Copie_de_tp1_prepa_feature.ipynb

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    "colab": {
      "name": "Copie de tp1_prepa_feature.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alex-cailler/alexandre-wordpress/blob/master/Copie_de_tp1_prepa_feature.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lWqJEbtdEZJ7"
      },
      "source": [
        "## Import des librairies ##"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NdU2q13DEZKB"
      },
      "source": [
        "#Imports librairies de base\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "#Bibliothèques pour Cross validation\n",
        "\n",
        "#Affichage de Graphes\n",
        "from IPython.display import Markdown, display, HTML"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "25b1e1db-8bc5-7029-f719-91da523bd121",
        "_uuid": "5c867fcbb300bcf3c9b8986bba9949da2a2df931",
        "id": "p646pPhGEZKC"
      },
      "source": [
        "## 1. Lecture des données ##"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "63Ka21PIEZKC"
      },
      "source": [
        "1.1 Charger les données d'apprentissage (Data/passagers.csv) dans un DataFrame nommé train. Afficher 10 exemples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "2ce68358-02ec-556d-ba88-e773a50bc18b",
        "_uuid": "5ec0878acc5c7ab3903410e671c2a2c6cfeafeea",
        "id": "mL3Q-d9HEZKC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "a1b731dd-8ae5-4382-c260-4ee6bd57d52f"
      },
      "source": [
        "train = pd.read_csv('Data/passagers.csv', dtype={'PassengerId': np.int})\n",
        "\n",
        "train.sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>331</th>\n",
              "      <td>332</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Partner, Mr. Austen</td>\n",
              "      <td>male</td>\n",
              "      <td>45.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113043</td>\n",
              "      <td>28.5000</td>\n",
              "      <td>C124</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>834</th>\n",
              "      <td>835</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allum, Mr. Owen George</td>\n",
              "      <td>male</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2223</td>\n",
              "      <td>8.3000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>866</th>\n",
              "      <td>867</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Duran y More, Miss. Asuncion</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>SC/PARIS 2149</td>\n",
              "      <td>13.8583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>345</th>\n",
              "      <td>346</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Brown, Miss. Amelia \"Mildred\"</td>\n",
              "      <td>female</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>248733</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>F33</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>581</th>\n",
              "      <td>582</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Thayer, Mrs. John Borland (Marian Longstreth M...</td>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>17421</td>\n",
              "      <td>110.8833</td>\n",
              "      <td>C68</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Peter, Miss. Anna</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2668</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>F E69</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>397</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Olsson, Miss. Elina</td>\n",
              "      <td>female</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>350407</td>\n",
              "      <td>7.8542</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>544</th>\n",
              "      <td>545</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Douglas, Mr. Walter Donald</td>\n",
              "      <td>male</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17761</td>\n",
              "      <td>106.4250</td>\n",
              "      <td>C86</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>132</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Coelho, Mr. Domingos Fernandeo</td>\n",
              "      <td>male</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SOTON/O.Q. 3101307</td>\n",
              "      <td>7.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>557</th>\n",
              "      <td>558</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Robbins, Mr. Victor</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17757</td>\n",
              "      <td>227.5250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...      Fare  Cabin  Embarked\n",
              "331          332         0       1  ...   28.5000   C124         S\n",
