{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TP2. Outils numériques pour les Statistiques Descriptives \n", "\n", "Les commandes suivantes, écrites en Python, s'exécutent directement dans ce notebook (cahier d'exercices) Jupyter avec la commande `Ctrl+Entrée`, ou `Shift+Entrée` pour passer directement à la cellule suivante." ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "# on importe les bibliothèques qui nous seront utiles\n", "from pandas import * # pour lire, importer et manipuler des données sous forme\n", "# de tableur\n", "from numpy import * # pour faire des calculs\n", "from matplotlib.pylab import * # pour faire des graphiques" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les instructions ci-dessous permettent d'importer dans \"df\" (c'est un dataframe) les données depuis le fichier footprint.xlsx et d'en afficher les premières lignes. Exécuter et comparer au fichier xlsx." ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountrySDGiLife ExectancyHDIPer Capita GDPRegionIncome GroupPopulation (millions)Total Ecological Footprint (Production)Total Ecological Footprint (Consumption)Total biocapacityEcological (Deficit) or ReserveNumber of Earths requiredNumber of Countries required
0Afghanistan51.84788663.5650.4882439.68Middle East/Central AsiaLI38.0420.7504440.8850110.578479-0.3065320.5707631.529894
1Albania71.48606179.2820.8113862.6Other EuropeUM2.8811.5665692.1022761.124433-0.9778421.3558041.869631
2Algeria70.51091776.4740.74811412.2AfricaUM43.0531.8127742.3674080.702314-1.6650941.5267943.370868
3Angola50.97480362.4480.5957034.84AfricaLM31.8250.6860621.0069831.7385730.7315900.6494260.579201
4Antigua and BarbudaNaN78.6910.822000.2Central America/CaribbeanHI0.0971.5419823.9195530.931722-2.9878322.5278074.206785
\n", "
" ], "text/plain": [ " Country SDGi Life Exectancy HDI Per Capita GDP \\\n", "0 Afghanistan 51.847886 63.565 0.488 2439.68 \n", "1 Albania 71.486061 79.282 0.81 13862.6 \n", "2 Algeria 70.510917 76.474 0.748 11412.2 \n", "3 Angola 50.974803 62.448 0.595 7034.84 \n", "4 Antigua and Barbuda NaN 78.691 0.8 22000.2 \n", "\n", " Region Income Group Population (millions) \\\n", "0 Middle East/Central Asia LI 38.042 \n", "1 Other Europe UM 2.881 \n", "2 Africa UM 43.053 \n", "3 Africa LM 31.825 \n", "4 Central America/Caribbean HI 0.097 \n", "\n", " Total Ecological Footprint (Production) \\\n", "0 0.750444 \n", "1 1.566569 \n", "2 1.812774 \n", "3 0.686062 \n", "4 1.541982 \n", "\n", " Total Ecological Footprint (Consumption) Total biocapacity \\\n", "0 0.885011 0.578479 \n", "1 2.102276 1.124433 \n", "2 2.367408 0.702314 \n", "3 1.006983 1.738573 \n", "4 3.919553 0.931722 \n", "\n", " Ecological (Deficit) or Reserve Number of Earths required \\\n", "0 -0.306532 0.570763 \n", "1 -0.977842 1.355804 \n", "2 -1.665094 1.526794 \n", "3 0.731590 0.649426 \n", "4 -2.987832 2.527807 \n", "\n", " Number of Countries required \n", "0 1.529894 \n", "1 1.869631 \n", "2 3.370868 \n", "3 0.579201 \n", "4 4.206785 " ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = read_excel('footprint.xlsx')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Ex 0.** Exécuter les lignes de commande, observer les sorties, puis commenter chaque ligne en décrivant les variables crées ou les affichages demandés. **Attention!** Vous devrez savoir effectuer ces manipulations tout seul.e par la suite. " ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "['Afghanistan' 51.8478863852544 63.565 0.488 2439.68\n", " 'Middle East/Central Asia' 'LI' 38.042 0.750444266722299\n", " 0.885011196656737 0.57847872813873 -0.306532468518007 0.570763381554606\n", " 1.52989410605344]\n", "(181, 14)\n", "181\n", "2534\n", "Index(['Country', 'SDGi', 'Life Exectancy', 'HDI', 'Per Capita GDP'], dtype='str')\n", "['Afghanistan' 'Albania' 'Algeria' 'Angola' 'Antigua and Barbuda']\n", "['Middle East/Central Asia' 'Other Europe' 'Africa' 'Africa'\n", " 'Central America/Caribbean']\n" ] } ], "source": [ "data = df.values # data contient les données sans les noms des variables.\n", "print(type(data)) # data est du type 'numpy.ndarray'. C'est la classe\n", "# permettant de représenter les tableaux dans numpy.