {file = "xxhash-3.6.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:5f059d9faeacd49c0215d66f4056e1326c80503f51a1532ca336a385edadd033"},
+    {file = "xxhash-3.6.0-cp313-cp313t-win32.whl", hash = "sha256:1244460adc3a9be84731d72b8e80625788e5815b68da3da8b83f78115a40a7ec"},
+    {file = "xxhash-3.6.0-cp313-cp313t-win_amd64.whl", hash = "sha256:b1e420ef35c503869c4064f4a2f2b08ad6431ab7b229a05cce39d74268bca6b8"},
+    {file = "xxhash-3.6.0-cp313-cp313t-win_arm64.whl", hash = "sha256:ec44b73a4220623235f67a996c862049f375df3b1052d9899f40a6382c32d746"},
+    {file = "xxhash-3.6.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:a40a3d35b204b7cc7643cbcf8c9976d818cb47befcfac8bbefec8038ac363f3e"},
+    {file = "xxhash-3.6.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:a54844be970d3fc22630b32d515e79a90d0a3ddb2644d8d7402e3c4c8da61405"},
+    {file = "xxhash-3.6.0-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:016e9190af8f0a4e3741343777710e3d5717427f175adfdc3e72508f59e2a7f3"},
+    {file = "xxhash-3.6.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4f6f72232f849eb9d0141e2ebe2677ece15adfd0fa599bc058aad83c714bb2c6"},
+    {file = "xxhash-3.6.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:63275a8aba7865e44b1813d2177e0f5ea7eadad3dd063a21f7cf9afdc7054063"},
+    {file = "xxhash-3.6.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3cd01fa2aa00d8b017c97eb46b9a794fbdca53fc14f845f5a328c71254b0abb7"},
+    {file = "xxhash-3.6.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0226aa89035b62b6a86d3c68df4d7c1f47a342b8683da2b60cedcddb46c4d95b"},
+    {file = "xxhash-3.6.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c6e193e9f56e4ca4923c61238cdaced324f0feac782544eb4c6d55ad5cc99ddd"},
+    {file = "xxhash-3.6.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:9176dcaddf4ca963d4deb93866d739a343c01c969231dbe21680e13a5d1a5bf0"},
+    {file = "xxhash-3.6.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:c1ce4009c97a752e682b897aa99aef84191077a9433eb237774689f14f8ec152"},
+    {file = "xxhash-3.6.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:8cb2f4f679b01513b7adbb9b1b2f0f9cdc31b70007eaf9d59d0878809f385b11"},
+    {file = "xxhash-3.6.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:653a91d7c2ab54a92c19ccf43508b6a555440b9be1bc8be553376778be7f20b5"},
+    {file = "xxhash-3.6.0-cp314-cp314-win32.whl", hash = "sha256:a756fe893389483ee8c394d06b5ab765d96e68fbbfe6fde7aa17e11f5720559f"},
+    {file = "xxhash-3.6.0-cp314-cp314-win_amd64.whl", hash = "sha256:39be8e4e142550ef69629c9cd71b88c90e9a5db703fecbcf265546d9536ca4ad"},
+    {file = "xxhash-3.6.0-cp314-cp314-win_arm64.whl", hash = "sha256:25915e6000338999236f1eb68a02a32c3275ac338628a7eaa5a269c401995679"},
+    {file = "xxhash-3.6.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:c5294f596a9017ca5a3e3f8884c00b91ab2ad2933cf288f4923c3fd4346cf3d4"},
+    {file = "xxhash-3.6.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1cf9dcc4ab9cff01dfbba78544297a3a01dafd60f3bde4e2bfd016cf7e4ddc67"},
+    {file = "xxhash-3.6.0-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:01262da8798422d0685f7cef03b2bd3f4f46511b02830861df548d7def4402ad"},
+    {file = "xxhash-3.6.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:51a73fb7cb3a3ead9f7a8b583ffd9b8038e277cdb8cb87cf890e88b3456afa0b"},
+    {file = "xxhash-3.6.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b9c6df83594f7df8f7f708ce5ebeacfc69f72c9fbaaababf6cf4758eaada0c9b"},
+    {file = "xxhash-3.6.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:627f0af069b0ea56f312fd5189001c24578868643203bca1abbc2c52d3a6f3ca"},
+    {file = "xxhash-3.6.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:aa912c62f842dfd013c5f21a642c9c10cd9f4c4e943e0af83618b4a404d9091a"},
+    {file = "xxhash-3.6.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:b465afd7909db30168ab62afe40b2fcf79eedc0b89a6c0ab3123515dc0df8b99"},
+    {file = "xxhash-3.6.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:a881851cf38b0a70e7c4d3ce81fc7afd86fbc2a024f4cfb2a97cf49ce04b75d3"},
+    {file = "xxhash-3.6.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:9b3222c686a919a0f3253cfc12bb118b8b103506612253b5baeaac10d8027cf6"},
+    {file = "xxhash-3.6.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:c5aa639bc113e9286137cec8fadc20e9cd732b2cc385c0b7fa673b84fc1f2a93"},
+    {file = "xxhash-3.6.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5c1343d49ac102799905e115aee590183c3921d475356cb24b4de29a4bc56518"},
+    {file = "xxhash-3.6.0-cp314-cp314t-win32.whl", hash = "sha256:5851f033c3030dd95c086b4a36a2683c2ff4a799b23af60977188b057e467119"},
+    {file = "xxhash-3.6.0-cp314-cp314t-win_amd64.whl", hash = "sha256:0444e7967dac37569052d2409b00a8860c2135cff05502df4da80267d384849f"},
+    {file = "xxhash-3.6.0-cp314-cp314t-win_arm64.whl", hash = "sha256:bb79b1e63f6fd84ec778a4b1916dfe0a7c3fdb986c06addd5db3a0d413819d95"},
+    {file = "xxhash-3.6.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:7dac94fad14a3d1c92affb661021e1d5cbcf3876be5f5b4d90730775ccb7ac41"},
+    {file = "xxhash-3.6.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:6965e0e90f1f0e6cb78da568c13d4a348eeb7f40acfd6d43690a666a459458b8"},
+    {file = "xxhash-3.6.0-cp38-cp38-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:2ab89a6b80f22214b43d98693c30da66af910c04f9858dd39c8e570749593d7e"},
+    {file = "xxhash-3.6.0-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4903530e866b7a9c1eadfd3fa2fbe1b97d3aed4739a80abf506eb9318561c850"},
+    {file = "xxhash-3.6.0-cp38-cp38-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4da8168ae52c01ac64c511d6f4a709479da8b7a4a1d7621ed51652f93747dffa"},
+    {file = "xxhash-3.6.0-cp38-cp38-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:97460eec202017f719e839a0d3551fbc0b2fcc9c6c6ffaa5af85bbd5de432788"},
+    {file = "xxhash-3.6.0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:45aae0c9df92e7fa46fbb738737324a563c727990755ec1965a6a339ea10a1df"},
+    {file = "xxhash-3.6.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:0d50101e57aad86f4344ca9b32d091a2135a9d0a4396f19133426c88025b09f1"},
+    {file = "xxhash-3.6.0-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:9085e798c163ce310d91f8aa6b325dda3c2944c93c6ce1edb314030d4167cc65"},
+    {file = "xxhash-3.6.0-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:a87f271a33fad0e5bf3be282be55d78df3a45ae457950deb5241998790326f87"},
+    {file = "xxhash-3.6.0-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:9e040d3e762f84500961791fa3709ffa4784d4dcd7690afc655c095e02fff05f"},
+    {file = "xxhash-3.6.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:b0359391c3dad6de872fefb0cf5b69d55b0655c55ee78b1bb7a568979b2ce96b"},
+    {file = "xxhash-3.6.0-cp38-cp38-win32.whl", hash = "sha256:e4ff728a2894e7f436b9e94c667b0f426b9c74b71f900cf37d5468c6b5da0536"},
+    {file = "xxhash-3.6.0-cp38-cp38-win_amd64.whl", hash = "sha256:01be0c5b500c5362871fc9cfdf58c69b3e5c4f531a82229ddb9eb1eb14138004"},
+    {file = "xxhash-3.6.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:cc604dc06027dbeb8281aeac5899c35fcfe7c77b25212833709f0bff4ce74d2a"},
+    {file = "xxhash-3.6.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:277175a73900ad43a8caeb8b99b9604f21fe8d7c842f2f9061a364a7e220ddb7"},
+    {file = "xxhash-3.6.0-cp39-cp39-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:cfbc5b91397c8c2972fdac13fb3e4ed2f7f8ccac85cd2c644887557780a9b6e2"},
+    {file = "xxhash-3.6.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2762bfff264c4e73c0e507274b40634ff465e025f0eaf050897e88ec8367575d"},
+    {file = "xxhash-3.6.0-cp39-cp39-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2f171a900d59d51511209f7476933c34a0c2c711078d3c80e74e0fe4f38680ec"},
+    {file = "xxhash-3.6.0-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:780b90c313348f030b811efc37b0fa1431163cb8db8064cf88a7936b6ce5f222"},
+    {file = "xxhash-3.6.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:18b242455eccdfcd1fa4134c431a30737d2b4f045770f8fe84356b3469d4b919"},
+    {file = "xxhash-3.6.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:a75ffc1bd5def584129774c158e108e5d768e10b75813f2b32650bb041066ed6"},
+    {file = "xxhash-3.6.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:1fc1ed882d1e8df932a66e2999429ba6cc4d5172914c904ab193381fba825360"},
+    {file = "xxhash-3.6.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:44e342e8cc11b4e79dae5c57f2fb6360c3c20cc57d32049af8f567f5b4bcb5f4"},
+    {file = "xxhash-3.6.0-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:c2f9ccd5c4be370939a2e17602fbc49995299203da72a3429db013d44d590e86"},
+    {file = "xxhash-3.6.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:02ea4cb627c76f48cd9fb37cf7ab22bd51e57e1b519807234b473faebe526796"},
+    {file = "xxhash-3.6.0-cp39-cp39-win32.whl", hash = "sha256:6551880383f0e6971dc23e512c9ccc986147ce7bfa1cd2e4b520b876c53e9f3d"},
+    {file = "xxhash-3.6.0-cp39-cp39-win_amd64.whl", hash = "sha256:7c35c4cdc65f2a29f34425c446f2f5cdcd0e3c34158931e1cc927ece925ab802"},
+    {file = "xxhash-3.6.0-cp39-cp39-win_arm64.whl", hash = "sha256:ffc578717a347baf25be8397cb10d2528802d24f94cfc005c0e44fef44b5cdd6"},
+    {file = "xxhash-3.6.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:0f7b7e2ec26c1666ad5fc9dbfa426a6a3367ceaf79db5dd76264659d509d73b0"},
+    {file = "xxhash-3.6.0-pp311-pypy311_pp73-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:5dc1e14d14fa0f5789ec29a7062004b5933964bb9b02aae6622b8f530dc40296"},
+    {file = "xxhash-3.6.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:881b47fc47e051b37d94d13e7455131054b56749b91b508b0907eb07900d1c13"},
+    {file = "xxhash-3.6.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c6dc31591899f5e5666f04cc2e529e69b4072827085c1ef15294d91a004bc1bd"},
+    {file = "xxhash-3.6.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:15e0dac10eb9309508bfc41f7f9deaa7755c69e35af835db9cb10751adebc35d"},
+    {file = "xxhash-3.6.0.tar.gz", hash = "sha256:f0162a78b13a0d7617b2845b90c763339d1f1d82bb04a4b07f4ab535cc5e05d6"},
+]
+
+[[package]]
+name = "yarl"
+version = "1.22.0"
+description = "Yet another URL library"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "yarl-1.22.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:c7bd6683587567e5a49ee6e336e0612bec8329be1b7d4c8af5687dcdeb67ee1e"},
+    {file = "yarl-1.22.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5cdac20da754f3a723cceea5b3448e1a2074866406adeb4ef35b469d089adb8f"},
+    {file = "yarl-1.22.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:07a524d84df0c10f41e3ee918846e1974aba4ec017f990dc735aad487a0bdfdf"},
+    {file = "yarl-1.22.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e1b329cb8146d7b736677a2440e422eadd775d1806a81db2d4cded80a48efc1a"},
+    {file = "yarl-1.22.0-cp310-cp310-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:75976c6945d85dbb9ee6308cd7ff7b1fb9409380c82d6119bd778d8fcfe2931c"},
+    {file = "yarl-1.22.0-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:80ddf7a5f8c86cb3eb4bc9028b07bbbf1f08a96c5c0bc1244be5e8fefcb94147"},
+    {file = "yarl-1.22.0-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d332fc2e3c94dad927f2112395772a4e4fedbcf8f80efc21ed7cdfae4d574fdb"},
+    {file = "yarl-1.22.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0cf71bf877efeac18b38d3930594c0948c82b64547c1cf420ba48722fe5509f6"},
+    {file = "yarl-1.22.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:663e1cadaddae26be034a6ab6072449a8426ddb03d500f43daf952b74553bba0"},
+    {file = "yarl-1.22.0-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:6dcbb0829c671f305be48a7227918cfcd11276c2d637a8033a99a02b67bf9eda"},
+    {file = "yarl-1.22.0-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:f0d97c18dfd9a9af4490631905a3f131a8e4c9e80a39353919e2cfed8f00aedc"},
+    {file = "yarl-1.22.0-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:437840083abe022c978470b942ff832c3940b2ad3734d424b7eaffcd07f76737"},
+    {file = "yarl-1.22.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:a899cbd98dce6f5d8de1aad31cb712ec0a530abc0a86bd6edaa47c1090138467"},
+    {file = "yarl-1.22.0-cp310-cp310-win32.whl", hash = "sha256:595697f68bd1f0c1c159fcb97b661fc9c3f5db46498043555d04805430e79bea"},
+    {file = "yarl-1.22.0-cp310-cp310-win_amd64.whl", hash = "sha256:cb95a9b1adaa48e41815a55ae740cfda005758104049a640a398120bf02515ca"},
+    {file = "yarl-1.22.0-cp310-cp310-win_arm64.whl", hash = "sha256:b85b982afde6df99ecc996990d4ad7ccbdbb70e2a4ba4de0aecde5922ba98a0b"},
+    {file = "yarl-1.22.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:1ab72135b1f2db3fed3997d7e7dc1b80573c67138023852b6efb336a5eae6511"},
+    {file = "yarl-1.22.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:669930400e375570189492dc8d8341301578e8493aec04aebc20d4717f899dd6"},
+    {file = "yarl-1.22.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:792a2af6d58177ef7c19cbf0097aba92ca1b9cb3ffdd9c7470e156c8f9b5e028"},
+    {file = "yarl-1.22.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3ea66b1c11c9150f1372f69afb6b8116f2dd7286f38e14ea71a44eee9ec51b9d"},
+    {file = "yarl-1.22.0-cp311-cp311-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3e2daa88dc91870215961e96a039ec73e4937da13cf77ce17f9cad0c18df3503"},
+    {file = "yarl-1.22.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ba440ae430c00eee41509353628600212112cd5018d5def7e9b05ea7ac34eb65"},
+    {file = "yarl-1.22.0-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:e6438cc8f23a9c1478633d216b16104a586b9761db62bfacb6425bac0a36679e"},
+    {file = "yarl-1.22.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4c52a6e78aef5cf47a98ef8e934755abf53953379b7d53e68b15ff4420e6683d"},
+    {file = "yarl-1.22.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:3b06bcadaac49c70f4c88af4ffcfbe3dc155aab3163e75777818092478bcbbe7"},
+    {file = "yarl-1.22.0-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:6944b2dc72c4d7f7052683487e3677456050ff77fcf5e6204e98caf785ad1967"},
+    {file = "yarl-1.22.0-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:d5372ca1df0f91a86b047d1277c2aaf1edb32d78bbcefffc81b40ffd18f027ed"},
+    {file = "yarl-1.22.0-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:51af598701f5299012b8416486b40fceef8c26fc87dc6d7d1f6fc30609ea0aa6"},
+    {file = "yarl-1.22.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:b266bd01fedeffeeac01a79ae181719ff848a5a13ce10075adbefc8f1daee70e"},
+    {file = "yarl-1.22.0-cp311-cp311-win32.whl", hash = "sha256:a9b1ba5610a4e20f655258d5a1fdc7ebe3d837bb0e45b581398b99eb98b1f5ca"},
+    {file = "yarl-1.22.0-cp311-cp311-win_amd64.whl", hash = "sha256:078278b9b0b11568937d9509b589ee83ef98ed6d561dfe2020e24a9fd08eaa2b"},
+    {file = "yarl-1.22.0-cp311-cp311-win_arm64.whl", hash = "sha256:b6a6f620cfe13ccec221fa312139135166e47ae169f8253f72a0abc0dae94376"},
+    {file = "yarl-1.22.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:e340382d1afa5d32b892b3ff062436d592ec3d692aeea3bef3a5cfe11bbf8c6f"},
+    {file = "yarl-1.22.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f1e09112a2c31ffe8d80be1b0988fa6a18c5d5cad92a9ffbb1c04c91bfe52ad2"},
+    {file = "yarl-1.22.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:939fe60db294c786f6b7c2d2e121576628468f65453d86b0fe36cb52f987bd74"},
+    {file = "yarl-1.22.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e1651bf8e0398574646744c1885a41198eba53dc8a9312b954073f845c90a8df"},
+    {file = "yarl-1.22.0-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:b8a0588521a26bf92a57a1705b77b8b59044cdceccac7151bd8d229e66b8dedb"},
+    {file = "yarl-1.22.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:42188e6a615c1a75bcaa6e150c3fe8f3e8680471a6b10150c5f7e83f47cc34d2"},
+    {file = "yarl-1.22.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f6d2cb59377d99718913ad9a151030d6f83ef420a2b8f521d94609ecc106ee82"},
+    {file = "yarl-1.22.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50678a3b71c751d58d7908edc96d332af328839eea883bb554a43f539101277a"},
+    {file = "yarl-1.22.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1e8fbaa7cec507aa24ea27a01456e8dd4b6fab829059b69844bd348f2d467124"},
+    {file = "yarl-1.22.0-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:433885ab5431bc3d3d4f2f9bd15bfa1614c522b0f1405d62c4f926ccd69d04fa"},
+    {file = "yarl-1.22.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:b790b39c7e9a4192dc2e201a282109ed2985a1ddbd5ac08dc56d0e121400a8f7"},
+    {file = "yarl-1.22.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:31f0b53913220599446872d757257be5898019c85e7971599065bc55065dc99d"},
+    {file = "yarl-1.22.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:a49370e8f711daec68d09b821a34e1167792ee2d24d405cbc2387be4f158b520"},
+    {file = "yarl-1.22.0-cp312-cp312-win32.whl", hash = "sha256:70dfd4f241c04bd9239d53b17f11e6ab672b9f1420364af63e8531198e3f5fe8"},
+    {file = "yarl-1.22.0-cp312-cp312-win_amd64.whl", hash = "sha256:8884d8b332a5e9b88e23f60bb166890009429391864c685e17bd73a9eda9105c"},
+    {file = "yarl-1.22.0-cp312-cp312-win_arm64.whl", hash = "sha256:ea70f61a47f3cc93bdf8b2f368ed359ef02a01ca6393916bc8ff877427181e74"},
+    {file = "yarl-1.22.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:8dee9c25c74997f6a750cd317b8ca63545169c098faee42c84aa5e506c819b53"},
+    {file = "yarl-1.22.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:01e73b85a5434f89fc4fe27dcda2aff08ddf35e4d47bbbea3bdcd25321af538a"},
+    {file = "yarl-1.22.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:22965c2af250d20c873cdbee8ff958fb809940aeb2e74ba5f20aaf6b7ac8c70c"},
+    {file = "yarl-1.22.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b4f15793aa49793ec8d1c708ab7f9eded1aa72edc5174cae703651555ed1b601"},
+    {file = "yarl-1.22.0-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e5542339dcf2747135c5c85f68680353d5cb9ffd741c0f2e8d832d054d41f35a"},
+    {file = "yarl-1.22.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5c401e05ad47a75869c3ab3e35137f8468b846770587e70d71e11de797d113df"},
+    {file = "yarl-1.22.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:243dda95d901c733f5b59214d28b0120893d91777cb8aa043e6ef059d3cddfe2"},
+    {file = "yarl-1.22.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bec03d0d388060058f5d291a813f21c011041938a441c593374da6077fe21b1b"},
+    {file = "yarl-1.22.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:b0748275abb8c1e1e09301ee3cf90c8a99678a4e92e4373705f2a2570d581273"},
+    {file = "yarl-1.22.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:47fdb18187e2a4e18fda2c25c05d8251a9e4a521edaed757fef033e7d8498d9a"},
+    {file = "yarl-1.22.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:c7044802eec4524fde550afc28edda0dd5784c4c45f0be151a2d3ba017daca7d"},
+    {file = "yarl-1.22.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:139718f35149ff544caba20fce6e8a2f71f1e39b92c700d8438a0b1d2a631a02"},
+    {file = "yarl-1.22.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e1b51bebd221006d3d2f95fbe124b22b247136647ae5dcc8c7acafba66e5ee67"},
+    {file = "yarl-1.22.0-cp313-cp313-win32.whl", hash = "sha256:d3e32536234a95f513bd374e93d717cf6b2231a791758de6c509e3653f234c95"},
+    {file = "yarl-1.22.0-cp313-cp313-win_amd64.whl", hash = "sha256:47743b82b76d89a1d20b83e60d5c20314cbd5ba2befc9cda8f28300c4a08ed4d"},
+    {file = "yarl-1.22.0-cp313-cp313-win_arm64.whl", hash = "sha256:5d0fcda9608875f7d052eff120c7a5da474a6796fe4d83e152e0e4d42f6d1a9b"},
+    {file = "yarl-1.22.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:719ae08b6972befcba4310e49edb1161a88cdd331e3a694b84466bd938a6ab10"},
+    {file = "yarl-1.22.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:47d8a5c446df1c4db9d21b49619ffdba90e77c89ec6e283f453856c74b50b9e3"},
+    {file = "yarl-1.22.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:cfebc0ac8333520d2d0423cbbe43ae43c8838862ddb898f5ca68565e395516e9"},
+    {file = "yarl-1.22.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4398557cbf484207df000309235979c79c4356518fd5c99158c7d38203c4da4f"},
+    {file = "yarl-1.22.0-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:2ca6fd72a8cd803be290d42f2dec5cdcd5299eeb93c2d929bf060ad9efaf5de0"},
+    {file = "yarl-1.22.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ca1f59c4e1ab6e72f0a23c13fca5430f889634166be85dbf1013683e49e3278e"},
+    {file = "yarl-1.22.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6c5010a52015e7c70f86eb967db0f37f3c8bd503a695a49f8d45700144667708"},
+    {file = "yarl-1.22.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9d7672ecf7557476642c88497c2f8d8542f8e36596e928e9bcba0e42e1e7d71f"},
+    {file = "yarl-1.22.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:3b7c88eeef021579d600e50363e0b6ee4f7f6f728cd3486b9d0f3ee7b946398d"},
+    {file = "yarl-1.22.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:f4afb5c34f2c6fecdcc182dfcfc6af6cccf1aa923eed4d6a12e9d96904e1a0d8"},
+    {file = "yarl-1.22.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:59c189e3e99a59cf8d83cbb31d4db02d66cda5a1a4374e8a012b51255341abf5"},
+    {file = "yarl-1.22.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:5a3bf7f62a289fa90f1990422dc8dff5a458469ea71d1624585ec3a4c8d6960f"},
+    {file = "yarl-1.22.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:de6b9a04c606978fdfe72666fa216ffcf2d1a9f6a381058d4378f8d7b1e5de62"},
+    {file = "yarl-1.22.0-cp313-cp313t-win32.whl", hash = "sha256:1834bb90991cc2999f10f97f5f01317f99b143284766d197e43cd5b45eb18d03"},
+    {file = "yarl-1.22.0-cp313-cp313t-win_amd64.whl", hash = "sha256:ff86011bd159a9d2dfc89c34cfd8aff12875980e3bd6a39ff097887520e60249"},
+    {file = "yarl-1.22.0-cp313-cp313t-win_arm64.whl", hash = "sha256:7861058d0582b847bc4e3a4a4c46828a410bca738673f35a29ba3ca5db0b473b"},
+    {file = "yarl-1.22.0-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:34b36c2c57124530884d89d50ed2c1478697ad7473efd59cfd479945c95650e4"},
+    {file = "yarl-1.22.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:0dd9a702591ca2e543631c2a017e4a547e38a5c0f29eece37d9097e04a7ac683"},
+    {file = "yarl-1.22.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:594fcab1032e2d2cc3321bb2e51271e7cd2b516c7d9aee780ece81b07ff8244b"},
+    {file = "yarl-1.22.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f3d7a87a78d46a2e3d5b72587ac14b4c16952dd0887dbb051451eceac774411e"},
+    {file = "yarl-1.22.0-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:852863707010316c973162e703bddabec35e8757e67fcb8ad58829de1ebc8590"},
+    {file = "yarl-1.22.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:131a085a53bfe839a477c0845acf21efc77457ba2bcf5899618136d64f3303a2"},
+    {file = "yarl-1.22.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:078a8aefd263f4d4f923a9677b942b445a2be970ca24548a8102689a3a8ab8da"},
+    {file = "yarl-1.22.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bca03b91c323036913993ff5c738d0842fc9c60c4648e5c8d98331526df89784"},
+    {file = "yarl-1.22.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:68986a61557d37bb90d3051a45b91fa3d5c516d177dfc6dd6f2f436a07ff2b6b"},
+    {file = "yarl-1.22.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:4792b262d585ff0dff6bcb787f8492e40698443ec982a3568c2096433660c694"},
+    {file = "yarl-1.22.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:ebd4549b108d732dba1d4ace67614b9545b21ece30937a63a65dd34efa19732d"},
+    {file = "yarl-1.22.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:f87ac53513d22240c7d59203f25cc3beac1e574c6cd681bbfd321987b69f95fd"},
+    {file = "yarl-1.22.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:22b029f2881599e2f1b06f8f1db2ee63bd309e2293ba2d566e008ba12778b8da"},
+    {file = "yarl-1.22.0-cp314-cp314-win32.whl", hash = "sha256:6a635ea45ba4ea8238463b4f7d0e721bad669f80878b7bfd1f89266e2ae63da2"},
+    {file = "yarl-1.22.0-cp314-cp314-win_amd64.whl", hash = "sha256:0d6e6885777af0f110b0e5d7e5dda8b704efed3894da26220b7f3d887b839a79"},
+    {file = "yarl-1.22.0-cp314-cp314-win_arm64.whl", hash = "sha256:8218f4e98d3c10d683584cb40f0424f4b9fd6e95610232dd75e13743b070ee33"},
+    {file = "yarl-1.22.0-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:45c2842ff0e0d1b35a6bf1cd6c690939dacb617a70827f715232b2e0494d55d1"},
+    {file = "yarl-1.22.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:d947071e6ebcf2e2bee8fce76e10faca8f7a14808ca36a910263acaacef08eca"},
+    {file = "yarl-1.22.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:334b8721303e61b00019474cc103bdac3d7b1f65e91f0bfedeec2d56dfe74b53"},
+    {file = "yarl-1.22.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e7ce67c34138a058fd092f67d07a72b8e31ff0c9236e751957465a24b28910c"},
+    {file = "yarl-1.22.0-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:d77e1b2c6d04711478cb1c4ab90db07f1609ccf06a287d5607fcd90dc9863acf"},
+    {file = "yarl-1.22.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c4647674b6150d2cae088fc07de2738a84b8bcedebef29802cf0b0a82ab6face"},
+    {file = "yarl-1.22.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:efb07073be061c8f79d03d04139a80ba33cbd390ca8f0297aae9cce6411e4c6b"},
+    {file = "yarl-1.22.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e51ac5435758ba97ad69617e13233da53908beccc6cfcd6c34bbed8dcbede486"},
+    {file = "yarl-1.22.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:33e32a0dd0c8205efa8e83d04fc9f19313772b78522d1bdc7d9aed706bfd6138"},
+    {file = "yarl-1.22.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:bf4a21e58b9cde0e401e683ebd00f6ed30a06d14e93f7c8fd059f8b6e8f87b6a"},
+    {file = "yarl-1.22.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:e4b582bab49ac33c8deb97e058cd67c2c50dac0dd134874106d9c774fd272529"},
+    {file = "yarl-1.22.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:0b5bcc1a9c4839e7e30b7b30dd47fe5e7e44fb7054ec29b5bb8d526aa1041093"},
+    {file = "yarl-1.22.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:c0232bce2170103ec23c454e54a57008a9a72b5d1c3105dc2496750da8cfa47c"},
+    {file = "yarl-1.22.0-cp314-cp314t-win32.whl", hash = "sha256:8009b3173bcd637be650922ac455946197d858b3630b6d8787aa9e5c4564533e"},
+    {file = "yarl-1.22.0-cp314-cp314t-win_amd64.whl", hash = "sha256:9fb17ea16e972c63d25d4a97f016d235c78dd2344820eb35bc034bc32012ee27"},
+    {file = "yarl-1.22.0-cp314-cp314t-win_arm64.whl", hash = "sha256:9f6d73c1436b934e3f01df1e1b21ff765cd1d28c77dfb9ace207f746d4610ee1"},
+    {file = "yarl-1.22.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:3aa27acb6de7a23785d81557577491f6c38a5209a254d1191519d07d8fe51748"},
+    {file = "yarl-1.22.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:af74f05666a5e531289cb1cc9c883d1de2088b8e5b4de48004e5ca8a830ac859"},
+    {file = "yarl-1.22.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:62441e55958977b8167b2709c164c91a6363e25da322d87ae6dd9c6019ceecf9"},
+    {file = "yarl-1.22.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b580e71cac3f8113d3135888770903eaf2f507e9421e5697d6ee6d8cd1c7f054"},
+    {file = "yarl-1.22.0-cp39-cp39-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e81fda2fb4a07eda1a2252b216aa0df23ebcd4d584894e9612e80999a78fd95b"},
+    {file = "yarl-1.22.0-cp39-cp39-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:99b6fc1d55782461b78221e95fc357b47ad98b041e8e20f47c1411d0aacddc60"},
+    {file = "yarl-1.22.0-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:088e4e08f033db4be2ccd1f34cf29fe994772fb54cfe004bbf54db320af56890"},
+    {file = "yarl-1.22.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2e4e1f6f0b4da23e61188676e3ed027ef0baa833a2e633c29ff8530800edccba"},
+    {file = "yarl-1.22.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:84fc3ec96fce86ce5aa305eb4aa9358279d1aa644b71fab7b8ed33fe3ba1a7ca"},
+    {file = "yarl-1.22.0-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:5dbeefd6ca588b33576a01b0ad58aa934bc1b41ef89dee505bf2932b22ddffba"},
+    {file = "yarl-1.22.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:14291620375b1060613f4aab9ebf21850058b6b1b438f386cc814813d901c60b"},
+    {file = "yarl-1.22.0-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:a4fcfc8eb2c34148c118dfa02e6427ca278bfd0f3df7c5f99e33d2c0e81eae3e"},
+    {file = "yarl-1.22.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:029866bde8d7b0878b9c160e72305bbf0a7342bcd20b9999381704ae03308dc8"},
+    {file = "yarl-1.22.0-cp39-cp39-win32.whl", hash = "sha256:4dcc74149ccc8bba31ce1944acee24813e93cfdee2acda3c172df844948ddf7b"},
+    {file = "yarl-1.22.0-cp39-cp39-win_amd64.whl", hash = "sha256:10619d9fdee46d20edc49d3479e2f8269d0779f1b031e6f7c2aa1c76be04b7ed"},
+    {file = "yarl-1.22.0-cp39-cp39-win_arm64.whl", hash = "sha256:dd7afd3f8b0bfb4e0d9fc3c31bfe8a4ec7debe124cfd90619305def3c8ca8cd2"},
+    {file = "yarl-1.22.0-py3-none-any.whl", hash = "sha256:1380560bdba02b6b6c90de54133c81c9f2a453dee9912fe58c1dcced1edb7cff"},
+    {file = "yarl-1.22.0.tar.gz", hash = "sha256:bebf8557577d4401ba8bd9ff33906f1376c877aa78d1fe216ad01b4d6745af71"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.1"
+
+[[package]]
+name = "zipp"
+version = "3.23.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"},
+    {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"},
+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
+[metadata]
+lock-version = "2.1"
+python-versions = ">=3.10,<3.14"
+content-hash = "d0513d30f2da53c60a544ebf648306bb275f0e4a152840b625be0edc792a4468"
diff --git a/pyproject.toml b/pyproject.toml
index 696c9b4..8ab5dc5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,49 +1,81 @@
-[project]
+[tool.poetry]
 name = "handson-mlp"
 version = "1.0.0"
 description = "A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn and PyTorch."