              "834          835         0       3  ...    8.3000    NaN         S\n",
              "866          867         1       2  ...   13.8583    NaN         C\n",
              "345          346         1       2  ...   13.0000    F33         S\n",
              "581          582         1       1  ...  110.8833    C68         C\n",
              "128          129         1       3  ...   22.3583  F E69         C\n",
              "396          397         0       3  ...    7.8542    NaN         S\n",
              "544          545         0       1  ...  106.4250    C86         C\n",
              "131          132         0       3  ...    7.0500    NaN         S\n",
              "557          558         0       1  ...  227.5250    NaN         C\n",
              "\n",
              "[10 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_P860sjEZKD"
      },
      "source": [
        "1.2 Charger les données d'apprentissage (Data/test.csv) dans un Dataframe nommé test. Afficher 10 exemples. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mww8ymdSEZKD"
      },
      "source": [
        "test = pd.read_csv('Data/test.csv', dtype={'PassengerId': np.int})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "V-K0bG2oJGj1",
        "outputId": "93094911-f8ac-44e6-cf3d-2d0becb5faf9"
      },
      "source": [
        "test.sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>PassengerId</th>\n",
              "      <th>Pclass</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>938</td>\n",
              "      <td>1</td>\n",
              "      <td>Chevre, Mr. Paul Romaine</td>\n",
              "      <td>male</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17594</td>\n",
              "      <td>29.7000</td>\n",
              "      <td>A9</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>361</th>\n",
              "      <td>1253</td>\n",
              "      <td>2</td>\n",
              "      <td>Mallet, Mrs. Albert (Antoinette Magnin)</td>\n",
              "      <td>female</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>S.C./PARIS 2079</td>\n",
              "      <td>37.0042</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>1104</td>\n",
              "      <td>2</td>\n",
              "      <td>Deacon, Mr. Percy William</td>\n",
              "      <td>male</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>S.O.C. 14879</td>\n",
              "      <td>73.5000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>1041</td>\n",
              "      <td>2</td>\n",
              "      <td>Lahtinen, Rev. William</td>\n",
              "      <td>male</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>250651</td>\n",
              "      <td>26.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>1174</td>\n",
              "      <td>3</td>\n",
              "      <td>Fleming, Miss. Honora</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>364859</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>1072</td>\n",
              "      <td>2</td>\n",
              "      <td>McCrie, Mr. James Matthew</td>\n",
              "      <td>male</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>233478</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>1144</td>\n",
              "      <td>1</td>\n",
              "      <td>Clark, Mr. Walter Miller</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>13508</td>\n",
              "      <td>136.7792</td>\n",
              "      <td>C89</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254</th>\n",
              "      <td>1146</td>\n",
              "      <td>3</td>\n",
              "      <td>Wenzel, Mr. Linhart</td>\n",
              "      <td>male</td>\n",
              "      <td>32.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>345775</td>\n",
              "      <td>9.5000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>1079</td>\n",
              "      <td>3</td>\n",
              "      <td>Davies, Mr. Joseph</td>\n",
              "      <td>male</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>A/4 48873</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>996</td>\n",
              "      <td>3</td>\n",
              "      <td>Thomas, Mrs. Alexander (Thamine Thelma\")\"</td>\n",
              "      <td>female</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2625</td>\n",
              "      <td>8.5167</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Pclass  ... Cabin Embarked\n",
              "46           938       1  ...    A9        C\n",
              "361         1253       2  ...   NaN        C\n",
              "212         1104       2  ...   NaN        S\n",
              "149         1041       2  ...   NaN        S\n",
              "282         1174       3  ...   NaN        Q\n",
