Régression_logistique_Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Une régression logistique sur le jeu de données Titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Une-régression-logistique-sur-le-jeu-de-données-Titanic\" data-toc-modified-id=\"Une-régression-logistique-sur-le-jeu-de-données-Titanic-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Une régression logistique sur le jeu de données Titanic</a></span><ul class=\"toc-item\"><li><span><a href=\"#Importer-les-librairies\" data-toc-modified-id=\"Importer-les-librairies-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Importer les librairies</a></span></li><li><span><a href=\"#Importer-les-données-et-commencer-à-explorer-le-dataset\" data-toc-modified-id=\"Importer-les-données-et-commencer-à-explorer-le-dataset-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Importer les données et commencer à explorer le dataset</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Le-jeu-de-données-titanic.csv-est-chargé-dans-votre-environnement.\" data-toc-modified-id=\"Le-jeu-de-données-titanic.csv-est-chargé-dans-votre-environnement.-1.2.0.1\"><span class=\"toc-item-num\">1.2.0.1&nbsp;&nbsp;</span>Le jeu de données titanic.csv est chargé dans votre environnement.</a></span></li><li><span><a href=\"#Utiliser-les-méthodes-des-DataFrames-pour-avoir-un-premier-aperçu-de-vos-données-:-info(),-describe()-et-count()\" data-toc-modified-id=\"Utiliser-les-méthodes-des-DataFrames-pour-avoir-un-premier-aperçu-de-vos-données-:-info(),-describe()-et-count()-1.2.0.2\"><span class=\"toc-item-num\">1.2.0.2&nbsp;&nbsp;</span>Utiliser les méthodes des DataFrames pour avoir un premier aperçu de vos données : info(), describe() et count()</a></span></li></ul></li></ul></li><li><span><a href=\"#Gestion-des-valeurs-manquantes\" data-toc-modified-id=\"Gestion-des-valeurs-manquantes-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Gestion des valeurs manquantes</a></span><ul class=\"toc-item\"><li><span><a href=\"#Utiliser-la-fonction-heatmap-de-seaborn-pour-visualiser-les-valeurs-manquantes-sur-un-graphique-et-identifier-les-valeurs-manquantes\" data-toc-modified-id=\"Utiliser-la-fonction-heatmap-de-seaborn-pour-visualiser-les-valeurs-manquantes-sur-un-graphique-et-identifier-les-valeurs-manquantes-1.3.1\"><span class=\"toc-item-num\">1.3.1&nbsp;&nbsp;</span>Utiliser la fonction heatmap de seaborn pour visualiser les valeurs manquantes sur un graphique et identifier les valeurs manquantes</a></span></li><li><span><a href=\"#Supprimer-les-colonnes-et/ou-lignes-qui-vous-paraissent-inutilisables\" data-toc-modified-id=\"Supprimer-les-colonnes-et/ou-lignes-qui-vous-paraissent-inutilisables-1.3.2\"><span class=\"toc-item-num\">1.3.2&nbsp;&nbsp;</span>Supprimer les colonnes et/ou lignes qui vous paraissent inutilisables</a></span><ul class=\"toc-item\"><li><span><a href=\"#&quot;cabin&quot;\" data-toc-modified-id=\"&quot;cabin&quot;-1.3.2.1\"><span class=\"toc-item-num\">1.3.2.1&nbsp;&nbsp;</span>\"cabin\"</a></span></li><li><span><a href=\"#&quot;body&quot;\" data-toc-modified-id=\"&quot;body&quot;-1.3.2.2\"><span class=\"toc-item-num\">1.3.2.2&nbsp;&nbsp;</span>\"body\"</a></span></li><li><span><a href=\"#&quot;boat&quot;\" data-toc-modified-id=\"&quot;boat&quot;-1.3.2.3\"><span class=\"toc-item-num\">1.3.2.3&nbsp;&nbsp;</span>\"boat\"</a></span></li><li><span><a href=\"#&quot;home.dest&quot;\" data-toc-modified-id=\"&quot;home.dest&quot;-1.3.2.4\"><span class=\"toc-item-num\">1.3.2.4&nbsp;&nbsp;</span>\"home.dest\"</a></span></li><li><span><a href=\"#&quot;age&quot;\" data-toc-modified-id=\"&quot;age&quot;-1.3.2.5\"><span class=\"toc-item-num\">1.3.2.5&nbsp;&nbsp;</span>\"age\"</a></span></li><li><span><a href=\"#age-et-titre\" data-toc-modified-id=\"age-et-titre-1.3.2.6\"><span class=\"toc-item-num\">1.3.2.6&nbsp;&nbsp;</span>age et titre</a></span></li></ul></li><li><span><a href=\"#heatmap\" data-toc-modified-id=\"heatmap-1.3.3\"><span class=\"toc-item-num\">1.3.3&nbsp;&nbsp;</span>heatmap</a></span><ul class=\"toc-item\"><li><span><a href=\"#&quot;fare&quot;\" data-toc-modified-id=\"&quot;fare&quot;-1.3.3.1\"><span class=\"toc-item-num\">1.3.3.1&nbsp;&nbsp;</span>\"fare\"</a></span></li><li><span><a href=\"#&quot;embarked&quot;\" data-toc-modified-id=\"&quot;embarked&quot;-1.3.3.2\"><span class=\"toc-item-num\">1.3.3.2&nbsp;&nbsp;</span>\"embarked\"</a></span></li><li><span><a href=\"#&quot;sibsp&quot;\" data-toc-modified-id=\"&quot;sibsp&quot;-1.3.3.3\"><span class=\"toc-item-num\">1.3.3.3&nbsp;&nbsp;</span>\"sibsp\"</a></span></li><li><span><a href=\"#final\" data-toc-modified-id=\"final-1.3.3.4\"><span class=\"toc-item-num\">1.3.3.4&nbsp;&nbsp;</span>final</a></span></li></ul></li></ul></li><li><span><a href=\"#Analyse-de-données-:-visualisation\" data-toc-modified-id=\"Analyse-de-données-:-visualisation-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Analyse de données : visualisation</a></span><ul class=\"toc-item\"><li><span><a href=\"#Survived\" data-toc-modified-id=\"Survived-1.4.1\"><span class=\"toc-item-num\">1.4.1&nbsp;&nbsp;</span>Survived</a></span></li><li><span><a href=\"#&quot;pclass&quot;\" data-toc-modified-id=\"&quot;pclass&quot;-1.4.2\"><span class=\"toc-item-num\">1.4.2&nbsp;&nbsp;</span>\"pclass\"</a></span></li><li><span><a href=\"#sex\" data-toc-modified-id=\"sex-1.4.3\"><span class=\"toc-item-num\">1.4.3&nbsp;&nbsp;</span>sex</a></span></li><li><span><a href=\"#toutes-les-features-numerique\" data-toc-modified-id=\"toutes-les-features-numerique-1.4.4\"><span class=\"toc-item-num\">1.4.4&nbsp;&nbsp;</span>toutes les features numerique</a></span></li></ul></li><li><span><a href=\"#Gestion-des-variables-catégoriques\" data-toc-modified-id=\"Gestion-des-variables-catégoriques-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Gestion des variables catégoriques</a></span><ul class=\"toc-item\"><li><span><a href=\"#Identifier-les-variables-catégoriques-et-les-transformer-de-sorte-à-pouvoir-les-utiliser-ensuite-dans-votre-modèle\" data-toc-modified-id=\"Identifier-les-variables-catégoriques-et-les-transformer-de-sorte-à-pouvoir-les-utiliser-ensuite-dans-votre-modèle-1.5.1\"><span class=\"toc-item-num\">1.5.1&nbsp;&nbsp;</span>Identifier les variables catégoriques et les transformer de sorte à pouvoir les utiliser ensuite dans votre modèle</a></span><ul class=\"toc-item\"><li><span><a href=\"#pclass\" data-toc-modified-id=\"pclass-1.5.1.1\"><span class=\"toc-item-num\">1.5.1.1&nbsp;&nbsp;</span>pclass</a></span></li><li><span><a href=\"#Embarked\" data-toc-modified-id=\"Embarked-1.5.1.2\"><span class=\"toc-item-num\">1.5.1.2&nbsp;&nbsp;</span>Embarked</a></span></li><li><span><a href=\"#sex\" data-toc-modified-id=\"sex-1.5.1.3\"><span class=\"toc-item-num\">1.5.1.3&nbsp;&nbsp;</span>sex</a></span></li><li><span><a href=\"#title\" data-toc-modified-id=\"title-1.5.1.4\"><span class=\"toc-item-num\">1.5.1.4&nbsp;&nbsp;</span>title</a></span></li><li><span><a href=\"#age\" data-toc-modified-id=\"age-1.5.1.5\"><span class=\"toc-item-num\">1.5.1.5&nbsp;&nbsp;</span><strong>age</strong></a></span></li><li><span><a href=\"#&quot;Name&quot;\" data-toc-modified-id=\"&quot;Name&quot;-1.5.1.6\"><span class=\"toc-item-num\">1.5.1.6&nbsp;&nbsp;</span>\"Name\"</a></span></li><li><span><a href=\"#&quot;ticket&quot;\" data-toc-modified-id=\"&quot;ticket&quot;-1.5.1.7\"><span class=\"toc-item-num\">1.5.1.7&nbsp;&nbsp;</span>\"ticket\"</a></span></li><li><span><a href=\"#Type-float64\" data-toc-modified-id=\"Type-float64-1.5.1.8\"><span class=\"toc-item-num\">1.5.1.8&nbsp;&nbsp;</span>Type float64</a></span></li></ul></li></ul></li><li><span><a href=\"#Création-d'un-train-set-et-test-set\" data-toc-modified-id=\"Création-d'un-train-set-et-test-set-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>Création d'un train set et test set</a></span></li><li><span><a href=\"#Création-du-modèle,-entraînement-et-prédictions\" data-toc-modified-id=\"Création-du-modèle,-entraînement-et-prédictions-1.7\"><span class=\"toc-item-num\">1.7&nbsp;&nbsp;</span>Création du modèle, entraînement et prédictions</a></span></li><li><span><a href=\"#Évaluation-du-modèle\" data-toc-modified-id=\"Évaluation-du-modèle-1.8\"><span class=\"toc-item-num\">1.8&nbsp;&nbsp;</span>Évaluation du modèle</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "#<img src=\"img/titanic.jpeg\" width=\"800\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"/img/titanic.jpeg\" width=\"800\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#<img src=\"/img/titanic.JPG\" width=\"800\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#<img src=\"img/titanic.JPG\" width=\"800\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importer les librairies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vous aurez besoin de pandas, matplotlib, seaborn (et numpy ?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Importer les données et commencer à explorer le dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Le jeu de données titanic.csv est chargé dans votre environnement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = pd.read_csv('data/titanic.csv',sep=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Utiliser les méthodes des DataFrames pour avoir un premier aperçu de vos données : info(), describe() et count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1310 entries, 0 to 1309\n",
      "Data columns (total 14 columns):\n",
      "pclass       1309 non-null float64\n",
      "survived     1309 non-null float64\n",
      "name         1309 non-null object\n",
      "sex          1309 non-null object\n",
      "age          1046 non-null float64\n",
      "sibsp        1309 non-null float64\n",
      "parch        1309 non-null float64\n",
      "ticket       1309 non-null object\n",
      "fare         1308 non-null float64\n",
      "cabin        295 non-null object\n",
      "embarked     1307 non-null object\n",
      "boat         486 non-null object\n",
      "body         121 non-null float64\n",
      "home.dest    745 non-null object\n",
      "dtypes: float64(7), object(7)\n",
      "memory usage: 143.4+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* survived : 1 si le passager a survécu, sinon 0\n",
    "* pclass : La classe dans laquelle le passager a voyagé (1, 2 ou 3)\n",
    "* name : Le titre et le nom\n",
    "* sex : Homme ou femme\n",
    "* age :\n",
    "* sibSp : Nombre de conjoints et frères/sœurs à bord\n",
    "* parch : Nombre de parents/enfants à bord\n",
    "* ticket : référence du ticket\n",
    "* fare : prix payé\n",
    "* cabin : numéro de cabine (pour ceux qui en ont)\n",
    "* embarked : Port de départ (S = Southampton, C = Cherbourg, Q = Queenstown)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1310, 14)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.0000    0.0    0.0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass       float64\n",
       "survived     float64\n",
       "name          object\n",
