Gett_Analysis.ipynb

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        "#**Description**:\n",
        "\n",
        "This notebook is for the analysis of the Gett's company data which is an Israeli-developed technology platform solely focused on corporate Ground Transportation Management (GTM). They have an application where clients can order taxis, and drivers can accept their rides (offers). At the moment, when the client clicks the Order button in the application, the matching system searches for the most relevant drivers and offers them the order. In this task, we would like to investigate some matching metrics for orders that did not completed successfully, i.e., the customer didn't end up getting a car.\n",
        "\n",
        "\n",
        "This project aims to analyse the data and provide insights on the following queries:\n",
        "\n",
        "\n",
        "*   Build up distribution of orders according to reasons for failure: cancellations before and after driver assignment and reasons for order rejection. Analyze the resulting plot. Which category has the highest number of orders?\n",
        "\n",
        "*   Plot the distribution of failed orders by hours. Is there a trend that certain hours have an abnormally high proportion of one category or another? What hours are the biggest failures? Explain.\n",
        "\n",
        "*   Plot the average time to cancellation with and without driver, by the hour. If there are any outliers in the data, it would be better to remove them. Can we draw any conclusions from this plot?\n",
        "\n",
        "*   Plot the distribution of average ETA by hours. How can this plot be explained?\n",
        "\n",
        "\n",
        "This is a project under the GirlScript Summer of Code'23, issue no #258.\n",
        "\n",
        "\n",
        "Developed by: Sarah Shruti Kujur\n",
        "\n",
        "Connect on GitHub: [sarahcodebyte](https://github.com/sarahcodebyte)"
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        "# **Importing Dependencies:**"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
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      "source": [
        "# **Loading Datasets:**"
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      "source": [
        "offers = pd.read_csv('/content/data_offers.csv')\n",
        "order = pd.read_csv('/content/data_orders.csv')"
      ],
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        "id": "pWwMHHHJnw_z"
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        "offers.head()"
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              "        order_gk      offer_id\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 116508 entries, 0 to 116507\n",
            "Data columns (total 2 columns):\n",
            " #   Column    Non-Null Count   Dtype\n",
            "---  ------    --------------   -----\n",
            " 0   order_gk  116508 non-null  int64\n",
            " 1   offer_id  116508 non-null  int64\n",
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              "  order_datetime  origin_longitude  origin_latitude  m_order_eta  \\\n",
              "0       18:08:07         -0.978916        51.456173         60.0   \n",
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              "3  3000582941169                 4                       1   \n",
              "4  3000583140877                 9                       0   \n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10716 entries, 0 to 10715\n",
            "Data columns (total 8 columns):\n",
            " #   Column                         Non-Null Count  Dtype  \n",
            "---  ------                         --------------  -----  \n",
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      "cell_type": "markdown",
      "source": [
        "# **Data Preprocessing and Analysis:**"
      ],
      "metadata": {
        "id": "bpRWSwrasonm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order.isna().mean().mul(100).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Yqw_oJ1suZ4",
        "outputId": "fb0b21b2-ad3c-4738-e71a-3a7ad1353e27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_datetime                    0.00\n",
              "origin_longitude                  0.00\n",
              "origin_latitude                   0.00\n",
              "m_order_eta                      73.74\n",
              "order_gk                          0.00\n",
              "order_status_key                  0.00\n",
              "is_driver_assigned_key            0.00\n",
              "cancellations_time_in_seconds    31.81\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   73% of data in the `column m_order_eta` is null or empty\n",
        "*   31% of data in the `cancellation_time_in_seconds` is null or empty\n",
        "\n"
      ],
      "metadata": {
        "id": "Ft8ZFnlj4Pht"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "offers.isna().mean().mul(100).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vR1s_fyg3vMa",
        "outputId": "3d11a802-60d4-4275-caf9-c111c2c08065"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_gk    0.0\n",
              "offer_id    0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***1. Distribution of Cancellations***"
      ],
      "metadata": {
        "id": "T0bmlAobrXb6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#distribution of cancellations\n",
        "plot = order.groupby(['is_driver_assigned_key', 'order_status_key'])['order_gk'].count().reset_index()\n",
        "plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "iLqIcs9rpWoS",
        "outputId": "701e57fc-ab61-4493-e7be-5f9553adc5d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   is_driver_assigned_key  order_status_key  order_gk\n",
              "0                       0                 4      4496\n",
              "1                       0                 9      3406\n",
              "2                       1                 4      2811\n",
              "3                       1                 9         3"
            ],
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    {
      "cell_type": "code",
      "source": [
        "order[order['is_driver_assigned_key']==0]['order_status_key'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
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      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    0.56897\n",
              "9    0.43103\n",
              "Name: order_status_key, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "order[order['is_driver_assigned_key']==1]['order_status_key'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
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        },
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    0.998934\n",
              "9    0.001066\n",
              "Name: order_status_key, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "order['is_cancelled_by_customer'] = (order['order_status_key']==4)*1"
      ],
      "metadata": {
        "id": "6rmYhzpmrv2e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cancel_plot = sns.barplot(data = plot, x = 'order_status_key', y = 'order_gk', hue = 'is_driver_assigned_key')\n",
        "cancel_plot.set_title('Distribution of orders by reasons of failure')\n",
        "cancel_plot.set_ylabel('The number of orders')\n",
        "cancel_plot.set_xlabel('Category')"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "VayI65WZsOm2",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Category')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Client cancelations are higher than the company rejections."
      ],
      "metadata": {
        "id": "D8nzItDrCyKi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "sns.barplot(\n",
        "    x='is_driver_assigned_key',\n",
        "    y='order_gk',\n",
        "    data=plot,\n",
        "    alpha = 0.5\n",
        "    )\n",
        "\n",
        "plt.xlabel(\"Is driver assigned\", size=15, labelpad=10)\n",
        "plt.ylabel(\"Count of cancellations\", size=15, labelpad=10)\n",
        "plt.title(\"Order canceled by user\", size=20)\n",
        "sns.despine()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "cAKpdP1F7YpH",
        "outputId": "b2cdded9-a722-4fbe-f052-3b081629b703"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***2. Cancelation by time***"
      ],
      "metadata": {
        "id": "oSOYCLoNCoEQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order['hours'] = order['order_datetime'].str[:2]"
      ],
      "metadata": {
        "id": "_wJVm8ODAhf0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "order"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "-p9pdH70BHrn",
        "outputId": "3bd1e69d-8cef-4dc4-fb55-5a022684a221"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      order_datetime  origin_longitude  origin_latitude  m_order_eta  \\\n",
              "0           18:08:07         -0.978916        51.456173         60.0   \n",
              "1           20:57:32         -0.950385        51.456843          NaN   \n",
              "2           12:07:50         -0.969520        51.455544        477.0   \n",
              "3           13:50:20         -1.054671        51.460544        658.0   \n",
              "4           21:24:45         -0.967605        51.458236          NaN   \n",
              "...              ...               ...              ...          ...   \n",
              "10711       13:11:35         -0.975372        51.457846          NaN   \n",
              "10712       13:13:55         -0.975372        51.457846          NaN   \n",
              "10713       13:17:21         -0.972926        51.457693         60.0   \n",
              "10714       13:16:28         -0.975372        51.457846          NaN   \n",
              "10715       11:49:35         -0.974738        51.458180        177.0   \n",
              "\n",
              "            order_gk  order_status_key  is_driver_assigned_key  \\\n",
              "0      3000583041974                 4                       1   \n",
              "1      3000583116437                 4                       0   \n",
              "2      3000582891479                 4                       1   \n",
              "3      3000582941169                 4                       1   \n",
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              "...              ...               ...                     ...   \n",
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              "\n",
              "       cancellations_time_in_seconds  is_cancelled_by_customer hours  \n",
              "0                              198.0                         1    18  \n",
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              "2                               46.0                         1    12  \n",
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              "...                              ...                       ...   ...  \n",
              "10711                           36.0                         1    13  \n",
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              "10713                           34.0                         1    13  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "client_cans = order[order['order_status_key']==4].groupby('hours')['is_cancelled_by_customer'].agg(['count'])"
      ],
      "metadata": {
        "id": "ynL6UMjf-oOY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reject_cans = order[order['order_status_key']==9].groupby('hours')['is_cancelled_by_customer'].agg(['count'])"
      ],
      "metadata": {
        "id": "J5RHK0dCBNn7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a = sns.lineplot(data=reject_cans,  x = 'hours', y = 'count', color = 'red')\n",
        "b = sns.lineplot(data=client_cans,  x = 'hours', y = 'count', color = 'green')\n",
        "\n",
        "a.set_xlabel('Time')\n",
        "a.set_ylabel('No of orders')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "SGvGJBbjBNkP",
        "outputId": "34a49d47-3484-4d4d-d834-e75e2a4a9f5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'No of orders')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A sharp increase in cancelations can be seen at around 8:00AM in both client refusals and rejections by the app."