              "180         1072       2  ...   NaN        S\n",
              "252         1144       1  ...   C89        C\n",
              "254         1146       3  ...   NaN        S\n",
              "187         1079       3  ...   NaN        S\n",
              "104          996       3  ...   NaN        C\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KVdVVuvCEZKD"
      },
      "source": [
        "1.3 Afficher les informations des deux DataFrames train et Test."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "2ce68358-02ec-556d-ba88-e773a50bc18b",
        "_uuid": "5ec0878acc5c7ab3903410e671c2a2c6cfeafeea",
        "id": "y9rtsNWgEZKD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a787f87-1778-4a87-899a-fbc4b4dddb1b"
      },
      "source": [
        "test.info\n",
        "train.info"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of      PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0              1         0       3  ...   7.2500   NaN         S\n",
              "1              2         1       1  ...  71.2833   C85         C\n",
              "2              3         1       3  ...   7.9250   NaN         S\n",
              "3              4         1       1  ...  53.1000  C123         S\n",
              "4              5         0       3  ...   8.0500   NaN         S\n",
              "..           ...       ...     ...  ...      ...   ...       ...\n",
              "886          887         0       2  ...  13.0000   NaN         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500   NaN         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500   NaN         Q\n",
              "\n",
              "[891 rows x 12 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2y9zldwEZKE"
      },
      "source": [
        "1.4 Calculer le taux global de survie"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "weMPeC-AEZKE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3b13a90-ffdd-460c-d90a-0a223a4c8c02"
      },
      "source": [
        "train[\"Survived\"].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3838383838383838"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MVJD62-eKG31",
        "outputId": "ea917bb6-0571-4e20-ba4b-3ebd1d756e1c"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FamilySize\n",
              "1     537\n",
              "2     161\n",
              "3     102\n",
              "4      29\n",
              "5      15\n",
              "6      22\n",
              "7      12\n",
              "8       6\n",
              "11      7\n",
              "Name: PassengerId, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Qh04lJmEZKE"
      },
      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AE12VzqDEZKE"
      },
      "source": [
        "#### Question 1 : Quel Travail voyez-vous pour que les données soient prêtes à faire tourner dans un modèle ?\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#### Question 2 : Quelle différence y-a-t'il entre les données train et les données test ? \n",
        "\n",
        "La différence est que dans les données de test il n'y a pas de donnée concernant la survie.\n",
        "\n",
        "\n",
        "\n",
        "#### Question 3 : Quel est le rôle de ces deux sets de données ?\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "f9595646-65c9-6fc4-395f-0befc4d122ce",
        "_uuid": "66273d64a2548d7a88464ab2a73dbdedfbdc488b",
        "id": "VnM1LzROEZKE"
      },
      "source": [
        "## 2. Analyse des features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "9b4c278b-aaca-e92c-ba77-b9b48379d1f1",
        "_uuid": "c2b62e14d493c270ec8df9f3af1938c479361ef3",
        "id": "q72lN8wlEZKF"
      },
      "source": [
        "2.1 Analyser l'effet de la variable Classe sur le taux de survie, en affichant le % de survie par classe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_dotq9GkEZKF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "d1faea9d-81e4-4e0d-cfb3-d129f8579d2b"
      },
      "source": [
        "train[['Survived', 'Pclass']].groupby('Pclass').mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.629630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.472826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.242363</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Survived\n",
              "Pclass          \n",
              "1       0.629630\n",
              "2       0.472826\n",
              "3       0.242363"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "4680d950-cf7d-a6ae-e813-535e2247d88e",
        "_uuid": "f02533e7b85bba0cca7fcf2cc598c8da92d7646d",
        "id": "shJa0_vsEZKF"
      },
      "source": [
        "2.2 Analyser l'effet de la variable Genre (Sex) sur le taux de survie, en affichant le % de survie par genre"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "4680d950-cf7d-a6ae-e813-535e2247d88e",