+authors = ["Aurélien Géron]
+license = "Apache-2.0"
 readme = "README.md"
-requires-python = ">=3.10,<3.14"
-dependencies = [
-    "accelerate",
-    # "box2d" installed separately because there's no binary on some platforms
-    "datasets",
-    "diffusers",
-    "google-genai",
-    "gymnasium[atari]",
-    "joblib",
-    "jupyter",
-    "jupyterlab",
-    "lion-pytorch",
-    "matplotlib",
-    "nbdime",
-    "nltk",
-    "numexpr",
-    "numpy",
-    "onnx",
-    "optuna",
-    "pandas",
-    "peft",
-    "pillow",
-    "xgboost",
-    "pygame",
-    "graphviz",
-    "torch",
-    "scikit-learn",
-    "scipy",
-    "sentence-transformers",
-    "sentencepiece",
-    "stable-baselines3",
-    "statsmodels",
-    "tensorboard",
-    "torchaudio",
-    "torchmetrics",
-    "torchvision",
-    "transformers",
-    "trl",
-    "ultralytics",
-    "urlextract",
-    "widgetsnbextension",
-    "ale-py>=0.11.2",
-    "einops>=0.8.1",
-]
+repository = "https://github.com/ageron/handson-mlp"
+packages = []
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.14"
+
+# Core scientific stack
+numpy = "^1.24.0"
+pandas = "^2.0.0"
+scipy = "^1.10.0"
+matplotlib = "^3.7.0"
+numexpr = "^2.8.0"
+
+# Jupyter environment
+jupyter = "^1.0.0"
+jupyterlab = "^4.0.0"
+widgetsnbextension = "^4.0.0"
+nbdime = "^3.2.0"
+
+# Machine Learning
+scikit-learn = "^1.3.0"
+xgboost = "^2.0.0"
+optuna = "^3.5.0"
+statsmodels = "^0.14.0"
+
+# Deep Learning core
+torch = "^2.0.0"
+torchvision = "^0.15.0"
+torchaudio = "^2.0.0"
+torchmetrics = "^1.0.0"
+accelerate = "^0.24.0"
+
+# NLP & Transformers
+transformers = "^4.35.0"
+datasets = "^2.14.0"
+sentence-transformers = "^2.2.0"
+sentencepiece = "^0.1.99"
+nltk = "^3.8.0"
+peft = "^0.6.0"
+trl = "^0.7.0"
+einops = ">=0.8.1"
+
+# Reinforcement Learning
+gymnasium = {extras = ["atari"], version = "^0.29.0"}
+stable-baselines3 = "^2.1.0"
+ale-py = ">=0.11.2"
+pygame = "^2.5.0"
+
+# Computer Vision & Generation
+diffusers = "^0.23.0"
+ultralytics = "^8.0.0"
+lion-pytorch = "^0.1.0"
+onnx = "^1.15.0"
+tensorboard = "^2.14.0"
+
+# Other utilities & APIs
+joblib = "^1.3.0"
+pillow = "^10.0.0"
+graphviz = "^0.20.0"
+urlextract = "^1.8.0"
+google-genai = "^0.3.0"
+requests = "^2.31.0"
+tqdm = "^4.65.0"
+
+# Note: box2d is excluded—install manually if needed
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.4.0"
+black = {version = "^23.0.0", extras = ["jupyter"]}
+flake8 = "^6.0.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/tools_pandas.ipynb b/tools_pandas.ipynb
index ad56e13..f7c8682 100644
--- a/tools_pandas.ipynb
+++ b/tools_pandas.ipynb
@@ -1,2757 +1,33640 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Tools - pandas**\n",
-    "\n",
-    "*The `pandas` library provides high-performance, easy-to-use data structures and data analysis tools. The main data structure is the `DataFrame`, which you can think of as an in-memory 2D table (like a spreadsheet, with column names and row labels). Many features available in Excel are available programmatically, such as creating pivot tables, computing columns based on other columns, plotting graphs, etc. You can also group rows by column value, or join tables much like in SQL. Pandas is also great at handling time series.*\n",
-    "\n",
-    "Prerequisites:\n",
-    "* NumPy – if you are not familiar with NumPy, we recommend that you go through the [NumPy tutorial](tools_numpy.ipynb) now."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<table align=\"left\">\n",
-    "  <td>\n",
-    "    <a href=\"https://colab.research.google.com/github/ageron/handson-mlp/blob/main/tools_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
-    "  </td>\n",
-    "  <td>\n",
-    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-mlp/blob/main/tools_pandas.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
-    "  </td>\n",
-    "</table>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, let's import `pandas`. People usually import it as `pd`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# `Series` objects\n",
-    "The `pandas` library contains the following useful data structures:\n",
-    "* `Series` objects, that we will discuss now. A `Series` object is 1D array, similar to a column in a spreadsheet (with a column name and row labels).\n",
-    "* `DataFrame` objects. This is a 2D table, similar to a spreadsheet (with column names and row labels).\n",
-    "* `Panel` objects. You can see a `Panel` as a dictionary of `DataFrame`s. These are less used, so we will not discuss them here."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating a `Series`\n",
-    "Let's start by creating our first `Series` object!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s = pd.Series([2,-1,3,5])\n",
-    "s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Similar to a 1D `ndarray`\n",
-    "`Series` objects behave much like one-dimensional NumPy `ndarray`s, and you can often pass them as parameters to NumPy functions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "np.exp(s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Arithmetic operations on `Series` are also possible, and they apply *elementwise*, just like for `ndarray`s:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s + [1000,2000,3000,4000]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similar to NumPy, if you add a single number to a `Series`, that number is added to all items in the `Series`. This is called * broadcasting*:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s + 1000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The same is true for all binary operations such as `*` or `/`, and even conditional operations:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s < 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Index labels\n",
-    "Each item in a `Series` object has a unique identifier called the *index label*. By default, it is simply the rank of the item in the `Series` (starting from `0`) but you can also set the index labels manually:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s2 = pd.Series([68, 83, 112, 68], index=[\"alice\", \"bob\", \"charles\", \"darwin\"])\n",
-    "s2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can then use the `Series` just like a `dict`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s2[\"bob\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To make it clear when you are accessing by label or by integer location, it is recommended to always use the `loc` attribute when accessing by label, and the `iloc` attribute when accessing by integer location:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s2.loc[\"bob\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s2.iloc[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Slicing a `Series` also slices the index labels:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s2.iloc[1:3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This can lead to unexpected results when using the default numeric labels, so be careful:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "surprise = pd.Series([1000, 1001, 1002, 1003])\n",
-    "surprise"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "surprise_slice = surprise[2:]\n",
-    "surprise_slice"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Oh, look! The first element has index label `2`. The element with index label `0` is absent from the slice:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    surprise_slice[0]\n",
-    "except KeyError as e:\n",
-    "    print(\"Key error:\", e)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But remember that you can access elements by integer location using the `iloc` attribute. This illustrates another reason why it's always better to use `loc` and `iloc` to access `Series` objects:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "surprise_slice.iloc[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Init from `dict`\n",
-    "You can create a `Series` object from a `dict`. The keys will be used as index labels:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "weights = {\"alice\": 68, \"bob\": 83, \"colin\": 86, \"darwin\": 68}\n",
-    "s3 = pd.Series(weights)\n",
-    "s3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can control which elements you want to include in the `Series` and in what order by explicitly specifying the desired `index`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s4 = pd.Series(weights, index = [\"colin\", \"alice\"])\n",
-    "s4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Automatic alignment\n",
-    "When an operation involves multiple `Series` objects, `pandas` automatically aligns items by matching index labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(s2.keys())\n",
-    "print(s3.keys())\n",
-    "\n",
-    "s2 + s3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The resulting `Series` contains the union of index labels from `s2` and `s3`. Since `\"colin\"` is missing from `s2` and `\"charles\"` is missing from `s3`, these items have a `NaN` result value (i.e. Not-a-Number means *missing*).\n",
-    "\n",
-    "Automatic alignment is very handy when working with data that may come from various sources with varying structure and missing items. But if you forget to set the right index labels, you can have surprising results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s5 = pd.Series([1000,1000,1000,1000])\n",
-    "print(\"s2 =\", s2.values)\n",
-    "print(\"s5 =\", s5.values)\n",
-    "\n",
-    "s2 + s5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pandas could not align the `Series`, since their labels do not match at all, hence the full `NaN` result."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Init with a scalar\n",
-    "You can also initialize a `Series` object using a scalar and a list of index labels: all items will be set to the scalar."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "meaning = pd.Series(42, [\"life\", \"universe\", \"everything\"])\n",
-    "meaning"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## `Series` name\n",
-    "A `Series` can have a `name`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s6 = pd.Series([83, 68], index=[\"bob\", \"alice\"], name=\"weights\")\n",
-    "s6"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting a `Series`\n",
-    "Pandas makes it easy to plot `Series` data using matplotlib (for more details on matplotlib, check out the [matplotlib tutorial](tools_matplotlib.ipynb)). Just import matplotlib and call the `plot()` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "temperatures = [4.4,5.1,6.1,6.2,6.1,6.1,5.7,5.2,4.7,4.1,3.9,3.5]\n",
-    "s7 = pd.Series(temperatures, name=\"Temperature\")\n",
-    "s7.plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There are *many* options for plotting your data. It is not necessary to list them all here: if you need a particular type of plot (histograms, pie charts, etc.), just look for it in the excellent [Visualization](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html) section of pandas' documentation, and look at the example code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Handling time\n",
-    "Many datasets have timestamps, and pandas is awesome at manipulating such data:\n",
-    "* it can represent periods (such as 2016Q3) and frequencies (such as \"monthly\"),\n",
-    "* it can convert periods to actual timestamps, and *vice versa*,\n",
-    "* it can resample data and aggregate values any way you like,\n",
-    "* it can handle timezones.\n",
-    "\n",
-    "## Time range\n",
-    "Let's start by creating a time series using `pd.date_range()`. It returns a `DatetimeIndex` containing one datetime per hour for 12 hours starting on October 29th 2016 at 5:30pm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='h')\n",
-    "dates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This `DatetimeIndex` may be used as an index in a `Series`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series = pd.Series(temperatures, dates)\n",
-    "temp_series"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's plot this series:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series.plot(kind=\"bar\")\n",
-    "\n",
-    "plt.grid(True)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Resampling\n",
-    "Pandas lets us resample a time series very simply. Just call the `resample()` method and specify a new frequency:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series_freq_2h = temp_series.resample(\"2h\")\n",
-    "temp_series_freq_2h"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The resampling operation is actually a deferred operation, which is why we did not get a `Series` object, but a `DatetimeIndexResampler` object instead. To actually perform the resampling operation, we can simply call the `mean()` method. Pandas will compute the mean of every pair of consecutive hours:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series_freq_2h = temp_series_freq_2h.mean()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's plot the result:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series_freq_2h.plot(kind=\"bar\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note how the values have automatically been aggregated into 2-hour periods. If we look at the 6-8pm period, for example, we had a value of `5.1` at 6:30pm, and `6.1` at 7:30pm. After resampling, we just have one value of `5.6`, which is the mean of `5.1` and `6.1`. Rather than computing the mean, we could have used any other aggregation function, for example we can decide to keep the minimum value of each period:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series_freq_2h = temp_series.resample(\"2h\").min()\n",
-    "temp_series_freq_2h"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or, equivalently, we could use the `apply()` method instead:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series_freq_2h = temp_series.resample(\"2h\").apply(\"min\")\n",
-    "temp_series_freq_2h"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Upsampling and interpolation\n",
-    "It was an example of downsampling. We can also upsample (i.e. increase the frequency), but it will create holes in our data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series_freq_15min = temp_series.resample(\"15Min\").mean()\n",
-    "temp_series_freq_15min.head(n=10) # `head` displays the top n values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One solution is to fill the gaps by interpolating. We just call the `interpolate()` method. The default is to use linear interpolation, but we can also select another method, such as cubic interpolation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series_freq_15min = temp_series.resample(\"15Min\").interpolate(method=\"cubic\")\n",
-    "temp_series_freq_15min.head(n=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series.plot(label=\"Period: 1 hour\")\n",
-    "temp_series_freq_15min.plot(label=\"Period: 15 minutes\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Timezones\n",
-    "By default, datetimes are *naive*: they are not aware of timezones, so 2016-10-30 02:30 might mean October 30th 2016 at 2:30am in Paris or in New York. We can make datetimes timezone *aware* by calling the `tz_localize()` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series_ny = temp_series.tz_localize(\"America/New_York\")\n",
-    "temp_series_ny"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that `-04:00` is now appended to all the datetimes. It means that these datetimes refer to [UTC](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) - 4 hours.\n",
-    "\n",
-    "We can convert these datetimes to Paris time like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series_paris = temp_series_ny.tz_convert(\"Europe/Paris\")\n",
-    "temp_series_paris"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You may have noticed that the UTC offset changes from `+02:00` to `+01:00`: this is because France switches to winter time at 3am that particular night (time goes back to 2am). Notice that 2:30am occurs twice! Let's go back to a naive representation (if you log some data hourly using local time, without storing the timezone, you might get something like this):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series_paris_naive = temp_series_paris.tz_localize(None)\n",
-    "temp_series_paris_naive"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now `02:30` is really ambiguous. If we try to localize these naive datetimes to the Paris timezone, we get an error:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    temp_series_paris_naive.tz_localize(\"Europe/Paris\")\n",
-    "except Exception as e:\n",
-    "    print(type(e))\n",
-    "    print(e)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Fortunately, by using the `ambiguous` argument we can tell pandas to infer the right DST (Daylight Saving Time) based on the order of the ambiguous timestamps:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_series_paris_naive.tz_localize(\"Europe/Paris\", ambiguous=\"infer\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Periods\n",
-    "The `pd.period_range()` function returns a `PeriodIndex` instead of a `DatetimeIndex`. For example, let's get all quarters in 2016 and 2017:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quarters = pd.period_range('2016Q1', periods=8, freq='Q')\n",
-    "quarters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Adding a number `N` to a `PeriodIndex` shifts the periods by `N` times the `PeriodIndex`'s frequency:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quarters + 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `asfreq()` method lets us change the frequency of the `PeriodIndex`. All periods are lengthened or shortened accordingly. For example, let's convert all the quarterly periods to monthly periods (zooming in):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quarters.asfreq(\"M\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By default, the `asfreq` zooms on the end of each period. We can tell it to zoom on the start of each period instead:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quarters.asfreq(\"M\", how=\"start\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can zoom out:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quarters.asfreq(\"Y\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Of course, we can create a `Series` with a `PeriodIndex`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quarterly_revenue = pd.Series([300, 320, 290, 390, 320, 360, 310, 410], index=quarters)\n",
-    "quarterly_revenue"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quarterly_revenue.plot(kind=\"line\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can convert periods to timestamps by calling `to_timestamp`. By default, it will give us the first day of each period, but by setting `how` and `freq`, we can get the last hour of each period:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "last_hours = quarterly_revenue.to_timestamp(how=\"end\", freq=\"h\")\n",
-    "last_hours"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And back to periods by calling `to_period`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "last_hours.to_period()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pandas also provides many other time-related functions that we recommend you check out in the [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html). To whet your appetite, here is one way to get the last business day of each month in 2016, at 9am:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "months_2016 = pd.period_range(\"2016\", periods=12, freq=\"M\")\n",
-    "one_day_after_last_days = months_2016.asfreq(\"D\") + 1\n",
-    "last_bdays = one_day_after_last_days.to_timestamp() - pd.tseries.offsets.BDay()\n",
-    "last_bdays.to_period(\"h\") + 9"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# `DataFrame` objects\n",
-    "A DataFrame object represents a spreadsheet, with cell values, column names and row index labels. You can define expressions to compute columns based on other columns, create pivot-tables, group rows, draw graphs, etc. You can see `DataFrame`s as dictionaries of `Series`.\n",
-    "\n",
-    "## Creating a `DataFrame`\n",
-    "You can create a DataFrame by passing a dictionary of `Series` objects:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people_dict = {\n",
-    "    \"weight\": pd.Series([68, 83, 112], index=[\"alice\", \"bob\", \"charles\"]),\n",
-    "    \"birthyear\": pd.Series([1984, 1985, 1992], index=[\"bob\", \"alice\", \"charles\"], name=\"year\"),\n",
-    "    \"children\": pd.Series([0, 3], index=[\"charles\", \"bob\"]),\n",
-    "    \"hobby\": pd.Series([\"Biking\", \"Dancing\"], index=[\"alice\", \"bob\"]),\n",
-    "}\n",
-    "people = pd.DataFrame(people_dict)\n",
-    "people"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A few things to note:\n",
-    "* the `Series` were automatically aligned based on their index,\n",
-    "* missing values are represented as `NaN`,\n",
-    "* `Series` names are ignored (the name `\"year\"` was dropped),\n",
-    "* `DataFrame`s are displayed nicely in Jupyter notebooks, woohoo!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can access columns pretty much as you would expect. They are returned as `Series` objects:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people[\"birthyear\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also get multiple columns at once:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people[[\"birthyear\", \"hobby\"]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you pass a list of columns and/or index row labels to the `DataFrame` constructor, it will guarantee that these columns and/or rows will exist, in that order, and no other column/row will exist. For example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d2 = pd.DataFrame(\n",
-    "        people_dict,\n",
-    "        columns=[\"birthyear\", \"weight\", \"height\"],\n",
-    "        index=[\"bob\", \"alice\", \"eugene\"]\n",
-    "     )\n",
-    "d2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another convenient way to create a `DataFrame` is to pass all the values to the constructor as an `ndarray`, or a list of lists, and specify the column names and row index labels separately:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "values = [\n",
-    "            [1985, np.nan, \"Biking\",   68],\n",
-    "            [1984, 3,      \"Dancing\",  83],\n",
-    "            [1992, 0,      np.nan,    112]\n",
-    "         ]\n",
-    "d3 = pd.DataFrame(\n",
-    "        values,\n",
-    "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
-    "        index=[\"alice\", \"bob\", \"charles\"]\n",
-    "     )\n",
-    "d3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To specify missing values, you can use either `np.nan` or NumPy's masked arrays:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "masked_array = np.ma.asarray(values, dtype=object)\n",
-    "masked_array[(0, 2), (1, 2)] = np.ma.masked\n",
-    "d3 = pd.DataFrame(\n",
-    "        masked_array,\n",
-    "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
-    "        index=[\"alice\", \"bob\", \"charles\"]\n",
-    "     )\n",
-    "d3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instead of an `ndarray`, you can also pass a `DataFrame` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d4 = pd.DataFrame(\n",
-    "         d3,\n",
-    "         columns=[\"hobby\", \"children\"],\n",
-    "         index=[\"alice\", \"bob\"]\n",
-    "     )\n",
-    "d4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is also possible to create a `DataFrame` with a dictionary (or list) of dictionaries (or lists):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people = pd.DataFrame({\n",
-    "    \"birthyear\": {\"alice\": 1985, \"bob\": 1984, \"charles\": 1992},\n",
-    "    \"hobby\": {\"alice\": \"Biking\", \"bob\": \"Dancing\"},\n",
-    "    \"weight\": {\"alice\": 68, \"bob\": 83, \"charles\": 112},\n",
-    "    \"children\": {\"bob\": 3, \"charles\": 0}\n",
-    "})\n",
-    "people"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Multi-indexing\n",
-    "If all columns are tuples of the same size, then they are understood as a multi-index. The same goes for row index labels. For example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d5 = pd.DataFrame(\n",
-    "  {\n",
-    "    (\"public\", \"birthyear\"):\n",
-    "        {(\"Paris\",\"alice\"): 1985, (\"Paris\",\"bob\"): 1984, (\"London\",\"charles\"): 1992},\n",
-    "    (\"public\", \"hobby\"):\n",
-    "        {(\"Paris\",\"alice\"): \"Biking\", (\"Paris\",\"bob\"): \"Dancing\"},\n",
-    "    (\"private\", \"weight\"):\n",
-    "        {(\"Paris\",\"alice\"): 68, (\"Paris\",\"bob\"): 83, (\"London\",\"charles\"): 112},\n",
-    "    (\"private\", \"children\"):\n",
-    "        {(\"Paris\", \"alice\"): np.nan, (\"Paris\",\"bob\"): 3, (\"London\",\"charles\"): 0}\n",
-    "  }\n",
-    ")\n",
-    "d5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can now get a `DataFrame` containing all the `\"public\"` columns very simply:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d5[\"public\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d5[\"public\", \"hobby\"]  # Same result as d5[\"public\"][\"hobby\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Dropping a level\n",
-    "Let's look at `d5` again:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There are two levels of columns, and two levels of indices. We can drop a column level by calling `droplevel()` (the same goes for indices):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d5.columns = d5.columns.droplevel(level = 0)\n",
-    "d5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Transposing\n",
-    "You can swap columns and indices using the `T` attribute:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d6 = d5.T\n",
-    "d6"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Stacking and unstacking levels\n",
-    "Calling the `stack()` method will push the lowest column level after the lowest index:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d7 = d6.stack()\n",
-    "d7"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that many `NaN` values appeared. This makes sense because many new combinations did not exist before (e.g. there was no `bob` in `London`).\n",
-    "\n",
-    "Calling `unstack()` will do the reverse, once again creating many `NaN` values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d8 = d7.unstack()\n",
-    "d8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we call `unstack` again, we end up with a `Series` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d9 = d8.unstack()\n",
-    "d9"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `stack()` and `unstack()` methods let you select the `level` to stack/unstack. You can even stack/unstack multiple levels at once:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d10 = d9.unstack(level = (0,1))\n",
-    "d10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Most methods return modified copies\n",
-    "As you may have noticed, the `stack()` and `unstack()` methods do not modify the object they are called on. Instead, they work on a copy and return that copy. This is true of most methods in pandas."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Accessing rows\n",
-    "Let's go back to the `people` `DataFrame`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `loc` attribute lets you access rows instead of columns. The result is a `Series` object in which the `DataFrame`'s column names are mapped to row index labels:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.loc[\"charles\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also access rows by integer location using the `iloc` attribute:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.iloc[2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also get a slice of rows, and this returns a `DataFrame` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.iloc[1:3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, you can pass a boolean array to get the matching rows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people[np.array([True, False, True])]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is most useful when combined with boolean expressions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people[people[\"birthyear\"] < 1990]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Adding and removing columns\n",
-    "You can generally treat `DataFrame` objects like dictionaries of `Series`, so the following works fine:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people[\"age\"] = 2018 - people[\"birthyear\"]  # adds a new column \"age\"\n",
-    "people[\"over 30\"] = people[\"age\"] > 30      # adds another column \"over 30\"\n",
-    "birthyears = people.pop(\"birthyear\")\n",
-    "del people[\"children\"]\n",
-    "\n",
-    "people"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "birthyears"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you add a new column, it must have the same number of rows. Missing rows are filled with NaN, and extra rows are ignored:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people[\"pets\"] = pd.Series({\"bob\": 0, \"charles\": 5, \"eugene\": 1})  # alice is missing, eugene is ignored\n",
-    "people"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When adding a new column, it is added at the end (on the right) by default. You can also insert a column anywhere else using the `insert()` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.insert(1, \"height\", [172, 181, 185])\n",
-    "people"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assigning new columns\n",
-    "You can also create new columns by calling the `assign()` method. Note that this returns a new `DataFrame` object, the original is not modified:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.assign(\n",
-    "    body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
-    "    has_pets = people[\"pets\"] > 0\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that you cannot access columns created within the same assignment:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    people.assign(\n",
-    "        body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
-    "        overweight = people[\"body_mass_index\"] > 25\n",
-    "    )\n",
-    "except KeyError as e:\n",
-    "    print(\"Key error:\", e)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The solution is to split this assignment in two consecutive assignments:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d6 = people.assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
-    "d6.assign(overweight = d6[\"body_mass_index\"] > 25)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Having to create a temporary variable `d6` is not very convenient. You may want to just chain the assignment calls, but it does not work because the `people` object is not actually modified by the first assignment:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    (people\n",
-    "         .assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
-    "         .assign(overweight = people[\"body_mass_index\"] > 25)\n",
-    "    )\n",
-    "except KeyError as e:\n",
-    "    print(\"Key error:\", e)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But fear not, there is a simple solution. You can pass a function to the `assign()` method (typically a `lambda` function), and this function will be called with the `DataFrame` as a parameter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(people\n",
-    "     .assign(body_mass_index = lambda df: df[\"weight\"] / (df[\"height\"] / 100) ** 2)\n",
-    "     .assign(overweight = lambda df: df[\"body_mass_index\"] > 25)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Problem solved!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluating an expression\n",
-    "A great feature supported by pandas is expression evaluation. It relies on the `numexpr` library which must be installed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.eval(\"weight / (height/100) ** 2 > 25\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Assignment expressions are also supported. Let's set `inplace=True` to directly modify the `DataFrame` rather than getting a modified copy:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.eval(\"body_mass_index = weight / (height/100) ** 2\", inplace=True)\n",
-    "people"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can use a local or global variable in an expression by prefixing it with `'@'`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "overweight_threshold = 30\n",
-    "people.eval(\"overweight = body_mass_index > @overweight_threshold\", inplace=True)\n",
-    "people"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Querying a `DataFrame`\n",
-    "The `query()` method lets you filter a `DataFrame` based on a query expression:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.query(\"age > 30 and pets == 0\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sorting a `DataFrame`\n",
-    "You can sort a `DataFrame` by calling its `sort_index` method. By default, it sorts the rows by their index label, in ascending order, but let's reverse the order:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.sort_index(ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that `sort_index` returned a sorted *copy* of the `DataFrame`. To modify `people` directly, we can set the `inplace` argument to `True`. Also, we can sort the columns instead of the rows by setting `axis=1`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.sort_index(axis=1, inplace=True)\n",
-    "people"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To sort the `DataFrame` by the values instead of the labels, we can use `sort_values` and specify the column to sort by:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.sort_values(by=\"age\", inplace=True)\n",
-    "people"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting a `DataFrame`\n",
-    "Just like for `Series`, pandas makes it easy to draw nice graphs based on a `DataFrame`.\n",
-    "\n",
-    "For example, it is trivial to create a line plot from a `DataFrame`'s data by calling its `plot` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.sort_values(by=\"body_mass_index\", inplace=True)\n",
-    "people.plot(kind=\"line\", x=\"body_mass_index\", y=[\"height\", \"weight\"])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can pass extra arguments supported by matplotlib's functions. For example, we can create scatterplot and pass it a list of sizes using the `s` argument of matplotlib's `scatter()` function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "people.plot(kind=\"scatter\", x=\"height\", y=\"weight\", s=[40, 120, 200])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Again, there are way too many options to list here: the best option is to scroll through the [Visualization](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html) page in pandas' documentation, find the plot you are interested in and look at the example code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Operations on `DataFrame`s\n",
-    "Although `DataFrame`s do not try to mimic NumPy arrays, there are a few similarities. Let's create a `DataFrame` to demonstrate this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grades_array = np.array([[8, 8, 9], [10, 9, 9], [4, 8, 2], [9, 10, 10]])\n",
-    "grades = pd.DataFrame(grades_array, columns=[\"sep\", \"oct\", \"nov\"], index=[\"alice\", \"bob\", \"charles\", \"darwin\"])\n",
-    "grades"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can apply NumPy mathematical functions on a `DataFrame`: the function is applied to all values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.sqrt(grades)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly, adding a single value to a `DataFrame` will add that value to all elements in the `DataFrame`. This is called *broadcasting*:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grades + 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Of course, the same is true for all other binary operations, including arithmetic (`*`,`/`,`**`...) and conditional (`>`, `==`...) operations:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grades >= 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Aggregation operations, such as computing the `max`, the `sum` or the `mean` of a `DataFrame`, apply to each column, and you get back a `Series` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grades.mean()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `all` method is also an aggregation operation: it checks whether all values are `True` or not. Let's see during which months all students got a grade greater than `5`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(grades > 5).all()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Most of these functions take an optional `axis` parameter which lets you specify along which axis of the `DataFrame` you want the operation executed. The default is `axis=0`, meaning that the operation is executed vertically (on each column). You can set `axis=1` to execute the operation horizontally (on each row). For example, let's find out which students had all grades greater than `5`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(grades > 5).all(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `any` method returns `True` if any value is True. Let's see who got at least one grade 10:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(grades == 10).any(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you add a `Series` object to a `DataFrame` (or execute any other binary operation), pandas attempts to broadcast the operation to all *rows* in the `DataFrame`. This only works if the `Series` has the same size as the `DataFrame`s rows. For example, let's subtract the `mean` of the `DataFrame` (a `Series` object) from the `DataFrame`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grades - grades.mean()  # equivalent to: grades - [7.75, 8.75, 7.50]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We subtracted `7.75` from all September grades, `8.75` from October grades and `7.50` from November grades. It is equivalent to subtracting this `DataFrame`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.DataFrame([[7.75, 8.75, 7.50]]*4, index=grades.index, columns=grades.columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to subtract the global mean from every grade, here is one way to do it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grades - grades.values.mean() # subtracts the global mean (8.00) from all grades"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Automatic alignment\n",
-    "Similar to `Series`, when operating on multiple `DataFrame`s, pandas automatically aligns them by row index label, but also by column names. Let's create a `DataFrame` with bonus points for each person from October to December:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bonus_array = np.array([[0, np.nan, 2], [np.nan, 1, 0], [0, 1, 0], [3, 3, 0]])\n",
-    "bonus_points = pd.DataFrame(bonus_array, columns=[\"oct\", \"nov\", \"dec\"], index=[\"bob\", \"colin\", \"darwin\", \"charles\"])\n",
-    "bonus_points"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grades + bonus_points"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looks like the addition worked in some cases but way too many elements are now empty. That's because when aligning the `DataFrame`s, some columns and rows were only present on one side, and thus they were considered missing on the other side (`NaN`). Then adding `NaN` to a number results in `NaN`, hence the result.\n",
-    "\n",
-    "## Handling missing data\n",
-    "Dealing with missing data is a frequent task when working with real life data. Pandas offers a few tools to handle missing data.\n",
-    " \n",
-    "Let's try to fix the problem above. For example, we can decide that missing data should result in a zero, instead of `NaN`. We can replace all `NaN` values by any value using the `fillna()` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(grades + bonus_points).fillna(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's a bit unfair that we're setting grades to zero in September, though. Perhaps we should decide that missing grades are missing grades, but missing bonus points should be replaced by zeros:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fixed_bonus_points = bonus_points.fillna(0)\n",
-    "fixed_bonus_points.insert(0, \"sep\", 0)\n",
-    "fixed_bonus_points.loc[\"alice\"] = 0\n",
-    "grades + fixed_bonus_points"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's much better: although we made up some data, we have not been too unfair.\n",
-    "\n",
-    "Another way to handle missing data is to interpolate. Let's look at the `bonus_points` `DataFrame` again:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bonus_points"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's call the `interpolate` method. By default, it interpolates vertically (`axis=0`), so let's tell it to interpolate horizontally (`axis=1`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bonus_points.interpolate(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Bob had 0 bonus points in October, and 2 in December. When we interpolate for November, we get the mean: 1 bonus point. Colin had 1 bonus point in November, but we do not know how many bonus points he had in September, so we cannot interpolate, this is why there is still a missing value in October after interpolation. To fix this, we can set the September bonus points to 0 before interpolation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "better_bonus_points = bonus_points.copy()\n",
-    "better_bonus_points.insert(0, \"sep\", 0)\n",
-    "better_bonus_points.loc[\"alice\"] = 0\n",
-    "better_bonus_points = better_bonus_points.interpolate(axis=1)\n",
-    "better_bonus_points"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Great, now we have reasonable bonus points everywhere. Let's find out the final grades:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grades + better_bonus_points"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is slightly annoying that the September column ends up on the right. This is because the `DataFrame`s we are adding do not have the exact same columns (the `grades` `DataFrame` is missing the `\"dec\"` column), so to make things predictable, pandas orders the final columns alphabetically. To fix this, we can simply add the missing column before adding:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grades[\"dec\"] = np.nan\n",
-    "final_grades = grades + better_bonus_points\n",
-    "final_grades"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There's not much we can do about December and Colin: it's bad enough that we are making up bonus points, but we can't reasonably make up grades (well, I guess some teachers probably do). So let's call the `dropna()` method to get rid of rows that are full of `NaN`s:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "final_grades_clean = final_grades.dropna(how=\"all\")\n",
-    "final_grades_clean"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's remove columns that are full of `NaN`s by setting the `axis` argument to `1`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "final_grades_clean = final_grades_clean.dropna(axis=1, how=\"all\")\n",
-    "final_grades_clean"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Aggregating with `groupby`\n",
-    "Similar to the SQL language, pandas allows grouping your data into groups to run calculations over each group.\n",
-    "\n",
-    "First, let's add some extra data about each person so we can group them, and let's go back to the `final_grades` `DataFrame` so we can see how `NaN` values are handled:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "final_grades[\"hobby\"] = [\"Biking\", \"Dancing\", np.nan, \"Dancing\", \"Biking\"]\n",
-    "final_grades"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's group data in this `DataFrame` by hobby:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grouped_grades = final_grades.groupby(\"hobby\")\n",
-    "grouped_grades"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We are ready to compute the average grade per hobby:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grouped_grades.mean()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That was easy! Note that the `NaN` values have simply been skipped when computing the means."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Pivot tables\n",
-    "Pandas supports spreadsheet-like [pivot tables](https://en.wikipedia.org/wiki/Pivot_table) that allow quick data summarization. To illustrate this, let's create a simple `DataFrame`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bonus_points"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "more_grades = final_grades_clean.stack().reset_index()\n",
-    "more_grades.columns = [\"name\", \"month\", \"grade\"]\n",
-    "more_grades[\"bonus\"] = [np.nan, np.nan, np.nan, 0, np.nan, 2, 3, 3, 0, 0, 1, 0]\n",
-    "more_grades"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can call the `pd.pivot_table()` function for this `DataFrame`, asking to group by the `name` column. By default, `pivot_table()` computes the mean of each numeric column:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.pivot_table(more_grades[[\"name\", \"grade\", \"bonus\"]], index=\"name\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can change the aggregation function by setting the `aggfunc` argument, and we can also specify the list of columns whose values will be aggregated:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.pivot_table(more_grades, index=\"name\", values=[\"grade\", \"bonus\"], aggfunc=\"max\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also specify the `columns` to aggregate over horizontally, and request the grand totals for each row and column by setting `margins=True`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.pivot_table(more_grades, index=\"name\", values=\"grade\", columns=\"month\", margins=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we can specify multiple index or column names, and pandas will create multi-level indices:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.pivot_table(more_grades, index=(\"name\", \"month\"), margins=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Overview functions\n",
-    "When dealing with large `DataFrames`, it is useful to get a quick overview of its content. Pandas offers a few functions for this. First, let's create a large `DataFrame` with a mix of numeric values, missing values and text values. Notice how Jupyter displays only the corners of the `DataFrame`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "much_data = np.fromfunction(lambda x,y: (x+y*y)%17*11, (10000, 26))\n",
-    "large_df = pd.DataFrame(much_data, columns=list(\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"))\n",
-    "large_df[large_df % 16 == 0] = np.nan\n",
-    "large_df.insert(3, \"some_text\", \"Blabla\")\n",
-    "large_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `head()` method returns the top 5 rows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "large_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Of course, there's also a `tail()` function to view the bottom 5 rows. You can pass the number of rows you want:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "large_df.tail(n=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `info()` method prints out a summary of each column's contents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "large_df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, the `describe()` method gives a nice overview of the main aggregated values over each column:\n",
-    "* `count`: number of non-null (not NaN) values\n",
-    "* `mean`: mean of non-null values\n",
-    "* `std`: [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of non-null values\n",
-    "* `min`: minimum of non-null values\n",
-    "* `25%`, `50%`, `75%`: 25th, 50th and 75th [percentile](https://en.wikipedia.org/wiki/Percentile) of non-null values\n",
-    "* `max`: maximum of non-null values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "large_df.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Saving & loading\n",
-    "Pandas can save `DataFrame`s to various backends, including file formats such as CSV, Excel, JSON, HTML and HDF5, or to a SQL database. Let's create a `DataFrame` to demonstrate this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_df = pd.DataFrame(\n",
-    "    [[\"Biking\", 68.5, 1985, np.nan], [\"Dancing\", 83.1, 1984, 3]], \n",
-    "    columns=[\"hobby\", \"weight\", \"birthyear\", \"children\"],\n",
-    "    index=[\"alice\", \"bob\"]\n",
-    ")\n",
-    "my_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Saving\n",
-    "Let's save it to CSV, HTML and JSON:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_df.to_csv(\"my_df.csv\")\n",
-    "my_df.to_html(\"my_df.html\")\n",
-    "my_df.to_json(\"my_df.json\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Done! Let's take a peek at what was saved:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "\n",
-    "for filename in (\"my_df.csv\", \"my_df.html\", \"my_df.json\"):\n",
-    "    print(\"#\", filename)\n",
-    "    print(Path(filename).read_text())\n",
-    "    print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that the index is saved as the first column in a CSV file, as `<th>` tags in HTML and as keys in JSON. The index column has no name by default, but you can fix that by setting `my_df.index.name` to any name you want.\n",
-    "\n",
-    "Saving to other formats works very similarly, but some formats require extra libraries to be installed. For example, saving to Excel requires the openpyxl library:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    my_df.to_excel(\"my_df.xlsx\", sheet_name='People')\n",
-    "except ImportError as e:\n",
-    "    print(e)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading\n",
-    "Now let's load our CSV file back into a `DataFrame`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_df_loaded = pd.read_csv(\"my_df.csv\", index_col=0)\n",
-    "my_df_loaded"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you might guess, there are similar `read_json`, `read_html`, `read_excel` functions as well.  We can also read data straight from the Internet. For example, let's load the top 1,000 U.S. cities from GitHub:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "us_cities = None\n",
-    "try:\n",
-    "    csv_url = \"https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv\"\n",
-    "    us_cities = pd.read_csv(csv_url, index_col=0)\n",
-    "    us_cities = us_cities.head()\n",
-    "except IOError as e:\n",
-    "    print(e)\n",
-    "us_cities"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There are more options available, in particular regarding datetime format. Check out the [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html) for more details."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Combining `DataFrame`s\n",
-    "\n",
-    "## SQL-like joins\n",
-    "One powerful feature of pandas is its ability to perform SQL-like joins on `DataFrame`s. Various types of joins are supported: inner joins, left/right outer joins and full joins. To illustrate this, let's start by creating a couple of simple `DataFrame`s:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "city_loc = pd.DataFrame(\n",
-    "    [\n",
-    "        [\"CA\", \"San Francisco\", 37.781334, -122.416728],\n",
-    "        [\"NY\", \"New York\", 40.705649, -74.008344],\n",
-    "        [\"FL\", \"Miami\", 25.791100, -80.320733],\n",
-    "        [\"OH\", \"Cleveland\", 41.473508, -81.739791],\n",
-    "        [\"UT\", \"Salt Lake City\", 40.755851, -111.896657]\n",
-    "    ], columns=[\"state\", \"city\", \"lat\", \"lng\"])\n",
-    "city_loc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "city_pop = pd.DataFrame(\n",
-    "    [\n",
-    "        [808976, \"San Francisco\", \"California\"],\n",
-    "        [8363710, \"New York\", \"New-York\"],\n",
-    "        [413201, \"Miami\", \"Florida\"],\n",
-    "        [2242193, \"Houston\", \"Texas\"]\n",
-    "    ], index=[3,4,5,6], columns=[\"population\", \"city\", \"state\"])\n",
-    "city_pop"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's join these `DataFrame`s using the `merge()` function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.merge(left=city_loc, right=city_pop, on=\"city\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that both `DataFrame`s have a column named `state`, so in the result they got renamed to `state_x` and `state_y`.\n",
-    "\n",
-    "Also, note that Cleveland, Salt Lake City and Houston were dropped because they don't exist in *both* `DataFrame`s. This is the equivalent of a SQL `INNER JOIN`. If you want a `FULL OUTER JOIN`, where no city gets dropped and `NaN` values are added, you must specify `how=\"outer\"`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_cities = pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"outer\")\n",
-    "all_cities"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Of course, `LEFT OUTER JOIN` is also available by setting `how=\"left\"`: only the cities present in the left `DataFrame` end up in the result. Similarly, with `how=\"right\"` only cities in the right `DataFrame` appear in the result. For example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"right\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If the key to join on is actually in one (or both) `DataFrame`'s index, you must use `left_index=True` and/or `right_index=True`. If the key column names differ, you must use `left_on` and `right_on`. For example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "city_pop2 = city_pop.copy()\n",
-    "city_pop2.columns = [\"population\", \"name\", \"state\"]\n",
-    "pd.merge(left=city_loc, right=city_pop2, left_on=\"city\", right_on=\"name\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Concatenation\n",
-    "Rather than joining `DataFrame`s, we may just want to concatenate them. That's what `concat()` is for:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result_concat = pd.concat([city_loc, city_pop])\n",
-    "result_concat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that this operation aligned the data horizontally (by columns) but not vertically (by rows). In this example, we end up with multiple rows having the same index (e.g. 3). Pandas handles this rather gracefully:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result_concat.loc[3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or you can tell pandas to just ignore the index:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.concat([city_loc, city_pop], ignore_index=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice that when a column does not exist in a `DataFrame`, it acts as if it was filled with `NaN` values. If we set `join=\"inner\"`, then only columns that exist in *both* `DataFrame`s are returned:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.concat([city_loc, city_pop], join=\"inner\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can concatenate `DataFrame`s horizontally instead of vertically by setting `axis=1`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.concat([city_loc, city_pop], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this case it really does not make much sense because the indices do not align well (e.g. Cleveland and San Francisco end up on the same row, because they shared the index label `3`). So let's reindex the `DataFrame`s by city name before concatenating:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.concat([city_loc.set_index(\"city\"), city_pop.set_index(\"city\")], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This looks a lot like a `FULL OUTER JOIN`, except that the `state` columns were not renamed to `state_x` and `state_y`, and the `city` column is now the index."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Categories\n",
-    "It is quite frequent to have values that represent categories, for example `1` for female and `2` for male, or `\"A\"` for Good, `\"B\"` for Average, `\"C\"` for Bad. These categorical values can be hard to read and cumbersome to handle, but fortunately pandas makes it easy. To illustrate this, let's take the `city_pop` `DataFrame` we created earlier, and add a column that represents a category:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "city_eco = city_pop.copy()\n",
-    "city_eco[\"eco_code\"] = [17, 17, 34, 20]\n",
-    "city_eco"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Right now the `eco_code` column is full of apparently meaningless codes. Let's fix that by creating a new categorical column based on the `eco_code`s:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "city_eco['economy'] = pd.Categorical(\n",
-    "    city_eco['eco_code'].map({17: 'Banking', 20: 'Energy', 34: 'Tourism'})\n",
-    ")\n",
-    "city_eco"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can get a categorical column's list of categories like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "city_eco['economy'].cat.categories"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And you can rename categories using `rename_categories`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "city_eco['economy'] = city_eco['economy'].cat.rename_categories({'Banking': 'Finance'})\n",
-    "city_eco"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# What's next?\n",
-    "As you probably noticed by now, pandas is quite a large library with *many* features. Although we went through the most important features, there is still a lot to discover. Probably the best way to learn more is to get your hands dirty with some real-life data. It is also a good idea to go through pandas' excellent [documentation](https://pandas.pydata.org/pandas-docs/stable/index.html), in particular the [Cookbook](https://pandas.pydata.org/pandas-docs/stable/user_guide/cookbook.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/bertramwooster/Geron-handson-ml-2025/blob/new_branch/tools_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Dy_d5yEQ3k0m"
+      },
+      "source": [
+        "**Tools - pandas**\n",
+        "\n",
+        "*The `pandas` library provides high-performance, easy-to-use data structures and data analysis tools. The main data structure is the `DataFrame`, which you can think of as an in-memory 2D table (like a spreadsheet, with column names and row labels). Many features available in Excel are available programmatically, such as creating pivot tables, computing columns based on other columns, plotting graphs, etc. You can also group rows by column value, or join tables much like in SQL. Pandas is also great at handling time series.*\n",
+        "\n",
+        "Prerequisites:\n",
+        "* NumPy – if you are not familiar with NumPy, we recommend that you go through the [NumPy tutorial](tools_numpy.ipynb) now."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r_gvseUj3k0q"
+      },
+      "source": [
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a href=\"https://colab.research.google.com/github/ageron/handson-mlp/blob/main/tools_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-mlp/blob/main/tools_pandas.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SJ8AW9zQ3k0u"
+      },
+      "source": [
+        "# Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UEH9H8Yr3k0w"
+      },
+      "source": [
+        "First, let's import `pandas`. People usually import it as `pd`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qxJtWzCR3k0x"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tut8ipH83k0z"
+      },
+      "source": [
+        "# `Series` objects\n",
+        "The `pandas` library contains the following useful data structures:\n",
+        "* `Series` objects, that we will discuss now. A `Series` object is 1D array, similar to a column in a spreadsheet (with a column name and row labels).\n",
+        "* `DataFrame` objects. This is a 2D table, similar to a spreadsheet (with column names and row labels).\n",
+        "* `Panel` objects. You can see a `Panel` as a dictionary of `DataFrame`s. These are less used, so we will not discuss them here."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FP6PbRki3k01"
+      },
+      "source": [
+        "## Creating a `Series`\n",
+        "Let's start by creating our first `Series` object!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "x_rstimi3k02"
+      },
+      "outputs": [],
+      "source": [
+        "s = pd.Series([2,-1,3,5])\n",
+        "s"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NtO-q4F53k04"
+      },
+      "source": [
+        "## Similar to a 1D `ndarray`\n",
+        "`Series` objects behave much like one-dimensional NumPy `ndarray`s, and you can often pass them as parameters to NumPy functions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "wPdHgKq43k04"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "np.exp(s)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d1Tc_FbF3k08"
+      },
+      "source": [
+        "Arithmetic operations on `Series` are also possible, and they apply *elementwise*, just like for `ndarray`s:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9wOWnU_x3k09"
+      },
+      "outputs": [],
+      "source": [
+        "s + [1000,2000,3000,4000]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jn5R100P3k0-"
+      },
+      "source": [
+        "Similar to NumPy, if you add a single number to a `Series`, that number is added to all items in the `Series`. This is called * broadcasting*:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qSGDPZj_3k0-"
+      },
+      "outputs": [],
+      "source": [
+        "s + 1000"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "o1trcau93k1D"
+      },
+      "source": [
+        "The same is true for all binary operations such as `*` or `/`, and even conditional operations:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LBpQWpym3k1E"
+      },
+      "outputs": [],
+      "source": [
+        "s < 0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-H_Hs6f33k1F"
+      },
+      "source": [
+        "## Index labels\n",
+        "Each item in a `Series` object has a unique identifier called the *index label*. By default, it is simply the rank of the item in the `Series` (starting from `0`) but you can also set the index labels manually:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-wmmuXXh3k1I"
+      },
+      "outputs": [],
+      "source": [
+        "s2 = pd.Series([68, 83, 112, 68], index=[\"alice\", \"bob\", \"charles\", \"darwin\"])\n",
+        "s2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "liK_2bCp3k2G"
+      },
+      "source": [
+        "You can then use the `Series` just like a `dict`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jeYIu5T13k2H"
+      },
+      "outputs": [],
+      "source": [
+        "s2[\"bob\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h929Rgk03k2H"
+      },
+      "source": [
+        "To make it clear when you are accessing by label or by integer location, it is recommended to always use the `loc` attribute when accessing by label, and the `iloc` attribute when accessing by integer location:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OukUPmCi3k2H"
+      },
+      "outputs": [],
+      "source": [
+        "s2.loc[\"bob\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rdJze3xF3k2H"
+      },
+      "outputs": [],
+      "source": [
+        "s2.iloc[1]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wP3Si83d3k2I"
+      },
+      "source": [
+        "Slicing a `Series` also slices the index labels:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gsTJK2-x3k2I"
+      },
+      "outputs": [],
+      "source": [
+        "s2.iloc[1:3]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GatKOUBA3k2I"
+      },
+      "source": [
+        "This can lead to unexpected results when using the default numeric labels, so be careful:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UZJYL_sn3k2I"
+      },
+      "outputs": [],
+      "source": [
+        "surprise = pd.Series([1000, 1001, 1002, 1003])\n",
+        "surprise"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LIhufEhc3k2I"
+      },
+      "outputs": [],
+      "source": [
+        "surprise_slice = surprise[2:]\n",
+        "surprise_slice"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sxqs3uUt3k2I"
+      },
+      "source": [
+        "Oh, look! The first element has index label `2`. The element with index label `0` is absent from the slice:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oO6NBfPz3k2I"
+      },
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    surprise_slice[0]\n",
+        "except KeyError as e:\n",
+        "    print(\"Key error:\", e)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yAZuwT3f3k2J"
+      },
+      "source": [
+        "But remember that you can access elements by integer location using the `iloc` attribute. This illustrates another reason why it's always better to use `loc` and `iloc` to access `Series` objects:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nwnEYwES3k2J"
+      },
+      "outputs": [],
+      "source": [
+        "surprise_slice.iloc[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gTR6nqaZ3k2M"
+      },
+      "source": [
+        "## Init from `dict`\n",
+        "You can create a `Series` object from a `dict`. The keys will be used as index labels:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TGqtCH713k2N"
+      },
+      "outputs": [],
+      "source": [
+        "weights = {\"alice\": 68, \"bob\": 83, \"colin\": 86, \"darwin\": 68}\n",
+        "s3 = pd.Series(weights)\n",
+        "s3"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2j6V-B9J3k2N"
+      },
+      "source": [
+        "You can control which elements you want to include in the `Series` and in what order by explicitly specifying the desired `index`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MIqK_Ymt3k2N"
+      },
+      "outputs": [],
+      "source": [
+        "s4 = pd.Series(weights, index = [\"colin\", \"alice\"])\n",
+        "s4"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ajKmTDWu3k2O"
+      },
+      "source": [
+        "## Automatic alignment\n",
+        "When an operation involves multiple `Series` objects, `pandas` automatically aligns items by matching index labels."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CS_J9UpH3k2O"
+      },
+      "outputs": [],
+      "source": [
+        "print(s2.keys())\n",
+        "print(s3.keys())\n",
+        "\n",
+        "s2 + s3"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hT0CUy9J3k2O"
+      },
+      "source": [
+        "The resulting `Series` contains the union of index labels from `s2` and `s3`. Since `\"colin\"` is missing from `s2` and `\"charles\"` is missing from `s3`, these items have a `NaN` result value (i.e. Not-a-Number means *missing*).\n",