      ],
      "metadata": {
        "id": "amY7VyUBCQSq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "no_driver = order.query('is_driver_assigned_key == 0').groupby('order_status_key').agg({'order_gk' : 'count'}).reset_index()"
      ],
      "metadata": {
        "id": "t7QxD8smDB-_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_driver['distribution'] = no_driver['order_gk']/no_driver['order_gk'].sum()"
      ],
      "metadata": {
        "id": "GPOJMmdfCDiJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with_driver = order.query('is_driver_assigned_key == 1').groupby('order_status_key').agg({'order_gk' : 'count'}).reset_index()"
      ],
      "metadata": {
        "id": "UifYs5e7Ce99"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with_driver['distribution'] = with_driver['order_gk']/with_driver['order_gk'].sum()"
      ],
      "metadata": {
        "id": "gtc1KlUQCpfy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(no_driver['distribution'].round(3))\n",
        "print(with_driver['distribution'].round(3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HX6VD1ySCx44",
        "outputId": "85adf74f-899b-4779-f68e-759f9f0dbfb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    0.569\n",
            "1    0.431\n",
            "Name: distribution, dtype: float64\n",
            "0    0.999\n",
            "1    0.001\n",
            "Name: distribution, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   If a driver is assigned, customer-initiated cancelations are upto 57% of orders, while the system cancels around 43% of the orders\n",
        "*   If no driver is assigned, customer-initiated cancelations are upto 99% of orders and system cancels 1% of the order\n"
      ],
      "metadata": {
        "id": "q6Zer0BkDdcG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***3. Average time to cancel the ride with and without the driver being assigned***"
      ],
      "metadata": {
        "id": "9PzGtot3EgRN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "ljKAXwP4FM65",
        "outputId": "7a9ce11c-43b6-41d6-ccf1-e6066234f698"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  order_datetime  origin_longitude  origin_latitude  m_order_eta  \\\n",
              "0       18:08:07         -0.978916        51.456173         60.0   \n",
              "1       20:57:32         -0.950385        51.456843          NaN   \n",
              "2       12:07:50         -0.969520        51.455544        477.0   \n",
              "3       13:50:20         -1.054671        51.460544        658.0   \n",
              "4       21:24:45         -0.967605        51.458236          NaN   \n",
              "\n",
              "        order_gk  order_status_key  is_driver_assigned_key  \\\n",
              "0  3000583041974                 4                       1   \n",
              "1  3000583116437                 4                       0   \n",
              "2  3000582891479                 4                       1   \n",
              "3  3000582941169                 4                       1   \n",
              "4  3000583140877                 9                       0   \n",
              "\n",
              "   cancellations_time_in_seconds  is_cancelled_by_customer hours  \n",
              "0                          198.0                         1    18  \n",
              "1                          128.0                         1    20  \n",
              "2                           46.0                         1    12  \n",
              "3                           62.0                         1    13  \n",
              "4                            NaN                         0    21  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-19d3dcda-68a5-4d73-aa2d-9e37988d6a03\">\n",
              "    <div 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>order_datetime</th>\n",
              "      <th>origin_longitude</th>\n",
              "      <th>origin_latitude</th>\n",
              "      <th>m_order_eta</th>\n",
              "      <th>order_gk</th>\n",
              "      <th>order_status_key</th>\n",
              "      <th>is_driver_assigned_key</th>\n",
              "      <th>cancellations_time_in_seconds</th>\n",
              "      <th>is_cancelled_by_customer</th>\n",
              "      <th>hours</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18:08:07</td>\n",
              "      <td>-0.978916</td>\n",
              "      <td>51.456173</td>\n",
              "      <td>60.0</td>\n",
              "      <td>3000583041974</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>198.0</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20:57:32</td>\n",
              "      <td>-0.950385</td>\n",
              "      <td>51.456843</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3000583116437</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12:07:50</td>\n",
              "      <td>-0.969520</td>\n",
              "      <td>51.455544</td>\n",
              "      <td>477.0</td>\n",
              "      <td>3000582891479</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13:50:20</td>\n",
              "      <td>-1.054671</td>\n",
              "      <td>51.460544</td>\n",
              "      <td>658.0</td>\n",
              "      <td>3000582941169</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>62.0</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>21:24:45</td>\n",
              "      <td>-0.967605</td>\n",
