2022-2023/2nd/12_Indicateurs_statistiques/5E_temperature_age.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Etudes statistiques\n",
    "\n",
    "Dans ce TP, vous allez réaliser 2 études statistiques basées sur des données issues de l'INSEE ([L’Institut national de la statistique et des études économiques](https://www.insee.fr/fr/accueil))\n",
    "\n",
    "- [Températures moyennes entre 1900 et 2017](#Temperature)\n",
    "- [Population totale par sexe et âge au 1er janvier 2019, France](#Population)\n",
    "\n",
    "Vous trouverez en fin de TP, un [mémo sur les graphiques](#Graphiques)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temperature\n",
    "\n",
    "Voici les données des températures moyenne en France de 1900 à 2017 ([source](https://www.insee.fr/fr/statistiques/3676581?sommaire=3696937))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature = [\n",
    "12.14413, 11.11613, 11.18313, 11.48013, 11.93013, 11.22013, 11.75313, 11.44013,\n",
    "11.35113, 10.82313, 11.37413, 12.32013, 11.37913, 11.89413, 11.48813, 11.43513,\n",
    "11.59413, 10.57213, 11.64313, 11.09013, 11.83913, 12.42113, 11.11913, 11.71713,\n",
    "11.43513, 11.16313, 12.10113, 11.67313, 12.27613, 11.56013, 12.16513, 11.19613,\n",
    "11.54013, 11.59013, 12.20913, 11.72313, 11.77113, 12.28713, 11.73213, 11.51313,\n",
    "10.85413, 10.87613, 11.43513, 12.52613, 11.41213, 12.37413, 11.47213, 12.59313,\n",
    "12.11813, 12.62013, 12.03913, 11.69713, 11.85613, 11.85013, 11.34113, 11.84213,\n",
    "10.58113, 11.84313, 11.77313, 12.59413, 11.79813, 12.58413, 11.03513, 10.68313,\n",
    "11.71513, 11.29313, 12.02013, 11.92813, 11.59013, 11.57513, 11.66613, 11.57613,\n",
    "11.34313, 11.62213, 11.89313, 11.77913, 12.08513, 11.88713, 11.38613, 11.59513,\n",
    "11.16713, 11.91113, 12.63613, 12.36213, 11.61013, 11.34313, 11.64413, 11.65413,\n",
    "12.46513, 12.95013, 12.99913, 11.99113, 12.31813, 12.04713, 13.29813, 12.83713,\n",
    "11.85613, 13.12113, 12.53113, 12.99513, 13.12313, 12.76313, 13.14513, 13.48113,\n",
    "12.59113, 12.58813, 13.23913, 12.91113, 12.54513, 12.96413, 11.86613, 13.60113,\n",
    "12.79113, 12.38113, 13.72713, 13.512, 13, 13.4\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour toutes les questions suivantes, les réponses doivent être données par votre programme.\n",
    "\n",
    "1. Décrire la série statistique (population, individus, caractère)\n",
    "2. Quelle a été la température en 1900, 1918, 1945, 1990 et en 2000?\n",
    "3. Calculer les 5 indicateurs et donner une interprétation de chacun de ces indicateurs.\n",
    "4. Tracer la courbe d'évolution des températures.\n",
    "\n",
    "Vous pouvez utiliser la commande suivante pour générer les années."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "annee = list(range(1900, 2018))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Population\n",
    "\n",
    "Voici l'estimation de la population totale par sexe et âge au 1er janvier 2019. Chaque élément de la liste correspond à une tranche d'age.\n",
    "\n",
    "- le premier élément (347749 pour les femmes) correspond au nombre de personnes ayant 0 an (nés en 2018)\n",
    "- le deuxime élément (370453 pour les hommes) correspond au nombre de personnes ayant 1 an (nés en 2019)\n",
    "- le dernier élément correspon au nombre de personnes ayant plus de 100ans (nés avant 1918)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "femmes = [\n",
    "347749, 355472, 363162, 372402, 387042, 389920, 396835, 403349, 412555, 408232,\n",
    "410703, 408166, 415280, 405218, 403761, 402532, 403441, 409037, 412560, 390002,\n",
    "384532, 370258, 374177, 367951, 358614, 357966, 376224, 385366, 397080, 405038,\n",
    "409842, 413955, 422167, 420790, 417815, 414133, 438390, 442482, 448307, 424441,\n",