+        "\n",
+        "Automatic alignment is very handy when working with data that may come from various sources with varying structure and missing items. But if you forget to set the right index labels, you can have surprising results:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZodQQ1U23k2P"
+      },
+      "outputs": [],
+      "source": [
+        "s5 = pd.Series([1000,1000,1000,1000])\n",
+        "print(\"s2 =\", s2.values)\n",
+        "print(\"s5 =\", s5.values)\n",
+        "\n",
+        "s2 + s5"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wfEXlPn83k2R"
+      },
+      "source": [
+        "Pandas could not align the `Series`, since their labels do not match at all, hence the full `NaN` result."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qcY0T3cH3k2S"
+      },
+      "source": [
+        "## Init with a scalar\n",
+        "You can also initialize a `Series` object using a scalar and a list of index labels: all items will be set to the scalar."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "185BLjl83k2S"
+      },
+      "outputs": [],
+      "source": [
+        "meaning = pd.Series(42, [\"life\", \"universe\", \"everything\"])\n",
+        "meaning"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Isw4A3Ks3k2S"
+      },
+      "source": [
+        "## `Series` name\n",
+        "A `Series` can have a `name`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IRcOsiT13k2T"
+      },
+      "outputs": [],
+      "source": [
+        "s6 = pd.Series([83, 68], index=[\"bob\", \"alice\"], name=\"weights\")\n",
+        "s6"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "frR1f5K63k2T"
+      },
+      "source": [
+        "## Plotting a `Series`\n",
+        "Pandas makes it easy to plot `Series` data using matplotlib (for more details on matplotlib, check out the [matplotlib tutorial](tools_matplotlib.ipynb)). Just import matplotlib and call the `plot()` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "id": "Vp_wJGUH3k2T",
+        "outputId": "ae6d2c60-140a-4193-e839-17c6a20b31ba"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "temperatures = [4.4,5.1,6.1,6.2,6.1,6.1,5.7,5.2,4.7,4.1,3.9,3.5]\n",
+        "s7 = pd.Series(temperatures, name=\"Temperature\")\n",
+        "s7.plot()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VjrGrqoz3k2T"
+      },
+      "source": [
+        "There are *many* options for plotting your data. It is not necessary to list them all here: if you need a particular type of plot (histograms, pie charts, etc.), just look for it in the excellent [Visualization](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html) section of pandas' documentation, and look at the example code."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YcqbZ3Hx3k2U"
+      },
+      "source": [
+        "# Handling time\n",
+        "Many datasets have timestamps, and pandas is awesome at manipulating such data:\n",
+        "* it can represent periods (such as 2016Q3) and frequencies (such as \"monthly\"),\n",
+        "* it can convert periods to actual timestamps, and *vice versa*,\n",
+        "* it can resample data and aggregate values any way you like,\n",
+        "* it can handle timezones.\n",
+        "\n",
+        "## Time range\n",
+        "Let's start by creating a time series using `pd.date_range()`. It returns a `DatetimeIndex` containing one datetime per hour for 12 hours starting on October 29th 2016 at 5:30pm."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Bi4EHcaS3k2U",
+        "outputId": "41b7c698-3805-4ecd-b8a9-6b9ae18729e2"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DatetimeIndex(['2016-10-29 17:30:00', '2016-10-29 18:30:00',\n",
+              "               '2016-10-29 19:30:00', '2016-10-29 20:30:00',\n",
+              "               '2016-10-29 21:30:00', '2016-10-29 22:30:00',\n",
+              "               '2016-10-29 23:30:00', '2016-10-30 00:30:00',\n",
+              "               '2016-10-30 01:30:00', '2016-10-30 02:30:00',\n",
+              "               '2016-10-30 03:30:00', '2016-10-30 04:30:00'],\n",
+              "              dtype='datetime64[ns]', freq='h')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='h')\n",
+        "dates"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BEfNCVvS3k2U"
+      },
+      "source": [
+        "This `DatetimeIndex` may be used as an index in a `Series`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 460
+        },
+        "id": "OCslif2B3k2U",
+        "outputId": "7db8114e-1faf-493a-ec17-a71814a7fced"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2016-10-29 17:30:00    4.4\n",
+              "2016-10-29 18:30:00    5.1\n",
+              "2016-10-29 19:30:00    6.1\n",
+              "2016-10-29 20:30:00    6.2\n",
+              "2016-10-29 21:30:00    6.1\n",
+              "2016-10-29 22:30:00    6.1\n",
+              "2016-10-29 23:30:00    5.7\n",
+              "2016-10-30 00:30:00    5.2\n",
+              "2016-10-30 01:30:00    4.7\n",
+              "2016-10-30 02:30:00    4.1\n",
+              "2016-10-30 03:30:00    3.9\n",
+              "2016-10-30 04:30:00    3.5\n",
+              "Freq: h, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 17:30:00</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 18:30:00</th>\n",
+              "      <td>5.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 19:30:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 20:30:00</th>\n",
+              "      <td>6.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 21:30:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 22:30:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 23:30:00</th>\n",
+              "      <td>5.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 00:30:00</th>\n",
+              "      <td>5.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 01:30:00</th>\n",
+              "      <td>4.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 02:30:00</th>\n",
+              "      <td>4.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 03:30:00</th>\n",
+              "      <td>3.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 04:30:00</th>\n",
+              "      <td>3.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "temp_series = pd.Series(temperatures, dates)\n",
+        "temp_series"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "temp_series.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7aSc5_By4Ah7",
+        "outputId": "df9fddd6-c8cf-4234-a7f4-59b1699d54ae"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.series.Series'>\n",
+            "DatetimeIndex: 12 entries, 2016-10-29 17:30:00 to 2016-10-30 04:30:00\n",
+            "Freq: h\n",
+            "Series name: None\n",
+            "Non-Null Count  Dtype  \n",
+            "--------------  -----  \n",
+            "12 non-null     float64\n",
+            "dtypes: float64(1)\n",
+            "memory usage: 192.0 bytes\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RnY8CoAx3k2V"
+      },
+      "source": [
+        "Let's plot this series:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 562
+        },
+        "id": "wVlgZulX3k2W",
+        "outputId": "d41abf78-8aa1-4cdc-f9a0-bec2276e2ea3"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "temp_series.plot(kind=\"bar\")\n",
+        "\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "md6Q7UUq3k2W"
+      },
+      "source": [
+        "## Resampling\n",
+        "Pandas lets us resample a time series very simply. Just call the `resample()` method and specify a new frequency:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oWkcplZN3k2W",
+        "outputId": "b27ebdeb-3cec-46bf-e34e-b99929b048cc"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<pandas.core.resample.DatetimeIndexResampler object at 0x7b75672e56d0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "temp_series_freq_2h = temp_series.resample(\"2h\")\n",
+        "temp_series_freq_2h"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fwqvfATZ3k2X"
+      },
+      "source": [
+        "The resampling operation is actually a deferred operation, which is why we did not get a `Series` object, but a `DatetimeIndexResampler` object instead. To actually perform the resampling operation, we can simply call the `mean()` method. Pandas will compute the mean of every pair of consecutive hours:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tBdl-HQC3k2X"
+      },
+      "outputs": [],
+      "source": [
+        "temp_series_freq_2h = temp_series_freq_2h.mean()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sNFRR0ke3k2Y"
+      },
+      "source": [
+        "Let's plot the result:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 562
+        },
+        "id": "DV5Nn5kB3k2Y",
+        "outputId": "1b760b98-07bd-447d-ee8b-62edd85ab615"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "temp_series_freq_2h.plot(kind=\"bar\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hb1JkUEj3k2Y"
+      },
+      "source": [
+        "Note how the values have automatically been aggregated into 2-hour periods. If we look at the 6-8pm period, for example, we had a value of `5.1` at 6:30pm, and `6.1` at 7:30pm. After resampling, we just have one value of `5.6`, which is the mean of `5.1` and `6.1`. Rather than computing the mean, we could have used any other aggregation function, for example we can decide to keep the minimum value of each period:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 303
+        },
+        "id": "bRR63ICJ3k2Z",
+        "outputId": "9c25b946-d2b6-430d-bead-10fa89b96ec0"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2016-10-29 16:00:00    4.4\n",
+              "2016-10-29 18:00:00    5.1\n",
+              "2016-10-29 20:00:00    6.1\n",
+              "2016-10-29 22:00:00    5.7\n",
+              "2016-10-30 00:00:00    4.7\n",
+              "2016-10-30 02:00:00    3.9\n",
+              "2016-10-30 04:00:00    3.5\n",
+              "Freq: 2h, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 16:00:00</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 18:00:00</th>\n",
+              "      <td>5.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 20:00:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 22:00:00</th>\n",
+              "      <td>5.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 00:00:00</th>\n",
+              "      <td>4.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 02:00:00</th>\n",
+              "      <td>3.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 04:00:00</th>\n",
+              "      <td>3.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ],
+      "source": [
+        "temp_series_freq_2h = temp_series.resample(\"2h\").min()\n",
+        "temp_series_freq_2h"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V6VWfFFd3k2a"
+      },
+      "source": [
+        "Or, equivalently, we could use the `apply()` method instead:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 303
+        },
+        "id": "10SYqnIY3k2a",
+        "outputId": "aba2870e-8101-42e8-c78a-8963fa894851"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2016-10-29 16:00:00    4.4\n",
+              "2016-10-29 18:00:00    5.1\n",
+              "2016-10-29 20:00:00    6.1\n",
+              "2016-10-29 22:00:00    5.7\n",
+              "2016-10-30 00:00:00    4.7\n",
+              "2016-10-30 02:00:00    3.9\n",
+              "2016-10-30 04:00:00    3.5\n",
+              "Freq: 2h, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 16:00:00</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 18:00:00</th>\n",
+              "      <td>5.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 20:00:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 22:00:00</th>\n",
+              "      <td>5.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 00:00:00</th>\n",
+              "      <td>4.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 02:00:00</th>\n",
+              "      <td>3.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 04:00:00</th>\n",
+              "      <td>3.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "temp_series_freq_2h = temp_series.resample(\"2h\").apply(\"min\")\n",
+        "temp_series_freq_2h"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JMfMse2q3k2a"
+      },
+      "source": [
+        "## Upsampling and interpolation\n",
+        "It was an example of downsampling. We can also upsample (i.e. increase the frequency), but it will create holes in our data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 397
+        },
+        "id": "Pw8oeyo13k2a",
+        "outputId": "bbc5b305-edfc-45ee-e10c-72dcb98cf8ad"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2016-10-29 17:30:00    4.4\n",
+              "2016-10-29 17:45:00    NaN\n",
+              "2016-10-29 18:00:00    NaN\n",
+              "2016-10-29 18:15:00    NaN\n",
+              "2016-10-29 18:30:00    5.1\n",
+              "2016-10-29 18:45:00    NaN\n",
+              "2016-10-29 19:00:00    NaN\n",
+              "2016-10-29 19:15:00    NaN\n",
+              "2016-10-29 19:30:00    6.1\n",
+              "2016-10-29 19:45:00    NaN\n",
+              "Freq: 15min, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 17:30:00</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 17:45:00</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 18:00:00</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 18:15:00</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 18:30:00</th>\n",
+              "      <td>5.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 18:45:00</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 19:00:00</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 19:15:00</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 19:30:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 19:45:00</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ],
+      "source": [
+        "temp_series_freq_15min = temp_series.resample(\"15Min\").min()\n",
+        "temp_series_freq_15min.head(n=10) # `head` displays the top n values"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2lwjWx4h3k2a"
+      },
+      "source": [
+        "One solution is to fill the gaps by interpolating. We just call the `interpolate()` method. The default is to use linear interpolation, but we can also select another method, such as cubic interpolation or even splines:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 397
+        },
+        "id": "3mWtCXZ83k2b",
+        "outputId": "d40224b6-43d0-4811-a576-00dc88265ff0"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2016-10-29 17:30:00    4.400000\n",
+              "2016-10-29 17:45:00    4.607135\n",
+              "2016-10-29 18:00:00    4.868166\n",
+              "2016-10-29 18:15:00    5.103398\n",
+              "2016-10-29 18:30:00    5.100000\n",
+              "2016-10-29 18:45:00    5.500022\n",
+              "2016-10-29 19:00:00    5.663194\n",
+              "2016-10-29 19:15:00    5.804127\n",
+              "2016-10-29 19:30:00    6.100000\n",
+              "2016-10-29 19:45:00    6.022832\n",
+              "Freq: 15min, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 17:30:00</th>\n",
+              "      <td>4.400000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 17:45:00</th>\n",
+              "      <td>4.607135</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 18:00:00</th>\n",
+              "      <td>4.868166</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 18:15:00</th>\n",
+              "      <td>5.103398</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 18:30:00</th>\n",
+              "      <td>5.100000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 18:45:00</th>\n",
+              "      <td>5.500022</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 19:00:00</th>\n",
+              "      <td>5.663194</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 19:15:00</th>\n",
+              "      <td>5.804127</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 19:30:00</th>\n",
+              "      <td>6.100000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 19:45:00</th>\n",
+              "      <td>6.022832</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ],
+      "source": [
+        "temp_series_freq_15min = temp_series.resample(\"15Min\").interpolate(method=\"spline\", order=3)\n",
+        "temp_series_freq_15min.head(n=10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "x9B60Iht3k2b",
+        "outputId": "fd88a28d-bcb4-435f-8164-a015aafeed1d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "temp_series.plot(label=\"Period: 1 hour\")\n",
+        "temp_series_freq_15min.plot(label=\"Period: 15 minutes\")\n",
+        "#temp_series_freq_15min.resample(\"1h\").mean().plot(label=\"Period: 1 hour but recons\")\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q43GAn5C3k2b"
+      },
+      "source": [
+        "## Timezones\n",
+        "By default, datetimes are *naive*: they are not aware of timezones, so 2016-10-30 02:30 might mean October 30th 2016 at 2:30am in Paris or in New York. We can make datetimes timezone *aware* by calling the `tz_localize()` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 460
+        },
+        "id": "4p17aqS73k2c",
+        "outputId": "af3a2304-76bf-4074-cfe3-e7c30e41175d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2016-10-29 17:30:00-04:00    4.4\n",
+              "2016-10-29 18:30:00-04:00    5.1\n",
+              "2016-10-29 19:30:00-04:00    6.1\n",
+              "2016-10-29 20:30:00-04:00    6.2\n",
+              "2016-10-29 21:30:00-04:00    6.1\n",
+              "2016-10-29 22:30:00-04:00    6.1\n",
+              "2016-10-29 23:30:00-04:00    5.7\n",
+              "2016-10-30 00:30:00-04:00    5.2\n",
+              "2016-10-30 01:30:00-04:00    4.7\n",
+              "2016-10-30 02:30:00-04:00    4.1\n",
+              "2016-10-30 03:30:00-04:00    3.9\n",
+              "2016-10-30 04:30:00-04:00    3.5\n",
+              "dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 17:30:00-04:00</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 18:30:00-04:00</th>\n",
+              "      <td>5.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 19:30:00-04:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 20:30:00-04:00</th>\n",
+              "      <td>6.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 21:30:00-04:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 22:30:00-04:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 23:30:00-04:00</th>\n",
+              "      <td>5.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 00:30:00-04:00</th>\n",
+              "      <td>5.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 01:30:00-04:00</th>\n",
+              "      <td>4.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 02:30:00-04:00</th>\n",
+              "      <td>4.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 03:30:00-04:00</th>\n",
+              "      <td>3.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 04:30:00-04:00</th>\n",
+              "      <td>3.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ],
+      "source": [
+        "temp_series_ny = temp_series.tz_localize(\"America/New_York\")\n",
+        "temp_series_ny"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aglbPxxk3k2c"
+      },
+      "source": [
+        "Note that `-04:00` is now appended to all the datetimes. It means that these datetimes refer to [UTC](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) - 4 hours.\n",
+        "\n",
+        "We can convert these datetimes to Paris time like this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 460
+        },
+        "id": "Yqpp0K9f3k2c",
+        "outputId": "346ea418-b56a-4023-a56e-7be7027a19d1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2016-10-29 23:30:00+02:00    4.4\n",
+              "2016-10-30 00:30:00+02:00    5.1\n",
+              "2016-10-30 01:30:00+02:00    6.1\n",
+              "2016-10-30 02:30:00+02:00    6.2\n",
+              "2016-10-30 02:30:00+01:00    6.1\n",
+              "2016-10-30 03:30:00+01:00    6.1\n",
+              "2016-10-30 04:30:00+01:00    5.7\n",
+              "2016-10-30 05:30:00+01:00    5.2\n",
+              "2016-10-30 06:30:00+01:00    4.7\n",
+              "2016-10-30 07:30:00+01:00    4.1\n",
+              "2016-10-30 08:30:00+01:00    3.9\n",
+              "2016-10-30 09:30:00+01:00    3.5\n",
+              "dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 23:30:00+02:00</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 00:30:00+02:00</th>\n",
+              "      <td>5.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 01:30:00+02:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 02:30:00+02:00</th>\n",
+              "      <td>6.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 02:30:00+01:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 03:30:00+01:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 04:30:00+01:00</th>\n",
+              "      <td>5.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 05:30:00+01:00</th>\n",
+              "      <td>5.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 06:30:00+01:00</th>\n",
+              "      <td>4.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 07:30:00+01:00</th>\n",
+              "      <td>4.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 08:30:00+01:00</th>\n",
+              "      <td>3.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 09:30:00+01:00</th>\n",
+              "      <td>3.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ],
+      "source": [
+        "temp_series_paris = temp_series_ny.tz_convert(\"Europe/Paris\")\n",
+        "temp_series_paris"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TjH4Xku73k2d"
+      },
+      "source": [
+        "You may have noticed that the UTC offset changes from `+02:00` to `+01:00`: this is because France switches to winter time at 3am that particular night (time goes back to 2am). Notice that 2:30am occurs twice! Let's go back to a naive representation (if you log some data hourly using local time, without storing the timezone, you might get something like this):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 460
+        },
+        "id": "wCuipOw13k2d",
+        "outputId": "0683f6f2-15e6-4561-f157-b49acdb5cd94"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2016-10-29 23:30:00    4.4\n",
+              "2016-10-30 00:30:00    5.1\n",
+              "2016-10-30 01:30:00    6.1\n",
+              "2016-10-30 02:30:00    6.2\n",
+              "2016-10-30 02:30:00    6.1\n",
+              "2016-10-30 03:30:00    6.1\n",
+              "2016-10-30 04:30:00    5.7\n",
+              "2016-10-30 05:30:00    5.2\n",
+              "2016-10-30 06:30:00    4.7\n",
+              "2016-10-30 07:30:00    4.1\n",
+              "2016-10-30 08:30:00    3.9\n",
+              "2016-10-30 09:30:00    3.5\n",
+              "dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 23:30:00</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 00:30:00</th>\n",
+              "      <td>5.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 01:30:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 02:30:00</th>\n",
+              "      <td>6.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 02:30:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 03:30:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 04:30:00</th>\n",
+              "      <td>5.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 05:30:00</th>\n",
+              "      <td>5.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 06:30:00</th>\n",
+              "      <td>4.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 07:30:00</th>\n",
+              "      <td>4.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 08:30:00</th>\n",
+              "      <td>3.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 09:30:00</th>\n",
+              "      <td>3.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ],
+      "source": [
+        "temp_series_paris_naive = temp_series_paris.tz_localize(None)\n",
+        "temp_series_paris_naive"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XV6sddea3k2d"
+      },
+      "source": [
+        "Now `02:30` is really ambiguous. If we try to localize these naive datetimes to the Paris timezone, we get an error:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DlL-91Jl3k2e",
+        "outputId": "eefa26ea-2c25-4952-8e5a-2c30cc6d3e09"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pytz.exceptions.AmbiguousTimeError'>\n",
+            "Cannot infer dst time from 2016-10-30 02:30:00, try using the 'ambiguous' argument\n"
+          ]
+        }
+      ],
+      "source": [
+        "try:\n",
+        "    temp_series_paris_naive.tz_localize(\"Europe/Paris\")\n",
+        "except Exception as e:\n",
+        "    print(type(e))\n",
+        "    print(e)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "howphgzb3k2h"
+      },
+      "source": [
+        "Fortunately, by using the `ambiguous` argument we can tell pandas to infer the right DST (Daylight Saving Time) based on the order of the ambiguous timestamps:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 460
+        },
+        "id": "MVmI6Ibw3k2i",
+        "outputId": "d14d2714-1f3c-46fa-9434-b8277758d67b"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2016-10-29 23:30:00+02:00    4.4\n",
+              "2016-10-30 00:30:00+02:00    5.1\n",
+              "2016-10-30 01:30:00+02:00    6.1\n",
+              "2016-10-30 02:30:00+02:00    6.2\n",
+              "2016-10-30 02:30:00+01:00    6.1\n",
+              "2016-10-30 03:30:00+01:00    6.1\n",
+              "2016-10-30 04:30:00+01:00    5.7\n",
+              "2016-10-30 05:30:00+01:00    5.2\n",
+              "2016-10-30 06:30:00+01:00    4.7\n",
+              "2016-10-30 07:30:00+01:00    4.1\n",
+              "2016-10-30 08:30:00+01:00    3.9\n",
+              "2016-10-30 09:30:00+01:00    3.5\n",
+              "dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016-10-29 23:30:00+02:00</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 00:30:00+02:00</th>\n",
+              "      <td>5.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 01:30:00+02:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 02:30:00+02:00</th>\n",
+              "      <td>6.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 02:30:00+01:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 03:30:00+01:00</th>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 04:30:00+01:00</th>\n",
+              "      <td>5.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 05:30:00+01:00</th>\n",
+              "      <td>5.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 06:30:00+01:00</th>\n",
+              "      <td>4.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 07:30:00+01:00</th>\n",
+              "      <td>4.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 08:30:00+01:00</th>\n",
+              "      <td>3.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-10-30 09:30:00+01:00</th>\n",
+              "      <td>3.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ],
+      "source": [
+        "temp_series_paris_naive.tz_localize(\"Europe/Paris\", ambiguous=\"infer\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XPJxa3Sd3k2i"
+      },
+      "source": [
+        "## Periods\n",
+        "The `pd.period_range()` function returns a `PeriodIndex` instead of a `DatetimeIndex`. For example, let's get all quarters in 2016 and 2017:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8EI6lIpg3k2l",
+        "outputId": "c7953faa-c3e4-46cb-bfbd-95cc3a482465"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PeriodIndex(['2016Q1', '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2',\n",
+              "             '2017Q3', '2017Q4'],\n",
+              "            dtype='period[Q-DEC]')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ],
+      "source": [
+        "quarters = pd.period_range('2016Q1', periods=8, freq='Q')\n",
+        "quarters"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C2v0ZewA3k2m"
+      },
+      "source": [
+        "Adding a number `N` to a `PeriodIndex` shifts the periods by `N` times the `PeriodIndex`'s frequency:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nAxKe8Dt3k2m",
+        "outputId": "cdd18ba2-e2bb-45f0-92d7-d0a60a4e08d6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PeriodIndex(['2016Q4', '2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1',\n",
+              "             '2018Q2', '2018Q3'],\n",
+              "            dtype='period[Q-DEC]')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ],
+      "source": [
+        "quarters + 3"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tznrbm-73k2n"
+      },
+      "source": [
+        "The `asfreq()` method lets us change the frequency of the `PeriodIndex`. All periods are lengthened or shortened accordingly. For example, let's convert all the quarterly periods to monthly periods (zooming in):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HbEEequp3k2o",
+        "outputId": "806d87bf-1e9a-434b-d852-a3729e3f8c5c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PeriodIndex(['2016-03', '2016-06', '2016-09', '2016-12', '2017-03', '2017-06',\n",
+              "             '2017-09', '2017-12'],\n",
+              "            dtype='period[M]')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 43
+        }
+      ],
+      "source": [
+        "quarters.asfreq(\"M\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FlSFamaE3k2o"
+      },
+      "source": [
+        "By default, the `asfreq` zooms on the end of each period. We can tell it to zoom on the start of each period instead:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "llFAgDCx3k2o",
+        "outputId": "57b4a913-a6de-4115-a1b8-02ecb731236e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PeriodIndex(['2016-01', '2016-04', '2016-07', '2016-10', '2017-01', '2017-04',\n",
+              "             '2017-07', '2017-10'],\n",
+              "            dtype='period[M]')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ],
+      "source": [
+        "quarters.asfreq(\"M\", how=\"start\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h1DV5evu3k2o"
+      },
+      "source": [
+        "And we can zoom out:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QvUwZxMA3k2o",
+        "outputId": "86cc8962-2455-4053-ef53-abfade9f41f2"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PeriodIndex(['2016', '2016', '2016', '2016', '2017', '2017', '2017', '2017'], dtype='period[Y-DEC]')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ],
+      "source": [
+        "quarters.asfreq(\"Y\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gVE9oRo33k2p"
+      },
+      "source": [
+        "Of course, we can create a `Series` with a `PeriodIndex`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 335
+        },
+        "id": "hIAxS4wB3k2p",
+        "outputId": "3ec2adfb-437c-41bc-b22c-dcd313eaa4af"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2016Q1    300\n",
+              "2016Q2    320\n",
+              "2016Q3    290\n",
+              "2016Q4    390\n",
+              "2017Q1    320\n",
+              "2017Q2    360\n",
+              "2017Q3    310\n",
+              "2017Q4    410\n",
+              "Freq: Q-DEC, Name: Q Revenue, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Q Revenue</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016Q1</th>\n",
+              "      <td>300</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016Q2</th>\n",
+              "      <td>320</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016Q3</th>\n",
+              "      <td>290</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016Q4</th>\n",
+              "      <td>390</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017Q1</th>\n",
+              "      <td>320</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017Q2</th>\n",
+              "      <td>360</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017Q3</th>\n",
+              "      <td>310</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017Q4</th>\n",
+              "      <td>410</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 47
+        }
+      ],
+      "source": [
+        "quarterly_revenue = pd.Series([300, 320, 290, 390, 320, 360, 310, 410], index=quarters, name = \"Q Revenue\")\n",
+        "quarterly_revenue"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 468
+        },
+        "id": "Oa27x_723k2p",
+        "outputId": "5a155601-2ece-4680-f404-6f670f7af799"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "quarterly_revenue.plot(kind=\"line\", title = \"Quarterly Performance\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "quarterly_revenue_and_expenses = pd.DataFrame(\n",
+        "    {\"revenue\": quarterly_revenue, \"expenses\": quarterly_revenue *0.2}\n",
+        ")\n",
+        "quarterly_revenue_and_expenses.plot(kind = \"line\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 463
+        },
+        "id": "O3Qqr3uf-TnL",
+        "outputId": "4010f12b-10c3-4481-c2bb-2169d40c3a7e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 57
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n3n_UaLO3k2p"
+      },
+      "source": [
+        "We can convert periods to timestamps by calling `to_timestamp`. By default, it will give us the first day of each period, but by setting `how` and `freq`, we can get the last hour of each period:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 335
+        },
+        "id": "yHNocn223k2p",
+        "outputId": "27877428-3032-46da-8817-4e899dcf0158"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2016-03-31 23:59:59.999999999    300\n",
+              "2016-06-30 23:59:59.999999999    320\n",
+              "2016-09-30 23:59:59.999999999    290\n",
+              "2016-12-31 23:59:59.999999999    390\n",
+              "2017-03-31 23:59:59.999999999    320\n",
+              "2017-06-30 23:59:59.999999999    360\n",
+              "2017-09-30 23:59:59.999999999    310\n",
+              "2017-12-31 23:59:59.999999999    410\n",
+              "Name: Q Revenue, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Q Revenue</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016-03-31 23:59:59.999999999</th>\n",
+              "      <td>300</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-06-30 23:59:59.999999999</th>\n",
+              "      <td>320</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-09-30 23:59:59.999999999</th>\n",
+              "      <td>290</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-12-31 23:59:59.999999999</th>\n",
+              "      <td>390</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-03-31 23:59:59.999999999</th>\n",
+              "      <td>320</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-06-30 23:59:59.999999999</th>\n",
+              "      <td>360</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-09-30 23:59:59.999999999</th>\n",
+              "      <td>310</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-12-31 23:59:59.999999999</th>\n",
+              "      <td>410</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 58
+        }
+      ],
+      "source": [
+        "last_hours = quarterly_revenue.to_timestamp(how=\"end\", freq=\"h\")\n",
+        "last_hours"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ri2ucNa-3k2p"
+      },
+      "source": [
+        "And back to periods by calling `to_period`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 335
+        },
+        "id": "6T9vKToF3k2p",
+        "outputId": "a579a210-1c00-4d04-e020-fdb95c17641a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2016Q1    300\n",
+              "2016Q2    320\n",
+              "2016Q3    290\n",
+              "2016Q4    390\n",
+              "2017Q1    320\n",
+              "2017Q2    360\n",
+              "2017Q3    310\n",
+              "2017Q4    410\n",
+              "Freq: Q-DEC, Name: Q Revenue, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Q Revenue</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016Q1</th>\n",
+              "      <td>300</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016Q2</th>\n",
+              "      <td>320</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016Q3</th>\n",
+              "      <td>290</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016Q4</th>\n",
+              "      <td>390</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017Q1</th>\n",
+              "      <td>320</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017Q2</th>\n",
+              "      <td>360</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017Q3</th>\n",
+              "      <td>310</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017Q4</th>\n",
+              "      <td>410</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 59
+        }
+      ],
+      "source": [
+        "last_hours.to_period()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "O-NzSdml3k2p"
+      },
+      "source": [
+        "Pandas also provides many other time-related functions that we recommend you check out in the [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html). To whet your appetite, here is one way to get the last business day of each month in 2016, at 9am:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iiTG9cN53k2q",
+        "outputId": "cbd941de-ebef-44ca-fb53-c939b2857b38"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PeriodIndex(['2016-01-29 09:00', '2016-02-29 09:00', '2016-03-31 09:00',\n",
+              "             '2016-04-29 09:00', '2016-05-31 09:00', '2016-06-30 09:00',\n",
+              "             '2016-07-29 09:00', '2016-08-31 09:00', '2016-09-30 09:00',\n",
+              "             '2016-10-31 09:00', '2016-11-30 09:00', '2016-12-30 09:00'],\n",
+              "            dtype='period[h]')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ],
+      "source": [
+        "months_2016 = pd.period_range(\"2016\", periods=12, freq=\"M\")\n",
+        "one_day_after_last_days = months_2016.asfreq(\"D\") + 1\n",
+        "last_bdays = one_day_after_last_days.to_timestamp() - pd.tseries.offsets.BDay()\n",
+        "last_bdays.to_period(\"h\") + 9"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8iwepcKu3k2q"
+      },
+      "source": [
+        "# `DataFrame` objects\n",
+        "A DataFrame object represents a spreadsheet, with cell values, column names and row index labels. You can define expressions to compute columns based on other columns, create pivot-tables, group rows, draw graphs, etc. You can see `DataFrame`s as dictionaries of `Series`.\n",
+        "\n",
+        "## Creating a `DataFrame`\n",
+        "You can create a DataFrame by passing a dictionary of `Series` objects:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "id": "SREq0QKC3k2q",
+        "outputId": "af6e4de5-add7-4d47-dec6-e58544ae2280"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         weight  birthyear  children    hobby\n",
+              "alice        68       1985       NaN   Biking\n",
+              "bob          83       1984       3.0  Dancing\n",
+              "charles     112       1992       0.0      NaN"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a5f6281d-a69c-44f3-a891-9bdb2f1a283d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>weight</th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>children</th>\n",
+              "      <th>hobby</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>68</td>\n",
+              "      <td>1985</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Biking</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>83</td>\n",
+              "      <td>1984</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>Dancing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>112</td>\n",
+              "      <td>1992</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5f6281d-a69c-44f3-a891-9bdb2f1a283d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a5f6281d-a69c-44f3-a891-9bdb2f1a283d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a5f6281d-a69c-44f3-a891-9bdb2f1a283d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-738b3b71-e99f-4af3-934f-8324b7bec5c6\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-738b3b71-e99f-4af3-934f-8324b7bec5c6')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-738b3b71-e99f-4af3-934f-8324b7bec5c6 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_9f2fd3ff-366e-4bfa-993c-932b62de79be\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('people')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_9f2fd3ff-366e-4bfa-993c-932b62de79be button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('people');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "people",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1984,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1985,\n          1984,\n          1992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1213203435596424,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ],
+      "source": [
+        "people_dict = {\n",
+        "    \"weight\": pd.Series([68, 83, 112], index=[\"alice\", \"bob\", \"charles\"]),\n",
+        "    \"birthyear\": pd.Series([1984, 1985, 1992], index=[\"bob\", \"alice\", \"charles\"], name=\"year\"),\n",
+        "    \"children\": pd.Series([0, 3], index=[\"charles\", \"bob\"]),\n",
+        "    \"hobby\": pd.Series([\"Biking\", \"Dancing\"], index=[\"alice\", \"bob\"]),\n",
+        "}\n",
+        "people = pd.DataFrame(people_dict)\n",
+        "people"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ftdghbIf3k2q"
+      },
+      "source": [
+        "A few things to note:\n",
+        "* the `Series` were automatically aligned based on their index,\n",
+        "* missing values are represented as `NaN`,\n",
+        "* `Series` names are ignored (the name `\"year\"` was dropped),\n",
+        "* `DataFrame`s are displayed nicely in Jupyter notebooks, woohoo!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "exPJdPwL3k2q"
+      },
+      "source": [
+        "You can access columns pretty much as you would expect. They are returned as `Series` objects:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 193
+        },
+        "id": "ZAW1NQex3k2q",
+        "outputId": "90eefde3-b51c-41ef-cdcc-226e07df907e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "alice      1985\n",
+              "bob        1984\n",
+              "charles    1992\n",
+              "Name: birthyear, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
+      "source": [
+        "people[\"birthyear\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pXDxCVXi3k2r"
+      },
+      "source": [
+        "You can also get multiple columns at once:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "id": "ECNOiihD3k2r",
+        "outputId": "82c53ebb-5dda-4929-9c69-a7b3a63ba84c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         birthyear    hobby\n",
+              "alice         1985   Biking\n",
+              "bob           1984  Dancing\n",
+              "charles       1992      NaN"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-124f80bc-8f11-43fe-af92-41ac264983ff\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>hobby</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>Biking</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>Dancing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-124f80bc-8f11-43fe-af92-41ac264983ff')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-124f80bc-8f11-43fe-af92-41ac264983ff button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-124f80bc-8f11-43fe-af92-41ac264983ff');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-5ba58da5-489c-4ac9-835f-78e15912447a\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5ba58da5-489c-4ac9-835f-78e15912447a')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-5ba58da5-489c-4ac9-835f-78e15912447a button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"people[[\\\"birthyear\\\", \\\"hobby\\\"]]\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1984,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1985,\n          1984,\n          1992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ],
+      "source": [
+        "people[[\"birthyear\", \"hobby\"]]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JKxInxUD3k2r"
+      },
+      "source": [
+        "If you pass a list of columns and/or index row labels to the `DataFrame` constructor, it will guarantee that these columns and/or rows will exist, in that order, and no other column/row will exist. For example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "id": "3dCWTEkZ3k2r",
+        "outputId": "332f5669-35bd-468b-ee1f-cf0cfea2b8e3"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        birthyear  weight height\n",
+              "bob        1984.0    83.0    NaN\n",
+              "alice      1985.0    68.0    NaN\n",
+              "eugene        NaN     NaN    NaN"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3112dc60-8948-44ae-b9e0-80c36c896824\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>height</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984.0</td>\n",
+              "      <td>83.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985.0</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>eugene</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3112dc60-8948-44ae-b9e0-80c36c896824')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3112dc60-8948-44ae-b9e0-80c36c896824 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3112dc60-8948-44ae-b9e0-80c36c896824');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-dc10c043-6def-46d2-b183-9a17dc94de12\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dc10c043-6def-46d2-b183-9a17dc94de12')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-dc10c043-6def-46d2-b183-9a17dc94de12 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_c6ead485-9c65-4e2a-a7dc-214af9a06f0e\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('d2')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_c6ead485-9c65-4e2a-a7dc-214af9a06f0e button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('d2');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "d2",
+              "repr_error": "Out of range float values are not JSON compliant: nan"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "d2 = pd.DataFrame(\n",
+        "        people_dict,\n",
+        "        columns=[\"birthyear\", \"weight\", \"height\"],\n",
+        "        index=[\"bob\", \"alice\", \"eugene\"]\n",
+        "     )\n",
+        "d2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "joMufLbD3k2r"
+      },
+      "source": [
+        "Another convenient way to create a `DataFrame` is to pass all the values to the constructor as an `ndarray`, or a list of lists, and specify the column names and row index labels separately:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "id": "0LP0F-g73k2s",
+        "outputId": "d7e57709-7762-47ab-d88d-a6af403ddffc"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         birthyear  children    hobby  weight\n",
+              "alice         1985       NaN   Biking      68\n",
+              "bob           1984       3.0  Dancing      83\n",
+              "charles       1992       0.0      NaN     112"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-33fe475a-b33e-48d9-9c7a-647dee30c0c4\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>children</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33fe475a-b33e-48d9-9c7a-647dee30c0c4')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-33fe475a-b33e-48d9-9c7a-647dee30c0c4 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-33fe475a-b33e-48d9-9c7a-647dee30c0c4');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6ceb423b-4c49-463c-aaf8-7ece92258427\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6ceb423b-4c49-463c-aaf8-7ece92258427')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6ceb423b-4c49-463c-aaf8-7ece92258427 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_7a9ab5c3-cf7b-48ab-8e0b-591f8c3ce625\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('d3')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_7a9ab5c3-cf7b-48ab-8e0b-591f8c3ce625 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('d3');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "d3",
+              "summary": "{\n  \"name\": \"d3\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1984,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1985,\n          1984,\n          1992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1213203435596424,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "values = [\n",
+        "            [1985, np.nan, \"Biking\",   68],\n",
+        "            [1984, 3,      \"Dancing\",  83],\n",
+        "            [1992, 0,      np.nan,    112]\n",
+        "         ]\n",
+        "d3 = pd.DataFrame(\n",
+        "        values,\n",
+        "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
+        "        index=[\"alice\", \"bob\", \"charles\"]\n",
+        "     )\n",
+        "d3"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ny8INXJy3k2s"
+      },
+      "source": [
+        "To specify missing values, you can use either `np.nan` or NumPy's masked arrays:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "id": "wziYAu6L3k2s",
+        "outputId": "0acde1d6-8c02-4820-92b2-dfd4ad56ed93"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        birthyear children    hobby weight\n",
+              "alice        1985      NaN   Biking     68\n",
+              "bob          1984        3  Dancing     83\n",
+              "charles      1992        0      NaN    112"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-785969b4-d0f8-42de-b26b-b1d0dc13f135\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>children</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-785969b4-d0f8-42de-b26b-b1d0dc13f135')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-785969b4-d0f8-42de-b26b-b1d0dc13f135 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-785969b4-d0f8-42de-b26b-b1d0dc13f135');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-de0f25af-2bb6-4ad5-8761-2a2e963bfec5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-de0f25af-2bb6-4ad5-8761-2a2e963bfec5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-de0f25af-2bb6-4ad5-8761-2a2e963bfec5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_58d3d751-5f92-4b77-bb68-aadae9e71738\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('d3')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_58d3d751-5f92-4b77-bb68-aadae9e71738 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('d3');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "d3",
+              "summary": "{\n  \"name\": \"d3\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1984,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1985,\n          1984,\n          1992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ],
+      "source": [
+        "masked_array = np.ma.asarray(values, dtype=object)\n",
+        "masked_array[(0, 2), (1, 2)] = np.ma.masked\n",
+        "d3 = pd.DataFrame(\n",
+        "        masked_array,\n",
+        "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
+        "        index=[\"alice\", \"bob\", \"charles\"]\n",
+        "     )\n",
+        "d3"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5b146464",
+        "outputId": "228a1977-34aa-4b6d-ec20-a9a20125ed93"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import numpy.ma as ma\n",
+        "\n",
+        "# 1. Creating a simple masked array\n",
+        "data = np.array([1, 2, 3, 4, 5])\n",
+        "mask = [False, False, True, False, False] # True means masked\n",
+        "masked_arr = ma.array(data, mask=mask)\n",
+        "\n",
+        "print(\"Original data:\", data)\n",
+        "print(\"Mask:\", mask)\n",
+        "print(\"Masked array:\\n\", masked_arr)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Original data: [1 2 3 4 5]\n",
+            "Mask: [False, False, True, False, False]\n",
+            "Masked array:\n",
+            " [1 2 -- 4 5]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c77bd458",
+        "outputId": "b0a90cff-b523-48cb-cab5-d581fe5a3f7c"
+      },
+      "source": [
+        "# 2. Masking individual elements using np.ma.masked\n",
+        "data2 = np.array([10, 20, 30, 40, 50])\n",
+        "masked_arr2 = ma.array(data2)\n",
+        "\n",
+        "print(\"Initial masked array2:\\n\", masked_arr2)\n",
+        "\n",
+        "# Mask the second element (index 1)\n",
+        "masked_arr2[1] = ma.masked\n",
+        "print(\"Masked array2 after masking index 1:\\n\", masked_arr2)\n",
+        "\n",
+        "# Mask multiple elements\n",
+        "masked_arr2[[0, 4]] = ma.masked\n",
+        "print(\"Masked array2 after masking indices 0 and 4:\\n\", masked_arr2)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initial masked array2:\n",
+            " [10 20 30 40 50]\n",
+            "Masked array2 after masking index 1:\n",
+            " [10 -- 30 40 50]\n",
+            "Masked array2 after masking indices 0 and 4:\n",
+            " [-- -- 30 40 --]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "76c856cf",
+        "outputId": "cc6627b7-053e-4b90-9df5-c3acd5ede8b6"
+      },
+      "source": [
+        "# 3. Operations on masked arrays\n",
+        "data3 = np.array([1, 2, 3, 4, 5])\n",
+        "mask3 = [False, False, True, False, True]\n",
+        "masked_arr3 = ma.array(data3, mask=mask3)\n",
+        "\n",
+        "print(\"Masked array for operations:\\n\", masked_arr3)\n",
+        "\n",
+        "# Sum of valid elements\n",
+        "print(\"Sum of elements (masked ignored):\", masked_arr3.sum())\n",
+        "\n",
+        "# Mean of valid elements\n",
+        "print(\"Mean of elements (masked ignored):\", masked_arr3.mean())\n",
+        "\n",
+        "# Standard deviation of valid elements\n",
+        "print(\"Standard deviation (masked ignored):\", masked_arr3.std())"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Masked array for operations:\n",
+            " [1 2 -- 4 --]\n",
+            "Sum of elements (masked ignored): 7\n",
+            "Mean of elements (masked ignored): 2.3333333333333335\n",
+            "Standard deviation (masked ignored): 1.247219128924647\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r37UaGmk3k2s"
+      },
+      "source": [
+        "Instead of an `ndarray`, you can also pass a `DataFrame` object:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gsdLK0KB3k2t",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 125
+        },
+        "outputId": "b3a5e956-0dae-40cc-cbcb-366b7448a3ef"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         hobby children\n",
+              "alice   Biking      NaN\n",
+              "bob    Dancing        3"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d6c1f8f4-8f49-47ba-b5f2-6c50fc657083\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6c1f8f4-8f49-47ba-b5f2-6c50fc657083')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d6c1f8f4-8f49-47ba-b5f2-6c50fc657083 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d6c1f8f4-8f49-47ba-b5f2-6c50fc657083');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-79e6db84-73ea-45d7-9ee0-bfca94c7d836\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79e6db84-73ea-45d7-9ee0-bfca94c7d836')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-79e6db84-73ea-45d7-9ee0-bfca94c7d836 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_f87f9d79-0f29-4974-983f-eca1fc5bdf22\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('d4')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_f87f9d79-0f29-4974-983f-eca1fc5bdf22 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('d4');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "d4",
+              "summary": "{\n  \"name\": \"d4\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ],
+      "source": [
+        "d4 = pd.DataFrame(\n",
+        "         d3,\n",
+        "         columns=[\"hobby\", \"children\"],\n",
+        "         index=[\"alice\", \"bob\"]\n",
+        "     )\n",
+        "d4"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "olFmjR683k2u"
+      },
+      "source": [
+        "It is also possible to create a `DataFrame` with a dictionary (or list) of dictionaries (or lists):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gfNvovlf3k2u",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "a69ae288-d574-4d76-a676-4da761f55765"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         birthyear    hobby  weight  children\n",
+              "alice         1985   Biking      68       NaN\n",
+              "bob           1984  Dancing      83       3.0\n",
+              "charles       1992      NaN     112       0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2a5f42dc-557a-42f1-92db-4f9724caa669\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a5f42dc-557a-42f1-92db-4f9724caa669')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2a5f42dc-557a-42f1-92db-4f9724caa669 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2a5f42dc-557a-42f1-92db-4f9724caa669');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-fb24f95f-f084-4477-9cb8-e28e8b17470e\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fb24f95f-f084-4477-9cb8-e28e8b17470e')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-fb24f95f-f084-4477-9cb8-e28e8b17470e button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_70c58f00-37b5-429f-b7c9-af990c5e1fbe\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('people')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_70c58f00-37b5-429f-b7c9-af990c5e1fbe button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('people');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "people",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1984,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1985,\n          1984,\n          1992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1213203435596424,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "people = pd.DataFrame({\n",
+        "    \"birthyear\": {\"alice\": 1985, \"bob\": 1984, \"charles\": 1992},\n",
+        "    \"hobby\": {\"alice\": \"Biking\", \"bob\": \"Dancing\"},\n",
+        "    \"weight\": {\"alice\": 68, \"bob\": 83, \"charles\": 112},\n",
+        "    \"children\": {\"bob\": 3, \"charles\": 0}\n",
+        "})\n",
+        "people"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xAUb8NFl3k2u"
+      },
+      "source": [
+        "## Multi-indexing\n",
+        "If all columns are tuples of the same size, then they are understood as a multi-index. The same goes for row index labels. For example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tIg0XPic3k2v",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "66c501f8-ae51-43ce-dbd6-398a0cba2e2a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  public          private         \n",
+              "               birthyear    hobby  weight children\n",
+              "Paris  alice        1985   Biking      68      NaN\n",
+              "       bob          1984  Dancing      83      3.0\n",
+              "London charles      1992      NaN     112      0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-bd6b3ff6-b492-4c46-a1c3-8f83b28913b1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"2\" halign=\"left\">public</th>\n",
+              "      <th colspan=\"2\" halign=\"left\">private</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>London</th>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd6b3ff6-b492-4c46-a1c3-8f83b28913b1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-bd6b3ff6-b492-4c46-a1c3-8f83b28913b1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-bd6b3ff6-b492-4c46-a1c3-8f83b28913b1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-80ad4cdf-62e1-4f3b-8877-613a8a5e5fa3\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-80ad4cdf-62e1-4f3b-8877-613a8a5e5fa3')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-80ad4cdf-62e1-4f3b-8877-613a8a5e5fa3 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_33da37c0-006e-4036-826a-b2f1fd6ccdfd\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('d5')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_33da37c0-006e-4036-826a-b2f1fd6ccdfd button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('d5');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "d5",
+              "summary": "{\n  \"name\": \"d5\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": [\n        \"public\",\n        \"birthyear\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1984,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1985,\n          1984,\n          1992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"public\",\n        \"hobby\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"private\",\n        \"weight\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"private\",\n        \"children\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1213203435596424,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ],
+      "source": [
+        "d5 = pd.DataFrame(\n",
+        "  {\n",
+        "    (\"public\", \"birthyear\"):\n",
+        "        {(\"Paris\",\"alice\"): 1985, (\"Paris\",\"bob\"): 1984, (\"London\",\"charles\"): 1992},\n",
+        "    (\"public\", \"hobby\"):\n",
+        "        {(\"Paris\",\"alice\"): \"Biking\", (\"Paris\",\"bob\"): \"Dancing\"},\n",
+        "    (\"private\", \"weight\"):\n",
+        "        {(\"Paris\",\"alice\"): 68, (\"Paris\",\"bob\"): 83, (\"London\",\"charles\"): 112},\n",
+        "    (\"private\", \"children\"):\n",
+        "        {(\"Paris\", \"alice\"): np.nan, (\"Paris\",\"bob\"): 3, (\"London\",\"charles\"): 0}\n",
+        "  }\n",
+        ")\n",
+        "d5"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tBgg3LNY3k2v"
+      },
+      "source": [
+        "You can now get a `DataFrame` containing all the `\"public\"` columns very simply:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Dg3Pta9U3k2v",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "f7f5344a-d437-428c-8ed6-03145e66229f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                birthyear    hobby\n",
+              "Paris  alice         1985   Biking\n",
+              "       bob           1984  Dancing\n",
+              "London charles       1992      NaN"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3b207134-84a3-4272-8a56-64135f758f4f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>hobby</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>Biking</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>Dancing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>London</th>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b207134-84a3-4272-8a56-64135f758f4f')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3b207134-84a3-4272-8a56-64135f758f4f button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3b207134-84a3-4272-8a56-64135f758f4f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-806c8dd3-bb02-48a0-bd31-ebe134360a6a\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-806c8dd3-bb02-48a0-bd31-ebe134360a6a')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-806c8dd3-bb02-48a0-bd31-ebe134360a6a button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"d5[\\\"public\\\"]\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1984,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1985,\n          1984,\n          1992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ],
+      "source": [
+        "d5[\"public\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "R7AgBFCT3k2v",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 227
+        },
+        "outputId": "86786b6d-9978-4dee-f174-9e873ed5c94e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Paris   alice       Biking\n",
+              "        bob        Dancing\n",
+              "London  charles        NaN\n",
+              "Name: (public, hobby), dtype: object"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>public</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>London</th>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> object</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ],
+      "source": [
+        "d5[\"public\", \"hobby\"]  # Same result as d5[\"public\"][\"hobby\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "INuIXCy33k2w"
+      },
+      "source": [
+        "## Dropping a level\n",
+        "Let's look at `d5` again:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4aW8girz3k2w",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "4309d416-18e7-4686-a1ae-a1b77425a640"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  public          private         \n",
+              "               birthyear    hobby  weight children\n",
+              "Paris  alice        1985   Biking      68      NaN\n",
+              "       bob          1984  Dancing      83      3.0\n",
+              "London charles      1992      NaN     112      0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-27496f4b-3ffd-40bf-b3be-727d0cce2bb7\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"2\" halign=\"left\">public</th>\n",
+              "      <th colspan=\"2\" halign=\"left\">private</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>London</th>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27496f4b-3ffd-40bf-b3be-727d0cce2bb7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-27496f4b-3ffd-40bf-b3be-727d0cce2bb7 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-27496f4b-3ffd-40bf-b3be-727d0cce2bb7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f2041270-392b-4bcd-8151-6bd42ec4e05c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f2041270-392b-4bcd-8151-6bd42ec4e05c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-f2041270-392b-4bcd-8151-6bd42ec4e05c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_6c4a3283-1777-4fb2-baa7-cfd02a899e98\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('d5')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_6c4a3283-1777-4fb2-baa7-cfd02a899e98 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('d5');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "d5",
+              "summary": "{\n  \"name\": \"d5\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": [\n        \"public\",\n        \"birthyear\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1984,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1985,\n          1984,\n          1992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"public\",\n        \"hobby\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"private\",\n        \"weight\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"private\",\n        \"children\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1213203435596424,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "d5"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jk8YDTHs3k2w"
