lecture12.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From: https://gist.github.com/mim/dc9e9b57b83ec9749cf5106602cb0604\n",
    "import collections\n",
    "\n",
    "from typing import Iterable\n",
    "\n",
    "\n",
    "def stem_and_leaf(x: Iterable[int], interval: int = 10, sep: str = \"\") -> None:\n",
    "    \"\"\"Prints a stem-and-leaf plot. Adapted from:\n",
    "  \n",
    "    https://www.rosettacode.org/wiki/Stem-and-leaf_plot#Python\n",
    "\n",
    "    The interval must be a power of 10 for this to work as expected.\n",
    "    \"\"\"\n",
    "    assert interval % 10 == 0, \"Interval not divisible by 10\"\n",
    "    # Maps from prefix (12|) to suffixes (2)\n",
    "    d = collections.defaultdict(list)\n",
    "    for val in x:\n",
    "        prefix = val // interval\n",
    "        suffix = val - prefix * interval\n",
    "        d[prefix].append(suffix)\n",
    "    max_pref_len = max(len(str(val)) for val in d.keys())\n",
    "    for (prefix, suffix) in sorted(d.items()):\n",
    "        # Left-pads it.\n",
    "        prefix_str = str(prefix).rjust(max_pref_len)\n",
    "        suffix_str = sep.join(str(val) for val in sorted(suffix))\n",
    "        print(f\"{prefix_str} | {suffix_str}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leaf and stem plots with data from Johnson (2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "alb = pd.read_csv(\"albuquerque.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .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>Temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>360</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>362</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Temp\n",
       "0      30\n",
       "1      35\n",
       "2      36\n",
       "3      33\n",
       "4      34\n",
       "..    ...\n",
       "360    39\n",
       "361    40\n",
       "362    35\n",
       "363    37\n",
       "364    22\n",
       "\n",
       "[365 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      30\n",
       "1      35\n",
       "2      36\n",
       "3      33\n",
       "4      34\n",
       "       ..\n",
       "360    39\n",
       "361    40\n",
       "362    35\n",
       "363    37\n",
       "364    22\n",
       "Name: Temp, Length: 365, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alb['Temp']"
   ]
  },
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   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "text": [
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    }
   ],
   "source": [
    "for temp in alb['Temp']:\n",
    "    print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for temp in alb['Temp'][:10]:\n",
    "    print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 | 2599\n",
      "3 | 000022234444444445555555556666666777788888888889999999\n",
      "4 | 0000000000011111122222222222222233333333444444444455555555555556666667777778888999\n",
      "5 | 000001112222233344445555555666778888888999999\n",
      "6 | 00000111222222333333334444555555555666666677778999\n",
      "7 | 000001111112222222222233333333333334444444444455555555555555666666666667777777788888888889999999\n",
      "8 | 0000000111112222222333334444445677\n"
     ]
    }
   ],
   "source": [
    "stem_and_leaf(alb['Temp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas with data from Peterson and Barney (1952)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pb52 = pd.read_csv(\"pb52.tsv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scales:\n",
    " * Nominal: Type, Sex, Vowel\n",
    " * Ordinal: Speaker, Repetition\n",
    " * Interval\n",
    " * Ratio: F0, F1, F2, F3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Vowel</th>\n",
       "      <th>Repetition</th>\n",
       "      <th>F0</th>\n",
       "      <th>F1</th>\n",
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       "      <td>0</td>\n",
       "      <td>m</td>\n",
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       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>240</td>\n",
       "      <td>2280</td>\n",
       "      <td>2850</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>m</td>\n",
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       "      <td>2</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>1</td>\n",
       "      <td>203</td>\n",
       "      <td>390</td>\n",
       "      <td>2030</td>\n",
       "      <td>2640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>2</td>\n",
       "      <td>192</td>\n",
       "      <td>310</td>\n",
       "      <td>1980</td>\n",
       "      <td>2550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>490</td>\n",
       "      <td>1870</td>\n",
       "      <td>2420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <td>1515</td>\n",
       "      <td>c</td>\n",
       "      <td>f</td>\n",
       "      <td>76</td>\n",
       "      <td>U</td>\n",
       "      <td>2</td>\n",
       "      <td>322</td>\n",
       "      <td>610</td>\n",
       "      <td>1550</td>\n",
       "      <td>3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1516</td>\n",
       "      <td>c</td>\n",
       "      <td>f</td>\n",
       "      <td>76</td>\n",
       "      <td>u</td>\n",
       "      <td>1</td>\n",
       "      <td>345</td>\n",
       "      <td>520</td>\n",
       "      <td>1250</td>\n",
       "      <td>3460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1517</td>\n",
       "      <td>c</td>\n",
       "      <td>f</td>\n",
       "      <td>76</td>\n",
       "      <td>u</td>\n",
       "      <td>2</td>\n",
       "      <td>334</td>\n",
       "      <td>500</td>\n",
       "      <td>1140</td>\n",
       "      <td>3380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1518</td>\n",
       "      <td>c</td>\n",
       "      <td>f</td>\n",