              "      <td>51.458236</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3000583140877</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-19d3dcda-68a5-4d73-aa2d-9e37988d6a03')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-19d3dcda-68a5-4d73-aa2d-9e37988d6a03 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-19d3dcda-68a5-4d73-aa2d-9e37988d6a03');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "order.cancellations_time_in_seconds.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "EST0Ba9FDYG-",
        "outputId": "bb1e8e8c-7bf8-4171-887c-37e1d847ccf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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HzlcTCoV04sQJpaamnnVt5eXlmjNnzhnnq6qqlJaWFs8222Te6NZ2n/NSWrduXUcv4bLz+/0dvQRcAD1KfPQo8XW2Hh0/frxNdXEFmAkTJth/HzZsmMaMGaMBAwZo1apV5wwWl0tZWZlKS0vt41AopOzsbBUUFMjtdrfb+0QiEfn9fv1wd7LCrUntNu+ltm/2lXP7Ldqj8ePHy+FwdPRycBb0KPHRo8TXWXsUvYNyIXHfQjpdRkaG/umf/kl/+tOfNH78eDU3N6upqSnmKkxDQ4P9mZnMzEzt3LkzZo7oU0qn13z6yaWGhga53e7zhiSXyyWXy3XGeYfDcUkaG25NUrjFnADTmb652+pS9R7thx4lPnqU+Dpbj9q6l8/0c2COHj2q//mf/1Hfvn01atQoORwOVVdX2+MHDhxQfX29vF6vJMnr9Wrv3r1qbGy0a/x+v9xut3Jzc+2a0+eI1kTnAAAAiCvAfO9739PmzZv15z//Wdu2bdOdd96pLl266J577lF6eromT56s0tJSvfnmm6qpqdH9998vr9ervLw8SVJBQYFyc3N177336q233tKGDRs0c+ZMFRcX21dPpkyZovfff18zZszQu+++qyVLlmjVqlWaPn16++8eAAAYKa5bSP/7v/+re+65R3//+9919dVX66abbtL27dt19dVXS5IWLVqk5ORkTZw4UeFwWD6fT0uWLLFf36VLF61Zs0ZTp06V1+tVt27dVFRUpLlz59o1OTk5Wrt2raZPn67FixerX79+eu6553iEGgAA2OIKMCtXrjzveEpKiioqKlRRUXHOmgEDBlzwiZixY8dqz5498SwNAABcQfhdSAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4nynAzJ8/X0lJSSopKbHPnTx5UsXFxerVq5e6d++uiRMnqqGhIeZ19fX1KiwsVFpamvr06aNHHnlEp06diqnZtGmTRo4cKZfLpYEDB6qysvKzLBUAAHQiFx1gdu3apZ/97GcaNmxYzPnp06frtdde0+rVq7V582Z9+OGHuuuuu+zxlpYWFRYWqrm5Wdu2bdMLL7ygyspKzZo1y645ePCgCgsLdeutt6q2tlYlJSV68MEHtWHDhotdLgAA6EQuKsAcPXpUkyZN0n/913+pR48e9vnDhw9r2bJleuKJJ3Tbbbdp1KhRWr58ubZt26bt27dLkqqqqrR//3798pe/1PDhwzVhwgTNmzdPFRUVam5uliQtXbpUOTk5WrhwoYYMGaJp06bpX//1X7Vo0aJ22DIAADBd14t5UXFxsQoLC5Wfn6/HHnvMPl9TU6NIJKL8/Hz73ODBg9W/f38FAgHl5eUpEAho6NCh8ng8do3P59PUqVNVV1enESNGKBAIxMwRrTn9VtWnhcNhhcNh+zgUCkmSIpGIIpHIxWzzrKJzuZKtdpvzcmjPr0Gii+71StqzaehR4qNHia+z9qit+4k7wKxcuVJ/+MMftGvXrjPGgsGgnE6nMjIyYs57PB4Fg0G75vTwEh2Pjp2vJhQK6cSJE0pNTT3jvcvLyzVnzpwzzldVVSktLa3tG2yjeaNb233OS2ndunUdvYTLzu/3d/QScAH0KPHRo8TX2Xp0/PjxNtXFFWA++OADfec735Hf71dKSspFLexSKSsrU2lpqX0cCoWUnZ2tgoICud3udnufSCQiv9+vH+5OVrg1qd3mvdT2zfZ19BIum2iPxo8fL4fD0dHLwVnQo8RHjxJfZ+1R9A7KhcQVYGpqatTY2KiRI0fa51paWrRlyxY988wz2rBhg5qbm9XU1BRzFaahoUGZmZmSpMzMTO3cuTNm3uhTSqfXfPrJpYaGBrnd7rNefZEkl8sll8t1xnmHw3FJGhtuTVK4xZwA05m+udvqUvUe7YceJT56lPg6W4/aupe4PsQ7btw47d27V7W1tfaf0aNHa9KkSfbfHQ6Hqqur7dccOHBA9fX18nq9kiSv16u9e/eqsbHRrvH7/XK73crNzbVrTp8jWhOdAwAAXNniugJz1VVX6brrros5161bN/Xq1cs+P3nyZJWWlqpnz55yu9369re/La/Xq7y8PElSQUGBcnNzde+992rBggUKBoOaOXOmiouL7SsoU6ZM0TPPPKMZM2bogQce0MaNG7Vq1SqtXbu2PfYMAAAMd1FPIZ3PokWLlJycrIkTJyocDsvn82nJkiX2eJcuXbRmzRpNnTpVXq9X3bp1U1FRkebOnWvX5OTkaO3atZo+fboWL16sfv366bnnnpPPd+V8jgMAAJzbZw4wmzZtijlOSUlRRUWFKioqzvmaAQMGXPCpmLFjx2rPnj2fdXkAAKAT4nchAQAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOPEFWCeffZZDRs2TG63W263W16vV6+//ro9fvLkSRUXF6tXr17q3r27Jk6cqIaGhpg56uvrVVhYqLS0NPXp00ePPPKITp06FVOzadMmjRw5Ui6XSwMHDlRlZeXF7xAAAHQ6cQWYfv36af78+aqpqdHu3bt122236fbbb1ddXZ0kafr06Xrttde0evVqbd68WR9++KHuuusu+/UtLS0qLCxUc3Oztm3bphdeeEGVlZWaNWuWXXPw4EEVFhbq1ltvVW1trUpKSvTggw9qw4YN7bRlAABguq7xFH/1q1+NOf7P//xPPfvss9q+fbv69eunZcuWacWKFbrtttskScuXL9eQIUO0fft25eXlqaqqSvv379cbb7whj8ej4cOHa968eXr00Uc1e/ZsOZ1OLV26VDk5OVq4cKEkaciQIdq6dasWLVokn8/XTtsGAAAmiyvAnK6lpUWrV6/WsWPH5PV6VVNTo0gkovz8fLtm8ODB6t+/vwKBgPLy8hQIBDR06FB5PB67xufzaerUqaqrq9OIESMUCARi5ojWlJSUnHc94XBY4XDYPg6FQpKkSCSiSCRysds8Q3QuV7LVbnNeDu35NUh00b1eSXs2DT1KfPQo8XXWHrV1P3EHmL1798rr9erkyZPq3r27Xn75ZeXm5qq2tlZOp1MZGRkx9R6PR8FgUJIUDAZjwkt0PDp2vppQKKQTJ04oNTX1rOsqLy/XnDlzzjhfVVWltLS0eLd5QfNGt7b7nJfSunXrOnoJl53f7+/oJeAC6FHio0eJr7P16Pjx422qizvADBo0SLW1tTp8+LB+/etfq6ioSJs3b457ge2trKxMpaWl9nEoFFJ2drYKCgrkdrvb7X0ikYj8fr9+uDtZ4dakdpv3Uts3+8q5/Rbt0fjx4+VwODp6OTgLepT46FHi66w9it5BuZC4A4zT6dTAgQMlSaNGjdKuXbu0ePFiff3rX1dzc7OamppirsI0NDQoMzNTkpSZmamdO3fGzBd9Sun0mk8/udTQ0CC3233Oqy+S5HK55HK5zjjvcDguSWPDrUkKt5gTYDrTN3dbXareo/3Qo8RHjxJfZ+tRW/fymX8OTGtrq8LhsEaNGiWHw6Hq6mp77MCBA6qvr5fX65Ukeb1e7d27V42NjXaN3++X2+1Wbm6uXXP6HNGa6BwAAABxXYEpKyvThAkT1L9/fx05ckQrVqzQpk2btGHDBqWnp2vy5MkqLS1Vz5495Xa79e1vf1ter1d5eXmSpIKCAuXm5uree+/VggULFAwGNXPmTBUXF9tXT6ZMmaJnnnlGM2bM0AMPPKCNGzdq1apVWrt2bfvvHgAAGCmuANPY2Kj77rtPH330kdLT0zVs2DBt2LBB48ePlyQtWrRIycnJmjhxosLhsHw+n5YsWWK/vkuXLlqzZo2mTp0qr9erbt26qaioSHPnzrVrcnJytHbtWk2fPl2LFy9Wv3799Nxzz/EINQAAsMUVYJYtW3be8ZSUFFVUVKiiouKcNQMGDLjgEzFjx47Vnj174lkaAAC4gvC7kAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBx4gow5eXl+sIXvqCrrrpKffr00R133KEDBw7E1Jw8eVLFxcXq1auXunfvrokTJ6qhoSGmpr6+XoWFhUpLS1OfPn30yCOP6NSpUzE1mzZt0siRI+VyuTRw4EBVVlZe3A4BAECnE1eA2bx5s4qLi7V9+3b5/X5FIhEVFBTo2LFjds306dP12muvafXq1dq8ebM+/PBD3XXXXfZ4S0uLCgsL1dzcrG3btumFF15QZWWlZs2aZdccPHhQhYWFuvXWW1VbW6uSkhI9+OCD2rBhQztsGQAAmK5rPMXr16+POa6srFSfPn1UU1OjW265RYcPH9ayZcu0YsUK3XbbbZKk5cuXa8iQIdq+fbvy8vJUVVWl/fv364033pDH49Hw4cM1b948Pfroo5o9e7acTqeWLl2qnJwcLVy4UJI0ZMgQbd26VYsWLZLP52unrQMAAFPFFWA+7fDhw5Kknj17SpJqamoUiUSUn59v1wwePFj9+/dXIBBQXl6eAoGAhg4dKo/HY9f4fD5NnTpVdXV1GjFihAKBQMwc0ZqSkpJzriUcDiscDtvHoVBIkhSJRBSJRD7LNmNE53IlW+025+XQnl+DRBfd65W0Z9PQo8RHjxJfZ+1RW/dz0QGmtbVVJSUluvHGG3XddddJkoLBoJxOpzIyMmJqPR6PgsGgXXN6eImOR8fOVxMKhXTixAmlpqaesZ7y8nLNmTPnjPNVVVVKS0u7uE2ex7zRre0+56W0bt26jl7CZef3+zt6CbgAepT46FHi62w9On78eJvqLjrAFBcXa9++fdq6devFTtGuysrKVFpaah+HQiFlZ2eroKBAbre73d4nEonI7/frh7uTFW5Nard5L7V9s6+cW2/RHo0fP14Oh6Ojl4OzoEeJjx4lvs7ao+gdlAu5qAAzbdo0rVmzRlu2bFG/fv3s85mZmWpublZTU1PMVZiGhgZlZmbaNTt37oyZL/qU0uk1n35yqaGhQW63+6xXXyTJ5XLJ5XKdcd7hcFySxoZbkxRuMSfAdKZv7ra6VL1H+6FHiY8eJb7O1qO27iWup5Asy9K0adP08ssva+PGjcrJyYkZHzVqlBwOh6qrq+1zBw4cUH19vbxeryTJ6/Vq7969amxstGv8fr/cbrdyc3PtmtPniNZE5wAAAFe2uK7AFBcXa8WKFfrtb3+rq666yv7MSnp6ulJTU5Wenq7JkyertLRUPXv2lNvt1re//W15vV7l5eVJkgoKCpSbm6t7771XCxYsUDAY1MyZM1VcXGxfQZkyZYqeeeYZzZgxQw888IA2btyoVatWae3ate28fQAAYKK4rsA8++yzOnz4sMaOHau+ffvaf1566SW7ZtGiRfrKV76iiRMn6pZbblFmZqZ+85vf2ONdunTRmjVr1KVLF3m9Xn3zm9/Ufffdp7lz59o1OTk5Wrt2rfx+v66//notXLhQzz33HI9QAwAASXFegbGsCz86nJKSooqKClVUVJyzZsCAARd8Kmbs2LHas2dPPMsDAABXCH4XEgAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADBO3AFmy5Yt+upXv6qsrCwlJSXplVdeiRm3LEuzZs1S3759lZqaqvz8fL333nsxNYcOHdKkSZPkdruVkZGhyZMn6+jRozE1b7/9tm6++WalpKQoOztbCxYsiH93AACgU4o7wBw7dkzXX3+9Kioqzjq+YMECPfXUU1q6dKl27Nihbt26yefz6eTJk3bNpEmTVFdXJ7/frzVr1mjLli16+OGH7fFQKKSCggINGDBANTU1evzxxzV79mz9/Oc/v4gtAgCAzqZrvC+YMGGCJkyYcNYxy7L05JNPaubMmbr99tslSb/4xS/k8Xj0yiuv6O6779Y777yj9evXa9euXRo9erQk6emnn9aXv/xl/fSnP1VWVpZefPFFNTc36/nnn5fT6dS1116r2tpaPfHEEzFBBwAAXJniDjDnc/DgQQWDQeXn59vn0tPTNWbMGAUCAd19990KBALKyMiww4sk5efnKzk5WTt27NCdd96pQCCgW265RU6n067x+Xz6yU9+oo8//lg9evQ4473D4bDC4bB9HAqFJEmRSESRSKTd9hidy5Vstducl0N7fg0SXXSvV9KeTUOPEh89SnydtUdt3U+7BphgMChJ8ng8Mec9Ho89FgwG1adPn9hFdO2qnj17xtTk5OScMUd07GwBpry8XHPmzDnjfFVVldLS0i5yR+c2b3Rru895Ka1bt66jl3DZ+f3+jl4CLoAeJT56lPg6W4+OHz/eprp2DTAdqaysTKWlpfZxKBRSdna2CgoK5Ha72+19IpGI/H6/frg7WeHWpHab91LbN9vX0Uu4bKI9Gj9+vBwOR0cvB2dBjxIfPUp8nbVH0TsoF9KuASYzM1OS1NDQoL59+9rnGxoaNHz4cLumsbEx5nWnTp3SoUOH7NdnZmaqoaEhpiZ6HK35NJfLJZfLdcZ5h8NxSRobbk1SuMWcANOZvrnb6lL1Hu2HHiU+epT4OluP2rqXdv05MDk5OcrMzFR1dbV9LhQKaceOHfJ6vZIkr9erpqYm1dTU2DUbN25Ua2urxowZY9ds2bIl5j6Y3+/XoEGDznr7CAAAXFniDjBHjx5VbW2tamtrJX3ywd3a2lrV19crKSlJJSUleuyxx/Tqq69q7969uu+++5SVlaU77rhDkjRkyBB96Utf0kMPPaSdO3fq97//vaZNm6a7775bWVlZkqRvfOMbcjqdmjx5surq6vTSSy9p8eLFMbeIAADAlSvuW0i7d+/Wrbfeah9HQ0VRUZEqKys1Y8YMHTt2TA8//LCampp00003af369UpJSbFf8+KLL2ratGkaN26ckpOTNXHiRD311FP2eHp6uqqqqlRcXKxRo0apd+/emjVrFo9QAwAASRcRYMaOHSvLOvcjxElJSZo7d67mzp17zpqePXtqxYoV532fYcOG6Xe/+128ywMAAFcAfhcSAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOF07egG4PK75/tqOXsJF+fP8wo5eAgAgAXEFBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABina0cv4HwqKir0+OOPKxgM6vrrr9fTTz+tG264oaOXhcvomu+vjfs1ri6WFtwgXTd7g8ItSZdgVef35/mFl/09AeBKk7BXYF566SWVlpbqRz/6kf7whz/o+uuvl8/nU2NjY0cvDQAAdLCEDTBPPPGEHnroId1///3Kzc3V0qVLlZaWpueff76jlwYAADpYQt5Cam5uVk1NjcrKyuxzycnJys/PVyAQOOtrwuGwwuGwfXz48GFJ0qFDhxSJRNptbZFIRMePH1fXSLJaWi//7QlcWNdWS8ePt3ZYjwZ+b9Vlf8/PakfZuMv6ftF/R3//+9/lcDgu63ujbehR4uusPTpy5IgkybKs89YlZID529/+ppaWFnk8npjzHo9H77777llfU15erjlz5pxxPicn55KsEYntGx29AMP0XtjRKwCAWEeOHFF6evo5xxMywFyMsrIylZaW2setra06dOiQevXqpaSk9vuv8FAopOzsbH3wwQdyu93tNi/aDz1KfPQo8dGjxNdZe2RZlo4cOaKsrKzz1iVkgOndu7e6dOmihoaGmPMNDQ3KzMw862tcLpdcLlfMuYyMjEu1RLnd7k71DdMZ0aPER48SHz1KfJ2xR+e78hKVkB/idTqdGjVqlKqrq+1zra2tqq6ultfr7cCVAQCARJCQV2AkqbS0VEVFRRo9erRuuOEGPfnkkzp27Jjuv//+jl4aAADoYAkbYL7+9a/r//7v/zRr1iwFg0ENHz5c69evP+ODvZeby+XSj370ozNuVyFx0KPER48SHz1KfFd6j5KsCz2nBAAAkGAS8jMwAAAA50OAAQAAxiHAAAAA4xBgAACAcQgwcaqoqNA111yjlJQUjRkzRjt37uzoJXVKW7Zs0Ve/+lVlZWUpKSlJr7zySsy4ZVmaNWuW+vbtq9TUVOXn5+u9996LqTl06JAmTZokt9utjIwMTZ48WUePHo2pefvtt3XzzTcrJSVF2dnZWrBgwaXeWqdQXl6uL3zhC7rqqqvUp08f3XHHHTpw4EBMzcmTJ1VcXKxevXqpe/fumjhx4hk/nLK+vl6FhYVKS0tTnz599Mgjj+jUqVMxNZs2bdLIkSPlcrk0cOBAVVZWXurtdQrPPvushg0bZv+QM6/Xq9dff90epz+JZ/78+UpKSlJJSYl9jj6dh4U2W7lypeV0Oq3nn3/eqqursx566CErIyPDamho6OildTrr1q2z/uM//sP6zW9+Y0myXn755Zjx+fPnW+np6dYrr7xivfXWW9a//Mu/WDk5OdaJEyfsmi996UvW9ddfb23fvt363e9+Zw0cONC655577PHDhw9bHo/HmjRpkrVv3z7rV7/6lZWammr97Gc/u1zbNJbP57OWL19u7du3z6qtrbW+/OUvW/3797eOHj1q10yZMsXKzs62qqurrd27d1t5eXnWP//zP9vjp06dsq677jorPz/f2rNnj7Vu3Tqrd+/eVllZmV3z/vvvW2lpaVZpaam1f/9+6+mnn7a6dOlirV+//rLu10SvvvqqtXbtWuuPf/yjdeDAAesHP/iB5XA4rH379lmWRX8Szc6dO61rrrnGGjZsmPWd73zHPk+fzo0AE4cbbrjBKi4uto9bWlqsrKwsq7y8vANX1fl9OsC0trZamZmZ1uOPP26fa2pqslwul/WrX/3KsizL2r9/vyXJ2rVrl13z+uuvW0lJSdZf//pXy7Isa8mSJVaPHj2scDhs1zz66KPWoEGDLvGOOp/GxkZLkrV582bLsj7ph8PhsFavXm3XvPPOO5YkKxAIWJb1SUhNTk62gsGgXfPss89abrfb7smMGTOsa6+9Nua9vv71r1s+n+9Sb6lT6tGjh/Xcc8/RnwRz5MgR6/Of/7zl9/utL37xi3aAoU/nxy2kNmpublZNTY3y8/Ptc8nJycrPz1cgEOjAlV15Dh48qGAwGNOL9PR0jRkzxu5FIBBQRkaGRo8ebdfk5+crOTlZO3bssGtuueUWOZ1Ou8bn8+nAgQP6+OOPL9NuOofDhw9Lknr27ClJqqmpUSQSienR4MGD1b9//5geDR06NOaHU/p8PoVCIdXV1dk1p88RreHfXHxaWlq0cuVKHTt2TF6vl/4kmOLiYhUWFp7xtaRP55ewP4k30fztb39TS0vLGT8J2OPx6N133+2gVV2ZgsGgJJ21F9GxYDCoPn36xIx37dpVPXv2jKnJyck5Y47oWI8ePS7J+jub1tZWlZSU6MYbb9R1110n6ZOvn9PpPOMXqn66R2frYXTsfDWhUEgnTpxQamrqpdhSp7F37155vV6dPHlS3bt318svv6zc3FzV1tbSnwSxcuVK/eEPf9CuXbvOGOPf0fkRYAB8JsXFxdq3b5+2bt3a0UvBpwwaNEi1tbU6fPiwfv3rX6uoqEibN2/u6GXh//nggw/0ne98R36/XykpKR29HONwC6mNevfurS5dupzx6e+GhgZlZmZ20KquTNGv9/l6kZmZqcbGxpjxU6dO6dChQzE1Z5vj9PfA+U2bNk1r1qzRm2++qX79+tnnMzMz1dzcrKamppj6T/foQl//c9W43W5j/6vxcnI6nRo4cKBGjRql8vJyXX/99Vq8eDH9SRA1NTVqbGzUyJEj1bVrV3Xt2lWbN2/WU089pa5du8rj8dCn8yDAtJHT6dSoUaNUXV1tn2ttbVV1dbW8Xm8HruzKk5OTo8zMzJhehEIh7dixw+6F1+tVU1OTampq7JqNGzeqtbVVY8aMsWu2bNmiSCRi1/j9fg0aNIjbRxdgWZamTZuml19+WRs3bjzjVtyoUaPkcDhienTgwAHV19fH9Gjv3r0xQdPv98vtdis3N9euOX2OaA3/5i5Oa2urwuEw/UkQ48aN0969e1VbW2v/GT16tCZNmmT/nT6dR0d/itgkK1eutFwul1VZWWnt37/fevjhh62MjIyYT3+jfRw5csTas2ePtWfPHkuS9cQTT1h79uyx/vKXv1iW9clj1BkZGdZvf/tb6+2337Zuv/32sz5GPWLECGvHjh3W1q1brc9//vMxj1E3NTVZHo/Huvfee619+/ZZK1eutNLS0niMug2mTp1qpaenW5s2bbI++ugj+8/x48ftmilTplj9+/e3Nm7caO3evdvyer2W1+u1x6OPfxYUFFi1tbXW+vXrrauvvvqsj38+8sgj1jvvvGNVVFR0isc/L4fvf//71ubNm62DBw9ab7/9tvX973/fSkpKsqqqqizLoj+J6vSnkCyLPp0PASZOTz/9tNW/f3/L6XRaN9xwg7V9+/aOXlKn9Oabb1qSzvhTVFRkWdYnj1L/8Ic/tDwej+Vyuaxx48ZZBw4ciJnj73//u3XPPfdY3bt3t9xut3X//fdbR44cial56623rJtuuslyuVzW5z73OWv+/PmXa4tGO1tvJFnLly+3a06cOGF961vfsnr06GGlpaVZd955p/XRRx/FzPPnP//ZmjBhgpWammr17t3b+u53v2tFIpGYmjfffNMaPny45XQ6rX/4h3+IeQ+c2wMPPGANGDDAcjqd1tVXX22NGzfODi+WRX8S1acDDH06tyTLsqyOufYDAABwcfgMDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADG+f8A3YDK92w6k7oAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(order.cancellations_time_in_seconds>1000).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1iuOX9hFc7_",