+      },
+      "source": [
+        "There are two levels of columns, and two levels of indices. We can drop a column level by calling `droplevel()` (the same goes for indices):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "unDeX9cQ3k2x",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "ad75351b-9cf5-4c65-9e89-a9309aca2f19"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                birthyear    hobby  weight  children\n",
+              "Paris  alice         1985   Biking      68       NaN\n",
+              "       bob           1984  Dancing      83       3.0\n",
+              "London charles       1992      NaN     112       0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-70df8cba-ba63-4059-bd80-1f5a94c9c0ba\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>London</th>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70df8cba-ba63-4059-bd80-1f5a94c9c0ba')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-70df8cba-ba63-4059-bd80-1f5a94c9c0ba button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-70df8cba-ba63-4059-bd80-1f5a94c9c0ba');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-db017491-3451-4174-af4d-5acf4db40567\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-db017491-3451-4174-af4d-5acf4db40567')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-db017491-3451-4174-af4d-5acf4db40567 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_1a2a8d44-4bde-4667-8536-9a42b95ed921\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('d5')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_1a2a8d44-4bde-4667-8536-9a42b95ed921 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('d5');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "d5",
+              "summary": "{\n  \"name\": \"d5\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1984,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1985,\n          1984,\n          1992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1213203435596424,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ],
+      "source": [
+        "d5.columns = d5.columns.droplevel(level = 0)\n",
+        "d5"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m_qDM5eO3k2x"
+      },
+      "source": [
+        "## Transposing\n",
+        "You can swap columns and indices using the `T` attribute:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dzkh68yd3k2x",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "bf7b22a0-8e1a-470f-a465-8a392301f770"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Paris           London\n",
+              "            alice      bob charles\n",
+              "birthyear    1985     1984    1992\n",
+              "hobby      Biking  Dancing     NaN\n",
+              "weight         68       83     112\n",
+              "children      NaN      3.0     0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f6693622-d283-474d-ab09-e829e3d18b65\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"2\" halign=\"left\">Paris</th>\n",
+              "      <th>London</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th>alice</th>\n",
+              "      <th>bob</th>\n",
+              "      <th>charles</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>birthyear</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>1984</td>\n",
+              "      <td>1992</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hobby</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>weight</th>\n",
+              "      <td>68</td>\n",
+              "      <td>83</td>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>children</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f6693622-d283-474d-ab09-e829e3d18b65')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f6693622-d283-474d-ab09-e829e3d18b65 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f6693622-d283-474d-ab09-e829e3d18b65');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-edbb93b9-641a-45a5-96a7-1cf449530f4f\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-edbb93b9-641a-45a5-96a7-1cf449530f4f')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-edbb93b9-641a-45a5-96a7-1cf449530f4f button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_0105bb21-12af-42f1-80fb-675f86780444\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('d6')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_0105bb21-12af-42f1-80fb-675f86780444 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('d6');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "d6",
+              "summary": "{\n  \"name\": \"d6\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": [\n        \"Paris\",\n        \"alice\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1985,\n          \"Biking\",\n          68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Paris\",\n        \"bob\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Dancing\",\n          3.0,\n          1984\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"London\",\n        \"charles\"\n      ],\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1992,\n          112,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ],
+      "source": [
+        "d6 = d5.T\n",
+        "d6"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qocZ7rom3k2y"
+      },
+      "source": [
+        "## Stacking and unstacking levels\n",
+        "Calling the `stack()` method will push the lowest column level after the lowest index:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GxyT9irq3k2z",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 458
+        },
+        "outputId": "e40c8440-486c-460f-f5a0-d2580a3fa528"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     Paris London\n",
+              "birthyear alice       1985    NaN\n",
+              "          bob         1984    NaN\n",
+              "          charles      NaN   1992\n",
+              "hobby     alice     Biking    NaN\n",
+              "          bob      Dancing    NaN\n",
+              "          charles      NaN    NaN\n",
+              "weight    alice         68    NaN\n",
+              "          bob           83    NaN\n",
+              "          charles      NaN    112\n",
+              "children  alice        NaN    NaN\n",
+              "          bob          3.0    NaN\n",
+              "          charles      NaN    0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3e568955-e46e-41ea-85d0-6ef35fffc98d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>Paris</th>\n",
+              "      <th>London</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">birthyear</th>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1992</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">hobby</th>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">weight</th>\n",
+              "      <th>alice</th>\n",
+              "      <td>68</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>83</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">children</th>\n",
+              "      <th>alice</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e568955-e46e-41ea-85d0-6ef35fffc98d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3e568955-e46e-41ea-85d0-6ef35fffc98d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3e568955-e46e-41ea-85d0-6ef35fffc98d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-ff4e43fa-6a5d-433a-9c15-6ac19b588bb2\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ff4e43fa-6a5d-433a-9c15-6ac19b588bb2')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-ff4e43fa-6a5d-433a-9c15-6ac19b588bb2 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_e11c18ea-3b8e-4e0c-b8f0-1c167492c1cd\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('d7')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_e11c18ea-3b8e-4e0c-b8f0-1c167492c1cd button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('d7');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "d7",
+              "summary": "{\n  \"name\": \"d7\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"Paris\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1985,\n          1984,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"London\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1992,\n          112,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
+      "source": [
+        "d7 = d6.stack(future_stack=True)\n",
+        "d7"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g_yBBYwP3k2z"
+      },
+      "source": [
+        "Note that many `NaN` values appeared. This makes sense because many new combinations did not exist before (e.g. there was no `bob` in `London`).\n",
+        "\n",
+        "Calling `unstack()` will do the reverse, once again creating many `NaN` values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7fN5spIR3k2z",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "b66d615b-42ab-47e5-d66d-50e3760e30ea"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Paris                  London             \n",
+              "            alice      bob charles  alice  bob charles\n",
+              "birthyear    1985     1984     NaN    NaN  NaN    1992\n",
+              "hobby      Biking  Dancing     NaN    NaN  NaN     NaN\n",
+              "weight         68       83     NaN    NaN  NaN     112\n",
+              "children      NaN      3.0     NaN    NaN  NaN     0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ac1bb8e8-42bc-4332-bdb1-6d7046e94458\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"3\" halign=\"left\">Paris</th>\n",
+              "      <th colspan=\"3\" halign=\"left\">London</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th>alice</th>\n",
+              "      <th>bob</th>\n",
+              "      <th>charles</th>\n",
+              "      <th>alice</th>\n",
+              "      <th>bob</th>\n",
+              "      <th>charles</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>birthyear</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>1984</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1992</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hobby</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>weight</th>\n",
+              "      <td>68</td>\n",
+              "      <td>83</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>children</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ac1bb8e8-42bc-4332-bdb1-6d7046e94458')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ac1bb8e8-42bc-4332-bdb1-6d7046e94458 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ac1bb8e8-42bc-4332-bdb1-6d7046e94458');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-7aac8aab-8f6a-4dd5-b935-d1e714c85c9f\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7aac8aab-8f6a-4dd5-b935-d1e714c85c9f')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-7aac8aab-8f6a-4dd5-b935-d1e714c85c9f button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_268ee11b-d426-4bca-80dc-e56b31c38f97\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('d8')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_268ee11b-d426-4bca-80dc-e56b31c38f97 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('d8');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "d8",
+              "repr_error": "Out of range float values are not JSON compliant: nan"
+            }
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ],
+      "source": [
+        "d8 = d7.unstack()\n",
+        "d8"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TtqQdpai3k2z"
+      },
+      "source": [
+        "If we call `unstack` again, we end up with a `Series` object:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Gmvf3Ang3k20",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 903
+        },
+        "outputId": "39c569c6-ade0-44ab-fecc-52789c1d9fed"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Paris   alice    birthyear       1985\n",
+              "                 hobby         Biking\n",
+              "                 weight            68\n",
+              "                 children         NaN\n",
+              "        bob      birthyear       1984\n",
+              "                 hobby        Dancing\n",
+              "                 weight            83\n",
+              "                 children         3.0\n",
+              "        charles  birthyear        NaN\n",
+              "                 hobby            NaN\n",
+              "                 weight           NaN\n",
+              "                 children         NaN\n",
+              "London  alice    birthyear        NaN\n",
+              "                 hobby            NaN\n",
+              "                 weight           NaN\n",
+              "                 children         NaN\n",
+              "        bob      birthyear        NaN\n",
+              "                 hobby            NaN\n",
+              "                 weight           NaN\n",
+              "                 children         NaN\n",
+              "        charles  birthyear       1992\n",
+              "                 hobby            NaN\n",
+              "                 weight           112\n",
+              "                 children         0.0\n",
+              "dtype: object"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"12\" valign=\"top\">Paris</th>\n",
+              "      <th rowspan=\"4\" valign=\"top\">alice</th>\n",
+              "      <th>birthyear</th>\n",
+              "      <td>1985</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hobby</th>\n",
+              "      <td>Biking</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>weight</th>\n",
+              "      <td>68</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>children</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"4\" valign=\"top\">bob</th>\n",
+              "      <th>birthyear</th>\n",
+              "      <td>1984</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hobby</th>\n",
+              "      <td>Dancing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>weight</th>\n",
+              "      <td>83</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>children</th>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"4\" valign=\"top\">charles</th>\n",
+              "      <th>birthyear</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hobby</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>weight</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>children</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"12\" valign=\"top\">London</th>\n",
+              "      <th rowspan=\"4\" valign=\"top\">alice</th>\n",
+              "      <th>birthyear</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hobby</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>weight</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>children</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"4\" valign=\"top\">bob</th>\n",
+              "      <th>birthyear</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hobby</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>weight</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>children</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"4\" valign=\"top\">charles</th>\n",
+              "      <th>birthyear</th>\n",
+              "      <td>1992</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hobby</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>weight</th>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>children</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> object</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ],
+      "source": [
+        "d9 = d8.unstack()\n",
+        "d9"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uZk2zvK03k20"
+      },
+      "source": [
+        "The `stack()` and `unstack()` methods let you select the `level` to stack/unstack. You can even stack/unstack multiple levels at once:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8dzS8niZ3k20",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "c0d1e906-f17d-4bee-bcb8-05b4799b9b56"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Paris                  London             \n",
+              "            alice      bob charles  alice  bob charles\n",
+              "birthyear    1985     1984     NaN    NaN  NaN    1992\n",
+              "hobby      Biking  Dancing     NaN    NaN  NaN     NaN\n",
+              "weight         68       83     NaN    NaN  NaN     112\n",
+              "children      NaN      3.0     NaN    NaN  NaN     0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-1e6b552a-9ea3-49db-b9bf-df22cf33e31e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"3\" halign=\"left\">Paris</th>\n",
+              "      <th colspan=\"3\" halign=\"left\">London</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th>alice</th>\n",
+              "      <th>bob</th>\n",
+              "      <th>charles</th>\n",
+              "      <th>alice</th>\n",
+              "      <th>bob</th>\n",
+              "      <th>charles</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>birthyear</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>1984</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1992</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hobby</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>weight</th>\n",
+              "      <td>68</td>\n",
+              "      <td>83</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>children</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e6b552a-9ea3-49db-b9bf-df22cf33e31e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1e6b552a-9ea3-49db-b9bf-df22cf33e31e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-1e6b552a-9ea3-49db-b9bf-df22cf33e31e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-963b4e08-3352-4680-b0dd-a2f16bc14e19\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-963b4e08-3352-4680-b0dd-a2f16bc14e19')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-963b4e08-3352-4680-b0dd-a2f16bc14e19 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_574a93fc-7628-49f1-91f8-dc7cc49fd875\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('d10')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_574a93fc-7628-49f1-91f8-dc7cc49fd875 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('d10');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "d10",
+              "repr_error": "Out of range float values are not JSON compliant: nan"
+            }
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ],
+      "source": [
+        "d10 = d9.unstack(level = (0,1))\n",
+        "d10"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WN8sUJqp3k20"
+      },
+      "source": [
+        "## Most methods return modified copies\n",
+        "As you may have noticed, the `stack()` and `unstack()` methods do not modify the object they are called on. Instead, they work on a copy and return that copy. This is true of most methods in pandas."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q4b_0h783k21"
+      },
+      "source": [
+        "## Accessing rows\n",
+        "Let's go back to the `people` `DataFrame`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pfPk_exA3k21",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "eec66d34-cb67-4b3a-ba7c-82754ca3aee5"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         birthyear    hobby  weight  children\n",
+              "alice         1985   Biking      68       NaN\n",
+              "bob           1984  Dancing      83       3.0\n",
+              "charles       1992      NaN     112       0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f60d358d-44d8-4fd1-ae63-553592e761eb\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f60d358d-44d8-4fd1-ae63-553592e761eb')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f60d358d-44d8-4fd1-ae63-553592e761eb button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f60d358d-44d8-4fd1-ae63-553592e761eb');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-741b21f6-65d6-40b0-867e-8d46aab3a7eb\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-741b21f6-65d6-40b0-867e-8d46aab3a7eb')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-741b21f6-65d6-40b0-867e-8d46aab3a7eb button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_7b2078c5-d497-4872-9d9d-0d33568c92d0\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('people')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_7b2078c5-d497-4872-9d9d-0d33568c92d0 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('people');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "people",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1984,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1985,\n          1984,\n          1992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1213203435596424,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ],
+      "source": [
+        "people"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PBpxawt83k21"
+      },
+      "source": [
+        "The `loc` attribute lets you access rows instead of columns. The result is a `Series` object in which the `DataFrame`'s column names are mapped to row index labels:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OYRjsfq23k21",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 227
+        },
+        "outputId": "1ff7c062-4565-4138-acc9-18bbe836653d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "birthyear    1992\n",
+              "hobby         NaN\n",
+              "weight        112\n",
+              "children      0.0\n",
+              "Name: charles, dtype: object"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>charles</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>birthyear</th>\n",
+              "      <td>1992</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hobby</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>weight</th>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>children</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> object</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ],
+      "source": [
+        "people.loc[\"charles\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DaQXnxVB3k22"
+      },
+      "source": [
+        "You can also access rows by integer location using the `iloc` attribute:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_XSBHaE53k22",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 119
+        },
+        "outputId": "e10c87e7-ec77-415d-932e-77a3f92f7dbc"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         birthyear   hobby  weight  children\n",
+              "alice         1985  Biking      68       NaN\n",
+              "charles       1992     NaN     112       0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2fc22312-6dfd-47d8-ac5f-6ec09b9b3c20\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fc22312-6dfd-47d8-ac5f-6ec09b9b3c20')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2fc22312-6dfd-47d8-ac5f-6ec09b9b3c20 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2fc22312-6dfd-47d8-ac5f-6ec09b9b3c20');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f1b8410e-9e58-4e73-b581-38befa02cbb1\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f1b8410e-9e58-4e73-b581-38befa02cbb1')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-f1b8410e-9e58-4e73-b581-38befa02cbb1 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1985,\n        \"max\": 1992,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1992,\n          1985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ],
+      "source": [
+        "people.iloc[[0, 2]]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WM0yYmDM3k23"
+      },
+      "source": [
+        "You can also get a slice of rows, and this returns a `DataFrame` object:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3vn20_HZ3k23",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 119
+        },
+        "outputId": "7e1d9670-d98d-4e3f-dde0-29e451d4ed44"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         birthyear    hobby  weight  children\n",
+              "bob           1984  Dancing      83       3.0\n",
+              "charles       1992      NaN     112       0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-610751f7-9557-49de-9780-9ccd0b1dc5c4\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-610751f7-9557-49de-9780-9ccd0b1dc5c4')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-610751f7-9557-49de-9780-9ccd0b1dc5c4 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-610751f7-9557-49de-9780-9ccd0b1dc5c4');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-030dbe31-2a4a-47a7-a117-11b92bf3b433\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-030dbe31-2a4a-47a7-a117-11b92bf3b433')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-030dbe31-2a4a-47a7-a117-11b92bf3b433 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 1984,\n        \"max\": 1992,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1992,\n          1984\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Dancing\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 83,\n        \"max\": 112,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1213203435596424,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ],
+      "source": [
+        "people.iloc[1:3]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CZZModcr3k23"
+      },
+      "source": [
+        "Finally, you can pass a boolean array to get the matching rows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eNl351MP3k24",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 119
+        },
+        "outputId": "49f3afbd-b380-46a7-87c3-fa6ea8012cb5"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         birthyear   hobby  weight  children\n",
+              "alice         1985  Biking      68       NaN\n",
+              "charles       1992     NaN     112       0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cf8ce520-0b0f-4a6f-8387-3fdeb12f3c0b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf8ce520-0b0f-4a6f-8387-3fdeb12f3c0b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-cf8ce520-0b0f-4a6f-8387-3fdeb12f3c0b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-cf8ce520-0b0f-4a6f-8387-3fdeb12f3c0b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-a35b4a8e-2c8c-4a18-8c28-fa15cd0a18ef\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a35b4a8e-2c8c-4a18-8c28-fa15cd0a18ef')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-a35b4a8e-2c8c-4a18-8c28-fa15cd0a18ef button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"people[np\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1985,\n        \"max\": 1992,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1992,\n          1985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ],
+      "source": [
+        "people[np.array([True, False, True])]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Cs8d-8nO3k24"
+      },
+      "source": [
+        "This is most useful when combined with boolean expressions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rFhN6RMj3k24",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 119
+        },
+        "outputId": "0c5a2904-06ab-4daa-b461-c54e3ec4fe06"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       birthyear    hobby  weight  children\n",
+              "alice       1985   Biking      68       NaN\n",
+              "bob         1984  Dancing      83       3.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-be918323-b6cb-4602-bc44-7c638e56b086\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be918323-b6cb-4602-bc44-7c638e56b086')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-be918323-b6cb-4602-bc44-7c638e56b086 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-be918323-b6cb-4602-bc44-7c638e56b086');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-12b75bb3-3f30-4dc9-83c3-9d0d23ba1cfd\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-12b75bb3-3f30-4dc9-83c3-9d0d23ba1cfd')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-12b75bb3-3f30-4dc9-83c3-9d0d23ba1cfd button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"people[people[\\\"birthyear\\\"] < 1990]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1984,\n        \"max\": 1985,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1984,\n          1985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 68,\n        \"max\": 83,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          83,\n          68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ],
+      "source": [
+        "people[people[\"birthyear\"] < 1990]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "blb6OV7m3k24"
+      },
+      "source": [
+        "## Adding and removing columns\n",
+        "You can generally treat `DataFrame` objects like dictionaries of `Series`, so the following works fine:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "L37gzIV-3k25",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "7cd1e0a2-ed51-439b-e14f-729c416fdb54"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         birthyear    hobby  weight  children\n",
+              "alice         1985   Biking      68       NaN\n",
+              "bob           1984  Dancing      83       3.0\n",
+              "charles       1992      NaN     112       0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-71fa01aa-c454-41e9-b185-94670458db7d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71fa01aa-c454-41e9-b185-94670458db7d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-71fa01aa-c454-41e9-b185-94670458db7d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-71fa01aa-c454-41e9-b185-94670458db7d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-8d0c65d6-35d7-4e53-a9ba-ad5457c53c1b\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8d0c65d6-35d7-4e53-a9ba-ad5457c53c1b')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-8d0c65d6-35d7-4e53-a9ba-ad5457c53c1b button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_f3592631-5fa2-431a-8276-dc57c205f9db\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('people')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_f3592631-5fa2-431a-8276-dc57c205f9db button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('people');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "people",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1984,\n        \"max\": 1992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1985,\n          1984,\n          1992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1213203435596424,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ],
+      "source": [
+        "people"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "I7bqCQV73k25",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "56b59a7a-cc60-4d7b-e6eb-1579ebf9a1fb"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           hobby  weight  age  over 30\n",
+              "alice     Biking      68   33     True\n",
+              "bob      Dancing      83   34     True\n",
+              "charles      NaN     112   26    False"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-30e5c4c6-98f5-4165-9eeb-40af12e5eed7\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>age</th>\n",
+              "      <th>over 30</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "      <td>33</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83</td>\n",
+              "      <td>34</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "      <td>26</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30e5c4c6-98f5-4165-9eeb-40af12e5eed7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-30e5c4c6-98f5-4165-9eeb-40af12e5eed7 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-30e5c4c6-98f5-4165-9eeb-40af12e5eed7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-fd33b33e-ec03-47fa-944d-1c7a6ef00a53\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fd33b33e-ec03-47fa-944d-1c7a6ef00a53')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-fd33b33e-ec03-47fa-944d-1c7a6ef00a53 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_aa2cbb3f-b4a5-480c-8b51-f7e7676fd60e\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('people')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_aa2cbb3f-b4a5-480c-8b51-f7e7676fd60e button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('people');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "people",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 26,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          33,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"over 30\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ],
+      "source": [
+        "people[\"age\"] = 2018 - people[\"birthyear\"]  # adds a new column \"age\"\n",
+        "people[\"over 30\"] = people[\"age\"] > 30      # adds another column \"over 30\"\n",
+        "birthyears = people.pop(\"birthyear\")\n",
+        "del people[\"children\"]\n",
+        "\n",
+        "people"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TdK-e36H3k26",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 193
+        },
+        "outputId": "40894b9b-16bc-43a2-ae57-41a8b0d130db"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "alice      1985\n",
+              "bob        1984\n",
+              "charles    1992\n",
+              "Name: birthyear, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>birthyear</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>1985</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1984</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>1992</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ],
+      "source": [
+        "birthyears"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JuWyxOFh3k26"
+      },
+      "source": [
+        "When you add a new column, it must have the same number of rows. Missing rows are filled with NaN, and extra rows are ignored:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2veIC1UC3k26",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "48d3f155-72de-4c01-c184-7112f055af6d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           hobby  weight  age  over 30  pets\n",
+              "alice     Biking      68   33     True   NaN\n",
+              "bob      Dancing      83   34     True   0.0\n",
+              "charles      NaN     112   26    False   5.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c99aae6c-abf8-4317-b79d-8ed54d19cd09\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>age</th>\n",
+              "      <th>over 30</th>\n",
+              "      <th>pets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68</td>\n",
+              "      <td>33</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83</td>\n",
+              "      <td>34</td>\n",
+              "      <td>True</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>112</td>\n",
+              "      <td>26</td>\n",
+              "      <td>False</td>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c99aae6c-abf8-4317-b79d-8ed54d19cd09')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c99aae6c-abf8-4317-b79d-8ed54d19cd09 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c99aae6c-abf8-4317-b79d-8ed54d19cd09');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-86232618-f68b-455f-ac3a-0badbc9675d0\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-86232618-f68b-455f-ac3a-0badbc9675d0')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-86232618-f68b-455f-ac3a-0badbc9675d0 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_a94e9e65-1ee2-4087-80db-9fa495a0e9a4\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('people')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_a94e9e65-1ee2-4087-80db-9fa495a0e9a4 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('people');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "people",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 26,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          33,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"over 30\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5355339059327378,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ],
+      "source": [
+        "people[\"pets\"] = pd.Series({\"bob\": 0, \"charles\": 5, \"eugene\": 1})  # alice is missing, eugene is ignored\n",
+        "people"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wfinVoak3k27"
+      },
+      "source": [
+        "When adding a new column, it is added at the end (on the right) by default. You can also insert a column anywhere else using the `insert()` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-ftKxX6x3k27",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "8142d4e4-907a-4981-f95b-a986f34b0166"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           hobby  height  weight  age  over 30  pets\n",
+              "alice     Biking     172      68   33     True   NaN\n",
+              "bob      Dancing     181      83   34     True   0.0\n",
+              "charles      NaN     185     112   26    False   5.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3f1d156b-3609-480a-abf4-aa2155489767\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>age</th>\n",
+              "      <th>over 30</th>\n",
+              "      <th>pets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>172</td>\n",
+              "      <td>68</td>\n",
+              "      <td>33</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>181</td>\n",
+              "      <td>83</td>\n",
+              "      <td>34</td>\n",
+              "      <td>True</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>185</td>\n",
+              "      <td>112</td>\n",
+              "      <td>26</td>\n",
+              "      <td>False</td>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f1d156b-3609-480a-abf4-aa2155489767')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3f1d156b-3609-480a-abf4-aa2155489767 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3f1d156b-3609-480a-abf4-aa2155489767');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-882fc16e-94d3-475c-8c01-b558c6bc229a\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-882fc16e-94d3-475c-8c01-b558c6bc229a')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-882fc16e-94d3-475c-8c01-b558c6bc229a button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_70e4253f-110c-423c-b9b8-34ee73d53c8b\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('people')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_70e4253f-110c-423c-b9b8-34ee73d53c8b button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('people');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "people",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 172,\n        \"max\": 185,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          172,\n          181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 26,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          33,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"over 30\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5355339059327378,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 39
+        }
+      ],
+      "source": [
+        "people.insert(1, \"height\", [172, 181, 185])\n",
+        "people"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EHaO0Zc_3k27"
+      },
+      "source": [
+        "## Assigning new columns\n",
+        "You can also create new columns by calling the `assign()` method. Note that this returns a new `DataFrame` object, the original is not modified:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Eot-_LZv3k27",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "b1639938-d47a-4478-e2cd-c6ffe0b9df83"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
+              "alice     Biking     172      68   33     True   NaN        22.985398   \n",
+              "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
+              "charles      NaN     185     112   26    False   5.0        32.724617   \n",
+              "\n",
+              "         has_pets  \n",
+              "alice       False  \n",
+              "bob         False  \n",
+              "charles      True  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f7fd6c7e-fb51-4464-b992-40d066864ef5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>age</th>\n",
+              "      <th>over 30</th>\n",
+              "      <th>pets</th>\n",
+              "      <th>body_mass_index</th>\n",
+              "      <th>has_pets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>172</td>\n",
+              "      <td>68</td>\n",
+              "      <td>33</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>22.985398</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>181</td>\n",
+              "      <td>83</td>\n",
+              "      <td>34</td>\n",
+              "      <td>True</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>25.335002</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>185</td>\n",
+              "      <td>112</td>\n",
+              "      <td>26</td>\n",
+              "      <td>False</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>32.724617</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7fd6c7e-fb51-4464-b992-40d066864ef5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f7fd6c7e-fb51-4464-b992-40d066864ef5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f7fd6c7e-fb51-4464-b992-40d066864ef5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-caa45cbf-5622-4b4b-8ec5-5f0e26aa6c44\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-caa45cbf-5622-4b4b-8ec5-5f0e26aa6c44')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-caa45cbf-5622-4b4b-8ec5-5f0e26aa6c44 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \")\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 172,\n        \"max\": 185,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          172,\n          181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 26,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          33,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"over 30\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5355339059327378,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.082312968343663,\n        \"min\": 22.985397512168742,\n        \"max\": 32.72461650840029,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          22.985397512168742,\n          25.33500198406642\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"has_pets\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ],
+      "source": [
+        "people.assign(\n",
+        "    body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
+        "    has_pets = people[\"pets\"] > 0\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L3W85v6f3k27"
+      },
+      "source": [
+        "Note that you cannot access columns created within the same assignment:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ehD8lLH73k27",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2ddd8e5b-1ac5-4327-b5b6-f430202d5705"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Key error: 'body_mass_index'\n"
+          ]
+        }
+      ],
+      "source": [
+        "try:\n",
+        "    people.assign(\n",
+        "        body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
+        "        overweight = people[\"body_mass_index\"] > 25\n",
+        "    )\n",
+        "except KeyError as e:\n",
+        "    print(\"Key error:\", e)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ibD1W-bt3k28"
+      },
+      "source": [
+        "The solution is to split this assignment in two consecutive assignments:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sVGwW3QX3k28",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "41808b6f-000f-41d8-8f84-d43e82cd1f60"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
+              "alice     Biking     172      68   33     True   NaN        22.985398   \n",
+              "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
+              "charles      NaN     185     112   26    False   5.0        32.724617   \n",
+              "\n",
+              "         overweight  \n",
+              "alice         False  \n",
+              "bob            True  \n",
+              "charles        True  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8570b394-bb37-435d-9bd2-97b3cc00864e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>age</th>\n",
+              "      <th>over 30</th>\n",
+              "      <th>pets</th>\n",
+              "      <th>body_mass_index</th>\n",
+              "      <th>overweight</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>172</td>\n",
+              "      <td>68</td>\n",
+              "      <td>33</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>22.985398</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>181</td>\n",
+              "      <td>83</td>\n",
+              "      <td>34</td>\n",
+              "      <td>True</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>25.335002</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>185</td>\n",
+              "      <td>112</td>\n",
+              "      <td>26</td>\n",
+              "      <td>False</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>32.724617</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8570b394-bb37-435d-9bd2-97b3cc00864e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8570b394-bb37-435d-9bd2-97b3cc00864e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8570b394-bb37-435d-9bd2-97b3cc00864e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-a2d57d82-1c70-4901-9945-01f1112d0f16\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a2d57d82-1c70-4901-9945-01f1112d0f16')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-a2d57d82-1c70-4901-9945-01f1112d0f16 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"d6\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 172,\n        \"max\": 185,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          172,\n          181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 26,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          33,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"over 30\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5355339059327378,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.082312968343663,\n        \"min\": 22.985397512168742,\n        \"max\": 32.72461650840029,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          22.985397512168742,\n          25.33500198406642\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"overweight\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ],
+      "source": [
+        "d6 = people.assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
+        "d6.assign(overweight = d6[\"body_mass_index\"] > 25)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Bn3G5HKR3k29"
+      },
+      "source": [
+        "Having to create a temporary variable `d6` is not very convenient. You may want to just chain the assignment calls, but it does not work because the `people` object is not actually modified by the first assignment:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3imbH3eG3k29",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4b5ba7d9-1f42-419e-915a-3a102f7b2544"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Key error: 'body_mass_index'\n"
+          ]
+        }
+      ],
+      "source": [
+        "try:\n",
+        "    (people\n",
+        "         .assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
+        "         .assign(overweight = people[\"body_mass_index\"] > 25)\n",
+        "    )\n",
+        "except KeyError as e:\n",
+        "    print(\"Key error:\", e)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E2mOai213k29"
+      },
+      "source": [
+        "But fear not, there is a simple solution. You can pass a function to the `assign()` method (typically a `lambda` function), and this function will be called with the `DataFrame` as a parameter:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JLeWl6O63k29",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "073e0075-6ae1-4f24-f59d-5a40307c0c62"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
+              "alice     Biking     172      68   33     True   NaN        22.985398   \n",
+              "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
+              "charles      NaN     185     112   26    False   5.0        32.724617   \n",
+              "\n",
+              "         overweight  \n",
+              "alice         False  \n",
+              "bob            True  \n",
+              "charles        True  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-288177dd-7fe8-4e10-ab56-a6af4d7bd2c3\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>age</th>\n",
+              "      <th>over 30</th>\n",
+              "      <th>pets</th>\n",
+              "      <th>body_mass_index</th>\n",
+              "      <th>overweight</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>172</td>\n",
+              "      <td>68</td>\n",
+              "      <td>33</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>22.985398</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>181</td>\n",
+              "      <td>83</td>\n",
+              "      <td>34</td>\n",
+              "      <td>True</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>25.335002</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>185</td>\n",
+              "      <td>112</td>\n",
+              "      <td>26</td>\n",
+              "      <td>False</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>32.724617</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-288177dd-7fe8-4e10-ab56-a6af4d7bd2c3')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-288177dd-7fe8-4e10-ab56-a6af4d7bd2c3 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-288177dd-7fe8-4e10-ab56-a6af4d7bd2c3');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-241ecb1f-da52-4249-9e32-7708e07eba88\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-241ecb1f-da52-4249-9e32-7708e07eba88')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-241ecb1f-da52-4249-9e32-7708e07eba88 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \")\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 172,\n        \"max\": 185,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          172,\n          181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 26,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          33,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"over 30\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5355339059327378,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.082312968343663,\n        \"min\": 22.985397512168742,\n        \"max\": 32.72461650840029,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          22.985397512168742,\n          25.33500198406642\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"overweight\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ],
+      "source": [
+        "(people\n",
+        "     .assign(body_mass_index = lambda df: df[\"weight\"] / (df[\"height\"] / 100) ** 2)\n",
+        "     .assign(overweight = lambda df: df[\"body_mass_index\"] > 25)\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8E6XJof53k2-"
+      },
+      "source": [
+        "Problem solved!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3lFRFNmd3k2-"
+      },
+      "source": [
+        "## Evaluating an expression\n",
+        "A great feature supported by pandas is expression evaluation. It relies on the `numexpr` library which must be installed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "people#.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "id": "ORXbrjeTXwoq",
+        "outputId": "92155d53-4190-4995-f951-9986f1f2b3d8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           hobby  height  weight  age  over 30  pets\n",
+              "alice     Biking     172      68   33     True   NaN\n",
+              "bob      Dancing     181      83   34     True   0.0\n",
+              "charles      NaN     185     112   26    False   5.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7566d752-e2c2-4367-80fe-944a969ec4bc\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>age</th>\n",
+              "      <th>over 30</th>\n",
+              "      <th>pets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>172</td>\n",
+              "      <td>68</td>\n",
+              "      <td>33</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>181</td>\n",
+              "      <td>83</td>\n",
+              "      <td>34</td>\n",
+              "      <td>True</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>185</td>\n",
+              "      <td>112</td>\n",
+              "      <td>26</td>\n",
+              "      <td>False</td>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7566d752-e2c2-4367-80fe-944a969ec4bc')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7566d752-e2c2-4367-80fe-944a969ec4bc button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7566d752-e2c2-4367-80fe-944a969ec4bc');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-8b22fa5f-db00-4bba-a49f-cb410e3dda8c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b22fa5f-db00-4bba-a49f-cb410e3dda8c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-8b22fa5f-db00-4bba-a49f-cb410e3dda8c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_caa7b55a-c177-448b-9cb6-cfb2c4928071\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('people')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_caa7b55a-c177-448b-9cb6-cfb2c4928071 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('people');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "people",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 172,\n        \"max\": 185,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          172,\n          181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 26,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          33,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"over 30\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5355339059327378,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 56
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WPVHEcgh3k2-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 384
+        },
+        "collapsed": true,
+        "outputId": "5f69e30c-64bf-4490-b91d-f1603e8ef805"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ValueError",
+          "evalue": "Expression ((weight) / (((height) / (np.float64(100.0))) ** (np.float64(2.0)))) > (25) has forbidden control characters.",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-3365752211.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"weight / (height/100) ** 2 > 25\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, expr, inplace, **kwargs)\u001b[0m\n\u001b[1;32m   4947\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"resolvers\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"resolvers\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mresolvers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4949\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4950\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mselect_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSelf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/eval.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(expr, parser, engine, local_dict, global_dict, resolvers, level, target, inplace)\u001b[0m\n\u001b[1;32m    355\u001b[0m         \u001b[0meng\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mENGINES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0meng_inst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meng\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparsed_expr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meng_inst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mparsed_expr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massigner\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/engines.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;31m# make sure no names in resolvers and locals/globals clash\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m         return reconstruct_object(\n\u001b[1;32m     83\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maligned_axes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/engines.py\u001b[0m in \u001b[0;36m_evaluate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0mscope\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_scope\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0m_check_ne_builtin_clash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mne\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/numexpr/necompiler.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(ex, local_dict, global_dict, out, order, casting, sanitize, _frame_depth, **kwargs)\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mre_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocal_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mglobal_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_frame_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_frame_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    990\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 991\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    993\u001b[0m def re_evaluate(local_dict: Optional[Dict] = None,\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/numexpr/necompiler.py\u001b[0m in \u001b[0;36mvalidate\u001b[0;34m(ex, local_dict, global_dict, out, order, casting, _frame_depth, sanitize, **kwargs)\u001b[0m\n\u001b[1;32m    892\u001b[0m         \u001b[0mexpr_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mexpr_key\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_names_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 894\u001b[0;31m             \u001b[0m_names_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpr_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetExprNames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msanitize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msanitize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    895\u001b[0m         \u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex_uses_vml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_names_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpr_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m         \u001b[0marguments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetArguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_frame_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_frame_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/numexpr/necompiler.py\u001b[0m in \u001b[0;36mgetExprNames\u001b[0;34m(text, context, sanitize)\u001b[0m\n\u001b[1;32m    735\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgetExprNames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msanitize\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m     \u001b[0mex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstringToExpression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msanitize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    738\u001b[0m     \u001b[0mast\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressionToAST\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    739\u001b[0m     \u001b[0minput_order\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetInputOrder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mast\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/numexpr/necompiler.py\u001b[0m in \u001b[0;36mstringToExpression\u001b[0;34m(s, types, context, sanitize)\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0mskip_quotes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'(\\'[^\\']*\\')'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mno_whitespace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_blacklist_re\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mskip_quotes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Expression {s} has forbidden control characters.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m     \u001b[0mold_ctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_current_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: Expression ((weight) / (((height) / (np.float64(100.0))) ** (np.float64(2.0)))) > (25) has forbidden control characters."
+          ]
+        }
+      ],
+      "source": [
+        "people.eval(\"weight / (height/100) ** 2 > 25\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5aWNID6H3k3G"
+      },
+      "source": [
+        "Assignment expressions are also supported. Let's set `inplace=True` to directly modify the `DataFrame` rather than getting a modified copy:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "n9VoaaA_3k3H",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 405
+        },
+        "outputId": "ca764089-cea1-41ad-9f75-e9f001f7ab3a"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ValueError",
+          "evalue": "Expression (weight) / (((height) / (np.float64(100.0))) ** (np.float64(2.0))) has forbidden control characters.",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-4113334437.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"body_mass_index = weight / (height/100) ** 2\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mpeople\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, expr, inplace, **kwargs)\u001b[0m\n\u001b[1;32m   4947\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"resolvers\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"resolvers\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mresolvers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4949\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4950\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mselect_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSelf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/eval.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(expr, parser, engine, local_dict, global_dict, resolvers, level, target, inplace)\u001b[0m\n\u001b[1;32m    355\u001b[0m         \u001b[0meng\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mENGINES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0meng_inst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meng\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparsed_expr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meng_inst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mparsed_expr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massigner\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/engines.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;31m# make sure no names in resolvers and locals/globals clash\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m         return reconstruct_object(\n\u001b[1;32m     83\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maligned_axes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/engines.py\u001b[0m in \u001b[0;36m_evaluate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0mscope\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_scope\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0m_check_ne_builtin_clash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mne\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/numexpr/necompiler.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(ex, local_dict, global_dict, out, order, casting, sanitize, _frame_depth, **kwargs)\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mre_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocal_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mglobal_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_frame_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_frame_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    990\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 991\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    993\u001b[0m def re_evaluate(local_dict: Optional[Dict] = None,\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/numexpr/necompiler.py\u001b[0m in \u001b[0;36mvalidate\u001b[0;34m(ex, local_dict, global_dict, out, order, casting, _frame_depth, sanitize, **kwargs)\u001b[0m\n\u001b[1;32m    892\u001b[0m         \u001b[0mexpr_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mexpr_key\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_names_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 894\u001b[0;31m             \u001b[0m_names_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpr_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetExprNames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msanitize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msanitize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    895\u001b[0m         \u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex_uses_vml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_names_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpr_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m         \u001b[0marguments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetArguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_frame_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_frame_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/numexpr/necompiler.py\u001b[0m in \u001b[0;36mgetExprNames\u001b[0;34m(text, context, sanitize)\u001b[0m\n\u001b[1;32m    735\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgetExprNames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msanitize\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m     \u001b[0mex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstringToExpression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msanitize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    738\u001b[0m     \u001b[0mast\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressionToAST\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    739\u001b[0m     \u001b[0minput_order\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetInputOrder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mast\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/numexpr/necompiler.py\u001b[0m in \u001b[0;36mstringToExpression\u001b[0;34m(s, types, context, sanitize)\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0mskip_quotes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'(\\'[^\\']*\\')'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mno_whitespace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_blacklist_re\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mskip_quotes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Expression {s} has forbidden control characters.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m     \u001b[0mold_ctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_current_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: Expression (weight) / (((height) / (np.float64(100.0))) ** (np.float64(2.0))) has forbidden control characters."