       "      <td>76</td>\n",
       "      <td>3'</td>\n",
       "      <td>1</td>\n",
       "      <td>308</td>\n",
       "      <td>740</td>\n",
       "      <td>1850</td>\n",
       "      <td>2160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1519</td>\n",
       "      <td>c</td>\n",
       "      <td>f</td>\n",
       "      <td>76</td>\n",
       "      <td>3'</td>\n",
       "      <td>2</td>\n",
       "      <td>328</td>\n",
       "      <td>660</td>\n",
       "      <td>1830</td>\n",
       "      <td>2200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1520 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type Sex  Speaker Vowel  Repetition   F0   F1    F2    F3\n",
       "0       m   m        1     i           1  160  240  2280  2850\n",
       "1       m   m        1     i           2  186  280  2400  2790\n",
       "2       m   m        1     I           1  203  390  2030  2640\n",
       "3       m   m        1     I           2  192  310  1980  2550\n",
       "4       m   m        1     E           1  161  490  1870  2420\n",
       "...   ...  ..      ...   ...         ...  ...  ...   ...   ...\n",
       "1515    c   f       76     U           2  322  610  1550  3400\n",
       "1516    c   f       76     u           1  345  520  1250  3460\n",
       "1517    c   f       76     u           2  334  500  1140  3380\n",
       "1518    c   f       76    3'           1  308  740  1850  2160\n",
       "1519    c   f       76    3'           2  328  660  1830  2200\n",
       "\n",
       "[1520 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Type</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Vowel</th>\n",
       "      <th>Repetition</th>\n",
       "      <th>F0</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>LogF0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>i</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>240</td>\n",
       "      <td>2280</td>\n",
       "      <td>2850</td>\n",
       "      <td>7.321928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>i</td>\n",
       "      <td>2</td>\n",
       "      <td>186</td>\n",
       "      <td>280</td>\n",
       "      <td>2400</td>\n",
       "      <td>2790</td>\n",
       "      <td>7.539159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>1</td>\n",
       "      <td>203</td>\n",
       "      <td>390</td>\n",
       "      <td>2030</td>\n",
       "      <td>2640</td>\n",
       "      <td>7.665336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>2</td>\n",
       "      <td>192</td>\n",
       "      <td>310</td>\n",
       "      <td>1980</td>\n",
       "      <td>2550</td>\n",
       "      <td>7.584963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>490</td>\n",
       "      <td>1870</td>\n",
       "      <td>2420</td>\n",
       "      <td>7.330917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>155</td>\n",
       "      <td>570</td>\n",
       "      <td>1700</td>\n",
       "      <td>2600</td>\n",
       "      <td>7.276124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>{</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>560</td>\n",
       "      <td>1820</td>\n",
       "      <td>2660</td>\n",
       "      <td>7.129283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>{</td>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>630</td>\n",
       "      <td>1700</td>\n",
       "      <td>2550</td>\n",
       "      <td>7.491853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>V</td>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>590</td>\n",
       "      <td>1250</td>\n",
       "      <td>2620</td>\n",
       "      <td>7.169925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>V</td>\n",
       "      <td>2</td>\n",
       "      <td>148</td>\n",
       "      <td>620</td>\n",
       "      <td>1300</td>\n",
       "      <td>2530</td>\n",
       "      <td>7.209453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Type Sex  Speaker Vowel  Repetition   F0   F1    F2    F3     LogF0\n",
       "0    m   m        1     i           1  160  240  2280  2850  7.321928\n",
       "1    m   m        1     i           2  186  280  2400  2790  7.539159\n",
       "2    m   m        1     I           1  203  390  2030  2640  7.665336\n",
       "3    m   m        1     I           2  192  310  1980  2550  7.584963\n",
       "4    m   m        1     E           1  161  490  1870  2420  7.330917\n",
       "5    m   m        1     E           2  155  570  1700  2600  7.276124\n",
       "6    m   m        1     {           1  140  560  1820  2660  7.129283\n",
       "7    m   m        1     {           2  180  630  1700  2550  7.491853\n",
       "8    m   m        1     V           1  144  590  1250  2620  7.169925\n",
       "9    m   m        1     V           2  148  620  1300  2530  7.209453"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=1520, step=1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Type', 'Sex', 'Speaker', 'Vowel', 'Repetition', 'F0', 'F1', 'F2',\n",
       "       'F3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    760\n",
       "1    760\n",
       "Name: Repetition, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52['Repetition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "m    660\n",
       "w    560\n",
       "c    300\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributions, seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe63a90c550>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(pb52[\"F0\"], bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "1515    False\n",
       "1516    False\n",
       "1517    False\n",
       "1518    False\n",
       "1519    False\n",
       "Name: Type, Length: 1520, dtype: bool"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52['Type'] == \"w\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Type</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Vowel</th>\n",
