6_types_iterables.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Programmation Python 3 - Les conteneurs"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Définition"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Un conteneur (*container*) est un nom générique pour définir un objet Python qui contient une collection, *un ensemble* d'autres objets.  \n",
    "Les types de conteneur sont capables de supporter :  \n",
    "* Des tests d'appartenance\n",
    "* La fonction `len` qui renvoie la longueur (*taille*) du conteneur  \n",
    "Les types de conteneur peuvent également être :\n",
    "* Ordonné : Les éléments contenus sont placés dans un ordre précis et sont donc positionnés à une place en particulièr, appelé indice ou *index* en anglais. \n",
    "* Indexable : Chaque élément est accessible via son indice (*position dans le conteneur*). Il est également possible de retrouver une tranche d'éléments. La plupart du temps, tous les conteneurs sont ordonnés et indexables. \n",
    "* Itérable : Il est possible d'appliquer une boucle sur un conteneur (*e.g., un parcours des éléments avec une boucle for*). "
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Les conteneurs peuvent contenir n'importe quel type de données. Celles-ci ne sont pas nécessairement toutes de même type.",
   "attachments": {
    "Python_conteneurs.png": {
     "image/png": 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"
    }
   }
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Une séquence est un conteneur itérable, ordonné et indexable. Les éléments présents dans une séquence possèdent une position précise.  \n",
    "On retrouve notamment quatre types de données prédéfinis :  \n",
    "* Les chaînes\n",
    "* Les listes\n",
    "* Les tuples\n",
    "* Les sets"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Le nombre d'éléments, présents dans une séquence, est fini. L'indice du premier élément est 0, et l'indice du dernier élément est *n-1*, ce qui correspondant donc à *n* éléments au total.  \n"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Les chaînes de caractères"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Une chaîne de caractères est de type *str*. Dans une chaîne de caractères, il est possible d'utiliser des séquences de caractères particulières, elles commencent par le caractère *\\\\* (*anti-slash*) et permettent d'insérer des caractères spéciaux.  \n",
    "Ci-dessous la liste des principaux caractères spéciaux que vous pouvez utiliser :\n"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "|Syntaxe|Signification|\n|-|-|\n|\\a|\"Bell\": un bip sonore|\n|\\n|Retour à la ligne|\n|\\t|Tabulation|\n|\\u|Caractère avec sa valeur Unicode|\n|\\\\'|Insérer un '|\n|\\\\\"|Insérer un \"|\n"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "La liste des caractères Unicode est accessible via le lien suivant : https://koor.fr/Unicode.wp"
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:05.311217Z",
     "start_time": "2024-10-25T15:37:05.303714Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'utilisation des caractères spéciaux\n",
    "print(\"Ligne 1\\nLigne 2\\nLigne 3\")\n",
    "print('\\u231a\\t\\u26bd')\n",
    "print('Ajourd\\'hui')"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ligne 1\n",
      "Ligne 2\n",
      "Ligne 3\n",
      "⌚\t⚽\n",
      "Ajourd'hui\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Deux opérations sont possibles quand on traite les chaînes de caractères : \n",
    "* `+` permet la concaténation de chaînes de caractères\n",
    "* `*` permet de reproduire une chaîne de caractères un certain nombre de fois"
   ]
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:05.865376Z",
     "start_time": "2024-10-25T15:37:05.858162Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'une concaténation de chaînes de caractères\n",
    "love = \"I love \"\n",
    "who = \"Digicomp Academy\"\n",
    "print(love + who)\n",
    "# Exemple d'une répétition d'une chaîne de caractères\n",
    "cours = \"Python\"\n",
    "print(cours * 3)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I love Digicomp Academy\n",
      "PythonPythonPython\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Attention aux types de données sur lesquels vous appliquez les opérateurs d'addition/concaténation et de multiplication/duplication. Certaines opérations sont interdites. "
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:06.219838Z",
     "start_time": "2024-10-25T15:37:05.907812Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'opération interdite\n",
    "who = \"Digicomp Academy\"\n",
    "print(who * who)"
   ],