+          ]
+        }
+      ],
+      "source": [
+        "people.eval(\"body_mass_index = weight / (height/100) ** 2\", inplace=True)\n",
+        "people"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CcjCXXtD3k3H"
+      },
+      "source": [
+        "You can use a local or global variable in an expression by prefixing it with `'@'`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RBqfPJjg3k3H",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 642
+        },
+        "outputId": "df506a60-925b-450b-f2e5-a9b44f3aea23"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "UndefinedVariableError",
+          "evalue": "name 'body_mass_index' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/scope.py\u001b[0m in \u001b[0;36mresolve\u001b[0;34m(self, key, is_local)\u001b[0m\n\u001b[1;32m    230\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhas_resolvers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolvers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.12/collections/__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1014\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1015\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m            \u001b[0;31m# support subclasses that define __missing__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1016\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.12/collections/__init__.py\u001b[0m in \u001b[0;36m__missing__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1007\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1008\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyError\u001b[0m: 'body_mass_index'",
+            "\nDuring handling of the above exception, another exception occurred:\n",
+            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/scope.py\u001b[0m in \u001b[0;36mresolve\u001b[0;34m(self, key, is_local)\u001b[0m\n\u001b[1;32m    241\u001b[0m                 \u001b[0;31m# e.g., df[df > 0]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtemps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    243\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyError\u001b[0m: 'body_mass_index'",
+            "\nThe above exception was the direct cause of the following exception:\n",
+            "\u001b[0;31mUndefinedVariableError\u001b[0m                    Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-2627838370.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0moverweight_threshold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"overweight = body_mass_index > @overweight_threshold\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mpeople\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, expr, inplace, **kwargs)\u001b[0m\n\u001b[1;32m   4947\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"resolvers\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"resolvers\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mresolvers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4949\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4950\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4951\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mselect_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSelf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/eval.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(expr, parser, engine, local_dict, global_dict, resolvers, level, target, inplace)\u001b[0m\n\u001b[1;32m    334\u001b[0m         )\n\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 336\u001b[0;31m         \u001b[0mparsed_expr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m         if engine == \"numexpr\" and (\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, expr, engine, parser, env, level)\u001b[0m\n\u001b[1;32m    807\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    808\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_visitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPARSERS\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 809\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    811\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    826\u001b[0m         \u001b[0mParse\u001b[0m \u001b[0man\u001b[0m \u001b[0mexpression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m         \"\"\"\n\u001b[0;32m--> 828\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_visitor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    829\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36mvisit\u001b[0;34m(self, node, **kwargs)\u001b[0m\n\u001b[1;32m    410\u001b[0m         \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"visit_{type(node).__name__}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m         \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvisit_Module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36mvisit_Module\u001b[0;34m(self, node, **kwargs)\u001b[0m\n\u001b[1;32m    416\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mSyntaxError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"only a single expression is allowed\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m         \u001b[0mexpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvisit_Expr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36mvisit\u001b[0;34m(self, node, **kwargs)\u001b[0m\n\u001b[1;32m    410\u001b[0m         \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"visit_{type(node).__name__}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m         \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvisit_Module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36mvisit_Assign\u001b[0;34m(self, node, **kwargs)\u001b[0m\n\u001b[1;32m    633\u001b[0m             )\n\u001b[1;32m    634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 635\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvisit_Attribute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36mvisit\u001b[0;34m(self, node, **kwargs)\u001b[0m\n\u001b[1;32m    410\u001b[0m         \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"visit_{type(node).__name__}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m         \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvisit_Module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36mvisit_Compare\u001b[0;34m(self, node, **kwargs)\u001b[0m\n\u001b[1;32m    717\u001b[0m             \u001b[0mop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslate_In\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0mbinop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBinOp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcomps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 719\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbinop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m         \u001b[0;31m# recursive case: we have a chained comparison, a CMP b CMP c, etc.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36mvisit\u001b[0;34m(self, node, **kwargs)\u001b[0m\n\u001b[1;32m    410\u001b[0m         \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"visit_{type(node).__name__}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m         \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvisit_Module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36mvisit_BinOp\u001b[0;34m(self, node, **kwargs)\u001b[0m\n\u001b[1;32m    530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvisit_BinOp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 532\u001b[0;31m         \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_class\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_transform_eq_ne\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    533\u001b[0m         \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_downcast_constants\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_evaluate_binop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_class\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36m_maybe_transform_eq_ne\u001b[0;34m(self, node, left, right)\u001b[0m\n\u001b[1;32m    450\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_maybe_transform_eq_ne\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mleft\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"left\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    453\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mright\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m             \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"right\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36mvisit\u001b[0;34m(self, node, **kwargs)\u001b[0m\n\u001b[1;32m    410\u001b[0m         \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"visit_{type(node).__name__}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m         \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvisit_Module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36mvisit_Name\u001b[0;34m(self, node, **kwargs)\u001b[0m\n\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvisit_Name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTerm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterm_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m     \u001b[0;31m# TODO(py314): deprecated since Python 3.8. Remove after Python 3.14 is min\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, env, side, encoding)\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0mtname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_local\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLOCAL_TAG\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mDEFAULT_GLOBALS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_resolve_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/ops.py\u001b[0m in \u001b[0;36m_resolve_name\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    113\u001b[0m             \u001b[0mis_local\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocal_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_local\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_local\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/computation/scope.py\u001b[0m in \u001b[0;36mresolve\u001b[0;34m(self, key, is_local)\u001b[0m\n\u001b[1;32m    242\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtemps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mUndefinedVariableError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_local\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mswapkey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mold_key\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_key\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mUndefinedVariableError\u001b[0m: name 'body_mass_index' is not defined"
+          ]
+        }
+      ],
+      "source": [
+        "overweight_threshold = 30\n",
+        "people.eval(\"overweight = body_mass_index > @overweight_threshold\", inplace=True)\n",
+        "people"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rHAyS6Kc3k3I"
+      },
+      "source": [
+        "## Querying a `DataFrame`\n",
+        "The `query()` method lets you filter a `DataFrame` based on a query expression:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "H7-wGpTM3k3I",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 119
+        },
+        "outputId": "fcef54d6-15f0-47c6-eb43-7f78b36c7329"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          hobby  height  weight  age  over 30  pets\n",
+              "alice    Biking     172      68   33     True   NaN\n",
+              "charles     NaN     185     112   26    False   5.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3e23aa82-90e5-406a-8faf-cc5ad558cde6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>age</th>\n",
+              "      <th>over 30</th>\n",
+              "      <th>pets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>172</td>\n",
+              "      <td>68</td>\n",
+              "      <td>33</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>185</td>\n",
+              "      <td>112</td>\n",
+              "      <td>26</td>\n",
+              "      <td>False</td>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e23aa82-90e5-406a-8faf-cc5ad558cde6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3e23aa82-90e5-406a-8faf-cc5ad558cde6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3e23aa82-90e5-406a-8faf-cc5ad558cde6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-e0028123-e0c8-4b5d-877b-27fd3b72a2b5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e0028123-e0c8-4b5d-877b-27fd3b72a2b5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-e0028123-e0c8-4b5d-877b-27fd3b72a2b5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 172,\n        \"max\": 185,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          185\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 26,\n        \"max\": 33,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"over 30\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 72
+        }
+      ],
+      "source": [
+        "people.query(\"age > 20 and pets != 0\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WqqJBcL13k3I"
+      },
+      "source": [
+        "## Sorting a `DataFrame`\n",
+        "You can sort a `DataFrame` by calling its `sort_index` method. By default, it sorts the rows by their index label, in ascending order, but let's reverse the order:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ge1OUc4O3k3J",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "3df13a44-2e36-412c-bf5e-99f3e3f7695d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           hobby  height  weight  age  over 30  pets\n",
+              "charles      NaN     185     112   26    False   5.0\n",
+              "bob      Dancing     181      83   34     True   0.0\n",
+              "alice     Biking     172      68   33     True   NaN"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-aacfe9af-d814-4d4e-8fee-3ff7f8e42801\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>age</th>\n",
+              "      <th>over 30</th>\n",
+              "      <th>pets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>185</td>\n",
+              "      <td>112</td>\n",
+              "      <td>26</td>\n",
+              "      <td>False</td>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>181</td>\n",
+              "      <td>83</td>\n",
+              "      <td>34</td>\n",
+              "      <td>True</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>172</td>\n",
+              "      <td>68</td>\n",
+              "      <td>33</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aacfe9af-d814-4d4e-8fee-3ff7f8e42801')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-aacfe9af-d814-4d4e-8fee-3ff7f8e42801 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-aacfe9af-d814-4d4e-8fee-3ff7f8e42801');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-24b720cf-0115-4048-a7d7-f87a958fbd02\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-24b720cf-0115-4048-a7d7-f87a958fbd02')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-24b720cf-0115-4048-a7d7-f87a958fbd02 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Biking\",\n          \"Dancing\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 172,\n        \"max\": 185,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          185,\n          181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          112,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 26,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          26,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"over 30\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5355339059327378,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 73
+        }
+      ],
+      "source": [
+        "people.sort_index(ascending=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hPK1MZmW3k3J"
+      },
+      "source": [
+        "Note that `sort_index` returned a sorted *copy* of the `DataFrame`. To modify `people` directly, we can set the `inplace` argument to `True`. Also, we can sort the columns instead of the rows by setting `axis=1`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "v-_Rno3m3k3J",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "f9d1e91a-09f0-437e-93f1-11b227e44781"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         age  height    hobby  over 30  pets  weight\n",
+              "alice     33     172   Biking     True   NaN      68\n",
+              "bob       34     181  Dancing     True   0.0      83\n",
+              "charles   26     185      NaN    False   5.0     112"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-44011550-320d-4560-a3c3-9103f8875069\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>height</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>over 30</th>\n",
+              "      <th>pets</th>\n",
+              "      <th>weight</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>33</td>\n",
+              "      <td>172</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>68</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>34</td>\n",
+              "      <td>181</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>True</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>83</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>26</td>\n",
+              "      <td>185</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>False</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44011550-320d-4560-a3c3-9103f8875069')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-44011550-320d-4560-a3c3-9103f8875069 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-44011550-320d-4560-a3c3-9103f8875069');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-18de459b-951d-4ce5-a957-9e467e041b74\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-18de459b-951d-4ce5-a957-9e467e041b74')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-18de459b-951d-4ce5-a957-9e467e041b74 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_366c4604-19a7-4d75-8ebd-ddf1fde00ba2\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('people')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_366c4604-19a7-4d75-8ebd-ddf1fde00ba2 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('people');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "people",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 26,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          33,\n          34,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 172,\n        \"max\": 185,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          172,\n          181,\n          185\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"over 30\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5355339059327378,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 74
+        }
+      ],
+      "source": [
+        "people.sort_index(axis=1, inplace=True)\n",
+        "people"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4Q3vKuCF3k3J"
+      },
+      "source": [
+        "To sort the `DataFrame` by the values instead of the labels, we can use `sort_values` and specify the column to sort by:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "koS_3AmT3k3J",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "2c7c8645-33bc-4cc1-e47e-834ebe0c4048"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         age  height    hobby  over 30  pets  weight\n",
+              "charles   26     185      NaN    False   5.0     112\n",
+              "alice     33     172   Biking     True   NaN      68\n",
+              "bob       34     181  Dancing     True   0.0      83"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2294ee19-4aa3-4087-aa9a-836163700ef2\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>height</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>over 30</th>\n",
+              "      <th>pets</th>\n",
+              "      <th>weight</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>26</td>\n",
+              "      <td>185</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>False</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>33</td>\n",
+              "      <td>172</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>68</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>34</td>\n",
+              "      <td>181</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>True</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>83</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2294ee19-4aa3-4087-aa9a-836163700ef2')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2294ee19-4aa3-4087-aa9a-836163700ef2 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2294ee19-4aa3-4087-aa9a-836163700ef2');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f4856a85-3f7e-4e2a-a96c-b8f763ddbd2c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f4856a85-3f7e-4e2a-a96c-b8f763ddbd2c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-f4856a85-3f7e-4e2a-a96c-b8f763ddbd2c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_7b3ebe94-d035-452b-b2ab-f5ccec44ff1e\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('people')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_7b3ebe94-d035-452b-b2ab-f5ccec44ff1e button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('people');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "people",
+              "summary": "{\n  \"name\": \"people\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 26,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          26,\n          33,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 172,\n        \"max\": 185,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          185,\n          172,\n          181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"over 30\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5355339059327378,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 68,\n        \"max\": 112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          112,\n          68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 75
+        }
+      ],
+      "source": [
+        "people.sort_values(by=\"age\", inplace=True)\n",
+        "people"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cOgqOwaT3k3K"
+      },
+      "source": [
+        "## Plotting a `DataFrame`\n",
+        "Just like for `Series`, pandas makes it easy to draw nice graphs based on a `DataFrame`.\n",
+        "\n",
+        "For example, it is trivial to create a line plot from a `DataFrame`'s data by calling its `plot` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "A0ji9jxY3k3L",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "outputId": "acf5b269-bb0d-49aa-ed47-c8c984ed6c41"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGwCAYAAACD0J42AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAS6lJREFUeJzt3Xlc1HX+B/DXDAPDOYMgp4LgCZr3FeaZ5JFiamWHW65ZdlmbWqkdVlut1dqxtW2lbdph2y93V1e0PNIQNTIvtAxREE+EUZEZhmMYZr6/P74wwwgCgwPf7wyv5+PxfezOfIeZ90dIXn4+38/7qxAEQQARERGRjCilLoCIiIjoagwoREREJDsMKERERCQ7DChEREQkOwwoREREJDsMKERERCQ7DChEREQkOyqpC2gOq9WK/Px8BAUFQaFQSF0OERERNYEgCCgpKUF0dDSUyobnSNwyoOTn5yMmJkbqMoiIiKgZzp49i44dOzb4GrcMKEFBQQDEAWo0GomrISIioqYwGAyIiYmx/R5viFsGlJplHY1Gw4BCRETkZppyeQYvkiUiIiLZYUAhIiIi2WFAISIiItlhQCEiIiLZYUAhIiIi2WFAISIiItlhQCEiIiLZYUAhIiIi2WFAISIiItlhQCEiIiLZYUAhIiIi2WFAISIiItlhQKmlvNKC3ItGGCrMEARB6nKIiIjaLLe8m3FLOXKuGHet+BkAoFYpERakRniQGmE1R6AvwjVqhAXan2sfqIaPijmPiIjIlRhQaimrtCBIrUKJqQqmKivOXSnHuSvljX5dO3/vWiFGjXCNr0OIqQk6Wj/vJt1imoiIqK1TCG64lmEwGKDVaqHX66HRaFz+/uWVFlwymqArMeFiSQUulpjEwyj+r67mcYkJVdam//F5eylqBRdfhwAjBhv77Iyvt5fLx0VERCQlZ35/cwalHn4+XogJ8UdMiH+Dr7NaBejLzfbAYrSHmdoh5qLRhOIyM8wWAfn6CuTrKwDoG3zvIF9VreUl+4yMw5JTkBoh/j5QKjkrQ0REnoUB5ToolQq0C/BBuwAf9IgMavC1pioLLhkr7aGlxATdVbMzOoP4v5VVVpRUVKGkogq5F0sbfF8vpQLtA31sszBiiPGts7wUFqSGvw+/3URE5B74G6uVqFVe6BDshw7Bfg2+ThAEGCqq6g0wtcPNxRITLpdWwmIVUGgwodBgarSGAB+vWqHF12F5Kax6eSk8SI2QAB+ovHjhLxERSYcBRWYUCgW0ft7Q+nmja3hgg681W6y4XDMr08Dyks5gQrnZgtJKC0ovl+HU5bJGagBCAxyvj6lveSk8SI1AtYoX/hIRkcsxoLgxby8lIrW+iNT6AtBe83WCIKC00lL/0tJVy0uXjSZYBeCS0YRLRhOyLjRcg6+38trLSzUX/gapERrA7dhERNR0DChtgEKhQKBahUC1CvHtAxp8rcUqoKi0st7lpZqZmUvV/1tiqkKF2YqzReU4W9T07dj1hpha27K5HZuIiBhQyIGXUmELCo0pq6zCpZLKRpeXLhnF7dhXysy4UmbG8UJjg+/r4yXOyrRvYHmJ27GJiDwbAwo1m7+PCrGhKsSGNr4du7jc3OjupYslJujLzai0WHG+uBznixufldH4qq594W+QfXamHbdjExG5FQYUanFKpQIhAT4IcWI7ts5Qcc3meDVHpcUKQ0UVDE5sx7aFmKsCTO3ZGW7HJqK27Mi5YnySfhLDu7bHPUNiJavD6b+J09PT8de//hUHDhzAhQsXsG7dOkydOtV23mg0YvHixVi/fj0uX76M+Ph4PPnkk3jkkUdsr6moqMDChQvxzTffwGQyYfz48fjHP/6BiIgIlwyK3JdT27HLq3DRWFFveKk9M1Pk5HbsQLXKIcTUOaqvmwkNUMOLszJE5AEEQUDa8YtYsfMkMk5eBgD8nm/AXYNiJJt9djqglJaWom/fvnjggQcwffr0OucXLFiAHTt24KuvvkJcXBy2bt2Kxx57DNHR0ZgyZQoAYP78+di0aRPWrl0LrVaLefPmYfr06dizZ8/1j4jaBIVCAa2/N7T+3uga3vCsTO3t2A0tL+lKKlBhtsJoqoLRVIW8Sw3PyigVQEhAPdfI1HPdDLdjE5EcVVZZkXo4HyvSTyK7sAQAoFIqkNI3Gg+N6Czp0vh13YtHoVDUmUG54YYbcNddd+HFF1+0PTdw4EBMnDgRr732GvR6PcLCwvD111/jjjvuAAAcO3YMiYmJyMjIwI033ljnc0wmE0wm+798DQYDYmJiWuxePNQ21WzHbnR5qdZ27Kby9VbWXV6qZ3amfaAa3mySR0QtrKTCjG9+OYt/7s5DgaECgNjM854hsXhgeDyiG5nFbi5J78UzbNgwbNiwAQ888ACio6ORlpaG48eP49133wUAHDhwAGazGcnJybavSUhIQGxs7DUDyrJly/DKK6+4ulQiB7bt2GGB6BzWcJO8KosVRWWV19y9dLHWzIyxejv2maIynClquEkeAIQE+DS4e0m8bsYXGj/OyhCRcwoNFfhsTx6+/vkMSkxVAICwIDVm3xSHmUM7QevnLXGFdi4PKB988AHmzp2Ljh07QqVSQalUYuXKlRg5ciQAoKCgAD4+PggODnb4uoiICBQUFNT7nkuWLMGCBQtsj2tmUIikovISZ0TCg3wbfW3Ndmzb8lLNzEytEHOxxL4du6i0EkWllbbp1mup2Y5d3/br8KtmZbgdm6htO1FYghXpJ7E+8zzMFnH6t0tYAOaO7Iyp/TtArZLf3xEtElB+/vlnbNiwAZ06dUJ6ejoef/xxREdHO8yaOEOtVkOtbrwvB5EcObMd+0pZpUNoqW95SWeogKGiyqnt2Fo/7zoX/ta9dYEvgv28uR2byEMIgoBf8oqwIv0kth/T2Z4fHNcOc0d2wdiEcFn/9+7SgFJeXo7nnnsO69atw6RJkwAAffr0QWZmJpYvX47k5GRERkaisrISxcXFDrMohYWFiIyMdGU5RG5FqVQgNFCN0EA1Ehr5T6HCbMGl+q6RqfVcTcffSosV+nIz9OVm5OgabpKnUirQ/lohptbyUliQGn4+8vsXFxGJHcG3Hi3AJ+knkXm2GIB4j7VxPSMwd2QXDOzUTtoCm8ilAcVsNsNsNkOpdLzIz8vLC1arFYB4way3tze2b9+O22+/HQCQnZ2NM2fOICkpyZXlEHksX28vdGznj47tGp6VqdmOXWd5qZ6ZmaLSSlRZBRQYKmwXzTXEth27kZtKcjs2UeuoMFvw7wPn8Omuk7abwvqolLh9QEc8NCK+0Wvr5MbpgGI0GpGTk2N7nJeXh8zMTISEhCA2NhajRo3CM888Az8/P3Tq1Ak7d+7EF198gXfeeQcAoNVqMWfOHCxYsAAhISHQaDR44oknkJSUVO8FskTUfLW3Y3eLaHg7dmWVFZdLG1leKqmAzmCCqcq57dihgQ0sL9ma5vkiwMeLF/4SOelKaSW+/Pk0Pv/pFC6XVgIQl3XvT+qE+5PimnTrEjlyeptxWloaxowZU+f5WbNmYfXq1SgoKMCSJUuwdetWFBUVoVOnTpg7dy7mz59v+4unplHbv/71L4dGbU1d4nFmmxIRuZYgCDCaqhpcXqp57nKpCc78DePn7VU3xDh0/RWXl0IDfbgdm9q8s0Vl+HTXSXy7/xzKzRYAQIdgPzw4Ih4zBsUgQC2/rtjO/P6+rj4oUmFAIXIPVRZr9d2xHXcs1Xfhb2mlxan3DgnwqRNiHC/65XZs8ky/ntPjk/RcfPfrBVs/pl7RGswd2RmTekdBJePwLmkfFCKiGiovJcI1vgjXNL4du9RUVf+Fv7WWl8Tt2OKtC2q2Yx8raGQ7tkrZpOWl9oE+stxqSQSIM5fpJy7hk525+Cn3su35Ed3a4+GRXXBT11CPC+IMKEQkCwFqFQLUKnQKDWjwdTXbsa+9vGS/nYGhogqVVc5vx65vZqb23bK5HZtai9lib0VfE8a9lAqk9InCQyM7o1e0VuIKWw4DChG5ldrbsROjGn5thdniEGDq7/pbgYtGE8wWocnbsb29am3HvubsDLdjU/MZTVX45pcz+OfuPFzQi7vq/H28cPfgWDwwPK7RHXyegAGFiDyWr7cXYkL8ERPS+HZsfbm5gQt/7bMyV8rMMFsEXNBX2H5xNCSoejt2+2td/Fs9MxMS4MPt2ASdoQKrfjqFr34+jZIKsRV9+0CxFf0fhnaC1l8+rehbGgMKEbV5CoUCwf4+CPb3adJ27EvGhkNMzZ2yTVVWlJiqUGKqwskmbsdubHmp5u7Y5FlydEasTD+JdYfOo9Ii9g3r3N7eir4t3q6CP+VERE7wUSkRHezX6N1eBUFASfV27GstL+kMFbhkNOFyaSWsAmznGuPv49Xo8lK4Ro3QAB9Z7+ho6wRBwP7TV/DJzlz8kGVvRT+wUzs8PLIzkhMj2vS1TgwoREQtQKFQQOPrDY2vN7o05e7Ypde68LfCIeSUVlpQVmnB6ctlOH254btjKxRAiL9PAzeVtM/MaHy5Hbu1WKwCtv0utqI/dKYYgPi9uiUxAg+P6oyBnUKkLVAmGFCIiCTm7Hbsuhf+VtQJNjXbsS+XVuJyE7Zjq1XKOgGmvuWlsEA1fFSclWmOCrMF/zl4Dp/uyrN1YBZb0XfAgyM6Nxpk2xoGFCIiN1KzHTuufcPbsS01d8duYHmpJsyUVFTBVGXFuSvlOHel8e3Ywf7ejS4vhQWqEezvzVkZAMVllfgy4zQ+zziFS0axFb3GV4X7kjph1rA4hAc1HkzbIgYUIiIP5FV9Z+r2TmzH1l01C3PxquWlmu3YxWVmFJeZccKJ7dh1L/71dXjeEy8CPVtUhn/uzsO3+8+irNLeiv6B4fG4a3AML3ZuBP90iIjauKZux7Zaq7djN7C8pDOI/1vcnO3YmgaWl6qfd4ft2L+d12NF+kls+vUCLNW96BOjNHh4ZGdM6hPF+0g1EQMKERE1iVKpQLsAH7QL8EH3RrZjm6osuGysrGd5qcIWYmrOVdZsx75YhZMXG96O7aVUIDTAp+7yUmDdWZnWvFmeIAjYdeISVqSfxO6cS7bnh3dtj4dHdcbwru253OUkBhQiInI5tcrLqe3YOkPDu5dqtmNbrAJ01cGmMTXbsa/VHK/muJ7t2GaLFZuOXMAn6SeRdcEAQAxRk/tE4aERnXFDB89tRd/SGFCIiEgytbdjdw1veBeLuXo7dmPLSzqDCeVm57Zjhwb42G9fcI3lpXCNGkFqcTt2TSv6VXtO2e7z5O/jhbsGx+CBm+IbXS6jxjGgEBGRW/D2UiJC44sIJ7Zj25eXKhxCTO2ZGasAXDJW4pKx6dux9eXmWq3offDHYXH4w42dEOzv45KxEgMKERF5IGe2Y9tmZa6xvCTeVNKEEpN9OzYgtqJ/cERnTB/QNlvRtzQGFCIiarO8lArbUk5jyistuGQUZ2UEQUD/2Hay31HkzhhQiIiImsDPp2nbsck1uBmbiIiIZIcBhYiIiGSHAYWIiIhkhwGFiIiIZIcBhYiIiGSHAYWIiIhkhwGFiIiIZIcBhYiIiGSHAYWIiIhkhwGFiIiIZIcBhYiIiGSHAYWIiIhkhwGFiIiIZIcBhYiIiGSHAYWIiIhkhwGFiIiIZIcBhYiIiGSHAYWIiIhkhwGFiIiIZIcBhYiIiGSHAYWIiIhkhwGFiIiIZIcBhYiIiGSHAYWIiIhkhwGFiIiIZIcBhYiIiGSHAYWIiIhkhwGFiIiIZMfpgJKeno6UlBRER0dDoVBg/fr1dV6TlZWFKVOmQKvVIiAgAIMHD8aZM2ds5ysqKvD4448jNDQUgYGBuP3221FYWHhdAyEiIiLP4XRAKS0tRd++ffHhhx/Wez43NxfDhw9HQkIC0tLScOTIEbz44ovw9fW1vWb+/PlITU3F2rVrsXPnTuTn52P69OnNHwURERF5FIUgCEKzv1ihwLp16zB16lTbc3fffTe8vb3x5Zdf1vs1er0eYWFh+Prrr3HHHXcAAI4dO4bExERkZGTgxhtvbPRzDQYDtFot9Ho9NBpNc8snIiKiVuTM72+XXoNitVqxadMmdO/eHePHj0d4eDiGDh3qsAx04MABmM1mJCcn255LSEhAbGwsMjIy6n1fk8kEg8HgcBAREZHncmlA0el0MBqNeOONNzBhwgRs3boV06ZNw/Tp07Fz504AQEFBAXx8fBAcHOzwtRERESgoKKj3fZctWwatVms7YmJiXFk2ERERyYzLZ1AA4LbbbsP8+fPRr18/LF68GJMnT8bHH3/c7PddsmQJ9Hq97Th79qyrSiYiIiIZUrnyzdq3bw+VSoWePXs6PJ+YmIjdu3cDACIjI1FZWYni4mKHWZTCwkJERkbW+75qtRpqtdqVpRIREZGMuXQGxcfHB4MHD0Z2drbD88ePH0enTp0AAAMHDoS3tze2b99uO5+dnY0zZ84gKSnJleUQERGRm3J6BsVoNCInJ8f2OC8vD5mZmQgJCUFsbCyeeeYZ3HXXXRg5ciTGjBmDzZs3IzU1FWlpaQAArVaLOXPmYMGCBQgJCYFGo8ETTzyBpKSkJu3gISIiIs/n9DbjtLQ0jBkzps7zs2bNwurVqwEAn332GZYtW4Zz586hR48eeOWVV3DbbbfZXltRUYGFCxfiX//6F0wmE8aPH49//OMf11ziuRq3GRMREbkfZ35/X1cfFKkwoBAREbkfyfqgEBEREbkCAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJDgMKERERyQ4DChEREckOAwoRERHJjtMBJT09HSkpKYiOjoZCocD69euv+dpHHnkECoUC7733nsPzRUVFmDlzJjQaDYKDgzFnzhwYjUZnSyEiIiIP5XRAKS0tRd++ffHhhx82+Lp169bh559/RnR0dJ1zM2fOxNGjR7Ft2zZs3LgR6enpmDt3rrOlEBERkYdSOfsFEydOxMSJExt8zfnz5/HEE09gy5YtmDRpksO5rKwsbN68Gfv27cOgQYMAAB988AFuvfVWLF++vN5AYzKZYDKZbI8NBoOzZRMREZEbcfk1KFarFffddx+eeeYZ9OrVq875jIwMBAcH28IJACQnJ0OpVGLv3r31vueyZcug1WptR0xMjKvLJiIiIhlxeUB58803oVKp8OSTT9Z7vqCgAOHh4Q7PqVQqhISEoKCgoN6vWbJkCfR6ve04e/asq8smIiIiGXF6iachBw4cwN/+9jccPHgQCoXCZe+rVquhVqtd9n5EREQkby6dQdm1axd0Oh1iY2OhUqmgUqlw+vRpLFy4EHFxcQCAyMhI6HQ6h6+rqqpCUVERIiMjXVkOERERuSmXzqDcd999SE5Odnhu/PjxuO+++zB79mwAQFJSEoqLi3HgwAEMHDgQALBjxw5YrVYMHTrUleUQERGRm3I6oBiNRuTk5Nge5+XlITMzEyEhIYiNjUVoaKjD6729vREZGYkePXoAABITEzFhwgQ89NBD+Pjjj2E2mzFv3jzcfffd9e7gISIiorbH6SWe/fv3o3///ujfvz8AYMGCBejfvz+WLl3a5PdYs2YNEhISMHbsWNx6660YPnw4VqxY4WwpRERE5KEUgiAIUhfhLIPBAK1WC71eD41GI3U5RERE1ATO/P7mvXiIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdpwOKOnp6UhJSUF0dDQUCgXWr19vO2c2m7Fo0SL07t0bAQEBiI6Oxv3334/8/HyH9ygqKsLMmTOh0WgQHByMOXPmwGg0XvdgiIiIyDM4HVBKS0vRt29ffPjhh3XOlZWV4eDBg3jxxRdx8OBB/Pe//0V2djamTJni8LqZM2fi6NGj2LZtGzZu3Ij09HTMnTu3+aMgIiIij6IQBEFo9hcrFFi3bh2mTp16zdfs27cPQ4YMwenTpxEbG4usrCz07NkT+/btw6BBgwAAmzdvxq233opz584hOjq60c81GAzQarXQ6/XQaDTNLZ+IiIhakTO/v1v8GhS9Xg+FQoHg4GAAQEZGBoKDg23hBACSk5OhVCqxd+/eet/DZDLBYDA4HEREROS5WjSgVFRUYNGiRbjnnntsSamgoADh4eEOr1OpVAgJCUFBQUG977Ns2TJotVrbERMT05JlExERkcRaLKCYzWbMmDEDgiDgo48+uq73WrJkCfR6ve04e/asi6okIiIiOVK1xJvWhJPTp09jx44dDutMkZGR0Ol0Dq+vqqpCUVERIiMj630/tVoNtVrdEqUSERGRDLl8BqUmnJw4cQI//PADQkNDHc4nJSWhuLgYBw4csD23Y8cOWK1WDB061NXlEBERkRtyegbFaDQiJyfH9jgvLw+ZmZkICQlBVFQU7rjjDhw8eBAbN26ExWKxXVcSEhICHx8fJCYmYsKECXjooYfw8ccfw2w2Y968ebj77rubtIOHiIiIPJ/T24zT0tIwZsyYOs/PmjULL7/8MuLj4+v9uh9//BGjR48GIDZqmzdvHlJTU6FUKnH77bfj/fffR2BgYJNq4DZjIiIi9+PM7+/r6oMiFQYUIiIi9yOrPihEREREzmJAISIiItlhQCEiIiLZYUAhIiIi2WFAISIiItlhQCEiIiLZYUAhIiIi2WFAISIiItlhQCEiIiLZYUAhIiIi2WFAISIiItlhQCEiIiLZYUAhIiIi2WFAISIiItlhQKnNXA5seALI3gyYK6SuhoiIqM1SSV2ArOTuAA5+IR4+gUC3cUBiCtDtFkAdJHV1REREbQYDSm0hnYEhDwNZqUBJPnD0v+LhpQa63CyGlR4TAf8QqSslIiLyaApBEASpi3CWwWCAVquFXq+HRqNx/QdYrUD+ISBrg3gUnbSfU3gBccPFsJIwGdBEuf7ziYiIPJAzv78ZUBojCIAuS5xVyUoFCn91PN9xiBhWEieLMzBERERULwaUllR0EsjaKIaVc784novoXR1WUoDwREChaN3aiIiIZIwBpdUKyQeObRLDyqndgGCxnwvpUh1WpgDR/QElN0wREVHbxoAihbIiIPt7Mazk7gAsJvs5TQfxepXEFCA2CfDitclERNT2MKBIzVQCnNgmhpUTW4FKo/2cfyjQ41ZxZqXzKECllq5OIiKiVsSAIifmCuBkmhhWsjcB5Vfs53yCgO7jxZmVrsmAOlCyMomIiFoaA4pcWaqA03vEsHJsI1BywX5O5Qt0GSuGle7j2WuFiIg8DgOKO7BagfMHqnutpAJX8uznlCogboS4dTlhMhAUKV2dRERELsKA4m4EASg8au+1ojta66QCiBli377cLk6qKomIiK4LA4q7u5xrDyvn9zuei+wtXmCbOAUI68FeK0RE5DYYUDyJ/nx1r5UN4vUrgtV+LrSrfWYlegDDChERyRoDiqcqvQxkfyfOrJz8EbBU2s9pOorXrNT0WlF6SVcnERFRPRhQ2oIKg9hjJStV7LliLrWf828PJFT3WokfyV4rREQkCwwobY25HMj9sbrXyndARbH9nFrj2GvFJ0CyMomIqG1jQGnLLGZ7r5WsjYCxwH5O5SuGlJpeK37tpKuTiIjaHAYUElmtwLl99l4rxaft55QqcfknMQXoMQkIipCuTiIiahMYUKguQQAKfhU72GalArrfa51UALE3imElYTLQrpNkZRIRkediQKHGXcoBjtX0WjngeC6qb/X25epeK0RERC7AgELO0Z+r7rWSWrfXSvvu9l4rUf3Ya4WIiJqNAYWar/SSvddK7o+A1Ww/p42xh5WYoey1QkRETmFAIdeo0Is9VrI2VPdaKbOfCwgDEiaJYSVuJKDyka5OIiJyCwwo5HrmciB3R61eK3r7ObUW6DFBDCtdxgI+/tLVSUREssWAQi3LYgZO7bL3WinV2c+p/IBuyeIFtt3GAX7BkpVJRETywoBCrcdqqe61kiouBRWfsZ9TegOdR9l7rQSGSVcnERFJjgGFpCEIQMGR6rCSClw8Zj+nUIo3MazptRIcI12dREQkCQYUkoeLx+29VvIPOZ6L7m/vtdK+mzT1ERFRq2JAIfkpPlOr18pPAGr92IUl2LcvR/ZhrxUiIg/FgELyZtTZe62c3OnYayU4VpxVSUwBOg4BlErp6iQiIpdy5ve303/7p6enIyUlBdHR0VAoFFi/fr3DeUEQsHTpUkRFRcHPzw/Jyck4ceKEw2uKioowc+ZMaDQaBAcHY86cOTAajc6WQu4qMBwY+EfgD/8BnskBpq8UA4nKT5xpyfg78Nl44J0EYON8cXuzxdzo2xIRkedwOqCUlpaib9+++PDDD+s9/9Zbb+H999/Hxx9/jL179yIgIADjx49HRUWF7TUzZ87E0aNHsW3bNmzcuBHp6emYO3du80dB7ssvGOgzA7jrK+DZk+L/9rlL7K1iLAT2fwZ8OQ34axdg3SPiMpG5XOqqiYiohV3XEo9CocC6deswdepUAOLsSXR0NBYuXIinn34aAKDX6xEREYHVq1fj7rvvRlZWFnr27Il9+/Zh0KBBAIDNmzfj1ltvxblz5xAdHd3o53KJpw2oqgROpYvLQMc2AaUX7ee8/YFut9h7rfjyZ4CIyB206BJPQ/Ly8lBQUIDk5GTbc1qtFkOHDkVGRgYAICMjA8HBwbZwAgDJyclQKpXYu3dvve9rMplgMBgcDvJwKh+gazKQ8jdgYTYw+3vgxsfE+wGZy4Df/wf8Z444s7LmTuDgF+J9hIiIyCOoXPlmBQUFAICIiAiH5yMiImznCgoKEB4e7liESoWQkBDba662bNkyvPLKK64sldyJ0gvoNEw8xv8FuJApzqz8vgG4fAI4sVU8FH8COt1U3WtlEqDtKHXlRETUTG6xRWLJkiXQ6/W24+zZs1KXRFJRKMQeKmOXAk/sBx7/Bbj5BSCqLyBYxRb83z8LvNsLWHkzsPtd4FKO1FUTEZGTXDqDEhkZCQAoLCxEVFSU7fnCwkL069fP9hqdTufwdVVVVSgqKrJ9/dXUajXUarUrSyVPEdYDCHsGGPkMcOU0cGyjOLty5mfg/AHx+OFlILynvYttZG/2WiEikjmXzqDEx8cjMjIS27dvtz1nMBiwd+9eJCUlAQCSkpJQXFyMAwcO2F6zY8cOWK1WDB061JXlUFvTrhOQ9DjwwGbxupXJ7wJdbgaUKkD3O7DzTeCTEcDf+gJbngfO7AWsVqmrJiKieji9i8doNCInR5wy79+/P9555x2MGTMGISEhiI2NxZtvvok33ngDn3/+OeLj4/Hiiy/iyJEj+P333+Hr6wsAmDhxIgoLC/Hxxx/DbDZj9uzZGDRoEL7++usm1cBdPOSU8ivA8S3izErOD0CVfcs7AiOBxMni7EqnmwAvb+nqJCLycC3aSTYtLQ1jxoyp8/ysWbOwevVqCIKAl156CStWrEBxcTGGDx+Of/zjH+jevbvttUVFRZg3bx5SU1OhVCpx++234/3330dgYKDLB0jkoLIUyNku3nn5+BbAVGtHmF87oPtEMax0GQN4+0lXJxGRB2Kre6KmqDIBeeliWDm2CSi7bD/nHVDdayWFvVaIiFyEAYXIWVYLcCZDXAbK2ggYztnPefkAnceIYaXHrUBAqHR1EhG5MQYUoushCED+oeqwsgG4XGubskJZ3WtlSnWvlQ7S1UlE5GYYUIhcRRCAi9n2sFJwxPF8h0HizEpiChDaRZoaiYjcBAMKUUu5ckpcAspKBc7uBVDrP5/wXvawEtGLvVaIiK7CgELUGkoKxItrs1LFi20Fi/1cu/jqsDIF6DAQULpF02YiohbFgELU2sqK7L1Wcrc79loJihI72Np6rbi0gTMRkdtgQCGSkskoNoTLShVDS2WJ/ZxfiLgTKDEF6Dwa8PaVrEwiotbGgEIkF1Um4OROe6+V8iL7OZ9AscdKYorYc0UdJF2dREStgAGFSI4sVbV6raQCJfn2c15q8b5BiSlAj4mAf4h0dRIRtRAGFCK5s1qre61sEI+ik/ZzCi8gbrj97suaqGu/DxGRG2FAIXInggDosuwzK4W/Op7vOKR6R9BkIKSzNDUSEbkAAwqROys6ae+1cu4Xx3MRve29VsIT2WuFiNwKAwqRpzDk23utnNrt2GslpEutXisDGFaISPYYUIg8UVkRkP19da+VHYDFZD+n6WDvtRKbxF4rRCRLDChEns5UApzYJoaVE1uBSqP9nH9oda+VKUDnUYBKLV2dRES1MKAQtSXmCuBkmhhWsjcB5Vfs53yCgO7jxZmVrsmAOlCyMomIGFCI2ipLFXB6jxhWjm0ESi7Yz6l8gS5jxbDSfTx7rRBRq2NAISKx18r5A9W9VlKBK3n2c0oVEDeiutfKJCAoUro6iajNYEAhIkeCABQetfda0R2tdVIBxAy191ppFydVlUTk4RhQiKhhl3PtYeX8fsdzkX3EC2wTU4CwHty+TEQuw4BCRE2nP1/da2WDeP2KYLWfC+1mbwwX3Z9hhYiuCwMKETVP6WUg+ztxZuXkj4Cl0n5O09EeVmJvBJRe0tVJRG6JAYWIrl+FQeyxkpUq9lwxl9rP+bcXL65NTAHiR7LXChE1CQMKEbmWuRzI/bG618p3QEWx/Zxa49hrxSdAsjKJSN4YUIio5VjM9l4rWRsBY4H9nMoP6DpWvMi2+3jAL1iyMolIfhhQiKh1WK3AuX32XivFp+3nlCpx+ScxBegxCQiKkK5OIpIFBhQian2CABT8KnawzUoFdL/XOqkQL6xNTBFvatiuk2RlEpF0GFCISHqXcoBjNb1WDjiei+pbvSNoithrhYjaBAYUIpIX/bnqXiupdXuttO9u374c1Y+9Vog8GAMKEclX6SV7r5XcHwGr2X5OG2MPKzFD2WuFyMMwoBCRe6jQiz1WsjZU91ops58LCLP3WokbCah8pKuTiFyCAYWI3I+5HMjdUavXit5+Tq0FekwQw0qXsYCPv3R1ElGzMaAQkXuzmIFTu+y9Vkp19nMqP6BbsniBbbdx7LVC5EYYUIjIc1gt1b1WUsWloOIz9nNKb6DzKHuvlcAw6eokokYxoBCRZxIEoOBIdVhJBS4es59TKIHYJHuvleAY6eokonoxoBBR23DxuL3XSv4hx3PR/e29Vtp3k6Y+InLAgEJEbU/xmVq9Vn4CUOuvtrAE+/blyD7stUIkEQYUImrbjDp7r5WTOx17rQTHirMqiSlAxyGAUildnURtDAMKEVGN8mLgxNbqXis/AFXl9nOBEbV6rYwAvLwlK5OoLWBAISKqT2UZkLu9utfKZsBUq9eKrxbocWt1r5WbAW8/6eok8lAMKEREjamqBE6li2Hl2Cag9KL9nLc/0O0We68VX/49Q21MVioQOwwICHXp2zKgEBE5w2oBzu61b1/Wn7Wf8/IBOo+u7rVyKxDQXrIyiVpcVSWw9QXgl0/Ers0z/+3S67QYUIiImksQgAuZYlD5fQNw+YT9nEIJdLqputfKJEDbUbIyiVyu+Cyw9o/A+f3i4+ELgJtfcOlNOxlQiIhc5WK2eIFtVipw4bDjuQ4Dq8NKCtC+qzT1EbnCiW3Afx8Cyq8AvsHAtE/E+1+5GAMKEVFLuHIaOLZRDCtnfoZDr5XwnvZeKxE3sNcKuQerBUhbBqT/VXwc3R+483OgXacW+TgGFCKillZSCGRXN4bLSwesVfZz7eLsXWw7DGKvFZInow74zxzx5xcABj8IjP8LoFK32EcyoBARtabyK8DxLWJYyfkBqKqwnwuMBBIni4Gl003stULycPonYO1swFgAeAcAU94Het/R4h/rzO9vl8d6i8WCF198EfHx8fDz80OXLl3w6quvonYOEgQBS5cuRVRUFPz8/JCcnIwTJ0408K5ERDLm1w7oezdw9xrg2ZPAjC+B3ncCao34C2Dfp8AXtwHLuwHrHwOyvwfMFY2/L5GrCQKw52/A6sniz2ZYAjD3x1YJJ85SufoN33zzTXz00Uf4/PPP0atXL+zfvx+zZ8+GVqvFk08+CQB466238P777+Pzzz9HfHw8XnzxRYwfPx6///47fH19XVaLxWKB2Wxu/IXkMt7e3vDyct0V30RuxycA6DlFPKpM4vR51gax10rZZSBzjXh4BwDdx4kzK93GAeogqSsnT1deXB2QN4mPe88AUt4Tf2ZlyOVLPJMnT0ZERAT++c9/2p67/fbb4efnh6+++gqCICA6OhoLFy7E008/DQDQ6/WIiIjA6tWrcffdd9d5T5PJBJPJZHtsMBgQExNzzSkiQRBQUFCA4uJiVw6Nmig4OBiRkZFQ8CJBIjurBTiTUd1rZSNgOGc/5+Ujdq9NTAG6T3R5cywi5GcC394PFJ8Wf94mvgkMnN3qF3M7s8Tj8hmUYcOGYcWKFTh+/Di6d++Ow4cPY/fu3XjnnXcAAHl5eSgoKEBycrLta7RaLYYOHYqMjIx6A8qyZcvwyiuvNLmGmnASHh4Of39//qJsJYIgoKysDDqdDgAQFRUlcUVEMqL0AuKGi8eEN4D8Q9VhZQNwOQc4vlk8bL1WpojXrmiipa6c3JkgAAdWA98vAiwmILgTMONzcbeOzLk8oCxevBgGgwEJCQnw8vKCxWLB66+/jpkzZwIQwwMAREREOHxdRESE7dzVlixZggULFtge18yg1MdisdjCSWgo/xXS2vz8xPuX6HQ6hIeHc7mHqD4KBdBhgHiMXVrda6U6rBQcAU7tEo/vnwE6Dq7utTIZCO0ideXkTipLgY0LgCPfiI973ApM/Yd4zZQbcHlA+fbbb7FmzRp8/fXX6NWrFzIzM/HUU08hOjoas2bNatZ7qtVqqNVN2/ZUc82Jv79/sz6Lrl/Nn73ZbGZAIWqMQgGEJ4jHqGeAK6fEJaCsVLH9/rl94rFtqdhfJaF6R1BEL/ZaoWu7eFxc0rmYBSi8gOSXgGFPutXPjMsDyjPPPIPFixfblmp69+6N06dPY9myZZg1axYiIyMBAIWFhQ5LAIWFhejXr5/L6uCyjnT4Z090HdrFAcPmiUdJgXhxbU2vlcLfxGPnG0C7+Fq9Vgay1wrZ/fpvIPVPQKVR3OZ+x2dA3E1SV+U0lweUsrIyKK/6D8XLywtWqxUAEB8fj8jISGzfvt0WSAwGA/bu3YtHH33U1eUQEbmvoEhg8BzxKCuy91rJ3Q5cyQN+el88gqLsMyudbgK8XP5XO7mDKlP1jf5WiI/jRojhJDBc2rqayeU/xSkpKXj99dcRGxuLXr164dChQ3jnnXfwwAMPABD/df3UU0/htddeQ7du3WzbjKOjozF16lRXl+NWRo8ejX79+uG9995r1te//PLLWL9+PTIzM1vtM4molfiHAP3uEQ+TUWwIl5UqhpaSC8C+leLhFyJea5CYIt6F2dt1rRtIxorPAN/OAvIPio9HPA2Mec6lN/prbS4PKB988AFefPFFPPbYY9DpdIiOjsbDDz+MpUuX2l7z7LPPorS0FHPnzkVxcTGGDx+OzZs3u7QHSlv09NNP44knnnD5+yoUCqxbt67NB0gi2VAHAr2mikeVCTi5095rpbwIyPxKPHwCxR4riSlAt1vYa8VTHd8q3uivoli8AHbaCrHHjptzeUAJCgrCe++91+C/yBUKBf785z/jz3/+s6s/vk0LDAxEYGCg1GUQUWtSqcVfRt3HAZPfq9VrJRUoyQeO/lc8vNT2Xis9JoozMuTeLFVA2l+AXW+LjzsMBO5cDQTHSlqWq7SJq6oEQUBZZZUkh7N98KxWK5599lmEhIQgMjISL7/8su1ccXExHnzwQYSFhUGj0eDmm2/G4cP227+//PLLDhcaV1VV4cknn0RwcDBCQ0OxaNEizJo1q85MSEOfGRcXBwCYNm0aFAqF7TERyZCXCogfAdz6FjD/KPDgDuCmp4CQzmIPjOPfA/97DPhrV+DzKcAvKwHDBamrpuYoKQS+nGoPJ0MeBmZv9phwArTADIoclZst6Ll0iySf/fufx8Pfp+l/zJ9//jkWLFiAvXv3IiMjA3/84x9x00034ZZbbsGdd94JPz8/fP/999Bqtfjkk08wduxYHD9+HCEhdf819Oabb2LNmjVYtWoVEhMT8be//Q3r16/HmDFjmvyZ+/btQ3h4OFatWoUJEyZw2zCRu1AqgY4DxSP5ZUCXZZ9ZKfwVyNspHt89DXQcUr0jKAUIiZe6cmrMqT3Av2cDxkJxGW/K+8ANt0tdlcu1iYDiTvr06YOXXnoJANCtWzf8/e9/x/bt2+Hn54dffvkFOp3O1hNm+fLlWL9+Pf79739j7ty5dd7rgw8+wJIlSzBt2jQAwN///nd89913Tf7MW265BWFhYQDs7euJyA0pFEBET/EYvQgoOmnvtXLuF/ux7UUgorc9rIQnulXfDI9ntYq7trb/GRAsQFgiMOMLIKy71JW1iDYRUPy8vfD7n8dL9tnO6NOnj8PjqKgo6HQ6HD58GEajsU533PLycuTm5tZ5H71ej8LCQgwZMsT2nJeXFwYOHGjb8t3YZxKRhwrpDNz0pHgYLgDHqsPKqd3i7Erhr+K1DSFdavVaGcCwIqXyK8C6R8VlOgDoczcw+R3Z3ujPFdpEQFEoFE4ts0jJ29vb4bFCoYDVaoXRaERUVBTS0tLqfE1wcHCLfCYRtQGaKGDIQ+JRVgRkf1/da2UHUJQL7HlPPDQd7L1WYpPYa6U15R+qvtHfGfFi51vfAgbM8vjAyJ8wNzFgwAAUFBRApVI16UJVrVaLiIgI7Nu3DyNHjgQg3qfo4MGDTnfs9fb2hsViaUbVRORW/EOA/jPFw1QCnNgmhpUTWwHDeeCXT8TDP7S618oUoPMocScRuZ4gAPs/AzYvBiyV1Tf6+wKI7id1Za2CAcVNJCcnIykpCVOnTsVbb72F7t27Iz8/H5s2bcK0adMwaNCgOl/zxBNPYNmyZejatSsSEhLwwQcf4MqVK063oo+Li8P27dtx0003Qa1Wo10797jRFBFdB3UQcMN08TBXACfTxLCSvQkouwwc+lI8fIKA7uPFmZWuyWKPFrp+JiOwcT7w67fi4x6Tqm/0FyxpWa2JAcVNKBQKfPfdd3j++ecxe/ZsXLx4EZGRkRg5cmSdO0PXWLRoEQoKCnD//ffDy8sLc+fOxfjx453eifP2229jwYIFWLlyJTp06IBTp065YERE5Da8fYEeE8TD8jfg9B4xrBzbKHax/e3f4qHyBbqMre61MsFt7porOxezgf+7D7iUXX2jv5eBYU94/JLO1RSCs406ZMBgMECr1UKv10Oj0Ticq6ioQF5eHuLj49mZ9ipWqxWJiYmYMWMGXn311Rb7HH4PiNoIqxU4f0DsYpuVKt4fqIZSJd4LJjFFvHYlqP5/SNFVjqwVb/RnLhVv9HfnKqDTMKmrcpmGfn9fjTMoHuz06dPYunUrRo0aBZPJhL///e/Iy8vDvffeK3VpROQJlEogZrB43PJnoPCovdeK7ihw8kfx2LQQiBlavSNosnjHZnJUZQI2LwH2/1N8HD8SuP2fbnujP1dgQPFgSqUSq1evxtNPPw1BEHDDDTfghx9+QGJiotSlEZGnUSiAyBvEY8wS4HKuPayc3w+c/Vk8tj4PRPYRL7BNTAHCerS5pYs6rpwG1s4Sd+sAwMhngdGL3fpGf67AJR5yOX4PiMiB/rx4I8OsDeL1K0KtNgah3eyN4aL7t72wkr0ZWPew/UZ/01eKN3b0UFziISIi+dB2AIbOFY/Sy0D2d+LMyskfgcsngN3viIemoz2sxN7o2TMIlirgx9eA3e+KjzsMqr7RX4ykZckJAwoREbWegFBgwH3iUWEQe6xkpYo9VwzngL0fiYd/eyBhkrgUFD8SUPlIXbnrlBQA/54DnN4tPh76CHDLq541RhdgQCEiImn4aoDed4iHuRzI/bG618p3QNkl4ODn4qHWAN0nVPdaGeve7d3zdgH/fgAo1Yk3+rvt70CvaVJXJUsMKEREJD1vPyDhVvGwmO29VrI2AsYCsWHZr98CKj8xpCROERvEuUvjMqtVvGXAjlfFa3DCe4pdYdt3k7oy2WJAISIiefHyBjqPFo+JfwXO7bP3Wik+LTaIO7ZR7LUSP6q618ok+W7JLSsC1j8KHN8sPu57LzDpbcDHX9q6ZI4BhYiI5EupBGKHise414CCX+13X9b9DuRuF4+N88WbGNb0WgmOlbpy0fmDwLezAH31jf4mLQf639f2dis1g1LqAqhlxMXF4b333mvy60+dOgWFQoHMzMwWq4mI6LooFEBUH2DMc8BjGcC8A2Ib+A4DAQjAmZ+ALUuA93oDn4wC0peLbeOlIAjAvk+Bz8aL4aRdPPDgD8CA+xlOmogzKB5q3759CAhw7YVkq1evxlNPPYXi4mKXvi8RUbO07woMny8e+nPVvVZSxetXLmSKx45Xgfbdq2dWpgBRfVs+IJiMYrv63/4tPk6YLN7oz1fbsp/rYRhQPFRYWJjUJRARtR5tR2Dow+JResneayX3R+DScWDX2+KhjbX3WokZ4vpeK7pjwLf3izf6U6qA5FeApMc5a9IMbWOJRxCAylJpjiY26t24cSOCg4NhsVgAAJmZmVAoFFi8eLHtNQ8++CD+8Ic/AAB2796NESNGwM/PDzExMXjyySdRWlpqe+3VSzzHjh3D8OHD4evri549e+KHH36AQqHA+vXrHeo4efIkxowZA39/f/Tt2xcZGRkAgLS0NMyePRt6vR4KhQIKhQIvv/xyM74ZREQtLKC9uJQycy3wbK54T5uetwHe/uJyy88fAqsmAG8niDMdOT8AVZXX/7lHvgVWjhHDSVA08MdNwLB5DCfN1DZmUMxlwF+ipfns5/KbtGd/xIgRKCkpwaFDhzBo0CDs3LkT7du3R1pamu01O3fuxKJFi5Cbm4sJEybgtddew2effYaLFy9i3rx5mDdvHlatWlXnvS0WC6ZOnYrY2Fjs3bsXJSUlWLhwYb11PP/881i+fDm6deuG559/Hvfccw9ycnIwbNgwvPfee1i6dCmys8U13cDAwOb9mRARtRZf7VW9VnbYe62U6oADq8VDrQV6VPda6TLWuR025grx2pf9n4mPO48Gpn8KBHIm+3q0jYDiBrRaLfr164e0tDQMGjQIaWlpmD9/Pl555RUYjUbo9Xrk5ORg1KhRWLZsGWbOnImnnnoKANCtWze8//77GDVqFD766KM697/Ztm0bcnNzkZaWhsjISADA66+/jltuqXu/h6effhqTJk0CALzyyivo1asXcnJykJCQAK1WC4VCYXsPIiK34u0nbkdOmCT2Wjm1y95rpVQHHPk/8VD5Ad2S7b1WGrp25MopcUnnwmEACmDUs8CoRZ7dpr+VtI2A4u0vzmRI9dlNNGrUKKSlpWHhwoXYtWsXli1bhm+//Ra7d+9GUVERoqOj0a1bNxw+fBhHjhzBmjVrbF8rCAKsVivy8vLq3K04OzsbMTExDsFiyJAh9dbQp08f2/+PiooCAOh0OiQkJDR5HEREsuflDXS5WTxuXV7dayVV7LdSfMZ+J2alN9C5utdKj0mOsyLHvgPWPwJU6AG/EOD2lUDXZOnG5GHaRkBRKNyiNfLo0aPx2Wef4fDhw/D29kZCQgJGjx6NtLQ0XLlyBaNGjQIAGI1GPPzww3jyySfrvEds7PXt/ff29rb9f0X1uqnVar3Wy4mI3J/SS7w5YeyN1b1WjtgDysVj4jUqOT849lox5AM/vS9+fcfB4o3+tB0lHYanaRsBxU3UXIfy7rvv2sLI6NGj8cYbb+DKlSu260YGDBiA33//HV27dm3S+/bo0QNnz55FYWEhIiIiAIjbkJ3l4+Nju4iXiMgjKRTiVuSovsDNLwAXjwPHqsNK/iFxC/PpPfbX3/iYuFOHN/pzubaxi8dNtGvXDn369MGaNWswevRoAMDIkSNx8OBBHD9+3BZaFi1ahJ9++gnz5s1DZmYmTpw4gf/973+YN29eve97yy23oEuXLpg1axaOHDmCPXv24IUXXgBgnyVpiri4OBiNRmzfvh2XLl1CWVnZ9Q2YiEjuwroDIxYCc9OAp34FJrwBxA4DNB2AOz8HJixjOGkhDCgyM2rUKFgsFltACQkJQc+ePREZGYkePXoAEK8T2blzJ44fP44RI0agf//+WLp0KaKj69+p5OXlhfXr18NoNGLw4MF48MEH8fzzzwNAnQtqGzJs2DA88sgjuOuuuxAWFoa33nrr+gZLROROgmOBGx8FHvgeWPA70Guq1BV5NIUgNLFRh4wYDAZotVro9XpoNBqHcxUVFcjLy0N8fLxTv3zbmj179mD48OHIyclBly5dXPre/B4QEVF9Gvr9fTVeg9JGrFu3DoGBgejWrRtycnLwpz/9CTfddJPLwwkREZErMKC0ESUlJVi0aBHOnDmD9u3bIzk5GW+//bbUZREREdWLAaWNuP/++3H//fdLXQYREVGT8CJZIiIikh2PDShueO2vx+CfPRERXS+PCyg1nVDZo0M6NX/2tbvSEhEROcPjrkHx8vJCcHAwdDodAMDf39+pZmTUfIIgoKysDDqdDsHBwfDy4s2yiIioeTwuoACw3RSvJqRQ6woODuYdj4mI6Lp4ZEBRKBSIiopCeHg4zGaz1OW0Kd7e3pw5ISKi6+aRAaWGl5cXf1kSERG5IY+7SJaIiIjcHwMKERERyQ4DChEREcmOW16DUtMIzGAwSFwJERERNVXN7+2mNPR0y4BSUlICAIiJiZG4EiIiInJWSUkJtFptg69RCG7Yl9xqtSI/Px9BQUEub8JmMBgQExODs2fPQqPRuPS95YDjc3+ePkZPHx/g+WPk+NxfS41REASUlJQgOjoaSmXDV5m45QyKUqlEx44dW/QzNBqNx/7gARyfJ/D0MXr6+ADPHyPH5/5aYoyNzZzU4EWyREREJDsMKERERCQ7DChXUavVeOmll6BWq6UupUVwfO7P08fo6eMDPH+MHJ/7k8MY3fIiWSIiIvJsnEEhIiIi2WFAISIiItlhQCEiIiLZYUAhIiIi2WmTAWXZsmUYPHgwgoKCEB4ejqlTpyI7O7vO6zIyMnDzzTcjICAAGo0GI0eORHl5uQQVO6ex8Z06dQoKhaLeY+3atRJW3nRN+R4WFBTgvvvuQ2RkJAICAjBgwAD85z//kahi5zRlfLm5uZg2bRrCwsKg0WgwY8YMFBYWSlSx8z766CP06dPH1ggqKSkJ33//ve18RUUFHn/8cYSGhiIwMBC33367R41vxYoVGD16NDQaDRQKBYqLi6UrthkaGl9RURGeeOIJ9OjRA35+foiNjcWTTz4JvV4vcdXOaex7+PDDD6NLly7w8/NDWFgYbrvtNhw7dkzCip3T2PhqCIKAiRMnQqFQYP369a1WX5sMKDt37sTjjz+On3/+Gdu2bYPZbMa4ceNQWlpqe01GRgYmTJiAcePG4ZdffsG+ffswb968RlvzykFj44uJicGFCxccjldeeQWBgYGYOHGixNU3TVO+h/fffz+ys7OxYcMG/Prrr5g+fTpmzJiBQ4cOSVh50zQ2vtLSUowbNw4KhQI7duzAnj17UFlZiZSUFFitVomrb5qOHTvijTfewIEDB7B//37cfPPNuO2223D06FEAwPz585Gamoq1a9di586dyM/Px/Tp0yWuuukaG19ZWRkmTJiA5557TuJKm6eh8eXn5yM/Px/Lly/Hb7/9htWrV2Pz5s2YM2eO1GU7pbHv4cCBA7Fq1SpkZWVhy5YtEAQB48aNg8VikbjypmlsfDXee+89l99WpkkEEnQ6nQBA2Llzp+25oUOHCi+88IKEVblOfeO7Wr9+/YQHHnigFatyrfrGGBAQIHzxxRcOrwsJCRFWrlzZ2uVdt6vHt2XLFkGpVAp6vd72muLiYkGhUAjbtm2Tqszr1q5dO+HTTz8ViouLBW9vb2Ht2rW2c1lZWQIAISMjQ8IKr0/N+Gr78ccfBQDClStXpCnKheobX41vv/1W8PHxEcxmcytX5VoNjfHw4cMCACEnJ6eVq3Kdq8d36NAhoUOHDsKFCxcEAMK6detarRb5Twe0gpppx5CQEACATqfD3r17ER4ejmHDhiEiIgKjRo3C7t27pSyz2a4e39UOHDiAzMxMt/vXTW31jXHYsGH4v//7PxQVFcFqteKbb75BRUUFRo8eLVGVzXf1+EwmExQKhUMTJV9fXyiVSrf8ObVYLPjmm29QWlqKpKQkHDhwAGazGcnJybbXJCQkIDY2FhkZGRJW2jxXj8/TNGV8er0eGo0GKpVb3gKu0TGWlpZi1apViI+PR0xMjAQVXp/6xldWVoZ7770XH374ISIjI1u/qFaLQjJlsViESZMmCTfddJPtuYyMDAGAEBISInz22WfCwYMHhaeeekrw8fERjh8/LmG1zqtvfFd79NFHhcTExFasyrWuNcYrV64I48aNEwAIKpVK0Gg0wpYtWySqsvnqG59OpxM0Go3wpz/9SSgtLRWMRqMwb948AYAwd+5cCat1zpEjR4SAgADBy8tL0Gq1wqZNmwRBEIQ1a9YIPj4+dV4/ePBg4dlnn23tMpvtWuOrzZ1nUJoyPkEQhIsXLwqxsbHCc88918oVXr/Gxvjhhx8KAQEBAgChR48ebjd70tD45s6dK8yZM8f2GK08g9LmA8ojjzwidOrUSTh79qztuT179ggAhCVLlji8tnfv3sLixYtbu8TrUt/4aisrKxO0Wq2wfPnyVq7Mda41xnnz5glDhgwRfvjhByEzM1N4+eWXBa1WKxw5ckSiSpvnWuPbsmWL0LlzZ0GhUAheXl7CH/7wB2HAgAHCI488IlGlzjOZTMKJEyeE/fv3C4sXLxbat28vHD161GMCyrXGV5s7B5SmjE+v1wtDhgwRJkyYIFRWVkpUafM1Nsbi4mLh+PHjws6dO4WUlBRhwIABQnl5uYQVO+da4/vf//4ndO3aVSgpKbG9lgGlFT3++ONCx44dhZMnTzo8f/LkSQGA8OWXXzo8P2PGDOHee+9tzRKvy7XGV9sXX3wheHt7CzqdrhUrc51rjTEnJ0cAIPz2228Oz48dO1Z4+OGHW7PE69KU7+HFixdtv9wiIiKEt956q5Wqc72xY8cKc+fOFbZv317vL+3Y2FjhnXfekaY4F6gZX23uHFCudvX4DAaDkJSUJIwdO9atfmk3pL7vYQ2TyST4+/sLX3/9dStX5To14/vTn/5k+8dPzQFAUCqVwqhRo1qlljZ5DYogCJg3bx7WrVuHHTt2ID4+3uF8XFwcoqOj62zrPH78ODp16tSapTZLY+Or7Z///CemTJmCsLCwVqzw+jU2xrKyMgCos+vKy8vLLXa5OPM9bN++PYKDg7Fjxw7odDpMmTKlFSt1LavVCpPJhIEDB8Lb2xvbt2+3ncvOzsaZM2fc+hqOmvF5qtrjMxgMGDduHHx8fLBhwwb4+vpKXJ1rNPQ9FMR/9Lv197hmfIsXL8aRI0eQmZlpOwDg3XffxapVq1qnmFaJQTLz6KOPClqtVkhLSxMuXLhgO8rKymyveffddwWNRiOsXbtWOHHihPDCCy8Ivr6+brG+2JTxCYIgnDhxQlAoFML3338vUaXN19gYKysrha5duwojRowQ9u7dK+Tk5AjLly8XFArFNdfJ5aQp38PPPvtMyMjIEHJycoQvv/xSCAkJERYsWCBh1c5ZvHixsHPnTiEvL084cuSIsHjxYkGhUAhbt24VBEFc2oqNjRV27Ngh7N+/X0hKShKSkpIkrrrpGhvfhQsXhEOHDgkrV64UAAjp6enCoUOHhMuXL0tcedM0ND69Xi8MHTpU6N27t5CTk+PwM1xVVSV16U3W0Bhzc3OFv/zlL8L+/fuF06dPC3v27BFSUlKEkJAQobCwUOrSm6Sxn9GrgUs8LQ9AvceqVascXrds2TKhY8eOgr+/v5CUlCTs2rVLmoKd1NTxLVmyRIiJiREsFos0hV6Hpozx+PHjwvTp04Xw8HDB399f6NOnT51tx3LVlPEtWrRIiIiIELy9vYVu3boJb7/9tmC1WqUr2kkPPPCA0KlTJ8HHx0cICwsTxo4d6/AXY3l5ufDYY48J7dq1E/z9/YVp06YJFy5ckLBi5zQ2vpdeeqlJ/53KVUPjq1m2qu/Iy8uTtnAnNDTG8+fPCxMnThTCw8MFb29voWPHjsK9994rHDt2TOKqm66xn9GrtXZAUVR/KBEREZFstMlrUIiIiEjeGFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiIiISHYYUIiIiEh2GFCIiIhIdhhQiKhVbN68GcOHD0dwcDBCQ0MxefJk5Obm2s7/9NNP6NevH3x9fTFo0CCsX78eCoXCdpt3APjtt98wceJEBAYGIiIiAvfddx8uXbokwWiIqKUxoBBRqygtLcWCBQuwf/9+bN++HUqlEtOmTYPVaoXBYEBKSgp69+6NgwcP4tVXX8WiRYscvr64uBg333wz+vfvj/3792Pz5s0oLCzEjBkzJBoREbUk3s2YiCRx6dIlhIWF4ddff8Xu3bvxwgsv4Ny5c/D19QUAfPrpp3jooYdw6NAh9OvXD6+99hp27dqFLVu22N7j3LlziImJQXZ2Nrp37y7VUIioBXAGhYhaxYkTJ3DPPfegc+fO0Gg0iIuLAwCcOXMG2dnZ6NOnjy2cAMCQIUMcvv7w4cP48ccfERgYaDsSEhIAwGGpiIg8g0rqAoiobUhJSUGnTp2wcuVKREdHw2q14oYbbkBlZWWTvt5oNCIlJQVvvvlmnXNRUVGuLpeIJMaAQkQt7vLly8jOzsbKlSsxYsQIAMDu3btt53v06IGvvvoKJpMJarUaALBv3z6H9xgwYAD+85//IC4uDioV/+oi8nRc4iGiFteuXTuEhoZixYoVyMnJwY4dO7BgwQLb+XvvvRdWqxVz585FVlYWtmzZguXLlwMAFAoFAODxxx9HUVER7rnnHuzbtw+5ubnYsmULZs+eDYvFIsm4iKjlMKAQUYtTKpX45ptvcODAAdxwww2YP38+/vrXv9rOazQapKamIjMzE/369cPzzz+PpUuXAoDtupTo6Gjs2bMHFosF48aNQ+/evfHUU08hODgYSiX/KiPyNNzFQ0SytGbNGsyePRt6vR5+fn5Sl0NErYwLuUQkC1988QU6d+6MDh064PDhw1i0aBFmzJjBcELURjGgEJEsFBQUYOnSpSgoKEBUVBTuvPNOvP7661KXRUQS4RIPERERyQ6vLCMiIiLZYUAhIiIi2WFAISIiItlhQCEiIiLZYUAhIiIi2WFAISIiItlhQCEiIiLZYUAhIiIi2fl/ZSjSE8rvwKQAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "people.sort_values(by=\"age\", inplace=True)\n",
+        "people.plot(kind=\"line\", x=\"age\", y=[\"height\", \"weight\"])\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3K9BbaU_3k3L"
+      },
+      "source": [
+        "You can pass extra arguments supported by matplotlib's functions. For example, we can create scatterplot and pass it a list of sizes using the `s` argument of matplotlib's `scatter()` function:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZpKWQlST3k3L",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "outputId": "9d5359b1-2a51-4efd-97e4-7bda068a6c66"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "people.plot(kind=\"scatter\", x=\"height\", y=\"weight\", s=[40, 120, 200])\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IEa4vM4i3k3L"
+      },
+      "source": [
+        "Again, there are way too many options to list here: the best option is to scroll through the [Visualization](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html) page in pandas' documentation, find the plot you are interested in and look at the example code."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iApn7kzb3k3M"
+      },
+      "source": [
+        "## Operations on `DataFrame`s\n",
+        "Although `DataFrame`s do not try to mimic NumPy arrays, there are a few similarities. Let's create a `DataFrame` to demonstrate this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "jze_Wb9k3k3M",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "fa0cec77-3a91-4635-cdab-b5520a480962"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         sep  oct  nov\n",
+              "alice      8    8    9\n",
+              "bob       10    9    9\n",
+              "charles    4    8    2\n",
+              "darwin     9   10   10"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cf253e89-fd02-4710-b6a6-b60fd7b330e1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>8</td>\n",
+              "      <td>8</td>\n",
+              "      <td>9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>10</td>\n",
+              "      <td>9</td>\n",
+              "      <td>9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>4</td>\n",
+              "      <td>8</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf253e89-fd02-4710-b6a6-b60fd7b330e1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-cf253e89-fd02-4710-b6a6-b60fd7b330e1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-cf253e89-fd02-4710-b6a6-b60fd7b330e1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-502f0da9-c841-4d7e-b336-682ff0268f0b\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-502f0da9-c841-4d7e-b336-682ff0268f0b')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-502f0da9-c841-4d7e-b336-682ff0268f0b button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_b459b50f-3324-4126-b9a5-23d56bc72d34\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('grades')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_b459b50f-3324-4126-b9a5-23d56bc72d34 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('grades');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "grades",
+              "summary": "{\n  \"name\": \"grades\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 4,\n        \"max\": 10,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10,\n          9,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 8,\n        \"max\": 10,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8,\n          9,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9,\n          2,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
+      "source": [
+        "grades_array = np.array([[8, 8, 9], [10, 9, 9], [4, 8, 2], [9, 10, 10]])\n",
+        "grades = pd.DataFrame(grades_array, columns=[\"sep\", \"oct\", \"nov\"], index=[\"alice\", \"bob\", \"charles\", \"darwin\"])\n",
+        "grades"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vwZxuySv3k3M"
+      },
+      "source": [
+        "You can apply NumPy mathematical functions on a `DataFrame`: the function is applied to all values:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "H5mtBS903k3M",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "48be473b-65d1-4de6-9383-1f92aecbb101"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              sep       oct       nov\n",
+              "alice    2.828427  2.828427  3.000000\n",
+              "bob      3.162278  3.000000  3.000000\n",
+              "charles  2.000000  2.828427  1.414214\n",
+              "darwin   3.000000  3.162278  3.162278"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cee1d7a8-6891-4eeb-bbcc-d9091f2a447a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>2.828427</td>\n",
+              "      <td>2.828427</td>\n",
+              "      <td>3.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>3.162278</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>2.828427</td>\n",
+              "      <td>1.414214</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>3.162278</td>\n",
+              "      <td>3.162278</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cee1d7a8-6891-4eeb-bbcc-d9091f2a447a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-cee1d7a8-6891-4eeb-bbcc-d9091f2a447a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-cee1d7a8-6891-4eeb-bbcc-d9091f2a447a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-5274d0e4-f85a-4630-b534-548f032b25cf\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5274d0e4-f85a-4630-b534-548f032b25cf')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-5274d0e4-f85a-4630-b534-548f032b25cf button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"np\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5167533527431944,\n        \"min\": 2.0,\n        \"max\": 3.1622776601683795,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3.1622776601683795,\n          3.0,\n          2.8284271247461903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16023963878069403,\n        \"min\": 2.8284271247461903,\n        \"max\": 3.1622776601683795,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2.8284271247461903,\n          3.0,\n          3.1622776601683795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8235003248078265,\n        \"min\": 1.4142135623730951,\n        \"max\": 3.1622776601683795,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.0,\n          1.4142135623730951,\n          3.1622776601683795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 82
+        }
+      ],
+      "source": [
+        "np.sqrt(grades)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sSbp9fPX3k3N"
+      },
+      "source": [
+        "Similarly, adding a single value to a `DataFrame` will add that value to all elements in the `DataFrame`. This is called *broadcasting*:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lF-cCA4K3k3N",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "2dbceb3d-6d74-402c-dfbe-536b26ca74f6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         sep  oct  nov\n",
+              "alice      9    9   10\n",
+              "bob       11   10   10\n",
+              "charles    5    9    3\n",
+              "darwin    10   11   11"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a196a8a3-eea3-4f99-813f-e519bc1cf219\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>9</td>\n",
+              "      <td>9</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>11</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>10</td>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a196a8a3-eea3-4f99-813f-e519bc1cf219')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a196a8a3-eea3-4f99-813f-e519bc1cf219 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a196a8a3-eea3-4f99-813f-e519bc1cf219');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-da966521-36d5-4b67-83d7-9c9990554c32\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da966521-36d5-4b67-83d7-9c9990554c32')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-da966521-36d5-4b67-83d7-9c9990554c32 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"grades + 1\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 5,\n        \"max\": 11,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          11,\n          10,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 9,\n        \"max\": 11,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9,\n          10,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 3,\n        \"max\": 11,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10,\n          3,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 83
+        }
+      ],
+      "source": [
+        "grades + 1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-BZYtZoq3k3N"
+      },
+      "source": [
+        "Of course, the same is true for all other binary operations, including arithmetic (`*`,`/`,`**`...) and conditional (`>`, `==`...) operations:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ciSO8zft3k3N",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "0219b6e3-25fb-4867-a624-53469ce11163"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           sep   oct    nov\n",
+              "alice     True  True   True\n",
+              "bob       True  True   True\n",
+              "charles  False  True  False\n",
+              "darwin    True  True   True"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b947f437-b2c7-4e0c-bc19-3a97117d7ca8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b947f437-b2c7-4e0c-bc19-3a97117d7ca8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b947f437-b2c7-4e0c-bc19-3a97117d7ca8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b947f437-b2c7-4e0c-bc19-3a97117d7ca8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-acd1de80-e31b-4788-bd2d-cb014315da94\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-acd1de80-e31b-4788-bd2d-cb014315da94')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-acd1de80-e31b-4788-bd2d-cb014315da94 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"grades >= 5\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 84
+        }
+      ],
+      "source": [
+        "grades >= 5"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "61AVqJLe3k3O"
+      },
+      "source": [
+        "Aggregation operations, such as computing the `max`, the `sum` or the `mean` of a `DataFrame`, apply to each column, and you get back a `Series` object:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PcpOArwQ3k3O",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 193
+        },
+        "outputId": "c9bf91f5-2331-48e7-ae7f-377ed3c9ec97"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "sep    7.75\n",
+              "oct    8.75\n",
+              "nov    7.50\n",
+              "dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>sep</th>\n",
+              "      <td>7.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>oct</th>\n",
+              "      <td>8.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nov</th>\n",
+              "      <td>7.50</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 87
+        }
+      ],
+      "source": [
