05_03_Bonus_Projet3_Analyse_des_donnees_du_Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sR1sundZsMw8"
   },
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xKa7I9uYr2dX"
   },
   "source": [
    "Dans ce notebook, nous allons analyser les données du Titanic. L'objectif est de comprendre les facteurs qui ont pu influencer la survie des passagers. Tout au long de ce rapport, nous nous poserons des questions pertinentes et essayerons d'y repondre avec du code et de la visualisation de données.\n",
    "\n",
    "Le plan de notre analyse est le suivant:\n",
    "\n",
    "* Import des différentes librairies de Python\n",
    "\n",
    "* Import et bref aperçu de nos données\n",
    "\n",
    "* Nettoyage et Transformation de nos données\n",
    "\n",
    "* Analyse univarié des colonnes ou attributs les plus important\n",
    "\n",
    "* Analyse bivariée des colonnes afin d'identifier les caractéristiques ayant une influence sur la survie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rSEUPEzxsrEj"
   },
   "source": [
    "# Import des librairies de Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UnrGiBMcspK2"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SDIV2LIOs_XI"
   },
   "source": [
    "# Import et Bref aperçu de nos données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VnlCTWUbs5Ff",
    "outputId": "c93aec40-7f4b-40f9-94ba-ca80090150a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /gdrive\n"
     ]
    }
   ],
   "source": [
    "# Montage du disque google drive\n",
    "from google.colab import drive\n",
    "drive.mount('/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "08Nvc-2PtKVO"
   },
   "outputs": [],
   "source": [
    "dossier = '/gdrive/My Drive/DataKev/data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5sOBNa3OtKVP"
   },
   "outputs": [],
   "source": [
    "titanic = pd.read_csv(dossier + 'train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "id": "yz4doRHUtiGK",
    "outputId": "80a92433-1de2-46d6-de52-58be58915932"
   },
   "outputs": [
    {
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       "  <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",
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       "      <th>Ticket</th>\n",
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       "      <th>Embarked</th>\n",
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       "      <th>543</th>\n",
       "      <td>544</td>\n",
       "      <td>1</td>\n",
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       "      <td>Beane, Mr. Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
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       "      <td>Farrell, Mr. James</td>\n",
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       "      <td>Adahl, Mr. Mauritz Nils Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>Smart, Mr. John Montgomery</td>\n",
       "      <td>male</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
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       "      <th>846</th>\n",
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       "      <td>Sage, Mr. Douglas Bullen</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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      "text/plain": [
       "     PassengerId  Survived  Pclass  ...   Fare Cabin  Embarked\n",
       "543          544         1       2  ...  26.00   NaN         S\n",
       "525          526         0       3  ...   7.75   NaN         Q\n",
       "365          366         0       3  ...   7.25   NaN         S\n",
       "467          468         0       1  ...  26.55   NaN         S\n",
       "846          847         0       3  ...  69.55   NaN         S\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pya-aLfttlGL",
    "outputId": "db7afd3c-a763-4b3a-e846-8510d277ba7d"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
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   ],
   "source": [
    "titanic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vHSXBuVItr4R",
    "outputId": "f6144263-f9c9-4566-e37a-cbcf89ec699a"
   },
   "outputs": [
    {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "ag8LrUlAtxMj",
    "outputId": "83d2f16f-7603-4f93-e390-11c616a8ef65"
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   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
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       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
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       "      <th>std</th>\n",
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       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n",
       "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
       "mean    446.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n",
       "std     257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n",
       "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
       "25%     223.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n",
       "50%     446.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n",
       "75%     668.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n",
       "max     891.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n",
       "\n",
       "[8 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7fQeHIyFuNmp"
   },
   "source": [