       "      <th>Repetition</th>\n",
       "      <th>F0</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>LogF0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>660</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>34</td>\n",
       "      <td>i</td>\n",
       "      <td>1</td>\n",
       "      <td>230</td>\n",
       "      <td>370</td>\n",
       "      <td>2670</td>\n",
       "      <td>3100</td>\n",
       "      <td>7.845490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>661</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>34</td>\n",
       "      <td>i</td>\n",
       "      <td>2</td>\n",
       "      <td>234</td>\n",
       "      <td>390</td>\n",
       "      <td>2760</td>\n",
       "      <td>3060</td>\n",
       "      <td>7.870365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>662</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>34</td>\n",
       "      <td>I</td>\n",
       "      <td>1</td>\n",
       "      <td>234</td>\n",
       "      <td>468</td>\n",
       "      <td>2330</td>\n",
       "      <td>2930</td>\n",
       "      <td>7.870365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>663</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>34</td>\n",
       "      <td>I</td>\n",
       "      <td>2</td>\n",
       "      <td>205</td>\n",
       "      <td>410</td>\n",
       "      <td>2380</td>\n",
       "      <td>2950</td>\n",
       "      <td>7.679480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>664</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>34</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>190</td>\n",
       "      <td>550</td>\n",
       "      <td>2200</td>\n",
       "      <td>2880</td>\n",
       "      <td>7.569856</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>1215</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>61</td>\n",
       "      <td>U</td>\n",
       "      <td>2</td>\n",
       "      <td>250</td>\n",
       "      <td>450</td>\n",
       "      <td>1050</td>\n",
       "      <td>2450</td>\n",
       "      <td>7.965784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1216</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>61</td>\n",
       "      <td>u</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>290</td>\n",
       "      <td>670</td>\n",
       "      <td>2380</td>\n",
       "      <td>8.022368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1217</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>61</td>\n",
       "      <td>u</td>\n",
       "      <td>2</td>\n",
       "      <td>275</td>\n",
       "      <td>330</td>\n",
       "      <td>630</td>\n",
       "      <td>2460</td>\n",
       "      <td>8.103288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1218</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>61</td>\n",
       "      <td>3'</td>\n",
       "      <td>1</td>\n",
       "      <td>240</td>\n",
       "      <td>500</td>\n",
       "      <td>1630</td>\n",
       "      <td>2040</td>\n",
       "      <td>7.906891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1219</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>61</td>\n",
       "      <td>3'</td>\n",
       "      <td>2</td>\n",
       "      <td>243</td>\n",
       "      <td>490</td>\n",
       "      <td>1580</td>\n",
       "      <td>2190</td>\n",
       "      <td>7.924813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>560 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type Sex  Speaker Vowel  Repetition   F0   F1    F2    F3     LogF0\n",
       "660     w   f       34     i           1  230  370  2670  3100  7.845490\n",
       "661     w   f       34     i           2  234  390  2760  3060  7.870365\n",
       "662     w   f       34     I           1  234  468  2330  2930  7.870365\n",
       "663     w   f       34     I           2  205  410  2380  2950  7.679480\n",
       "664     w   f       34     E           1  190  550  2200  2880  7.569856\n",
       "...   ...  ..      ...   ...         ...  ...  ...   ...   ...       ...\n",
       "1215    w   f       61     U           2  250  450  1050  2450  7.965784\n",
       "1216    w   f       61     u           1  260  290   670  2380  8.022368\n",
       "1217    w   f       61     u           2  275  330   630  2460  8.103288\n",
       "1218    w   f       61    3'           1  240  500  1630  2040  7.906891\n",
       "1219    w   f       61    3'           2  243  490  1580  2190  7.924813\n",
       "\n",
       "[560 rows x 10 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52[pb52['Type']==\"w\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe64253d650>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(pb52[pb52['Type']==\"w\"][\"F0\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "types = pb52['Type'].unique()\n",
    "for a in types:\n",
    "    sns.distplot(pb52[pb52['Type'] == a]['F0'], ax=ax)\n",
    "_ = ax.legend(types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe63a1aced0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Type', y='F0', data=pb52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 | 2599\n",
      "3 | 000022234444444445555555556666666777788888888889999999\n",
      "4 | 0000000000011111122222222222222233333333444444444455555555555556666667777778888999\n",
      "5 | 000001112222233344445555555666778888888999999\n",
      "6 | 00000111222222333333334444555555555666666677778999\n",
      "7 | 000001111112222222222233333333333334444444444455555555555555666666666667777777788888888889999999\n",
      "8 | 0000000111112222222333334444445677\n"
     ]
    }
   ],
   "source": [