\n", "print(data[0,:]) # Affiche la première ligne de data, c'est-à-dire les infos\n", "# concernant l'Afghanistan.\n", "print(shape(data)) # affiche les dimensions des données c'est-à-dire le\n", "# nombre de pays (lignes) et le nombre de variables (colonnes).\n", "print(len(data)) # affiche le nombre de pays (lignes)\n", "print(size(data)) # affiche le nombre de valeurs (ou de cases) du tableau de\n", "# données.\n", "labels = df.columns # labels contient les noms des variables (str)\n", "print(labels[0:5]) # Affiche les 5 premiers noms de variables\n", "pays = data[:,0] # La 1ère colonne de data contient les noms des pays, on\n", "# l'appelle 'pays'\n", "print(pays[0:5]) # Affiche les 5 premiers noms de pays.\n", "region = data[:,5] # La 5ème colonne de data contient les régions auxquelles\n", "# appartiennent les pays.\n", "print(region[0:5]) # Affiche les cinq premières régions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Ex 1. Indicateurs numériques pour la variable \"Population\"**\n", "\n", "Stocker dans un vecteur colonne (c'est-à-dire une matrice à 1 colonne) appelé `X` les données de la variable \"Population\". Vérifier que le vecteur `X` ainsi construit contient bien les informations attendues et a bien le format attendu en affichant les premières valeurs et en testant les fonctions `shape`, `len` et `size`.\n" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[38.042 2.881 43.053 31.825 0.097]\n", "\n", "(181,)\n", "181\n", "181\n" ] } ], "source": [ "# la variable population est à l'indice i = 7 dans la liste des noms de\n", "# variables.\n", "X = data[:,7]\n", "print(X[0:5])\n", "print(type(X))\n", "# shape renvoie un tuple d'un seul élément car les valeurs de population\n", "# constituent un vecteur colonne (tableau à une seule dimension). La virgule\n", "# qui apparaît dans l'affichage ne joue pas le rôle de séparateur d'élément\n", "# mais permet de distingier le tuple (181,) de la simple expression parenthésée\n", "# (181).\n", "print(shape(X))\n", "# len et size donnent le même résultat car les valeurs de population\n", "# constituent un vecteur colonne (tableau à une seule dimension).\n", "print(len(X))\n", "print(size(X))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**1)** Calculer la moyenne de la variable `X` (fonction `mean`). Ajuster le nombre de décimales pour que le résultat affiché soit « lisible » (fonction `round`). Enoncer et afficher une phrase présentant le résultat exprimé en millions d'habitants. " ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "La population moyenne des pays du monde est d'environ :\n", "42.39 millions d'habitants.\n" ] } ], "source": [ "m = mean(X)\n", "print(\"La population moyenne des pays du monde est d'environ :\")\n", "print(round(m, 2), \"millions d'habitants.\") # arrondi avec deux décimales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2)** Calculer la variance de la variable `X` de deux façons différentes:\n", "\n", "- en appliquant la formule donnée dans le cours (cf slides) \"moyenne des carrés moins carré de la moyenne\", autrement dit~: $\\left(\\frac{1}{n}\\sum_{i=1}^nX(i)^2\\right)~-~(\\overline{X})^2$\n", "\n", "- en utilisant la fonction appropriée de python. \n", "\n", "Afficher les décimales sans arrondir afin de comparer précisément les résulats." ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "23380.918212974895\n", "23380.91821297488\n" ] } ], "source": [ "# méthode 1\n", "n = len(X)\n", "print(1/n * sum(X ** 2) - m ** 2)\n", "# X ** 2 veux dire que chaque valeur du tableau X est élevée au carré. C'est un\n", "# comportement classique des opérations sur les numpy.ndarray qui s'appliquent\n", "# à chaque élément (element-wise en anglais).\n", "\n", "# méthode 2\n", "print(var(X))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3)** Calculer l’écart-type de la variable `X` de deux façons différentes : en utilisant la fonction appropiée (`std` pour 'standard deviation') et en partant de la variance déjà calculée (rappel: la fonction 'racine' s'appelle `sqrt` dans python). " ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "152.90820191531546\n", "152.90820191531546\n" ] } ], "source": [ "# méthode 1\n", "print(std(X))\n", "# méthode 2\n", "print(sqrt(var(X))) # la fonction sqrt vient de numpy ici" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**4)** Calculer la médiane de la variable `X`. Enoncer le résultat avec une phrase. " ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "La moitié des pays a une population inférieure à :\n", "9.75 millions d'habitants.