    "* On remarque déjà que 38% des passagers ont survécu.\n",
    "* Les Variables `Survived` et `Pclass` devraient être des catégories et non des floats\n",
    "* L'age moyen sur le titanic était de 29 ans\n",
    "*  Il y a des valeurs manquantes pour l'age des passagers\n",
    "* La variable Sibsp ne devraient-elle pas être une catégorie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 172
    },
    "id": "6yMj3bRit8mQ",
    "outputId": "8c84071f-536c-45bc-d244-7203d0e09bab"
   },
   "outputs": [
    {
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       "      <th>top</th>\n",
       "      <td>Robert, Mrs. Edward Scott (Elisabeth Walton Mc...</td>\n",
       "      <td>male</td>\n",
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      "text/plain": [
       "                                                     Name   Sex  ... Cabin Embarked\n",
       "count                                                 891   891  ...   204      889\n",
       "unique                                                891     2  ...   147        3\n",
       "top     Robert, Mrs. Edward Scott (Elisabeth Walton Mc...  male  ...    G6        S\n",
       "freq                                                    1   577  ...     4      644\n",
       "\n",
       "[4 rows x 5 columns]"
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     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LDaBPotuwAub"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5gXYAoE3wp4E"
   },
   "source": [
    "# Nettoyage et Transformation de nos données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yXJhIDEvwr0q",
    "outputId": "093e6a80-360c-4895-97e5-d5909e927e8e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C1mvtfwLw-_V",
    "outputId": "a489f492-7d2b-481c-a618-c027c1b7cf0b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0.000000\n",
       "Survived        0.000000\n",
       "Pclass          0.000000\n",
       "Name            0.000000\n",
       "Sex             0.000000\n",
       "Age            19.865320\n",
       "SibSp           0.000000\n",
       "Parch           0.000000\n",
       "Ticket          0.000000\n",
       "Fare            0.000000\n",
       "Cabin          77.104377\n",
       "Embarked        0.224467\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().mean(axis=0) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qRtd6_wbxFiZ"
   },
   "outputs": [],
   "source": [
    "titanic.drop(['PassengerId', 'Cabin'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uCJXqguvxXc0",
    "outputId": "f0cbbbf0-a82a-48bc-f71c-d1159bee14b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived     0.000000\n",
       "Pclass       0.000000\n",
       "Name         0.000000\n",
       "Sex          0.000000\n",
       "Age         19.865320\n",
       "SibSp        0.000000\n",
       "Parch        0.000000\n",
       "Ticket       0.000000\n",
       "Fare         0.000000\n",
       "Embarked     0.224467\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().mean(axis=0) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w560q2z5xe-o",
    "outputId": "d97d4c6a-b4e4-440d-b7ba-9db3e73db2b0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xUbmQzh3xkWg",
    "outputId": "63c050f4-b9af-4f2e-8cf2-fc7359125ca4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Age.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dbVA3_E0xuB8"
   },
   "outputs": [],
   "source": [
    "titanic['Age'] = titanic.Age.fillna(titanic.Age.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pswT1Bnjxt4o",
    "outputId": "e42c98e3-b3e3-412d-dc0e-0c8d32409e44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0.000000\n",
       "Pclass      0.000000\n",
       "Name        0.000000\n",
       "Sex         0.000000\n",
       "Age         0.000000\n",
       "SibSp       0.000000\n",
       "Parch       0.000000\n",
       "Ticket      0.000000\n",
       "Fare        0.000000\n",
       "Embarked    0.224467\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().mean(axis=0) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-v0OJ0zSxtsf",
    "outputId": "63c03343-03ae-4502-a586-32e14d8f4e01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S      644\n",
       "C      168\n",
       "Q       77\n",
       "NaN      2\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Embarked.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 110
    },
    "id": "97IRghSyyLsN",
    "outputId": "e2b5906d-5e3b-46d6-bf23-7d20a0ecfc10"
   },
   "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>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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  ...  Fare Embarked\n",
       "61          1       1  ...  80.0      NaN\n",
       "829         1       1  ...  80.0      NaN\n",
       "\n",
       "[2 rows x 10 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic.Embarked.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jfFvItKoxrEp",
    "outputId": "bc1b0ef0-9792-4961-d7d6-89f8301924b9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([61, 829], dtype='int64')"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines_to_delete = titanic[titanic.Embarked.isnull()].index\n",
    "lines_to_delete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oaHgYHI9ycxF"
   },
   "outputs": [],
   "source": [