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't multiply sequence by non-int of type 'str'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[4], line 3\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[38;5;66;03m# Exemple d'opération interdite\u001B[39;00m\n\u001B[1;32m      2\u001B[0m who \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mDigicomp Academy\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m----> 3\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[43mwho\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43m \u001B[49m\u001B[43mwho\u001B[49m)\n",
      "\u001B[0;31mTypeError\u001B[0m: can't multiply sequence by non-int of type 'str'"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Les listes"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Une liste est une collection de données ordonnées et modifiables.  \n",
    "Les éléments qui composent une liste sont séparés par des `,` et l'ensemble est délimité par des crochets `[]`. Tous les éléments peuvent être hétérogènes ou non, c’est-à-dire de même type ou non. "
   ]
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": [
    "# Exemple de création de listes\n",
    "liste_formes = [\"Triangle\", \"Cercle\", \"Quadrilatère\"]\n",
    "ma_liste = [\"Bonjour\", False, 0, 1, 3.14159]\n",
    "print(liste_formes)\n",
    "print(ma_liste)\n",
    "print(liste_formes + ma_liste)"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Les éléments dans une liste sont accessibles via leur indice, c’est-à-dire la place qu'ils occupent dans la liste.  \n",
    "Le premier élément est placé à l'indice `0`. Les éléments suivants voient leur indice incrémenté de 1 à chaque fois.  \n",
    "Il est possible de parcourir les éléments d'une liste par ordre décroissant, c’est-à-dire les éléments situés à la fin, en plaçant une valeur négative comme indice de la liste (*exemple: -1 correspond au premier élément en partant de la fin de la liste*). "
   ]
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'accès aux éléments d'une liste par leur indice (croissant)\n",
    "couleurs = [\"Rouge\", \"Orange\", \"Noir\", \"Marron\", \"Bleu\", \"Violet\", \"Jaune\"]\n",
    "print(couleurs[0])\n",
    "print(couleurs[4])\n",
    "#Exemple d'accès aux éléments d'une liste par leur indice (décroissant)\n",
    "print(couleurs[-1])\n",
    "print(couleurs[-3])"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "***À noter, que les chaînes de caractères sont manipulables de la même manière que les listes au travers des indices (places) des éléments (caractères) qui la composent.***"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Il est également possible de créer des listes en itérant une suite de nombres grâce à la fonction `range`. Cette fonction génère des suites arithmétiques.  "
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'initialisation de listes avec l'itérateur d'entiers range()\n",
    "ma_liste = list(range(10))\n",
    "print(ma_liste)\n",
    "ma_liste2 = list(range(5, 10))\n",
    "print(ma_liste2)\n",
    "liste_impair = list(range(1, 10, 2))\n",
    "print(liste_impair)\n",
    "liste_pair = list(range(0, 10, 2))\n",
    "print(liste_pair)"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Il existe un certain nombre de méthodes prédéfinies qui permettent de manipuler les listes et les éléments qui les composent.  "
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "***\n#### ***Exercice 1***"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "***Méthodes sur les listes***  \n",
    "Recherchez sur Internet, dans la documentation de la [standard library](https://docs.python.org/3/library/stdtypes.html#sequence-types-list-tuple-range), le rôle de chacune des méthodes proposées ci-dessous et proposez un exemple d'utilisation pour chacune d'elles: \n",
    "* `len`\n",
    "* `sort`\n",
    "* `append`\n",
    "* `reverse`\n",
    "* `remove`\n",
    "* `index`\n",
    "* `pop`\n",
    "* `count`\n",
    "* `extend` \n",
    "    - Attention aux commentaires\n",
    "    - Attention aux noms de variables.  \n",
    "*Faites valider votre script ainsi que son exécution.* \n",
    "***"
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "markdown",
   "source": "Il est possible de supprimer, remplacer ou insérer un ou plusieurs éléments dans une liste, il faut indiquer un interval ou tranche de valeurs à modifier. \n"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Pour spécifier une tranche de valeurs il faut insérer le caractère `:` en précisant la valeur de départ (indice de début) suivie de la valeur finale (indice de fin), cette dernière étant exclue. "
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:06.257442Z",