        "outputId": "fb7e0d10-115f-4d67-ac0e-d834e6907996"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.006718924972004479"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*  There is very small part of outliers.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "QE0zvDVSFyCu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order[order['cancellations_time_in_seconds']<=1000].groupby('is_driver_assigned_key')['cancellations_time_in_seconds'].agg(['mean', 'median', 'count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "QHiNrE65FxpJ",
        "outputId": "e83e2ff6-1803-4049-d958-2185782c27fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              mean  median  count\n",
              "is_driver_assigned_key                           \n",
              "0                       108.189087    88.0   4490\n",
              "1                       205.276503   121.0   2745"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c293f20e-479c-42df-a912-29c8ad957bfc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_driver_assigned_key</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>108.189087</td>\n",
              "      <td>88.0</td>\n",
              "      <td>4490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>205.276503</td>\n",
              "      <td>121.0</td>\n",
              "      <td>2745</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c293f20e-479c-42df-a912-29c8ad957bfc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\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",
              "    [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-c293f20e-479c-42df-a912-29c8ad957bfc 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-c293f20e-479c-42df-a912-29c8ad957bfc');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with_driver = order[order['is_driver_assigned_key']==1].groupby('hours')['cancellations_time_in_seconds'].agg(['mean', 'median'])"
      ],
      "metadata": {
        "id": "Db3bmUJsHBJN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_driver = order[order['is_driver_assigned_key']==0].groupby('hours')['cancellations_time_in_seconds'].agg(['mean', 'median'])"
      ],
      "metadata": {
        "id": "mbQJmkurIHh9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#avg cancellation time by hours with driver assigned\n",
        "cans_plot_with_driver = with_driver['mean'].plot(kind = 'bar')\n",
        "cans_plot_with_driver.set_xlabel('Time')\n",
        "cans_plot_with_driver.set_ylabel('Avg cancellation Time')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "VB3kwGv0IWfe",
        "outputId": "116eba2a-e4b3-4047-fa26-409a504f8d9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Avg cancellation Time')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#avg cancellation time by hours without any driver assigned\n",
        "cans_plot_no_driver = no_driver['mean'].plot(kind = 'bar')\n",
        "cans_plot_no_driver.set_xlabel('Time')\n",
        "cans_plot_no_driver.set_ylabel('Avg cancellation Time')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "cwpYbiRJJOHU",
        "outputId": "254ef6a1-db57-4f07-a475-3d42f10d2b92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Avg cancellation Time')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "no_d = no_driver['mean'].mean()\n",
        "with_d = with_driver['mean'].mean()\n",
        "\n",
        "print(f'Avg cancellation time if driver was assigned: {with_d}', f'Av cancellation time of no driver was assigned: {no_d}', sep = '\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o9ywuTQ-JNXv",
        "outputId": "f7878862-c43c-4fc8-ed6e-51dc37caf4a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Avg cancellation time if driver was assigned: 242.60632353041066\n",
            "Av cancellation time of no driver was assigned: 107.31176564166906\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   When no driver is assigned then the cancellation is less\n",
        "*   Both the graphs have distribution similar to uniform\n",
        "\n"
      ],
      "metadata": {
        "id": "zcUN9O4rL3BH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***4. Distribution of avg ETA(estimated time of arrival) by hours***"
      ],
      "metadata": {
        "id": "ACPUGk3fMlrl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "Oiy8BMLhNCn0",
        "outputId": "62b9a456-bfc3-409b-ad29-472e476feff0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  order_datetime  origin_longitude  origin_latitude  m_order_eta  \\\n",
              "0       18:08:07         -0.978916        51.456173         60.0   \n",
              "1       20:57:32         -0.950385        51.456843          NaN   \n",
              "2       12:07:50         -0.969520        51.455544        477.0   \n",
              "3       13:50:20         -1.054671        51.460544        658.0   \n",
              "4       21:24:45         -0.967605        51.458236          NaN   \n",
              "\n",
              "        order_gk  order_status_key  is_driver_assigned_key  \\\n",
              "0  3000583041974                 4                       1   \n",
              "1  3000583116437                 4                       0   \n",