\n" ] } ], "source": [ "med = median(X)\n", "print(\"La moitié des pays a une population inférieure à :\")\n", "print(round(med, 2), \"millions d'habitants.\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**5)** En utilisant la fonction `quantile`, calculer les 1er et 3ème quartiles de la variable `X`. Enoncer le résultat avec une phrase. Combien vaut le 2ème quartile? " ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Un quart des pays a une population inférieure à :\n", "2.76 millions d'habitants.\n", "Un quart des pays a une population supérieure à :\n", "30.418 millions d'habitants.\n", "La médiane 9.746 est égale au second quartile 9.746 .\n" ] } ], "source": [ "q1 = quantile(X, 0.25)\n", "q2 = quantile(X, 0.5)\n", "q3 = quantile(X, 0.75)\n", "print(\"Un quart des pays a une population inférieure à :\")\n", "print(q1, \"millions d'habitants.\")\n", "print(\"Un quart des pays a une population supérieure à :\")\n", "print(q3, \"millions d'habitants.\")\n", "print(\"La médiane\", med, \"est égale au second quartile\", q2, \".\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**6)** Proposer des indicateurs qui rendent compte des valeurs extrêmes de la variable `X`. Quel est le pays le plus peuplé dans l'échantillon étudié? le moins peuplé? (utiliser les fonctions `argmax` et `argmin`). Enoncer les résultats sous forme de phrases." ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dominica est le pays le moins peuplé avec :\n", "0.072 millions d'habitants.\n", "China est le pays le plus peuplé avec :\n", "1465.634 millions d'habitants.\n" ] } ], "source": [ "# Indice de la population minimum dans la liste des populations\n", "imin = argmin(X)\n", "# Indice de la population maximum dans la liste des populations\n", "imax = argmax(X)\n", "# Ces indices vont permettrent de retrouver les noms de pays associés dans la\n", "# liste pays.\n", "print(pays[imin], \"est le pays le moins peuplé avec :\")\n", "print(min(X), \"millions d'habitants.\")\n", "print(pays[imax], \"est le pays le plus peuplé avec :\")\n", "print(max(X), \"millions d'habitants.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**7)** Calculer les mêmes indicateurs (questions 1) à 6)) pour une autre variable du même type (quantitative continue), à choisir. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Ex 2. Tri à plat et représentation graphique de la variable \"Population\".** " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**1)** Effectuer le tri à plat de la variable `X` suivant les intervalles \\[0;10[, [10;50[, [50;100[, [100;500[, [500,...[ et tracer l'histogramme associé. \n", "\n", "*On pourra utiliser la fonction `hist` et se référer à http://python-simple.com/python-matplotlib/histogram.php pour connaître la syntaxe et les options.* " ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist1 = hist(X, bins=[0, 10, 50, 100, 500, 1000, 1500])\n", "title(\"Populations des pays du monde\")\n", "# gca veut dire get current axis (obtenir les axes courants)\n", "gca().set_xlabel(\"Millions d'habitants\")\n", "gca().set_ylabel(\"Nombre de pays\")\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2)** Recommencer avec les intervalles \\[0;375[, [375;750[, [750;1125[, [1125;1500[ qui sont tous les 4 de même longueur. Tester l'instruction `hist(X,4)` et commenter." ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist(X, bins=[0, 375, 750, 1125, 1500])\n", "title(\"Populations des pays du monde\")\n", "gca().set_xlabel(\"Millions d'habitants\")\n", "gca().set_ylabel(\"Nombre de pays\")\n", "show()\n", "# instruction hist(X, 4)\n", "title(\"Populations des pays du monde\")\n", "gca().set_xlabel(\"Millions d'habitants\")\n", "gca().set_ylabel(\"Nombre de pays\")\n", "hist(X, 4)\n", "show()\n", "# Les résultats sont identiques car les intervalles choisis à cette question\n", "# sont tous de même longueur. Ce travail de division en quatre intervalle de\n", "# même longueur est réalisé efficacement avec l'instruction hist(X, 4)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3)** Recommencer encore une fois avec les intervalles \\[0,Q1[,[Q1,Q2[,[Q2,Q3[,[Q3,max[, où les Qi désignent les quartiles. Le résultat était-il prévisible ? " ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist(X, bins=[0, q1, med, q3, max(X)])\n", "title(\"Populations des pays du monde\")\n", "gca().set_xlabel(\"Millions d'habitants\")\n", "gca().set_ylabel(\"Nombre de pays\")\n", "show()\n", "# Le résultat était prévisible. En effet les bornes choisies divisent la\n", "# population en quatre groupes possédant le même nombre de pays. Les rectangles\n", "# associés à chacun de ces groupes ont donc la même hauteur." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**4)** Comparer les 3 histogrammes et commenter en répondant en particulier à la question : « Etes-vous bien sûr qu’il s’agit des mêmes données ? »" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [], "source": [ "# commentaire\n", "# Les populations des pays du monde couvrent plusieurs ordres de grandeurs. Les\n", "# intervalles de taille croissante du premier histogramme sont donc adaptés\n", "# pour rendre compte de la diversité des pays de manière fine.\n", "# Les intervalles de taille identique du second histogramme montrent clairement\n", "# qu'il y a de nombreux pays très peu peuplés et très peu de pays très peuplés.\n", "# Cependant, cet histogramme rend compte de la diversité des pays de manière\n", "# beaucoup moins fine que le premier.\n", "# Le dernier histogramme n'a aucun interêt pour décrire les données. Il ne\n", "# fait que confirmer que les bornes min, q1, med, q2 et max divisent bien\n", "# les pays en quatre groupes composés du même nombre de pays.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**5)** A la'ide de la fonction `pie` de la bibliothèque `matplotlib`, tracer un camembert correspondant au tri à plat de la question 1). Insérer des légendes, afficher les pourcentages, etc. \n", "http://python-simple.com/python-matplotlib/pie.php" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[92. 60. 15. 12. 0. 2.]\n" ] } ], "source": [ "# hist1[0] permet de récupérer le tri a plat c'est-à-dire les effectifs\n", "# de chaque intervalles affiché dans l'histogramme hist1.\n", "print(hist1[0])" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['[0;10[', '[10;50[', '[50;100[', '[100;500[', '[500;1000[', '[1000;1500[']\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "effs = hist1[0] # effectifs de chaque intervalle\n", "bins = [0, 10, 50, 100, 500, 1000, 1500] # bornes des intervalles\n", "# liste en compréhension astucieuse pour créer les étiquettes\n", "labels = [f'[{bins[i]};{bins[i+1]}[' for i in range(len(bins) - 1)]\n", "# Si vous trouvez ça trop énigmatique, vous pouvez écrire cette liste à la\n", "# main:\n", "# labels = [\n", "# '[0;10[', '[10;50[', '[50;100[', '[100;500[', '[500;1000[', '[1000;1500['\n", "# ]\n", "print(labels)\n", "pie(\n", " effs,\n", " labels=labels,\n", " # pour l'affichage des pourcentages\n", " autopct = lambda x: str(round(x, 2)) + '%'\n", ")\n", "title(\"Populations des pays du monde (en millions d'habitants)\")\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Ex 3. Etude de la variable Region.** " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**1)** A l'aide de la fonction `hist`, effectuer le tri à plat puis tracer un histogramme pour représenter la distribution de la variable \"Region\". Commenter le résultat. Tester un histogramme horizontal." ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Middle East/Central Asia' 'Other Europe' 'Africa' 'Africa'\n", " 'Central America/Caribbean' 'South America' 'Middle East/Central Asia'\n", " 'Asia-Pacific' 'EU-28' 'Middle East/Central Asia']\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Les noms des régions sont situés dans la colonne d'indice 5.\n", "X = data[:,5]\n", "# Affichage des 10 premières régions (on voit qu'il y a des doublons car une\n", "# région réuni plusieurs pays.)