     "start_time": "2024-10-25T15:37:06.251630Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple de manipulation des tranches de listes\n",
    "couleurs = [\"Rouge\", \"Orange\", \"Noir\", \"Marron\", \"Bleu\", \"Violet\", \"Jaune\"]\n",
    "# Parcours dans un intervalle au milieu de la liste\n",
    "print(couleurs[2:6])\n",
    "# Parcours dans un intervalle depuis un certain indice jusqu'à la fin\n",
    "print(couleurs[2:])\n",
    "# Parcours dans un intervalle depuis le début jusqu'à un certain indice exclu\n",
    "print(couleurs[:4])"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Noir', 'Marron', 'Bleu', 'Violet']\n",
      "['Noir', 'Marron', 'Bleu', 'Violet', 'Jaune']\n",
      "['Rouge', 'Orange', 'Noir', 'Marron']\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": " L'assignation d'une liste se fait par référence, la liste étant *mutable*, en modifiant l'une on modifie l'autre. "
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:06.372664Z",
     "start_time": "2024-10-25T15:37:06.366098Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'une copie de liste\n",
    "couleurs = [\"Noir\", \"Marron\", \"Rouge\", \"Orange\"]\n",
    "colors = couleurs\n",
    "print(\"Liste copie: \", colors)\n",
    "colors[0] = \"Blanc\"\n",
    "print(\"Liste copie modifiée: \", colors)\n",
    "print(\"Liste souche: \", couleurs)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liste copie:  ['Noir', 'Marron', 'Rouge', 'Orange']\n",
      "Liste copie modifiée:  ['Blanc', 'Marron', 'Rouge', 'Orange']\n",
      "Liste souche:  ['Blanc', 'Marron', 'Rouge', 'Orange']\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "On constate donc que les deux listes ont été modifiées suite à l'égalité.\n",
    "Concernant la copie, la méthode `copy()` est peut-être plus lisible, la méthode `[:]` ressemble trop au slicing."
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Il est possible faire une copie qui est indépendante de la première par l'intermédiaire du caractère `:` ou de la méthode `copy()` : "
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:06.961186Z",
     "start_time": "2024-10-25T15:37:06.955243Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'une copie de liste indépendante\n",
    "couleurs = [\"Noir\", \"Marron\", \"Rouge\", \"Orange\"]\n",
    "colors = couleurs[:]\n",
    "colors_another_copy = couleurs.copy()\n",
    "print(\"Les copies sont identiques: \", colors == colors_another_copy)\n",
    "print(\"Liste copie: \", colors)\n",
    "colors[0] = \"Blanc\"\n",
    "print(\"Liste copie modifiée: \", colors)\n",
    "print(\"Liste souche: \", couleurs)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Les copies sont identiques:  True\n",
      "Liste copie:  ['Noir', 'Marron', 'Rouge', 'Orange']\n",
      "Liste copie modifiée:  ['Blanc', 'Marron', 'Rouge', 'Orange']\n",
      "Liste souche:  ['Noir', 'Marron', 'Rouge', 'Orange']\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "On constate donc que seule la liste copie a été modifiée et que la modification opérée n'a eu aucune incidence sur la liste souche. "
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "***\n#### ***Exercice 2***"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Écrire un script Python qui demande un mot à l'utilisateur et qui lui signifie si ce mot est un palindrome ou non.  \n- Attention aux commentaires\n- Attention aux noms de variables.  \n*Faites valider votre script ainsi que son exécution.* "
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### ***Exercice 3***"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Écrire un script Python qui demande un mot à l'utilisateur et qui affiche ce mot en doublant toutes les voyelles.  \n- Attention aux commentaires\n- Attention aux noms de variables.  \n*Faites valider votre script ainsi que son exécution.*\n"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### ***Exercice 4***"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Écrire un script Python qui permet de jouer au jeu du Pendu.  \n",
    "* Le joueur 1 entre un mot dans le programme. Les caractères qui composent le mot apparaitront sous forme de `_` pour le joueur. Si vous souhaitez que le mot saisi n'apparaisse pas à l'écran vous pouvez utiliser la méthode `getpass()` contenue dans le module `getpass` - Voir exemple ci-dessous. \n",
    "* Le joueur 2, propose une lettre\n",
    "* L'ordinateur indique alors si la lettre est présente ou non dans le mot. Si la lettre est présente dans le mot, elle est alors positionnée et affichée en lieu et place du ou des *_*.  \n",
    "* BONUS: Dessiner au fur et à mesure des erreurs, le personnage du pendu. \n",