    "titanic.drop(lines_to_delete, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GqyDhG1TymfM",
    "outputId": "4bf2d934-0966-44e6-d18b-67983b3940ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0.0\n",
       "Pclass      0.0\n",
       "Name        0.0\n",
       "Sex         0.0\n",
       "Age         0.0\n",
       "SibSp       0.0\n",
       "Parch       0.0\n",
       "Ticket      0.0\n",
       "Fare        0.0\n",
       "Embarked    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().mean(axis=0) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A0Bl52Dyy22O"
   },
   "source": [
    "# Analyse univarié des colonnes ou attributs les plus important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X7ryjI2_ysqM",
    "outputId": "1b7abeb9-6ea5-44a5-b908-d12c1d0c4f4a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    340\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "GcvnX9Ohy9hp",
    "outputId": "a4064ac8-6d44-41c8-f2ea-191cadec43c6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b2c6e320>"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.Survived.value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "5oXMaP0tzcqo",
    "outputId": "38c63be6-f3f6-403f-99a0-1826cb95c6c7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b198fc88>"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L8OE25hzg3Kp6JnDHY/6G0ogMBS16VfUA8DxgPTAFXJTk9Y+y2aaq+p/Wvhh4VWufBlwyx/iLgFe39untMw4FXgh8Ism1wN8wPWsBeBHw8da+YI++kPQYLBl3AdJCUFUPAlcBVyW5HlgL7OCh/3E6ZKdNvjdr221J7k7yLKb/8P/2HB+xCfizJEcyHUBXAE8E7quq43ZV1l5+HWmvOVPQopfk6UlWz+o6DrgNuJXpP+AAv/Eou7kIeCtwWFVdt/PKNhv5KtOHhT5dVQ9W1XeA/0pyaqsjSZ7dNvkS0zMKgNfu+beS9o6hIMGhwMYkNyW5junzBW8H3gGck2QSePBR9nEJ03/EL97NmIuA17X3Ga8F1iX5OnAjD/0U6huBs9qsxV/C07zxklRJUudMQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVL3/6xJyNXqjyNeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Survived', data=titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4XCM7xmozH_5"
   },
   "outputs": [],
   "source": [
    "# Matplotlib : plusieurs lignes pour afficher un truc simple\n",
    "#pandas : qui donne des paramètres par défaut et rend facile la visualisation\n",
    "# seaborn : des graphes plus élégants et plus parlant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 352
    },
    "id": "B-lsbDs50DPq",
    "outputId": "8ef265e2-b9f1-40f4-cfce-045af99e2fa7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b20f3a20>"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Sex', data=titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "0zSa-Cy-0DC0",
    "outputId": "c69eb5c6-87c3-41e6-d4e4-bf7edaaa8052"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b19674e0>"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Embarked', data=titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
    },
    "id": "b9oR5W8t0YPp",
    "outputId": "764f32af-e857-4c74-daad-28502c101767"
   },
   "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>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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  ...     Fare Embarked\n",
       "0         0       3  ...   7.2500        S\n",
       "1         1       1  ...  71.2833        C\n",
       "2         1       3  ...   7.9250        S\n",
       "3         1       1  ...  53.1000        S\n",
       "4         0       3  ...   8.0500        S\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "Jbdb2Q670mGU",
    "outputId": "7eb103b4-3dbb-456d-d8d1-ad35ecce9d3b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b18942b0>"
      ]
     },
     "execution_count": 43,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.Age.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "HkrGw0zs0rRO",
    "outputId": "2b3389bb-7942-44a3-c351-ddda9708166a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b17c07f0>"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uH7FRUdxw9nSvSwloxqRk9h5SC0ImpuN1Mf3C//3741OOTCRNHT08vrGSlYumkpsaOoPTQ83MSebRkh6aO3tJT9SiATKxjHSxvv80szQzizWzl8ys3sxuCHZxEtkefvsAnb393HTeDK9LOar3D1SLTCwj7fS91DnXAnwU31pMs4H/FayiJPL19A3wwBv7OG/OJOZPTvO6nKMaOtVVZKIZaUAMdkV9BHjMOaf1B+SkPPtOFbUt3Xz+3NBtPQAUZiURG23srlMLQiaeke4ot8bMdgKdwFfMLAfoCl5ZEsmcc/zqtb3Mzk3h/DmhNbV1uNjoKGZMSmZ3bavXpYiMu5Eu9307cA6w1DnXC7QDK4NZmESut8ob2F7Vwk3nziAqyo7/Ao/NyUtlV50CQiaeE9mTej6++yGGvubBMa5HJoD7Xy8nKzmOqxZP87qUEZmbm8qz26rp6OkjKS6o27iLhJSRLvf9EDAL2AL0+w87FBBygsrr23hpZx23XjSHhNhor8sZkbl5vplMe+raOD0/w+NqRMbPSP85tBQods65YBYjkW/wxrhPLw/NG+MCmZPnuyd0V60CQiaWkc5ieheYHMxCJPI1dfTw2MYKVi6aSk5qvNfljFhRdhJx0VEaqJYJZ6QtiEnADjN7G+gePOicuzIoVUlE+t36A3T1DoT0jXGBxERHMTMnmV0KCJlgRhoQ3wtmERL5evsHePDN0L8x7mjm5KWyaX+j12WIjKuRTnN9Fd8d1LH+xxuATUGsSyLMS6V11LZ085mzi7wuZVTmT07lYFMnzZ29XpciMm5GuhbTF4HHgV/4D00D/hisoiTyPPz2ASanJYTcng8jVTzV1+oprW7xuBKR8TPSQeqvASuAFgDn3G4gN1hFSWSpaOjgtd31XHtmQUju+TASp/oDYnuVAkImjpH+be12zvUMPvHfLKcprzIij26owIBrzyzwupRRy01NICc1nh0KCJlARhoQr5rZd4BEM7sEeAx4JnhlSaTo7R/g0ZIKLpyXy9SMRK/LOSnFU9LYXqV1KmXiGGlA3A7UA+8AXwKeA/4lWEVJ5HiptI761m5WLyv0upSTdurUNPbUtdHd13/8i0UiwIimuTrnBszsj8AfnXP1Qa5JIsjDbx9gSnoCF4Tp4PRQxVPT6Btw7K5t47Rp6V6XIxJ0x2xBmM/3zOwQUAaU+XeT++74lCfhrKa5i9d21/OJpeE7OD3UqVN9oaBuJpkojve39lv4Zi+d6ZzLcs5lAWcBK8zsW0GvTsLamm1VOAcfXzTV61LGxPSsJFLjY9haqYCQieF4AfFpYLVzbu/gAedcOXAD8JlgFibh75lt1Zw2Le3Ivs7hLirKWFSYweYDTV6XIjIujhcQsc65Q8MP+schYo/3w83sMjMrM7M9ZnZ7gPPxZvao//x6MyvyH7/EzDaa2Tv+7xeN7NeRUHHgcAdbK5r42OmR0XoYtLggg7KaFjp6+rwuRSTojhcQPaM8h5lFA/cAlwPFwGozKx522U1Ao3NuNnAXcIf/+CHgY865BcCNwEPHqVNCzDPbqgD4yOlTPK5kbC0qzGDAwTZ1M8kEcLyAWGhmLQG+WoEFx3ntMmCPc67cf5PdI3xwm9KVwAP+x48DF5uZOec2O+eq/Me347v/InzWhxae2VrFGdMzyc9M8rqUMbWoIBNA3UwyIRwzIJxz0c65tABfqc6543UxTQMqhjyv9B8LeI1zrg9oBrKHXXMNsMk51z3sOGZ2s5mVmFlJfb1m34aK3bWt7Kxp5WMR1noAyEqOoyg7ic0HtLKrRL6QnntoZqfi63b6UqDzzrn7nHNLnXNLc3LCf559pHhmWzVRBldEYEAALC7MZHNFE9pgUSJdMAPiIDB08Z18/7GA1/jXd0oHDvuf5wNPAZ9xzr0XxDplDDnnWLO1iuUzs8lNTfC6nKBYMj2T+tZu9h/u8LoUkaAKZkBsAOaY2QwziwOuA54eds3T+AahAVYBa51zzswygGeB251z64JYo4yxXbVtlB9qj7jB6aHOnunrBX2z/LDHlYgEV9ACwj+mcAvwPFAK/ME5t93MfmBmg1uV3g9km9ke4DZ8az7hf91s4LtmtsX/peXFw8DanXUAXDw/z+NKgmdWTjI5qfG8+Z4CQiLbSLccHRXn3HP4FvYbeuy7Qx53AZ8I8LofAj8MZm0SHC/vrKN4ShqT0yOzewnAzFg+M5s3yw/jnMPMvC5JJChCepBawktzRy8bDzRy0fzIb+ydPTOb+tZuyg+1e12KSNAoIGTM/G13Pf0DjgsnQkDM8o9DqJtJIpgCQsbMyzvryEyKZVFBhtelBF1RdhLTMhL52y7dfyORSwEhY6J/wPHKrnrOn5tDdFTk98mbGRfOz+H1PYfo6tUGQhKZFBAyJrZVNtHQ3jMhupcGXTw/j46eftbvbfC6FJGgUEDImHh5Zx1RBufPnTh3tJ89K5uE2Che9k/tFYk0CggZE2vL6lhSmElGUpzXpYybhNhoVsyaxEs7a7XshkQkBYSctLqWLt492DKhupcGfbg4j4qGTnZUt3hdisiYU0DISXulzDeTZyLc/zDcZadOJibKeHpr1fEvFgkzCgg5aWt31jElPYH5k1O9LmXcZSbHcd6cSazZWs3AgLqZJLIoIOSk9PQN8PqeQ1wwL3fCLjlx5aKpHGzqZJP2iJAIo4CQk1Kyr4G27r4J2b006JLiycTHRPHU5uGr2YuENwWEnJS1O+uIi4lixezhGwFOHCnxMXxkwRT+tKWK9u4+r8sRGTMKCDkpa8vqWD4zm6S4oC4MHPKuX15IW3efBqsloiggZNT2H26nvL6dC+dNnJvjjmZJYSbzJ6fy27f2654IiRgKCBm1wTuIJ/L4wyAz4/rl09le1cKGfRqslsiggJBRW1tWz8ycZKZnJ3tdSkhYtSSfrOQ47n1lj9eliIwJBYSMSkdPH2+VH+aieWo9DEqMi+bzK4p4paye7VXNXpcjctIUEDIq6/YcpqdvYEIur3Esnz67iNT4GH760m6vSxE5aQoIGZWXy+pIiY/hzKIsr0sJKemJsXzhvJk8v72Wjfs1FiHhTQEhJ8w5x8s76zh39iTiYvRHaLgvfmgGOanx/Oi5Us1okrCmv91ywnbWtFLd3KXZS0eRFBfDbZfMZeP+Rv60RfdFSPhSQMgJW+uf3nqB7n84qk8uLWBRQQb/tmYHTR09XpcjMioKCDlhr5TVcdq0NHLTErwuJWRFRxk/umoBTZ29/PDZUq/LERkVBYSckKaOHjbub9T01hEonprGV86fxeMbK/nzO9VelyNywhQQckJe3VXPgEPTW0foGx+ew8L8dG5/8h0ONnV6XY7ICVFAyAl5payerOQ4Ts/P8LqUsBAbHcVPrlvMwIDjyw9tpKu33+uSREZMASEj1j/geKWsjgvm5hAdNTE3BxqNoknJ3HntIt452Mw/PbFNO89J2FBAyIhtqWiisaNX3UujcElxHv/rH+bxpy1V/GDNDt0fIWFhYi/iLyfkr9triI02ztf01lH56gWzaGjv4f7X95KRFMs3PzzX65JEjkkBISPinOP57TWcPWsSaQmxXpcTlsyM//eKU2ju7OV/XtxNSnwMXzhvptdliRyVAkJGZFdtG/sOd+h/aCcpKsr4j6sX0N7dxw+fLaWxo4dvXzoPM43pSOjRGISMyF+312AGlxbneV1K2IuJjuLu1YtZvayAe15+j398bCu9/QNelyXyAWpByIg8v6OGxQUZunt6jMRER/GjqxYwJT2RO1/YRW1LFz+9bjHZKfFelyZyhFoQclyVjR28e7CFfzh1stelRBQz4+sXz+F/rzqdDfsaueKnr7G+/LDXZYkcEdSAMLPLzKzMzPaY2e0Bzseb2aP+8+vNrMh/PNvMXjazNjP7WTBrlOP76/ZaAAVEkHxiaQFPffUckuJiWP3Lt7j7pd30qctJQkDQAsLMooF7gMuBYmC1mRUPu+wmoNE5Nxu4C7jDf7wL+Ffg28GqT0bu+e01zMtLpWiS9p4OllOnpvPMrefy0dOn8t8v7GLlPet496C2LRVvBbMFsQzY45wrd871AI8AK4ddsxJ4wP/4ceBiMzPnXLtz7nV8QSEeOtzWzYZ9DfzDqRqcDraU+Bh+unoxP79+CbUt3ay8Zx0//nMp7d19XpcmE1QwA2IaUDHkeaX/WMBrnHN9QDOQPdI3MLObzazEzErq6+tPslwJ5K87ahlwcKm6l8bN5Qum8NJt53PNkmn84tVyLvyvV3h8Y6WW6JBxF9aD1M65+5xzS51zS3NydHdvMDy1+SCzcpI5dWqa16VMKOlJsfznqoU8+dVzmJKRyLcf28pV965j4/4Gr0uTCSSYAXEQKBjyPN9/LOA1ZhYDpAOaxhEiKhs7eHtvA1ctnqYbuTyypDCTp75yDnd+ciE1LV1c8/M3+frvN1OlpcNlHAQzIDYAc8xshpnFAdcBTw+75mngRv/jVcBap1XMQsbgfsorFw3vGZTxFBVlXL0kn7X/eAG3XjSb57fXcNF/v8JPXtxNZ4+WD5fgCVpA+McUbgGeB0qBPzjntpvZD8zsSv9l9wPZZrYHuA04MhXWzPYBdwKfNbPKADOgJIicczy5qZIzizIpyEryuhwBkuNj+MdL5/Hibedz8fw87npxFxf/9ys8s7VKq8NKUFik/MFaunSpKykp8bqMiLG+/DDX3vcW/3vV6XxiacHxXyDjbn35Yb7/zA52VLdw/twc/v2q08jPVJjLiTGzjc65pYHOhfUgtQTP79YfIDUhho+ePtXrUuQozpqZzTO3nsv3rzyVkn0NXHrX3/j163s120nGjAJCPuBwWzd/freaa5bkkxgX7XU5cgzRUcaN5xTx19vOZ9mMLH6wZgc33L+e2hbdQiQnTwEhH/D7tw/Q2+/41FmFXpciIzQtI5HffPZM7rhmAZsPNHHZ//yNF3bUel2WhDkFhLxPV28/v1m3jwvm5TA3L9XrcuQEmBnXnlnIM7eey5T0RL74YAk/eq5U6zrJqCkg5H0eK6ngcHsPXzl/ltelyCjNzk3hqa+dww3LC7nvb+V87v9soKmjx+uyJAwpIOSI7r5+/v9Xy1lSmMGyGVlelyMnIT4mmh9+fAF3XLOA9eUNfOxnr1Na3eJ1WRJmFBByxANv7ONgUyffumSu7pyOENeeWcgjX1pOT98AV9/7Bmu2VXldkoQRBYQA0NDew91r93DBvBzOm6N1rSLJksJMnrnlXIqnpnHLw5v5r+fLNBVWRkQBIQD8+7OldPT0850rTvG6FAmC3LQEHv7iWVy7tICfvbyHL/12I21aRlyOQ3tSh6jy+jae2FTJ5gNNVDR2EGXG5LQETp2azoXzc1g+M5vY6LHJ97+8W8MTmyq59aLZmrkUweJjovmPaxZwypRU/u3ZUq6+dx2//MxSpmdrIygJTEtthJia5i7+7dkdPLutmugo47SpaUd2cqto6GBHdQtdvQPkpcXzmbOLWL2skKzkuFG/395D7Vx97zqmZSby1FdXjFnoSGhbt+cQX3t4EwD3fGoJK2ZP8rgi8cqxltpQQISQV8rquPX3m+nuG+DLH5rJDcunk5uW8L5rOnv6eXVXPb9bv5/Xdh8iPiaKVWfk8+XzZ53wonr1rd1c8/M3aOvu44mvnMMMbSk6oew/3M4XHyzhvfp2/vUjp3DjOUWanDABKSDCwENv7ef/+9O7zJucxs+vXzKi/Z931bby69f38uSmg/Q7x8qFU/nyBbNG1E20o6qFLz5YQkN7D7+/eTmLCjLG4teQMNPW3cc3H9nCi6W1XLFgMj+++nTSE2O9LkvGkQIixP32rf38yx/f5eL5udz9qcUkxZ3Y0FBNcxe/fK2ch9cfoLO3n7NnZnP1kmlcUpxHRtL7u5/qWrq4f91efrNuH5lJsdz36aUsVDhMaAMDjl+9Xs5//qWMvLQE7v7UYpYUZnpdlowTBUQI+/M71Xzld5u4eH4uP7/hDOJiRj8G0NDew6MbKnj47f1UNPh2HJudm8K0jERiooyDTZ3srGnFDK5aNI3br5hPbmrCcX6qTBRbKpq49febqGrq4gvnzuCbH56rxRonAAVEiNpa0cQnf/Emp05N4+EvLichdmz+Mjrn2HTlw4AAAAwYSURBVFLRxLo9h9hS0Ux9axc9/Y6c1HiWFWVy2WlTmJ2bMibvJZGlpauXHz9Xyu/frqAwK4kfX71AA9gRTgERgpo7ernip68B8KdbVjApJd7jikT+7o33DvGdJ99h3+EOLinO458um8fsXE2BjkTaMCjEOOe4/clt1LZ0cc/1SxQOEnLOmTWJv3zzQ3z70rm8+d5hLr3rb/zzk9uoaOjwujQZR7pRzgMPv32AP79bw+2Xz9fsIQlZCbHR3HLRHFYvK+TutXv43fr9PLqhgisWTOFLH5rFgvx0r0uUIFMX0zgrq2nlyp+9zrIZWTzwuWVERWneuYSHmuYufvPGXh5+6wCt3X0sLMhg9ZkFfHThVFLi9W/NcKUxiBDR2dPPlT97ncaOXv78jfPISVXXkoSflq5eHiup5JG3D7C7ro3kuGg+tnAq155ZwKKCDN1sF2aOFRCK/XH0gzU72F3XxoOfX6ZwkLCVlhDLTefO4PMrith0oIlHNxzgT1uqeGRDBbNzU1h1Rj5XL572gVUAJPyoBTFOnt1Wzdce3sSXz5/F7ZfP97ockTHV2tXLc+9U81hJJSX7G4kyOH9uDp9YWsDFp+QSH6P7KUKVupg8VtHQwRU/fY1ZOSk89uWztSCeRLS9h9p5fGMFT2w8SE1LF+mJsaxcNJVPnVXI/MlpXpcnwyggPNTd18+1v3iL9+raeO4b553wgnoi4ap/wLFuzyEe21jJ89tr6Okb4OyZ2Xx2RREfPiWPaE3QCAkag/DQD9eUsqWiiZ9fv0ThIBNKdJTxobk5fGhuDk0dvmVgHnxzP196aCPTMhK58ZzpXLu0kPQkLQ4YqtSCCKKnNlfyrUe3cvOHZmqnNhGgr3+AF0vr+D9v7OWt8gYSYqO4anE+n1tRpM2qPKIuJg+UVrdw1b3rWJifwe++cBYxGncQeZ/S6hYeeGMfT20+SHffAOfOnsTnVhRx4bxc3R80jhQQ46yutYtrfv4G3b0DrPn6uVoxVeQYGtp7+P3bB3jwzX3UtnQzY1IyN549nWvOyCc1Qd1PwaaAGEdt3X1cd9+bvFfXziM3L9deCyIj1Ns/wJ/freE36/ay+UATibHRXHbaZFadkc/ZM7PVqggSDVKPk9auXj77mw2UVrfyq89oIx6RExEbHcWVC6dy5cKpbKlo4tENFazZVsVTmw+SkxrPh0/J5eL5eayYPUn7VIwTtSDGSH1rN194sITtB5u5e/ViLl8wxbNaRCJFV28/L+yo5S/ba3i1rJ627j5io43TpqWzdHomiwszmTc5laLsZE2bHSV1MQXZuweb+dJDGznc3s3dq5dwSXGeJ3WIRLKevgHW7z3MG+8dpmRfA1srmunpHwAgPiaKOXkpzMtLY97kFObkpjInz7ebotaGOjZ1MQVJT98Av3ytnP95cRfZyfE89qVztASySJDExURx3pwczpuTA/haF7tqWymr8X/VtvK33fU8sanyyGuS46KZnZfKnNwU5ualMMf/WMExMgqIUejzD6bd9cIuyg+1c/lpk/nRVQvITI7zujSRCSMhNprT8zM4Pf/9Y31NHT3srmtjV20ru2t931/dVc/jG/8eHCnxMf4WRypz8lKZl5fK3Mkp5KTEKziGCGoXk5ldBvwEiAZ+5Zz7j2Hn44EHgTOAw8C1zrl9/nP/DNwE9ANfd849f6z3CnYXU0/fAO9WNbO2tI6nNh/kYFMns3KS+ZePFHPh/Nygva+IjI3G9qHB4WtxlNW00tjRe+SazKRY5uSlkp+ZyNT0RKZmJDIlI4G81ATSk2JJT4wlOS46okLEky4mM4sG7gEuASqBDWb2tHNux5DLbgIanXOzzew64A7gWjMrBq4DTgWmAi+a2VznXP9Y1+mco7tvgLbuPtq7+/zf+2lo76aysZPKxk521rSwpaKJrt4BogxWzJ7Ev360mEuL8zT1TiRMZCbHsWxGFstmZB055pzjUFsPu2pbj3ztrm3jzfcOU9vSxUCAfz/HRBlpibEkxUWTGBtNYlw0CbG+r8TYqPcdS4yN/uBz/+viY6OIj4kiLjqa2BgjLjqK2Ogo4mKifI/936OjDAPMGPdgCmYX0zJgj3OuHMDMHgFWAkMDYiXwPf/jx4Gfme+/wErgEedcN7DXzPb4f96bY13k5oomrr73jaOeT46LZmZOCquXFbKsKIszZ2RpD2mRCGFm5KTGk5Maz4rZk953rq9/gLrWbqqbO6lv7aa5s/d9Xx09/XT19tPZ009nbz/Nnb3UNvsed/X+/Xtv/9j20kQZRJkdCQwDPrJgCndeu2hM3weCGxDTgIohzyuBs452jXOuz8yagWz/8beGvXba8Dcws5uBm/1P28ysbGxKf78dwJpg/GCYBBwKzo8OWfqdJwb9zuNoF3DXdaN++fSjnQjrQWrn3H3AfV7XMVpmVnK0vr9Ipd95YtDvHBmCuYLcQaBgyPN8/7GA15hZDJCOb7B6JK8VEZEgCmZAbADmmNkMM4vDN+j89LBrngZu9D9eBax1vmlVTwPXmVm8mc0A5gBvB7FWEREZJmhdTP4xhVuA5/FNc/21c267mf0AKHHOPQ3cDzzkH4RuwBci+K/7A77u/z7ga8GYwRQCwrZ77CTod54Y9DtHgIhZakNERMaWdrEREZGAFBAiIhKQAsIDZnaZmZWZ2R4zu93reoLBzArM7GUz22Fm283sG/7jWWb2gpnt9n/P9LrWsWZm0Wa22czW+J/PMLP1/s/7Uf+kjYhiZhlm9riZ7TSzUjM7O9I/azP7lv/P9rtm9nszS4i0z1oBMc6GLEFyOVAMrPYvLRJp+oB/dM4VA8uBr/l/z9uBl5xzc4CX/M8jzTeA0iHP7wDucs7NBhrxLTETaX4C/MU5Nx9YiO/3j9jP2symAV8HljrnTsM3EWdwuaCI+awVEOPvyBIkzrkeYHAJkojinKt2zm3yP27F9z+Mafh+1wf8lz0AfNybCoPDzPKBjwC/8j834CJ8S8lAZP7O6cCH8M1KxDnX45xrIsI/a3yzQBP993AlAdVE2GetgBh/gZYg+cAyIpHEzIqAxcB6IM85V+0/VQNE2u5K/wP8P8CA/3k20OSc6/M/j8TPewZQD/zG37X2KzNLJoI/a+fcQeC/gAP4gqEZ2EiEfdYKCAkqM0sBngC+6ZxrGXrOf1NkxMyzNrOPAnXOuY1e1zLOYoAlwM+dc4uBdoZ1J0XgZ52Jr4U0A9+K08nAZZ4WFQQKiPE3YZYRMbNYfOHwO+fck/7DtWY2xX9+ClDnVX1BsAK40sz24es6vAhf33yGvxsCIvPzrgQqnXPr/c8fxxcYkfxZfxjY65yrd871Ak/i+/wj6rNWQIy/kSxBEvb8fe/3A6XOuTuHnBq6vMqNwJ/Gu7Zgcc79s3Mu3zlXhO9zXeucux54Gd9SMhBhvzOAc64GqDCzef5DF+NbBSFiP2t8XUvLzSzJ/2d98HeOqM9ad1J7wMyuwNdXPbgEyb97XNKYM7NzgdeAd/h7f/x38I1D/AEoBPYDn3TONXhSZBCZ2QXAt51zHzWzmfhaFFnAZuAG/14nEcPMFuEbmI8DyoHP4fsHaMR+1mb2feBafDP2NgNfwDfmEDGftQJCREQCUheTiIgEpIAQEZGAFBAiIhKQAkJERAJSQIiISEAKCJExYGYfNzNnZvO9rkVkrCggRMbGauB1/3eRiKCAEDlJ/vWmzsW3tPN1/mNRZnavf3+EF8zsOTNb5T93hpm9amYbzez5weUoREKNAkLk5K3EtxfCLuCwmZ0BXA0U4dvz49PA2XBkfaq7gVXOuTOAXwMRdye9RIaY418iIsexGt+ifOBbZmE1vr9bjznnBoAaM3vZf34ecBrwgm8JH6LxLRctEnIUECInwcyy8K3ausDMHL7/4TvgqaO9BNjunDt7nEoUGTV1MYmcnFXAQ8656c65IudcAbAXaACu8Y9F5AEX+K8vA3LM7EiXk5md6kXhIsejgBA5Oav5YGvhCWAyvn0SdgC/BTYBzf5tZlcBd5jZVmALcM74lSsyclrNVSRIzCzFOddmZtnA28AK/94JImFBYxAiwbPGzDLw7ZHwbwoHCTdqQYiISEAagxARkYAUECIiEpACQkREAlJAiIhIQAoIEREJ6P8CYC5RI5tvDS4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=titanic, x=\"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "U4A9R3Dw3hgq",
    "outputId": "21b9edaf-7f06-47cd-aabb-054742e3d9cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b15439b0>"
      ]
     },
     "execution_count": 53,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=titanic, x=\"Fare\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "VcLF-Sn73b0N",
    "outputId": "64f6279a-7957-4401-82df-25a5bb95bf23"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b16340f0>"
      ]
     },
     "execution_count": 52,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Fare', data=titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "vGfDsM4b1NXT",
    "outputId": "2ccc2de0-f24f-4664-ebe7-cdaca7568302"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b179d4e0>"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EMDIvvVfGzUo+QHNnumyWmf1jeo/bg2a238xuT9ctNbN/S2+K893ix1qBrCGAMR18Scn9bH8oacTMlkr6SyW3GL1B0l1K7lNRvLdGn6Tb3X2ppB2SHo0oGphMbvKHAOFWK7npjpTchGe1kmt3t7ufl/SWmfWn66+T9GlJB5OP8atJycfQgcwhgJFpZjZfyV3PFpuZKwlUV3K3sXE3kfS6u9/UoBKBKWMIAll3u6Svu/vH3T3v7tcq+Q8P70j6q3QseKGSm/NIyX9TuMrMSkMSZvb7EYUDkyGAkXWrdWFv95uSfk/Jne3ekPTPSv5l0rvu/hslof1VM3tNyR3m/qhx5QKXjruhYdoys3nuftrMmpXcJvALntz/FpgWGAPGdPZietP42ZL+jvDFdEMPGACCMAYMAEEIYAAIQgADQBACGACCEMAAEOT/AWhCvr5p23K8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Age', data=titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "BcGdbxA-1b18",
    "outputId": "5deffa36-ea3a-4d3a-b627-bb0c5eaee8c4"
   },
   "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>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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Master. Alden Gates</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Helene Barbara</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Eugenie</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hamalainen, Master. Viljo</td>\n",