    "stem_and_leaf(alb['Temp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe639f8c310>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(alb['Temp'], bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    42\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alb['Temp'].mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Central tendencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Speaker</th>\n",
       "      <th>Repetition</th>\n",
       "      <th>F0</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>1520.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>191.295395</td>\n",
       "      <td>563.299342</td>\n",
       "      <td>1624.377632</td>\n",
       "      <td>2707.813816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>21.944631</td>\n",
       "      <td>0.500165</td>\n",
       "      <td>60.362272</td>\n",
       "      <td>201.254297</td>\n",
       "      <td>637.012780</td>\n",
       "      <td>519.455448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>19.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>2370.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>2680.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>57.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>681.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>3030.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>3610.000000</td>\n",
       "      <td>4380.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Speaker   Repetition           F0           F1           F2  \\\n",
       "count  1520.000000  1520.000000  1520.000000  1520.000000  1520.000000   \n",
       "mean     38.500000     1.500000   191.295395   563.299342  1624.377632   \n",
       "std      21.944631     0.500165    60.362272   201.254297   637.012780   \n",
       "min       1.000000     1.000000    91.000000   190.000000   560.000000   \n",
       "25%      19.750000     1.000000   133.000000   420.000000  1100.000000   \n",
       "50%      38.500000     1.500000   200.000000   540.000000  1470.000000   \n",
       "75%      57.250000     2.000000   238.000000   681.000000  2100.000000   \n",
       "max      76.000000     2.000000   350.000000  1300.000000  3610.000000   \n",
       "\n",
       "                F3  \n",
       "count  1520.000000  \n",
       "mean   2707.813816  \n",
       "std     519.455448  \n",
       "min    1400.000000  \n",
       "25%    2370.000000  \n",
       "50%    2680.000000  \n",
       "75%    3030.000000  \n",
       "max    4380.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Speaker</th>\n",
       "      <th>Repetition</th>\n",
       "      <th>F0</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>LogF0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>560.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>222.960714</td>\n",
       "      <td>578.089286</td>\n",
       "      <td>1695.362500</td>\n",
       "      <td>2782.098214</td>\n",
       "      <td>7.790247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>8.084969</td>\n",
       "      <td>0.500447</td>\n",
       "      <td>26.840786</td>\n",
       "      <td>199.468917</td>\n",
       "      <td>654.206452</td>\n",
       "      <td>383.692244</td>\n",
       "      <td>0.173557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>590.000000</td>\n",
       "      <td>1480.000000</td>\n",
       "      <td>7.228819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>40.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>203.750000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>1137.500000</td>\n",
       "      <td>2600.000000</td>\n",
       "      <td>7.670653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>533.500000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>2820.000000</td>\n",
       "      <td>7.794416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>54.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>735.250000</td>\n",
       "      <td>2300.000000</td>\n",
       "      <td>3030.000000</td>\n",
       "      <td>7.906891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>3100.000000</td>\n",
       "      <td>3900.000000</td>\n",
       "      <td>8.357552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Speaker  Repetition          F0           F1           F2  \\\n",
       "count  560.000000  560.000000  560.000000   560.000000   560.000000   \n",
       "mean    47.500000    1.500000  222.960714   578.089286  1695.362500   \n",
       "std      8.084969    0.500447   26.840786   199.468917   654.206452   \n",
       "min     34.000000    1.000000  150.000000   220.000000   590.000000   \n",
       "25%     40.750000    1.000000  203.750000   420.000000  1137.500000   \n",
       "50%     47.500000    1.500000  222.000000   533.500000  1500.000000   \n",
       "75%     54.250000    2.000000  240.000000   735.250000  2300.000000   \n",
       "max     61.000000    2.000000  328.000000  1110.000000  3100.000000   \n",
       "\n",
       "                F3       LogF0  \n",
       "count   560.000000  560.000000  \n",
       "mean   2782.098214    7.790247  \n",
       "std     383.692244    0.173557  \n",
       "min    1480.000000    7.228819  \n",
       "25%    2600.000000    7.670653  \n",
       "50%    2820.000000    7.794416  \n",
       "75%    3030.000000    7.906891  \n",
       "max    3900.000000    8.357552  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52[pb52['Type'] == \"w\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fe634a23510>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52.groupby('Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Repetition</th>\n",
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       "      <th>F1</th>\n",
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       "      <th>Type</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>264.243333</td>\n",
       "      <td>675.186667</td>\n",
       "      <td>1931.200000</td>\n",