+        "grades.mean(axis = 0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ojbOXpBw3k3O"
+      },
+      "source": [
+        "The `all` method is also an aggregation operation: it checks whether all values are `True` or not. Let's see during which months all students got a grade greater than `5`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qL-WRGtn3k3O",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 193
+        },
+        "outputId": "a91d859f-cd74-4e34-c7df-c3c8310d2343"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "sep    False\n",
+              "oct     True\n",
+              "nov    False\n",
+              "dtype: bool"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>sep</th>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>oct</th>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nov</th>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> bool</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 89
+        }
+      ],
+      "source": [
+        "(grades > 5).all(axis = 0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vvIX3MOw3k3O"
+      },
+      "source": [
+        "Most of these functions take an optional `axis` parameter which lets you specify along which axis of the `DataFrame` you want the operation executed. The default is `axis=0`, meaning that the operation is executed vertically (on each column). You can set `axis=1` to execute the operation horizontally (on each row). For example, let's find out which students had all grades greater than `5`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3kY7gAHo3k3P",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 227
+        },
+        "outputId": "871b774c-8690-4a9c-f7c4-35ba7bb91c64"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "alice       True\n",
+              "bob         True\n",
+              "charles    False\n",
+              "darwin      True\n",
+              "dtype: bool"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> bool</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 90
+        }
+      ],
+      "source": [
+        "(grades > 5).all(axis=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Nv2SaGXG3k3P"
+      },
+      "source": [
+        "The `any` method returns `True` if any value is True. Let's see who got at least one grade 10:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8QdC7CJC3k3Q",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 227
+        },
+        "outputId": "e4219fa6-bf68-4b70-8a83-d5b039180860"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "alice      False\n",
+              "bob         True\n",
+              "charles    False\n",
+              "darwin      True\n",
+              "dtype: bool"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> bool</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 91
+        }
+      ],
+      "source": [
+        "(grades == 10).any(axis=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ld9F6Poj3k3Q"
+      },
+      "source": [
+        "If you add a `Series` object to a `DataFrame` (or execute any other binary operation), pandas attempts to broadcast the operation to all *rows* in the `DataFrame`. This only works if the `Series` has the same size as the `DataFrame`s rows. For example, let's subtract the `mean` of the `DataFrame` (a `Series` object) from the `DataFrame`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8uOgLHUd3k3Q",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "823ed15f-f873-4666-8177-3b2363ac5ce6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          sep   oct  nov\n",
+              "alice    0.25 -0.75  1.5\n",
+              "bob      2.25  0.25  1.5\n",
+              "charles -3.75 -0.75 -5.5\n",
+              "darwin   1.25  1.25  2.5"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-affe1a78-7876-49a1-af54-0cdca3b406d5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>0.25</td>\n",
+              "      <td>-0.75</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>2.25</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>-3.75</td>\n",
+              "      <td>-0.75</td>\n",
+              "      <td>-5.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>1.25</td>\n",
+              "      <td>1.25</td>\n",
+              "      <td>2.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-affe1a78-7876-49a1-af54-0cdca3b406d5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-affe1a78-7876-49a1-af54-0cdca3b406d5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-affe1a78-7876-49a1-af54-0cdca3b406d5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-ddf44cc6-9a2f-4812-802e-c63ffea62183\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ddf44cc6-9a2f-4812-802e-c63ffea62183')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-ddf44cc6-9a2f-4812-802e-c63ffea62183 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"grades - grades\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": -3.75,\n        \"max\": 2.25,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.25,\n          1.25,\n          0.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9574271077563381,\n        \"min\": -0.75,\n        \"max\": 1.25,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -0.75,\n          0.25,\n          1.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.696845502136472,\n        \"min\": -5.5,\n        \"max\": 2.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.5,\n          -5.5,\n          2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 92
+        }
+      ],
+      "source": [
+        "grades - grades.mean(axis = 0)  # equivalent to: grades - [7.75, 8.75, 7.50]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "(grades - grades.mean(axis = 0))/grades.std()  # normalisation"
+      ],
+      "metadata": {
+        "id": "woQfe_n6fIHg",
+        "outputId": "1a79589e-117e-4792-fad5-c23bfe7d95f6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              sep       oct       nov\n",
+              "alice    0.095059 -0.783349  0.405751\n",
+              "bob      0.855528  0.261116  0.405751\n",
+              "charles -1.425880 -0.783349 -1.487755\n",
+              "darwin   0.475293  1.305582  0.676252"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0b86eed2-eb89-4bba-ba5e-0427254c20c2\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>0.095059</td>\n",
+              "      <td>-0.783349</td>\n",
+              "      <td>0.405751</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>0.855528</td>\n",
+              "      <td>0.261116</td>\n",
+              "      <td>0.405751</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>-1.425880</td>\n",
+              "      <td>-0.783349</td>\n",
+              "      <td>-1.487755</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>0.475293</td>\n",
+              "      <td>1.305582</td>\n",
+              "      <td>0.676252</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b86eed2-eb89-4bba-ba5e-0427254c20c2')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0b86eed2-eb89-4bba-ba5e-0427254c20c2 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0b86eed2-eb89-4bba-ba5e-0427254c20c2');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-4012ad11-2708-473b-9c8d-2fc9eb7526fe\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4012ad11-2708-473b-9c8d-2fc9eb7526fe')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-4012ad11-2708-473b-9c8d-2fc9eb7526fe button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"(grades - grades\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -1.4258795636800754,\n        \"max\": 0.8555277382080453,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8555277382080453,\n          0.4752931878933585,\n          0.0950586375786717\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -0.7833494518006403,\n        \"max\": 1.3055824196677337,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -0.7833494518006403,\n          0.26111648393354675,\n          1.3055824196677337\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -1.4877548972012633,\n        \"max\": 0.6762522260005742,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.40575133560034454,\n          -1.4877548972012633,\n          0.6762522260005742\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 95
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W96jWlQq3k3R"
+      },
+      "source": [
+        "We subtracted `7.75` from all September grades, `8.75` from October grades and `7.50` from November grades. It is equivalent to subtracting this `DataFrame`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VtIDYijA3k3R",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "fe71960b-3338-4801-f357-32ac806fe8c7"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          sep   oct  nov\n",
+              "alice    7.75  8.75  7.5\n",
+              "bob      7.75  8.75  7.5\n",
+              "charles  7.75  8.75  7.5\n",
+              "darwin   7.75  8.75  7.5"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-087a90a4-491e-43fd-97b6-afea06031ed6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>7.75</td>\n",
+              "      <td>8.75</td>\n",
+              "      <td>7.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>7.75</td>\n",
+              "      <td>8.75</td>\n",
+              "      <td>7.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>7.75</td>\n",
+              "      <td>8.75</td>\n",
+              "      <td>7.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>7.75</td>\n",
+              "      <td>8.75</td>\n",
+              "      <td>7.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-087a90a4-491e-43fd-97b6-afea06031ed6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-087a90a4-491e-43fd-97b6-afea06031ed6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-087a90a4-491e-43fd-97b6-afea06031ed6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-771528d9-ff21-4143-9391-f3632df0f831\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-771528d9-ff21-4143-9391-f3632df0f831')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-771528d9-ff21-4143-9391-f3632df0f831 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 7.75,\n        \"max\": 7.75,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          7.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 8.75,\n        \"max\": 8.75,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 7.5,\n        \"max\": 7.5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          7.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 93
+        }
+      ],
+      "source": [
+        "pd.DataFrame([[7.75, 8.75, 7.50]]*4, index=grades.index, columns=grades.columns)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9PIMwJ3E3k3R"
+      },
+      "source": [
+        "If you want to subtract the global mean from every grade, here is one way to do it:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VOBf3J-M3k3R",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "0b2cdd24-88f6-44d2-a657-26ee5ff913db"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         sep  oct  nov\n",
+              "alice    0.0  0.0  1.0\n",
+              "bob      2.0  1.0  1.0\n",
+              "charles -4.0  0.0 -6.0\n",
+              "darwin   1.0  2.0  2.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-170443de-01a4-40ba-aaf2-9d238626b701\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>-4.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-6.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-170443de-01a4-40ba-aaf2-9d238626b701')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-170443de-01a4-40ba-aaf2-9d238626b701 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-170443de-01a4-40ba-aaf2-9d238626b701');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-85f2de01-cf72-4615-b251-301048de39ab\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-85f2de01-cf72-4615-b251-301048de39ab')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-85f2de01-cf72-4615-b251-301048de39ab button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"grades - grades\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": -4.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.0,\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9574271077563381,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.696845502136472,\n        \"min\": -6.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          -6.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 94
+        }
+      ],
+      "source": [
+        "grades - grades.values.mean() # subtracts the global mean (8.00) from all grades"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5mBE67i_3k3R"
+      },
+      "source": [
+        "## Automatic alignment\n",
+        "Similar to `Series`, when operating on multiple `DataFrame`s, pandas automatically aligns them by row index label, but also by column names. Let's create a `DataFrame` with bonus points for each person from October to December:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "7YfeAj623k3S",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "34a5d399-748c-49db-fac6-fe5d4a9df259"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         oct  nov  dec\n",
+              "bob      0.0  NaN  2.0\n",
+              "colin    NaN  1.0  0.0\n",
+              "darwin   0.0  1.0  0.0\n",
+              "charles  3.0  3.0  0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c20b08c6-b98d-42cb-9041-76c51f6ee03e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>dec</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>colin</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c20b08c6-b98d-42cb-9041-76c51f6ee03e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c20b08c6-b98d-42cb-9041-76c51f6ee03e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c20b08c6-b98d-42cb-9041-76c51f6ee03e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f3c4e06a-5294-465b-ad68-5acbcc27ded5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3c4e06a-5294-465b-ad68-5acbcc27ded5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-f3c4e06a-5294-465b-ad68-5acbcc27ded5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_62314e32-7895-4342-914e-2b2409ea8446\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('bonus_points')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_62314e32-7895-4342-914e-2b2409ea8446 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('bonus_points');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "bonus_points",
+              "summary": "{\n  \"name\": \"bonus_points\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7320508075688772,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1547005383792517,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "bonus_array = np.array([[0, np.nan, 2], [np.nan, 1, 0], [0, 1, 0], [3, 3, 0]])\n",
+        "bonus_points = pd.DataFrame(bonus_array, columns=[\"oct\", \"nov\", \"dec\"], index=[\"bob\", \"colin\", \"darwin\", \"charles\"])\n",
+        "bonus_points"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "Cj263V0r3k3S",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "4d06daa3-aaf2-4965-a012-97c89723d6c0"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         dec   nov   oct  sep\n",
+              "alice    NaN   NaN   NaN  NaN\n",
+              "bob      NaN   NaN   9.0  NaN\n",
+              "charles  NaN   5.0  11.0  NaN\n",
+              "colin    NaN   NaN   NaN  NaN\n",
+              "darwin   NaN  11.0  10.0  NaN"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-89a1fddc-92cc-4093-b52e-b31f55c572ed\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>dec</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>sep</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>colin</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-89a1fddc-92cc-4093-b52e-b31f55c572ed')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-89a1fddc-92cc-4093-b52e-b31f55c572ed button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-89a1fddc-92cc-4093-b52e-b31f55c572ed');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-0a816bfb-5434-44c1-953f-18f6c0123fb7\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0a816bfb-5434-44c1-953f-18f6c0123fb7')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-0a816bfb-5434-44c1-953f-18f6c0123fb7 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"grades + bonus_points\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.242640687119285,\n        \"min\": 5.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 9.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ],
+      "source": [
+        "grades + bonus_points"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "flocsVia3k3S"
+      },
+      "source": [
+        "Looks like the addition worked in some cases but way too many elements are now empty. That's because when aligning the `DataFrame`s, some columns and rows were only present on one side, and thus they were considered missing on the other side (`NaN`). Then adding `NaN` to a number results in `NaN`, hence the result.\n",
+        "\n",
+        "## Handling missing data\n",
+        "Dealing with missing data is a frequent task when working with real life data. Pandas offers a few tools to handle missing data.\n",
+        "\n",
+        "Let's try to fix the problem above. For example, we can decide that missing data should result in a zero, instead of `NaN`. We can replace all `NaN` values by any value using the `fillna()` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "lZqRka4j3k3S",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "fc01de76-d05e-4f7c-b303-e85c2e14ab98"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         dec   nov   oct  sep\n",
+              "alice    0.0   0.0   0.0  0.0\n",
+              "bob      0.0   0.0   9.0  0.0\n",
+              "charles  0.0   5.0  11.0  0.0\n",
+              "colin    0.0   0.0   0.0  0.0\n",
+              "darwin   0.0  11.0  10.0  0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6e969519-eb8e-4dfc-968b-cca68870660e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>dec</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>sep</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>colin</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e969519-eb8e-4dfc-968b-cca68870660e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6e969519-eb8e-4dfc-968b-cca68870660e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6e969519-eb8e-4dfc-968b-cca68870660e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-9910b1a2-4002-46a3-906e-04e7520c60c8\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9910b1a2-4002-46a3-906e-04e7520c60c8')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-9910b1a2-4002-46a3-906e-04e7520c60c8 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"(grades + bonus_points)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.868264577855235,\n        \"min\": 0.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.522680508593631,\n        \"min\": 0.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "(grades + bonus_points).fillna(0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HVhS4Gvy3k3S"
+      },
+      "source": [
+        "It's a bit unfair that we're setting grades to zero in September, though. Perhaps we should decide that missing grades are missing grades, but missing bonus points should be replaced by zeros:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "NlDpdgbf3k3T",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "8f814c6a-997e-476b-a518-04c791411244"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         dec   nov   oct   sep\n",
+              "alice    NaN   9.0   8.0   8.0\n",
+              "bob      NaN   9.0   9.0  10.0\n",
+              "charles  NaN   5.0  11.0   4.0\n",
+              "colin    NaN   NaN   NaN   NaN\n",
+              "darwin   NaN  11.0  10.0   9.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-11a5e36f-a762-47ef-a744-3a15ae79d472\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>dec</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>sep</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>colin</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>9.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11a5e36f-a762-47ef-a744-3a15ae79d472')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-11a5e36f-a762-47ef-a744-3a15ae79d472 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-11a5e36f-a762-47ef-a744-3a15ae79d472');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-e0bd8adb-92b8-4fb6-bd38-31bed13380c4\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e0bd8adb-92b8-4fb6-bd38-31bed13380c4')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-e0bd8adb-92b8-4fb6-bd38-31bed13380c4 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"grades + fixed_bonus_points\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.516611478423583,\n        \"min\": 5.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2909944487358056,\n        \"min\": 8.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 4,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": 4.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 4,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ],
+      "source": [
+        "fixed_bonus_points = bonus_points.fillna(0)\n",
+        "fixed_bonus_points.insert(0, \"sep\", 0)\n",
+        "fixed_bonus_points.loc[\"alice\"] = 0\n",
+        "grades + fixed_bonus_points"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RkmI8mp23k3T"
+      },
+      "source": [
+        "That's much better: although we made up some data, we have not been too unfair.\n",
+        "\n",
+        "Another way to handle missing data is to interpolate. Let's look at the `bonus_points` `DataFrame` again:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "VRLcOOHN3k3T",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "edef622a-7e32-4735-834f-ddbd652db176"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         oct  nov  dec\n",
+              "bob      0.0  NaN  2.0\n",
+              "colin    NaN  1.0  0.0\n",
+              "darwin   0.0  1.0  0.0\n",
+              "charles  3.0  3.0  0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d9af4c28-7799-4679-9fe6-64cace487490\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>dec</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>colin</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d9af4c28-7799-4679-9fe6-64cace487490')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d9af4c28-7799-4679-9fe6-64cace487490 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d9af4c28-7799-4679-9fe6-64cace487490');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-ffc5704d-636f-4777-b848-e9a8641db8ff\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ffc5704d-636f-4777-b848-e9a8641db8ff')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-ffc5704d-636f-4777-b848-e9a8641db8ff button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_4efcbe4e-5355-4475-83d4-6c2645663fe9\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('bonus_points')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_4efcbe4e-5355-4475-83d4-6c2645663fe9 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('bonus_points');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "bonus_points",
+              "summary": "{\n  \"name\": \"bonus_points\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7320508075688772,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1547005383792517,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ],
+      "source": [
+        "bonus_points"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HwmjjDv43k3T"
+      },
+      "source": [
+        "Now let's call the `interpolate` method. By default, it interpolates vertically (`axis=0`), so let's tell it to interpolate horizontally (`axis=1`)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "AHUsfJwD3k3T",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "1f84643e-1ad5-42ef-b77e-c4beb8039c1b"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         oct  nov  dec\n",
+              "bob      0.0  1.0  2.0\n",
+              "colin    NaN  1.0  0.0\n",
+              "darwin   0.0  1.0  0.0\n",
+              "charles  3.0  3.0  0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ec7d8e40-c127-465d-95b6-50cf395dcda0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>dec</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>colin</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec7d8e40-c127-465d-95b6-50cf395dcda0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ec7d8e40-c127-465d-95b6-50cf395dcda0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ec7d8e40-c127-465d-95b6-50cf395dcda0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-4711c05e-abc6-440e-9c05-0b2828fb9dd5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4711c05e-abc6-440e-9c05-0b2828fb9dd5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-4711c05e-abc6-440e-9c05-0b2828fb9dd5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"bonus_points\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7320508075688772,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ],
+      "source": [
+        "bonus_points.interpolate(axis=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RA6Ghi1o3k3T"
+      },
+      "source": [
+        "Bob had 0 bonus points in October, and 2 in December. When we interpolate for November, we get the mean: 1 bonus point. Colin had 1 bonus point in November, but we do not know how many bonus points he had in September, so we cannot interpolate, this is why there is still a missing value in October after interpolation. To fix this, we can set the September bonus points to 0 before interpolation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "Rp8vIJb53k3T",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "5bdd4ecd-9cbd-43ac-8dfe-4c782d06047b"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         sep  oct  nov  dec\n",
+              "bob      0.0  0.0  1.0  2.0\n",
+              "colin    0.0  0.5  1.0  0.0\n",
+              "darwin   0.0  0.0  1.0  0.0\n",
+              "charles  0.0  3.0  3.0  0.0\n",
+              "alice    0.0  0.0  0.0  0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-71310974-8305-4d0b-98ad-6b570807d075\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>dec</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>colin</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71310974-8305-4d0b-98ad-6b570807d075')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-71310974-8305-4d0b-98ad-6b570807d075 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-71310974-8305-4d0b-98ad-6b570807d075');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-d879dab0-e749-4fd3-b564-aae4e200a4c8\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d879dab0-e749-4fd3-b564-aae4e200a4c8')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d879dab0-e749-4fd3-b564-aae4e200a4c8 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_385ff919-5a38-44da-8e0b-382c75b716d9\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('better_bonus_points')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_385ff919-5a38-44da-8e0b-382c75b716d9 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('better_bonus_points');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "better_bonus_points",
+              "summary": "{\n  \"name\": \"better_bonus_points\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3038404810405297,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0954451150103324,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8944271909999161,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "better_bonus_points = bonus_points.copy()\n",
+        "better_bonus_points.insert(0, \"sep\", 0)\n",
+        "better_bonus_points.loc[\"alice\"] = 0\n",
+        "better_bonus_points = better_bonus_points.interpolate(axis=1)\n",
+        "better_bonus_points"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ub9iHJsk3k3U"
+      },
+      "source": [
+        "Great, now we have reasonable bonus points everywhere. Let's find out the final grades:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "hglNVeCC3k3U",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "777e00be-0528-481f-b72e-48796a0cfdfe"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         dec   nov   oct   sep\n",
+              "alice    NaN   9.0   8.0   8.0\n",
+              "bob      NaN  10.0   9.0  10.0\n",
+              "charles  NaN   5.0  11.0   4.0\n",
+              "colin    NaN   NaN   NaN   NaN\n",
+              "darwin   NaN  11.0  10.0   9.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fff12c5e-3692-4e39-9b32-19d0d633c042\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>dec</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>sep</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>colin</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>9.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fff12c5e-3692-4e39-9b32-19d0d633c042')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fff12c5e-3692-4e39-9b32-19d0d633c042 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fff12c5e-3692-4e39-9b32-19d0d633c042');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6f05b277-9be4-4939-b09b-03e50e416844\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f05b277-9be4-4939-b09b-03e50e416844')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6f05b277-9be4-4939-b09b-03e50e416844 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"grades + better_bonus_points\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": 5.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 4,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2909944487358056,\n        \"min\": 8.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 4,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": 4.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 4,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ],
+      "source": [
+        "grades + better_bonus_points"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5b9lHFb13k3U"
+      },
+      "source": [
+        "It is slightly annoying that the September column ends up on the right. This is because the `DataFrame`s we are adding do not have the exact same columns (the `grades` `DataFrame` is missing the `\"dec\"` column), so to make things predictable, pandas orders the final columns alphabetically. To fix this, we can simply add the missing column before adding:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "iMig8f1-3k3V",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "71226831-8f94-4154-8cd1-27e514039e6d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          sep   oct   nov  dec\n",
+              "alice     8.0   8.0   9.0  NaN\n",
+              "bob      10.0   9.0  10.0  NaN\n",
+              "charles   4.0  11.0   5.0  NaN\n",
+              "colin     NaN   NaN   NaN  NaN\n",
+              "darwin    9.0  10.0  11.0  NaN"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-57274325-50e5-48e4-9076-758817044f1b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>dec</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>10.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>4.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>colin</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57274325-50e5-48e4-9076-758817044f1b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-57274325-50e5-48e4-9076-758817044f1b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-57274325-50e5-48e4-9076-758817044f1b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6ac74c97-d2aa-400d-8077-68d001ba157b\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6ac74c97-d2aa-400d-8077-68d001ba157b')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6ac74c97-d2aa-400d-8077-68d001ba157b button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_4cf6033f-381a-4f24-9896-3a6ee93da06b\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('final_grades')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_4cf6033f-381a-4f24-9896-3a6ee93da06b button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('final_grades');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "final_grades",
+              "summary": "{\n  \"name\": \"final_grades\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": 4.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10.0,\n          9.0,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2909944487358056,\n        \"min\": 8.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9.0,\n          10.0,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": 5.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10.0,\n          11.0,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ],
+      "source": [
+        "grades[\"dec\"] = np.nan\n",
+        "final_grades = grades + better_bonus_points\n",
+        "final_grades"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "39AupCAN3k3V"
+      },
+      "source": [
+        "There's not much we can do about December and Colin: it's bad enough that we are making up bonus points, but we can't reasonably make up grades (well, I guess some teachers probably do). So let's call the `dropna()` method to get rid of rows that are full of `NaN`s:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "1ce45hkw3k3V",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "9e19e85e-2496-4e97-b4a7-9192a9237c37"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          sep   oct   nov  dec\n",
+              "alice     8.0   8.0   9.0  NaN\n",
+              "bob      10.0   9.0  10.0  NaN\n",
+              "charles   4.0  11.0   5.0  NaN\n",
+              "darwin    9.0  10.0  11.0  NaN"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2513d026-4c9a-42c3-8603-1a53c392644b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>dec</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>10.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>4.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2513d026-4c9a-42c3-8603-1a53c392644b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2513d026-4c9a-42c3-8603-1a53c392644b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2513d026-4c9a-42c3-8603-1a53c392644b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-83574c27-1676-4a8a-88d4-f209cb1f1400\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-83574c27-1676-4a8a-88d4-f209cb1f1400')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-83574c27-1676-4a8a-88d4-f209cb1f1400 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_7586fb0b-2937-427d-9cc6-4fd262bd9b17\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('final_grades_clean')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_7586fb0b-2937-427d-9cc6-4fd262bd9b17 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('final_grades_clean');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "final_grades_clean",
+              "summary": "{\n  \"name\": \"final_grades_clean\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": 4.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10.0,\n          9.0,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2909944487358056,\n        \"min\": 8.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9.0,\n          10.0,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": 5.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10.0,\n          11.0,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ],
+      "source": [
+        "final_grades_clean = final_grades.dropna(how=\"all\")\n",
+        "final_grades_clean"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zxqcoCzn3k3V"
+      },
+      "source": [
+        "Now let's remove columns that are full of `NaN`s by setting the `axis` argument to `1`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "fTtlfxup3k3V",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "3dda3fc4-7cd8-48a3-e9a0-8e1112abe6d3"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          sep   oct   nov\n",
+              "alice     8.0   8.0   9.0\n",
+              "bob      10.0   9.0  10.0\n",
+              "charles   4.0  11.0   5.0\n",
+              "darwin    9.0  10.0  11.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-267627b6-1ad6-4c33-aba2-567d52bdd0d4\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>9.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>10.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>4.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>11.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-267627b6-1ad6-4c33-aba2-567d52bdd0d4')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-267627b6-1ad6-4c33-aba2-567d52bdd0d4 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-267627b6-1ad6-4c33-aba2-567d52bdd0d4');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-38142035-e894-4cf8-85b9-0159e3a7f4a2\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-38142035-e894-4cf8-85b9-0159e3a7f4a2')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-38142035-e894-4cf8-85b9-0159e3a7f4a2 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_8decc637-ad1f-4379-87c9-43b2cf034e60\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('final_grades_clean')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_8decc637-ad1f-4379-87c9-43b2cf034e60 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('final_grades_clean');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "final_grades_clean",
+              "summary": "{\n  \"name\": \"final_grades_clean\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": 4.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10.0,\n          9.0,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2909944487358056,\n        \"min\": 8.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9.0,\n          10.0,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": 5.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10.0,\n          11.0,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "final_grades_clean = final_grades_clean.dropna(axis=1, how=\"all\")\n",
+        "final_grades_clean"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BCVtMHsH3k3W"
+      },
+      "source": [
+        "## Aggregating with `groupby`\n",
+        "Similar to the SQL language, pandas allows grouping your data into groups to run calculations over each group.\n",
+        "\n",
+        "First, let's add some extra data about each person so we can group them, and let's go back to the `final_grades` `DataFrame` so we can see how `NaN` values are handled:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "ykUCYFfa3k3W",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "d2300a68-f143-4d6e-a66d-9ac4f947aa72"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          sep   oct   nov  dec    hobby\n",
+              "alice     8.0   8.0   9.0  NaN   Biking\n",
+              "bob      10.0   9.0  10.0  NaN  Dancing\n",
+              "charles   4.0  11.0   5.0  NaN      NaN\n",
+              "colin     NaN   NaN   NaN  NaN  Dancing\n",
+              "darwin    9.0  10.0  11.0  NaN   Biking"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0c388cb8-76b9-407c-bcf7-4b26a54aebd7\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>dec</th>\n",
+              "      <th>hobby</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Biking</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>10.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Dancing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>4.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>colin</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Dancing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Biking</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0c388cb8-76b9-407c-bcf7-4b26a54aebd7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0c388cb8-76b9-407c-bcf7-4b26a54aebd7 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0c388cb8-76b9-407c-bcf7-4b26a54aebd7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-085fb3fd-87c6-45de-9f62-732177678028\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-085fb3fd-87c6-45de-9f62-732177678028')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-085fb3fd-87c6-45de-9f62-732177678028 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_095f443d-24a0-4002-9bd9-d6526800d408\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('final_grades')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_095f443d-24a0-4002-9bd9-d6526800d408 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('final_grades');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "final_grades",
+              "summary": "{\n  \"name\": \"final_grades\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": 4.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10.0,\n          9.0,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2909944487358056,\n        \"min\": 8.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9.0,\n          10.0,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6299556396765835,\n        \"min\": 5.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10.0,\n          11.0,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ],
+      "source": [
+        "final_grades[\"hobby\"] = [\"Biking\", \"Dancing\", np.nan, \"Dancing\", \"Biking\"]\n",
+        "final_grades"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Qo_wJAJL3k3W"
+      },
+      "source": [
+        "Now let's group data in this `DataFrame` by hobby:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "xapxvwBk3k3W",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7b8484b2-8557-4d07-e064-1ff599a96ac3"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7edc00956630>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ],
+      "source": [
+        "grouped_grades = final_grades.groupby(\"hobby\")\n",
+        "grouped_grades"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E8JMd6Lb3k3X"
+      },
+      "source": [
+        "We are ready to compute the average grade per hobby:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "G1UC0-Ij3k3X",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "e47af45e-563b-45c8-e634-a6391a79de13"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          sep  oct   nov  dec\n",
+              "hobby                        \n",
+              "Biking    8.5  9.0  10.0  NaN\n",
+              "Dancing  10.0  9.0  10.0  NaN"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2035de3f-597e-43a0-b671-d4c9388433ff\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sep</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>dec</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hobby</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Biking</th>\n",
+              "      <td>8.5</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Dancing</th>\n",
+              "      <td>10.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2035de3f-597e-43a0-b671-d4c9388433ff')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2035de3f-597e-43a0-b671-d4c9388433ff button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2035de3f-597e-43a0-b671-d4c9388433ff');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-b220dd50-88b0-493e-af6f-012604d6b26c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b220dd50-88b0-493e-af6f-012604d6b26c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-b220dd50-88b0-493e-af6f-012604d6b26c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"grouped_grades\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0606601717798212,\n        \"min\": 8.5,\n        \"max\": 10.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          10.0,\n          8.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 9.0,\n        \"max\": 9.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 10.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ],
+      "source": [
+        "grouped_grades.mean()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cNyKWs_R3k3X"
+      },
+      "source": [
+        "That was easy! Note that the `NaN` values have simply been skipped when computing the means."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HfXZC0ZP3k3X"
+      },
+      "source": [
+        "## Pivot tables\n",
+        "Pandas supports spreadsheet-like [pivot tables](https://en.wikipedia.org/wiki/Pivot_table) that allow quick data summarization. To illustrate this, let's create a simple `DataFrame`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "_rhxgA3M3k3Y",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "d826d2b9-b1d4-4f3b-835f-0663441c8964"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         oct  nov  dec\n",
+              "bob      0.0  NaN  2.0\n",
+              "colin    NaN  1.0  0.0\n",
+              "darwin   0.0  1.0  0.0\n",
+              "charles  3.0  3.0  0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a6a8a913-b629-4f1d-985e-2529d509dd9c\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>oct</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>dec</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>colin</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a6a8a913-b629-4f1d-985e-2529d509dd9c')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a6a8a913-b629-4f1d-985e-2529d509dd9c button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a6a8a913-b629-4f1d-985e-2529d509dd9c');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-2ace1df5-9318-4a23-a8eb-296ad5176239\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2ace1df5-9318-4a23-a8eb-296ad5176239')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-2ace1df5-9318-4a23-a8eb-296ad5176239 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_c23e3cdc-c9a3-4891-8905-56edb080180e\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('bonus_points')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_c23e3cdc-c9a3-4891-8905-56edb080180e button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('bonus_points');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "bonus_points",
+              "summary": "{\n  \"name\": \"bonus_points\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7320508075688772,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1547005383792517,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
+      "source": [
+        "bonus_points"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "final_grades_clean.stack().reset_index()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 458
+        },
+        "id": "Odnb2T0EIsyj",
+        "outputId": "b38542e6-1a1a-4820-a89c-8a79e46048b4"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    level_0 level_1     0\n",
+              "0     alice     sep   8.0\n",
+              "1     alice     oct   8.0\n",
+              "2     alice     nov   9.0\n",
+              "3       bob     sep  10.0\n",
+              "4       bob     oct   9.0\n",
+              "5       bob     nov  10.0\n",
+              "6   charles     sep   4.0\n",
+              "7   charles     oct  11.0\n",
+              "8   charles     nov   5.0\n",
+              "9    darwin     sep   9.0\n",
+              "10   darwin     oct  10.0\n",
+              "11   darwin     nov  11.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8d3c51cb-a27b-45c3-b666-41d7219c365e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>level_0</th>\n",
+              "      <th>level_1</th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>alice</td>\n",
+              "      <td>sep</td>\n",
+              "      <td>8.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>alice</td>\n",
+              "      <td>oct</td>\n",
+              "      <td>8.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>alice</td>\n",
+              "      <td>nov</td>\n",
+              "      <td>9.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>bob</td>\n",
+              "      <td>sep</td>\n",
+              "      <td>10.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>bob</td>\n",
+              "      <td>oct</td>\n",
+              "      <td>9.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>bob</td>\n",
+              "      <td>nov</td>\n",
+              "      <td>10.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>charles</td>\n",
+              "      <td>sep</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>charles</td>\n",
+              "      <td>oct</td>\n",
+              "      <td>11.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>charles</td>\n",
+              "      <td>nov</td>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>darwin</td>\n",
+              "      <td>sep</td>\n",
+              "      <td>9.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>darwin</td>\n",
+              "      <td>oct</td>\n",
+              "      <td>10.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>darwin</td>\n",
+              "      <td>nov</td>\n",
+              "      <td>11.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d3c51cb-a27b-45c3-b666-41d7219c365e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8d3c51cb-a27b-45c3-b666-41d7219c365e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8d3c51cb-a27b-45c3-b666-41d7219c365e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-a7c41d3f-9095-4c72-b575-5c575551456c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a7c41d3f-9095-4c72-b575-5c575551456c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-a7c41d3f-9095-4c72-b575-5c575551456c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"final_grades_clean\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"level_0\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"bob\",\n          \"darwin\",\n          \"alice\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"level_1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"sep\",\n          \"oct\",\n          \"nov\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.188122205883115,\n        \"min\": 4.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          8.0,\n          9.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "cDZr3wCs3k3Y",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 458
+        },
+        "outputId": "ecfe7c59-4897-43d1-c75e-5f2294fe9c6d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       name month  grade  bonus\n",
+              "0     alice   sep    8.0    NaN\n",
+              "1     alice   oct    8.0    NaN\n",
+              "2     alice   nov    9.0    NaN\n",
+              "3       bob   sep   10.0    0.0\n",
+              "4       bob   oct    9.0    NaN\n",
+              "5       bob   nov   10.0    2.0\n",
+              "6   charles   sep    4.0    3.0\n",
+              "7   charles   oct   11.0    3.0\n",
+              "8   charles   nov    5.0    0.0\n",
+              "9    darwin   sep    9.0    0.0\n",
+              "10   darwin   oct   10.0    1.0\n",
+              "11   darwin   nov   11.0    0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c3681175-f399-471b-abe7-4594b4c574dc\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>month</th>\n",
+              "      <th>grade</th>\n",
+              "      <th>bonus</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>alice</td>\n",
+              "      <td>sep</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>alice</td>\n",
+              "      <td>oct</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>alice</td>\n",
+              "      <td>nov</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>bob</td>\n",
+              "      <td>sep</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>bob</td>\n",
+              "      <td>oct</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>bob</td>\n",
+              "      <td>nov</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>charles</td>\n",
+              "      <td>sep</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>charles</td>\n",
+              "      <td>oct</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>charles</td>\n",
+              "      <td>nov</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>darwin</td>\n",
+              "      <td>sep</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>darwin</td>\n",
+              "      <td>oct</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>darwin</td>\n",
+              "      <td>nov</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3681175-f399-471b-abe7-4594b4c574dc')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c3681175-f399-471b-abe7-4594b4c574dc button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c3681175-f399-471b-abe7-4594b4c574dc');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-8cbd8a9a-e2c7-4295-8931-a99668e4851b\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8cbd8a9a-e2c7-4295-8931-a99668e4851b')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-8cbd8a9a-e2c7-4295-8931-a99668e4851b button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_0152c977-cb9a-4493-b552-4afe07a3f47e\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('more_grades')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_0152c977-cb9a-4493-b552-4afe07a3f47e button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('more_grades');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "more_grades",
+              "summary": "{\n  \"name\": \"more_grades\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"bob\",\n          \"darwin\",\n          \"alice\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"sep\",\n          \"oct\",\n          \"nov\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.188122205883115,\n        \"min\": 4.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          8.0,\n          9.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3562026818605375,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.0,\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ],
+      "source": [
+        "more_grades = final_grades_clean.stack().reset_index()\n",
+        "more_grades.columns = [\"name\", \"month\", \"grade\"]\n",
+        "more_grades[\"bonus\"] = [np.nan, np.nan, np.nan, 0, np.nan, 2, 3, 3, 0, 0, 1, 0]\n",
+        "more_grades"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n5WWl1J_3k3e"
+      },
+      "source": [
+        "Now we can call the `pd.pivot_table()` function for this `DataFrame`, asking to group by the `name` column. By default, `pivot_table()` computes the mean of each numeric column:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "GGAE298X3k3f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "91a0b920-b2fd-457b-9ff8-3b62bac023c1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            bonus      grade\n",
+              "name                        \n",
+              "alice         NaN   8.333333\n",
+              "bob      1.000000   9.666667\n",
+              "charles  2.000000   6.666667\n",
+              "darwin   0.333333  10.000000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-750dae59-b18a-4ac3-bc69-b4d8e85b2125\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>bonus</th>\n",
+              "      <th>grade</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>name</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>8.333333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>9.666667</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>6.666667</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>0.333333</td>\n",
+              "      <td>10.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-750dae59-b18a-4ac3-bc69-b4d8e85b2125')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-750dae59-b18a-4ac3-bc69-b4d8e85b2125 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-750dae59-b18a-4ac3-bc69-b4d8e85b2125');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-1da74036-dde0-446e-8351-61a79091e7de\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1da74036-dde0-446e-8351-61a79091e7de')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-1da74036-dde0-446e-8351-61a79091e7de button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"bob\",\n          \"darwin\",\n          \"alice\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8388704928078612,\n        \"min\": 0.3333333333333333,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          2.0,\n          0.3333333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.515353521887317,\n        \"min\": 6.666666666666667,\n        \"max\": 10.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9.666666666666666,\n          10.0,\n          8.333333333333334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ],
+      "source": [
+        "pd.pivot_table(more_grades[[\"name\", \"grade\", \"bonus\"]], index=\"name\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f4Y0z9Ey3k3f"
+      },
+      "source": [
+        "We can change the aggregation function by setting the `aggfunc` argument, and we can also specify the list of columns whose values will be aggregated:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "mQk2C1rZ3k3g",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "0f053701-533a-4c7f-b017-218bfe395719"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         bonus  grade\n",
+              "name                 \n",
+              "alice      NaN    9.0\n",
+              "bob        2.0   10.0\n",
+              "charles    3.0   11.0\n",
+              "darwin     1.0   11.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8e5d742f-58c5-434f-96ab-678ae5ba9594\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>bonus</th>\n",
+              "      <th>grade</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>name</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>9.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>10.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>11.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>11.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e5d742f-58c5-434f-96ab-678ae5ba9594')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8e5d742f-58c5-434f-96ab-678ae5ba9594 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8e5d742f-58c5-434f-96ab-678ae5ba9594');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-989d0aa5-30ec-43aa-b298-6e0b203c0835\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-989d0aa5-30ec-43aa-b298-6e0b203c0835')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-989d0aa5-30ec-43aa-b298-6e0b203c0835 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"bob\",\n          \"darwin\",\n          \"alice\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2.0,\n          3.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9574271077563381,\n        \"min\": 9.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9.0,\n          10.0,\n          11.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ],