    "\n",
    "- Attention aux commentaires\n",
    "- Attention aux noms de variables.  \n",
    "*Faites valider votre script ainsi que son exécution.*\n"
   ]
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:13.401634Z",
     "start_time": "2024-10-25T15:37:07.012803Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple de saisie cachée\n",
    "from getpass import getpass\n",
    "\n",
    "mot = getpass(\"Veuillez saisir le mot secret: \")"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### ***Exercice 5***"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "**€€€ La monnaie SVP €€€**  \n",
    "Un distributeur de banque dispose de billets de 10, 20, 50, 100, 200 et 500 €. On suppose que la banque ne connaît pas la crise et possède donc un nombre illimité de billets.  \n",
    "Écrire un script Python qui demande la somme désirée par l'utilisateur et propose une répartition en proposant le moins de billets possible. Dans le cas où la banque ne peut pas donner l'intégralité de la somme souhaitée, elle distribue le montant possible et spécifie à l'utilisateur le montant manquant.  \n",
    "* Exemple : 57€ demandé = 50 € x 1. Les 7 € manquants ne peuvent pas être distribués.*  \n",
    "\n",
    "- Attention aux commentaires\n",
    "- Attention aux noms de variables.  \n",
    "*Faites valider votre script ainsi que son exécution.*"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### ***Exercice 6***  \n"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "**Blackjack**  \n",
    "Le blackjack est un jeu opposant un joueur à la banque, le but est de se rapprocher de 21 sans dépasser ce total. Les valeurs des 13 cartes seront représentées dans une liste (Figure = 10 points, As = 11 ou 1 points si le total dépasse 21). Le banquier et le joueur choisissent chacun à leur tour le nombre de cartes à tirer. Si l'un des deux dépasse 21, il perd la partie. S'il y a égalité, c'est le joueur ayant le moins de cartes qui gagne. Le joueur avec le score le plus proche ou égale à 21 remporte la partie. Si les deux joueurs dépassent 21, la partie est nulle. La saisie du 0 permet d'arrêter la partie. La banque continue de tirer des cartes tant que son score n’est pas au moins égale à 17. À vous de définir la stratégie de jeu de l'ordinateur, c’est-à-dire le nombre de cartes à tirer ou non en fonction de la valeur acquise\n",
    "\n",
    "Voir https://www.casinosbarriere.com/fr/nos-jeux/jeux-de-table/blackjack.html.\n",
    "\n",
    "- Attention aux commentaires\n",
    "- Attention aux noms de variables.  \n",
    "*Faites valider votre script ainsi que son exécution.*\n",
    "***"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Les tuples"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Un tuple est similaire à une liste, c’est-à-dire une collection d'objets.  \n",
    "Pour créer un tuple, il faut utiliser les caractères `()` au lieu des `[]` pour une liste. Tout comme pour une liste, les éléments sont séparés par des `,`. "
   ]
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:13.409711Z",
     "start_time": "2024-10-25T15:37:13.404363Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple de création d'un tuple et d'une liste\n",
    "couleurs_tuple = (\"Noir\", \"Marron\", \"Rouge\", \"Orange\")\n",
    "print(type(couleurs_tuple))\n",
    "couleurs_liste = [\"Noir\", \"Marron\", \"Rouge\", \"Orange\"]\n",
    "print(type(couleurs_liste))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tuple'>\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Pour accéder aux éléments d'un tuple, il faut procéder de la même manière qu'avec une liste. Chaque élément à une place précise, appelée indice. La fonction `len()` est également utilisable et renvoie la longueur du tuple, c'est-à-dire le nombre d'éléments qui le compose. "
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:13.520897Z",
     "start_time": "2024-10-25T15:37:13.512723Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple de manipulation des éléments d'un tuple\n",
    "couleurs_tuple = (\"Noir\", \"Marron\", \"Rouge\", \"Orange\")\n",
    "print(len(couleurs_tuple))\n",
    "print(couleurs_tuple[0])\n",
    "print(couleurs_tuple[:3])\n",
    "print(couleurs_tuple[2:])"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "Noir\n",
      "('Noir', 'Marron', 'Rouge')\n",
      "('Rouge', 'Orange')\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "**Quelle est alors la différence entre une liste et un tuple ?**  \n",
    "Un tuple ne peut pas être modifié, en effet, on ne peut ni modifier les éléments qui le composent, ni ajouter ou supprimer des éléments !"