       "      <td>male</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>250649</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thomas, Master. Assad Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2625</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Richards, Master. George Sibley</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29106</td>\n",
       "      <td>18.7500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  ...      Fare Embarked\n",
       "78          1       2  ...   29.0000        S\n",
       "305         1       1  ...  151.5500        S\n",
       "469         1       3  ...   19.2583        C\n",
       "644         1       3  ...   19.2583        C\n",
       "755         1       2  ...   14.5000        S\n",
       "803         1       3  ...    8.5167        C\n",
       "831         1       2  ...   18.7500        S\n",
       "\n",
       "[7 rows x 10 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic['Age'] < 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QW41BfPV2Q40"
   },
   "source": [
    "# Analyse bivariée des colonnes afin d'identifier les caractéristiques ayant une influence sur la survie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "4fyfWNTy1r58",
    "outputId": "05f96618-7e17-4066-a29f-4f2201cfa886"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b1682a90>"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Sex', data=titanic, hue='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "60tKvDMD2oQB",
    "outputId": "68c6e568-a3a8-48f6-ef59-7d9b63316b25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b1657470>"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Pclass', data=titanic, hue='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "41E0jVjA4B4z",
    "outputId": "814051d0-a1d5-4ccd-94df-94b22799bdc6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b135a240>"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Embarked', data=titanic, hue='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8GRisxbS3L_g",
    "outputId": "3e1facb5-30e8-4e73-8a21-e6ea54f31dad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
       "       'Fare', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uXsaZtlp3XmQ",
    "outputId": "2f9f9de1-9a32-4bf4-e4a4-1aa6421f38cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "599    Duff Gordon, Sir. Cosmo Edmund (\"Mr Morgan\")\n",
       "618                     Becker, Miss. Marion Louise\n",
       "369                   Aubart, Mme. Leontine Pauline\n",
       "79                         Dowdell, Miss. Elizabeth\n",
       "658                    Eitemiller, Mr. George Floyd\n",
       "793                        Hoyt, Mr. William Fisher\n",
       "814                      Tomlin, Mr. Ernest Portage\n",
       "178                              Hale, Mr. Reginald\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Name.sample(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2tJNMoKO33a6"
   },
   "outputs": [],
   "source": [
    "# Plus jeune passager sans parent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OcsEe6RJ5IIF",
    "outputId": "9837ca00-3393-4781-8db1-e93ece2f25ad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
       "       'Fare', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "djwc6keQ5HSo",
    "outputId": "61e79da2-ecdc-474e-cad6-7246803ff0f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic.Parch == 0].Age.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "id": "3Ps6Dvvf5tm0",
    "outputId": "e1895e41-a761-477e-c9c6-20c6c0c48829"
   },
   "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>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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Emanuel, Miss. Virginia Ethel</td>\n",
       "      <td>female</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364516</td>\n",
       "      <td>12.4750</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hassan, Mr. Houssein G N</td>\n",
       "      <td>male</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2699</td>\n",
       "      <td>18.7875</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Nicola-Yarred, Master. Elias</td>\n",
       "      <td>male</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2651</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Ayoub, Miss. Banoura</td>\n",
       "      <td>female</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2687</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Connors, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>70.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370369</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Artagaveytia, Mr. Ramon</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17609</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347060</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27042</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>676 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  ...     Fare Embarked\n",
       "777         1       3  ...  12.4750        S\n",
       "731         0       3  ...  18.7875        C\n",
       "125         1       3  ...  11.2417        C\n",
       "780         1       3  ...   7.2292        C\n",
       "14          0       3  ...   7.8542        S\n",
       "..        ...     ...  ...      ...      ...\n",
       "116         0       3  ...   7.7500        Q\n",
       "493         0       1  ...  49.5042        C\n",
       "96          0       1  ...  34.6542        C\n",
