{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Avis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour faire fonctionner ce notebook, vous devez avoir préalablement installé le package simgen-creei et avoir obtenu le fichier .csv de la BDSPS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7HbaYlcajapp"
   },
   "source": [
    "# Choix des paramètres d'utilisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hmko935mjf9P"
   },
   "source": [
    "### Chemin d'accès et le nom du fichier de la BDSPS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modifiez le chemin d'accès ci-dessous (et le nom au besoin) pour qu'il corresponde à l'endroit où vous avez enregistré le fichier .csv de la BDSPS fourni par l'équipe CREEi (bdsps2017_slice.csv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "qAQy4F3Ows7q"
   },
   "outputs": [],
   "source": [
    "donnees_brutes = '.../Desktop/bdsps2017_slice.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-MOCuwZr1_k6"
   },
   "source": [
    "### Année de fin \n",
    " - valeurs possibles : 2018 à 2100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "p8krlNEa2CLX"
   },
   "outputs": [],
   "source": [
    "annee_fin = 2050"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8AlKE8ss2Cku"
   },
   "source": [
    "### Nombre de réplications \n",
    "- valeurs possibles : 1 à 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "07twCcp1C7NX"
   },
   "outputs": [],
   "source": [
    "nb_rep = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nwxo4b7DDBVk"
   },
   "source": [
    "### Hypothèses du modèle\n",
    "\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tfaeNgtmj2t2"
   },
   "source": [
    "#### Fécondité"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0qL2q0R_lfHC"
   },
   "source": [
    "Choix :\n",
    "- weak (faible)\n",
    "- reference (moyenne)\n",
    "- strong (élevée)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "WEmcyKBBGiEJ"
   },
   "outputs": [],
   "source": [
    "fecondite = 'reference'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fbmoD1mhGiWL"
   },
   "source": [
    "#### Mortalité\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J3oN1u-Il7_9"
   },
   "source": [
    "Choix :\n",
    "- low (faible)\n",
    "- medium (moyen)\n",
    "- high (élevé)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "__NE2RZsG4bP"
   },
   "outputs": [],
   "source": [
    "mortalite = 'low'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7MTzaXmXawU1"
   },
   "source": [
    "#### Immigration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h8DZhM_ba9pb"
   },
   "source": [
    "- valeurs possibles : 0 à 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mJfs6YIEd55A"
   },
   "source": [
    "**Taux prospectif d'immigration internationnale**\n",
    "\n",
    "(une valeur de 0,0066 corresponds à 55 000 nouveaux immigrants en 2017 sur une population de 8 302 063)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "UuFC08UBa2Yl"
   },
   "outputs": [],
   "source": [
    "taux_immigration = 0.0066"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-xq6a8zVnGdA"
   },
   "source": [
    "# Initialisation du modèle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-1i8ACm2qmWQ"
   },
   "source": [
    "## Importation des packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "xzifaxU4BH9m"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MzgZcJb6oGZR"
   },
   "source": [
    "## Importation des fonctions et des classes de SIMGEN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si vous avez utilisé l'installation manuelle, vous devez ajouter dans une cellule ci-dessous le code suivant:\n",
    "\n",
    "import sys\n",
    "sys.path.append('.../packages')\n",
    "\n",
    "où '.../packages' correspond au chemin d'accès du dossier où vous avez sauvegardé le package *simgen*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "g_wshE-Vn9eQ"
   },
   "outputs": [],
   "source": [
    "import simgen\n",
    "from simgen import model, formating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YmLS2-9nqSNG"
   },
   "source": [
    "## Formattage données de départ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_2WqQUJbys7F"