   ]
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:13.685439Z",
     "start_time": "2024-10-25T15:37:13.647412Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple modification d'un tuple\n",
    "couleurs_tuple = (\"Noir\", \"Marron\", \"Rouge\", \"Orange\")\n",
    "couleurs_tuple[0] = \"Blanc\"\n",
    "print(couleurs_tuple)"
   ],
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[11], line 3\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[38;5;66;03m# Exemple modification d'un tuple\u001B[39;00m\n\u001B[1;32m      2\u001B[0m couleurs_tuple \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNoir\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMarron\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRouge\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mOrange\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m----> 3\u001B[0m \u001B[43mcouleurs_tuple\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m]\u001B[49m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mBlanc\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m      4\u001B[0m \u001B[38;5;28mprint\u001B[39m(couleurs_tuple)\n",
      "\u001B[0;31mTypeError\u001B[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Mutabilité"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Il existe différents objets qui peuvent être modifiables ou non. Les objets modifiables sont appelés **mutables**, comme les listes par exemple, tandis que les objets non modifiables sont appelés **immuables**, comme par exemple les tuples ou les chaînes de caractères.  "
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:13.857188Z",
     "start_time": "2024-10-25T15:37:13.851403Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'objets immuables\n",
    "note_info = 9\n",
    "moyenne_info = note_info\n",
    "note_info = 16\n",
    "print(note_info)\n",
    "print(moyenne_info)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "9\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Les tuples sont immuables, c'est-à-dire qu'ils ne peuvent pas être modifiés :"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:14.000233Z",
     "start_time": "2024-10-25T15:37:13.928823Z"
    }
   },
   "cell_type": "code",
   "source": [
    "inchangeable = (1, 2, 3)\n",
    "inchangeable[0] = 4"
   ],
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[13], line 2\u001B[0m\n\u001B[1;32m      1\u001B[0m inchangeable \u001B[38;5;241m=\u001B[39m (\u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m3\u001B[39m)\n\u001B[0;32m----> 2\u001B[0m \u001B[43minchangeable\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m]\u001B[49m \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m4\u001B[39m\n",
      "\u001B[0;31mTypeError\u001B[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Les vertus de l'immutabilité\n",
    "\n",
    "En design logiciel les objets immuables sont souvent considérés comme plus safe.\n",
    "En effet leur immutabilité permet entre autre d'éviter des effets de bord, des modifications involontaires, des bugs difficiles à débugger."