+      "source": [
+        "pd.pivot_table(more_grades, index=\"name\", values=[\"grade\", \"bonus\"], aggfunc=\"max\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xJIHE4qk3k3g"
+      },
+      "source": [
+        "We can also specify the `columns` to aggregate over horizontally, and request the grand totals for each row and column by setting `margins=True`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "5_mM6ffO3k3g",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 255
+        },
+        "outputId": "2f7f214c-d013-4591-e6fb-ad49cbaa8e24"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "month      nov   oct    sep        All\n",
+              "name                                  \n",
+              "alice     9.00   8.0   8.00   8.333333\n",
+              "bob      10.00   9.0  10.00   9.666667\n",
+              "charles   5.00  11.0   4.00   6.666667\n",
+              "darwin   11.00  10.0   9.00  10.000000\n",
+              "All       8.75   9.5   7.75   8.666667"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a1428c8b-ed14-43ef-9c60-4bfe640ee307\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>month</th>\n",
+              "      <th>nov</th>\n",
+              "      <th>oct</th>\n",
+              "      <th>sep</th>\n",
+              "      <th>All</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>name</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>9.00</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.00</td>\n",
+              "      <td>8.333333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>10.00</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>10.00</td>\n",
+              "      <td>9.666667</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>charles</th>\n",
+              "      <td>5.00</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>4.00</td>\n",
+              "      <td>6.666667</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>darwin</th>\n",
+              "      <td>11.00</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>9.00</td>\n",
+              "      <td>10.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>All</th>\n",
+              "      <td>8.75</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>7.75</td>\n",
+              "      <td>8.666667</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1428c8b-ed14-43ef-9c60-4bfe640ee307')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a1428c8b-ed14-43ef-9c60-4bfe640ee307 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a1428c8b-ed14-43ef-9c60-4bfe640ee307');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-c9a55a49-19bd-4df7-9930-7e5c2817c564\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c9a55a49-19bd-4df7-9930-7e5c2817c564')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c9a55a49-19bd-4df7-9930-7e5c2817c564 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"bob\",\n          \"All\",\n          \"charles\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.277608394786075,\n        \"min\": 5.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          10.0,\n          8.75,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.118033988749895,\n        \"min\": 8.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.0,\n          9.5,\n          11.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.277608394786075,\n        \"min\": 4.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          10.0,\n          7.75,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"All\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.312334645668635,\n        \"min\": 6.666666666666667,\n        \"max\": 10.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.666666666666666,\n          8.666666666666666,\n          6.666666666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ],
+      "source": [
+        "pd.pivot_table(more_grades, index=\"name\", values=\"grade\", columns=\"month\", margins=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BIHqBOEH3k3h"
+      },
+      "source": [
+        "Finally, we can specify multiple index or column names, and pandas will create multi-level indices:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "gIlCHpyn3k3h",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 525
+        },
+        "outputId": "99f680f4-8c80-4784-c899-aa769bcf5ac8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               bonus  grade\n",
+              "name    month              \n",
+              "alice   nov      NaN   9.00\n",
+              "        oct      NaN   8.00\n",
+              "        sep      NaN   8.00\n",
+              "bob     nov    2.000  10.00\n",
+              "        oct      NaN   9.00\n",
+              "        sep    0.000  10.00\n",
+              "charles nov    0.000   5.00\n",
+              "        oct    3.000  11.00\n",
+              "        sep    3.000   4.00\n",
+              "darwin  nov    0.000  11.00\n",
+              "        oct    1.000  10.00\n",
+              "        sep    0.000   9.00\n",
+              "All            1.125   8.75"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f3d1ec42-0dd2-4e9f-b784-c6b1eba0efee\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>bonus</th>\n",
+              "      <th>grade</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>name</th>\n",
+              "      <th>month</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">alice</th>\n",
+              "      <th>nov</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>9.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>oct</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>8.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sep</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>8.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">bob</th>\n",
+              "      <th>nov</th>\n",
+              "      <td>2.000</td>\n",
+              "      <td>10.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>oct</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>9.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sep</th>\n",
+              "      <td>0.000</td>\n",
+              "      <td>10.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">charles</th>\n",
+              "      <th>nov</th>\n",
+              "      <td>0.000</td>\n",
+              "      <td>5.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>oct</th>\n",
+              "      <td>3.000</td>\n",
+              "      <td>11.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sep</th>\n",
+              "      <td>3.000</td>\n",
+              "      <td>4.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">darwin</th>\n",
+              "      <th>nov</th>\n",
+              "      <td>0.000</td>\n",
+              "      <td>11.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>oct</th>\n",
+              "      <td>1.000</td>\n",
+              "      <td>10.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sep</th>\n",
+              "      <td>0.000</td>\n",
+              "      <td>9.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>All</th>\n",
+              "      <th></th>\n",
+              "      <td>1.125</td>\n",
+              "      <td>8.75</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3d1ec42-0dd2-4e9f-b784-c6b1eba0efee')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f3d1ec42-0dd2-4e9f-b784-c6b1eba0efee button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f3d1ec42-0dd2-4e9f-b784-c6b1eba0efee');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-24c8555a-12a0-4844-9c64-9d2f0cd79372\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-24c8555a-12a0-4844-9c64-9d2f0cd79372')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-24c8555a-12a0-4844-9c64-9d2f0cd79372 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2686114456365274,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          1.125,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.095095004271424,\n        \"min\": 4.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          9.0,\n          8.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ],
+      "source": [
+        "pd.pivot_table(more_grades, index=(\"name\", \"month\"), margins=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NMsa49Qc3k3h"
+      },
+      "source": [
+        "## Overview functions\n",
+        "When dealing with large `DataFrames`, it is useful to get a quick overview of its content. Pandas offers a few functions for this. First, let's create a large `DataFrame` with a mix of numeric values, missing values and text values. Notice how Jupyter displays only the corners of the `DataFrame`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "id": "DnoPfOIX3k3i",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 476
+        },
+        "outputId": "1409d4f5-9624-43be-a898-ef56928751c8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         A     B     C some_text      D      E      F     G      H      I  \\\n",
+              "0      NaN  11.0  44.0    Blabla   99.0    NaN   88.0  22.0  165.0  143.0   \n",
+              "1     11.0  22.0  55.0    Blabla  110.0    NaN   99.0  33.0    NaN  154.0   \n",
+              "2     22.0  33.0  66.0    Blabla  121.0   11.0  110.0  44.0    NaN  165.0   \n",
+              "3     33.0  44.0  77.0    Blabla  132.0   22.0  121.0  55.0   11.0    NaN   \n",
+              "4     44.0  55.0  88.0    Blabla  143.0   33.0  132.0  66.0   22.0    NaN   \n",
+              "...    ...   ...   ...       ...    ...    ...    ...   ...    ...    ...   \n",
+              "9995   NaN   NaN  33.0    Blabla   88.0  165.0   77.0  11.0  154.0  132.0   \n",
+              "9996   NaN  11.0  44.0    Blabla   99.0    NaN   88.0  22.0  165.0  143.0   \n",
+              "9997  11.0  22.0  55.0    Blabla  110.0    NaN   99.0  33.0    NaN  154.0   \n",
+              "9998  22.0  33.0  66.0    Blabla  121.0   11.0  110.0  44.0    NaN  165.0   \n",
+              "9999  33.0  44.0  77.0    Blabla  132.0   22.0  121.0  55.0   11.0    NaN   \n",
+              "\n",
+              "      ...     Q     R     S     T      U      V      W     X      Y      Z  \n",
+              "0     ...  11.0   NaN  11.0  44.0   99.0    NaN   88.0  22.0  165.0  143.0  \n",
+              "1     ...  22.0  11.0  22.0  55.0  110.0    NaN   99.0  33.0    NaN  154.0  \n",
+              "2     ...  33.0  22.0  33.0  66.0  121.0   11.0  110.0  44.0    NaN  165.0  \n",
+              "3     ...  44.0  33.0  44.0  77.0  132.0   22.0  121.0  55.0   11.0    NaN  \n",
+              "4     ...  55.0  44.0  55.0  88.0  143.0   33.0  132.0  66.0   22.0    NaN  \n",
+              "...   ...   ...   ...   ...   ...    ...    ...    ...   ...    ...    ...  \n",
+              "9995  ...   NaN   NaN   NaN  33.0   88.0  165.0   77.0  11.0  154.0  132.0  \n",
+              "9996  ...  11.0   NaN  11.0  44.0   99.0    NaN   88.0  22.0  165.0  143.0  \n",
+              "9997  ...  22.0  11.0  22.0  55.0  110.0    NaN   99.0  33.0    NaN  154.0  \n",
+              "9998  ...  33.0  22.0  33.0  66.0  121.0   11.0  110.0  44.0    NaN  165.0  \n",
+              "9999  ...  44.0  33.0  44.0  77.0  132.0   22.0  121.0  55.0   11.0    NaN  \n",
+              "\n",
+              "[10000 rows x 27 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7d9118bc-2171-46b9-95f6-1d786db6791b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>A</th>\n",
+              "      <th>B</th>\n",
+              "      <th>C</th>\n",
+              "      <th>some_text</th>\n",
+              "      <th>D</th>\n",
+              "      <th>E</th>\n",
+              "      <th>F</th>\n",
+              "      <th>G</th>\n",
+              "      <th>H</th>\n",
+              "      <th>I</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Q</th>\n",
+              "      <th>R</th>\n",
+              "      <th>S</th>\n",
+              "      <th>T</th>\n",
+              "      <th>U</th>\n",
+              "      <th>V</th>\n",
+              "      <th>W</th>\n",
+              "      <th>X</th>\n",
+              "      <th>Y</th>\n",
+              "      <th>Z</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>143.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>143.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>11.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>154.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>154.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>22.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>165.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>33.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>44.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>143.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>143.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9995</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>154.0</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>154.0</td>\n",
+              "      <td>132.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9996</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>143.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>143.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9997</th>\n",
+              "      <td>11.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>154.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>154.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9998</th>\n",
+              "      <td>22.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>165.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9999</th>\n",
+              "      <td>33.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10000 rows × 27 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d9118bc-2171-46b9-95f6-1d786db6791b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7d9118bc-2171-46b9-95f6-1d786db6791b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7d9118bc-2171-46b9-95f6-1d786db6791b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-042e37bb-1cb6-46d5-8c0e-41deb3980c2c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-042e37bb-1cb6-46d5-8c0e-41deb3980c2c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-042e37bb-1cb6-46d5-8c0e-41deb3980c2c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_d912117f-9630-49e9-8b2a-f1f8b4106212\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('large_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_d912117f-9630-49e9-8b2a-f1f8b4106212 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('large_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "large_df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ],
+      "source": [
+        "much_data = np.fromfunction(lambda x,y: (x+y*y)%17*11, (10000, 26))\n",
+        "large_df = pd.DataFrame(much_data, columns=list(\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"))\n",
+        "large_df[large_df % 16 == 0] = np.nan\n",
+        "large_df.insert(3, \"some_text\", \"Blabla\")\n",
+        "large_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n-X7Ah-13k3i"
+      },
+      "source": [
+        "The `head()` method returns the top 5 rows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "id": "z6pgCqs93k3j",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273
+        },
+        "outputId": "f9a52bbd-d4de-4d7e-d881-daab8648db54"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      A     B     C some_text      D     E      F     G      H      I  ...  \\\n",
+              "0   NaN  11.0  44.0    Blabla   99.0   NaN   88.0  22.0  165.0  143.0  ...   \n",
+              "1  11.0  22.0  55.0    Blabla  110.0   NaN   99.0  33.0    NaN  154.0  ...   \n",
+              "2  22.0  33.0  66.0    Blabla  121.0  11.0  110.0  44.0    NaN  165.0  ...   \n",
+              "3  33.0  44.0  77.0    Blabla  132.0  22.0  121.0  55.0   11.0    NaN  ...   \n",
+              "4  44.0  55.0  88.0    Blabla  143.0  33.0  132.0  66.0   22.0    NaN  ...   \n",
+              "\n",
+              "      Q     R     S     T      U     V      W     X      Y      Z  \n",
+              "0  11.0   NaN  11.0  44.0   99.0   NaN   88.0  22.0  165.0  143.0  \n",
+              "1  22.0  11.0  22.0  55.0  110.0   NaN   99.0  33.0    NaN  154.0  \n",
+              "2  33.0  22.0  33.0  66.0  121.0  11.0  110.0  44.0    NaN  165.0  \n",
+              "3  44.0  33.0  44.0  77.0  132.0  22.0  121.0  55.0   11.0    NaN  \n",
+              "4  55.0  44.0  55.0  88.0  143.0  33.0  132.0  66.0   22.0    NaN  \n",
+              "\n",
+              "[5 rows x 27 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-98d73591-42d4-40c3-bc40-ad5f27f521ef\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>A</th>\n",
+              "      <th>B</th>\n",
+              "      <th>C</th>\n",
+              "      <th>some_text</th>\n",
+              "      <th>D</th>\n",
+              "      <th>E</th>\n",
+              "      <th>F</th>\n",
+              "      <th>G</th>\n",
+              "      <th>H</th>\n",
+              "      <th>I</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Q</th>\n",
+              "      <th>R</th>\n",
+              "      <th>S</th>\n",
+              "      <th>T</th>\n",
+              "      <th>U</th>\n",
+              "      <th>V</th>\n",
+              "      <th>W</th>\n",
+              "      <th>X</th>\n",
+              "      <th>Y</th>\n",
+              "      <th>Z</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>143.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>143.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>11.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>154.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>154.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>22.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>165.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>33.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>44.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>143.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>143.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 27 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98d73591-42d4-40c3-bc40-ad5f27f521ef')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-98d73591-42d4-40c3-bc40-ad5f27f521ef button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-98d73591-42d4-40c3-bc40-ad5f27f521ef');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-35448776-8db2-4f9b-b9e6-a955d8284f5e\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-35448776-8db2-4f9b-b9e6-a955d8284f5e')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-35448776-8db2-4f9b-b9e6-a955d8284f5e button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "large_df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ],
+      "source": [
+        "large_df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dqMXVKuY3k3k"
+      },
+      "source": [
+        "Of course, there's also a `tail()` function to view the bottom 5 rows. You can pass the number of rows you want:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "id": "LLe0RnMF3k3l",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 171
+        },
+        "outputId": "0a922cf8-1a02-43c5-9814-bd6acf4a85a1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         A     B     C some_text      D     E      F     G     H      I  ...  \\\n",
+              "9998  22.0  33.0  66.0    Blabla  121.0  11.0  110.0  44.0   NaN  165.0  ...   \n",
+              "9999  33.0  44.0  77.0    Blabla  132.0  22.0  121.0  55.0  11.0    NaN  ...   \n",
+              "\n",
+              "         Q     R     S     T      U     V      W     X     Y      Z  \n",
+              "9998  33.0  22.0  33.0  66.0  121.0  11.0  110.0  44.0   NaN  165.0  \n",
+              "9999  44.0  33.0  44.0  77.0  132.0  22.0  121.0  55.0  11.0    NaN  \n",
+              "\n",
+              "[2 rows x 27 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b2643c63-cf61-4f45-b9f8-b302bd08f898\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>A</th>\n",
+              "      <th>B</th>\n",
+              "      <th>C</th>\n",
+              "      <th>some_text</th>\n",
+              "      <th>D</th>\n",
+              "      <th>E</th>\n",
+              "      <th>F</th>\n",
+              "      <th>G</th>\n",
+              "      <th>H</th>\n",
+              "      <th>I</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Q</th>\n",
+              "      <th>R</th>\n",
+              "      <th>S</th>\n",
+              "      <th>T</th>\n",
+              "      <th>U</th>\n",
+              "      <th>V</th>\n",
+              "      <th>W</th>\n",
+              "      <th>X</th>\n",
+              "      <th>Y</th>\n",
+              "      <th>Z</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>9998</th>\n",
+              "      <td>22.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>165.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9999</th>\n",
+              "      <td>33.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>Blabla</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>2 rows × 27 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2643c63-cf61-4f45-b9f8-b302bd08f898')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b2643c63-cf61-4f45-b9f8-b302bd08f898 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b2643c63-cf61-4f45-b9f8-b302bd08f898');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-a97cf8eb-9de4-436c-bbd3-7252b7407c51\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a97cf8eb-9de4-436c-bbd3-7252b7407c51')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-a97cf8eb-9de4-436c-bbd3-7252b7407c51 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ],
+      "source": [
+        "large_df.tail(n=2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QwBqCXLD3k3m"
+      },
+      "source": [
+        "The `info()` method prints out a summary of each column's contents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "f2Aw0SAO3k3m",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b12b2b40-ab8d-4a4f-8fa8-844aa45f7eaf"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 10000 entries, 0 to 9999\n",
+            "Data columns (total 27 columns):\n",
+            " #   Column     Non-Null Count  Dtype  \n",
+            "---  ------     --------------  -----  \n",
+            " 0   A          8823 non-null   float64\n",
+            " 1   B          8824 non-null   float64\n",
+            " 2   C          8824 non-null   float64\n",
+            " 3   some_text  10000 non-null  object \n",
+            " 4   D          8824 non-null   float64\n",
+            " 5   E          8822 non-null   float64\n",
+            " 6   F          8824 non-null   float64\n",
+            " 7   G          8824 non-null   float64\n",
+            " 8   H          8822 non-null   float64\n",
+            " 9   I          8823 non-null   float64\n",
+            " 10  J          8823 non-null   float64\n",
+            " 11  K          8822 non-null   float64\n",
+            " 12  L          8824 non-null   float64\n",
+            " 13  M          8824 non-null   float64\n",
+            " 14  N          8822 non-null   float64\n",
+            " 15  O          8824 non-null   float64\n",
+            " 16  P          8824 non-null   float64\n",
+            " 17  Q          8824 non-null   float64\n",
+            " 18  R          8823 non-null   float64\n",
+            " 19  S          8824 non-null   float64\n",
+            " 20  T          8824 non-null   float64\n",
+            " 21  U          8824 non-null   float64\n",
+            " 22  V          8822 non-null   float64\n",
+            " 23  W          8824 non-null   float64\n",
+            " 24  X          8824 non-null   float64\n",
+            " 25  Y          8822 non-null   float64\n",
+            " 26  Z          8823 non-null   float64\n",
+            "dtypes: float64(26), object(1)\n",
+            "memory usage: 2.1+ MB\n"
+          ]
+        }
+      ],
+      "source": [
+        "large_df.info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I9uZPgbd3k3n"
+      },
+      "source": [
+        "Finally, the `describe()` method gives a nice overview of the main aggregated values over each column:\n",
+        "* `count`: number of non-null (not NaN) values\n",
+        "* `mean`: mean of non-null values\n",
+        "* `std`: [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of non-null values\n",
+        "* `min`: minimum of non-null values\n",
+        "* `25%`, `50%`, `75%`: 25th, 50th and 75th [percentile](https://en.wikipedia.org/wiki/Percentile) of non-null values\n",
+        "* `max`: maximum of non-null values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "id": "o1hOBHXj3k3n",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        },
+        "outputId": "1201b753-89ac-49bd-eb58-4157ccbc57dc"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                 A            B            C            D            E  \\\n",
+              "count  8823.000000  8824.000000  8824.000000  8824.000000  8822.000000   \n",
+              "mean     87.977559    87.972575    87.987534    88.012466    87.983791   \n",
+              "std      47.535911    47.535523    47.521679    47.521679    47.535001   \n",
+              "min      11.000000    11.000000    11.000000    11.000000    11.000000   \n",
+              "25%      44.000000    44.000000    44.000000    44.000000    44.000000   \n",
+              "50%      88.000000    88.000000    88.000000    88.000000    88.000000   \n",
+              "75%     132.000000   132.000000   132.000000   132.000000   132.000000   \n",
+              "max     165.000000   165.000000   165.000000   165.000000   165.000000   \n",
+              "\n",
+              "                 F            G            H            I            J  ...  \\\n",
+              "count  8824.000000  8824.000000  8822.000000  8823.000000  8823.000000  ...   \n",
+              "mean     88.007480    87.977561    88.000000    88.022441    88.022441  ...   \n",
+              "std      47.519371    47.529755    47.536879    47.535911    47.535911  ...   \n",
+              "min      11.000000    11.000000    11.000000    11.000000    11.000000  ...   \n",
+              "25%      44.000000    44.000000    44.000000    44.000000    44.000000  ...   \n",
+              "50%      88.000000    88.000000    88.000000    88.000000    88.000000  ...   \n",
+              "75%     132.000000   132.000000   132.000000   132.000000   132.000000  ...   \n",
+              "max     165.000000   165.000000   165.000000   165.000000   165.000000  ...   \n",
+              "\n",
+              "                 Q            R            S            T            U  \\\n",
+              "count  8824.000000  8823.000000  8824.000000  8824.000000  8824.000000   \n",
+              "mean     87.972575    87.977559    87.972575    87.987534    88.012466   \n",
+              "std      47.535523    47.535911    47.535523    47.521679    47.521679   \n",
+              "min      11.000000    11.000000    11.000000    11.000000    11.000000   \n",
+              "25%      44.000000    44.000000    44.000000    44.000000    44.000000   \n",
+              "50%      88.000000    88.000000    88.000000    88.000000    88.000000   \n",
+              "75%     132.000000   132.000000   132.000000   132.000000   132.000000   \n",
+              "max     165.000000   165.000000   165.000000   165.000000   165.000000   \n",
+              "\n",
+              "                 V            W            X            Y            Z  \n",
+              "count  8822.000000  8824.000000  8824.000000  8822.000000  8823.000000  \n",
+              "mean     87.983791    88.007480    87.977561    88.000000    88.022441  \n",
+              "std      47.535001    47.519371    47.529755    47.536879    47.535911  \n",
+              "min      11.000000    11.000000    11.000000    11.000000    11.000000  \n",
+              "25%      44.000000    44.000000    44.000000    44.000000    44.000000  \n",
+              "50%      88.000000    88.000000    88.000000    88.000000    88.000000  \n",
+              "75%     132.000000   132.000000   132.000000   132.000000   132.000000  \n",
+              "max     165.000000   165.000000   165.000000   165.000000   165.000000  \n",
+              "\n",
+              "[8 rows x 26 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2154936f-e46a-4dd3-81ef-497f3b3f5bba\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>A</th>\n",
+              "      <th>B</th>\n",
+              "      <th>C</th>\n",
+              "      <th>D</th>\n",
+              "      <th>E</th>\n",
+              "      <th>F</th>\n",
+              "      <th>G</th>\n",
+              "      <th>H</th>\n",
+              "      <th>I</th>\n",
+              "      <th>J</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Q</th>\n",
+              "      <th>R</th>\n",
+              "      <th>S</th>\n",
+              "      <th>T</th>\n",
+              "      <th>U</th>\n",
+              "      <th>V</th>\n",
+              "      <th>W</th>\n",
+              "      <th>X</th>\n",
+              "      <th>Y</th>\n",
+              "      <th>Z</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>8823.000000</td>\n",
+              "      <td>8824.000000</td>\n",
+              "      <td>8824.000000</td>\n",
+              "      <td>8824.000000</td>\n",
+              "      <td>8822.000000</td>\n",
+              "      <td>8824.000000</td>\n",
+              "      <td>8824.000000</td>\n",
+              "      <td>8822.000000</td>\n",
+              "      <td>8823.000000</td>\n",
+              "      <td>8823.000000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>8824.000000</td>\n",
+              "      <td>8823.000000</td>\n",
+              "      <td>8824.000000</td>\n",
+              "      <td>8824.000000</td>\n",
+              "      <td>8824.000000</td>\n",
+              "      <td>8822.000000</td>\n",
+              "      <td>8824.000000</td>\n",
+              "      <td>8824.000000</td>\n",
+              "      <td>8822.000000</td>\n",
+              "      <td>8823.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>87.977559</td>\n",
+              "      <td>87.972575</td>\n",
+              "      <td>87.987534</td>\n",
+              "      <td>88.012466</td>\n",
+              "      <td>87.983791</td>\n",
+              "      <td>88.007480</td>\n",
+              "      <td>87.977561</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.022441</td>\n",
+              "      <td>88.022441</td>\n",
+              "      <td>...</td>\n",
+              "      <td>87.972575</td>\n",
+              "      <td>87.977559</td>\n",
+              "      <td>87.972575</td>\n",
+              "      <td>87.987534</td>\n",
+              "      <td>88.012466</td>\n",
+              "      <td>87.983791</td>\n",
+              "      <td>88.007480</td>\n",
+              "      <td>87.977561</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.022441</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>47.535911</td>\n",
+              "      <td>47.535523</td>\n",
+              "      <td>47.521679</td>\n",
+              "      <td>47.521679</td>\n",
+              "      <td>47.535001</td>\n",
+              "      <td>47.519371</td>\n",
+              "      <td>47.529755</td>\n",
+              "      <td>47.536879</td>\n",
+              "      <td>47.535911</td>\n",
+              "      <td>47.535911</td>\n",
+              "      <td>...</td>\n",
+              "      <td>47.535523</td>\n",
+              "      <td>47.535911</td>\n",
+              "      <td>47.535523</td>\n",
+              "      <td>47.521679</td>\n",
+              "      <td>47.521679</td>\n",
+              "      <td>47.535001</td>\n",
+              "      <td>47.519371</td>\n",
+              "      <td>47.529755</td>\n",
+              "      <td>47.536879</td>\n",
+              "      <td>47.535911</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>44.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "      <td>132.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 26 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2154936f-e46a-4dd3-81ef-497f3b3f5bba')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2154936f-e46a-4dd3-81ef-497f3b3f5bba button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2154936f-e46a-4dd3-81ef-497f3b3f5bba');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-dc66d000-47be-4886-917f-86954f21f33e\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dc66d000-47be-4886-917f-86954f21f33e')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-dc66d000-47be-4886-917f-86954f21f33e button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ],
+      "source": [
+        "large_df.describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6d_EJFxN3k3o"
+      },
+      "source": [
+        "# Saving & loading\n",
+        "Pandas can save `DataFrame`s to various backends, including file formats such as CSV, Excel, JSON, HTML and HDF5, or to a SQL database. Let's create a `DataFrame` to demonstrate this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "id": "kUqoQGfZ3k3o",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "520af4e4-8039-4e97-ea5e-058ddbd6847d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          hobby  weight  birthyear  children\n",
+              "People                                      \n",
+              "alice    Biking    68.5       1985       NaN\n",
+              "bob     Dancing    83.1       1984       3.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8776d207-e6cd-4fdc-8492-982f2bb49ab0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>People</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68.5</td>\n",
+              "      <td>1985</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83.1</td>\n",
+              "      <td>1984</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8776d207-e6cd-4fdc-8492-982f2bb49ab0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8776d207-e6cd-4fdc-8492-982f2bb49ab0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8776d207-e6cd-4fdc-8492-982f2bb49ab0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-12bf8c26-f564-4b60-83f5-c0c4bc36287f\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-12bf8c26-f564-4b60-83f5-c0c4bc36287f')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-12bf8c26-f564-4b60-83f5-c0c4bc36287f button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_391b9d2f-4621-40a6-bec6-491bf3e2b2f5\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('my_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_391b9d2f-4621-40a6-bec6-491bf3e2b2f5 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('my_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "my_df",
+              "summary": "{\n  \"name\": \"my_df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"People\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"bob\",\n          \"alice\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.32375900532359,\n        \"min\": 68.5,\n        \"max\": 83.1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          83.1,\n          68.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1984,\n        \"max\": 1985,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1984,\n          1985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 55
+        }
+      ],
+      "source": [
+        "my_df = pd.DataFrame(\n",
+        "    [[\"Biking\", 68.5, 1985, np.nan], [\"Dancing\", 83.1, 1984, 3]],\n",
+        "    columns=[\"hobby\", \"weight\", \"birthyear\", \"children\"],\n",
+        "    index=[\"alice\", \"bob\"]\n",
+        ")\n",
+        "my_df.index.name = 'People'\n",
+        "my_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QKvKAMbX3k3o"
+      },
+      "source": [
+        "## Saving\n",
+        "Let's save it to CSV, HTML and JSON:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {
+        "id": "DR_V9_5g3k3o"
+      },
+      "outputs": [],
+      "source": [
+        "my_df.to_csv(\"my_df.csv\")\n",
+        "my_df.to_html(\"my_df.html\")\n",
+        "my_df.to_json(\"my_df.json\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zear04Ai3k3p"
+      },
+      "source": [
+        "Done! Let's take a peek at what was saved:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {
+        "id": "NHzzAlaC3k3q",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "23176f18-93d7-4c0a-d77a-8e57a0b9e6ae"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "#### my_df.csv\n",
+            "People,hobby,weight,birthyear,children\n",
+            "alice,Biking,68.5,1985,\n",
+            "bob,Dancing,83.1,1984,3.0\n",
+            "\n",
+            "\n",
+            "#### my_df.html\n",
+            "<table border=\"1\" class=\"dataframe\">\n",
+            "  <thead>\n",
+            "    <tr style=\"text-align: right;\">\n",
+            "      <th></th>\n",
+            "      <th>hobby</th>\n",
+            "      <th>weight</th>\n",
+            "      <th>birthyear</th>\n",
+            "      <th>children</th>\n",
+            "    </tr>\n",
+            "    <tr>\n",
+            "      <th>People</th>\n",
+            "      <th></th>\n",
+            "      <th></th>\n",
+            "      <th></th>\n",
+            "      <th></th>\n",
+            "    </tr>\n",
+            "  </thead>\n",
+            "  <tbody>\n",
+            "    <tr>\n",
+            "      <th>alice</th>\n",
+            "      <td>Biking</td>\n",
+            "      <td>68.5</td>\n",
+            "      <td>1985</td>\n",
+            "      <td>NaN</td>\n",
+            "    </tr>\n",
+            "    <tr>\n",
+            "      <th>bob</th>\n",
+            "      <td>Dancing</td>\n",
+            "      <td>83.1</td>\n",
+            "      <td>1984</td>\n",
+            "      <td>3.0</td>\n",
+            "    </tr>\n",
+            "  </tbody>\n",
+            "</table>\n",
+            "\n",
+            "#### my_df.json\n",
+            "{\"hobby\":{\"alice\":\"Biking\",\"bob\":\"Dancing\"},\"weight\":{\"alice\":68.5,\"bob\":83.1},\"birthyear\":{\"alice\":1985,\"bob\":1984},\"children\":{\"alice\":null,\"bob\":3.0}}\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "from pathlib import Path\n",
+        "\n",
+        "for filename in (\"my_df.csv\", \"my_df.html\", \"my_df.json\"):\n",
+        "    print(\"#\"*4, filename)\n",
+        "    print(Path(filename).read_text())\n",
+        "    print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "j4JnGbfX3k3q"
+      },
+      "source": [
+        "Note that the index is saved as the first column in a CSV file, as `<th>` tags in HTML and as keys in JSON. The index column has no name by default, but you can fix that by setting `my_df.index.name` to any name you want.\n",
+        "\n",
+        "Saving to other formats works very similarly, but some formats require extra libraries to be installed. For example, saving to Excel requires the openpyxl library:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "id": "b6E2nAgN3k3q"
+      },
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    my_df.to_excel(\"my_df.xlsx\", sheet_name='People')\n",
+        "except ImportError as e:\n",
+        "    print(e)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.read_excel(\"my_df.xlsx\", sheet_name='People')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 119
+        },
+        "id": "PErFbQL3WO6u",
+        "outputId": "992fdb2b-7ec9-4e5a-c4ec-f2852093780a"
+      },
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  People    hobby  weight  birthyear  children\n",
+              "0  alice   Biking    68.5       1985       NaN\n",
+              "1    bob  Dancing    83.1       1984       3.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6c624d9c-c655-4cda-ac3b-1fa510c13f96\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>People</th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>alice</td>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68.5</td>\n",
+              "      <td>1985</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>bob</td>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83.1</td>\n",
+              "      <td>1984</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c624d9c-c655-4cda-ac3b-1fa510c13f96')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6c624d9c-c655-4cda-ac3b-1fa510c13f96 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6c624d9c-c655-4cda-ac3b-1fa510c13f96');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-ed0a088f-da97-4aa2-9f6c-406f442a56e0\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ed0a088f-da97-4aa2-9f6c-406f442a56e0')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-ed0a088f-da97-4aa2-9f6c-406f442a56e0 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"People\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"bob\",\n          \"alice\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.32375900532359,\n        \"min\": 68.5,\n        \"max\": 83.1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          83.1,\n          68.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1984,\n        \"max\": 1985,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1984,\n          1985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 63
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Js7WjPzX3k3r"
+      },
+      "source": [
+        "## Loading\n",
+        "Now let's load our CSV file back into a `DataFrame`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "id": "vSb4Y0yT3k3r",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "c3c39fac-c943-45ea-dd75-573263614837"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          hobby  weight  birthyear  children\n",
+              "People                                      \n",
+              "alice    Biking    68.5       1985       NaN\n",
+              "bob     Dancing    83.1       1984       3.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-16a4f3a4-fc16-44d5-8b64-034f77ed5ce8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>hobby</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>birthyear</th>\n",
+              "      <th>children</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>People</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>alice</th>\n",
+              "      <td>Biking</td>\n",
+              "      <td>68.5</td>\n",
+              "      <td>1985</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bob</th>\n",
+              "      <td>Dancing</td>\n",
+              "      <td>83.1</td>\n",
+              "      <td>1984</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16a4f3a4-fc16-44d5-8b64-034f77ed5ce8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-16a4f3a4-fc16-44d5-8b64-034f77ed5ce8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-16a4f3a4-fc16-44d5-8b64-034f77ed5ce8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-8e259bc1-e15a-4096-a6bc-4d960370f976\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8e259bc1-e15a-4096-a6bc-4d960370f976')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-8e259bc1-e15a-4096-a6bc-4d960370f976 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_4308daf1-33c9-4fff-a414-8f88dea3918e\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('my_df_loaded')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_4308daf1-33c9-4fff-a414-8f88dea3918e button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('my_df_loaded');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "my_df_loaded",
+              "summary": "{\n  \"name\": \"my_df_loaded\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"People\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"bob\",\n          \"alice\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hobby\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dancing\",\n          \"Biking\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.32375900532359,\n        \"min\": 68.5,\n        \"max\": 83.1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          83.1,\n          68.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birthyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1984,\n        \"max\": 1985,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1984,\n          1985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 66
+        }
+      ],
+      "source": [
+        "my_df_loaded = pd.read_csv(\"my_df.csv\", index_col=0)\n",
+        "my_df_loaded"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a8awNsWG3k3r"
+      },
+      "source": [
+        "As you might guess, there are similar `read_json`, `read_html`, `read_excel` functions as well.  We can also read data straight from the Internet. For example, let's load the top 1,000 U.S. cities from GitHub:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 67,
+      "metadata": {
+        "id": "LnvT7iqn3k3r",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 255
+        },
+        "outputId": "ebcd00b0-88a4-4c15-a4b1-0121d2e6b71e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     State  Population        lat         lon\n",
+              "City                                                         \n",
+              "Marysville      Washington       63269  48.051764 -122.177082\n",
+              "Perris          California       72326  33.782519 -117.228648\n",
+              "Cleveland             Ohio      390113  41.499320  -81.694361\n",
+              "Worcester    Massachusetts      182544  42.262593  -71.802293\n",
+              "Columbia    South Carolina      133358  34.000710  -81.034814"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fabbd4d9-9dd0-4b14-a81b-e51b656e42c7\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>State</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lon</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>City</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Marysville</th>\n",
+              "      <td>Washington</td>\n",
+              "      <td>63269</td>\n",
+              "      <td>48.051764</td>\n",
+              "      <td>-122.177082</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Perris</th>\n",
+              "      <td>California</td>\n",
+              "      <td>72326</td>\n",
+              "      <td>33.782519</td>\n",
+              "      <td>-117.228648</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Cleveland</th>\n",
+              "      <td>Ohio</td>\n",
+              "      <td>390113</td>\n",
+              "      <td>41.499320</td>\n",
+              "      <td>-81.694361</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Worcester</th>\n",
+              "      <td>Massachusetts</td>\n",
+              "      <td>182544</td>\n",
+              "      <td>42.262593</td>\n",
+              "      <td>-71.802293</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Columbia</th>\n",
+              "      <td>South Carolina</td>\n",
+              "      <td>133358</td>\n",
+              "      <td>34.000710</td>\n",
+              "      <td>-81.034814</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fabbd4d9-9dd0-4b14-a81b-e51b656e42c7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fabbd4d9-9dd0-4b14-a81b-e51b656e42c7 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fabbd4d9-9dd0-4b14-a81b-e51b656e42c7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-fd3b533c-9997-44eb-8c6e-013547424272\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fd3b533c-9997-44eb-8c6e-013547424272')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-fd3b533c-9997-44eb-8c6e-013547424272 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_7fcb4027-2705-4a1f-962d-94e9c4c4a9a3\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('us_cities')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_7fcb4027-2705-4a1f-962d-94e9c4c4a9a3 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('us_cities');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "us_cities",
+              "summary": "{\n  \"name\": \"us_cities\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Perris\",\n          \"Columbia\",\n          \"Cleveland\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"California\",\n          \"South Carolina\",\n          \"Ohio\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 133104,\n        \"min\": 63269,\n        \"max\": 390113,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          72326,\n          133358,\n          390113\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.058346123449606,\n        \"min\": 33.7825194,\n        \"max\": 48.0517637,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          33.7825194,\n          34.0007104,\n          41.49932\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.144549527727225,\n        \"min\": -122.1770818,\n        \"max\": -71.8022934,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -117.2286478,\n          -81.0348144,\n          -81.6943605\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 67
+        }
+      ],
+      "source": [
+        "us_cities = None\n",
+        "try:\n",
+        "    csv_url = \"https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv\"\n",
+        "    us_cities = pd.read_csv(csv_url, index_col=0)\n",
+        "    us_cities = us_cities.head()\n",
+        "except IOError as e:\n",
+        "    print(e)\n",
+        "us_cities"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QhbRFwF93k3s"
+      },
+      "source": [
+        "There are more options available, in particular regarding datetime format. Check out the [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html) for more details."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1ZZTcch33k3s"
+      },
+      "source": [
+        "# Combining `DataFrame`s\n",
+        "\n",
+        "## SQL-like joins\n",
+        "One powerful feature of pandas is its ability to perform SQL-like joins on `DataFrame`s. Various types of joins are supported: inner joins, left/right outer joins and full joins. To illustrate this, let's start by creating a couple of simple `DataFrame`s:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 68,
+      "metadata": {
+        "id": "Mvd7Bn5u3k3s",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "010d4559-1380-458a-b102-d0957d2f376c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  state            city        lat         lng\n",
+              "0    CA   San Francisco  37.781334 -122.416728\n",
+              "1    NY        New York  40.705649  -74.008344\n",
+              "2    FL           Miami  25.791100  -80.320733\n",
+              "3    OH       Cleveland  41.473508  -81.739791\n",
+              "4    UT  Salt Lake City  40.755851 -111.896657"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c1f45c4b-dc34-4d4a-aeb5-78648003d80e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>state</th>\n",
+              "      <th>city</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lng</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>CA</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>37.781334</td>\n",
+              "      <td>-122.416728</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>NY</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>40.705649</td>\n",
+              "      <td>-74.008344</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>FL</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>25.791100</td>\n",
+              "      <td>-80.320733</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>OH</td>\n",
+              "      <td>Cleveland</td>\n",
+              "      <td>41.473508</td>\n",
+              "      <td>-81.739791</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>UT</td>\n",
+              "      <td>Salt Lake City</td>\n",
+              "      <td>40.755851</td>\n",
+              "      <td>-111.896657</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c1f45c4b-dc34-4d4a-aeb5-78648003d80e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c1f45c4b-dc34-4d4a-aeb5-78648003d80e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c1f45c4b-dc34-4d4a-aeb5-78648003d80e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-22ff16f8-1693-4e88-9673-1d215baf8548\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-22ff16f8-1693-4e88-9673-1d215baf8548')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-22ff16f8-1693-4e88-9673-1d215baf8548 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_b3ccabea-51fc-408e-b120-39a82cf5c0e1\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('city_loc')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_b3ccabea-51fc-408e-b120-39a82cf5c0e1 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('city_loc');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "city_loc",
+              "summary": "{\n  \"name\": \"city_loc\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"NY\",\n          \"UT\",\n          \"FL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"New York\",\n          \"Salt Lake City\",\n          \"Miami\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5887398420401535,\n        \"min\": 25.7911,\n        \"max\": 41.473508,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          40.705649,\n          40.755851,\n          25.7911\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lng\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.592087199560734,\n        \"min\": -122.416728,\n        \"max\": -74.008344,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -74.008344,\n          -111.896657,\n          -80.320733\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 68
+        }
+      ],
+      "source": [
+        "city_loc = pd.DataFrame(\n",
+        "    [\n",
+        "        [\"CA\", \"San Francisco\", 37.781334, -122.416728],\n",
+        "        [\"NY\", \"New York\", 40.705649, -74.008344],\n",
+        "        [\"FL\", \"Miami\", 25.791100, -80.320733],\n",
+        "        [\"OH\", \"Cleveland\", 41.473508, -81.739791],\n",
+        "        [\"UT\", \"Salt Lake City\", 40.755851, -111.896657]\n",
+        "    ], columns=[\"state\", \"city\", \"lat\", \"lng\"])\n",
+        "city_loc"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 69,
+      "metadata": {
+        "id": "y0LtUW1O3k3s",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "2083a267-5caa-4d73-a6cc-982a182fbc17"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   population           city       state\n",
+              "3      808976  San Francisco  California\n",
+              "4     8363710       New York    New-York\n",
+              "5      413201          Miami     Florida\n",
+              "6     2242193        Houston       Texas"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-547dc584-beab-4430-8d25-ebd19cc8b4bc\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>population</th>\n",
+              "      <th>city</th>\n",
+              "      <th>state</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>808976</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>California</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>8363710</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>New-York</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>413201</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>Florida</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2242193</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Texas</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-547dc584-beab-4430-8d25-ebd19cc8b4bc')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-547dc584-beab-4430-8d25-ebd19cc8b4bc button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-547dc584-beab-4430-8d25-ebd19cc8b4bc');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-a63513ca-58e7-41b6-8952-b231dd366107\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a63513ca-58e7-41b6-8952-b231dd366107')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-a63513ca-58e7-41b6-8952-b231dd366107 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_2ffd515c-dc68-4ed2-8ac3-92e43b790e53\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('city_pop')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_2ffd515c-dc68-4ed2-8ac3-92e43b790e53 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('city_pop');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "city_pop",
+              "summary": "{\n  \"name\": \"city_pop\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3689100,\n        \"min\": 413201,\n        \"max\": 8363710,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8363710,\n          2242193,\n          808976\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New York\",\n          \"Houston\",\n          \"San Francisco\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New-York\",\n          \"Texas\",\n          \"California\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 69
+        }
+      ],
+      "source": [
+        "city_pop = pd.DataFrame(\n",
+        "    [\n",
+        "        [808976, \"San Francisco\", \"California\"],\n",
+        "        [8363710, \"New York\", \"New-York\"],\n",
+        "        [413201, \"Miami\", \"Florida\"],\n",
+        "        [2242193, \"Houston\", \"Texas\"]\n",
+        "    ], index=[3,4,5,6], columns=[\"population\", \"city\", \"state\"])\n",
+        "city_pop"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5sobzSd73k3s"
+      },
+      "source": [
+        "Now let's join these `DataFrame`s using the `merge()` function:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "id": "GahDZXtS3k3t",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "33e860d0-f795-4547-ab96-b11b4ad89c55"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  state_x           city        lat         lng  population     state_y\n",
+              "0      CA  San Francisco  37.781334 -122.416728      808976  California\n",
+              "1      NY       New York  40.705649  -74.008344     8363710    New-York\n",
+              "2      FL          Miami  25.791100  -80.320733      413201     Florida"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4d83c8c6-2254-4f1f-be39-0e81a86074ed\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>state_x</th>\n",
+              "      <th>city</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lng</th>\n",
+              "      <th>population</th>\n",
+              "      <th>state_y</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>CA</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>37.781334</td>\n",