       "      <td>3285.266667</td>\n",
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       "    <tr>\n",
       "      <td>m</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>131.269697</td>\n",
       "      <td>499.892424</td>\n",
       "      <td>1424.683333</td>\n",
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       "    <tr>\n",
       "      <td>w</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>222.960714</td>\n",
       "      <td>578.089286</td>\n",
       "      <td>1695.362500</td>\n",
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       "      Speaker  Repetition          F0          F1           F2           F3\n",
       "Type                                                                       \n",
       "c        69.0         1.5  264.243333  675.186667  1931.200000  3285.266667\n",
       "m        17.0         1.5  131.269697  499.892424  1424.683333  2382.306061\n",
       "w        47.5         1.5  222.960714  578.089286  1695.362500  2782.098214"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52.groupby('Type').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Type</th>\n",
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       "      <td rowspan=\"2\" valign=\"top\">c</td>\n",
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       "      <td>270.350000</td>\n",
       "      <td>708.025000</td>\n",
       "      <td>1964.125000</td>\n",
       "      <td>3442.812500</td>\n",
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       "      <td>m</td>\n",
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       "      <td>637.657143</td>\n",
       "      <td>1893.571429</td>\n",
       "      <td>3105.214286</td>\n",
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       "    <tr>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>131.269697</td>\n",
       "      <td>499.892424</td>\n",
       "      <td>1424.683333</td>\n",
       "      <td>2382.306061</td>\n",
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       "    <tr>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>222.960714</td>\n",
       "      <td>578.089286</td>\n",
       "      <td>1695.362500</td>\n",
       "      <td>2782.098214</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "            Speaker  Repetition          F0          F1           F2  \\\n",
       "Type Sex                                                               \n",
       "c    f    67.500000         1.5  270.350000  708.025000  1964.125000   \n",
       "     m    70.714286         1.5  257.264286  637.657143  1893.571429   \n",
       "m    m    17.000000         1.5  131.269697  499.892424  1424.683333   \n",
       "w    f    47.500000         1.5  222.960714  578.089286  1695.362500   \n",
       "\n",
       "                   F3  \n",
       "Type Sex               \n",
       "c    f    3442.812500  \n",
       "     m    3105.214286  \n",
       "m    m    2382.306061  \n",
       "w    f    2782.098214  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52.groupby(['Type', 'Sex']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <td>A</td>\n",
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       "      <td>1199.335526</td>\n",
       "      <td>2714.881579</td>\n",
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       "      <td>E</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>187.934211</td>\n",
       "      <td>590.703947</td>\n",
       "      <td>2181.203947</td>\n",
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       "    <tr>\n",
       "      <td>I</td>\n",
       "      <td>38.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>197.855263</td>\n",
       "      <td>438.197368</td>\n",
       "      <td>2318.467105</td>\n",
       "      <td>2955.032895</td>\n",
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       "      <td>O</td>\n",
       "      <td>38.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>186.098684</td>\n",
       "      <td>599.848684</td>\n",
       "      <td>910.138158</td>\n",
       "      <td>2695.447368</td>\n",
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       "      <td>U</td>\n",
       "      <td>38.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>199.111842</td>\n",
       "      <td>473.065789</td>\n",
       "      <td>1149.065789</td>\n",
       "      <td>2621.894737</td>\n",
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       "      <td>1350.690789</td>\n",
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       "      <td>301.263158</td>\n",
       "      <td>2648.355263</td>\n",
       "      <td>3238.289474</td>\n",
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       "    <tr>\n",
       "      <td>u</td>\n",
       "      <td>38.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>201.986842</td>\n",
       "      <td>358.006579</td>\n",
       "      <td>969.776316</td>\n",
       "      <td>2596.026316</td>\n",
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       "    <tr>\n",
       "      <td>{</td>\n",
       "      <td>38.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>179.835526</td>\n",
       "      <td>806.690789</td>\n",
       "      <td>1965.401316</td>\n",
       "      <td>2758.552632</td>\n",
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      ],
      "text/plain": [
       "       Speaker  Repetition          F0          F1           F2           F3\n",
       "Vowel                                                                       \n",
       "3'        38.5         1.5  190.434211  509.651316  1551.342105  1903.276316\n",
       "A         38.5         1.5  182.723684  833.592105  1199.335526  2714.881579\n",
       "E         38.5         1.5  187.934211  590.703947  2181.203947  2885.703947\n",
       "I         38.5         1.5  197.855263  438.197368  2318.467105  2955.032895\n",