    "414208, 413671, 404350, 413722, 435157, 460384, 469527, 466462, 457896, 452879,\n",
    "450472, 447421, 457665, 459310, 464153, 460412, 445047, 444896, 444709, 442263,\n",
    "433635, 430912, 427893, 424094, 421875, 413428, 418007, 408050, 422019, 413673,\n",
    "409072, 400876, 378561, 286325, 279055, 269401, 249057, 221914, 231318, 239598,\n",
    "232663, 226088, 222853, 213902, 210980, 195596, 192550, 175872, 164803, 139226,\n",
    "124322, 105456, 91072, 76447, 61235, 48398, 37882, 27754, 19813, 8273, 12670\n",
    "]\n",
    "hommes = [\n",
    "364155, 370453, 378518, 387906, 399232, 407611, 417471, 418623,\n",
    "429919, 427917, 430934, 426744, 433073, 424141, 422877, 422127,\n",
    "423901, 431086, 433377, 410714, 398993, 384384, 381869, 371731,\n",
    "357849, 356195, 373660, 377772, 384835, 385034, 390899, 392786,\n",
    "397979, 398786, 396435, 391214, 416777, 421707, 427643, 405581,\n",
    "399149, 404816, 390441, 404346, 426173, 448213, 459886, 457822,\n",
    "448697, 441572, 434971, 432749, 441979, 442828, 444960, 438142,\n",
    "422099, 421161, 416331, 410415, 400042, 395817, 390345, 382395,\n",
    "381146, 371165, 374781, 364694, 374817, 364312, 361485, 350179,\n",
    "327085, 242793, 234112, 224687, 204674, 177799, 179151, 182015,\n",
    "171854, 160969, 153145, 139041, 131872, 116712, 108339, 95104,\n",
    "83373, 66602, 55382, 44797, 34519, 27317, 20525, 14477, 10101, \n",
    "7239, 4977, 2058, 2976\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Décrire les séries statistiques (population, individues, caractères)\n",
    "\n",
    "2. Combien y a-t-il d'homme en tout? De femmes? De personnes?\n",
    "\n",
    "3. Sur un même graphique tracer la répartition en fonction de l'age de la population féminine et masculine.\n",
    "\n",
    "4. Pour comparer la **répartition** de la population les **quantités** ne sont pas adapté, on préfèrera la **fréquence** (effectif divisé par l'effectif total). Créer 2 autres listes pour calculer la fréquence de chaque classe d'age pour les hommes et les femmes. Tracer à nouveau la répartition selon les ages.\n",
    "\n",
    "5. Calculer les 5 indicateurs pour les 2 séries. Interpréter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graphiques\n",
    "\n",
    "Pour tracer des graphiques, on utilisera la librairie Maplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour tracer une courbe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# le graphique\n",
    "fig, ax = plt.subplots()\n",
    "# Abscisses\n",
    "x = [10, 100, 1000, 10000]\n",
    "# Ordonnées\n",
    "y = [1, 2, 3, 4]\n",
    "# On ajoute la courbe\n",
    "ax.plot(x, y)\n",
    "# On affiche\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour mettre plusieurs courbes sur le même graphique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# le graphique\n",
    "fig, ax = plt.subplots()\n",
    "# Abscisses\n",
    "x = [1000, 3000, 6000, 10000]\n",
    "# Ordonnées\n",
    "y = [1, 2, 3, 4]\n",
    "# On ajoute la courbe\n",
    "ax.plot(x, y)\n",
    "# On ajoute une autre courbe\n",
    "x = [10, 100, 1000, 10000]\n",
    "y2 = [4, 3, 2, 1]\n",
    "ax.plot(x, y2)\n",
    "# On affiche\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour faire des barres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# le graphique\n",
    "fig, ax = plt.subplots()\n",
    "# Abscisses\n",
    "x = [10, 30, 50, 70]\n",
    "# Ordonnées\n",
    "y = [1, 2, 3, 2]\n",
    "# On ajoute la courbe\n",
    "ax.bar(x, y, label=y)\n",
    "# Une legende\n",
    "ax.legend(title='Panier')\n",
    "# Nom de l'axe des y\n",
    "ax.set_ylabel('Effectif')\n",
    "# Titre du graphique\n",
    "ax.set_title('Quantité et effectifs')\n",
    "# Afficher le graphique\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}