{"id":911,"date":"2023-11-08T15:27:32","date_gmt":"2023-11-08T15:27:32","guid":{"rendered":"https:\/\/lasstatistique.com\/?p=911"},"modified":"2024-08-14T08:04:07","modified_gmt":"2024-08-14T08:04:07","slug":"cours-1-python","status":"publish","type":"post","link":"https:\/\/lasstatistique.com\/?p=911","title":{"rendered":"Cours 1 : Python"},"content":{"rendered":"

CHAPITRE II : INTRODUCTION A NUMPY<\/b><\/span><\/p>\n

    \n
  1. D\u00e9finition et importation du package<\/i><\/b><\/span><\/li>\n<\/ol>\n
      \n
    • Numpy <\/span>(Numerical Python) est le package python<\/span> sp\u00e9cialis\u00e9 dans le calcul num\u00e9rique.<\/li>\n
    • Pour l\u2019utiliser sur python<\/span>, il faut tout d\u2019abord l\u2019importer. A syntaxe d\u2019importation est la suivante : import numpy as np<\/span><\/li>\n
    • Pour plus d\u2019information par rapport \u00e0 numpy, faire : help(np)<\/span> apr\u00e8s l\u2019importation.<\/li>\n
    • Pour utiliser une fonction de numpy, faire : np.nom_de_la_fonction()<\/span><\/li>\n<\/ul>\n
        \n
      1. Cr\u00e9ation des tableaux<\/i><\/b><\/span><\/li>\n<\/ol>\n
          \n
        • On utilise la m\u00e9thode array()<\/span> \u00e0 partir d\u2019une liste.<\/li>\n<\/ul>\n

          Exemple :<\/p>\n

          \"\"<\/p>\n

            \n
          • Pour cr\u00e9er un tableau \u00e0 plusieurs dimension, on utilise la commande array()<\/span> avec une liste de liste.<\/li>\n<\/ul>\n

            Exemple :<\/p>\n

            \"\"<\/p>\n

            On peut \u00e9galement cr\u00e9er des tableaux pr\u00e9-remplis gr\u00e2ce aux fonctions zeros()<\/span> et full() de numpy<\/span>.<\/p>\n

              \n
            • La fonction zeros()<\/span> permet de cr\u00e9er un tableau o\u00f9 tous les \u00e9l\u00e9ments sont tous nuls. La syntaxe est : np.zeros(n_ligne, n_col)<\/span>.<\/li>\n
            • La fonction full()<\/span> permet de cr\u00e9er un tableau o\u00f9 toutes les cellules contiennent la m\u00eame valeur. Syntaxe : np.full((n_ligne, n_col), valeur)<\/span>.<\/li>\n<\/ul>\n

              Exemple : Si on fait np.full((9,5), 12)<\/span>, on obtient un tableau de taille 9 X 5 et contenant 12 dans chaque cellule.<\/p>\n

                \n
              1. Extraction des \u00e9l\u00e9ments du tableau<\/i><\/b><\/span><\/li>\n<\/ol>\n
                  \n
                • On acc\u00e8de aux \u00e9l\u00e9ments du tableau par l\u2019indice. En python<\/span>, l\u2019indice commence \u00e0 0.<\/li>\n
                • Pour acc\u00e9der \u00e0 une cellule particuli\u00e8re, on utilise l\u2019indice si c\u2019est un tableau \u00e0 une dimension et le num\u00e9ro de la cellule si c\u2019est un tableau \u00e0 plusieurs dimension. On identifie la cellule par son num\u00e9ro de ligne et son num\u00e9ro de colonne.<\/li>\n<\/ul>\n

                  Syntaxe : nom_tableau[n_ligne, n_colonne]<\/span><\/p>\n

                    \n
                  • On peut \u00e9galement acc\u00e9der \u00e0 une partie du tableau en utilisant le slicing<\/span>.<\/li>\n<\/ul>\n

                    Exemple : nom_tableau[:, 1]<\/span> donne acc\u00e8s \u00e0 la premi\u00e8re colonne du tableau. Ici, : signifie qu\u2019on veut toutes la ligne et 1 signifie qu\u2019on veut la premi\u00e8re colonne.<\/p>\n

                      \n
                    • Pour modifier la valeur d\u2019une cellule, il suffit de choisir la cellule et affecter la valeur.<\/li>\n<\/ul>\n

                      Exemple : tableau[1,2] = 5<\/span> affecte la valeur 2 \u00e0 la cellule de ligne 1 et de colonne 2.<\/p>\n","protected":false},"excerpt":{"rendered":"

                      CHAPITRE II : INTRODUCTION A NUMPY D\u00e9finition et importation du package Numpy (Numerical Python) est le package python sp\u00e9cialis\u00e9 dans<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"colormag_page_container_layout":"default_layout","colormag_page_sidebar_layout":"default_layout","footnotes":""},"categories":[40,46],"tags":[],"class_list":["post-911","post","type-post","status-publish","format-standard","hentry","category-cours","category-python"],"yoast_head":"\nCours 1 : Python - LASStatistique<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/lasstatistique.com\/?p=911\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cours 1 : Python - 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