{
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   "source": [
    "# TP3 _TiTANIC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e796dd4a",
   "metadata": {},
   "source": [
    "L’objectif de cette partie et de faire quelques manipulations basique sur des données avec python.\n",
    "Pour cela, le jeu de données que nous utiliserons est extrait de https://github.com/caesar0301/\n",
    "awesome-public-datasets et concerne les caractéristiques des passagers du Titanic. La description\n",
    "des données originales est disponible sur cette page web : https://www.kaggle.com/c/titanic/\n",
    "data.\n",
    "\n",
    "ou bien accéder à https://www.kaggle.com/code/djousto/titanic-in-french?scriptVersionId=81122543\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d14fba9",
   "metadata": {},
   "source": [
    "#### Importation des bibliothéques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7b85725",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np #(A compléter) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b31e548",
   "metadata": {},
   "source": [
    "## Partie 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54731c31",
   "metadata": {},
   "source": [
    "#### 1. Charger les données, sous forme de DataFrame, dans la variable data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7722f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "data="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4b0f0d",
   "metadata": {},
   "source": [
    "#### Observer les noms des variables à l’aide data.columns et faire le bilan de celles-ci à l’aide des informations disponibles sur le web."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ebc571",
   "metadata": {},
   "source": [
    "Var 1: PassengerId=........\n",
    "\n",
    "\n",
    "Var 2: Age= ......."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "481cadd2",
   "metadata": {},
   "source": [
    "#### Puis, donner le type de chaque variable et vérifier si il y’a des valeurs manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "929b12ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3f5b747a",
   "metadata": {},
   "source": [
    "#### a. Afficher seulement les mineurs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ce9e37a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "92989184",
   "metadata": {},
   "source": [
    "#### b. Afficher somme des mineurs suivant le sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33231694",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "86e68e24",
   "metadata": {},
   "source": [
    "#### c. Vérifier si chaque passager un est mineur ou non"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21e1246c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "587f01b1",
   "metadata": {},
   "source": [
    "####  2. Stocker le nombre de variables dans la variable n et celui des d’observations dans la variable p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28421579",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dcf850db",
   "metadata": {},
   "source": [
    "#### 3. Enlever les variables Fare, Name, SibSp, Ticket, Cabin, Parch.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42b0be27",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "21df05c1",
   "metadata": {},
   "source": [
    "#### 4. Recoder la variable Sex en variable binaire (male= 0, female=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1184c208",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "15737df1",
   "metadata": {},
   "source": [
    "#### 5. Remplacer les valeurs manquantes dans la variable Age par 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80b83472",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6982861a",
   "metadata": {},
   "source": [
    "#### 6. Transformer la variable Age en entier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "671d415a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0b4244b5",
   "metadata": {},
   "source": [
    "#### 7. Faite une analyse descriptive de vos données (min, max, moyenne,...)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99af7ca0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "535bd7d7",
   "metadata": {},
   "source": [
    "#### 8. Tracer et commenter la boite à moustache des variables Age, Sex..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a3349d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "73fd186d",
   "metadata": {},
   "source": [
    "#### 9. Tracer la densité des variables Age, Sex..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86e52ebf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "50ba472b",
   "metadata": {},
   "source": [
    "#### 10. Donner Une représentation graphique, de votre choix, pour variables: Age, Sex et Pclass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e7a6040",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "451e4b9d",
   "metadata": {},
   "source": [
    "## Partie 2: Tableau de contingence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "108ddf55",
   "metadata": {},
   "source": [
    "#### 1. Calculer le tableau de contingence de la variable Survived vs le genre des passagers.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53ef4320",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f4be1338",
   "metadata": {},
   "source": [
    "#### 2. Calculer le tableau de contingence sur la survie vs la classe des passagers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef01f91f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fa8f8c79",
   "metadata": {},
   "source": [
    "#### 3. Calculer le tableau de contingence sur la survie vs le port d’embarquement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecfb2c03",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3a7d0371",
   "metadata": {},
   "source": [
    "#### 4. Analyser et commenter les résultats "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc758a38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e47ab404",
   "metadata": {},
   "source": [
    "## Partie 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299b94ac",
   "metadata": {},
   "source": [
    "#### 1. Récupérer la variable Fare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d7433c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "194f15ab",
   "metadata": {},
   "source": [
    "#### 2. Tracer le nuage de points de ces deux variables (PassengerId et Fare)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7faef2b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "156835b3",
   "metadata": {},
   "source": [
    "#### 3. Penser vous qu’il existe une relation entre ces deux variables ?. Justifier votre réponse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c8a94de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4ed9f029",
   "metadata": {},
   "source": [
    "#### 4. Calculer et commenter le coefficient de corrélation entre ces deux variables. Cela est-il cohérentavec les résultats précédents ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee10cfc6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4c2813ab",
   "metadata": {},
   "source": [
    "#### 5. Trouver les estimateurs des moindres carrés ordinaire de a et b qu’on notera respectivement par a et b.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "301069cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "df36c2b4",
   "metadata": {},
   "source": [
    "#### 6. Tracer sur le nuage de points (PassengerId et Fare) la droite des moindre  carrée (notée DMC) DMC = aX + b.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7d240ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "04c2bf31",
   "metadata": {},
   "source": [
    " #### Commenter les résultats obtenues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46e54458",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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