\n", "print(X[:10])\n", "hist(X)\n", "title(\"Répartition des pays du monde par région\")\n", "gca().set_xlabel(\"Région\")\n", "gca().set_ylabel(\"Nombre de pays\")\n", "show()\n", "# Remarque. Les noms des pays sont illisibles, donc on va orienter\n", "# l'histogramme horizontalement.\n" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# on trace un histogramme horizontal pour avoir assez de place pour les noms\n", "# des régions\n", "hist(X, orientation='horizontal')\n", "title(\"Répartition des pays du monde par région\")\n", "gca().set_xlabel(\"Nombre de pays\")\n", "gca().set_ylabel(\"Région\")\n", "show()\n", "# Remarque. On voit que les nom des régions sont mal positionnés. En fait,\n", "# l'histogramme n'est pas la représentation adaptée pour la variable\n", "# qualitative Région. Il faut mieux utiliser un diagramme en bâtons abordé à la\n", "# question suivante." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2)** Tester la fonction `value_counts` de la bibliothèque Panda (https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.value_counts.html) pour effectuer le tri à plat de la variable Region. \n", "\n", "Puis utiliser la fonction `bar` ou `barh` (de la bibliothèque `matplotlib`) pour tracer l'histogramme de la variable Region (voir http://python-simple.com/python-matplotlib/barplot.php pour la syntaxe). Adapter la légende pour que l'affichage soit correct." ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Region\n", "Africa 53\n", "Asia-Pacific 30\n", "EU-28 27\n", "Middle East/Central Asia 23\n", "Central America/Caribbean 20\n", "South America 13\n", "Other Europe 12\n", "North America 3\n", "Name: count, dtype: int64\n", "\n", "['Africa' 'Asia-Pacific' 'EU-28' 'Middle East/Central Asia'\n", " 'Central America/Caribbean' 'South America' 'Other Europe'\n", " 'North America']\n", "[53 30 27 23 20 13 12 3]\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# fonction value_counts\n", "# On récupère les nombres de pays par région\n", "tri_plat = df.Region.value_counts()\n", "print(tri_plat)\n", "print(type(tri_plat))\n", "# On extrait de cette pandas.Series\n", "# 1. les noms de régions (to_numpy permet d'obtenir une numpy.ndarray)\n", "noms_regions = tri_plat.index.to_numpy()\n", "print(noms_regions)\n", "# 2. les nombres de pays par région (to_numpy permet d'obtenir une\n", "# numpy.ndarray)\n", "nbres_pays = tri_plat.to_numpy()\n", "print(nbres_pays)\n", "# Diagramme en bâton vertical\n", "bar(noms_regions, nbres_pays)\n", "title(\"Pays du monde par région\")\n", "gca().set_xlabel(\"Région\")\n", "gca().set_ylabel(\"Nombre de pays\")\n", "show()\n", "# Remarque. Avec bar l'affichage est vertical ce qui rend illisible les noms\n", "# des régions." ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Diagramme en bâton horizontal avec barh (au lieu de bar)\n", "title(\"Pays du monde par région\")\n", "barh(noms_regions, nbres_pays)\n", "gca().set_xlabel(\"Nombre de pays\")\n", "gca().set_ylabel(\"Région\")\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3)** Quelle est la région du monde la plus représentée parmi les pays étudiés? Répondre de deux façons différentes: \n", "\n", "- en utilisant le résultat du tri à plat précédent,\n", "\n", "- en utilisant la fonction `mode` de la bibliothèque `statistics` (commencer par importer cette bibliothèque)" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Africa\n", "Africa\n" ] } ], "source": [ "# méthode 1\n", "imax = argmax(nbres_pays)\n", "print(noms_regions[imax])\n", "# méthode 2\n", "from statistics import mode\n", "print(mode(df.Region))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**4)** Tracer un camembert pour représenter la distribution de la variable Region. " ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pie(\n", " nbres_pays,\n", " labels=noms_regions,\n", " # pour l'affichage des pourcentages\n", " autopct = lambda x: str(round(x, 2)) + '%'\n", ")\n", "title(\"Pays du monde par région (en nombre de pays)\")\n", "show()" ] } ], "metadata": { "kernelspec": { "display_name": "iut", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 2 }