       "O         38.5         1.5  186.098684  599.848684   910.138158  2695.447368\n",
       "U         38.5         1.5  199.111842  473.065789  1149.065789  2621.894737\n",
       "V         38.5         1.5  189.631579  721.973684  1350.690789  2709.032895\n",
       "i         38.5         1.5  197.342105  301.263158  2648.355263  3238.289474\n",
       "u         38.5         1.5  201.986842  358.006579   969.776316  2596.026316\n",
       "{         38.5         1.5  179.835526  806.690789  1965.401316  2758.552632"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52.groupby('Vowel').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1383.000000</td>\n",
       "      <td>3187.666667</td>\n",
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       "      <td>E</td>\n",
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       "      <td>259.033333</td>\n",
       "      <td>699.866667</td>\n",
       "      <td>2615.500000</td>\n",
       "      <td>3564.000000</td>\n",
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       "      <td>I</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>270.333333</td>\n",
       "      <td>533.833333</td>\n",
       "      <td>2744.000000</td>\n",
       "      <td>3604.000000</td>\n",
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       "    <tr>\n",
       "      <td>O</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>259.233333</td>\n",
       "      <td>694.000000</td>\n",
       "      <td>1064.333333</td>\n",
       "      <td>3263.333333</td>\n",
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       "    <tr>\n",
       "      <td>U</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>273.233333</td>\n",
       "      <td>560.166667</td>\n",
       "      <td>1402.166667</td>\n",
       "      <td>3332.333333</td>\n",
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       "      <td>V</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>262.600000</td>\n",
       "      <td>855.366667</td>\n",
       "      <td>1591.666667</td>\n",
       "      <td>3328.000000</td>\n",
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       "    <tr>\n",
       "      <td>i</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>269.566667</td>\n",
       "      <td>360.200000</td>\n",
       "      <td>3177.833333</td>\n",
       "      <td>3763.000000</td>\n",
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       "    <tr>\n",
       "      <td>u</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>278.300000</td>\n",
       "      <td>432.366667</td>\n",
       "      <td>1193.333333</td>\n",
       "      <td>3250.333333</td>\n",
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       "    <tr>\n",
       "      <td>{</td>\n",
       "      <td>69.0</td>\n",
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       "      <td>248.100000</td>\n",
       "      <td>1017.100000</td>\n",
       "      <td>2334.333333</td>\n",
       "      <td>3366.000000</td>\n",
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       "    <tr>\n",
       "      <td rowspan=\"10\" valign=\"top\">m</td>\n",
       "      <td>3'</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>132.712121</td>\n",
       "      <td>488.515152</td>\n",
       "      <td>1359.833333</td>\n",
       "      <td>1708.878788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>123.848485</td>\n",
       "      <td>718.424242</td>\n",
       "      <td>1090.893939</td>\n",
       "      <td>2442.121212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>E</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>128.409091</td>\n",
       "      <td>526.196970</td>\n",
       "      <td>1854.409091</td>\n",
       "      <td>2481.439394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>I</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>135.787879</td>\n",
       "      <td>392.348485</td>\n",
       "      <td>1993.439394</td>\n",
       "      <td>2568.712121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>O</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>127.045455</td>\n",
       "      <td>568.318182</td>\n",
       "      <td>836.257576</td>\n",
       "      <td>2403.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>U</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>136.106061</td>\n",
       "      <td>436.772727</td>\n",
       "      <td>1023.272727</td>\n",
       "      <td>2245.287879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>V</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>128.560606</td>\n",
       "      <td>630.606061</td>\n",
       "      <td>1191.863636</td>\n",
       "      <td>2377.348485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>i</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>266.712121</td>\n",
       "      <td>2293.787879</td>\n",
       "      <td>2937.424242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>u</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>139.378788</td>\n",
       "      <td>307.363636</td>\n",
       "      <td>875.969697</td>\n",
       "      <td>2238.954545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>{</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>124.848485</td>\n",
       "      <td>663.666667</td>\n",
       "      <td>1727.106061</td>\n",
       "      <td>2419.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"10\" valign=\"top\">w</td>\n",
       "      <td>3'</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>219.285714</td>\n",
       "      <td>502.785714</td>\n",
       "      <td>1640.714286</td>\n",
       "      <td>1976.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>211.535714</td>\n",
       "      <td>864.107143</td>\n",
       "      <td>1228.750000</td>\n",