       "sex           object\n",
       "age          float64\n",
       "sibsp        float64\n",
       "parch        float64\n",
       "ticket        object\n",
       "fare         float64\n",
       "cabin         object\n",
       "embarked      object\n",
       "boat          object\n",
       "body         float64\n",
       "home.dest     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dir(titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass       1309\n",
       "survived     1309\n",
       "name         1309\n",
       "sex          1309\n",
       "age          1046\n",
       "sibsp        1309\n",
       "parch        1309\n",
       "ticket       1309\n",
       "fare         1308\n",
       "cabin         295\n",
       "embarked     1307\n",
       "boat          486\n",
       "body          121\n",
       "home.dest     745\n",
       "dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.0000    0.0    0.0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>body</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.381971</td>\n",
       "      <td>29.881135</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>160.809917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.837836</td>\n",
       "      <td>0.486055</td>\n",
       "      <td>14.413500</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>97.696922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>256.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>328.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pclass     survived          age        sibsp        parch  \\\n",
       "count  1309.000000  1309.000000  1046.000000  1309.000000  1309.000000   \n",
       "mean      2.294882     0.381971    29.881135     0.498854     0.385027   \n",
       "std       0.837836     0.486055    14.413500     1.041658     0.865560   \n",
       "min       1.000000     0.000000     0.166700     0.000000     0.000000   \n",
       "25%       2.000000     0.000000    21.000000     0.000000     0.000000   \n",
       "50%       3.000000     0.000000    28.000000     0.000000     0.000000   \n",
       "75%       3.000000     1.000000    39.000000     1.000000     0.000000   \n",
       "max       3.000000     1.000000    80.000000     8.000000     9.000000   \n",
       "\n",
       "              fare        body  \n",
       "count  1308.000000  121.000000  \n",
       "mean     33.295479  160.809917  \n",
       "std      51.758668   97.696922  \n",
       "min       0.000000    1.000000  \n",
       "25%       7.895800   72.000000  \n",
       "50%      14.454200  155.000000  \n",
       "75%      31.275000  256.000000  \n",
       "max     512.329200  328.000000  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gestion des valeurs manquantes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utiliser la fonction heatmap de seaborn pour visualiser les valeurs manquantes sur un graphique et identifier les valeurs manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass          1\n",
       "survived        1\n",
       "name            1\n",
       "sex             1\n",
       "age           264\n",
       "sibsp           1\n",
       "parch           1\n",
       "ticket          1\n",
       "fare            2\n",
       "cabin        1015\n",
       "embarked        3\n",
       "boat          824\n",
       "body         1189\n",
       "home.dest     565\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(titanic).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x156366dc608>"
      ]
     },
     "execution_count": 109,
     "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.heatmap(titanic.isnull(), cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Données manquante du Dataset Titanic')"
      ]
     },
     "execution_count": 110,
     "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.heatmap(titanic.isnull(), yticklabels=False, cbar = False, cmap = 'viridis')\n",
    "plt.title(\"Données manquante du Dataset Titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install missingno\n",
    "#conda install -c conda-forge missingno\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15636166688>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1563612cf08>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.heatmap(titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supprimer les colonnes et/ou lignes qui vous paraissent inutilisables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic[\"pclass\"].dropna(axis=0, inplace=True)\n",
    "titanic[\"survived\"].dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"cabin\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le pourcentage de valeurs manquantes sur la colonne \"Cabin\" est de 77.48%\n"
     ]
    }
   ],
   "source": [
    "print('Le pourcentage de valeurs manquantes sur la colonne \"Cabin\" est de %.2f%%' %((titanic['cabin'].isnull().sum()/titanic.shape[0])*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Est-ce que les valeurs manquantes correspondent aux personnes sans cabine ?\n",
    "* A priori ce n'est pas le cas\n",
    "* on supprime donc cette colonne car supprimer les lignes manquantes impacterait trop les résultats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.drop('cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"body\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.drop('body',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"boat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.drop('boat',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"home.dest\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.drop('home.dest',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"age\"\n",
    "\n",
    "Focus sur l'âge : peu de valeurs manquantes, on peut donc faire un peu d'imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* On pourrait imputer tout simplement l'âge moyen pour toutes les valeurs manquantes.\n",
    "* C'est une solution viable et assez simple. \n",
    "* Mais on peut faire mieux en s'intéressant par exemple à l'âge moyen au sein de certain groupes...  \n",
    "* Regardez en utilisant les graphiques adéquats la répartition de l'âge par classe et par sexe.\n",
    "* Conclure sur quelle imputation serait pertinente. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### age et survie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare embarked  \n",
       "0  29.0000    0.0    0.0   24160  211.3375        S  \n",
       "1   0.9167    1.0    2.0  113781  151.5500        S  \n",
       "2   2.0000    1.0    2.0  113781  151.5500        S  \n",
       "3  30.0000    1.0    2.0  113781  151.5500        S  \n",
       "4  25.0000    1.0    2.0  113781  151.5500        S  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_temp = titanic\n",
    "titanic_temp[\"age\"].dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare embarked  \n",
       "0  29.0000    0.0    0.0   24160  211.3375        S  \n",
       "1   0.9167    1.0    2.0  113781  151.5500        S  \n",
       "2   2.0000    1.0    2.0  113781  151.5500        S  \n",
       "3  30.0000    1.0    2.0  113781  151.5500        S  \n",
       "4  25.0000    1.0    2.0  113781  151.5500        S  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.kdeplot(titanic_temp[\"age\"][titanic_temp.survived == 1], color=\"darkturquoise\", shade=True)\n",
    "sns.kdeplot(titanic_temp[\"age\"][titanic_temp.survived == 0], color=\"lightcoral\", shade=True)\n",
    "plt.legend(['survived', 'died'])\n",
    "plt.title('Density Plot of Age for Surviving Population and Deceased Population')\n",
    "ax.set(xlabel='Age')\n",
    "plt.xlim(-10,85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* La répartition par âge des survivants et des décédés est en fait très similaire. \n",
    "* Une différence notable est que, parmi les survivants, une plus grande proportion étaient des enfants. \n",
    "* Les passagers ont manifestement tenté de sauver des enfants en leur donnant une place sur les radeaux de sauvetage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### age et classe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x156366d9e08>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(x='pclass',y='age',data=titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### age et titre\n",
    "\n",
    "Prise en compte du titre pour evaluer les ages manquants "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare embarked  \n",
       "0  29.0000    0.0    0.0   24160  211.3375        S  \n",
       "1   0.9167    1.0    2.0  113781  151.5500        S  \n",
       "2   2.0000    1.0    2.0  113781  151.5500        S  \n",
       "3  30.0000    1.0    2.0  113781  151.5500        S  \n",
       "4  25.0000    1.0    2.0  113781  151.5500        S  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1310 entries, 0 to 1309\n",
      "Data columns (total 10 columns):\n",
      "pclass      1309 non-null float64\n",
      "survived    1309 non-null float64\n",
      "name        1309 non-null object\n",
      "sex         1309 non-null object\n",
      "age         1046 non-null float64\n",
      "sibsp       1309 non-null float64\n",
      "parch       1309 non-null float64\n",
      "ticket      1309 non-null object\n",
      "fare        1308 non-null float64\n",
      "embarked    1307 non-null object\n",
      "dtypes: float64(6), object(4)\n",
      "memory usage: 100.4+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic['name'] = titanic.name.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic['name'] = titanic['name'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic[\"name\"].dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                         Allen, Miss. Elisabeth Walton\n",
       "1                        Allison, Master. Hudson Trevor\n",
       "2                          Allison, Miss. Helen Loraine\n",
       "3                  Allison, Mr. Hudson Joshua Creighton\n",
       "4       Allison, Mrs. Hudson J C (Bessie Waldo Daniels)\n",
       "                             ...                       \n",
       "1304                               Zabour, Miss. Hileni\n",
       "1305                              Zabour, Miss. Thamine\n",
       "1306                          Zakarian, Mr. Mapriededer\n",
       "1307                                Zakarian, Mr. Ortin\n",
       "1308                                 Zimmerman, Mr. Leo\n",
       "Name: name, Length: 1309, dtype: object"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic['title'] = titanic['name'].apply(lambda name: name.split(',')[1].split('.')[0].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Miss', 'Master', 'Mr', 'Mrs', 'Col', 'Mme', 'Dr', 'Major', 'Capt',\n",
       "       'Lady', 'Sir', 'Mlle', 'Dona', 'Jonkheer', 'the Countess', 'Don',\n",
       "       'Rev', 'Ms', nan], dtype=object)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['title'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mr         757\n",