        "_uuid": "f02533e7b85bba0cca7fcf2cc598c8da92d7646d",
        "id": "NtAyWzktEZKF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "9bb1e3ec-6576-455e-e488-3f4623afe86a"
      },
      "source": [
        "train[['Survived', 'Sex']].groupby('Sex').mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.742038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.188908</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Survived\n",
              "Sex             \n",
              "female  0.742038\n",
              "male    0.188908"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "7c58b7ee-d6a1-0cc9-2346-81c47846a54a",
        "_uuid": "88185e9222c26d5d23caaeb209c18710a231b5f9",
        "id": "P0ldnt9aEZKF"
      },
      "source": [
        "2.3 Calculer une nouvelle colonne FamilySize contenant la taille de la famille, pour les DataFrame train et Test. Afficher pour 10 exemples l'Id du passager, le nom, l'age, le sexe et la taille de la famille."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "1a537f10-7cec-d0b7-8a34-fa9975655190",
        "_uuid": "ccc4a4cf7624dd4be450fd62ca3ad478d4e75696",
        "id": "LMrrxfRSEZKG"
      },
      "source": [
        "train['FamilySize'] = train['Parch'] + train['SibSp'] + 1\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyb1ro-qEZKG"
      },
      "source": [
        "2.4 Visualisez le nombre de personnes et le taux de survie par taille de famille."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "1a537f10-7cec-d0b7-8a34-fa9975655190",
        "_uuid": "ccc4a4cf7624dd4be450fd62ca3ad478d4e75696",
        "id": "vmbRK5WZEZKG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "4a0a3b29-3e97-4f99-f01d-eb7ae98f06e7"
      },
      "source": [
        "train[['Survived', 'PassengerId', 'FamilySize']].groupby('FamilySize') \\\n",
        "       .agg({'PassengerId':'count', 'Survived':'mean'})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FamilySize</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>537</td>\n",
              "      <td>0.303538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>161</td>\n",
              "      <td>0.552795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>102</td>\n",
              "      <td>0.578431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>29</td>\n",
              "      <td>0.724138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>15</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>22</td>\n",
              "      <td>0.136364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>12</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>6</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>7</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            PassengerId  Survived\n",
              "FamilySize                       \n",
              "1                   537  0.303538\n",
              "2                   161  0.552795\n",
              "3                   102  0.578431\n",
              "4                    29  0.724138\n",
              "5                    15  0.200000\n",
              "6                    22  0.136364\n",
              "7                    12  0.333333\n",
              "8                     6  0.000000\n",
              "11                    7  0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "7c58b7ee-d6a1-0cc9-2346-81c47846a54a",
        "_uuid": "88185e9222c26d5d23caaeb209c18710a231b5f9",
        "id": "6dvyYLpxEZKG"
      },
      "source": [
        "2.5 Calculer une nouvelle colonne IsAlone indiquant si le passager est seul ou pas, pour les DataFrame train et Test. Afficher pour 10 exemples l'Id du passager, le nom, l'age, le sexe et le status IsAlone."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "8c35e945-c928-e3bc-bd9c-d6ddb287e4c9",
        "_uuid": "87f79dc0711c29f39c0db1a4f7a2e8a84c0c7edb",
        "id": "wF25r704EZKG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "b3146b4c-3bc5-4c87-baa3-7383c12b47ad"
      },
      "source": [
        "\n",
        "train['isAlone'] = train['FamilySize'] == 1\n",
        "train[['PassengerId', 'Name', 'Sex', 'Age', 'isAlone']].sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>isAlone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>393</td>\n",
              "      <td>Gustafsson, Mr. Johan Birger</td>\n",
              "      <td>male</td>\n",
              "      <td>28.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>158</td>\n",
              "      <td>Corn, Mr. Harry</td>\n",
              "      <td>male</td>\n",
              "      <td>30.0</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>219</td>\n",
              "      <td>Bazzani, Miss. Albina</td>\n",
              "      <td>female</td>\n",
              "      <td>32.0</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>688</th>\n",
