CorrelaçãoByFederer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlação entre 2 variáveis com 'tabela de contingência' & 'qui-quadrado'\n",
    "fonte: [Link](https://github.com/ipython-books/cookbook-2nd-code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "player = 'Roger Federer'\n",
    "df = pd.read_csv('https://github.com/ipython-books/'\n",
    "                 'cookbook-2nd-data/blob/master/'\n",
    "                 'federer.csv?raw=true',\n",
    "                 parse_dates=['start date'],\n",
    "                 dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>year</th>\n",
       "      <th>atp ranking</th>\n",
       "      <th>time</th>\n",
       "      <th>player1 aces</th>\n",
       "      <th>player1 double faults</th>\n",
       "      <th>player1 1st serves in</th>\n",
       "      <th>player1 1st serves total</th>\n",
       "      <th>player1 1st serve points won</th>\n",
       "      <th>player1 1st serve points total</th>\n",
       "      <th>player1 2nd serve points won</th>\n",
       "      <th>...</th>\n",
       "      <th>player2 2nd serve return points total</th>\n",
       "      <th>player2 break points converted won</th>\n",
       "      <th>player2 break points converted total</th>\n",
       "      <th>player2 return games played</th>\n",
       "      <th>player2 total service points won</th>\n",
       "      <th>player2 total service points total</th>\n",
       "      <th>player2 total return points won</th>\n",
       "      <th>player2 total return points total</th>\n",
       "      <th>player2 total points won</th>\n",
       "      <th>player2 total points total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1179.000000</td>\n",
       "      <td>1177.000000</td>\n",
       "      <td>1179.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2005.822731</td>\n",
       "      <td>15.402719</td>\n",
       "      <td>88.014419</td>\n",
       "      <td>7.658228</td>\n",
       "      <td>1.972736</td>\n",
       "      <td>48.074976</td>\n",
       "      <td>78.050633</td>\n",
       "      <td>36.891918</td>\n",
       "      <td>48.074976</td>\n",
       "      <td>16.935735</td>\n",
       "      <td>...</td>\n",
       "      <td>29.975657</td>\n",
       "      <td>1.515093</td>\n",
       "      <td>4.582278</td>\n",
       "      <td>12.798442</td>\n",
       "      <td>49.671860</td>\n",
       "      <td>82.675755</td>\n",
       "      <td>24.245375</td>\n",
       "      <td>78.050633</td>\n",
       "      <td>73.917235</td>\n",
       "      <td>160.726388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.893527</td>\n",
       "      <td>61.331354</td>\n",
       "      <td>50.208925</td>\n",
       "      <td>4.791261</td>\n",
       "      <td>1.927344</td>\n",
       "      <td>17.828652</td>\n",
       "      <td>28.282779</td>\n",
       "      <td>13.340020</td>\n",
       "      <td>17.828652</td>\n",
       "      <td>6.512229</td>\n",
       "      <td>...</td>\n",
       "      <td>12.366416</td>\n",
       "      <td>1.666818</td>\n",
       "      <td>4.072658</td>\n",
       "      <td>4.167927</td>\n",
       "      <td>20.230553</td>\n",
       "      <td>29.701783</td>\n",
       "      <td>12.214704</td>\n",
       "      <td>28.282779</td>\n",
       "      <td>30.274990</td>\n",
       "      <td>56.441072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2006.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2009.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>191.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>436.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              year  atp ranking         time  player1 aces  \\\n",
       "count  1179.000000  1177.000000  1179.000000   1027.000000   \n",
       "mean   2005.822731    15.402719    88.014419      7.658228   \n",
       "std       3.893527    61.331354    50.208925      4.791261   \n",
       "min    1998.000000     1.000000     0.000000      0.000000   \n",
       "25%    2003.000000     1.000000    63.000000      4.000000   \n",
       "50%    2006.000000     2.000000    86.000000      7.000000   \n",
       "75%    2009.000000     6.000000   116.000000     10.000000   \n",
       "max    2012.000000   878.000000   305.000000     50.000000   \n",
       "\n",
       "       player1 double faults  player1 1st serves in  player1 1st serves total  \\\n",
       "count            1027.000000            1027.000000               1027.000000   \n",
       "mean                1.972736              48.074976                 78.050633   \n",
       "std                 1.927344              17.828652                 28.282779   \n",
       "min                 0.000000               3.000000                  5.000000   \n",
       "25%                 1.000000              35.500000                 57.000000   \n",
       "50%                 1.000000              45.000000                 73.000000   \n",
       "75%                 3.000000              57.000000                 93.000000   \n",
       "max                12.000000             127.000000                197.000000   \n",
       "\n",
       "       player1 1st serve points won  player1 1st serve points total  \\\n",