      "Miss       262\n",
      "Mrs        200\n",
      "Master      61\n",
      "Officer     23\n",
      "Royalty      6\n",
      "Name: title, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# normalize the titles\n",
    "standardized_titles = {\n",
    "    \"Capt\":       \"Officer\",\n",
    "    \"Col\":        \"Officer\",\n",
    "    \"Major\":      \"Officer\",\n",
    "    \"Jonkheer\":   \"Royalty\",\n",
    "    \"Don\":        \"Royalty\",\n",
    "    \"Sir\" :       \"Royalty\",\n",
    "    \"Dr\":         \"Officer\",\n",
    "    \"Rev\":        \"Officer\",\n",
    "    \"the Countess\":\"Royalty\",\n",
    "    \"Dona\":       \"Royalty\",\n",
    "    \"Mme\":        \"Mrs\",\n",
    "    \"Mlle\":       \"Miss\",\n",
    "    \"Ms\":         \"Mrs\",\n",
    "    \"Mr\" :        \"Mr\",\n",
    "    \"Mrs\" :       \"Mrs\",\n",
    "    \"Miss\" :      \"Miss\",\n",
    "    \"Master\" :    \"Master\",\n",
    "    \"Lady\" :      \"Royalty\"\n",
    "}# map the normalized titles to the current titles \n",
    "titanic.title = titanic.title.map(standardized_titles)# view value counts for the normalized titles\n",
    "print(titanic.title.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source : https://medium.com/i-like-big-data-and-i-cannot-lie/how-i-scored-in-the-top-9-of-kaggles-titanic-machine-learning-challenge-243b5f45c8e9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Faire l'imputation choisie pour les âges manquants.  \n",
    "_Indication : une solution est de créer une fonction qui renvoie l'âge voulu selon les cas puis de l'appliquer sur les lignes avec la méthode apply_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "? moyenne ou mediane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex     pclass  title  \n",
       "female  1.0     Miss       30.0\n",
       "                Mrs        45.0\n",
       "                Officer    49.0\n",
       "                Royalty    39.0\n",
       "        2.0     Miss       20.0\n",
       "                Mrs        30.0\n",
       "        3.0     Miss       18.0\n",
       "                Mrs        31.0\n",
       "male    1.0     Master      6.0\n",
       "                Mr         41.5\n",
       "                Officer    52.0\n",
       "                Royalty    40.0\n",
       "        2.0     Master      2.0\n",
       "                Mr         30.0\n",
       "                Officer    41.5\n",
       "        3.0     Master      6.0\n",
       "                Mr         26.0\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by Sex, Pclass, and Title \n",
    "grouped = titanic.groupby(['sex','pclass', 'title']) \n",
    "grouped.age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the grouped median value on the Age NaN\n",
    "titanic.age = grouped.age.apply(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# autre méthode sans prendre en compte les titres\n",
    "\n",
    "def age_by_pclass(ref):\n",
    "    age = ref[0]\n",
    "    pclass = ref[1]\n",
    "    if pd.isnull(age):\n",
    "        if pclass == 1:\n",
    "            return 37\n",
    "        elif pclass == 2:\n",
    "            return 29\n",
    "        else:\n",
    "            return 24\n",
    "\n",
    "    else:\n",
    "        return age"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "titanic['age'] = titanic[['age','pclass']].apply(age_by_pclass,axis=1)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "titanic_data['age']=titanic_data['age'].fillna(titanic_data.groupby(['pclass','sex'])['age'].transform('mean'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29.    ,  0.9167,  2.    , 30.    , 25.    , 48.    , 63.    ,\n",
       "       39.    , 53.    , 71.    , 47.    , 18.    , 24.    , 26.    ,\n",
       "       80.    , 41.5   , 50.    , 32.    , 36.    , 37.    , 42.    ,\n",
       "       19.    , 35.    , 28.    , 45.    , 40.    , 58.    , 22.    ,\n",
       "       41.    , 52.    , 44.    , 59.    , 60.    , 33.    , 17.    ,\n",
       "       11.    , 14.    , 49.    , 76.    , 46.    , 27.    , 64.    ,\n",
       "       55.    , 70.    , 38.    , 51.    , 31.    ,  4.    , 54.    ,\n",
       "       23.    , 43.    , 16.    , 32.5   , 21.    , 15.    , 65.    ,\n",
       "       28.5   , 45.5   , 56.    , 13.    , 61.    , 34.    ,  6.    ,\n",
       "       57.    , 62.    , 67.    ,  1.    , 12.    , 20.    ,  0.8333,\n",
       "        8.    ,  0.6667,  7.    ,  3.    , 36.5   , 18.5   ,  5.    ,\n",
       "       66.    ,  9.    ,  0.75  , 70.5   , 22.5   ,  0.3333,  0.1667,\n",
       "       40.5   , 10.    , 23.5   , 34.5   , 20.5   , 30.5   , 55.5   ,\n",
       "       38.5   , 14.5   , 24.5   , 60.5   , 74.    ,  0.4167, 11.5   ,\n",
       "       26.5   ,     nan])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.age.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Autres solutions pour l'age :\n",
    "* imputer"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "imputer = Imputer(strategy=\"median\")\n",
    "X = imputer.fit_transform(data_binarized)\n",
    "# reinject in pandas.Dataframe: \n",
    "data_median = pd.DataFrame(X, columns=data_binarized.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Réafficher la visualisation des données manquantes avec heatmap. Que constatez-vous ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15635b12948>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.heatmap(titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vérifier où sont les données encore manquantes avec la méthode DataFrame.info() par exemple puis supprimer les lignes concernées avec la méthode adaptée.  \n",
    "Vérifier à nouveau ce qu'il en est des valeurs manquantes avec DataFrame.count() pour changer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass      1309\n",
       "survived    1309\n",
       "name        1309\n",
       "sex         1309\n",
       "age         1309\n",
       "sibsp       1309\n",
       "parch       1309\n",
       "ticket      1309\n",
       "fare        1308\n",
       "embarked    1307\n",
       "title       1309\n",
       "dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1310 entries, 0 to 1309\n",
      "Data columns (total 11 columns):\n",
      "pclass      1309 non-null float64\n",
      "survived    1309 non-null float64\n",
      "name        1309 non-null object\n",
      "sex         1309 non-null object\n",
      "age         1309 non-null float64\n",
      "sibsp       1309 non-null float64\n",
      "parch       1309 non-null float64\n",
      "ticket      1309 non-null object\n",
      "fare        1308 non-null float64\n",
      "embarked    1307 non-null object\n",
      "title       1309 non-null object\n",
      "dtypes: float64(6), object(5)\n",
      "memory usage: 112.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"fare\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "le prix du billet semble fortement corrélé avec les chances de survie, il faut garder ces données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On supprime les lignes pour les valeurs manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic[\"fare\"].dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1310 entries, 0 to 1309\n",
      "Data columns (total 11 columns):\n",
      "pclass      1309 non-null float64\n",
      "survived    1309 non-null float64\n",
      "name        1309 non-null object\n",
      "sex         1309 non-null object\n",
      "age         1309 non-null float64\n",
      "sibsp       1309 non-null float64\n",
      "parch       1309 non-null float64\n",
      "ticket      1309 non-null object\n",
      "fare        1308 non-null float64\n",
      "embarked    1307 non-null object\n",
      "title       1309 non-null object\n",
      "dtypes: float64(6), object(5)\n",
      "memory usage: 112.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.kdeplot(titanic[\"fare\"][titanic.survived == 1], color=\"darkturquoise\", shade=True)\n",
    "sns.kdeplot(titanic[\"fare\"][titanic.survived == 0], color=\"lightcoral\", shade=True)\n",
    "plt.legend(['survivant', 'décédé'])\n",
    "plt.title('densité ')\n",
    "ax.set(xlabel='Fare')\n",
    "plt.xlim(-20,200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"embarked\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of missing \"embarked\" records is 0.23%\n"
     ]
    }
   ],
   "source": [
    "print('Percent of missing \"embarked\" records is %.2f%%' %((titanic['embarked'].isnull().sum()/titanic.shape[0])*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supression des lignes avec valeurs manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic[\"embarked\"].dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['embarked'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boarded passengers grouped by port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton):\n",
      "S    914\n",
      "C    270\n",
      "Q    123\n",
      "Name: embarked, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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DH8jLdzcP8Y6IH5LuRdqCNJDgk8A1gB/3YGZWs3aPfC4gHWHcLOmEiFjQWCFpMnAu6Qij7PpNyyLiUuDSLtZdSJr/rZXtXAtcuzm1mJlZ32t3hoNrJJ1HOor4taQnSTdtjge2Ic10cF5EXNXnlZqZ2aDR9mO0I+JTpHnWbgKeBl5OmoX6RtLpr8066jEzs8GvtzMc/Az4WR/XYmZmHaLtIx8zM7PN5fAxM7PKOXzMzKxyDh8zM6ucw8fMzCrn8DEzs8o5fMzMrHIOHzMzq5zDx8zMKufwMTOzyjl8zMyscg4fMzOrnMPHzMwq5/AxM7PKOXzMzKxyDh8zM6ucw8fMzCrn8DEzs8r16jHaZoPViu+cWncJg95OH/tG3SXYAOAjHzMzq5zDx8zMKufwMTOzyjl8zMyscg4fMzOrnMPHzMwq5/AxM7PKOXzMzKxyDh8zM6tc7eEjaS9JZ0u6S9IqSWskLZR0vCSV9D9W0iJJ6yQtze/dqottHyppgaS1klZImilpTP/vlZmZdaf28AFOBf4FuBM4DfgssA74DnBhsaOkU4GLgIeATwJX5OV1zUEl6XDgJ/nXk4DzgfcBt0oa1U/7YmZmLRgIc7udB/xLRKxrNEg6D/gv4FhJ346I30vaETgDuB04MCKezX0XAxcAhwE/zm3DgHOAxcCbI2JNbr8DuAGYDpxVze6ZmVmz2o98IuJXxeDJbc8C1+Rf/yEvDwVGATMawZN9H3gKOKrQtj8wDri4ETx5u7OA+5r6mplZxWoPn26Mz8tH8nLvvJxf7BQR64G7gUmF5tK+hbbXShreR3WamVmbBsJpt+eRNBY4DlgCzM3NjTBaVvKWZcCbJCkiooW+Q4Cdgb82fe5oYHRT/4lt74CZmXVrwIWPpBGkazfbAYdHxIa8asu8XF/ytnWko7gR+eee+ha3V3QicHovyjYzszYMqNNukoYCVwH7AMdHxM2F1Y3QGFHy1pFAsClseuoLsLZk3TnAbk2vqa3Wb2ZmrRkwRz6ShpCGTr8bOCEivtfUZWlejgf+0rRuPLAsn3Jr7ntvSd+NwPLmGiJiFbCqqa429sLMzFoxII58JG0BXEa6D+fTEXFeSbeFeTml6b0jgD1J9wl12zebDCzKAxXMzKwGtYdPDp5LSMOfPxcRZ3fR9TrS6bTp+T0NxwBbkY6aGmaTjmymFW8olXQQsHtTXzMzq9hAOO32TeBoYAHwN0kfaFr/3xFxf0SskHQm8FXgRklXkYLkJOBW4OrGGyJig6STSSEzR9JMYCfgFOAeYEZ/75SZmXVtIITPG/JyEnB5yfoPA/cDRMTXJD0OnECaLufRvPxi4XoPue8PJa0HPk8aSPAk6cbV04o3npqZWfVqD5+I2L/N/hfSNOdbN32vBa7tRVlmZtaPar/mY2ZmncfhY2ZmlXP4mJlZ5Rw+ZmZWOYePmZlVzuFjZmaVc/iYmVnlHD5mZlY5h4+ZmVWu9hkOzMz6ygU/mFd3CYPexz+wb59sx0c+ZmZWOYePmZlVzuFjZmaVc/iYmVnlHD5mZlY5h4+ZmVXO4WNmZpVz+JiZWeUcPmZmVjmHj5mZVc7hY2ZmlXP4mJlZ5Rw+ZmZWOYePmZlVzuFjZmaVc/iYmVnlHD5mZlY5h4+ZmVXO4WNmZpVz+JiZWeUcPmZmVjmHj5mZVW5Qh4+kIZL+j6T7JK2X9BdJX5A0tO7azMw62aAOH+A84GvAQmA68EvgS8CFdRZlZtbpBu0RgKR/AI4HroyII3LzxZJWA6dK+m5ELKivQjOzzjWYj3yOAASc29Te+P3IassxM7OGQXvkA+wNPAvcWWyMiAclPQhMan6DpNHA6KbmXQCWLl3a0oeufviR3tRqbXrggQf6ZbuPrlzdL9u1Tdb003cHsPKRh/pt25a0+t9e4W/mkLL1ioi+qWiAkfQ7YKeIGFuy7g5gTES8tKn9DOD0aio0M+sIUyNiXnPjYD7y2RJY38W6dXl9s3OAS5vahgMvBf4MbOyr4gaQicBcYCrQ2uGdDST+/l64Bvt3NwQYB5ReWx/M4bMO2LqLdSOBtc2NEbEKWFXS/099WNeAIqnx49KIeKDGUqwX/P29cHXId/eXrlYM5gEHS4ExkoaXrBsPPFhxPWZmlg3m8FlI2r+9i42SJgATaBqIYGZm1RnM4XMlEMAJTe2N339YbTlmZtYwaK/5RMRvJF0EHKd0cvUm0lHQccB/RMT8WgscOFYBZ1J+rcsGPn9/L1wd/d0N2qHWAHkOt88A00gjS5YBlwBfjYin66zNzKyTDerwMTOzgWkwX/MxM7MByuFjZmaVc/h0KEkvl3SppD9LWifpUUl3STpP0kt73oLVRdLWkk6V9GtJqyRtkLRU0tWSDq67PuuapO0lfUXSIklPSXpS0sL8fY6qu74q+ZpPB5K0N3AbsIE0AONe4EXA/wIOAaZFxI/rq9C6IukVwCzSlE83ALeQRktNAA4mTZj7zxFxRW1FWqn8mJcbgbGkW0HmkEYcv430393vgAMjoiNmR3X4dCBJ1wPvAl4XEb9tWjcS2DIiHqulOOuSpK2Au0gzrb8nIm4o6XMI8GxEXF91fdY1SVsDvyXNrvLOiLilaf0HgMuBecB+0QF/mH3arTPtDqxsDh6AiFjn4BmwppG+u2+UBQ9ARPzUwTMgHQvsBpzVHDwAEfED0o3v+5KOYAc9h09nWgzskP8v2V443puXM2utwnrjPXn5/W76NL7X93TTZ9DwabcOJGk/0rWCocA9pHPPvwZmRcSKOmuzrklaCQyNiO3qrsXa08p3J2l7YCWwMCL27qrfYOEjnw4UEXOAN5Eueo4Hjic9x+ghSd/L56dt4NkWeKLuIqxXWvnuGuu37edaBgSHT4eKiDsj4ghge+BVwCdIp+OmAefWWZt16Uk65A/TINTKd9dY/3A/1zIgOHw6XCR/iIjzgSnAauCf87x4NrD8HthW0m51F2Jta+W72zMv76ugnto5fOzvImIl6cmDw4ExNZdjz3dtXh5TaxXWGz/Jy+6+u8a6H/RzLQOCBxx0IElvBW6NiI1N7S8FFgFPAWMj4tk66rNy+Vrc3cCL6fo+n4MBPNx6YJG0Dek+n52BgyJidtP6I4H/BG4F3toJ9/k4fDqQpEXAaOCnpLB5BngF8CFgR+C4iLiovgqtK5L2IM1wsAvPneFgPOnG4TcCR0bEj2or0kpJei1phoOdSPf0zAOGkGY4OJR0A/Hb8hmIQc/h04EkHUi6l2Af0rQs25CGeC4AZkTEL2osz3qQ/y96Ouk73AMYBawA/hu4JCJm1ViedUPSDsAppOl0diN9dwBXA0dFxDN11VY1h4+ZWU0kjSadatsD+N8RcWvNJVXG4WNmViNJO5Ju9J5Iut4zv+aSKuHwMTOzynmotZmZVc7hY2ZmlXP4mJlZ5Rw+ZmZWOYePmZlVzuFjViNJD0h6oMbPD0mzK/7M/fPnnlHl59rA4vAxM7PKOXzMzKxyDh8zM6ucw8csk3SYpNmSVktaK+kuSR8t6XdGvmaxv6SPSPp97v9HSR/MfYZL+rKkJZLWSVog6Y3dfPYOki6RtELSGkm/knRASb83SDo/f+YTkp6S9D+SpktSSf/I+/QSSVdIeiS37dpNLUMlXZb7nVXcrqSX5TqXStqQlxfkKWLKtvMFSYvzv8G9ko7r6nOts/hplWaApG8CnwaWAFcCa0hT3V8o6VURcVLJ204C9iU9mmI2cARwmaSVwHGkx5P/jPT4iiOBWZJ2jYjVTdsZDvwSGAZcQnqQ35HATZLeGRE3FfoeS3p0whzg56QZyd8OzAB2B04sqXMH4FfAcuDyXM+GLv4dRgJXAQcDn4uIrxbWvYn0SICReb8WkybEPB44UNKkiHissLnvAx8E/gycB2wHfDPXbp0uIvzyq6NfwIFAANcBIwvtw3JbAJMK7WfktkeAXQrtb8jtjwO3AaMK607J605u+uwHcvtNwNBC+yTSc5YWA1sU2l9S/D23DSWFwsZiPXld5Nd3yXM5lqyfnX/eLte9ETi2qd9wUjA/Bryqad378nZmFNrektvuaPo3fTWwLq87o+7v3q/6Xj7tZpaejRPARyNiXaMxIp4GvpB/fX/J+86NiCWF/gtJjyEfDXwhItYW+l6Zl6/rooZ/jcKzXCJiAemx2buSjq4a7X+NpifM5vddRDqN/rxTdcB60lFMl7MIS9qJNLX/G4H3R8T3mrq8ixR8/x4R9zZ9/tXAQtKRX8MH8vKMpn/Te4DLuqrDOodPu5nBFOAJ4OMll02G5eUeJe/7TUnbcuBlJeuW5+X4kvc8TTpCaDaPdFTxOvKpKkkjgBNIYfhKYOum94wr2c7ieO7psGZj82eNB94VEb8s6TMlL1/Txf05o4AdJI2JiEfZFLJzS/rOI50+tA7m8DGD7Un/LZzeTZ+tStqeKGl7BiAinrMuIp7JwTas5D0rm49msofzcttC2zXAO4E/AFeQTv09QzpC+hAwomQ7K0raisblz7iXdARTZvu8/FAP29oKeJR0Cm9dRDxZ0ufhkjbrMA4fsxQiayNiYk2fv4OkLUoCaGxePgEgaRIpeG4E3lnsL+n9dB0MPT20627SQITvATdL+seIeLypTyNM3xYRN/ewPYDVwMskbVMSQGPL3mCdxdd8zNIprwmSXlzT5w8DJpe0N671NE7hvSwvf14SVPtsTgERMZM0am1P4Jf58c5FjdOCXQ4Xb9KoeWrJun1L2qzDOHzM0jBggJmStmteKWm37u6L6SNfkvT3MxH5KOe9pNFw83LzX/PyOUGT7x963v1I7YqIi4CPA3uRhnkX/y2uA/4GnCbpeUEpaZSkKYWmH+TlGXn4dqPfq4GjN7dWe+HzaTfreBFxg6SvAp8F7pP0C2ApsCPpXp03AkeRgqA/PES6t+d/JM1i030+ARxXOMqZD9wJHCFpZ2AB8FLg3aT7bg7b3EIi4ruStgDOB34h6e0R8URErJf0PmAW8GtJNwH3kP6G7Aq8GbgdeEfezn9Jupx0n89vJP2MdF3pSOBm0ulD62AOHzMgIj4naS7wCdIf0G1JF/PvAz5D+oPZXzaQbmg9CziGNILtLuDzEXFrocaNkt4FfJ10Y+kU0iCBDwMP0gfhkz/ngjyrwQzgRkkHRsSTETFf0p7AqcBBpGHda0hBfRnPH0J9DOkG04+QRujdn9/7Bxw+HU/dDP03MzPrF77mY2ZmlXP4mJlZ5Rw+ZmZWOYePmZlVzuFjZmaVc/iYmVnlHD5mZlY5h4+ZmVXO4WNmZpVz+JiZWeX+Px8iH2//qgubAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Boarded passengers grouped by port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton):')\n",
    "print(titanic['embarked'].value_counts())\n",
    "sns.countplot(x='embarked', data=titanic, palette='Set2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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": [
    "sns.barplot('embarked', 'survived', data=titanic, color=\"teal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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": [
    "sns.countplot(x='embarked', data=titanic, palette='hls', hue='survived')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Les passagers embarqués à Cherbourg semblent avoir le taux de survie le plus élevé.\n",
    "* Les passagers embarqués à Southhampton étaient légèrement moins susceptibles de survivre que ceux embarqués à Queenstown.\n",
    "* Cela est probablement lié à la classe de passagers, ou à l'ordre d'attribution des chambres\n",
    "    * Il est possible qu'on ait attribué aux premiers passagers une chambre plus proches du pont"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On pourrait supprimer cette colonne :\n",
    "> titanic.drop('embarked',axis=1,inplace=True)\n",
    "\n",
    "mais on la garde.\n",
    "* on la convertie en variable catégorique (au format numérique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>Master</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                            name     sex      age  sibsp  \\\n",
       "0     1.0       1.0   Allen, Miss. Elisabeth Walton  female  29.0000    0.0   \n",
       "1     1.0       1.0  Allison, Master. Hudson Trevor    male   0.9167    1.0   \n",
       "2     1.0       0.0    Allison, Miss. Helen Loraine  female   2.0000    1.0   \n",
       "\n",
       "   parch  ticket      fare embarked   title  \n",
       "0    0.0   24160  211.3375        S    Miss  \n",
       "1    2.0  113781  151.5500        S  Master  \n",
       "2    2.0  113781  151.5500        S    Miss  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1310 entries, 0 to 1309\n",
      "Data columns (total 11 columns):\n",
      "pclass      1309 non-null float64\n",
      "survived    1309 non-null float64\n",
      "name        1309 non-null object\n",
      "sex         1309 non-null object\n",
      "age         1309 non-null float64\n",
      "sibsp       1309 non-null float64\n",
      "parch       1309 non-null float64\n",
      "ticket      1309 non-null object\n",
      "fare        1308 non-null float64\n",
      "embarked    1307 non-null object\n",
      "title       1309 non-null object\n",
      "dtypes: float64(6), object(5)\n",
      "memory usage: 112.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>Master</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                            name     sex      age  sibsp  \\\n",
       "0     1.0       1.0   Allen, Miss. Elisabeth Walton  female  29.0000    0.0   \n",
       "1     1.0       1.0  Allison, Master. Hudson Trevor    male   0.9167    1.0   \n",
       "2     1.0       0.0    Allison, Miss. Helen Loraine  female   2.0000    1.0   \n",
       "\n",
       "   parch  ticket      fare embarked   title  \n",
       "0    0.0   24160  211.3375        S    Miss  \n",
       "1    2.0  113781  151.5500        S  Master  \n",
       "2    2.0  113781  151.5500        S    Miss  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q'], dtype=object)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['embarked'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"sibsp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass      float64\n",
       "survived    float64\n",
       "name         object\n",
       "sex          object\n",
       "age         float64\n",
       "sibsp       float64\n",
       "parch       float64\n",
       "ticket       object\n",
       "fare        float64\n",
       "embarked     object\n",
       "title        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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": [
    "sns.barplot('sibsp', 'survived', data=titanic, color=\"mediumturquoise\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sibsp vs survie avec catplot\n",
    "sns.catplot(x='sibsp',col='survived', data=titanic, kind=\"count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eOARIkuuBjwBHV9Xd7fYd2/WlvTtV1aokVwI79TQP7NvTtleSDatqde+GJFsCW/b1XzL0K5GkecxcKWkhGYdi+V7g34FzgOtp5hb/GfBe4MnAy9p+i9v18v4B2ranJUk7b3mqvusBD2ufr9dBwOFr9jIkacEwV0paMEZeLFfV9cCze9uSnAycDbw0ye5VdQEwMT1j1YBhVtJMKVnUfj1VX3r69DoWOLWvbQlw8eSvQpIWFHOlpAVj5MXyIFVVSY6imaf8XOAC7i9yFwH9V7LYCCjuL457+/bbqF0/4GoYVXUrcGtvW5Jhw5ekec1cKWkhGasT/Ppc264f2q6XtevFD+zKYmB5z6Xjpup7D3DDDMQoSZKkeWyci+VHteuft+sr2vUuvZ2SLKKZ23x5T/PAvq2dgauqatAUDUmSJOk+Iy+Wkzx0QNv6NNdUBvh8uz6XZnrF/kl6494X2BT4VE/bhTRHjvdLsnHPuC+guZFJb19JkiRpoHGYs/xPSTajudnIMmAb4BXA44ETq+pSgKq6McmRwFHAeUnOoCl8D6aZ0/yZiQGranWSQ2iK4ouSnNKO+xbgauD4uXpxkiRJWneNQ7H8BWBv4A3AQ2hOvPtvYO+q+kRvx6p6X5JbgDcDJwA3t+t39d7quu17WpJVwDtozty+AzgLOKz3RiWSJElSl5EXy1V1CnDKEP1PAk6aZt+zaS5BJ0mSJA1t5HOWJUmSpHFlsSxJkiR1sFiWJEmSOlgsS5IkSR0sliVJkqQOFsuSJElSB4tlSZIkqYPFsiRJktTBYlmSJEnqYLEsSZIkdbBYliRJkjpYLEuSJEkdLJYlSZKkDhbLkiRJUgeLZUmSJKmDxbIkSZLUwWJZkiRJ6mCxLEmSJHWwWJYkSZI6WCxLkiRJHYYqlpPsneSJU/T5/SR7r11YkiRJ0ugNe2T5VGCPKfr8H+BjaxQNkORxSVYnqSQvHLD9dUmuSrIyybIkxyTZtGOsPZJ8M8mKJDcmOSXJVmsamyRJkhaW2ZiGsQFw75rsmCTAScDqju2HAh8BfgYcAHyqXZ/b7tvbd0/gnPbhwcAJwMuAC5JsvCbxSZIkaWFZfw32qa4NSTYAdgVuWsN49gV2BI4Gjugbe+u27evA86rq3rZ9KXAi8BLgzJ44jgWWAs+qqrva9suALwL7A+9fwxglSZK0QEx5ZDnJjyeWtung3rae5TrgVuC5wBeGDaSdHvF3wHuB6wZ02QPYGDh+olBufRS4E3hlT9tuwHbAyROFMkBVfQn4YV9fSZIkaaDpHFl+EPcfTS4g7dLv18B3gQtoCt5hHQPcTHPEd1Axu2O7vrS3sapWJbkS2Gmqvj1teyXZsKp+Y7pHki2BLfv6L5le+JK0MJgrJS0kUxbLVbXDxNdJ7gX+oar+eiaDSLI7sDfw3Kpa3Tf9eMLidr18wLblwNOSpKpqGn3XAx4GXN+37SDg8CHDl6SFxlwpacEYds7yI2imWsyYJIuADwFnVNVXJum6SbteNWDbSpoj4Ivar6fq2zter2NprvjRawlw8SRxSdJCY66UtGAMVSxX1aC5xGvrMGB74NlT9JsochcBK/q2bUQzRWTVgL79NmrX/WNQVbfS989Ax1FuSVqwzJWSFpKhr4aRZBvgNTTzgrekmdLQr6pqquKXJNsBbwM+DGyc5FHtpm3a9XZt27XAsrZtMfCjvqEWA8vbKRj09f3egL73ADdMFZ8kSZIWtqGK5SRPAc6nKZInO4zQeXm5PtvSHP09sF36faRdPwK4ov16F3qK5XYax5PbuCb09v1q35g7A1dV1aApGpIkSdJ9hr0pyQeAhwBHAjsAG1TVgwYsg442D7KU5kYh/csJ7fb3tY9vBM6lmV6xf5LeuPcFNqW5QcmEC2mOHO/XewOSJC8AHt3XV5IkSRpo2GkYOwNnzdTVMKrqNtobifRKsln75SVV9fn267uSHAkcBZyX5AyawvdgmsvVfaZn3NVJDqEpii9KcgrN1I63AFcDx89E/JIkSZrfhi2W72LwDUPmRFW9L8ktwJtpjj7f3K7f1TNfeaLvaUlWAe+gOXP7DuAs4LDeG5VIkiRJXYYtlr9EczvrWVVVp/LAyxJNbDsJOGma45wNnD1jgUmSJGlBGXbO8luBrZJ8oHcusCRJkjQfDXtk+dM00xkOpDl57hrgtgH9pnXpOEmSJGmcDVss79bz9WbAUzr6TffScZIkSdLYGvYOfsNO25AkSZLWWRa/kiRJUgeLZUmSJKnDsLe7fvc0u1ZVvWcN4pEkSZLGxrAn+B0xxfYC0q4tliVJkrROG7ZY3r2jfQvgqcABNLee9nbSkiRJWucNezWMr02y+XNJPgF8C++aJ0mSpHlgRk/wq6ofA+cAh83kuJIkSdIozMbVMH4J/K9ZGFeSJEmaUzNaLCfZAngxcNNMjitJkiSNwkxdOm49YHvgT4CtgL9dy7gkSZKkkZvpS8fdCRwDHL5G0UiS5o0/+MuPj+y5r/j7vUf23JLml5m6dNy9wK3AD6pq9dqFJEmSJI2Hmbx0nCRJkjSvrNUJfkk2TbJdkk1nKiBJkiRpXAxdLCfZMMk7k1wD3A4sA25Pck3bvmjGo5QkSZJGYNirYWwCfBXYGVgN/DdwA7At8FjgSOBPkuxeVXfNcKySJEnSnBr2yPLbgF2AjwEPr6qnVNULquqpwMOBU4Cd2n6SJEnSOm3YYnlP4OtVtV9V3di7oapuqqo/B74OvHy6AyZ5TJLTkvwgye1J7kjynXZKx+YD+r8uyVVJViZZluSYrjnTSfZI8s0kK5LcmOSUJFsN+ZolSZK0QA1bLD8cuHCKPl8DfmeIMZcA2wBnAn8F/CVwKfBu4MIkG050THIo8BHgZ8ABwKfa9blJ0jtokj2Bc9qHBwMnAC8DLkiy8RDxSZIkaYEa9jrLt9PcqW8y2wN3THfAqvoqzTzo35DkB8DfAc8HPptka5qbonwdeF5V3dv2WwqcCLyEpuAmyQbAscBS4FkT86eTXAZ8EdgfeP90Y5QkSdLCNOyR5YuBlyd5+qCNSZ5GM1VjJq7HfG27fki73gPYGDh+olBufZTmzoGv7GnbDdgOOLn3RMOq+hLww76+kiRJ0kBrcrvr59NMjziHpij+Oc3VMJ4J/F9gFfDXwwbSTo3YlKYgfgpwdDvWhW2XHdv1pb37VdWqJFfSnFjIZH172vZKsmH/3QaTbAls2dd/yXCvRJLmN3OlpIVk2Dv4fSfJ82muhvES4P9vN03MF/4xsG9V/fcaxHIgcFTP4+8Be1TVde3jxe16+YB9lwNPS5Kqqmn0XQ94GHB937aDgMPXIHZJWkjMlZIWjGGPLFNVFyd5NPB04MnAFjRzma8E/qMtVtfEacDlNEcrdqWZSrFZz/ZN2vWqAfuupJlSsqj9eqq+veP1OhY4ta9tCc30E0lSw1wpacEYulgGaAvii5nBxNgeQZ44inxmklcAn0nyx1V1PvcXuYuAFX27bwQU9xfHvX37bdSu+8egqm4Fbu1t67vIhiQteOZKSQvJUCf4JVmS5EXtfLVB2x/Sbp/qihnTcSbwa+A17eNl7XrxgL6LgeU9R7Wn6nsPzZ0HJUmSpE7DXg3jXTTzlR9wVLZ1F83VKd6+NkG1NqCZWzxxNYwr2vUuvZ2SLKKZDnJ5T/PAvq2dgauqatAUDUmSJOk+wxbL/xv4cleh2bb/G/Cc6Q6YZNuOTW9o45u4osW5NNMr9k/SG/e+NFfR+FRP24U0R473670BSZIXAI/u6ytJkiQNNOyc5e2Bz0zR5zrgxUOM+eEk2wD/TnN1ii1oTu57Ic0VMY4FqKobkxxJc8WM85KcQVP4Hgxc0BtXVa1OcghNUXxRklNo7hL4FuBq4Pgh4pMkSdICNWyxvBL4rSn6/BZw9xBjfhp4Nc0R4q1p5ilfAxwJHFNV990NsKrel+QW4M00t6++uV2/q/8qHFV1WpJVwDtoCu47gLOAw3pvVCJJkiR1GbZY/hbw4iRvqao7+zcm2ZzmqPKV0x2wqk4HTh+i/0nASdPsezZw9nTHliRJknoNO2f5RJq79Z2fpPeOeSTZGfgKzXSHE2cmPEmSJGl0hr2D31lJjgMOAL6R5A6aO+ItBjanuZPfcVV1xoxHKkmSJM2xYY8sU1UHAnsAX6aZX/woYDVwHvCidrskSZK0zlvTO/h9FvjsDMciSZIkjZWhjyxLkiRJC4XFsiRJktTBYlmSJEnqYLEsSZIkdbBYliRJkjpYLEuSJEkdLJYlSZKkDhbLkiRJUgeLZUmSJKmDxbIkSZLUwWJZkiRJ6mCxLEmSJHWwWJYkSZI6WCxLkiRJHSyWJUmSpA4Wy5IkSVIHi2VJkiSpg8WyJEmS1GHkxXKSpyY5Jsm3ktya5K4kVyR5Q5IM6P+6JFclWZlkWbvvph1j75Hkm0lWJLkxySlJtpr9VyVJkqT5YOTFMnAosA9wOXAY8DZgJfAh4KTejkkOBT4C/Aw4APhUuz63v7BOsidwTvvwYOAE4GXABUk2nqXXIkmSpHlk/VEHABwH7FNVKycakhwH/DvwuiT/WFXfTbI1cATwdeB5VXVv23cpcCLwEuDMtm0D4FhgKfCsqrqrbb8M+CKwP/D+uXl5kiRJWleN/MhyVV3SWyi3bfcCZ7UPn9Cu9wA2Bo6fKJRbHwXuBF7Z07YbsB1w8kSh3I77JeCHfX0lSZKkgcbhyHKXxe36pna9Y7u+tLdTVa1KciWwU0/zwL49bXsl2bCqVvduSLIlsGVf/yXDBi5J85m5UtJCMpbFcpJtgdcD1wEXt80TxfPyAbssB56WJFVV0+i7HvAw4Pq+bQcBh69F6JK0EJgrJS0YY1csJ1lEM/f4wcCePUd/N2nXqwbstpJmSsmi9uup+vaO1+tY4NS+tiXcX7BLksyVkhaQsSqWk6wPnAHsCry+qs7v2TxR5C4CVvTtuhFQ3F8c9/btt1G77h+DqroVuLUvpumGL0kLgrlS0kIy8hP8JiRZj+ZScC8CDqyqf+rrsqxdL+aBFgPL2ykY0+l7D3DD2kUsSZKk+W4siuUkDwI+TnMd5LdW1XEDul3Rrnfp23cR8GSa6zRP2re1M3BVVQ2aoiFJkiTdZ+TFclsof4zmcm5vr6pjOrqeSzO9Yv92nwn7ApvSHJWecCHNkeP9em9AkuQFwKP7+kqSJEkDjcOc5b8H9ga+CfwkyZ/2bf/PqvpxVd2Y5EjgKOC8JGfQFL4HAxcAn5nYoapWJzmEpii+KMkpwDbAW4CrgeNn+0VJkiRp3TcOxfIftOudgE8M2P4a4McAVfW+JLcAb6a5ffXN7fpdPfOVafuelmQV8A6aM7fvoLnRyWG9NyqRJEmSuoy8WK6q3YbsfxJw0jT7ng2cvQZhSZIkSaOfsyxJkiSNK4tlSZIkqYPFsiRJktTBYlmSJEnqYLEsSZIkdbBYliRJkjpYLEuSJEkdLJYlSZKkDhbLkiRJUgeLZUmSJKmDxbIkSZLUwWJZkiRJ6mCxLEmSJHWwWJYkSZI6WCxLkiRJHSyWJUmSpA4Wy5IkSVIHi2VJkiSpg8WyJEmS1MFiWZIkSepgsSxJkiR1GHmxnGSzJIcn+VyS5UkqyZmT9H9dkquSrEyyLMkxSTbt6LtHkm8mWZHkxiSnJNlq9l6NJEmS5pORF8vAVsARwB8AV0zWMcmhwEeAnwEHAJ9q1+cmSV/fPYFz2ocHAycALwMuSLLxDMYvSZKkeWr9UQdAU/guqaqfAiSpQZ2SbE1TVH8deF5V3du2LwVOBF4CnNm2bQAcCywFnlVVd7XtlwFfBPYH3j97L0mSJEnzwciPLFfVqolCeQp7ABsDx08Uyq2PAncCr+xp2w3YDjh5olBun+tLwA/7+kqSJEkDjcOR5enasV1f2ttYVauSXAnsNFXfnra9kmxYVat7NyTZEtiyr/+SNQ9ZkuYfc6WkhWRdKpYXt+vlA7YtB56WJFVV0+i7HvAw4Pq+bQcBh89ArJI0n63TufL6v37CyJ77d979nZE9t6Q1sy4Vy5u061UDtq2kmVKyqP16qr694/U6Fji1r20JcPEwgUrSPGeulLRgrEvF8kSRuwhY0bdtI6C4vzju7dtvo3bdPwZVdStwa29b30U2JFlCLiEAABDZSURBVGnBM1dKWkhGfoLfEJa168UDti0GlrdTMKbT9x7ghpkNT5IkSfPNulQsT1yDeZfexiSLgCcDl0/Vt7UzcFVVDZqiIUmSJN1nXSqWz6WZXrF/kt649wU2pblByYQLaY4c79d7A5IkLwAe3ddXkiRJGmgs5iwneRO/eRmixyZ5Z/v1RVV1UVXdmORI4CjgvCRn0BS+BwMXAJ+Z2LmqVic5hKYovijJKcA2wFuAq4HjZ/1FSZIkaZ03FsUy8Fbg4T2PHw+8p/36SOAigKp6X5JbgDfT3L765nb9rp75yrR9T0uyCngHzZnbdwBnAYf13qhEkiRJ6jIWxXJV7TBE35OAk6bZ92zg7DUMS5IkSQvcujRnWZIkSZpTFsuSJElSh7GYhqGZMapbuHr7VkmSNF95ZFmSJEnqYLEsSZIkdbBYliRJkjo4Z1mSpDGw63G7juy5LzngkpE9tzTuLJY163wDkCRJ6yqnYUiSJEkdLJYlSZKkDhbLkiRJUgeLZUmSJKmDxbIkSZLUwWJZkiRJ6mCxLEmSJHWwWJYkSZI6WCxLkiRJHSyWJUmSpA4Wy5IkSVIHi2VJkiSpg8WyJEmS1GFeF8tJ1kvyV0l+mGRVkh8leWeS9UcdmyRJksbfvC6WgeOA9wFXAPsDXwH+GjhplEFJkiRp3TBvj7AmeQLwBuD0qtqrbT45yW3AoUk+XFXfHF2EkiRJGnfz+cjyXkCAD/a1Tzx+xdyGI0mSpHXNvD2yDOwI3Atc3ttYVT9N8lNgp/4dkmwJbNnX/HCAZcuW3dew6rabZjrWabn22msn3f7TW1bPTSB97p0irpW/WDk3gQww2ffsG3u+fO4C6fOHZ5zeue2f33v+HEbym179zud0bjv+oDfOYSS/6U3Hfmhkzz0fPOIRj9gBWFZVd8/EeOOeK2Hyv/1R5UqYPF+aKx9oslwpzbSuXJmqGkU8sy7Jd4BtqmrbAdsuA7aqqt/taz8COHxuIpSkOfWIqrp2JgYyV0qaxx6QK+dzsfwjYIOq+p0B2y4C/ldVPayvfdDRkg2B3wWuAe5Zy7CWABcDzwCWTdF3LhnXcIxrOOMaF4xvbLMR12wfWTZXjs64xgXjG5txDWchxfWAXDmfp2GsBDbr2LYRsKK/sapuBW4d0P9/ZiKgJBNfLpupIzwzwbiGY1zDGde4YHxjG9e4JpgrjWu6xjU24xrOQo9rPp/gtwzYKsmGA7YtBn46x/FIkiRpHTOfi+UraF7fjr2NSbYHtqfvxD9JkiSp33wulk8HCnhzX/vE49PmNhxJkiSta+btnOWq+naSjwCvTzOp5cs0R5lfD/xzVV06grBuBY5k8Fy/UTKu4RjXcMY1Lhjf2MY1rrkyrq/fuIY3rrEZ13AWdFzz9moYAEnWB/4S2I/mjMnlwMeAo6rq16OMTZIkSeNvXhfLkiRJ0tqYz3OWJUmSpLVisSxJkiR1sFiWJEmSOlgsr6Uk6yX5qyQ/TLIqyY+SvLM9uXA6+/9WkpOS3JBkRZIrkrxsBuLaLMnhST6XZHmSSnLmkGPMeGxJnprkmCTfSnJrkrvacd+QnlvxjCCuxyQ5LckPktye5I4k32l/lpuPKq4Bz/G4JKvbn+cLRxVXkh3aGAYt540qrp6xt07ywSRL27/LG5J8KcnvTWPfjZMcneQnSVYmuTrJG2cgpu2TfDTJz9qYrknyjiQbTHP/WYlrLo1jvjRXDh2XuXK4OMyVw8c0frmyqlzWYgFOpLme8+k0V934MHAvcMo09l0E/BewGvg74HXAV9rx/mwt49qhHWc58Ln26zOH2H9WYgM+DfwC+CfgDcCBwCXtuB8ZYVzPBr4K/A3wxja2k9vnuQLYcFQ/y57nCHAR8Kt23BeO8Ps18fv1BeBP+5b/PeLf/UfS3MHzOppLCu0LvLX93XvWNPb/Qvs3/OH2b/qMNq53rUVM27UxrQKOpbmE5cnAPcBp0xxjxuOa64UxzJeYK4eNy1y5Zr9f5srpxTSWuXKtfykX8gI8of2BfLqv/ej2B7PTFPu/qe33hp62BwH/CdwEbLwWsS0Ctu95POwbwKzEBuwKbNTX9iDgwvb5Hj+q71nH8/1l+3wvGnVcwGuBu4DDmf4bwGz9HHdox33/Gu4/m7/73wC+DTx4DfZ9YRvX+/raT2+T9/ZrGNMH23H37Gs/tG1/zijimstlXPOluXLGfr7mysHjmiuHG3csc+Va/UIu9IXmP+sC/qivffu2/QNT7H8JcAd9/4kDr5pO0hky1mHfAOYstnbcA9px9xqzuF7WjvvqUcYFbEVzlOntwD5DvAHMSly9bwDARsAmQ+4/W3Ht1rs/TSG0aIj9P9nuv31f+65t+5vXMK5v07x5p699u3bcU0cR11wu60q+NFeucVzmysHjmiuHi2ssc6VzltfOjjRHSi7vbayqnwI/BXbq2jHJg4CnAFdW1eq+zRN3F+zcfzaNKLbF7fqmUcbVznXaKslvJ3kRzVGvVTRHc0YWF3AMcDNNwp2WOYrr9TSJ7c4k17XzyiadfzrLcT2/Xd+S5CJgBbCynff5vGnsvyOwrP0b7vVNmr/1NY1rEbCy2qzd4852vfOI4ppL8y5fmivNlUMwV07PWOZKi+W1sxi4ecAvMTTz37afZN+HABu3/QbtyxT7z6Y5jS3JtjSJ5Drg4hHHdSDNm9D1wL8CK4E9quq6UcWVZHdgb+BNHb9ro4jrXuDfgbcBewB/TvPzey9w2gjjeky7Pgu4DdiLZl7lVsAXkjxniv0XD4qr/b7/Yi3i+h7wkCRP7GvfvV0vGVFcc2k+5ktzpblyKubK4YxlrpzWGcjqtAnNf9KDrGy3T7YvHfuv7Osz1+YstiSLgDOBB9PMUZoswc1FXKfRHPnakuZjm92AzabYZ9biar8/HwLOqKqvDLn7rMVVVdfTnOhznyQnA2cDL02ye1VdMNdxARNn43+f5uPFamM7nyYJ/w1w/iT7r83f9GQ+CLwYOD3JgW18OwLHAb+exrizFddcmo/50lxprpyUuXJoY5krPbK8dlbSfGQwyEY0H2tMti8d+2/UrifbfzbNSWztR1Bn0CTaN1TVZH+YcxJXVV1XVedX1ZlVdTDN2cefmeK/7NmM6zCa/4QPWYN95/R3rE22R7UPnzuiuCaS5Md7P8arqh/SzP3bKcmmU8S2pn/Tndo3w71pjtr8G82RpdOBjwFXArdPMcSsxDXH5mO+NFeaK4dmruw2rrnSYnntLAO2SrLhgG2LaebhdfkFzQ918YBtE22T7T+bZj22JOsBnwJeBBxYVf80DnENcCbNf7Ovmeu4kmxH89HdKcDGSR6V5FHANm2X7dq2rk+IRvH9urZdP3SSPrMZ10/a9Q0Dtt1Ac0mpB0+y/7JBcbV/4w9di7ioqn9px34q8AzgYVX1TuDhwA+m2H3W4ppD8zFfmivvZ64czrXt2lzZZxxzpcXy2rmC5nu4Y29jku1p/sO9fNBOAFV1L/At4MkD3jx2aded+8+m2Y6tPWnh4zRnT7+1qo4bh7g6bACsRzN3bK7j2pbmP+QDgWt6lqPb7R9pHw+cwzWi79ej2vXPuzrMclyXtetB35PfBu4GfjnJ/lcAS5L0J9udaP7W1+r7VVW/rqpvVdV/VNUvkjyZ5g19qpsTzGpcc2Te5Utz5W8wVw7HXDmJscuVa3IJDZf7LkXyJCa/buguPW2PBR7Z1+/NdF8/8WaGvMTMFLF2Xg5pLmNrx/jnduy3TdF3LuPatqP9kPb5Dp/ruGj+q3/pgOX49rmOah9vMoLv10MHtK0PfHmUv/s0Rw5+RZMQ1+9pfxLNRe2/0tP2SOCxffu/iMmv0blkTeLqiHVDmhN/ftn7+zfquGZrYR3Jl5grp4rLXDlcbObKtVwYg1w5Iy9kIS80d4ip9gfxWpoTDO6l71qAbZ9r+9o2opmDs4rmDaP3zjz7zEBsbwLe2S4FXNXz+JmjiI3msj5F819t/92M/hT43RHFdQ7NPK33tGO+hfvv5nU1PRdtH8XPsu+59mHAtUPn+Pt1Nk2yP7Id8x3t71cBJ4zydx/4i3acS2iuSXs4zceZvwKe1NPvWtrpg337n0fzN/whmr/p09vxjliLmDYDvtv+fr0W+Kv28Srg//T1nbO45nphTPMl5sph4jJXDheDuXK4mMYyV87YL+VCXWj+Q3wb8KP2h7kUeDewQV+/B/wRtO0Ppbmd6Y00c5P+C3j5DMV2bfu8g5YjRhEb9999qmvZZ0RxvRz4Is18ptU013S8EjgC2HzUP8u+59mHab4BzOL367XA12g+QlxNc+mhixlw+9UR/e6/nOa6miva2D4LPGHQ38eAfTcB/p77b7n6PWB/+i6SP2Q8G9Ik7OvaMW+imeP5pAF95yyuuV4Y03yJuXLYvy1z5fRjMFcOF89Y5sq0A0uSJEnq4wl+kiRJUgeLZUmSJKmDxbIkSZLUwWJZkiRJ6mCxLEmSJHWwWJYkSZI6WCxLkiRJHSyWpXkgybVJrh11HJI0zsyVWhMWy5IkSVIHi2VJkiSpg8WyJEmS1MFiWZpEkt2SVJIjkuye5JIkdya5KckpSbbq679vks8muS7JqiQ3J/nXJDsOGHufdux9kvzfJN9ox76wp8+DkrwuyX8mub3d/r0k/5DkIQPG3DzJB5P8LMnKJN9M8pxZ+eZIUstcqfksVTXqGKSxlWQ34ALgy8BuwGeBa4CntY+vBnauqjvb/iuAK4GrgF8ADwf2AALsVlXf6Bl7H+BjwHnAs4F/BX4E3F1V70zyIOCsdv9rgS8AK4BHAc8Fdq2qK9uxrgU2AK4HtgDOBx4CvAIoYMeq+u+Z/N5I0gRzpeaz9UcdgLSOeC7w6qr6+ERDkg8CBwB/Bby7bf69qlrau2OSxwGXAe8FBh25+GNg96q6uK/9TTTJ//PAS6pqdc+YDwbu6eu/GPgG8Myq+nXb76vAqcD+wOun+2IlaQ2ZKzXvOA1Dmp7vA5/oazuS5ujFn0409Cf/tu17NEdcnpFkwwFjnzMg+QO8EVgN7N+b/Nsxb6uqXw3Y5y0Tyb/1SeBu4AEfbUrSLDBXat6xWJam55Lqm7NUVb+geWN4RJLNAZI8KslHkyxt5+FVkgL+BNgQeOiAsS/vb0iyGfBY4AdVdf00Y7y1qq7ti/Fu4OfAltMcQ5LWhrlS847TMKTpuamj/efteoskD6P5CHFzmnlw5wC/Au6l+YjwScCiAWPcOKDtwe16+RAx3tbRfjew3hDjSNKaMldq3rFYlqZn6472bdv17cDbaY5KvLKqTuvtlGQXmjeAQQadZTuRzBcPGackjZK5UvOO0zCk6fmjJOltSPJQmo//llbVHcAj202f6+u3MfDUYZ6snWP3PeAxSX5njaOWpLllrtS8Y7EsTc/jgD/razsc2Bj4l/bxxHy5XSc6tG8afwtsswbP+SGauXvH95/skmSLdq6eJI0Tc6XmHadhSNPzZeCfkvwJD7x26NFtnw8DrwHOTnI6zceDzwAeAVzY9h/GCTTXFH0x8IMknwfuAn4XeAHwdJrrlErSuDBXat7xyLI0PV+nSbqLgQOBJ9BcJH+3iYvsV9V/Ac8Hvg28DNgbuA74w3Y9lKq6F3gJ8BfAzcC+NNcAfTxwEs3F9yVpnJgrNe94Bz9pEj13pTqyqo4YbTSSNJ7MlZrPPLIsSZIkdbBYliRJkjpYLEuSJEkdnLMsSZIkdfDIsiRJktTBYlmSJEnqYLEsSZIkdbBYliRJkjpYLEuSJEkd/h/7+nk2KIrNwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#parch vs survie avec catplot\n",
    "sns.catplot(x='parch',col='survived', data=titanic, kind=\"count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "il reste encore une ligne "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic = titanic.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass      0\n",
       "survived    0\n",
       "name        0\n",
       "sex         0\n",
       "age         0\n",
       "sibsp       0\n",
       "parch       0\n",
       "ticket      0\n",
       "fare        0\n",
       "embarked    0\n",
       "title       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(titanic).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\matrix.py:287: UserWarning: Attempting to set identical bottom == top == 0 results in singular transformations; automatically expanding.\n",
      "  ax.set(xlim=(0, self.data.shape[1]), ylim=(0, self.data.shape[0]))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\matrix.py:287: UserWarning: Attempting to set identical left == right == 0 results in singular transformations; automatically expanding.\n",