              "      <td>689</td>\n",
              "      <td>Fischer, Mr. Eberhard Thelander</td>\n",
              "      <td>male</td>\n",
              "      <td>18.0</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>370</td>\n",
              "      <td>Aubart, Mme. Leontine Pauline</td>\n",
              "      <td>female</td>\n",
              "      <td>24.0</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>839</th>\n",
              "      <td>840</td>\n",
              "      <td>Marechal, Mr. Pierre</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>112</td>\n",
              "      <td>Zabour, Miss. Hileni</td>\n",
              "      <td>female</td>\n",
              "      <td>14.5</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>861</th>\n",
              "      <td>862</td>\n",
              "      <td>Giles, Mr. Frederick Edward</td>\n",
              "      <td>male</td>\n",
              "      <td>21.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>138</td>\n",
              "      <td>Futrelle, Mr. Jacques Heath</td>\n",
              "      <td>male</td>\n",
              "      <td>37.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>711</th>\n",
              "      <td>712</td>\n",
              "      <td>Klaber, Mr. Herman</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId                             Name     Sex   Age  isAlone\n",
              "392          393     Gustafsson, Mr. Johan Birger    male  28.0    False\n",
              "157          158                  Corn, Mr. Harry    male  30.0     True\n",
              "218          219            Bazzani, Miss. Albina  female  32.0     True\n",
              "688          689  Fischer, Mr. Eberhard Thelander    male  18.0     True\n",
              "369          370    Aubart, Mme. Leontine Pauline  female  24.0     True\n",
              "839          840             Marechal, Mr. Pierre    male   NaN     True\n",
              "111          112             Zabour, Miss. Hileni  female  14.5    False\n",
              "861          862      Giles, Mr. Frederick Edward    male  21.0    False\n",
              "137          138      Futrelle, Mr. Jacques Heath    male  37.0    False\n",
              "711          712               Klaber, Mr. Herman    male   NaN     True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d94zOUn_EZKG"
      },
      "source": [
        "2.6 Visualisez le nombre de personnes et le taux de survie par statut seul ou pas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "8c35e945-c928-e3bc-bd9c-d6ddb287e4c9",
        "_uuid": "87f79dc0711c29f39c0db1a4f7a2e8a84c0c7edb",
        "id": "QfPfWi00EZKG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "4e73d5cc-91fa-4f6b-a22b-572648d0a099"
      },
      "source": [
        "train[['Survived', 'FamilySize', 'PassengerId', 'isAlone']].groupby('isAlone') \\\n",
        "       .agg({'PassengerId':'count', 'Survived':'mean'}) \\\n",
        "       .rename(columns={'PassengerId':'Qty'})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Qty</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>isAlone</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>False</th>\n",
              "      <td>354</td>\n",
              "      <td>0.505650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True</th>\n",
              "      <td>537</td>\n",
              "      <td>0.303538</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Qty  Survived\n",
              "isAlone               \n",
              "False    354  0.505650\n",
              "True     537  0.303538"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TZBBzu9EZKH"
      },
      "source": [
        "### Question 1 : La variable Classe est-elle utile pour prévoir la survie ? Pourquoi ?\n",
        "\n",
        "\n",
        "\n",
        "### Question 2 : Même question pour la variable Sex \n",
        "\n",
        "\n",
        "### Question 3 : Comment traiter les variables liées à la famille du passager ?\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrDm1kR9EZKH"
      },
      "source": [
        "## 3. Traitement des données manquantes (à faire sur les données Train et Test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "8aa419c0-6614-7efc-7797-97f4a5158b19",
        "_uuid": "dd18a31086cfeca6330b05f83caf3cc02f687253",
        "id": "r8N2Lml8EZKH"
      },
      "source": [
        "3.1 La variable \"Embarked\" possède des valeurs nulles, compléter par la valeur majoritaire (S)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "0e70e9af-d7cc-8c40-b7d4-2643889c376d",
        "_uuid": "b4f7ccb3df98da6915bda1c7c225b905fc37845b",
        "id": "v63MJmEFEZKH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55fd9eb3-7ee8-4463-a343-2bcdecb2dbec"
      },