       "      <td>2783.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>E</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>608.250000</td>\n",
       "      <td>2333.696429</td>\n",
       "      <td>2998.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>I</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>232.178571</td>\n",
       "      <td>441.000000</td>\n",
       "      <td>2473.571429</td>\n",
       "      <td>3062.678571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>O</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>216.517857</td>\n",
       "      <td>586.571429</td>\n",
       "      <td>914.607143</td>\n",
       "      <td>2735.696429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>U</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>233.660714</td>\n",
       "      <td>469.178571</td>\n",
       "      <td>1161.732143</td>\n",
       "      <td>2685.160714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>V</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>222.517857</td>\n",
       "      <td>758.196429</td>\n",
       "      <td>1408.785714</td>\n",
       "      <td>2768.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>i</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>230.946429</td>\n",
       "      <td>310.410714</td>\n",
       "      <td>2782.589286</td>\n",
       "      <td>3311.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>u</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>234.892857</td>\n",
       "      <td>377.857143</td>\n",
       "      <td>960.571429</td>\n",
       "      <td>2666.339286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>{</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>208.071429</td>\n",
       "      <td>862.535714</td>\n",
       "      <td>2048.607143</td>\n",
       "      <td>2832.464286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Speaker  Repetition          F0           F1           F2  \\\n",
       "Type Vowel                                                              \n",
       "c    3'        69.0         1.5  263.566667   568.966667  1805.833333   \n",
       "     A         69.0         1.5  258.466667  1030.000000  1383.000000   \n",
       "     E         69.0         1.5  259.033333   699.866667  2615.500000   \n",
       "     I         69.0         1.5  270.333333   533.833333  2744.000000   \n",
       "     O         69.0         1.5  259.233333   694.000000  1064.333333   \n",
       "     U         69.0         1.5  273.233333   560.166667  1402.166667   \n",
       "     V         69.0         1.5  262.600000   855.366667  1591.666667   \n",
       "     i         69.0         1.5  269.566667   360.200000  3177.833333   \n",
       "     u         69.0         1.5  278.300000   432.366667  1193.333333   \n",
       "     {         69.0         1.5  248.100000  1017.100000  2334.333333   \n",
       "m    3'        17.0         1.5  132.712121   488.515152  1359.833333   \n",
       "     A         17.0         1.5  123.848485   718.424242  1090.893939   \n",
       "     E         17.0         1.5  128.409091   526.196970  1854.409091   \n",
       "     I         17.0         1.5  135.787879   392.348485  1993.439394   \n",
       "     O         17.0         1.5  127.045455   568.318182   836.257576   \n",
       "     U         17.0         1.5  136.106061   436.772727  1023.272727   \n",
       "     V         17.0         1.5  128.560606   630.606061  1191.863636   \n",
       "     i         17.0         1.5  136.000000   266.712121  2293.787879   \n",
       "     u         17.0         1.5  139.378788   307.363636   875.969697   \n",
       "     {         17.0         1.5  124.848485   663.666667  1727.106061   \n",
       "w    3'        47.5         1.5  219.285714   502.785714  1640.714286   \n",
       "     A         47.5         1.5  211.535714   864.107143  1228.750000   \n",
       "     E         47.5         1.5  220.000000   608.250000  2333.696429   \n",
       "     I         47.5         1.5  232.178571   441.000000  2473.571429   \n",
       "     O         47.5         1.5  216.517857   586.571429   914.607143   \n",
       "     U         47.5         1.5  233.660714   469.178571  1161.732143   \n",
       "     V         47.5         1.5  222.517857   758.196429  1408.785714   \n",
       "     i         47.5         1.5  230.946429   310.410714  2782.589286   \n",
       "     u         47.5         1.5  234.892857   377.857143   960.571429   \n",
       "     {         47.5         1.5  208.071429   862.535714  2048.607143   \n",
       "\n",
       "                     F3  \n",
       "Type Vowel               \n",
       "c    3'     2194.000000  \n",
       "     A      3187.666667  \n",
       "     E      3564.000000  \n",
       "     I      3604.000000  \n",
       "     O      3263.333333  \n",
       "     U      3332.333333  \n",
       "     V      3328.000000  \n",
       "     i      3763.000000  \n",
       "     u      3250.333333  \n",
       "     {      3366.000000  \n",
       "m    3'     1708.878788  \n",
       "     A      2442.121212  \n",
       "     E      2481.439394  \n",
       "     I      2568.712121  \n",
       "     O      2403.166667  \n",
       "     U      2245.287879  \n",
       "     V      2377.348485  \n",
       "     i      2937.424242  \n",
       "     u      2238.954545  \n",
       "     {      2419.727273  \n",
       "w    3'     1976.642857  \n",
       "     A      2783.071429  \n",
       "     E      2998.785714  \n",
       "     I      3062.678571  \n",
       "     O      2735.696429  \n",
       "     U      2685.160714  \n",
       "     V      2768.357143  \n",
       "     i      3311.785714  \n",
       "     u      2666.339286  \n",
       "     {      2832.464286  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52.groupby(['Type', 'Vowel']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dispersion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append new column\n",
