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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\n",
      "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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\n",
      "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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\n",
      "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
}