      "source": [
        "train['Embarked'].fillna('S')\n",
        "test['Embarked'].fillna('S')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.fillna of     Embarked\n",
              "0          S\n",
              "1          C\n",
              "2          S\n",
              "3          S\n",
              "4          S\n",
              "..       ...\n",
              "886        S\n",
              "887        S\n",
              "888        S\n",
              "889        C\n",
              "890        Q\n",
              "\n",
              "[891 rows x 1 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EpODpzWLEZKH"
      },
      "source": [
        "3.2 Afficher l'influence du port d'embarquement sur la Survie "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "0e70e9af-d7cc-8c40-b7d4-2643889c376d",
        "_uuid": "b4f7ccb3df98da6915bda1c7c225b905fc37845b",
        "id": "UrD1N259EZKH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "2110801e-51b9-41c4-c7a9-1585450f88e6"
      },
      "source": [
        "train[['Embarked','Survived']].groupby(['Embarked']).mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q</th>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S</th>\n",
              "      <td>644</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Survived\n",
              "Embarked          \n",
              "C              168\n",
              "Q               77\n",
              "S              644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "e08c9ee8-d6d1-99b7-38bd-f0042c18a5d9",
        "_uuid": "bbeb369d4bb1b086fcc3257218fdeeb6bcdb53c0",
        "id": "fY41X85VEZKH"
      },
      "source": [
        "3.3 La variable \"Fare\" (prix du ticket) possède des valeurs manquantes. Remplacer ces valeurs par la valeur médiane."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "a21335bd-4e8d-66e8-e6a5-5d2173b72d3b",
        "_uuid": "f04975f236aad78d3f93812512088404b5c9280e",
        "id": "k9OwF5oMEZKI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73f05246-b9cc-4551-c407-1167277aadcd"
      },
      "source": [
        "train[[\"Fare\"]].fillna(train[[\"Fare\"]].median())\n",
        "test[[\"Fare\"]].fillna(test[[\"Fare\"]].median())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       7.2500\n",
              "1      71.2833\n",
              "2       7.9250\n",
              "3      53.1000\n",
              "4       8.0500\n",
              "        ...   \n",
              "886    13.0000\n",
              "887    30.0000\n",
              "888    23.4500\n",
              "889    30.0000\n",
              "890     7.7500\n",
              "Name: Fare, Length: 891, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKV9civhEZKI"
      },
      "source": [
        "3.4 Créer une variable CategoricalFare Découpant la valeur de ticket en 4 catégories de tailles à peu près égales. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "a21335bd-4e8d-66e8-e6a5-5d2173b72d3b",
        "_uuid": "f04975f236aad78d3f93812512088404b5c9280e",
        "id": "rz4U2iTsEZKI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "13897ba1-6eee-41a7-f024-733740b53cc9"
      },
      "source": [
        "train['CategoricalFare'] = pd.qcut(train['Fare'], 4)\n",
        "test['CategoricalFare'] = pd.qcut(test['Fare'], 4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-7b69f508ced9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CategoricalFare'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqcut\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Fare'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CategoricalFare'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqcut\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Fare'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLMV-XuVEZKI"
      },
      "source": [
        "3.5 Analyser l'effet de ces catégories de prix de ticket sur la survie."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "a21335bd-4e8d-66e8-e6a5-5d2173b72d3b",
        "_uuid": "f04975f236aad78d3f93812512088404b5c9280e",
        "id": "isc8cmTvEZKJ"
      },
      "source": [
        "train[['CategoricalFare','Survived']].groupby(['CategoricalFare']).mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCth7E8REZKJ"
      },
      "source": [
        "3.6 Combien de valeurs d'Age manquent ? Complète par des valeurs entières aléatoires entre (moyenne - écart type) et (moyenne + écart type)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "b90c2870-ce5d-ae0e-a33d-59e35445500e",
        "_uuid": "2af2b56d51752be08b84dbb2684466976758faa7",
        "id": "P75dhhN-EZKK"
      },
      "source": [
        "import random\n",
        "train['Age'] = train['Age'].fillna(random.uniform(train['Age'].mean() - train['Age'].std(), train['Age'].mean() + train['Age'].std()))\n",