      "  ax.set(xlim=(0, self.data.shape[1]), ylim=(0, self.data.shape[0]))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x156371b1548>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABBcAAAKaCAYAAABlQrENAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3df6zlZ50X8PfnDukvfhVk1e0kWHbXUFmJYFoIBo0uQV3duMQIthiUQrGLYFNWBbOiFP6hrIoFwkLrLtIFpW3AbIiJseKuJmSXQisghipbAs3SggvdHRZop9295/GPc2653D0zc2eeuc/pPPN6JSdn7vd8v9/7zP3vfu77/XyrtRYAAACAU7W16QUAAAAAZzbDBQAAAKCL4QIAAADQxXABAAAA6GK4AAAAAHR53KYXAAAAAAfhnOe+aqOPR3zksx+oTX7/kSQXAAAAgC6GCwAAAEAXwwUAAACgiz0XAAAAmFJtHdr0Es4akgsAAABAF8kFAAAApiS5MI7kAgAAANDFcAEAAADoohYBAADAlNQixpFcAAAAALpILgAAADAlyYVxJBcAAACALoYLAAAAQBe1CAAAAKZUh9QiRpFcAAAAALpILgAAADClLRs6DiO5AAAAAHQxXAAAAAC6qEUAAAAwpVKLGEZyAQAAAOgiuQAAAMCUJBfGkVwAAAAAuhguAAAAAF3UIgAAAJhSbfl7+ih+0gAAAEAXyQUAAACmZEPHcSQXAAAAgC6GCwAAAEAXtQgAAACmpBYxjuQCAAAA0MVwAQAAAOiiFgEAAMCU1CLGkVwAAAAAukguAAAAMKU6JLkwiuQCAAAA0MVwAQAAAOiiFgEAAMCUbOg4juQCAAAA0EVyAQAAgClJLowjuQAAAAB0MVwAAAAAuqhFAAAAMKUttYhhJBcAAACALpILAAAATMmGjuNILgAAAABdDBcAAACALmoRAAAATEktYhzJBQAAAKCL5AIAAABTklwYR3IBAAAA6GK4AAAAAHRRiwAAAGBKahHjSC4AAAAAXSQXAAAAmJLkwjiSCwAAAEAXwwUAAACgi1oEAAAAU6pDahGjSC4AAAAAXQwXAAAAgC6GCwAAAEyptg5t9HXS6606VFVvqqp7qurhqvpyVb25qva1pUFV/amq+khV3VtVD1XVV6rq31XVj530Yk6SPRcAAADgseE9SV6b5LYk1ye5NMnbkjwjyauPd2FVPSvJp5McSXJjkvuTXJLk6iQvqapnt9a+dlALN1wAAABgSqeSHtiUqnp2kp9Jcmtr7fLV4V+sqm8neWNVvb+19pnj3OLqJI9P8udaa/9r130/l+TDSf5WkhsOZvVqEQAAAPBYcHmSSvLuPcd3vr7iBNc/efX+9T3Hd75+8NSXdmKGCwAAALB5lyZZJLlz98HW2n1J7kty2Qmu/8Tq/eaquqyqDlfVT2SZVvhSkltO83p/gFoEAAAAU9p0LaKqLkxy4ZqPjrTWjuw5dlGSb7XWHllz/v1JDp/g2/37LPdYeEOWey/s+NUkL22t/d7+Vn1qJBcAAADgYFyb5CtrXteuOfeCJA8f4z5HV58fU2utre796dX9fzrJdUmel+RXquq41/eSXAAAAGBKW1u16SXckOSDa47vTS0kywHCE45xn/OSPHS8b1RVb0jy80me3Vr7P6vDH6+qLyT5WJZPofjX+1jzKTFcAAAAgAOwqj6sGySs87Ukl1TVOWuqERcl+eoJrr82yd27Bgs7Pp7k95P8+RzgcEEtAgAAADbvrix/R79098GqOpzlfgt3rrtolz+eZN0mE1ur14FuQGG4AAAAwJRqqzb6Okm3JmlJrtlzfOfrjzz6/6q6pKp+dM95d2eZfLh0z/GXZzlYONFwootaBAAAAGxYa+3zVXVTkqurqpLcnmWK4eokN7fW7th1+t1J7k1y8a5jb0vy0ST/rarem2WN4rlJrsryUZa/cJDrN1wAAABgSsvf0c8or89yaHBVkpdk+QjK65K8/UQXttb+Y1W9MMnPJXlFkj+W5JtJPpTkX7TWvnlAa05iuAAAAACPCa21P8hykHDcYUJrbe3UpLX260l+6gCWdkL2XAAAAAC6SC4AAAAwpa2T31SRUyS5AAAAAHSRXAAAAGBKp/A4SE6R5AIAAADQxXABAAAA6KIWAQAAwJTUIsaRXAAAAAC6GC4AAAAAXdQiAAAAmNJWqUWMIrkAAAAAdJFcAAAAYEo2dBxHcgEAAADoYrgAAAAAdFGLAAAAYEpqEeNILgAAAABdJBcAAACY0pbkwjCSCwAAAEAXwwUAAACgi1oEAAAAUyp/Th/GjxoAAADoIrkAAADAlKps6DiK5AIAAADQxXABAAAA6KIWAQAAwJS2ttQiRpFcAAAAALpILgAAADClklwYRnIBAAAA6GK4AAAAAHRRiwAAAGBKahHjSC4AAAAAXSQXAAAAmNJWSS6MIrkAAAAAdDFcAAAAALqoRQAAADAlGzqOI7kAAAAAdDFcAAAAALqoRQAAADAltYhxJBcAAACALpILAAAATGlLcmEYyQUAAACgi+ECAAAA0EUtAgAAgClVqUWMIrkAAAAAdJFcAAAAYErlz+nD+FEDAAAAXQwXAAAAgC5qEQAAAExpa8uGjqNILgAAAABdJBcAAACYUkkuDCO5AAAAAHQxXAAAAAC6qEUAAAAwpSq1iFEkFwAAAIAukgsAAABMyaMox5FcAAAAALoYLgAAAABd1CIAAACYUqlFDCO5AAAAAHSRXAAAAGBKhyQXhpFcAAAAALoYLgAAAABd1CIAAACYklrEOJILAAAAQBfDBQAAAKCLWgQAAABTUosYR3IBAAAA6CK5AAAAwJQkF8aRXAAAAAC6GC4AAAAAXdQiAAAAmJJaxDiSCwAAAEAXyQUAAACm9DjJhWEkFwAAAIAuhgsAAABAF7UIAAAApmRDx3EkFwAAAIAukgsAAABMSXJhHMkFAAAAoIvhAgAAANBFLQIAAIApHdry9/RR/KQBAACALpILAAAATMmGjuNILgAAAABdDBcAAACALmoRAAAATEktYhzJBQAAAKCL4QIAAADQRS0CAACAKalFjCO5AAAAAHSRXAAAAGBKh0pyYRTJBQAAAKCL4QIAAADQRS0CAACAKdnQcRzJBQAAAKCL5AIAAABTklwYR3IBAAAA6GK4AAAAAHRRiwAAAGBKj1OLGEZyAQAAAOgiuQAAAMCUbOg4juQCAAAA0MVwAQAAAOiiFgEAAMCU1CLGkVwAAAAAukguAAAAMCXJhXEkFwAAAIAuhgsAAABAF7UIAAAApqQWMY7kAgAAANBFcgEAAIApSS6MI7kAAAAAdDFcAAAAALqoRQAAADAltYhxJBcAAACALoYLAAAAQBe1CAAAAKakFjGO5AIAAAA8BlTVoap6U1XdU1UPV9WXq+rNVXXSwYCquqqq2ur1hINY726SCwAAAEzpDEwuvCfJa5PcluT6JJcmeVuSZyR59X5vUlU/lOQdSb6X5PGnf5l/mOECAAAAbFhVPTvJzyS5tbV2+erwL1bVt5O8sare31r7zD5v984kX0vyhSR/5/Sv9g9TiwAAAIDNuzxJJXn3nuM7X1+xn5tU1U9kOVB4XZI/OG2rOwHJBQAAAKa06VpEVV2Y5MI1Hx1prR3Zc+zSJIskd+4+2Fq7r6ruS3LZPr7fuUnel+Tm1tonq+qqU1v5yZNcAAAAgINxbZKvrHldu+bci5J8q7X2yJrP7k9yeB/f7+eS/FCSN57SajtILgAAADClTScXktyQ5INrju9NLSTJBUkePsZ9jq4+P6aqemaSNyV5Q2vtmyexxtPCcAEAAAAOwKr6sG6QsM7RJMd6ZOR5SR46wfXvS/K/k9y4z+93WhkuAAAAwOZ9LcklVXXOmmrERUm+eqwLq+pvJvlLSV6e5EeqHk1sPHH1/iNV9e3W2r2nd8nfZ7gAAADAlA7VxmsRJ+OuJH85y40df33nYFUdznK/hY8e59qnr97/wzE+/3ySe5Nc3L3KYzBcAAAAgM27Nck/TXJNdg0XVl8nyUd2DlTVJUl+v7X25dWh/5Rl8mGv1yX5i0lekeSB07zeH2C4AAAAwJS2zqDkQmvt81V1U5Kra9lruD3LFMPVWT5a8o5dp9+dXUmE1to9Se7Ze8+q+qnVP3+ltfbdA1y+4QIAAAA8Rrw+y6HBVUlekuUjKK9L8vYNrmlfqrW26TUAAADAaXfjHfdu9Bfeq5//J86c6EQnyQUAAACmdOis+dV+87Y2vQAAAADgzCa5AAAAwJS2tkQXRpFcAAAAALoYLgAAAABd1CIAAACY0qFSixhFcgEAAADoIrkAAADAlLYkF4aRXAAAAAC6GC4AAAAAXdQiAAAAmNIhrYhhJBcAAACALoYLAAAAQBe1CAAAAKa0taUXMYrkAgAAANBFcgEAAIApbZXkwiiSCwAAAEAXwwUAAACgi1oEAAAAUzqkFTGM5AIAAADQRXIBAACAKdnQcRzJBQAAAKCL4QIAAADQRS0CAACAKR3aUosYRXIBAAAA6CK5AAAAwJRs6DiO5AIAAADQxXABAAAA6KIWAQAAwJQOaUUMI7kAAAAAdJFcAAAAYEo2dBxHcgEAAADoYrgAAAAAdFGLAAAAYEqHttQiRpFcAAAAALoYLgAAAABd1CIAAACYklbEOJILAAAAQBfJBQAAAKZ0qEQXRpFcAAAAALoYLgAAAABd1CIAAACY0pZaxDCSCwAAAEAXyQUAAACmdMif04fxowYAAAC6GC4AAAAAXdQiAAAAmJINHceRXAAAAAC6SC4AAAAwpUOSC8NILgAAAABdDBcAAACALmoRAAAATMmGjuNILgAAAABdJBcAAACY0iF/Th/GjxoAAADoYrgAAAAAdFGLAAAAYEo2dBxHcgEAAADoIrkAAADAlAQXxpFcAAAAALoYLgAAAABd1CIAAACY0lb0IkaRXAAAAAC6GC4AAAAAXdQiAAAAmJKnRYwjuQAAAAB0kVwAAABgSluSC8NILgAAAABdDBcAAACALmoRAAAATMmGjuNILgAAAABdJBcAAACY0lZEF0aRXAAAAAC6GC4AAAAAXdQiAAAAmJINHceRXAAAAAC6SC4AAAAwpS3JhWEkFwAAAIAuhgsAAABAF7UIAAAApqQVMY7kAgAAANBFcgEAAIApbXkW5TCSCwAAAEAXwwUAAACgi1oEAAAAU9KKGEdyAQAAAOgiuQAAAMCU/DV9HD9rAAAAoIvhAgAAANBFLQIAAIAplR0dh5FcAAAAALoYLgAAAABd1CIAAACY0pZWxDCSCwAAAEAXyQUAAACmZD/HcSQXAAAAgC6GCwAAAEAXtQgAAACm5K/p4/hZAwAAAF0kFwAAAJhS2dFxGMkFAAAAoIvhAgAAANBFLQIAAIApbWlFDCO5AAAAAHSRXAAAAGBKggvjSC4AAAAAXQwXAAAAgC5qEQAAAEzJho7jSC4AAAAAXSQXAAAAmFKV6MIokgsAAABAF8MFAAAAoItaBAAAAFOyoeM4kgsAAABAF8kFAAAApiS4MI7kAgAAANDFcAEAAAAeA6rqUFW9qaruqaqHq+rLVfXmqtpX66CqnlpVN1bVN6rqoaq6q6peetDrTtQiAAAAmNRWnXHFiPckeW2S25Jcn+TSJG9L8owkrz7ehVV1bpJPJPnTSW5I8ptJXpbktqr6u621Dx3gulOttYO8PwAAAGzEgw8d3egvvBecf96+pxtV9ewkn09yW2vt8l3H35HkjUme11r7zHGuf31Ww4nW2vtXx7aSfDLJn0zy9NbaQ6f0H9kHtQgAAADYvMuz3IPy3XuO73x9xQmuvyLJd5N8YOdAa22R5L1JnpbkxadnmesZLgAAADClqs2+TtKlSRZJ7tx9sLV2X5L7klx27P9nbSV5bpLPtdYe2fPxHav3Y15/OthzAQAAAA5AVV2Y5MI1Hx1prR3Zc+yiJN9aMxxIkvuTHD7Ot3pKkvNX5627Nie4vpvkAgAAAFOq1jb6SnJtkq+seV27ZrkXJHn4GP+Vo6vPj2Xns3XXH91zzoGQXAAAAICDcUOSD645vje1kCyHAE84xn3OS3K8zRh3BgjnHuPanOD6boYLAAAAcABW1Yd1g4R1vpbkkqo6Z0014qIkXz3OtQ9kOWC4aM1nO8fu2+c6TolaBAAAAHNqi82+Ts5dWf6Ofunug1V1OMv9Eu5cd1Hy6FMhPpvkOVV1zp6Pn796P+b1p4PhAgAAAGzerUlakmv2HN/5+iM7B6rqkqr60T3n3ZJlreJVu87bSvK6LJMNt5/uBe+mFgEAAMCU6uTTAxvTWvt8Vd2U5OqqqiyHAZcmuTrJza21O3adfneSe5NcvOvYTVkOFt5VVc9Ick+SlyV5QZIrW2sPHuT6DRcAAADgseH1WQ4NrkrykiwfI3ldkref6MLW2tGqelGS65NcmeRJSb6Y5PLW2q0HteAd1ZaPxwAAAICpPPy972z0F95zH//E2uT3H0lyAQAAgDmdQbWIM50NHQEAAIAukgsAAADMyTYAw0guAAAAAF0MFwAAAIAuahEAAADMyYaOw0guAAAAAF0kFwAAAJhSSS4MI7kAAAAAdDFcAAAAALqoRQAAADAntYhhJBcAAACALoYLAAAAQBe1CAAAAOakFjGM5AIAAADQRXIBAACAOUkuDCO5AAAAAHQxXAAAAAC6qEUAAAAwp4VaxCiSCwAAAEAXyQUAAACmVDZ0HEZyAQAAAOhiuAAAAAB0UYsAAABgTmoRw0guAAAAAF0kFwAAAJhTa5tewVlDcgEAAADoYrgAAAAAdFGLAAAAYE42dBxGcgEAAADoIrkAAADAlEpyYRjJBQAAAKCL4QIAAADQRS0CAACAOalFDCO5AAAAAHSRXAAAAGBOkgvDSC4AAAAAXQwXAAAAgC5qEQAAAMxJLWIYyQUAAACgi+ECAAAA0EUtAgAAgCmVWsQwkgsAAABAF8kFAAAA5rSQXBhFcgEAAADoYrgAAAAAdFGLAAAAYE6tbXoFZw3JBQAAAKCL5AIAAABz8ijKYSQXAAAAgC6GCwAAAEAXtQgAAACmVGoRw0guAAAAAF0kFwAAAJiT5MIwkgsAAABAF8MFAAAAoItaBAAAAHNSixhGcgEAAADoIrkAAADAnBbbm17BWUNyAQAAAOhiuAAAAAB0UYsAAABgSm1hQ8dRJBcAAACALpILAAAAzMmGjsNILgAAAABdDBcAAACALmoRAAAAzEktYhjJBQAAAKCL4QIAAADQRS0CAACAKbVttYhRJBcAAACALpILAAAAzGmx2PQKzhqSCwAAAEAXwwUAAACgi1oEAAAAc1rY0HEUyQUAAACgi+QCAAAAU2qSC8NILgAAAABdDBcAAACALmoRAAAAzGmx2PQKzhqSCwAAAEAXyQUAAACmZEPHcSQXAAAAgC6GCwAAAEAXtQgAAADmpBYxjOQCAAAA0EVyAQAAgDl5FOUwkgsAAABAF8MFAAAAoItaBAAAAFNq2zZ0HEVyAQAAAOhiuAAAAAB0UYsAAABgTgu1iFEkFwAAAIAukgsAAADMSXJhGMkFAAAAoIvhAgAAANBFLQIAAIAptcVi00s4a0guAAAAAF0kFwAAAJiTDR2HkVwAAAAAuhguAAAAAF3UIgAAAJiTWsQwkgsAAABAF8kFAAAApuRRlONILgAAAABdDBcAAACALmoRAAAAzMmGjsNILgAAAABdJBcAAACYk+TCMJILAAAAQBfDBQAAAKCLWgQAAABTattqEaNILgAAAABdJBcAAACY02Kx6RWcNSQXAAAAgC6GCwAAAEAXwwUAAADmtNje7GugqnpqVd1YVd+oqoeq6q6qemnH/T5cVa2q7tzP+YYLAAAAcAarqnOTfCLJlUl+Ock1SX4nyW1V9YpTuN+Lklye5KH9XmO4AAAAAGe21yR5bpJrWmtvbK392yR/JclvJHlnVZ2/3xutBhXvS3Jjkt/e73WGCwAAAEypLbY3+hroiiTfTfKBR//vrS2SvDfJ05K8+CTu9c+SPHn1vm8eRQkAAAAHoKouTHLhmo+OtNaOnKbvsZVlauGu1tojez6+Y/V+WZKP7+Nez0zypiR/v7V2pKr2vQ7DBQAAAKbUFotNL+HaJG9Zc/ytSa47Td/jKUnOT3L/ms92jh3e573en+TTWe7bcFIMFwAAAOBg3JDkg2uOr00trPZGeP4+7320tfapJBesvn543Tmr9wvWfLb3e78yyQuT/NnWWtvnGh5luAAAAAAHYFV9OJn6w+Ekv7bPc+9NcnG+P0A4d805563ej/vUh6r6I0n+ZZL3tNa+sM/v/wMMFwAAAJhS2954LeJk/VaSF+zz3J2kwgNZDhguWnPOzrH7TnCvtyQ5J8mHqurHdh1/XJJzV8d+r7V2zKdHGC4AAADAY0Br7eEknzrJaxZV9dkkz6mqc/Zs6rhTsbjzBLd5epInJfmfaz47nOQ3k9yc5JXHuoHhAgAAAFM6A5MLp+qWLBMPr8pyU8adp0i8Lstkw+07J1bVBVkOE77dWvv66vA7knx4zX1/Icl3snyCxFePtwDDBQAAADiz3ZTlYOFdVfWMJPckeVmWA4crW2sP7jr3eVnu6/BoEqG19hvrblpV/yrJ77bWPnqiBRguAAAAwBmstXa0ql6U5PokV2ZZcfhikstba7eOWIPhAgAAAFNqi7OmFpHW2gNJXrN6He+8/56k9nnPi/f7/bf2eyIAAADAOpILAAAATOks2tBx4yQXAAAAgC6GCwAAAEAXtQgAAACmpBYxjuQCAAAA0EVyAQAAgCkttrc3vYSzhuQCAAAA0MVwAQAAAOiiFgEAAMCU2sKGjqNILgAAAABdJBcAAACYkkdRjiO5AAAAAHQxXAAAAAC6qEUAAAAwJbWIcSQXAAAAgC6GCwAAAEAXtQgAAACm1BZqEaNILgAAAABdJBcAAACY0sKGjsNILgAAAABdDBcAAACALmoRAAAATKmpRQwjuQAAAAB0kVwAAABgSpIL40guAAAAAF0MFwAAAIAuahEAAABMqS3UIkaRXAAAAAC6SC4AAAAwJRs6jiO5AAAAAHQxXAAAAAC6qEUAAAAwJbWIcSQXAAAAgC6SCwAAAExp4VGUw0guAAAAAF0MFwAAAIAuahEAAABMyYaO40guAAAAAF0MFwAAAIAuahEAAABMqW1vb3oJZw3JBQAAAKCL5AIAAABTagsbOo4iuQAAAAB0MVwAAAAAuqhFAAAAMKW2rRYxiuQCAAAA0EVyAQAAgClJLowjuQAAAAB0MVwAAAAAuqhFAAAAMKWFWsQwkgsAAABAF8kFAAAAptQWkgujSC4AAAAAXQwXAAAAgC5qEQAAAEyp2dBxGMkFAAAAoIvkAgAAAFNq223TSzhrSC4AAAAAXQwXAAAAgC5qEQAAAExpYUPHYSQXAAAAgC6SCwAAAEypLWzoOIrkAgAAANDFcAEAAADoohYBAADAlBbbahGjSC4AAAAAXQwXAAAAgC5qEQAAAEypbS82vYSzhuQCAAAA0EVyAQAAgCk1GzoOI7kAAAAAdDFcAAAAALqoRQAAADClhVrEMJILAAAAQBfJBQAAAKbkUZTjSC4AAAAAXQwXAAAAgC5qEQAAAExpsbCh4yiSCwAAAEAXyQUAAACm1DyKchjJBQAAAKCL4QIAAADQRS0CAACAKS22F5tewllDcgEAAADoIrkAAADAlGzoOI7kAgAAANDFcAEAAADoohYBAADAlNQixpFcAAAAALpILgAAADAlj6IcR3IBAAAA6GK4AAAAAHRRiwAAAGBKbWFDx1EkFwAAAIAuhgsAAABAF7UIAAAAprTYVosYRXIBAAAA6CK5AAAAwJTa9mLTSzhrSC4AAAAAXQwXAAAAgC5qEQAAAEyp2dBxGMkFAAAAoIvkAgAAAFPyKMpxJBcAAACALoYLAAAAQBe1CAAAAKbUFotNL+GsIbkAAAAAdJFcAAAAYEo2dBxHcgEAAADoYrgAAAAAdFGLAAAAYEpNLWIYyQUAAACgi+QCAAAAU2rbHkU5iuQCAAAA0MVwAQAAAOhiuAAAAMCUFttto6+RquqpVXVjVX2jqh6qqruq6qUneY+/WlW/WlX/r6q+V1V3V9Vbq+rJJ7rWcAEAAADOYFV1bpJPJLkyyS8nuSbJ7yS5rapesc97/L0k/znJE5K8PckbknwmyT9P8l+rqo53vQ0dAQAAmNJZ9CjK1yR5bpLXttbenyRV9UtJPpnknVX10dbaQye4x88m+XqSv9BaO7o6dlNVPZzkqiR/JsnnjnWx5AIAAACc2a5I8t0kH9g50FpbJHlvkqclefE+7vHkJL+7a7Cw4+ur9wePd7HhAgAAAJyhqmory9TC51prj+z5+I7V+2X7uNUnkjyrqn6+qp5ZVU+vqpdnWbG4pbX2peNdrBYBAADAlBZts7WIqrowyYVrPjrSWjtymr7NU5Kcn+T+NZ/tHDu8j/v8oyRPXb3/k13H/02Sf3yiiw0XAAAA4GBcm+Qta46/Ncl1ew9W1flJnr/Pex9trX0qyQWrrx9ed87q/YI1n+31cJL/m+T2JLdkWbP4ySz/Dy3LocMxGS4AAADAwbghyQfXHD9WauFwkl/b573vTXJxvj9AOHfNOeet3k+0mWOS3Jrkx5P8eGttZ1Dxsap6MMnPVtVHWmt3HutiwwUAAACmtL3hWsSq+nAy9YffSvKCfZ67MwB4IMsBw0Vrztk5dt/xblRVFyf5G0nes2uwsONjSf5hkhcmMVwAAACAx7LVL/afOslrFlX12STPqapz9mzquFOxOOZQYOWHV++H1nz2uON89ihPiwAAAGBK222zr4FuSfKEJK/aObB6isTrskw23L7r+AVVdUlV/fCu67+UZDvJT1fVE/fc+xWr9+MOKCQXAAAA4Mx2U5aDhXdV1TOS3JPkZVlWLK5srT2469znZbmvw81JXpkkrbUHquq9WT528q6q+qUk30ny15L89ST/pbX2P463AMMFAAAAOIO11o5W1YuSXJ/kyiRPSvLFJJe31m7d523ekOQzSf5Blo+efFKSryZ5W5K3n+jiahve4AIAAAAOwq1/9Fkb/YX3b//2F2uT338key4AAAAAXdQiAAAAmNLgTeIK6voAAAIASURBVBXPapILAAAAQBfDBQAAAKCLWgQAAABT2vYAg2EkFwAAAIAukgsAAABMyYaO40guAAAAAF0MFwAAAIAuahEAAABMyYaO40guAAAAAF0kFwAAAJiSDR3HkVwAAAAAuhguAAAAAF3UIgAAAJiSWsQ4kgsAAABAF8MFAAAAoItaBAAAAFPabnoRo0guAAAAAF0kFwAAAJiSDR3HkVwAAAAAuhguAAAAAF3UIgAAAJiSDR3HkVwAAAAAukguAAAAMCUbOo4juQAAAAB0MVwAAAAAuqhFAAAAMCUbOo4juQAAAAB0kVwAAABgSjZ0HEdyAQAAAOhiuAAAAAB0UYsAAABgSjZ0HEdyAQAAAOgiuQAAAMCUFptewFlEcgEAAADoYrgAAAAAdFGLAAAAYEo2dBxHcgEAAADoIrkAAADAlLYFF4aRXAAAAAC6GC4AAAAAXdQiAAAAmJINHceRXAAAAAC6GC4AAAAAXdQiAAAAmJKnRYwjuQAAAAB0kVwAAABgSjZ0HEdyAQAAAOhiuAAAAAB0UYsAAABgSjZ0HEdyAQAAAOgiuQAAAMCUbOg4juQCAAAA0MVwAQAAAOiiFgEAAMCUbOg4juQCAAAA0KWaDS4AAACADpILAAAAQBfDBQAAAKCL4QIAAADQxXABAAAA6GK4AAAAAHQxXAAAAAC6/H+KeD8m1bAuqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.heatmap(titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse de données : visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avec seaborn, afficher une série de graphiques pour avoir une idée de la répartition des variables indépendantes et visualiser les effets possibles des différentes variables indépendantes sur la survie ou non.  \n",