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Dans l'exemple ci-dessus on constate que la variable `note_info` a été modifiée mais que la variable *moyenne_info* elle ne l'a pas été. "
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:14.455688Z",
     "start_time": "2024-10-25T15:37:14.442342Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'objets mutables\n",
    "note_info1 = [19, 14, 10, 18]\n",
    "note_info2 = note_info1\n",
    "note_info1[0] = 9\n",
    "print(note_info1)\n",
    "print(note_info2)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9, 14, 10, 18]\n",
      "[9, 14, 10, 18]\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Dans l'exemple ci-dessus on constate que la variable `note_info1` a été modifiée et que la variable `note_info2` également. "
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "L'utilisation de la fonction `id()` qui renvoie l'identité de l'objet passé en paramètre. Il s'agit d'un nombre entier garanti unique et constant pour le dit objet durant sa durée de vie.  \n",
    "*Pour simplifier les choses cela peut être assimilé à l'adresse de l'objet en mémoire.* "
   ]
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:14.624621Z",
     "start_time": "2024-10-25T15:37:14.619166Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'objets immuables\n",
    "note_info = 9\n",
    "moyenne_info = note_info\n",
    "print(id(note_info), id(moyenne_info))\n",
    "note_info = 16\n",
    "print(id(note_info), id(moyenne_info))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11754152 11754152\n",
      "11754376 11754152\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "On constate donc qu'au moment de l'initialisation des deux variables, celles-ci pointent vers le même emplacement mémoire.  \n",
    "Après modification d'une des deux variables, celle-ci pointe alors vers un nouvel emplacement mémoire tandis que l'autre pointe toujours sur le même emplacement mémoire. "
   ]
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:14.707025Z",
     "start_time": "2024-10-25T15:37:14.699828Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'objets mutables\n",
    "note_info1 = [19, 14, 10, 18]\n",
    "note_info2 = note_info1\n",
    "print(id(note_info1), id(note_info2))\n",
    "note_info1[0] = 9\n",
    "print(id(note_info1), id(note_info2))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125278896047104 125278896047104\n",
      "125278896047104 125278896047104\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Avec des objets mutables Les variables pointent toujours vers les mêmes emplacements mémoire avant ou après modification. "
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "|Objets mutables|Objets immuables|\n|-|-|\n|list|int, float, bool|\n|dict|str|\n|set|tuple|"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Les dictionnaires"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Les dictionnaires sont des tableaux associatifs, c'est-à-dire qu'ils associent une clé à une valeur. Un tableau associatif, dictionnaire, est un **type de données** (*dict*) permettant de stocker des couples (*clé: valeur*). Les dictionnaires sont des collections non ordonnées d'objets. Il ne s'agit pas d'objets séquentiels comme peuvent l'être les listes, les tuples ou même les chaînes de caractères. L'accès aux valeurs est très rapide par l'intermédiaire des clés. **Les clés dans un dictionnaire étant uniques et immuables !**.  \n"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Pour créer un dictionnaire, il faut utiliser les caractères *{}*. Lors de l'initialisation il est possible de créer un dictionnaire vide ou de créer un dictionnaire avec les couples désirés. Pour insérer des couples lors de l'initialisation il est nécessaire de séparer avec le caractère *:* la clé de la valeur associée.  \n",
    "Les couples sont séparés entre eux par des `,` comme dans les listes, tuples, ...  "
   ]
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:14.819300Z",
     "start_time": "2024-10-25T15:37:14.813964Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple de création de dictionnaires\n",
    "couleurs_dico = {}\n",
    "print(type(couleurs_dico))\n",
    "couleurs_dico[\"Noir\"] = 0\n",
    "couleurs_dico[\"Marron\"] = 1\n",
    "print(couleurs_dico)\n",
    "notes_dico = {\"Info\": 16, \"Communication\": 13, \"Anglais\": 20}\n",
    "print(notes_dico)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "{'Noir': 0, 'Marron': 1}\n",