   },
   "source": [
    "La fonction ***bdsps_format***  transforme la BDSPS de Statistique Canada afin de mettre en forme certaines variables et créer les registres des individus (dominants, conjoints et enfants). Cette fonction calibre également les poids des répondants, par âge et sexe, afin de s’arrimer à la population québécoise de 2017, selon l’ISQ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 181
    },
    "id": "yoht3K1xo6dn",
    "outputId": "5435b5f1-671f-4e84-efb7-90345b91dd4f"
   },
   "outputs": [],
   "source": [
    "preparation_data=formating()\n",
    "preparation_data.bdsps_format(donnees_brutes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DHgRpFjpIFeb"
   },
   "source": [
    "## Création de l'instance du modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "BnNOAduV5uBi"
   },
   "outputs": [],
   "source": [
    "base = model(stop_yr=(annee_fin-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zqvvkN-hIajW"
   },
   "source": [
    "## Chargement des principaux intrants "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3jTMp-G2x9Lu"
   },
   "source": [
    "- Base de données de départ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "1A7QsGT5Iaw_"
   },
   "outputs": [],
   "source": [
    "base.startpop('start_pop')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RwsnBNJRIoeF"
   },
   "source": [
    "- Hypothèse de fécondité"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "AucdWJT1In0F"
   },
   "outputs": [],
   "source": [
    "base.birth_assumptions(scenario=fecondite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NUpmkWqDIo91"
   },
   "source": [
    "- Hypothèse de mortalité"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "6iqYRKGgIn3r"
   },
   "outputs": [],
   "source": [
    "base.dead_assumptions(scenario=mortalite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bXkrcGCkIcaj"
   },
   "source": [
    "- Base de données à partir de laquelle les immigrants sont sélectionnés"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "LwyO68SRIdZc"
   },
   "outputs": [],
   "source": [
    "base.immig_assumptions(init='imm_pop', num=taux_immigration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZV9nYFIy4k2G"
   },
   "source": [
    "# Lancement de la simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uFSHfAcNqKdh",
    "outputId": "50379575-b6d3-4691-92a0-9754e1614a8e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year: 2049 replication: 1\r"
     ]
    }
   ],
   "source": [
    "base.simulate(rep=nb_rep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pOg0dBUQ6NvR"
   },
   "source": [
    "# Affichage des résultats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T2yNyyh7X82b"
   },
   "source": [
    "Le paramètre *annee_debut_affi* sert d'année de départ pour l'affichage des résultats. Cette valeur doit être supérieure ou égale à 2017 et inférieure à l'année de fin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "UC6mahTFW-ZJ"
   },
   "outputs": [],
   "source": [
    "annee_debut_affi = 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZoYEfDet-WdL"
   },
   "source": [
    "## Taille de la population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uRGtoC-2Flm6"
   },
   "source": [
    "Calcul des fréquences pour la population totale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "Ze_hk7LdFplm"
   },
   "outputs": [],
   "source": [
    "pop = base.stats.freq()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dVGp-LfNPvHh"
   },
   "source": [
    "Affichage du graphique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "sAWI5hWc7Nwx",
    "outputId": "5fd1a8f5-8ea0-4182-f2d5-bd8aad053325"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(pop.loc[annee_debut_affi:]/1e6, color='red')\n",
    "plt.title('Population, Québec, '+str(annee_debut_affi)+'-'+str(annee_fin))\n",
    "plt.ylabel('Population (en millions)')\n",
    "plt.xlabel('Année')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m9Tpk4UYJn4d"
   },
   "source": [
    "Production du tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "b5kOjdR_6ZoA"
   },
   "outputs": [],
   "source": [
    "saut = int((len(pop.loc[annee_debut_affi:])-1)/3)\n",
    "\n",