        "test['Age'] = test['Age'].fillna(random.uniform(test['Age'].mean() - test['Age'].std(), test['Age'].mean() + test['Age'].std()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tw0N61XVEZKK"
      },
      "source": [
        "3.7 Créer une variable CategoricalAge Découpant la valeur d'age en 5 catégories égales. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "b90c2870-ce5d-ae0e-a33d-59e35445500e",
        "_uuid": "2af2b56d51752be08b84dbb2684466976758faa7",
        "id": "nWMYww-aEZKK"
      },
      "source": [
        "test['CategoricalAge'] = pd.qcut(test['Age'], 5)\n",
        "train['CategoricalAge'] = pd.qcut(train['Age'], 5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h1vK0gVSEZKK"
      },
      "source": [
        "3.8 Analyser l'effet de ces catégories d'age sur la survie."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCIOQQBmEZKK"
      },
      "source": [
        "train[['CategoricalAge','Survived']].groupby(['CategoricalAge']).mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cq0wxmPqEZKK"
      },
      "source": [
        "### Question 1 : Classer les variables par ordre d'intérêt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jh5lLiB5EZKK"
      },
      "source": [
        "## 4 : Traitement de features complexes (Optionnel) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "bd25ec3f-b601-c1cc-d701-991fac1621f9",
        "_uuid": "17f6d181260d68d89f0a5dfa99b40a82b723567c",
        "id": "vCbg4VQ2EZKK"
      },
      "source": [
        "4.1 : Le nom permet de retrouver le titre du passager. Trouver les titres lorsqu'ils existent."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "ad042f43-bfe0-ded0-4171-379d8caaa749",
        "_uuid": "5a56f5b4bad78d7e323167c5f0cc07cb3675187a",
        "id": "pYOrucgkEZKL"
      },
      "source": [
        "train['Title']=train['Name'].str.extract(r\"(?<=, )(.?)(?=.)\")\n",
        "test['Title']=test['Name'].str.extract(r\"(?<=, )(.?)(?=.)\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2gid1QnyEZKL"
      },
      "source": [
        "4.2 : En utilisant la fonction pandas crosstab, afficher pour train le nombre d'occurences par titres (en ligne) et par sexe (en colonne)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IeamJeYhEZKL"
      },
      "source": [
        "pd.crosstab(\n",
        "    train['Title'], [train['Title'].count(), train['Sex']], \n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "ca5fff8c-7a0d-6c18-2173-b8df6293c50a",
        "_uuid": "84862f8cbbe2ea72b69a9cba78c6b7e10c80626e",
        "id": "IvSZ7eyJEZKL"
      },
      "source": [
        "4.3 : \n",
        "Remplacer 'Melle et 'Ms' par 'Miss'\n",
        "Remplacer 'Mme' par 'Mrs'\n",
        "Creer une valeur \"Rare\" pour les autres titres avec moins de 10 occurences. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "8357238b-98fe-632a-acd5-33674a6132ce",
        "_uuid": "db0029466612ad0550fc0eebef4a06a5507d6c3d",
        "id": "ksL8DUk6EZKL"
      },
      "source": [
        "train['Title'].replace(['Ms', 'Mlle'], 'Miss')\n",
        "train['Title'].replace('Mme', 'Miss')\n",
        "\n",
        "train.loc[train.groupby('Title')['Title'].transform('count').lt(10), 'Title'] = 'Rare' \n",
        "\n",
        "\n",
        "test['Title'].replace(['Ms', 'Mlle'], 'Miss')\n",
        "test['Title'].replace('Mme', 'Miss')\n",
        "\n",
        "test.loc[test.groupby('Title')['Title'].transform('count').lt(10), 'Title'] = 'Rare'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "68fa2057-e27a-e252-0d1b-869c00a303ba",
        "_uuid": "52bcf7b36b8edb12d40f2a1f9e80060b55d91ad3",
        "id": "G_G6Fp-VEZKL"
      },
      "source": [
        "## 5. Mise en forme des données non numériques"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RO2mBsfqEZKL"
      },
      "source": [
        "5.1 Attribuer une valeur numérique au Genre. Par exemple 0 pour 'female' et 1 pour 'male'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U726FvO3EZKM"
      },
      "source": [
        "train.loc[\n",
        "    train[\"Sex\"]=='female', # Lignes\n",
        "                \"Sex\"\n",
        "    ]= 0\n",
        "train.loc[\n",
        "    train[\"Sex\"]=='male', # Lignes\n",
        "                \"Sex\"\n",
        "    ]= 1\n",
        "\n",
        "test.loc[\n",
        "    test[\"Sex\"]=='female', # Lignes\n",
        "                \"Sex\"\n",
        "    ]= 0\n",
        "test.loc[\n",
        "    test[\"Sex\"]=='male', # Lignes\n",
        "                \"Sex\"\n",
        "    ]= 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubAwAcdtEZKM"
      },
      "source": [
        "5.2 : Attribuer une valeur pour les possibilités de la variable \"Title\" ('Master','Miss','Mr','Mrs','Rare'). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3wdHp3FEZKM"
      },
      "source": [