    "\n",
    "pb52['LogF0'] = np.log2(pb52['F0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6422778d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Type', y='F0', data=pb52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6349acfd0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Type', y='LogF0', data=pb52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Speaker</th>\n",
       "      <th>Repetition</th>\n",
       "      <th>F0</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>LogF0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <td>c</td>\n",
       "      <td>4.327713</td>\n",
       "      <td>0.500835</td>\n",
       "      <td>34.629875</td>\n",
       "      <td>241.676126</td>\n",
       "      <td>723.255988</td>\n",
       "      <td>511.551770</td>\n",
       "      <td>0.190355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>m</td>\n",
       "      <td>9.529126</td>\n",
       "      <td>0.500379</td>\n",
       "      <td>18.979343</td>\n",
       "      <td>152.309880</td>\n",
       "      <td>497.876908</td>\n",
       "      <td>340.226347</td>\n",
       "      <td>0.204401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>w</td>\n",
       "      <td>8.084969</td>\n",
       "      <td>0.500447</td>\n",
       "      <td>26.840786</td>\n",
       "      <td>199.468917</td>\n",
       "      <td>654.206452</td>\n",
       "      <td>383.692244</td>\n",
       "      <td>0.173557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Speaker  Repetition         F0          F1          F2          F3  \\\n",
       "Type                                                                        \n",
       "c     4.327713    0.500835  34.629875  241.676126  723.255988  511.551770   \n",
       "m     9.529126    0.500379  18.979343  152.309880  497.876908  340.226347   \n",
       "w     8.084969    0.500447  26.840786  199.468917  654.206452  383.692244   \n",
       "\n",
       "         LogF0  \n",
       "Type            \n",
       "c     0.190355  \n",
       "m     0.204401  \n",
       "w     0.173557  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52.groupby('Type').std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Speaker</th>\n",
       "      <th>Repetition</th>\n",
       "      <th>F0</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>LogF0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <td>c</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150630</td>\n",
       "      <td>0.524620</td>\n",
       "      <td>0.432856</td>\n",
       "      <td>-0.887001</td>\n",
       "      <td>-0.119860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>m</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.649375</td>\n",
       "      <td>-0.060663</td>\n",
       "      <td>0.383807</td>\n",
       "      <td>-0.212248</td>\n",
       "      <td>0.219065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.322175</td>\n",
       "      <td>0.462590</td>\n",
       "      <td>0.330556</td>\n",
       "      <td>-0.648379</td>\n",
       "      <td>-0.055157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Speaker  Repetition        F0        F1        F2        F3     LogF0\n",
       "Type                                                                       \n",
       "c         0.0         0.0  0.150630  0.524620  0.432856 -0.887001 -0.119860\n",
       "m         0.0         0.0  0.649375 -0.060663  0.383807 -0.212248  0.219065\n",
       "w         0.0         0.0  0.322175  0.462590  0.330556 -0.648379 -0.055157"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52.groupby('Type').skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Speaker</th>\n",
       "      <th>Repetition</th>\n",
       "      <th>F0</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>LogF0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <td>c</td>\n",
       "      <td>-1.210868</td>\n",
       "      <td>-2.013468</td>\n",
       "      <td>-0.545983</td>\n",
       "      <td>-0.497488</td>\n",
       "      <td>-1.030446</td>\n",
       "      <td>1.018241</td>\n",
       "      <td>-0.644032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>m</td>\n",
       "      <td>-1.202217</td>\n",
       "      <td>-2.006088</td>\n",
       "      <td>0.642984</td>\n",
       "      <td>-0.913963</td>\n",
       "      <td>-1.024223</td>\n",
       "      <td>0.556897</td>\n",
       "      <td>-0.005167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>w</td>\n",
       "      <td>-1.203085</td>\n",
       "      <td>-2.007181</td>\n",
       "      <td>0.243463</td>\n",
       "      <td>-0.707460</td>\n",
       "      <td>-1.230952</td>\n",
       "      <td>1.127510</td>\n",
       "      <td>0.120756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Speaker  Repetition        F0        F1        F2        F3     LogF0\n",
       "Type                                                                        \n",
       "c    -1.210868   -2.013468 -0.545983 -0.497488 -1.030446  1.018241 -0.644032\n",
       "m    -1.202217   -2.006088  0.642984 -0.913963 -1.024223  0.556897 -0.005167\n",
       "w    -1.203085   -2.007181  0.243463 -0.707460 -1.230952  1.127510  0.120756"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52.groupby('Type').apply(pd.DataFrame.kurt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatterplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe642249f50>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=pb52[pb52['Type'] == 'w'], x='F1', y='F2', hue=\"Vowel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6406a3d50>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=pb52, x='F1', y='F2', hue=\"Vowel\", style=\"Type\")"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}