    "_Indication : vous pourrez regarder par exemple la distribution des âges, des tarifs et comparer sexe vs survie, classe vs survie,sibsp vs survie en utilisant les visualisations que vous souhaitez_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15637328348>"
      ]
     },
     "execution_count": 162,
     "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.countplot(x='survived',data=titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \"pclass\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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": [
    "sns.barplot('pclass', 'survived', data=titanic, color=\"darkturquoise\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plus de chances de survie en première classe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "on converti en dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>pclass</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.964789</td>\n",
       "      <td>0.886792</td>\n",
       "      <td>0.490741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.340782</td>\n",
       "      <td>0.146199</td>\n",
       "      <td>0.152439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "pclass       1.0       2.0       3.0\n",
       "sex                                 \n",
       "female  0.964789  0.886792  0.490741\n",
       "male    0.340782  0.146199  0.152439"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>pclass</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.552381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.968992</td>\n",
       "      <td>0.870588</td>\n",
       "      <td>0.432432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.321637</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.138350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "pclass                1.0       2.0       3.0\n",
       "sex    age                                   \n",
       "female (0, 18]   0.923077  0.952381  0.552381\n",
       "       (18, 80]  0.968992  0.870588  0.432432\n",
       "male   (0, 18]   0.750000  0.523810  0.225000\n",
       "       (18, 80]  0.321637  0.093333  0.138350"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.cut(titanic['age'], [0, 18, 80])\n",
    "titanic.pivot_table('survived', ['sex', age], 'pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "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": [
    "def plot_cat(data, x_axis, y_axis, hue):\n",
    "    plt.figure()    \n",
    "    sns.barplot(x=x_axis, y=y_axis, hue=hue, data=data)\n",
    "    sns.set_context(\"notebook\", font_scale=1.6)\n",
    "    plt.legend(loc=\"upper right\", fontsize=\"medium\")\n",
    "plot_cat(titanic,\"sex\", \"survived\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plot_cat(titanic,\"pclass\", \"survived\", \"sex\") \n",
    "plot_cat(titanic,\"pclass\", \"survived\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### toutes les features numerique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Selected KDE bandwidth is 0. Cannot estiamte density.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py\u001b[0m in \u001b[0;36mkdensityfft\u001b[1;34m(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid)\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 451\u001b[1;33m         \u001b[0mbw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    452\u001b[0m     \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'scott'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-168-6771fbd13615>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mnumeric_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumeric_features\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"level_1\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m\"Features\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m\"Value\"\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFacetGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mnumeric_features\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Features\"\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mcol_wrap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msharex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msharey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Value\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;34m'blue'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuptitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Histogrammes\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mmap\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m    752\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    753\u001b[0m             \u001b[1;31m# Draw the plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 754\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_facet_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    755\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    756\u001b[0m         \u001b[1;31m# Finalize the annotations and layout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36m_facet_plot\u001b[1;34m(self, func, ax, plot_args, plot_kwargs)\u001b[0m\n\u001b[0;32m    836\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    837\u001b[0m         \u001b[1;31m# Draw the plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 838\u001b[1;33m         \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mplot_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    839\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    840\u001b[0m         \u001b[1;31m# Sort out the supporting information\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mdistplot\u001b[1;34m(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax)\u001b[0m\n\u001b[0;32m    229\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    230\u001b[0m         \u001b[0mkde_color\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkde_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"color\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 231\u001b[1;33m         \u001b[0mkdeplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvertical\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvertical\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkde_color\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkde_kws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    232\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkde_color\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    233\u001b[0m             \u001b[0mkde_kws\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"color\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkde_color\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mkdeplot\u001b[1;34m(data, data2, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, **kwargs)\u001b[0m\n\u001b[0;32m    689\u001b[0m         ax = _univariate_kdeplot(data, shade, vertical, kernel, bw,\n\u001b[0;32m    690\u001b[0m                                  \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 691\u001b[1;33m                                  cumulative=cumulative, **kwargs)\n\u001b[0m\u001b[0;32m    692\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    693\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_univariate_kdeplot\u001b[1;34m(data, shade, vertical, kernel, bw, gridsize, cut, clip, legend, ax, cumulative, **kwargs)\u001b[0m\n\u001b[0;32m    281\u001b[0m         x, y = _statsmodels_univariate_kde(data, kernel, bw,\n\u001b[0;32m    282\u001b[0m                                            \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 283\u001b[1;33m                                            cumulative=cumulative)\n\u001b[0m\u001b[0;32m    284\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m         \u001b[1;31m# Fall back to scipy if missing statsmodels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_statsmodels_univariate_kde\u001b[1;34m(data, kernel, bw, gridsize, cut, clip, cumulative)\u001b[0m\n\u001b[0;32m    353\u001b[0m     \u001b[0mfft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkernel\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"gau\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    354\u001b[0m     \u001b[0mkde\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msmnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mKDEUnivariate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 355\u001b[1;33m     \u001b[0mkde\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclip\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    356\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcumulative\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    357\u001b[0m         \u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msupport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, kernel, bw, fft, weights, gridsize, adjust, cut, clip)\u001b[0m\n\u001b[0;32m    138\u001b[0m             density, grid, bw = kdensityfft(endog, kernel=kernel, bw=bw,\n\u001b[0;32m    139\u001b[0m                     \u001b[0madjust\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0madjust\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 140\u001b[1;33m                     clip=clip, cut=cut)\n\u001b[0m\u001b[0;32m    141\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    142\u001b[0m             density, grid, bw = kdensity(endog, kernel=kernel, bw=bw,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py\u001b[0m in \u001b[0;36mkdensityfft\u001b[1;34m(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid)\u001b[0m\n\u001b[0;32m    451\u001b[0m         \u001b[0mbw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    452\u001b[0m     \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 453\u001b[1;33m         \u001b[0mbw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbandwidths\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect_bandwidth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkern\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# will cross-val fit this pattern?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    454\u001b[0m     \u001b[0mbw\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[0madjust\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\bandwidths.py\u001b[0m in \u001b[0;36mselect_bandwidth\u001b[1;34m(x, bw, kernel)\u001b[0m\n\u001b[0;32m    172\u001b[0m         \u001b[1;31m# eventually this can fall back on another selection criterion.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m         \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Selected KDE bandwidth is 0. Cannot estiamte density.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 174\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    175\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mbandwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Selected KDE bandwidth is 0. Cannot estiamte density."