    "if len(pop)>=5:\n",
    "  table_annee = {1:annee_debut_affi,2:(annee_debut_affi+saut),3:(annee_debut_affi+2*saut),4:(annee_debut_affi+3*saut)}\n",
    "\n",
    "  table = pd.DataFrame(index=[str(table_annee[1]),str(table_annee[2]),str(table_annee[3]), str(table_annee[4])], columns=['Fréquence'])\n",
    "\n",
    "  for i in range(0,4):\n",
    "    j = annee_debut_affi + i*saut\n",
    "    table.iloc[i,0] = round(pop[j]/1e6,1)\n",
    "else:\n",
    "  table = pd.DataFrame(index=[str(annee_debut_affi),str(annee_debut_affi+1),str(annee_debut_affi+2), str(annee_debut_affi+3), str(annee_debut_affi+4)], columns=['Fréquence'])\n",
    "\n",
    "  for i in range(0,(len(pop))):\n",
    "    j = annee_debut_affi + i\n",
    "\n",
    "    table.iloc[i,0] = round(pop[j]/1e6,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l8HVLAgSJMo3"
   },
   "source": [
    "Affichage tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "yj1IRY39JNK-",
    "outputId": "91e1b9f2-bf24-4ec2-f495-d749b4cc7424"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fréquence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fréquence\n",
       "2020       8.4\n",
       "2030       8.9\n",
       "2040       9.3\n",
       "2050       9.6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YzDuUTiwG1_Z"
   },
   "source": [
    "### Selon le sexe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XC4B-Jh0FSbU"
   },
   "source": [
    "Calcul des fréquences selon le sexe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "M7OVa3N46DI5"
   },
   "outputs": [],
   "source": [
    "pop_m=base.stats.freq(sub='male==True')\n",
    "pop_f=base.stats.freq(sub='male==False')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t02lEOlHMLqs"
   },
   "source": [
    "Affichage du graphique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "A3KEN0DcC1RO",
    "outputId": "db8a71eb-33b7-4c54-85b1-1e0f75840f38"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(pop_m.loc[annee_debut_affi:]/1e6, label='Hommes', color='green')\n",
    "plt.plot(pop_f.loc[annee_debut_affi:]/1e6, label='Femmes', color='blue')\n",
    "plt.legend()\n",
    "plt.xlabel('Année')\n",
    "plt.ylabel('Population (en millions)')\n",
    "plt.title('Population selon le sexe, Québec, '+str(annee_debut_affi)+'-'+str(annee_fin))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jysC6GAHL68c"
   },
   "source": [
    "Production du tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "4NcWYUUlL7OS"
   },
   "outputs": [],
   "source": [
    "saut = int((len(pop.loc[annee_debut_affi:])-1)/3)\n",
    "\n",
    "if len(pop)>=5:\n",
    "  table_annee = {1:annee_debut_affi,2:(annee_debut_affi+saut),3:(annee_debut_affi+2*saut),4:(annee_debut_affi+3*saut)}\n",
    "\n",
    "  table = pd.DataFrame(index=[str(table_annee[1]),str(table_annee[2]),str(table_annee[3]), str(table_annee[4])], columns=['Hommes', 'Femmes'])\n",
    "\n",
    "  for i in range(0,4):\n",
    "    j = annee_debut_affi + i*saut\n",
    "\n",
    "    table.iloc[i,0] = round(pop_m[j]/1e6,1)\n",
    "    table.iloc[i,1] = round(pop_f[j]/1e6,1)\n",
    "else:\n",
    "  table = pd.DataFrame(index=[str(annee_debut_affi),str(annee_debut_affi+1),str(annee_debut_affi+2), str(annee_debut_affi+3), str(annee_debut_affi+4)], columns=['Hommes', 'Femmes'])\n",
    "\n",
    "  for i in range(0,(len(pop))):\n",
    "    j = annee_debut_affi + i\n",
    "\n",
    "    table.iloc[i,0] = round(pop_m[j]/1e6,1)\n",
    "    table.iloc[i,1] = round(pop_f[j]/1e6,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HpwnvMReL7eZ"
   },
   "source": [
    "Affichage tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "IZidZg__L7nq",
    "outputId": "19e1bdf8-cce0-43df-d06a-854782c6f523"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hommes</th>\n",
       "      <th>Femmes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>4.2</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>4.4</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>4.6</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>4.7</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Hommes Femmes\n",
       "2020    4.2    4.2\n",
       "2030    4.4    4.5\n",
       "2040    4.6    4.7\n",