              "2  3000582891479                 4                       1   \n",
              "3  3000582941169                 4                       1   \n",
              "4  3000583140877                 9                       0   \n",
              "\n",
              "   cancellations_time_in_seconds  is_cancelled_by_customer hours  \n",
              "0                          198.0                         1    18  \n",
              "1                          128.0                         1    20  \n",
              "2                           46.0                         1    12  \n",
              "3                           62.0                         1    13  \n",
              "4                            NaN                         0    21  "
            ],
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              "      <th>order_datetime</th>\n",
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              "      <th>m_order_eta</th>\n",
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              "      <th>cancellations_time_in_seconds</th>\n",
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              "      <td>18:08:07</td>\n",
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              "      <td>20:57:32</td>\n",
              "      <td>-0.950385</td>\n",
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              "      <th>2</th>\n",
              "      <td>12:07:50</td>\n",
              "      <td>-0.969520</td>\n",
              "      <td>51.455544</td>\n",
              "      <td>477.0</td>\n",
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              "      <td>4</td>\n",
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              "      <td>46.0</td>\n",
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              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13:50:20</td>\n",
              "      <td>-1.054671</td>\n",
              "      <td>51.460544</td>\n",
              "      <td>658.0</td>\n",
              "      <td>3000582941169</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>62.0</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>21:24:45</td>\n",
              "      <td>-0.967605</td>\n",
              "      <td>51.458236</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3000583140877</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "order['m_order_eta'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kongj8CSL2Ft",
        "outputId": "2c68d324-c2c6-4513-e480-299f38c2a8ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2814.000000\n",
              "mean      441.415423\n",
              "std       288.006379\n",
              "min        60.000000\n",
              "25%       233.000000\n",
              "50%       368.500000\n",
              "75%       653.000000\n",
              "max      1559.000000\n",
              "Name: m_order_eta, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "order['m_order_eta'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "lKF3xhMrNn8N",
        "outputId": "cbb0b9f0-17e5-432e-b2ed-f3a6e3071987"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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3RMsrJV5m8nbfHg0R/Z2XkydP6n/+5380aNAgjR49Wi6XS3V1dc72xsZGNTU1yev1SpK8Xq8OHDiglpYWZ05tba08Ho+KiooiWQoAAEgQYd3z8s///M+67bbbNHjwYB09elQ//OEPlZKSonvuuUeZmZmaOnWqqqurlZ2dLY/Ho/vvv19er1fFxcWSpNLSUhUVFWnKlClavny5fD6f5s2bp6qqqh7vWQEAADhXWOXlr3/9q+655x69//77uvzyy3XjjTdq165duvzyyyVJjz76qJKTk1VRUSG/36+ysjI9+eSTzvVTUlK0YcMGTZ8+XV6vV/3791dlZaUWL14c3VQAACBuhVVennvuufNuT09PV01NjWpqaj50zuDBg7Vx48ZwbhYAAMDBexsBAACrUF4AAIBVKC8AAMAqlBcAAGAVygsAALAK5QUAAFiF8gIAAKxCeQEAAFahvAAAAKtQXgAAgFUoLwAAwCqUFwAAYBXKCwAAsArlBQAAWIXyAgAArEJ5AQAAVqG8AAAAq1BeAACAVSgvAADAKpQXAABgFcoLAACwCuUFAABYhfICAACsQnkBAABWobwAAACrUF4AAIBVKC8AAMAqlBcAAGAVygsAALAK5QUAAFiF8gIAAKxCeQEAAFahvAAAAKukxnoBgE2GfP/lPrkdd4rR8rHS8IWb5e9Mimhff1lWHqVVAcDHA/e8AAAAq1BeAACAVSgvAADAKpQXAABglYjKy7Jly5SUlKSZM2c6Y6dPn1ZVVZUGDhyoAQMGqKKiQs3NzSHXa2pqUnl5uTIyMpSTk6PZs2frzJkzkSwFAAAkiF6Xl7179+qnP/2prr322pDxWbNm6aWXXtK6deu0fft2HT16VBMnTnS2d3Z2qry8XB0dHdq5c6eefvpprVmzRgsWLOh9CgAAkDB6VV5OnjypyZMn62c/+5kuvfRSZ/z48eNatWqVHnnkEd18880aPXq0Vq9erZ07d2rXrl2SpC1btujw4cN65plnNHLkSE2YMEFLlixRTU2NOjo6opMKAADErV6Vl6qqKpWXl6ukpCRkvKGhQYFAIGR82LBhKiwsVH19vSSpvr5eI0aMUG5urjOnrKxMbW1tOnToUG+WAwAAEkjYf6Tuueee02uvvaa9e/d22+bz+ZSWlqasrKyQ8dzcXPl8PmfO2cWla3vXtp74/X75/X7ncltbmyQpEAgoEAg4/z77cyTcKSbifVxs7mQT8tlG4RyraB7fSPTV90Y0j2+sv2YX4uNyfPtKouWVEi8zec8/L1JhlZd33nlHDzzwgGpra5Wenh6VBVyIpUuXatGiRd3Gt2zZooyMjJCx2traiG9v+diId9FnlowJxnoJvbZx48awrxON4xuJvv7eiMbx7c3XOVZifXz7WqLllRIvM3lDtbe3R+V2wiovDQ0Namlp0Wc+8xlnrLOzUzt27NC///u/a/Pmzero6FBra2vIvS/Nzc3Ky8uTJOXl5WnPnj0h++16NVLXnHPNnTtX1dXVzuW2tjYVFBSotLRUHo9H0gdtrra2VuPHj5fL5QonVjfDF26O6Pp9wZ1stGRMUPP3JcsfjOzPx8fKwYVlFzw3msc3En31vRHN4xvO1zlWPi7Ht68kWl4p8TKTt2ddj5xEKqzycsstt+jAgQMhY/fee6+GDRumOXPmqKCgQC6XS3V1daqoqJAkNTY2qqmpSV6vV5Lk9Xr14IMPqqWlRTk5OZI+aGoej0dFRUU93q7b7Zbb7e427nK5un2RehoLV6TvJdOX/MEkq9Z7tqvmb7nguV3v9TPqwW0xztu3tx2N42vTiTMaP782SbS8UuJlJm/37dEQVnm55JJLNHz48JCx/v37a+DAgc741KlTVV1drezsbHk8Ht1///3yer0qLi6WJJWWlqqoqEhTpkzR8uXL5fP5NG/ePFVVVfVYUAAAAM4W9XeVfvTRR5WcnKyKigr5/X6VlZXpySefdLanpKRow4YNmj59urxer/r376/KykotXrw42ksBAABxKOLy8rvf/S7kcnp6umpqalRTU/Oh1xk8eLBVTyIEAAAfH7y3EQAAsArlBQAAWIXyAgAArEJ5AQAAVqG8AAAAq1BeAACAVSgvAADAKpQXAABgFcoLAACwCuUFAABYhfICAACsQnkBAABWobwAAACrUF4AAIBVKC8AAMAqlBcAAGAVygsAALAK5QUAAFiF8gIAAKxCeQEAAFahvAAAAKtQXgAAgFUoLwAAwCqUFwAAYBXKCwAAsArlBQAAWIXyAgAArEJ5AQAAVqG8AAAAq1BeAACAVSgvAADAKpQXAABgFcoLAACwCuUFAABYhfICAACsQnkBAABWobwAAACrUF4AAIBVKC8