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_features= titanic.select_dtypes(exclude=[\"object\",\"bool\"])\n",
    "numeric_features = numeric_features.stack().reset_index().rename(columns = {\"level_1\":\"Features\",0:\"Value\"})\n",
    "g = sns.FacetGrid(data =numeric_features, col=\"Features\",  col_wrap=5, sharex=False, sharey=False)\n",
    "g = g.map(sns.distplot, \"Value\", color ='blue')\n",
    "plt.suptitle(\"Histogrammes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def titanic_corr(data):\n",
    "    correlation = data.corr()\n",
    "    sns.heatmap(correlation, annot=True, cbar=True)\n",
    "    \n",
    "titanic_corr(titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gestion des variables catégoriques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identifier les variables catégoriques et les transformer de sorte à pouvoir les utiliser ensuite dans votre modèle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Catégorique\n",
    "    * Pclass: ordinal\n",
    "    * Sex\n",
    "    * Embarked (colonne supprimée)\n",
    "* Numérique\n",
    "    * Age: continuous\n",
    "    * Fare: continuous\n",
    "    * SibSp: discrete\n",
    "    * Parch: discrete\n",
    "* Autres\n",
    "    * Name\n",
    "    * Ticket\n",
    "    * Cabin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "pclass_dummies = pd.get_dummies(titanic.pclass, prefix=\"pclass\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>pclass_1.0</th>\n",
       "      <th>pclass_2.0</th>\n",
       "      <th>pclass_3.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass_1.0  pclass_2.0  pclass_3.0\n",
       "0           1           0           0\n",
       "1           1           0           0\n",
       "2           1           0           0\n",
       "3           1           0           0\n",
       "4           1           0           0"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "embarked_dummies = pd.get_dummies(titanic.embarked, prefix=\"embarked\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   embarked_C  embarked_Q  embarked_S\n",
       "0           0           0           1\n",
       "1           0           0           1\n",
       "2           0           0           1\n",
       "3           0           0           1\n",
       "4           0           0           1"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "on souhaite le remplacement de : \n",
    "* male par 1\n",
    "* female par 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il y a plusieurs solutions :\n",
    "* La librairie Scikit-Learn possède une fonction LabelBinarizer dans son module de pré-traitement\n",
    "* utilisation de get_dummies de Pandas \n",
    "* directement "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.sex = titanic.sex.map({\"male\": 0, \"female\":1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "scrolled": true
   },
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>Master</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                            name  sex      age  sibsp  \\\n",
       "0     1.0       1.0   Allen, Miss. Elisabeth Walton    1  29.0000    0.0   \n",
       "1     1.0       1.0  Allison, Master. Hudson Trevor    0   0.9167    1.0   \n",
       "2     1.0       0.0    Allison, Miss. Helen Loraine    1   2.0000    1.0   \n",
       "\n",
       "   parch  ticket      fare embarked   title  \n",
       "0    0.0   24160  211.3375        S    Miss  \n",
       "1    2.0  113781  151.5500        S  Master  \n",
       "2    2.0  113781  151.5500        S    Miss  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head(3)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#autre methode\n",
    "titanic.loc[(titanic['sex'] == \"male\", \"sex\")]=1\n",
    "titanic.loc[(titanic['sex'] == \"female\", \"sex\")]=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_dummies = pd.get_dummies(titanic.title, prefix=\"title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>title_Master</th>\n",
       "      <th>title_Miss</th>\n",
       "      <th>title_Mr</th>\n",
       "      <th>title_Mrs</th>\n",
       "      <th>title_Officer</th>\n",
       "      <th>title_Royalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   title_Master  title_Miss  title_Mr  title_Mrs  title_Officer  title_Royalty\n",
       "0             0           1         0          0              0              0\n",
       "1             1           0         0          0              0              0\n",
       "2             0           1         0          0              0              0\n",
       "3             0           0         1          0              0              0\n",
       "4             0           0         0          1              0              0"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of \"Age\" is 29.22\n",
      "The median of \"Age\" is 26.00\n"
     ]
    }
   ],
   "source": [
    "# mean age\n",
    "print('The mean of \"Age\" is %.2f' %(titanic[\"age\"].mean(skipna=True)))\n",
    "# median age\n",
    "print('The median of \"Age\" is %.2f' %(titanic[\"age\"].median(skipna=True)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "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": [
    "ax = titanic[\"age\"].hist(bins=15, density=True, stacked=True, color='teal', alpha=0.6)\n",
    "titanic[\"age\"].plot(kind='density', color='teal')\n",
    "ax.set(xlabel='Age')\n",
    "plt.xlim(-10,85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creer des catégorie d'age :\n",
    "* 1 - petite enfance de 0 à 6\n",
    "* 2 - seconde enfance de 6 à 18\n",
    "* 3 - adultes de 18 à 60 ans\n",
    "* 4 - personnes âgées de 60 à 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.0"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(titanic[\"age\"])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "titanic[\"cat_age\"] = pd.cut(titanic[\"age\"],bins=[0,6,18,60,80], labels=[1,2,3,4])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "titanic[[\"age\", \"cat_age\"]].head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.310064</td>\n",
       "      <td>-0.121407</td>\n",
       "      <td>-0.454511</td>\n",
       "      <td>0.060149</td>\n",
       "      <td>0.017596</td>\n",
       "      <td>-0.557861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>-0.310064</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.527248</td>\n",
       "      <td>-0.039275</td>\n",
       "      <td>-0.027228</td>\n",
       "      <td>0.083400</td>\n",
       "      <td>0.242960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.121407</td>\n",
       "      <td>0.527248</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.072334</td>\n",
       "      <td>0.110529</td>\n",
       "      <td>0.214179</td>\n",
       "      <td>0.184034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.454511</td>\n",
       "      <td>-0.039275</td>\n",
       "      <td>-0.072334</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.219269</td>\n",
       "      <td>-0.132615</td>\n",
       "      <td>0.197843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibsp</th>\n",
       "      <td>0.060149</td>\n",
       "      <td>-0.027228</td>\n",
       "      <td>0.110529</td>\n",
       "      <td>-0.219269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.373281</td>\n",
       "      <td>0.161030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parch</th>\n",
       "      <td>0.017596</td>\n",
       "      <td>0.083400</td>\n",
       "      <td>0.214179</td>\n",
       "      <td>-0.132615</td>\n",
       "      <td>0.373281</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.222327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare</th>\n",
       "      <td>-0.557861</td>\n",
       "      <td>0.242960</td>\n",
       "      <td>0.184034</td>\n",
       "      <td>0.197843</td>\n",
       "      <td>0.161030</td>\n",
       "      <td>0.222327</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pclass  survived       sex       age     sibsp     parch      fare\n",
       "pclass    1.000000 -0.310064 -0.121407 -0.454511  0.060149  0.017596 -0.557861\n",
       "survived -0.310064  1.000000  0.527248 -0.039275 -0.027228  0.083400  0.242960\n",
       "sex      -0.121407  0.527248  1.000000 -0.072334  0.110529  0.214179  0.184034\n",
       "age      -0.454511 -0.039275 -0.072334  1.000000 -0.219269 -0.132615  0.197843\n",
       "sibsp     0.060149 -0.027228  0.110529 -0.219269  1.000000  0.373281  0.161030\n",
       "parch     0.017596  0.083400  0.214179 -0.132615  0.373281  1.000000  0.222327\n",
       "fare     -0.557861  0.242960  0.184034  0.197843  0.161030  0.222327  1.000000"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "la colone age n'est plus utile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic.drop(['age'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"Name\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "le nom du passager n'est pas utile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic.drop(['name'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"ticket\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "le numero du ticket n'est pas utile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.drop(['ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Type float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic = titanic.astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1306 entries, 0 to 1308\n",
      "Data columns (total 9 columns):\n",
      "pclass      1306 non-null float64\n",
      "survived    1306 non-null float64\n",
      "sex         1306 non-null int64\n",
      "age         1306 non-null float64\n",
      "sibsp       1306 non-null float64\n",
      "parch       1306 non-null float64\n",
      "fare        1306 non-null float64\n",
      "embarked    1306 non-null object\n",
      "title       1306 non-null object\n",
      "dtypes: float64(6), int64(1), object(2)\n",
      "memory usage: 142.0+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Création d'un train set et test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_dummies = pd.concat([titanic, pclass_dummies, title_dummies, embarked_dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop categorical fields\n",
    "titanic_dummies.drop(['pclass', 'title', 'embarked'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1306 entries, 0 to 1308\n",
      "Data columns (total 18 columns):\n",
      "survived         1306 non-null float64\n",
      "sex              1306 non-null int64\n",
      "age              1306 non-null float64\n",
      "sibsp            1306 non-null float64\n",
      "parch            1306 non-null float64\n",
      "fare             1306 non-null float64\n",
      "pclass_1.0       1306 non-null uint8\n",
      "pclass_2.0       1306 non-null uint8\n",
      "pclass_3.0       1306 non-null uint8\n",
      "title_Master     1306 non-null uint8\n",
      "title_Miss       1306 non-null uint8\n",
      "title_Mr         1306 non-null uint8\n",
      "title_Mrs        1306 non-null uint8\n",
      "title_Officer    1306 non-null uint8\n",
      "title_Royalty    1306 non-null uint8\n",
      "embarked_C       1306 non-null uint8\n",
      "embarked_Q       1306 non-null uint8\n",
      "embarked_S       1306 non-null uint8\n",
      "dtypes: float64(5), int64(1), uint8(12)\n",
      "memory usage: 126.7 KB\n"
     ]
    }
   ],
   "source": [
    "titanic_dummies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>pclass_1.0</th>\n",
       "      <th>pclass_2.0</th>\n",
       "      <th>pclass_3.0</th>\n",
       "      <th>title_Master</th>\n",
       "      <th>title_Miss</th>\n",
       "      <th>title_Mr</th>\n",
       "      <th>title_Mrs</th>\n",
       "      <th>title_Officer</th>\n",
       "      <th>title_Royalty</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  sex      age  sibsp  parch      fare  pclass_1.0  pclass_2.0  \\\n",
       "0       1.0    1  29.0000    0.0    0.0  211.3375           1           0   \n",
       "1       1.0    0   0.9167    1.0    2.0  151.5500           1           0   \n",
       "2       0.0    1   2.0000    1.0    2.0  151.5500           1           0   \n",
       "3       0.0    0  30.0000    1.0    2.0  151.5500           1           0   \n",
       "4       0.0    1  25.0000    1.0    2.0  151.5500           1           0   \n",
       "\n",
       "   pclass_3.0  title_Master  title_Miss  title_Mr  title_Mrs  title_Officer  \\\n",
       "0           0             0           1         0          0              0   \n",
       "1           0             1           0         0          0              0   \n",
       "2           0             0           1         0          0              0   \n",
       "3           0             0           0         1          0              0   \n",
       "4           0             0           0         0          1              0   \n",
       "\n",
       "   title_Royalty  embarked_C  embarked_Q  embarked_S  \n",
       "0              0           0           0           1  \n",
       "1              0           0           0           1  \n",
       "2              0           0           0           1  \n",
       "3              0           0           0           1  \n",
       "4              0           0           0           1  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "il faut supprimer une des colonne categorielle (pour chaque categorie)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_dummies.drop(['pclass_1.0', 'embarked_C', 'title_Master'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic_dummies[\"survived\"].dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic_dummies.survived = titanic_dummies.survived.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = titanic_dummies.drop('survived',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(titanic_dummies.drop('survived', axis=1), titanic_dummies['survived'], test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X = train.drop('Survived',axis=1)\n",
    "y = train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>pclass_2.0</th>\n",
       "      <th>pclass_3.0</th>\n",
       "      <th>title_Miss</th>\n",
       "      <th>title_Mr</th>\n",
       "      <th>title_Mrs</th>\n",
       "      <th>title_Officer</th>\n",
       "      <th>title_Royalty</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0042</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex   age  sibsp  parch     fare  pclass_2.0  pclass_3.0  title_Miss  \\\n",
       "743     1  22.0    0.0    0.0  10.5167           0           1           1   \n",
       "1153    1  47.0    1.0    0.0  14.5000           0           1           0   \n",
       "493     0  31.0    1.0    1.0  37.0042           1           0           0   \n",
       "497     0  40.0    0.0    0.0  16.0000           1           0           0   \n",
       "\n",
       "      title_Mr  title_Mrs  title_Officer  title_Royalty  embarked_Q  \\\n",
       "743          0          0              0              0           0   \n",
       "1153         0          1              0              0           0   \n",
       "493          1          0              0              0           0   \n",
       "497          1          0              0              0           0   \n",
       "\n",
       "      embarked_S  \n",
       "743            1  \n",
       "1153           1  \n",
       "493            0  \n",
       "497            1  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1044 entries, 743 to 1063\n",
      "Data columns (total 14 columns):\n",
      "sex              1044 non-null int64\n",
      "age              1044 non-null float64\n",
      "sibsp            1044 non-null float64\n",
      "parch            1044 non-null float64\n",
      "fare             1044 non-null float64\n",
      "pclass_2.0       1044 non-null uint8\n",
      "pclass_3.0       1044 non-null uint8\n",
      "title_Miss       1044 non-null uint8\n",
      "title_Mr         1044 non-null uint8\n",
      "title_Mrs        1044 non-null uint8\n",
      "title_Officer    1044 non-null uint8\n",
      "title_Royalty    1044 non-null uint8\n",
      "embarked_Q       1044 non-null uint8\n",
      "embarked_S       1044 non-null uint8\n",
      "dtypes: float64(4), int64(1), uint8(9)\n",
      "memory usage: 58.1 KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "743     0.0\n",
       "1153    0.0\n",
       "493     0.0\n",
       "497     0.0\n",
       "154     0.0\n",
       "Name: survived, dtype: float64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'info'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-585-6e837eca3a7a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5177\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5178\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5179\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5181\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'info'"
     ]
    }
   ],
   "source": [
    "y_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Création du modèle, entraînement et prédictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " standardization (in english, en français la normalisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le module de preprocessing de Scikit-Learn propose StandardScaler (un transformateur) \n",
    "! retourne un tableau Numpy (a remettre au format pd.Dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "std = StandardScaler()\n",
    "X_train = std.fit_transform(X_train)\n",
    "X_train = pd.DataFrame(X_train, columns=X_train.pclass)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "data_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instanciation et entraînement du modèle\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "reglog = LogisticRegression(solver='liblinear')\n",
    "reglog.fit(X_train, y_train)\n",
    "\n",
    "# Prédictions sur le test set\n",
    "y_pred = reglog.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "logmodel = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Évaluation du modèle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vous pourrez utiliser la fonction classification_report, la matrice de confusion ou tout autre méthode d'évaluation du modèle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8206106870229007\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.84      0.88      0.86       164\n",
      "         1.0       0.79      0.71      0.75        98\n",
      "\n",
      "    accuracy                           0.82       262\n",
      "   macro avg       0.81      0.80      0.80       262\n",
      "weighted avg       0.82      0.82      0.82       262\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[145  19]\n",
      " [ 28  70]]\n"
     ]
    }
   ],
   "source": [
    "# Matrice de confusion\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "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>Coefficients</th>\n",
       "      <th>OR</th>\n",
       "      <th>1/OR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>constante</th>\n",
       "      <td>2.201079</td>\n",
       "      <td>9.034758</td>\n",
       "      <td>0.110684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0.997284</td>\n",
       "      <td>2.710910</td>\n",
       "      <td>0.368880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.026933</td>\n",
       "      <td>0.973426</td>\n",
       "      <td>1.027299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibsp</th>\n",
       "      <td>-0.358217</td>\n",
       "      <td>0.698921</td>\n",
       "      <td>1.430776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parch</th>\n",
       "      <td>-0.221336</td>\n",
       "      <td>0.801448</td>\n",
       "      <td>1.247742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare</th>\n",
       "      <td>0.003986</td>\n",
       "      <td>1.003994</td>\n",
       "      <td>0.996022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass_2.0</th>\n",
       "      <td>-0.719226</td>\n",
       "      <td>0.487129</td>\n",
       "      <td>2.052843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass_3.0</th>\n",
       "      <td>-1.534017</td>\n",
       "      <td>0.215668</td>\n",
       "      <td>4.636766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_Miss</th>\n",
       "      <td>-0.263850</td>\n",
       "      <td>0.768089</td>\n",
       "      <td>1.301933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_Mr</th>\n",
       "      <td>-1.677768</td>\n",
       "      <td>0.186790</td>\n",
       "      <td>5.353593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_Mrs</th>\n",
       "      <td>0.442715</td>\n",
       "      <td>1.556928</td>\n",
       "      <td>0.642290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_Officer</th>\n",
       "      <td>-1.011182</td>\n",
       "      <td>0.363789</td>\n",
       "      <td>2.748849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_Royalty</th>\n",
       "      <td>-0.406159</td>\n",
       "      <td>0.666204</td>\n",
       "      <td>1.501041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embarked_Q</th>\n",
       "      <td>-0.288365</td>\n",
       "      <td>0.749488</td>\n",
       "      <td>1.334244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embarked_S</th>\n",
       "      <td>-0.422088</td>\n",
       "      <td>0.655677</td>\n",
       "      <td>1.525142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Coefficients        OR      1/OR\n",
       "constante          2.201079  9.034758  0.110684\n",
       "sex                0.997284  2.710910  0.368880\n",
       "age               -0.026933  0.973426  1.027299\n",
       "sibsp             -0.358217  0.698921  1.430776\n",
       "parch             -0.221336  0.801448  1.247742\n",
       "fare               0.003986  1.003994  0.996022\n",
       "pclass_2.0        -0.719226  0.487129  2.052843\n",
       "pclass_3.0        -1.534017  0.215668  4.636766\n",
       "title_Miss        -0.263850  0.768089  1.301933\n",
       "title_Mr          -1.677768  0.186790  5.353593\n",
       "title_Mrs          0.442715  1.556928  0.642290\n",
       "title_Officer     -1.011182  0.363789  2.748849\n",
       "title_Royalty     -0.406159  0.666204  1.501041\n",
       "embarked_Q        -0.288365  0.749488  1.334244\n",
       "embarked_S        -0.422088  0.655677  1.525142"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coefficients\n",
    "coef = pd.DataFrame(np.concatenate([reglog.intercept_.reshape(-1,1), reglog.coef_],axis=1),\n",
    "             index = [\"Coefficients\"],\n",
    "             columns = [\"constante\"]+list(X.columns)).T\n",
    "\n",
    "coef[\"OR\"] = np.exp(coef.Coefficients)\n",
    "coef[\"1/OR\"] = np.exp(-coef.Coefficients)\n",
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Courbe ROC\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "proba = reglog.predict_proba(X_test)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, proba[:, 1], pos_label=1, drop_intermediate=False)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6,6))\n",
    "ax.plot([0, 1], [0, 1], 'k--')\n",
    "# aucf = roc_auc_score(y_test == clr.classes_[0], probas[:, 0]) # première méthode\n",
    "aucf = auc(fpr, tpr)  # seconde méthode\n",
    "ax.plot(fpr, tpr, label='auc=%1.5f' % aucf)\n",
    "ax.set_title('Courbe ROC')\n",
    "ax.text(0.5, 0.3, \"plus mauvais que\\nle hasard dans\\ncette zone\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "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>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>pclass_2.0</th>\n",
       "      <th>pclass_3.0</th>\n",
       "      <th>title_Miss</th>\n",
       "      <th>title_Mr</th>\n",
       "      <th>title_Mrs</th>\n",
       "      <th>title_Officer</th>\n",
       "      <th>title_Royalty</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sex      age  sibsp  parch      fare  pclass_2.0  pclass_3.0  title_Miss  \\\n",
       "0    1  29.0000    0.0    0.0  211.3375           0           0           1   \n",
       "1    0   0.9167    1.0    2.0  151.5500           0           0           0   \n",
       "2    1   2.0000    1.0    2.0  151.5500           0           0           1   \n",
       "3    0  30.0000    1.0    2.0  151.5500           0           0           0   \n",
       "4    1  25.0000    1.0    2.0  151.5500           0           0           0   \n",
       "\n",
       "   title_Mr  title_Mrs  title_Officer  title_Royalty  embarked_Q  embarked_S  \n",
       "0         0          0              0              0           0           1  \n",
       "1         0          0              0              0           0           1  \n",
       "2         0          0              0              0           0           1  \n",
       "3         1          0              0              0           0           1  \n",
       "4         0          1              0              0           0           1  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": null,
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   "outputs": [],
   "source": []
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   "execution_count": null,
   "metadata": {},
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   "execution_count": 68,
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    {
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     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
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