+              "      <td>-122.416728</td>\n",
+              "      <td>808976</td>\n",
+              "      <td>California</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>NY</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>40.705649</td>\n",
+              "      <td>-74.008344</td>\n",
+              "      <td>8363710</td>\n",
+              "      <td>New-York</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>FL</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>25.791100</td>\n",
+              "      <td>-80.320733</td>\n",
+              "      <td>413201</td>\n",
+              "      <td>Florida</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d83c8c6-2254-4f1f-be39-0e81a86074ed')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4d83c8c6-2254-4f1f-be39-0e81a86074ed button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4d83c8c6-2254-4f1f-be39-0e81a86074ed');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6bcb23c5-0e8c-4404-abd0-9814a876433b\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6bcb23c5-0e8c-4404-abd0-9814a876433b')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6bcb23c5-0e8c-4404-abd0-9814a876433b button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"state_x\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"CA\",\n          \"NY\",\n          \"FL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"San Francisco\",\n          \"New York\",\n          \"Miami\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.903175537585953,\n        \"min\": 25.7911,\n        \"max\": 40.705649,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          37.781334,\n          40.705649,\n          25.7911\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lng\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.316315254415855,\n        \"min\": -122.416728,\n        \"max\": -74.008344,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -122.416728,\n          -74.008344,\n          -80.320733\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4480350,\n        \"min\": 413201,\n        \"max\": 8363710,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          808976,\n          8363710,\n          413201\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state_y\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"California\",\n          \"New-York\",\n          \"Florida\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 70
+        }
+      ],
+      "source": [
+        "pd.merge(left=city_loc, right=city_pop, on=\"city\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jiH2hvJT3k3t"
+      },
+      "source": [
+        "Note that both `DataFrame`s have a column named `state`, so in the result they got renamed to `state_x` and `state_y`.\n",
+        "\n",
+        "Also, note that Cleveland, Salt Lake City and Houston were dropped because they don't exist in *both* `DataFrame`s. This is the equivalent of a SQL `INNER JOIN`. If you want a `FULL OUTER JOIN`, where no city gets dropped and `NaN` values are added, you must specify `how=\"outer\"`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 71,
+      "metadata": {
+        "id": "E5E-aFTJ3k3t",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 255
+        },
+        "outputId": "dcbb980c-e848-44f2-8a7f-abb62dd803de"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  state_x            city        lat         lng  population     state_y\n",
+              "0      OH       Cleveland  41.473508  -81.739791         NaN         NaN\n",
+              "1     NaN         Houston        NaN         NaN   2242193.0       Texas\n",
+              "2      FL           Miami  25.791100  -80.320733    413201.0     Florida\n",
+              "3      NY        New York  40.705649  -74.008344   8363710.0    New-York\n",
+              "4      UT  Salt Lake City  40.755851 -111.896657         NaN         NaN\n",
+              "5      CA   San Francisco  37.781334 -122.416728    808976.0  California"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-675e4e1c-cf1d-4329-9b02-dde1b5eaec67\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>state_x</th>\n",
+              "      <th>city</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lng</th>\n",
+              "      <th>population</th>\n",
+              "      <th>state_y</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>OH</td>\n",
+              "      <td>Cleveland</td>\n",
+              "      <td>41.473508</td>\n",
+              "      <td>-81.739791</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2242193.0</td>\n",
+              "      <td>Texas</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>FL</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>25.791100</td>\n",
+              "      <td>-80.320733</td>\n",
+              "      <td>413201.0</td>\n",
+              "      <td>Florida</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>NY</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>40.705649</td>\n",
+              "      <td>-74.008344</td>\n",
+              "      <td>8363710.0</td>\n",
+              "      <td>New-York</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>UT</td>\n",
+              "      <td>Salt Lake City</td>\n",
+              "      <td>40.755851</td>\n",
+              "      <td>-111.896657</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>CA</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>37.781334</td>\n",
+              "      <td>-122.416728</td>\n",
+              "      <td>808976.0</td>\n",
+              "      <td>California</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-675e4e1c-cf1d-4329-9b02-dde1b5eaec67')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-675e4e1c-cf1d-4329-9b02-dde1b5eaec67 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-675e4e1c-cf1d-4329-9b02-dde1b5eaec67');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-a9bd2798-952a-4c3d-aef3-abb88e1f0812\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a9bd2798-952a-4c3d-aef3-abb88e1f0812')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-a9bd2798-952a-4c3d-aef3-abb88e1f0812 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_4d925abc-77ac-47fb-9ea6-9b84e347d9bb\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('all_cities')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_4d925abc-77ac-47fb-9ea6-9b84e347d9bb button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('all_cities');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "all_cities",
+              "summary": "{\n  \"name\": \"all_cities\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"state_x\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"FL\",\n          \"CA\",\n          \"NY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Cleveland\",\n          \"Houston\",\n          \"San Francisco\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5887398420401535,\n        \"min\": 25.7911,\n        \"max\": 41.473508,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          25.7911,\n          37.781334,\n          40.705649\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lng\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.592087199560734,\n        \"min\": -122.416728,\n        \"max\": -74.008344,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -80.320733,\n          -122.416728,\n          -74.008344\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3689100.275438715,\n        \"min\": 413201.0,\n        \"max\": 8363710.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          413201.0,\n          808976.0,\n          2242193.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state_y\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Florida\",\n          \"California\",\n          \"Texas\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 71
+        }
+      ],
+      "source": [
+        "all_cities = pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"outer\")\n",
+        "all_cities"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yau1SoO83k3t"
+      },
+      "source": [
+        "Of course, `LEFT OUTER JOIN` is also available by setting `how=\"left\"`: only the cities present in the left `DataFrame` end up in the result. Similarly, with `how=\"right\"` only cities in the right `DataFrame` appear in the result. For example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 72,
+      "metadata": {
+        "id": "zsd4aKSF3k3t",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "b86df73f-26e2-4aaf-aacd-faaa0bdd215c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  state_x           city        lat         lng  population     state_y\n",
+              "0      CA  San Francisco  37.781334 -122.416728      808976  California\n",
+              "1      NY       New York  40.705649  -74.008344     8363710    New-York\n",
+              "2      FL          Miami  25.791100  -80.320733      413201     Florida\n",
+              "3     NaN        Houston        NaN         NaN     2242193       Texas"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-193bc0d1-be0e-4e4f-bd73-01649a8bbf62\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>state_x</th>\n",
+              "      <th>city</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lng</th>\n",
+              "      <th>population</th>\n",
+              "      <th>state_y</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>CA</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>37.781334</td>\n",
+              "      <td>-122.416728</td>\n",
+              "      <td>808976</td>\n",
+              "      <td>California</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>NY</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>40.705649</td>\n",
+              "      <td>-74.008344</td>\n",
+              "      <td>8363710</td>\n",
+              "      <td>New-York</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>FL</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>25.791100</td>\n",
+              "      <td>-80.320733</td>\n",
+              "      <td>413201</td>\n",
+              "      <td>Florida</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2242193</td>\n",
+              "      <td>Texas</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-193bc0d1-be0e-4e4f-bd73-01649a8bbf62')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-193bc0d1-be0e-4e4f-bd73-01649a8bbf62 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-193bc0d1-be0e-4e4f-bd73-01649a8bbf62');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-1ab61bab-951c-4675-80a3-f65d65765217\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1ab61bab-951c-4675-80a3-f65d65765217')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-1ab61bab-951c-4675-80a3-f65d65765217 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"state_x\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"CA\",\n          \"NY\",\n          \"FL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New York\",\n          \"Houston\",\n          \"San Francisco\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.903175537585953,\n        \"min\": 25.7911,\n        \"max\": 40.705649,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          37.781334,\n          40.705649,\n          25.7911\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lng\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.316315254415855,\n        \"min\": -122.416728,\n        \"max\": -74.008344,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -122.416728,\n          -74.008344,\n          -80.320733\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3689100,\n        \"min\": 413201,\n        \"max\": 8363710,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8363710,\n          2242193,\n          808976\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state_y\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New-York\",\n          \"Texas\",\n          \"California\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 72
+        }
+      ],
+      "source": [
+        "pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"right\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cZLN4xHv3k3u"
+      },
+      "source": [
+        "If the key to join on is actually in one (or both) `DataFrame`'s index, you must use `left_index=True` and/or `right_index=True`. If the key column names differ, you must use `left_on` and `right_on`. For example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {
+        "id": "xLfSGLaT3k3u",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "06b0c534-18bf-47d9-bce8-9cb6686b956d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  state_x           city        lat         lng  population           name  \\\n",
+              "0      CA  San Francisco  37.781334 -122.416728      808976  San Francisco   \n",
+              "1      NY       New York  40.705649  -74.008344     8363710       New York   \n",
+              "2      FL          Miami  25.791100  -80.320733      413201          Miami   \n",
+              "\n",
+              "      state_y  \n",
+              "0  California  \n",
+              "1    New-York  \n",
+              "2     Florida  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5dade7de-d106-4961-bd7e-903008952da8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>state_x</th>\n",
+              "      <th>city</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lng</th>\n",
+              "      <th>population</th>\n",
+              "      <th>name</th>\n",
+              "      <th>state_y</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>CA</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>37.781334</td>\n",
+              "      <td>-122.416728</td>\n",
+              "      <td>808976</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>California</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>NY</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>40.705649</td>\n",
+              "      <td>-74.008344</td>\n",
+              "      <td>8363710</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>New-York</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>FL</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>25.791100</td>\n",
+              "      <td>-80.320733</td>\n",
+              "      <td>413201</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>Florida</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5dade7de-d106-4961-bd7e-903008952da8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-5dade7de-d106-4961-bd7e-903008952da8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-5dade7de-d106-4961-bd7e-903008952da8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-76f583dc-a45e-4f93-b84b-8d00b3191ed8\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-76f583dc-a45e-4f93-b84b-8d00b3191ed8')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-76f583dc-a45e-4f93-b84b-8d00b3191ed8 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"state_x\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"CA\",\n          \"NY\",\n          \"FL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"San Francisco\",\n          \"New York\",\n          \"Miami\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.903175537585953,\n        \"min\": 25.7911,\n        \"max\": 40.705649,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          37.781334,\n          40.705649,\n          25.7911\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lng\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.316315254415855,\n        \"min\": -122.416728,\n        \"max\": -74.008344,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -122.416728,\n          -74.008344,\n          -80.320733\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4480350,\n        \"min\": 413201,\n        \"max\": 8363710,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          808976,\n          8363710,\n          413201\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"San Francisco\",\n          \"New York\",\n          \"Miami\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state_y\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"California\",\n          \"New-York\",\n          \"Florida\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 73
+        }
+      ],
+      "source": [
+        "city_pop2 = city_pop.copy()\n",
+        "city_pop2.columns = [\"population\", \"name\", \"state\"]\n",
+        "pd.merge(left=city_loc, right=city_pop2, left_on=\"city\", right_on=\"name\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LuKKTp2b3k3u"
+      },
+      "source": [
+        "## Concatenation\n",
+        "Rather than joining `DataFrame`s, we may just want to concatenate them. That's what `concat()` is for:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 74,
+      "metadata": {
+        "id": "qdSGqOkt3k3u",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 356
+        },
+        "outputId": "000895db-26e3-4785-db5c-78351ce9cc94"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        state            city        lat         lng  population\n",
+              "0          CA   San Francisco  37.781334 -122.416728         NaN\n",
+              "1          NY        New York  40.705649  -74.008344         NaN\n",
+              "2          FL           Miami  25.791100  -80.320733         NaN\n",
+              "3          OH       Cleveland  41.473508  -81.739791         NaN\n",
+              "4          UT  Salt Lake City  40.755851 -111.896657         NaN\n",
+              "3  California   San Francisco        NaN         NaN    808976.0\n",
+              "4    New-York        New York        NaN         NaN   8363710.0\n",
+              "5     Florida           Miami        NaN         NaN    413201.0\n",
+              "6       Texas         Houston        NaN         NaN   2242193.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-737fcfda-2317-4b07-b8fc-af19859ec065\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>state</th>\n",
+              "      <th>city</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lng</th>\n",
+              "      <th>population</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>CA</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>37.781334</td>\n",
+              "      <td>-122.416728</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>NY</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>40.705649</td>\n",
+              "      <td>-74.008344</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>FL</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>25.791100</td>\n",
+              "      <td>-80.320733</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>OH</td>\n",
+              "      <td>Cleveland</td>\n",
+              "      <td>41.473508</td>\n",
+              "      <td>-81.739791</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>UT</td>\n",
+              "      <td>Salt Lake City</td>\n",
+              "      <td>40.755851</td>\n",
+              "      <td>-111.896657</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>California</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>808976.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>New-York</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>8363710.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>413201.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Texas</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2242193.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-737fcfda-2317-4b07-b8fc-af19859ec065')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-737fcfda-2317-4b07-b8fc-af19859ec065 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-737fcfda-2317-4b07-b8fc-af19859ec065');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-9b38f8f4-bca5-49a8-a434-545d75ffa494\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9b38f8f4-bca5-49a8-a434-545d75ffa494')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-9b38f8f4-bca5-49a8-a434-545d75ffa494 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_83a13ee1-3007-4854-9e76-bd06d74fb93f\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('result_concat')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_83a13ee1-3007-4854-9e76-bd06d74fb93f button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('result_concat');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "result_concat",
+              "summary": "{\n  \"name\": \"result_concat\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Florida\",\n          \"NY\",\n          \"California\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"San Francisco\",\n          \"New York\",\n          \"Houston\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5887398420401535,\n        \"min\": 25.7911,\n        \"max\": 41.473508,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          40.705649,\n          40.755851,\n          25.7911\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lng\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.592087199560734,\n        \"min\": -122.416728,\n        \"max\": -74.008344,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -74.008344,\n          -111.896657,\n          -80.320733\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3689100.275438715,\n        \"min\": 413201.0,\n        \"max\": 8363710.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8363710.0,\n          2242193.0,\n          808976.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 74
+        }
+      ],
+      "source": [
+        "result_concat = pd.concat([city_loc, city_pop])\n",
+        "result_concat"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fjP1VZ0K3k3u"
+      },
+      "source": [
+        "Note that this operation aligned the data horizontally (by columns) but not vertically (by rows). In this example, we end up with multiple rows having the same index (e.g. 3). Pandas handles this rather gracefully:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 75,
+      "metadata": {
+        "id": "47j_5LIy3k3v",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 119
+        },
+        "outputId": "e50bd164-f089-4446-9562-bd0a7e3e4783"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        state           city        lat        lng  population\n",
+              "3          OH      Cleveland  41.473508 -81.739791         NaN\n",
+              "3  California  San Francisco        NaN        NaN    808976.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-319d581b-fb1a-4ff1-976a-ceb9a251b881\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>state</th>\n",
+              "      <th>city</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lng</th>\n",
+              "      <th>population</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>OH</td>\n",
+              "      <td>Cleveland</td>\n",
+              "      <td>41.473508</td>\n",
+              "      <td>-81.739791</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>California</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>808976.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-319d581b-fb1a-4ff1-976a-ceb9a251b881')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-319d581b-fb1a-4ff1-976a-ceb9a251b881 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-319d581b-fb1a-4ff1-976a-ceb9a251b881');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-a53b292d-efcf-43e2-99d9-9b9b56210931\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a53b292d-efcf-43e2-99d9-9b9b56210931')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-a53b292d-efcf-43e2-99d9-9b9b56210931 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"result_concat\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"California\",\n          \"OH\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"San Francisco\",\n          \"Cleveland\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 41.473508,\n        \"max\": 41.473508,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          41.473508\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lng\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -81.739791,\n        \"max\": -81.739791,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -81.739791\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 808976.0,\n        \"max\": 808976.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          808976.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 75
+        }
+      ],
+      "source": [
+        "result_concat.loc[3]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "O4XafQMB3k3v"
+      },
+      "source": [
+        "Or you can tell pandas to just ignore the index:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {
+        "id": "j7P2bBr53k3v",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 356
+        },
+        "outputId": "ab3e5796-5a48-42ed-e2db-cb9b6fe27b64"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        state            city        lat         lng  population\n",
+              "0          CA   San Francisco  37.781334 -122.416728         NaN\n",
+              "1          NY        New York  40.705649  -74.008344         NaN\n",
+              "2          FL           Miami  25.791100  -80.320733         NaN\n",
+              "3          OH       Cleveland  41.473508  -81.739791         NaN\n",
+              "4          UT  Salt Lake City  40.755851 -111.896657         NaN\n",
+              "5  California   San Francisco        NaN         NaN    808976.0\n",
+              "6    New-York        New York        NaN         NaN   8363710.0\n",
+              "7     Florida           Miami        NaN         NaN    413201.0\n",
+              "8       Texas         Houston        NaN         NaN   2242193.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ef77dda8-5aca-4675-b73c-97882c512971\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>state</th>\n",
+              "      <th>city</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lng</th>\n",
+              "      <th>population</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>CA</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>37.781334</td>\n",
+              "      <td>-122.416728</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>NY</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>40.705649</td>\n",
+              "      <td>-74.008344</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>FL</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>25.791100</td>\n",
+              "      <td>-80.320733</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>OH</td>\n",
+              "      <td>Cleveland</td>\n",
+              "      <td>41.473508</td>\n",
+              "      <td>-81.739791</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>UT</td>\n",
+              "      <td>Salt Lake City</td>\n",
+              "      <td>40.755851</td>\n",
+              "      <td>-111.896657</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>California</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>808976.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>New-York</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>8363710.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>413201.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Texas</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2242193.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef77dda8-5aca-4675-b73c-97882c512971')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ef77dda8-5aca-4675-b73c-97882c512971 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ef77dda8-5aca-4675-b73c-97882c512971');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-dc4d5d23-607c-4c89-934a-11b8acf1447b\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dc4d5d23-607c-4c89-934a-11b8acf1447b')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-dc4d5d23-607c-4c89-934a-11b8acf1447b button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Florida\",\n          \"NY\",\n          \"California\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"San Francisco\",\n          \"New York\",\n          \"Houston\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5887398420401535,\n        \"min\": 25.7911,\n        \"max\": 41.473508,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          40.705649,\n          40.755851,\n          25.7911\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lng\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.592087199560734,\n        \"min\": -122.416728,\n        \"max\": -74.008344,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -74.008344,\n          -111.896657,\n          -80.320733\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3689100.275438715,\n        \"min\": 413201.0,\n        \"max\": 8363710.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8363710.0,\n          2242193.0,\n          808976.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 76
+        }
+      ],
+      "source": [
+        "pd.concat([city_loc, city_pop], ignore_index=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2qbPLtGF3k3v"
+      },
+      "source": [
+        "Notice that when a column does not exist in a `DataFrame`, it acts as if it was filled with `NaN` values. If we set `join=\"inner\"`, then only columns that exist in *both* `DataFrame`s are returned:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 77,
+      "metadata": {
+        "id": "z3VZ5tUq3k3v",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 356
+        },
+        "outputId": "a5ee653e-edb9-42b4-c310-8212b976cbe1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        state            city\n",
+              "0          CA   San Francisco\n",
+              "1          NY        New York\n",
+              "2          FL           Miami\n",
+              "3          OH       Cleveland\n",
+              "4          UT  Salt Lake City\n",
+              "3  California   San Francisco\n",
+              "4    New-York        New York\n",
+              "5     Florida           Miami\n",
+              "6       Texas         Houston"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-28c3a56e-9207-48c8-aad1-33dff0316529\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>state</th>\n",
+              "      <th>city</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>CA</td>\n",
+              "      <td>San Francisco</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>NY</td>\n",
+              "      <td>New York</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>FL</td>\n",
+              "      <td>Miami</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>OH</td>\n",
+              "      <td>Cleveland</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>UT</td>\n",
+              "      <td>Salt Lake City</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>California</td>\n",
+              "      <td>San Francisco</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>New-York</td>\n",
+              "      <td>New York</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Miami</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Texas</td>\n",
+              "      <td>Houston</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-28c3a56e-9207-48c8-aad1-33dff0316529')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-28c3a56e-9207-48c8-aad1-33dff0316529 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-28c3a56e-9207-48c8-aad1-33dff0316529');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-27b44008-0da7-4ed8-a70f-11a1410eec08\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-27b44008-0da7-4ed8-a70f-11a1410eec08')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-27b44008-0da7-4ed8-a70f-11a1410eec08 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Florida\",\n          \"NY\",\n          \"California\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"San Francisco\",\n          \"New York\",\n          \"Houston\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 77
+        }
+      ],
+      "source": [
+        "pd.concat([city_loc, city_pop], join=\"inner\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8g2Cd6Vd3k3w"
+      },
+      "source": [
+        "You can concatenate `DataFrame`s horizontally instead of vertically by setting `axis=1`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 78,
+      "metadata": {
+        "id": "_72wyxON3k3w",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 289
+        },
+        "outputId": "8a5f7e80-bf3c-4854-be9c-a70856fa0757"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  state            city        lat         lng  population           city  \\\n",
+              "0    CA   San Francisco  37.781334 -122.416728         NaN            NaN   \n",
+              "1    NY        New York  40.705649  -74.008344         NaN            NaN   \n",
+              "2    FL           Miami  25.791100  -80.320733         NaN            NaN   \n",
+              "3    OH       Cleveland  41.473508  -81.739791    808976.0  San Francisco   \n",
+              "4    UT  Salt Lake City  40.755851 -111.896657   8363710.0       New York   \n",
+              "5   NaN             NaN        NaN         NaN    413201.0          Miami   \n",
+              "6   NaN             NaN        NaN         NaN   2242193.0        Houston   \n",
+              "\n",
+              "        state  \n",
+              "0         NaN  \n",
+              "1         NaN  \n",
+              "2         NaN  \n",
+              "3  California  \n",
+              "4    New-York  \n",
+              "5     Florida  \n",
+              "6       Texas  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fe944468-6405-4816-8c28-5697b71c3333\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>state</th>\n",
+              "      <th>city</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lng</th>\n",
+              "      <th>population</th>\n",
+              "      <th>city</th>\n",
+              "      <th>state</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>CA</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>37.781334</td>\n",
+              "      <td>-122.416728</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>NY</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>40.705649</td>\n",
+              "      <td>-74.008344</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>FL</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>25.791100</td>\n",
+              "      <td>-80.320733</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>OH</td>\n",
+              "      <td>Cleveland</td>\n",
+              "      <td>41.473508</td>\n",
+              "      <td>-81.739791</td>\n",
+              "      <td>808976.0</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>California</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>UT</td>\n",
+              "      <td>Salt Lake City</td>\n",
+              "      <td>40.755851</td>\n",
+              "      <td>-111.896657</td>\n",
+              "      <td>8363710.0</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>New-York</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>413201.0</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>Florida</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2242193.0</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Texas</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe944468-6405-4816-8c28-5697b71c3333')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fe944468-6405-4816-8c28-5697b71c3333 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fe944468-6405-4816-8c28-5697b71c3333');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-a44fb80b-af76-4186-85bd-18b480ca9c5a\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a44fb80b-af76-4186-85bd-18b480ca9c5a')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-a44fb80b-af76-4186-85bd-18b480ca9c5a button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"NY\",\n          \"UT\",\n          \"FL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"New York\",\n          \"Salt Lake City\",\n          \"Miami\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5887398420401535,\n        \"min\": 25.7911,\n        \"max\": 41.473508,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          40.705649,\n          40.755851,\n          25.7911\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lng\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.592087199560734,\n        \"min\": -122.416728,\n        \"max\": -74.008344,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -74.008344,\n          -111.896657,\n          -80.320733\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3689100.275438715,\n        \"min\": 413201.0,\n        \"max\": 8363710.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8363710.0,\n          2242193.0,\n          808976.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New York\",\n          \"Houston\",\n          \"San Francisco\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New-York\",\n          \"Texas\",\n          \"California\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 78
+        }
+      ],
+      "source": [
+        "pd.concat([city_loc, city_pop], axis=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YV0jvRF53k3w"
+      },
+      "source": [
+        "In this case it really does not make much sense because the indices do not align well (e.g. Cleveland and San Francisco end up on the same row, because they shared the index label `3`). So let's reindex the `DataFrame`s by city name before concatenating:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 79,
+      "metadata": {
+        "id": "XOQtyiE33k3w",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 289
+        },
+        "outputId": "0b62b7b9-e0e9-451b-9163-cf96aa1b02ee"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               state        lat         lng  population       state\n",
+              "city                                                               \n",
+              "San Francisco     CA  37.781334 -122.416728    808976.0  California\n",
+              "New York          NY  40.705649  -74.008344   8363710.0    New-York\n",
+              "Miami             FL  25.791100  -80.320733    413201.0     Florida\n",
+              "Cleveland         OH  41.473508  -81.739791         NaN         NaN\n",
+              "Salt Lake City    UT  40.755851 -111.896657         NaN         NaN\n",
+              "Houston          NaN        NaN         NaN   2242193.0       Texas"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-43c1d779-a851-49a5-b979-ea61afc1e6f5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>state</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lng</th>\n",
+              "      <th>population</th>\n",
+              "      <th>state</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>city</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>San Francisco</th>\n",
+              "      <td>CA</td>\n",
+              "      <td>37.781334</td>\n",
+              "      <td>-122.416728</td>\n",
+              "      <td>808976.0</td>\n",
+              "      <td>California</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New York</th>\n",
+              "      <td>NY</td>\n",
+              "      <td>40.705649</td>\n",
+              "      <td>-74.008344</td>\n",
+              "      <td>8363710.0</td>\n",
+              "      <td>New-York</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Miami</th>\n",
+              "      <td>FL</td>\n",
+              "      <td>25.791100</td>\n",
+              "      <td>-80.320733</td>\n",
+              "      <td>413201.0</td>\n",
+              "      <td>Florida</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Cleveland</th>\n",
+              "      <td>OH</td>\n",
+              "      <td>41.473508</td>\n",
+              "      <td>-81.739791</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Salt Lake City</th>\n",
+              "      <td>UT</td>\n",
+              "      <td>40.755851</td>\n",
+              "      <td>-111.896657</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Houston</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2242193.0</td>\n",
+              "      <td>Texas</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43c1d779-a851-49a5-b979-ea61afc1e6f5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-43c1d779-a851-49a5-b979-ea61afc1e6f5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-43c1d779-a851-49a5-b979-ea61afc1e6f5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-5d2818b9-b192-497e-9804-ff7aaf5f6285\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5d2818b9-b192-497e-9804-ff7aaf5f6285')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-5d2818b9-b192-497e-9804-ff7aaf5f6285 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"San Francisco\",\n          \"New York\",\n          \"Houston\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"NY\",\n          \"UT\",\n          \"FL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5887398420401535,\n        \"min\": 25.7911,\n        \"max\": 41.473508,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          40.705649,\n          40.755851,\n          25.7911\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lng\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.592087199560734,\n        \"min\": -122.416728,\n        \"max\": -74.008344,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -74.008344,\n          -111.896657,\n          -80.320733\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3689100.275438715,\n        \"min\": 413201.0,\n        \"max\": 8363710.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8363710.0,\n          2242193.0,\n          808976.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New-York\",\n          \"Texas\",\n          \"California\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 79
+        }
+      ],
+      "source": [
+        "pd.concat([city_loc.set_index(\"city\"), city_pop.set_index(\"city\")], axis=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_g3FMUyq3k3w"
+      },
+      "source": [
+        "This looks a lot like a `FULL OUTER JOIN`, except that the `state` columns were not renamed to `state_x` and `state_y`, and the `city` column is now the index."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WAQlwwCL3k3w"
+      },
+      "source": [
+        "# Categories\n",
+        "It is quite frequent to have values that represent categories, for example `1` for female and `2` for male, or `\"A\"` for Good, `\"B\"` for Average, `\"C\"` for Bad. These categorical values can be hard to read and cumbersome to handle, but fortunately pandas makes it easy. To illustrate this, let's take the `city_pop` `DataFrame` we created earlier, and add a column that represents a category:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 80,
+      "metadata": {
+        "id": "2CBUdCuK3k3w",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "ecd92534-c674-4f21-8c13-3aaf0ce72539"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   population           city       state  eco_code\n",
+              "3      808976  San Francisco  California        17\n",
+              "4     8363710       New York    New-York        17\n",
+              "5      413201          Miami     Florida        34\n",
+              "6     2242193        Houston       Texas        20"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-afd7a860-04c6-49a2-a942-13f748c8a78d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>population</th>\n",
+              "      <th>city</th>\n",
+              "      <th>state</th>\n",
+              "      <th>eco_code</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>808976</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>California</td>\n",
+              "      <td>17</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>8363710</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>New-York</td>\n",
+              "      <td>17</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>413201</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>34</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2242193</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Texas</td>\n",
+              "      <td>20</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-afd7a860-04c6-49a2-a942-13f748c8a78d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-afd7a860-04c6-49a2-a942-13f748c8a78d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-afd7a860-04c6-49a2-a942-13f748c8a78d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-36815c29-c24c-4495-bb82-4510a7e7ecb5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-36815c29-c24c-4495-bb82-4510a7e7ecb5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-36815c29-c24c-4495-bb82-4510a7e7ecb5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_1ec404f4-c5d7-471b-8cea-a454b021b7ef\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('city_eco')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_1ec404f4-c5d7-471b-8cea-a454b021b7ef button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('city_eco');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "city_eco",
+              "summary": "{\n  \"name\": \"city_eco\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3689100,\n        \"min\": 413201,\n        \"max\": 8363710,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8363710,\n          2242193,\n          808976\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New York\",\n          \"Houston\",\n          \"San Francisco\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New-York\",\n          \"Texas\",\n          \"California\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"eco_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 17,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          17,\n          34,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 80
+        }
+      ],
+      "source": [
+        "city_eco = city_pop.copy()\n",
+        "city_eco[\"eco_code\"] = [17, 17, 34, 20]\n",
+        "city_eco"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PEoR2jbF3k3x"
+      },
+      "source": [
+        "Right now the `eco_code` column is full of apparently meaningless codes. Let's fix that by creating a new categorical column based on the `eco_code`s:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 81,
+      "metadata": {
+        "id": "2BV1ABz73k3x",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "72434193-7026-4c1b-b761-83e5d9073b3c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   population           city       state  eco_code  economy\n",
+              "3      808976  San Francisco  California        17  Banking\n",
+              "4     8363710       New York    New-York        17  Banking\n",
+              "5      413201          Miami     Florida        34  Tourism\n",
+              "6     2242193        Houston       Texas        20   Energy"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c8e294c7-bcf3-448d-94d5-da6f32a9bd08\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>population</th>\n",
+              "      <th>city</th>\n",
+              "      <th>state</th>\n",
+              "      <th>eco_code</th>\n",
+              "      <th>economy</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>808976</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>California</td>\n",
+              "      <td>17</td>\n",
+              "      <td>Banking</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>8363710</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>New-York</td>\n",
+              "      <td>17</td>\n",
+              "      <td>Banking</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>413201</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>34</td>\n",
+              "      <td>Tourism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2242193</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Texas</td>\n",
+              "      <td>20</td>\n",
+              "      <td>Energy</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c8e294c7-bcf3-448d-94d5-da6f32a9bd08')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c8e294c7-bcf3-448d-94d5-da6f32a9bd08 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c8e294c7-bcf3-448d-94d5-da6f32a9bd08');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-a2f954bb-7530-4395-b5bc-98603d571ee3\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a2f954bb-7530-4395-b5bc-98603d571ee3')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-a2f954bb-7530-4395-b5bc-98603d571ee3 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_585b43f2-081b-457d-b2ea-df657ceb7c86\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('city_eco')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_585b43f2-081b-457d-b2ea-df657ceb7c86 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('city_eco');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "city_eco",
+              "summary": "{\n  \"name\": \"city_eco\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3689100,\n        \"min\": 413201,\n        \"max\": 8363710,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8363710,\n          2242193,\n          808976\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New York\",\n          \"Houston\",\n          \"San Francisco\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New-York\",\n          \"Texas\",\n          \"California\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"eco_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 17,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          17,\n          34,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"economy\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Banking\",\n          \"Tourism\",\n          \"Energy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 81
+        }
+      ],
+      "source": [
+        "city_eco['economy'] = pd.Categorical(\n",
+        "    city_eco['eco_code'].map({17: 'Banking', 20: 'Energy', 34: 'Tourism'})\n",
+        ")\n",
+        "city_eco"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-dc-A9cQ3k3x"
+      },
+      "source": [
+        "You can get a categorical column's list of categories like this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 82,
+      "metadata": {
+        "id": "jUEquUCc3k3x",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1b601959-5bc2-4c64-9a0f-cb1353fb2537"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['Banking', 'Energy', 'Tourism'], dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 82
+        }
+      ],
+      "source": [
+        "city_eco['economy'].cat.categories"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NbPZILUO3k3x"
+      },
+      "source": [
+        "And you can rename categories using `rename_categories`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 83,
+      "metadata": {
+        "id": "lgcA4LFr3k3x",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "c23b5fe4-41a4-4040-da70-d38f24f93fec"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   population           city       state  eco_code  economy\n",
+              "3      808976  San Francisco  California        17  Finance\n",
+              "4     8363710       New York    New-York        17  Finance\n",
+              "5      413201          Miami     Florida        34  Tourism\n",
+              "6     2242193        Houston       Texas        20   Energy"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2da9c882-08df-4b1e-8d1f-e4798be4ab75\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>population</th>\n",
+              "      <th>city</th>\n",
+              "      <th>state</th>\n",
+              "      <th>eco_code</th>\n",
+              "      <th>economy</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>808976</td>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>California</td>\n",
+              "      <td>17</td>\n",
+              "      <td>Finance</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>8363710</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>New-York</td>\n",
+              "      <td>17</td>\n",
+              "      <td>Finance</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>413201</td>\n",
+              "      <td>Miami</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>34</td>\n",
+              "      <td>Tourism</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2242193</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Texas</td>\n",
+              "      <td>20</td>\n",
+              "      <td>Energy</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2da9c882-08df-4b1e-8d1f-e4798be4ab75')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2da9c882-08df-4b1e-8d1f-e4798be4ab75 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2da9c882-08df-4b1e-8d1f-e4798be4ab75');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-3e937720-2a7f-451b-b9a0-c53b4ee587a0\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3e937720-2a7f-451b-b9a0-c53b4ee587a0')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-3e937720-2a7f-451b-b9a0-c53b4ee587a0 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_98e41a56-e54b-42f2-af03-df5cd2735092\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('city_eco')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_98e41a56-e54b-42f2-af03-df5cd2735092 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('city_eco');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "city_eco",
+              "summary": "{\n  \"name\": \"city_eco\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3689100,\n        \"min\": 413201,\n        \"max\": 8363710,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8363710,\n          2242193,\n          808976\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New York\",\n          \"Houston\",\n          \"San Francisco\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"New-York\",\n          \"Texas\",\n          \"California\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"eco_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 17,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          17,\n          34,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"economy\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Finance\",\n          \"Tourism\",\n          \"Energy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 83
+        }
+      ],
+      "source": [
+        "city_eco['economy'] = city_eco['economy'].cat.rename_categories({'Banking': 'Finance'})\n",
+        "city_eco"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0PTY96sc3k3y"
+      },
+      "source": [
+        "# What's next?\n",
+        "As you probably noticed by now, pandas is quite a large library with *many* features. Although we went through the most important features, there is still a lot to discover. Probably the best way to learn more is to get your hands dirty with some real-life data. It is also a good idea to go through pandas' excellent [documentation](https://pandas.pydata.org/pandas-docs/stable/index.html), in particular the [Cookbook](https://pandas.pydata.org/pandas-docs/stable/user_guide/cookbook.html)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 85,
+      "metadata": {
+        "id": "zCWYLy6R3k3y",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "de4db164-dfc9-4626-f568-997f59d7bfdf"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Original city_eco DataFrame:\n",
+            "   population           city       state  eco_code  economy\n",
+            "3      808976  San Francisco  California        17  Finance\n",
+            "4     8363710       New York    New-York        17  Finance\n",
+            "5      413201          Miami     Florida        34  Tourism\n",
+            "6     2242193        Houston       Texas        20   Energy\n",
+            "\n",
+            "Original index: ['population', 'city', 'state', 'eco_code', 'economy']\n",
+            "New desired index order: ['economy', 'eco_code', 'state', 'city', 'population']\n",
+            "\n",
+            "Reordered city_eco DataFrame:\n",
+            "   economy  eco_code       state           city  population\n",
+            "3  Finance        17  California  San Francisco      808976\n",
+            "4  Finance        17    New-York       New York     8363710\n",
+            "5  Tourism        34     Florida          Miami      413201\n",
+            "6   Energy        20       Texas        Houston     2242193\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Original city_eco DataFrame:\")\n",
+        "print(city_eco)\n",
+        "\n",
+        "# Get the current column\n",
+        "original_index = city_eco.columns.tolist()\n",
+        "print(f\"\\nOriginal index: {original_index}\")\n",
+        "\n",
+        "# Define a new order for the columns\n",
+        "# For example, let's reverse the order of the original column\n",
+        "new_index_order = original_index[::-1]\n",
+        "print(f\"New desired index order: {new_index_order}\")\n",
+        "\n",
+        "# Reindex the DataFrame\n",
+        "reordered_city_eco = city_eco.reindex(new_index_order, axis = 1)\n",
+        "\n",
+        "print(\"\\nReordered city_eco DataFrame:\")\n",
+        "print(reordered_city_eco)\n",
+        "\n",
+        "# If you wanted to explore all 4! (24) permutations,\n",
+        "# you would use itertools.permutations:\n",
+        "# import itertools\n",
+        "# all_permutations = list(itertools.permutations(original_index))\n",
+        "# print(\"\\nFirst 3 permutations of indices:\")\n",
+        "# for i, perm in enumerate(all_permutations[:3]):\n",
+        "#     print(f\"Permutation {i+1}: {perm}\")\n",
+        "#     print(city_eco.reindex(list(perm)))\n",
+        "#     print()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import itertools\n",
+        "all_permutations = list(itertools.permutations(original_index))"
+      ],
+      "metadata": {
+        "id": "3Y7qchKrawzI"
+      },
+      "execution_count": 91,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "len(all_permutations)"
+      ],
+      "metadata": {
+        "id": "8ndi0kGmbLBe",
+        "outputId": "2df2adf0-1590-42ed-e8ab-410b5b3e8098",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 94,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "120"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 94
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    },
+    "colab": {
+      "provenance": [],
+      "toc_visible": true,
+      "include_colab_link": true
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file