       "2050    4.7    4.8"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vGTDJC80HCC3"
   },
   "source": [
    "### Selon le groupe d'âge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "42JOyCnb-oMz"
   },
   "source": [
    "Calcul des fréquences par groupe d'âge (moins de 25 ans, 25 à 64 ans, 65 et plus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "ViGa49LW9y-L"
   },
   "outputs": [],
   "source": [
    "pop_0_24=base.stats.freq(sub='age<25')\n",
    "pop_25_64=base.stats.freq(sub='age>24 and age<65')\n",
    "pop_65_plus=base.stats.freq(sub='age>64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6HvoLe0FK9z6"
   },
   "source": [
    "Affichage du graphique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "8nLS_h-5-e5D",
    "outputId": "bce5ea2b-af18-4a74-ba57-e71f194c1cfa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(pop_0_24.loc[annee_debut_affi:]/1e6, label='Moins de 25 ans', color='green')\n",
    "plt.plot(pop_25_64.loc[annee_debut_affi:]/1e6, label='25 à 64 ans', color='orange')\n",
    "plt.plot(pop_65_plus.loc[annee_debut_affi:]/1e6, label='65 ans et plus', color='red')\n",
    "plt.legend()\n",
    "plt.ylabel('Population (en millions)')\n",
    "plt.xlabel('Année')\n",
    "plt.title('Population par groupe d´âge, Québec, '+str(annee_debut_affi)+'-'+str(annee_fin))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HbUrgrnKMi8d"
   },
   "source": [
    "Production du tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "S669S_ooMi8d"
   },
   "outputs": [],
   "source": [
    "saut = int((len(pop.loc[annee_debut_affi:])-1)/3)\n",
    "\n",
    "if len(pop)>=5:\n",
    "  table_annee = {1:annee_debut_affi,2:(annee_debut_affi+saut),3:(annee_debut_affi+2*saut),4:(annee_debut_affi+3*saut)}\n",
    "\n",
    "  table = pd.DataFrame(index=[str(table_annee[1]),str(table_annee[2]),str(table_annee[3]), str(table_annee[4])], columns=['Moins de 25 ans', '25 à 64 ans','65 ans et plus'])\n",
    "\n",
    "  for i in range(0,4):\n",
    "    j = annee_debut_affi + i*saut\n",
    "\n",
    "    table.iloc[i,0] = round(pop_0_24[j]/1e6,1)\n",
    "    table.iloc[i,1] = round(pop_25_64[j]/1e6,1)\n",
    "    table.iloc[i,2] = round(pop_65_plus[j]/1e6,1)\n",
    "else:\n",
    "  table = pd.DataFrame(index=[str(annee_debut_affi),str(annee_debut_affi+1),str(annee_debut_affi+2), str(annee_debut_affi+3), str(annee_debut_affi+4)], columns=['Moins de 25 ans', '25 à 64 ans','65 ans et plus'])\n",
    "\n",
    "  for i in range(0,(len(pop))):\n",
    "    j = annee_debut_affi + i\n",
    "\n",
    "    table.iloc[i,0] = round(pop_0_24[j]/1e6,1)\n",
    "    table.iloc[i,1] = round(pop_25_64[j]/1e6,1)\n",
    "    table.iloc[i,2] = round(pop_65_plus[j]/1e6,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DDxMDMO6Mi8d"
   },
   "source": [
    "Affichage tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "LCCXSnlfMi8d",
    "outputId": "258771ab-eafb-419c-9b79-7a412d5954f7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Moins de 25 ans</th>\n",
       "      <th>25 à 64 ans</th>\n",
       "      <th>65 ans et plus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>2.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>2.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Moins de 25 ans 25 à 64 ans 65 ans et plus\n",
       "2020             2.2         4.6            1.7\n",
       "2030             2.1         4.6            2.2\n",
       "2040             2.1         4.8            2.4\n",
       "2050             2.1         4.8            2.7"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6VxdRRwwE92q"
   },
   "source": [
    "### Selon la tranche d'âge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "81A910-YHbgt"
   },
   "source": [
    "Calcul des fréquences par âge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "azwT6KDkHK_C"
   },
   "outputs": [],
   "source": [
    "baset=base.stats.freq('age').T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6K2GiPxNOdfX"
   },
   "source": [
    "Année de la première série"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "rJ_dNO7IOhOg"
   },
   "outputs": [],
   "source": [
    "annee_serie1 = annee_debut_affi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sHXNvM5kOlzC"