      "{'Info': 16, 'Communication': 13, 'Anglais': 20}\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Comme présenté dans l'exemple ci-dessus, pour ajouter des couples dans le dictionnaire, il faut préciser la clé entre *[]* puis affecter une valeur, cette méthode peut se rapprocher de la manière de procéder avec une liste ou un tuple où l'indice est précisé entre crochets, tandis que pour les dictionnaires c'est le clé qui est spécifiée.\n",
    "Si lors de l'ajout d'un élément dans un dictionnaire, la clé est déjà existante il n'y aura alors pas d'insertion de nouvelle valeur, mais un remplacement. "
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Pour accéder à la valeur associée à une clé, il est nécessaire de spécifier la clé entre `[]`. Pour connaître l'ensemble des couples clés/valeurs, il est possible d'itérer sur un dictionnaire avec une boucle `for`. "
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:14.889885Z",
     "start_time": "2024-10-25T15:37:14.883656Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'accès à une valeur\n",
    "notes_dico = {\"Info\": 16, \"Communication\": 13, \"Anglais\": 20}\n",
    "print(notes_dico[\"Communication\"])\n",
    "for cle in notes_dico:\n",
    "    print(cle, notes_dico[cle])"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n",
      "Info 16\n",
      "Communication 13\n",
      "Anglais 20\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Il existe des méthodes applicables sur un dictionnaire qui permettent de récupérer des collections de données en fonction de la méthode utilisée. Pour utiliser ces méthodes il suffit de spécifier le nom du dictionnaire visé suivi par la méthode à appliquer en séparant par le caractère `.`. \n",
    "* `notes_dico.keys()`: qui renvoie une collection contenant les clés comprises dans `notes_dico`.   \n",
    "* `notes_dico.values()`: qui renvoie une collection contenant les valeurs comprises dans `notes_dico`. \n",
    "* `notes_dico.items()`: qui renvoie une collection de tuples contenants chacun les couples clés/valeurs comprises dans `notes_dico`.  \n",
    "L'ensemble de ces méthodes sont itérables. "
   ]
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:15.060387Z",
     "start_time": "2024-10-25T15:37:15.053800Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple des méthodes à appliquer sur un dictionnaire\n",
    "notes_dico = {\"Info\": 16, \"Communication\": 13, \"Anglais\": 20}\n",
    "print(notes_dico.keys())\n",
    "print(notes_dico.values())\n",
    "print(notes_dico.items())"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['Info', 'Communication', 'Anglais'])\n",
      "dict_values([16, 13, 20])\n",
      "dict_items([('Info', 16), ('Communication', 13), ('Anglais', 20)])\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "L'ensemble de ces méthodes sont itérables. "
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:15.116396Z",
     "start_time": "2024-10-25T15:37:15.111005Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple des méthodes à appliquer sur un dictionnaire\n",
    "notes_dico = {\"Info\": 16, \"Communication\": 13, \"Anglais\": 20}\n",
    "for couple in notes_dico.items():\n",
    "    print(couple)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Info', 16)\n",
      "('Communication', 13)\n",
      "('Anglais', 20)\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Il existe également des méthodes permettant de supprimer des couples clés/valeurs présentes dans le dictionnaire. \n",
    "\n",
    "| Méthode   | Description                                                                    |\n",
    "|-----------|--------------------------------------------------------------------------------|\n",
    "| `pop`     | Supprime l'élément dont la clé est spécifiée en paramètre et renvoie la valeur |\n",
    "| `popitem` | Supprime le dernier élément ajouté au dictionnaire et renvoie le couple        |\n",
    "| `del`     | Supprime l'élément dont la clé est spécifiée en paramètre                      |\n",
    "| `clear`   | Supprime tous les éléments du dictionnaire                                     |\n"
   ]
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:15.276441Z",
     "start_time": "2024-10-25T15:37:15.269467Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple de suppression d'éléments dans un dictionnaire\n",
    "notes_dico = {\"Info\": 16, \"Communication\": 13, \"Anglais\": 20}\n",
    "print(notes_dico.popitem())\n",