       "851         0       3  ...   7.7750        S\n",
       "630         1       1  ...  30.0000        S\n",
       "\n",
       "[676 rows x 10 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic.Parch == 0].sort_values('Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "lSvzTkZk6NgY",
    "outputId": "c0106489-1b95-4465-e619-c1e6a71d1976"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b178dda0>"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0qjBJ0ugb7p7CnK2BAFBVTwNHdKYkSVK3DHdP4VVJ9t1mT2G4Yye0718yuzsb3tdnFUraecP9j/1y4B+T3NbMvwf4TGdKkiR1y3DvaF6cZBlwUtN0WlWt7lxZkqRuGPYhoCYEDAJJegXb6UdnS5JeuToWCkm+2DwnaeWgtouSrEuyvPmcOmjZJ5KsSfLdJL6rQZK6oJN7CtcD7xii/XNVNa/53AGQ5HDgLOCNzZi/SDKpg7VJkobQsVCoqn8Anh5m9wXALVX1QlX9M7AGOKZTtUmShtaNcwofTrKiOby0b9N2MLB2UJ/+pu0lkixKsizJsg0bNnS6VkmaUEY7FK4CXg/MY+AZSpfv7Aqq6pqq6quqvp4eH9QqSSNpVEOhqp6sqi1V9SJwLT87RLQOOGRQ196mTZI0ikY1FJJMHzT7bmDrlUlLgLOS7JlkJnAYcN9o1iZJ6uDzi5LcDLwVOCBJP/Bp4K1J5gEFPA58EKCqViW5lYGb4zYDH6qqLZ2qTZI0tI6FQlW9b4jm67bT/zP4PCVJ6irvaJYktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktTp2n8JYc9SFi7uy3dv36spmJWmXuKcgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKkVsdCIckXkzyVZOWgtv2S3J3k0ebnvk17kvx5kjVJViQ5slN1SZJeXif3FK4H3rFN28eBe6rqMOCeZh7gFOCw5rMIuKqDdUmSXkbHQqGq/gF4epvmBcANzfQNwLsGtS+uAd8C9kkyvVO1SZKGNtrnFA6qqvXN9A+Ag5rpg4G1g/r1N20vkWRRkmVJlm3YsKFzlUrSBNS1E81VVUDtwrhrqqqvqvp6eno6UJkkTVyjHQpPbj0s1Px8qmlfBxwyqF9v0yZJGkWjHQpLgHOb6XOBrwxqP6e5CulY4MeDDjNJkkZJx97RnORm4K3AAUn6gU8DfwTcmmQh8ARwZtP9DuBUYA3wU+B3OlWXJOnldSwUqup9L7Po5CH6FvChTtUiSRoe72iWJLUMBUlSy1CQJLU6dk5BGklHXbi4K9t94I/P6cp2pW5xT0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1PI+BUkaQcddcVxXtnvvR+4dkfW4pyBJahkKkqSWh4+kMWa8H37Q+OaegiSpZShIklqGgiSpZShIklqGgiSp1ZWrj5I8DjwDbAE2V1Vfkv2ALwMzgMeBM6vqX7pRnyRNVN3cU3hbVc2rqr5m/uPAPVV1GHBPMy9JGkVj6fDRAuCGZvoG4F1drEWSJqRuhUIBf5fkgSSLmraDqmp9M/0D4KChBiZZlGRZkmUbNmwYjVolacLo1h3Nx1fVuiQHAncn+afBC6uqktRQA6vqGuAagL6+viH7SJJ2TVf2FKpqXfPzKeB24BjgySTTAZqfT3WjNkmayEY9FJL8QpK9tk4DvwmsBJYA5zbdzgW+Mtq1SdJE143DRwcBtyfZuv3/UVV3JrkfuDXJQuAJ4Mwu1CZJE9qoh0JVPQbMHaJ9I3DyaNcjSfqZsXRJqiSpywwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVKrW6/jlMaF718ye/Q3uu/eo79NqeGegiSpZShIklqGgiSpZShIklqGgiSpZShIklpjLhSSvCPJd5OsSfLxbtcjSRPJmLpPIckk4AvA24F+4P4kS6pqdXcrkzTedOUeExj395mMqVAAjgHWVNVjAEluARYAhoI0jh114eJR3+bte436Jl8RxlooHAysHTTfD8wf3CHJImBRM/tsku+OUm275Jd3b/gBwA9HpJBRko+m2yWMqIn0/fnd/Zxx9d3BTn9/L/vPM9ZCYYeq6hrgmm7XMRqSLKuqvm7XoV3j9zd+TeTvbqydaF4HHDJovrdpkySNgrEWCvcDhyWZmeTVwFnAki7XJEkTxpg6fFRVm5N8GLgLmAR8sapWdbmsbpoQh8lewfz+xq8J+92lqrpdgyRpjBhrh48kSV1kKEiSWobCGJTkU0lWJVmRZHmS+TsepbEiyWuT3JLke0keSHJHkl/tdl3asSS9Sb6S5NEkjyW5Msme3a5rNBkKY0ySNwPvBI6sqjnAb/DzN/RpDEsS4HbgG1X1+qo6CvgEcFB3K9OONN/dXwN/U1WHAYcBU4H/1tXCRtmYuvpIAEwHflhVLwBU1bi6q1K8DdhUVVdvbaiqh7pYj4bvJOD5qvoSQFVtSfKfgCeSfKqqnu1ueaPDPYWx5++AQ5L83yR/keQt3S5IO+VNwAPdLkK75I1s891V1U+Ax4Ff6UZB3WAojDHNXyNHMfB8pw3Al5Oc19WiJE0YhsIYVFVbquobVfVp4MPA6d2uScO2ioFQ1/izmm2+uyR7A68FxvSDN0eSoTDGJPm1JIcNapoHPNGterTTlgJ7Nk/zBSDJnCQndLEmDc89wGuSnAPt+10uB66sque6WtkoMhTGnmnADUlWJ1kBHA5c1N2SNFw18IiAdwO/0VySugr4Q+AH3a1MOzLouzsjyaPARuDFqvpMdysbXT7mQpKGkOTXgZuBd1fVd7pdz2gxFCRJLQ8fSZJahoIkqWUoSJJahoIkqWUoaEJKsqV5Au3Wz8d3Yuxbk3x1N7f/jSS79GL4JNcnOWN3ti+9HB+Ip4nquaqa140NNzdFSWOSewrSIEkeT/KHzd7DsiRHJrmruRHt3w/quneSv03y3SRXJ3lVM/6qZtyqJBdvs97LknwHeM+g9lc1f/lfmmRSkj9Ocn/zLo0PNn3SPNf/u0n+HjhwlP45NAEZCpqopm5z+Oi9g5Z9v9mL+N/A9cAZwLHAxYP6HAN8hIE7zl8PnNa0f6qq+oA5wFuSzBk0ZmNVHVlVtzTzewA3AY9W1X8BFgI/rqqjgaOB300yk4G7bH+t2dY5wK+PzD+B9FIePtJEtb3DR0uanw8D06rqGeCZJC8k2adZdl9VPQaQ5GbgeOAvgTOb5x7twcC7MQ4HVjRjvrzNdv47cOugxyj8JjBn0PmCX2TgRS8nAjdX1Rbg/yVZumu/srRj7ilIL/VC8/PFQdNb57f+IbXtowCq+av+Y8DJzVvz/haYMqjPv24z5v8Ab0uytU+Aj1TVvOYzs6r+bjd/F2mnGArSrjkmyczmXMJ7gW8CezPwH/+PkxwEnLKDdVwH3AHcmmQP4C7gPySZDJDkV5P8AvAPwHubcw7TGXi7m9QRHj7SRDU1yfJB83dW1bAvSwXuB65k4I1cXwdur6oXkzwI/BMD79W+d0crqao/TfKLwI3A2cAM4DvN+4I3AO9i4J3PJzHwvP/vA/+4E3VKO8UH4kmSWh4+kiS1DAVJUstQkCS1DAVJUstQkCS1DAVJUstQkCS1/j+07c2MAPW7JQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Embarked', data=titanic, hue='Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bKCrAwFO7LWI"
   },
   "outputs": [],
   "source": [
    "#Age moyen des gens qui ont survécu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "v30buq6L7lrC",
    "outputId": "b3407811-510b-4428-b1f4-db34f44a9d80"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.423600346020766"
      ]
     },
     "execution_count": 77,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic.Survived == 1].Age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D-fJUt6p7tIx",
    "outputId": "f6979e5e-0287-42f9-ef2b-07de819b7377"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.415099646415896"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic.Survived == 0].Age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 234
    },
    "id": "WFHXFKml772V",
    "outputId": "ea2d23ee-7d2a-4222-8082-3f76fa443da4"
   },
   "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>Pclass</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>Survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.335549</td>\n",
       "      <td>-0.074673</td>\n",
       "      <td>-0.034040</td>\n",
       "      <td>0.083151</td>\n",
       "      <td>0.255290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.335549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.327954</td>\n",
       "      <td>0.081656</td>\n",