   },
   "source": [
    "Année de la deuxième série"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "WRbvY-EtOogE"
   },
   "outputs": [],
   "source": [
    "annee_serie2 = annee_fin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4eFKGH1kLb_A"
   },
   "source": [
    "Affichage du graphique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 352
    },
    "id": "4Ln1zENVHLlV",
    "outputId": "0c5e3706-321b-4f39-ab67-4164d211a24e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(baset[[annee_serie1]]/1000,label=str(annee_serie1))\n",
    "plt.plot(baset[[annee_serie2]]/1000,label=str(annee_serie2))\n",
    "plt.ylabel('Population (en milliers)')\n",
    "plt.xlabel('Âge')\n",
    "plt.legend()\n",
    "plt.title('Population par tranche d´âge, Québec, '+str(annee_debut_affi)+'-'+str(annee_fin))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2PdD2LyNMpVk"
   },
   "source": [
    "Production du tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "onxk9_MUMpVl"
   },
   "outputs": [],
   "source": [
    "table = pd.DataFrame(index=['0 ans','20 ans','40 ans', '60 ans','80 ans'], columns=[str(annee_serie1), str(annee_serie2)])\n",
    "\n",
    "for i in range(0,5):\n",
    "  j = i*20\n",
    "  table.iloc[i,0] = round(baset[annee_serie1].loc[j]/1000,1)\n",
    "  table.iloc[i,1] = round(baset[annee_serie2].loc[j]/1000,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V-QjRZJ1MpVl"
   },
   "source": [
    "Affichage tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "kh9VoTOkMpVl",
    "outputId": "85d39e86-7c44-4d7a-81b7-9eedaade35a3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2020</th>\n",
       "      <th>2050</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 ans</th>\n",
       "      <td>83.9</td>\n",
       "      <td>88.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20 ans</th>\n",
       "      <td>87.4</td>\n",
       "      <td>85.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40 ans</th>\n",
       "      <td>123</td>\n",
       "      <td>125.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60 ans</th>\n",
       "      <td>127.7</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80 ans</th>\n",
       "      <td>49.1</td>\n",
       "      <td>85.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         2020   2050\n",
       "0 ans    83.9   88.8\n",
       "20 ans   87.4   85.6\n",
       "40 ans    123  125.5\n",
       "60 ans  127.7    141\n",
       "80 ans   49.1   85.4"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GHroe-pn_2S_"
   },
   "source": [
    "## Niveau de scolarité"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9G8eBM90__gQ"
   },
   "source": [
    "Calcul des proportions par niveau de scolarité des individus de 25 à 64 ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "R2XLnp9LQLmw"
   },
   "outputs": [],
   "source": [
    "prop_educ = base.stats.prop('educ', sub=\"age>=25 and age<=64 and insch==False\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EcC49sqwLdW4"
   },
   "source": [
    "Affichage du graphique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "h2-O37dgAN8e",
    "outputId": "83b6f883-1c80-4796-8475-29aa0dc9b1d4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(prop_educ['dec'].loc[annee_debut_affi:]*100, label='Collégial', color='red')\n",
    "plt.plot(prop_educ['uni'].loc[annee_debut_affi:]*100, label='Universitaire', color='blue')\n",
    "plt.plot(prop_educ['des'].loc[annee_debut_affi:]*100, label='Secondaire', color='orange')\n",
    "plt.plot(prop_educ['none'].loc[annee_debut_affi:]*100, label='Sans diplôme', color='green')\n",
    "plt.ylim(0, 45)\n",
    "plt.legend()\n",
    "plt.ylabel('Part (en %)')\n",
    "plt.xlabel('Année')\n",
    "plt.title('Part de la population de 25 à 64 ans selon le niveau de scolarité, Québec, '+str(annee_debut_affi)+'-'+str(annee_fin))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xB9tCFGpMsSN"
   },
   "source": [
    "Production du tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "4j-Qu9ZxMsSN"
   },
   "outputs": [],
   "source": [
    "saut = int((len(pop.loc[annee_debut_affi:])-1)/3)\n",
    "\n",
    "if len(pop)>=5:\n",