       "count                   1027.000000                     1027.000000   \n",
       "mean                      36.891918                       48.074976   \n",
       "std                       13.340020                       17.828652   \n",
       "min                        2.000000                        3.000000   \n",
       "25%                       27.000000                       35.500000   \n",
       "50%                       35.000000                       45.000000   \n",
       "75%                       44.000000                       57.000000   \n",
       "max                      113.000000                      127.000000   \n",
       "\n",
       "       player1 2nd serve points won  ...  \\\n",
       "count                   1027.000000  ...   \n",
       "mean                      16.935735  ...   \n",
       "std                        6.512229  ...   \n",
       "min                        2.000000  ...   \n",
       "25%                       12.000000  ...   \n",
       "50%                       16.000000  ...   \n",
       "75%                       21.000000  ...   \n",
       "max                       48.000000  ...   \n",
       "\n",
       "       player2 2nd serve return points total  \\\n",
       "count                            1027.000000   \n",
       "mean                               29.975657   \n",
       "std                                12.366416   \n",
       "min                                 2.000000   \n",
       "25%                                21.000000   \n",
       "50%                                27.000000   \n",
       "75%                                37.000000   \n",
       "max                                88.000000   \n",
       "\n",
       "       player2 break points converted won  \\\n",
       "count                         1027.000000   \n",
       "mean                             1.515093   \n",
       "std                              1.666818   \n",
       "min                              0.000000   \n",
       "25%                              0.000000   \n",
       "50%                              1.000000   \n",
       "75%                              2.000000   \n",
       "max                             11.000000   \n",
       "\n",
       "       player2 break points converted total  player2 return games played  \\\n",
       "count                           1027.000000                  1027.000000   \n",
       "mean                               4.582278                    12.798442   \n",
       "std                                4.072658                     4.167927   \n",
       "min                                0.000000                     1.000000   \n",
       "25%                                1.000000                    10.000000   \n",
       "50%                                4.000000                    12.000000   \n",
       "75%                                7.000000                    15.000000   \n",
       "max                               26.000000                    37.000000   \n",
       "\n",
       "       player2 total service points won  player2 total service points total  \\\n",
       "count                       1027.000000                         1027.000000   \n",
       "mean                          49.671860                           82.675755   \n",
       "std                           20.230553                           29.701783   \n",
       "min                            1.000000                            9.000000   \n",
       "25%                           35.000000                           61.000000   \n",
       "50%                           46.000000                           78.000000   \n",
       "75%                           61.500000                           99.000000   \n",
       "max                          171.000000                          239.000000   \n",
       "\n",
       "       player2 total return points won  player2 total return points total  \\\n",
       "count                      1027.000000                        1027.000000   \n",
       "mean                         24.245375                          78.050633   \n",
       "std                          12.214704                          28.282779   \n",
       "min                           1.000000                           5.000000   \n",
       "25%                          15.000000                          57.000000   \n",
       "50%                          22.000000                          73.000000   \n",
       "75%                          31.000000                          93.000000   \n",
       "max                          86.000000                         197.000000   \n",
       "\n",
       "       player2 total points won  player2 total points total  \n",
       "count               1027.000000                 1027.000000  \n",
       "mean                  73.917235                  160.726388  \n",
       "std                   30.274990                   56.441072  \n",
       "min                    2.000000                   14.000000  \n",
       "25%                   51.000000                  118.000000  \n",
       "50%                   68.000000                  150.000000  \n",
       "75%                   92.000000                  191.500000  \n",