       "      <td>0.016824</td>\n",
       "      <td>-0.548193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.074673</td>\n",
       "      <td>-0.327954</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.231875</td>\n",
       "      <td>-0.178232</td>\n",
       "      <td>0.088604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.034040</td>\n",
       "      <td>0.081656</td>\n",
       "      <td>-0.231875</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414542</td>\n",
       "      <td>0.160887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.083151</td>\n",
       "      <td>0.016824</td>\n",
       "      <td>-0.178232</td>\n",
       "      <td>0.414542</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.217532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.255290</td>\n",
       "      <td>-0.548193</td>\n",
       "      <td>0.088604</td>\n",
       "      <td>0.160887</td>\n",
       "      <td>0.217532</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived    Pclass       Age     SibSp     Parch      Fare\n",
       "Survived  1.000000 -0.335549 -0.074673 -0.034040  0.083151  0.255290\n",
       "Pclass   -0.335549  1.000000 -0.327954  0.081656  0.016824 -0.548193\n",
       "Age      -0.074673 -0.327954  1.000000 -0.231875 -0.178232  0.088604\n",
       "SibSp    -0.034040  0.081656 -0.231875  1.000000  0.414542  0.160887\n",
       "Parch     0.083151  0.016824 -0.178232  0.414542  1.000000  0.217532\n",
       "Fare      0.255290 -0.548193  0.088604  0.160887  0.217532  1.000000"
      ]
     },
     "execution_count": 79,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "-YrPH-Xi8BO9",
    "outputId": "d4453f1d-3c97-425e-f6c8-b3aeda6fc4f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07b161c748>"
      ]
     },
     "execution_count": 80,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(titanic.corr(), annot=True, cmap=\"BrBG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 400
    },
    "id": "B1-HWdel8d6g",
    "outputId": "966d9dde-b254-42f8-cbc2-85e41be5893f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f07b148b6d8>"
      ]
     },
     "execution_count": 81,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Survived\", y=\"Age\", data=titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pp4JJHgJ9ZOu"
   },
   "outputs": [],
   "source": [
    "    # Plus vieux survivant du titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "id": "xXDlv_TC9uK4",
    "outputId": "5f6dac0d-2320-4fc1-8c18-a91d8bdb6bc5"
   },
   "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>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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>male</td>\n",
       "      <td>80.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27042</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
       "      <td>female</td>\n",
       "      <td>63.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13502</td>\n",
       "      <td>77.9583</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Turkula, Mrs. (Hedwig)</td>\n",
       "      <td>female</td>\n",
       "      <td>63.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4134</td>\n",
       "      <td>9.5875</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Harris, Mr. George</td>\n",
       "      <td>male</td>\n",
       "      <td>62.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.W./PP 752</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Warren, Mrs. Frank Manley (Anna Sophia Atkinson)</td>\n",
       "      <td>female</td>\n",
       "      <td>60.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110813</td>\n",
       "      <td>75.2500</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Richards, Master. George Sibley</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29106</td>\n",
       "      <td>18.7500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Helene Barbara</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Eugenie</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hamalainen, Master. Viljo</td>\n",
       "      <td>male</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>250649</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thomas, Master. Assad Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2625</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>340 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  ...     Fare Embarked\n",
       "630         1       1  ...  30.0000        S\n",
       "275         1       1  ...  77.9583        S\n",
       "483         1       3  ...   9.5875        S\n",
       "570         1       2  ...  10.5000        S\n",
       "366         1       1  ...  75.2500        C\n",
       "..        ...     ...  ...      ...      ...\n",
       "831         1       2  ...  18.7500        S\n",
       "469         1       3  ...  19.2583        C\n",
       "644         1       3  ...  19.2583        C\n",
       "755         1       2  ...  14.5000        S\n",
       "803         1       3  ...   8.5167        C\n",
       "\n",
       "[340 rows x 10 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic.Survived == 1].sort_values('Age', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CSwh8AoKGGuE"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "name": "Analyse Titanic.ipynb",
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