    "  table_annee = {1:annee_debut_affi,2:(annee_debut_affi+saut),3:(annee_debut_affi+2*saut),4:(annee_debut_affi+3*saut)}\n",
    "\n",
    "  table = pd.DataFrame(index=[str(table_annee[1]),str(table_annee[2]),str(table_annee[3]), str(table_annee[4])], columns=['Sans diplôme','Secondaire','Collégial','Universitaire'])\n",
    "\n",
    "  for i in range(0,4):\n",
    "    j = annee_debut_affi + i*saut\n",
    "\n",
    "    table.iloc[i,0] = round(prop_educ['none'].loc[j]*100,1)\n",
    "    table.iloc[i,1] = round(prop_educ['des'].loc[j]*100,1)\n",
    "    table.iloc[i,2] = round(prop_educ['dec'].loc[j]*100,1)\n",
    "    table.iloc[i,3] = round(prop_educ['uni'].loc[j]*100,1)\n",
    "    \n",
    "else:\n",
    "  table = pd.DataFrame(index=[str(annee_debut_affi),str(annee_debut_affi+1),str(annee_debut_affi+2), str(annee_debut_affi+3), str(annee_debut_affi+4)], columns=['Sans diplôme','Secondaire','Collégial','Universitaire'])\n",
    "\n",
    "  for i in range(0,(len(pop))):\n",
    "    j = annee_debut_affi + i\n",
    "    \n",
    "    table.iloc[i,0] = round(prop_educ['none'].loc[j]*100,1)\n",
    "    table.iloc[i,1] = round(prop_educ['des'].loc[j]*100,1)\n",
    "    table.iloc[i,2] = round(prop_educ['dec'].loc[j]*100,1)\n",
    "    table.iloc[i,3] = round(prop_educ['uni'].loc[j]*100,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J8uZY0u2MsSO"
   },
   "source": [
    "Affichage tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "YOIga5UwMsSO",
    "outputId": "0fea78b4-77a0-4ac3-c8cd-5fb137795b4b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sans diplôme</th>\n",
       "      <th>Secondaire</th>\n",
       "      <th>Collégial</th>\n",
       "      <th>Universitaire</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>14.1</td>\n",
       "      <td>21.3</td>\n",
       "      <td>37.7</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>10.3</td>\n",
       "      <td>19.3</td>\n",
       "      <td>38.5</td>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>8.4</td>\n",
       "      <td>17.2</td>\n",
       "      <td>39</td>\n",
       "      <td>35.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>7.7</td>\n",
       "      <td>17.4</td>\n",
       "      <td>37.1</td>\n",
       "      <td>37.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sans diplôme Secondaire Collégial Universitaire\n",
       "2020         14.1       21.3      37.7          26.9\n",
       "2030         10.3       19.3      38.5          31.9\n",
       "2040          8.4       17.2        39          35.4\n",
       "2050          7.7       17.4      37.1          37.8"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "grTFYCLBCCi9"
   },
   "source": [
    "## Personnes en couple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R3VK3LVYJcBB"
   },
   "source": [
    "Calcul de la proportion des personnes en couple auprès les 25 ans et plus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "jWQfeAsqEN6j"
   },
   "outputs": [],
   "source": [
    "propms15plus = base.stats.prop('married', sub=\"age>=15\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B7vkwPEtLe6_"
   },
   "source": [
    "Affichage du graphique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "i4wyv3SdJVJB",
    "outputId": "f51d96c7-eeb4-40c9-e829-8de35a07df1b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(propms15plus[True].loc[annee_debut_affi:]*100, color='blue')\n",
    "plt.ylim(50, 60)\n",
    "plt.title('Part de la population en couple, Québec, '+str(annee_debut_affi)+'-'+str(annee_fin))\n",
    "plt.ylabel('Part (en %)')\n",
    "plt.xlabel('Année')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KJxt_5qNNLt7"
   },
   "source": [
    "Production du tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "hMMFR_T1NLt7"
   },
   "outputs": [],
   "source": [
    "saut = int((len(pop.loc[annee_debut_affi:])-1)/3)\n",
    "\n",
    "if len(pop)>=5:\n",
    "  table_annee = {1:annee_debut_affi,2:(annee_debut_affi+saut),3:(annee_debut_affi+2*saut),4:(annee_debut_affi+3*saut)}\n",
    "\n",
    "  table = pd.DataFrame(index=[str(table_annee[1]),str(table_annee[2]),str(table_annee[3]), str(table_annee[4])], columns=['Part (en %)'])\n",