    "print(notes_dico)\n",
    "print(notes_dico.pop(\"Info\"))\n",
    "print(notes_dico)\n",
    "del (notes_dico[\"Communication\"])\n",
    "print(notes_dico)\n",
    "notes_dico = {\"Info\": 16, \"Communication\": 13, \"Anglais\": 20}\n",
    "notes_dico.clear()\n",
    "print(notes_dico)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Anglais', 20)\n",
      "{'Info': 16, 'Communication': 13}\n",
      "16\n",
      "{'Communication': 13}\n",
      "{}\n",
      "{}\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "***\n",
    "#### ***Exercice 8***\n",
    "\n",
    "**Moyenne !**  \n",
    "Écrire un script Python qui demande à l'utilisateur de saisir son prénom et ses notes dans les matières suivantes : \n",
    "* Scripting\n",
    "* Python\n",
    "* Code source  \n",
    "\n",
    "Présentez les résultats sous forme de dictionnaire faisant intervenir des couples clés/valeurs où la clé est le prénom de l'élève et la valeur est elle-même un dictionnaire. Ce sous-dictionnaire intègre également des couples clés/valeurs où la clé représente la matière et la valeur est une liste des notes dans la matière concernée. \n",
    "Une fois le dictionnaire de chaque étudiant créé vous avez constituez votre base de données. Complétez alors votre script pour calculer la moyenne dans chaque matière d'un étudiant, dont le prénom a été saisi par l'utilisateur, ainsi que sa moyenne générale. *On suppose que toutes les matières ont le même coefficient*.  \n",
    "\n",
    "- Attention aux commentaires\n",
    "- Attention aux noms de variables.  \n",
    "*Faites valider votre script ainsi que son exécution.*"
   ]
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:15.331503Z",
     "start_time": "2024-10-25T15:37:15.326880Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple de dictionnaire à réaliser pour l'exercice 8\n",
    "etudiant_dico = {\"Pierre\": {\"Scripting\": [12, 10],\n",
    "                            \"Python\": [18],\n",
    "                            \"Code source\": [15, 18]}}\n",
    "# Ce dictionnaire doit être complété avec les infos saisies par l'utilisateur"
   ],
   "outputs": [],
   "execution_count": 22
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Les sets"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Les objets de type *set* représentent un autre type d'ensembles. La particularité des sets est qu'ils sont modifiables. Il existe également des sets non modifiables appelés *frozenset*. On retrouve alors les mêmes différences qu'entre les listes (modifiables) et les tuples (non modifiables). Les données sont **non ordonnées** et **sans doublons**.  \n",
    "Attention, un *set* se déclare avec des `{}` comme un dictionnaire. Au moment de l'initialisation il est nécessaire de spécifier le contenu du set ou de spécifier son type. En effet, un objet vide déclaré avec `{}` de type dictionnaire ! Il faut le déclarer avec `set()`.\n",
    "\n",
    "Une des particularités est qu'il ne peut contenir qu'une seule fois une même valeur.\n",
    "Il est donc pratique pour éliminer les doublons d'une liste.\n",
    "Mais pour cela les éléments du set doivent être hashables... Pourquoi ?"
   ]
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:15.469187Z",
     "start_time": "2024-10-25T15:37:15.463322Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'initialisation d'un set\n",
    "matieres_set = {\"Scripting\", \"Python\", \"Code Source\", \"Python\"}\n",
    "notes_set = {}\n",
    "print(type(matieres_set))\n",
    "print(type(notes_set))\n",
    "empty_set = set()\n",
    "print(type(empty_set))\n",
    "notes_set = set(notes_set)\n",
    "print(type(notes_set))\n",
    "print(matieres_set)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'set'>\n",
      "<class 'dict'>\n",
      "<class 'set'>\n",
      "<class 'set'>\n",
      "{'Scripting', 'Python', 'Code Source'}\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "trusted": false,
    "ExecuteTime": {
     "end_time": "2024-10-25T15:37:15.521401Z",
     "start_time": "2024-10-25T15:37:15.515910Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Exemple d'initialisation d'un frozenset\n",
    "matieres_set = frozenset({\"Scripting\", \"Python\", \"Code Source\", \"Python\"})\n",
    "print(type(matieres_set))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'frozenset'>\n"
     ]
    }
   ],
   "execution_count": 24
  }
 ],
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