        "train.loc[\n",
        "    train[\"Title\"]=='Master',\n",
        "                \"Title\"\n",
        "    ]= 1\n",
        "train.loc[\n",
        "    train[\"Title\"]=='Miss',\n",
        "                \"Title\"\n",
        "    ]= 2\n",
        "train.loc[\n",
        "    train[\"Title\"]=='Mr',\n",
        "                \"Title\"\n",
        "    ]= 3\n",
        "train.loc[\n",
        "    train[\"Title\"]=='Mrs',\n",
        "                \"Title\"\n",
        "    ]= 4\n",
        "train.loc[\n",
        "    train[\"Title\"]=='Rare',\n",
        "                \"Title\"\n",
        "    ]= 5\n",
        "\n",
        "test.loc[\n",
        "    test[\"Title\"]=='Master',\n",
        "                \"Title\"\n",
        "    ]= 1\n",
        "test.loc[\n",
        "    test[\"Title\"]=='Miss',\n",
        "                \"Title\"\n",
        "    ]= 2\n",
        "test.loc[\n",
        "    test[\"Title\"]=='Mr',\n",
        "                \"Title\"\n",
        "    ]= 3\n",
        "test.loc[\n",
        "    test[\"Title\"]=='Mrs',\n",
        "                \"Title\"\n",
        "    ]= 4\n",
        "test.loc[\n",
        "    test[\"Title\"]=='Rare',\n",
        "                \"Title\"\n",
        "    ]= 5\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClOqjDs8EZKM"
      },
      "source": [
        "5.3 Remplacer les titres manquant par 0, et attribuer à la colonne le type entier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zni8lkjLEZKM"
      },
      "source": [
        " train['Title'].fillna(0)\n",
        "test['Title'].fillna(0) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lPcANhtaEZKM"
      },
      "source": [
        "5.4 Attribuer une valeur numérique aux 4 catégories de la variable Fare (question 3.6)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8K0GW0SEZKM"
      },
      "source": [
        "train['CategoricalFare'] = pd.qcut(train['Fare'],4, labels=[0, 1, 2, 3])\n",
        "test['CategoricalFare'] = pd.qcut(train['Fare'],4, labels=[0, 1, 2, 3])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SUuAHt1XEZKM"
      },
      "source": [
        "5.5 Attribuer une valeur numérique aux 5 catégories de la variable Age (question 3.7)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHDs_4tYEZKN"
      },
      "source": [
        "train['CategoricalAge'] = pd.qcut(train['Age'], 5, labels=[0, 1, 2, 3, 4])\n",
        "test['CategoricalAge'] = pd.qcut(test['Age'], 5, labels=[0, 1, 2, 3, 4])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzjj69JWEZKN"
      },
      "source": [
        "5.6 Attibuer une valeur numérique au Port d'embarquement (Embarked)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Buxenr2XEZKN"
      },
      "source": [
        "train.loc[\n",
        "    train[\"Embarked\"]=='C',\n",
        "                \"Embarked\"\n",
        "    ]= 1\n",
        "train.loc[\n",
        "    train[\"Embarked\"]=='Q',\n",
        "                \"Embarked\"\n",
        "    ]= 2\n",
        "train.loc[\n",
        "    train[\"Embarked\"]=='S',\n",
        "                \"Embarked\"\n",
        "    ]= 3\n",
        "\n",
        "test.loc[\n",
        "    test[\"Embarked\"]=='C',\n",
        "                \"Embarked\"\n",
        "    ]= 1\n",
        "test.loc[\n",
        "    test[\"Embarked\"]=='Q',\n",
        "                \"Embarked\"\n",
        "    ]= 2\n",
        "test.loc[\n",
        "    test[\"Embarked\"]=='S',\n",
        "                \"Embarked\"\n",
        "    ]= 3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BcUr0rFxEZKN"
      },
      "source": [
        "5.7 Supprimer les features inutiles"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAHD_CnAEZKN"
      },
      "source": [
        "train = train.drop(['Ticket', 'Cabin', 'Name'], axis=1)\n",
        "test = test.drop(['Ticket', 'Cabin', 'Name'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJ9i-WX9EZKN"
      },
      "source": [
        "5.8 Afficher les infos pour les deux datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9zbmpZgWEZKN"
      },
      "source": [
        "train.info()\n",
        "test.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YspQ8ZdSEZKN"
      },
      "source": [
        "# Sauver les deux datasets dans des fichier csv. Eviter d'écrire un index (n° de ligne)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "2502bb70-ce6f-2497-7331-7d1f80521470",
        "_uuid": "1aa110c1043f1f43c091a771abc64054a211f784",
        "id": "8IMB3RGNEZKO"
      },
      "source": [
        "train.to_csv(index=False)\n",
        "test.to_csv(index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EKNxVTTmEZKO"
      },
      "source": [
        "##### Question : Sur combien de questions avez-vous utilisé les conseils ?\n"
      ]
    }
  ]
}