    "\n",
    "  for i in range(0,4):\n",
    "    j = annee_debut_affi + i*saut\n",
    "    \n",
    "    table.iloc[i,0] = round(propms15plus[True].loc[j]*100,1)\n",
    "\n",
    "else:\n",
    "  table = pd.DataFrame(index=[str(annee_debut_affi),str(annee_debut_affi+1),str(annee_debut_affi+2), str(annee_debut_affi+3), str(annee_debut_affi+4)], columns=['Part (en %)'])\n",
    "\n",
    "  for i in range(0,(len(pop))):\n",
    "    j = annee_debut_affi + i\n",
    "\n",
    "    table.iloc[i,0] = round(propms15plus[True].loc[j]*100,1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VeouvWBGNLt8"
   },
   "source": [
    "Affichage tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "LBX8zXTUNLt8",
    "outputId": "47d6f5bd-307b-4fed-970a-baf825c816aa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Part (en %)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>57.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>56.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>56.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>56.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Part (en %)\n",
       "2020        57.3\n",
       "2030        56.8\n",
       "2040        56.6\n",
       "2050        56.7"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O9TEQoFXLGm4"
   },
   "source": [
    "### Selon le groupe d'âge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9uAA4JGsLKVO"
   },
   "source": [
    "Calcul des proportions selon le groupe d'âge (20 à 44 ans, 45 à 64 ans, 65 et plus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "tCYNOqA1K2G0"
   },
   "outputs": [],
   "source": [
    "propms18_44 = base.stats.prop('married', sub=\"age>=18 and age<=44\")\n",
    "propms45_64 = base.stats.prop('married', sub=\"age>=45 and age<=64\")\n",
    "propms65_plus = base.stats.prop('married', sub=\"age>=65\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FHAFwS33Lgcf"
   },
   "source": [
    "Affichage du graphique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "e48kHD_ZLq5l",
    "outputId": "dcb301eb-7e4c-4d12-f52e-44b808485450"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(propms18_44[True].loc[annee_debut_affi:]*100, label='18 à 44 ans', color='red')\n",
    "plt.plot(propms45_64[True].loc[annee_debut_affi:]*100, label='45 à 64 ans', color='blue')\n",
    "plt.plot(propms65_plus[True].loc[annee_debut_affi:]*100, label='65 ans et plus', color='green')\n",
    "plt.ylim(40, 80)\n",
    "plt.ylabel('Part (en %)')\n",
    "plt.xlabel('Année')\n",
    "plt.legend()\n",
    "plt.title('Proportion de personnes en couple selon le groupe d`âge, Québec, '+str(annee_debut_affi)+'-'+str(annee_fin))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KX8D0ReJNNO0"
   },
   "source": [
    "Production du tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "nJbQ54RQNNO1"
   },
   "outputs": [],
   "source": [
    "saut = int((len(pop.loc[annee_debut_affi:])-1)/3)\n",
    "\n",
    "if len(pop)>=5:\n",
    "  table_annee = {1:annee_debut_affi,2:(annee_debut_affi+saut),3:(annee_debut_affi+2*saut),4:(annee_debut_affi+3*saut)}\n",
    "\n",
    "  table = pd.DataFrame(index=[str(table_annee[1]),str(table_annee[2]),str(table_annee[3]), str(table_annee[4])], columns=['18 à 44 ans', '45 à 64 ans','65 ans et plus'])\n",
    "\n",
    "  for i in range(0,4):\n",
    "    j = annee_debut_affi + i*saut\n",
    "    \n",
    "    table.iloc[i,0] = round(propms18_44[True].loc[j]*100,1)\n",
    "    table.iloc[i,1] = round(propms45_64[True].loc[j]*100,1)\n",
    "    table.iloc[i,2] = round(propms65_plus[True].loc[j]*100,1)\n",
    "else:\n",
    "  table = pd.DataFrame(index=[str(annee_debut_affi),str(annee_debut_affi+1),str(annee_debut_affi+2), str(annee_debut_affi+3), str(annee_debut_affi+4)], columns=['18 à 44 ans', '45 à 64 ans','65 ans et plus'])\n",
    "\n",
    "  for i in range(0,(len(pop))):\n",
    "    j = annee_debut_affi + i\n",
    "\n",
    "    table.iloc[i,0] = round(propms18_44[True].loc[j]*100,1)\n",
    "    table.iloc[i,1] = round(propms45_64[True].loc[j]*100,1)\n",
    "    table.iloc[i,2] = round(propms65_plus[True].loc[j]*100,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9Pufcsr6NNO1"
   },