       "max                  213.000000                  436.000000  \n",
       "\n",
       "[8 rows x 51 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "podoc": {
     "output_text": "Some tournaments"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns: 70\n"
     ]
    },
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "\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>year</th>\n",
       "      <th>tournament</th>\n",
       "      <th>start date</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>2012</td>\n",
       "      <td>Australian Open, Australia</td>\n",
       "      <td>2012-01-16</td>\n",
       "      <td>GS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175</th>\n",
       "      <td>2012</td>\n",
       "      <td>Doha, Qatar</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>2012</td>\n",
       "      <td>Doha, Qatar</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>2012</td>\n",
       "      <td>Doha, Qatar</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>2012</td>\n",
       "      <td>Doha, Qatar</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year                  tournament start date type\n",
       "1174  2012  Australian Open, Australia 2012-01-16   GS\n",
       "1175  2012                 Doha, Qatar 2012-01-02  250\n",
       "1176  2012                 Doha, Qatar 2012-01-02  250\n",
       "1177  2012                 Doha, Qatar 2012-01-02  250\n",
       "1178  2012                 Doha, Qatar 2012-01-02  250"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Number of columns: {len(df.columns)}\")\n",
    "df[df.columns[:4]].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "npoints = df['player1 total points total']\n",
    "points = df['player1 total points won'] / npoints\n",
    "aces = df['player1 aces'] / npoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "podoc": {
     "output_text": "Aces and won points"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.1629310344827586)"
      ]
     },
     "execution_count": 8,
     "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": [
    "fig, ax = plt.subplots(1, 1)\n",
    "ax.plot(points, aces, '.')\n",
    "ax.set_xlabel('% of points won')\n",
    "ax.set_ylabel('% of aces')\n",
    "ax.set_xlim(0., 1.)\n",
    "ax.set_ylim(0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "podoc": {
     "output_text": "Aces and won points"
    }
   },
   "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>points</th>\n",
       "      <th>aces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1173</th>\n",
       "      <td>0.585366</td>\n",
       "      <td>0.024390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>0.471014</td>\n",
       "      <td>0.039855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175</th>\n",
       "      <td>0.639535</td>\n",
       "      <td>0.046512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>0.606061</td>\n",
       "      <td>0.020202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>0.531792</td>\n",
       "      <td>0.069364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        points      aces\n",
       "1173  0.585366  0.024390\n",
       "1174  0.471014  0.039855\n",
       "1175  0.639535  0.046512\n",
       "1176  0.606061  0.020202\n",
       "1177  0.531792  0.069364"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bis = pd.DataFrame({'points': points,\n",
    "                       'aces': aces}).dropna()\n",
    "df_bis.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "podoc": {
     "output_text": "Pearson correlation coefficient"
    }
   },
   "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>points</th>\n",
       "      <th>aces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>points</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.255457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aces</th>\n",
       "      <td>0.255457</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          points      aces\n",
       "points  1.000000  0.255457\n",
       "aces    0.255457  1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bis.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bis['result'] = (df_bis['points'] >\n",
    "                    df_bis['points'].median())\n",
    "df_bis['manyaces'] = (df_bis['aces'] >\n",
    "                      df_bis['aces'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "podoc": {
     "output_text": "Contingency table"
    }
   },
   "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>manyaces</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>300</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>214</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "manyaces  False  True \n",
       "result                \n",
       "False       300    214\n",
       "True        214    299"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_bis['result'], df_bis['manyaces'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27.809858855369555,\n",
       " 1.338423379963363e-07,\n",
       " 1,\n",
       " array([[257.25024343, 256.74975657],\n",
       "        [256.74975657, 256.25024343]]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.chi2_contingency(_)"
   ]
  }
 ],
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