AAMAqYZWXFStW6Nprr5XH45HH45HX69Urr7zibD99+rSqqqo0cOBADRgwQBUVFWpubg7ZR1NTk8rLy5WRkaGcnBzNnj1bZ86ciU4aAAAQ98IqL1deeaWWLVumhoYG7du3TzfffLPuuOMOHTp0SJI0a9YsvfTSS1q3bp22b9+uo0ePauLEic71Ozs7VV5ero6ODu3cuVNPP/201qxZowULFkQ3FQAAiFup4Uy+7bbbQi4/+OCDWrFihXbt2qUrr7xSq1at0tq1a3XzzTdLklavXq1rrrlGu3btUnFxsbZs2aLDhw9r69atys3N1ciRI7VkyRLNmTNHCxcuVFpaWvSSAQCAuBRWeTlbZ2en1q1bp1OnTsnr9aqhoUGBQEAlJSXOnGHDhqmwsFD19fUqLi5WfX29RowYodzcXGdOWVmZpk+frkOHDmnUqFE93pbf75ff73cut7W1SZICgYACgYDz77M/R8KdYiLex8XmTjYhn+MdeXsvGj8TF1s0f35tkGh5pcTLTN7zz4tU2OXlwIED8nq9On36tAYMGKAXXnhBRUVF2r9/v9LS0pSVlRUyPzc3Vz6fT5Lk8/lCikvX9q5tH2bp0qVatGhRt/EtW7YoIyMjZKy2tjbcSN0sHxvxLvrMkjHBWC+hT5E3fBs3bozCSvpGNH5+bZJoeaXEy0zeUO3t7VG5nbDLy9VXX639+/fr+PHj+o//+A9VVlZq+/btUVnMh5k7d66qq6udy21tbSooKFBpaak8Ho+kD9pcbW2txo8fL5fLFdHtDV+4OaLr9wV3stGSMUHN35csfzAp1su56MjbewcXlkVpVRdPNH9+bZBoeaXEy0zennU9chKpsMtLWlqaPvnJT0qSRo8erb179+onP/mJvvKVr6ijo0Otra0h9740NzcrLy9PkpSXl6c9e/aE7K/r1Uhdc3ridrvldru7jbtcrm5fpJ7GwuXvtOeXoz+YZNV6I0Xe8Nl04ozGz69NEi2vlHiZydt9ezRE/HdegsGg/H6/Ro8eLZfLpbq6OmdbY2Ojmpqa5PV6JUler1cHDhxQS0uLM6e2tlYej0dFRUWRLgUAACSAsO55mTt3riZMmKDCwkKdOHFCa9eu1e9+9ztt3rxZmZmZmjp1qqqrq5WdnS2Px6P7779fXq9XxcXFkqTS0lIVFRVpypQpWr58uXw+n+bNm6eqqqoe71kBAAA4V1jlpaWlRV//+tf13nvvKTMzU9dee602b96s8ePHS5IeffRRJScnq6KiQn6/X2VlZXryySed66ekpGjDhg2aPn26vF6v+vfvr8rKSi1evDi6qQAAQNwKq7ysWrXqvNvT09NVU1OjmpqaD50zePBgq179AAAAPl56/XdeAOBiGfL9l2O9hLD9ZVl5rJcAJAzemBEAAFiF8gIAAKxCeQEAAFahvAAAAKtQXgAAgFUoLwAAwCqUFwAAYBXKCwAAsArlBQAAWIXyAgAArEJ5AQAAVqG8AAAAq1BeAACAVSgvAADAKpQXAABgFcoLAACwCuUFAABYhfICAACsQnkBAABWobwAAACrUF4AAIBVKC8AAMAqlBcAAGAVygsAALAK5QUAAFiF8gIAAKxCeQEAAFahvAAAAKtQXgAAgFUoLwAAwCqUFwAAYBXKCwAAsArlBQAAWIXyAgAArEJ5AQAAVqG8AAAAq1BeAACAVcIqL0uXLtX111+vSy65RDk5ObrzzjvV2NgYMuf06dOqqqrSwIEDNWDAAFVUVKi5uTlkTlNTk8rLy5WRkaGcnBzNnj1bZ86ciTwNAACIe2GVl+3bt6uqqkq7du1SbW2tAoGASktLderUKWfOrFmz9NJLL2ndunXavn27jh49qokTJzrbOzs7VV5ero6ODu3cuVNPP/201qxZowULFkQvFQAAiFup4UzetGlTyOU1a9YoJydHDQ0N+tznPqfjx49r1apVWrt2rW6++WZJ0urVq3XNNddo165dKi4u1pYtW3T48GFt3bpVubm5GjlypJYsWaI5c+Zo4cKFSktLi146AAAQd8IqL+c6fvy4JCk7O1uS1NDQoEAgoJKSEmfOsGHDVFhYqPr6ehUXF6u+vl4jRoxQbm6uM6esrEzTp0/XoUOHNGrUqG634/f75ff7ncttbW2SpEAgoEAg4Pz77M+RcKeYiPdxsbmTTcjneEfe3ovGz8TFdu7Prw0/g+cK5+sczfOVLRItM3nPPy9SScaYXp0lgsGgbr/9drW2turVV1+VJK1du1b33ntvSNGQpLFjx+oLX/iCHn74YU2bNk1vv/22Nm/e7Gxvb29X//79tXHjRk2YMKHbbS1cuFCLFi3qNr527VplZGT0ZvkAAKCPtbe366tf/aqOHz8uj8fT6/30+p6XqqoqHTx40CkuF9PcuXNVXV3tXG5ra1NBQYFKS0ud8IFAQLW1tRo/frxcLldEtzd84eaPnhRj7mSjJWOCmr8vWf5gUqyXc9GRt/cOLiyL0qounnN/fm34GTxXOF/naJ6vbJFomcnbs65HTiLVq/IyY8YMbdiwQTt27NCVV17pjOfl5amjo0Otra3Kyspyxpubm5WXl+fM2bNnT8j+ul6N1DXnXG63W263u9u4y+Xq9kXqaSxc/k57fjn6g0lWrTdS5A2fTSfOrp9fG49xb77O0Thf2SbRMpO3+/ZoCOvVRsYYzZgxQy+88IK2bdumoUOHhmwfPXq0XC6X6urqnLHGxkY1NTXJ6/VKkrxerw4cOKCWlhZnTm1trTwej4qKiiLJAgAAEkBY97xUVVVp7dq1evHFF3XJJZfI5/NJkjIzM9WvXz9lZmZq6tSpqq6uVnZ2tjwej+6//355vV4VFxdLkkpLS1VUVKQpU6Zo+fLl8vl8mjdvnqqqqnq8dwUAAOBsYZWXFStWSJI+//nPh4yvXr1a3/jGNyRJjz76qJKTk1VRUSG/36+ysjI9+eSTztyUlBRt2LBB06dPl9frVf/+/VVZWanFixdHlgQAACSEsMrLhbwwKT09XTU1NaqpqfnQOYMHD9bGjRvDuWkAvTTk+y/HegkfyZ1itHzsB0+Wt/H5LgD6Fu9tBAAArEJ5AQAAVqG8AAAAq1BeAACAVSgvAADAKpQXAABgFcoLAACwCuUFAABYhfICAACsQnkBAABWobwAAACrUF4AAIBVKC8AAMAqlBcAAGAVygsAALAK5QUAAFiF8gIAAKxCeQEAAFahvAAAAKtQXgAAgFUoLwAAwCqUFwAAYBXKCwAAsArlBQAAWIXyAgAArEJ5AQAAVqG8AAAAq1BeAACAVSgvAADAKpQXAABgFcoLAACwCuUFAABYhfICAACsQnkBAABWobwAAACrUF4AAIBVKC8AAMAqlBcAAGCVsMvLjh07dNtttyk/P19JSUlav359yHZjjBYsWKBBgwapX79+Kikp0ZEjR0LmHDt2TJMnT5bH41FWVpamTp2qkydPRhQEAAAkhrDLy6lTp3Tdddeppqamx+3Lly/X448/rpUrV2r37t3q37+/ysrKdPr0aWfO5MmTdejQIdXW1mrDhg3asWOHpk2b1vsUAAAgYaSGe4UJEyZowoQJPW4zxuixxx7TvHnzdMcdd0iSfvGLXyg3N1fr16/XpEmT9MYbb2jTpk3au3evxowZI0l64okn9MUvflE//vGPlZ+fH0EcAAAQ78IuL+fz1ltvyefzqaSkxBnLzMzUuHHjVF9fr0mTJqm+vl5ZWVlOcZGkkpISJScna/fu3brrrru67dfv98vv9zuX29raJEmBQECBQMD599mfI+FOMRHv42JzJ5uQz/GOvPEtHvKGc+6J5vnKFomWmbznnxepqJYXn88nScrNzQ0Zz83Ndbb5fD7l5OSELiI1VdnZ2c6ccy1dulSLFi3qNr5lyxZlZGSEjNXW1vZ6/V2Wj414F31myZhgrJfQp8gb32zOu3HjxrCvE43zlW0SLTN5Q7W3t0fldqJaXi6WuXPnqrq62rnc1tamgoIClZaWyuPxSPqgzdXW1mr8+PFyuVwR3d7whZsjun5fcCcbLRkT1Px9yfIHk2K9nIuOvPEtHvIeXFh2wXOjeb6yRaJlJm/Puh45iVRUy0teXp4kqbm5WYMGDXLGm5ubNXLkSGdOS0tLyPXOnDmjY8eOOdc/l9vtltvt7jbucrm6fZF6GguXv9Oek6c/mGTVeiNF3vhmc97enHeicb6yTaJlJm/37dEQ1b/zMnToUOXl5amurs4Za2tr0+7du+X1eiVJXq9Xra2tamhocOZs27ZNwWBQ48aNi+ZyAABAHAr7npeTJ0/qzTffdC6/9dZb2r9/v7Kzs1VYWKiZM2fqRz/6ka666ioNHTpU8+fPV35+vu68805J0jXXXKNbb71V9913n1auXKlAIKAZM2Zo0qRJvNIIAAB8pLDLy759+/SFL3zBudz1XJTKykqtWbNG3/ve93Tq1ClNmzZNra2tuvHGG7Vp0yalp6c713n22Wc1Y8YM3XLLLUpOTlZFRYUef/zxKMQBAADxLuzy8vnPf17GfPjLGZOSkrR48WItXrz4Q+dkZ2dr7dq14d40AAAA720EAADsQnkBAABWobwAAACrUF4AAIBVKC8AAMAqlBcAAGAVygsAALAK5QUAAFjFineVBoCPuyHff/mC57pTjJaP/eAd7GP5RpR/WVYes9sGIsE9LwAAwCqUFwAAYBXKCwAAsArlBQAAWIXyAgAArEJ5AQAAVqG8AAAAq1BeAACAVSgvAADAKpQXAABgFcoLAACwCuUFAABYhTdmBIAEFc6bSUYqWm9GyZtJQuKeFwAAYBnKCwAAsArlBQAAWIXyAgAArEJ5AQAAVqG8AAAAq1BeAACAVSgvAADAKpQXAABgFcoLAACwCuUFAABYhfICAACsQnkBAABWobwAAACrUF4AAIBVKC8AAMAqMS0vNTU1GjJkiNLT0zVu3Djt2bMnlssBAAAWSI3VDf/qV79SdXW1Vq5cqXHjxumxxx5TWVmZGhsblZOTE6tlAQA+xoZ8/+VYL+GCuFOMlo+Vhi/crMYHvxTr5cSdmN3z8sgjj+i+++7Tvffeq6KiIq1cuVIZGRn6+c9/HqslAQAAC8TknpeOjg41NDRo7ty5zlhycrJKSkpUX1/fbb7f75ff73cuHz9+XJJ07NgxBQIBSVIgEFB7e7vef/99uVyuiNaXeuZURNfvC6lBo/b2oFIDyeoMJsV6ORcdeeMbeeNfomU+O+/7778f6+VcdBf6O/jEiROSJGNMZDdoYuDdd981kszOnTtDxmfPnm3Gjh3bbf4Pf/hDI4kPPvjggw8++IiDj3feeSeiHhGz57yEY+7cuaqurnYuB4NBHTt2TAMHDlRS0gcNvq2tTQUFBXrnnXfk8XhitdQ+Q974Rt74lmh5pcTLTN6eGWN04sQJ5efnR3R7MSkvl112mVJSUtTc3Bwy3tzcrLy8vG7z3W633G53yFhWVlaP+/Z4PAnxjdKFvPGNvPEt0fJKiZeZvN1lZmZGfDsxecJuWlqaRo8erbq6OmcsGAyqrq5OXq83FksCAACWiNnDRtXV1aqsrNSYMWM0duxYPfbYYzp16pTuvffeWC0JAABYIGbl5Stf+Yr+93//VwsWLJDP59PIkSO1adMm5ebm9mp/brdbP/zhD7s9vBSvyBvfyBvfEi2vlHiZyXtxJRkT6euVAAAA+g7vbQQAAKxCeQEAAFahvAAAAKtQXgAAgFXiorzU1NRoyJAhSk9P17hx47Rnz55YL6lXli5dquuvv16XXHKJcnJydOedd6qxsTFkzunTp1VVVaWBAwdqwIABqqio6PbH/pqamlReXq6MjAzl5ORo9uzZOnPmTF9GCduyZcuUlJSkmTNnOmPxmPXdd9/V1772NQ0cOFD9+vXTiBEjtG/fPme7MUYLFizQoEGD1K9fP5WUlOjIkSMh+zh27JgmT54sj8ejrKwsTZ06VSdPnuzrKB+ps7NT8+fP19ChQ9WvXz994hOf0JIlS0Le08TmvDt27NBtt92m/Px8JSUlaf369SHbo5Xtj3/8o/7f//t/Sk9PV0FBgZYvX36xo32o82UOBAKaM2eORowYof79+ys/P19f//rXdfTo0ZB92JT5o47x2b71rW8pKSlJjz32WMh4vOV94403dPvttyszM1P9+/fX9ddfr6amJmd7n523I3pzgY+B5557zqSlpZmf//zn5tChQ+a+++4zWVlZprm5OdZLC1tZWZlZvXq1OXjwoNm/f7/54he/aAoLC83JkyedOd/61rdMQUGBqaurM/v27TPFxcXms5/9rLP9zJkzZvjw4aakpMS8/vrrZuPGjeayyy4zc+fOjUWkC7Jnzx4zZMgQc+2115oHHnjAGY+3rMeOHTODBw823/jGN8zu3bvNn//8Z7N582bz5ptvOnOWLVtmMjMzzfr1680f/vAHc/vtt5uhQ4eav//9786cW2+91Vx33XVm165d5ve//7355Cc/ae65555YRDqvBx980AwcONBs2LDBvPXWW2bdunVmwIAB5ic/+Ykzx+a8GzduND/4wQ/M888/bySZF154IWR7NLIdP37c5ObmmsmTJ5uDBw+aX/7yl6Zfv37mpz/9aV/FDHG+zK2traakpMT86le/Mv/93/9t6uvrzdixY83o0aND9mFT5o86xl2ef/55c91115n8/Hzz6KOPhmyLp7xvvvmmyc7ONrNnzzavvfaaefPNN82LL74Y8vu2r87b1peXsWPHmqqqKudyZ2enyc/PN0uXLo3hqqKjpaXFSDLbt283xnxwcnC5XGbdunXOnDfeeMNIMvX19caYD775kpOTjc/nc+asWLHCeDwe4/f7+zbABThx4oS56qqrTG1trbnpppuc8hKPWefMmWNuvPHGD90eDAZNXl6e+dd//VdnrLW11bjdbvPLX/7SGGPM4cOHjSSzd+9eZ84rr7xikpKSzLvvvnvxFt8L5eXl5pvf/GbI2MSJE83kyZONMfGV99wTfbSyPfnkk+bSSy8N+X6eM2eOufrqqy9yoo92vl/mXfbs2WMkmbffftsYY3fmD8v717/+1VxxxRXm4MGDZvDgwSHlJd7yfuUrXzFf+9rXPvQ6fXnetvpho46ODjU0NKikpMQZS05OVklJierr62O4sug4fvy4JCk7O1uS1NDQoEAgEJJ32LBhKiwsdPLW19drxIgRIX/sr6ysTG1tbTp06FAfrv7CVFVVqby8PCSTFJ9Zf/Ob32jMmDG6++67lZOTo1GjRulnP/uZs/2tt96Sz+cLyZyZmalx48aFZM7KytKYMWOcOSUlJUpOTtbu3bv7LswF+OxnP6u6ujr96U9/kiT94Q9/0KuvvqoJEyZIir+8Z4tWtvr6en3uc59TWlqaM6esrEyNjY3629/+1kdpeu/48eNKSkpy3osu3jIHg0FNmTJFs2fP1qc//elu2+MpbzAY1Msvv6xPfepTKisrU05OjsaNGxfy0FJfnretLi//93//p87Ozm5/lTc3N1c+ny9Gq4qOYDComTNn6oYbbtDw4cMlST6fT2lpad3elPLsvD6fr8evR9e2j5PnnntOr732mpYuXdptW7xllaQ///nPWrFiha666ipt3rxZ06dP13e+8x09/fTTkv6x5vN9P/t8PuXk5IRsT01NVXZ29scu8/e//31NmjRJw4YNk8vl0qhRozRz5kxNnjxZUvzlPVu0stn2PX6206dPa86cObrnnnucN+qLt8wPP/ywUlNT9Z3vfKfH7fGUt6WlRSdPntSyZct06623asuWLbrrrrs0ceJEbd++XVLfnrdj9vYAOL+qqiodPHhQr776aqyXclG88847euCBB1RbW6v09PRYL6dPBINBjRkzRg899JAkadSoUTp48KBWrlypysrKGK8u+n7961/r2Wef1dq1a/XpT39a+/fv18yZM5Wfnx+XefEPgUBAX/7yl2WM0YoVK2K9nIuioaFBP/nJT/Taa68pKSkp1su56ILBoCTpjjvu0KxZsyRJI0eO1M6dO7Vy5UrddNNNfboeq+95ueyyy5SSktLtmczNzc3Ky8uL0aoiN2PGDG3YsEG//e1vdeWVVzrjeXl56ujoUGtra8j8s/Pm5eX1+PXo2vZx0dDQoJaWFn3mM59RamqqUlNTtX37dj3++ONKTU1Vbm5u3GTtMmjQIBUVFYWMXXPNNc4z9bvWfL7v57y8PLW0tIRsP3PmjI4dO/axyzx79mzn3pcRI0ZoypQpmjVrlnNPW7zlPVu0stn2PS79o7i8/fbbqq2tde51keIr8+9//3u1tLSosLDQOYe9/fbb+u53v6shQ4ZIiq+8l112mVJTUz/yHNZX522ry0taWppGjx6turo6ZywYDKqurk5erzeGK+sdY4xmzJihF154Qdu2bdPQoUNDto8ePVoulyskb2Njo5qampy8Xq9XBw4cCPmB6TqBnPtNF0u33HKLDhw4oP379zsfY8aM0eTJk51/x0vWLjfccEO3l77/6U9/0uDBgyVJQ4cOVV5eXkjmtrY27d69OyRza2urGhoanDnbtm1TMBjUuHHj+iDFhWtvb1dycugpJiUlxfkfXLzlPVu0snm9Xu3YsUOBQMCZU1tbq6uvvlqXXnppH6W5cF3F5ciRI9q6dasGDhwYsj2eMk+ZMkV//OMfQ85h+fn5mj17tjZv3iwpvvKmpaXp+uuvP+85rE9/R13wU3s/pp577jnjdrvNmjVrzOHDh820adNMVlZWyDOZbTF9+nSTmZlpfve735n33nvP+Whvb3fmfOtb3zKFhYVm27ZtZt++fcbr9Rqv1+ts73oZWmlpqdm/f7/ZtGmTufzyyz+2Lx8+29mvNjIm/rLu2bPHpKammgcffNAcOXLEPPvssyYjI8M888wzzpxly5aZrKws8+KLL5o//vGP5o477ujx5bWjRo0yu3fvNq+++qq56qqrPhYvHT5XZWWlueKKK5yXSj///PPmsssuM9/73vecOTbnPXHihHn99dfN66+/biSZRx55xLz++uvOK2uika21tdXk5uaaKVOmmIMHD5rnnnvOZGRkxOyl0ufL3NHRYW6//XZz5ZVXmv3794ecw85+FYlNmT/qGJ/r3FcbGRNfeZ9//nnjcrnMU089ZY4cOWKeeOIJk5KSYn7/+987++ir87b15cUYY5544glTWFho0tLSzNixY82uXbtivaRekdTjx+rVq505f//73823v/1tc+mll5qMjAxz1113mffeey9kP3/5y1/MhAkTTL9+/cxll11mvvvd75pAINDHacJ3bnmJx6wvvfSSGT58uHG73WbYsGHmqaeeCtkeDAbN/PnzTW5urnG73eaWW24xjY2NIXPef/99c88995gBAwYYj8dj7r33XnPixIm+jHFB2trazAMPPGAKCwtNenq6+ad/+ifzgx/8IOQXmc15f/vb3/b481pZWWmMiV62P/zhD+bGG280brfbXHHFFWbZsmV9FbGb82V+6623PvQc9tvf/tbZh02ZP+oYn6un8hJveVetWmU++clPmvT0dHPdddeZ9evXh+yjr87bScac9ecuAQAAPuasfs4LAABIPJQXAABgFcoLAACwCuUFAABYhfICAACsQnkBAABWobwAAACrUF4AAIBVKC8AAMAqlBcAAGAVygsAALAK5QUAAFjl/wMgql7//nGtpwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "order.m_order_eta.isna().mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4waqL6WNxQv",
        "outputId": "1d763259-0729-4645-aa18-484d18634aa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7374020156774916"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eta = order.groupby('hours')['m_order_eta'].agg(['mean', 'count', 'median']).reset_index()"
      ],
      "metadata": {
        "id": "EGeTKQbAN7i_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eta_plot = sns.barplot(data = eta, x = 'hours', y =  'median')\n",
        "eta_plot.set_title('Distribution of avg ETA by hour')\n",
        "eta_plot.set_ylabel('Median ETA')\n",
        "eta_plot.set_xlabel('Hours')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "FcNd_5jxOUIs",
        "outputId": "126f17cd-ebed-446e-e578-dc8cf4c9d675"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Hours')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eta_plot = sns.barplot(data = eta, x = 'hours', y =  'mean')\n",
        "eta_plot.set_title('Distribution of avg ETA by hour')\n",
        "eta_plot.set_ylabel('Avg ETA')\n",
        "eta_plot.set_xlabel('Hours')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "d84vKjE1PdDM",
        "outputId": "ca2ebe3f-07f3-46e5-9b6e-7d196796b2d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Hours')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   ETA is higher during morning rush hours\n",
        "\n"
      ],
      "metadata": {
        "id": "UvVm3T4bPr0v"
      }
    }
  ]
}