   "source": [
    "Affichage tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "mFA5mEC6NNO1",
    "outputId": "5df6b4ec-d080-4f5a-fec2-ef1a7ac50abf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>18 à 44 ans</th>\n",
       "      <th>45 à 64 ans</th>\n",
       "      <th>65 ans et plus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>53.1</td>\n",
       "      <td>70.9</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>51.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>59.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>51.8</td>\n",
       "      <td>65.4</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>51.4</td>\n",
       "      <td>65.4</td>\n",
       "      <td>58.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     18 à 44 ans 45 à 64 ans 65 ans et plus\n",
       "2020        53.1        70.9             54\n",
       "2030        51.5        67.6           59.2\n",
       "2040        51.8        65.4           58.5\n",
       "2050        51.4        65.4           58.8"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F8eZdS5_SqMx"
   },
   "source": [
    "## Nombre d'enfants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "LHipZyqdOD06"
   },
   "outputs": [],
   "source": [
    "prop_enfant = base.stats.prop('nkids', sub=\"age>=18 and age<=54\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dnROpwTvLhwb"
   },
   "source": [
    "Affichage du graphiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "TiiDQqdvtnN2",
    "outputId": "a7c19963-de64-4bb2-a960-8dd6aeead1f3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(100-prop_enfant.loc[annee_debut_affi:,0]*100, color='blue')\n",
    "plt.ylim(0, 50)\n",
    "plt.ylabel('Part (en %)')\n",
    "plt.xlabel('Année')\n",
    "plt.title('Part de la population de 18 à 54 ans avec au moins un enfant, Québec, '+str(annee_debut_affi)+'-'+str(annee_fin))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "smW2n5YaNPpU"
   },
   "source": [
    "Production du tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "vwwhE1RhNPpU"
   },
   "outputs": [],
   "source": [
    "saut = int((len(pop.loc[annee_debut_affi:])-1)/3)\n",
    "\n",
    "if len(pop)>=5:\n",
    "  table_annee = {1:annee_debut_affi,2:(annee_debut_affi+saut),3:(annee_debut_affi+2*saut),4:(annee_debut_affi+3*saut)}\n",
    "\n",
    "  table = pd.DataFrame(index=[str(table_annee[1]),str(table_annee[2]),str(table_annee[3]), str(table_annee[4])], columns=['Part (en %)'])\n",
    "\n",
    "  for i in range(0,4):\n",
    "    j = annee_debut_affi + i*saut\n",
    "\n",
    "    table.iloc[i,0] = round((1-prop_enfant.loc[j,0])*100,1)\n",
    "else:\n",
    "  table = pd.DataFrame(index=[str(annee_debut_affi),str(annee_debut_affi+1),str(annee_debut_affi+2), str(annee_debut_affi+3), str(annee_debut_affi+4)], columns=['Part (en %)'])\n",
    "\n",
    "  for i in range(0,(len(pop))):\n",
    "    j = annee_debut_affi + i\n",
    "    \n",
    "    table.iloc[i,0] = round((1-prop_enfant.loc[j,0])*100,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AdwGfWsdNPpV"
   },
   "source": [
    "Affichage tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "qTnUZmfzNPpV",
    "outputId": "310c47fc-dd61-4032-8b69-16d4e5d96e08"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Part (en %)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>23.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>20.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Part (en %)\n",
       "2020        37.9\n",
       "2030        32.1\n",
       "2040        23.6\n",
       "2050        20.3"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Aj9y0TtNaM8"
   },
   "source": [
    "Affichage du graphiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "uP7CLF25u4ME",
    "outputId": "71f4ac0e-d499-4d56-8d98-7d41779724c0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wr5H529FTI2yLYF8m4z/TCWaWbMV3Y34LHGe+/TIzs9H4UPJjhE0V3iywMdjuhfj/TCMys7Fm1iYY74M/pfBWMPtx/EX+g4PhUfz1LqUdG6kElxCYb2Ptd6UsF6kc482sR/CFvAO/z/fY72Z2YLBPE/E/jtlA/j4ex98E6/4+2O+j8IFwUnnLuw9eAm4OjrGW+OvLKtMcyVAzOzmoLZqA/2doeinLvgAcb2bHmll8cEyNCvm8hdrb8VyafTrO9yI1KMNG/GfgL+xZC1KW9YR8n5b1WTbfZtynkTbinPsIf5nKa2bWP9h3B+FPrT3nnFsYLDobODM4hoZRXHsD5dv354Z8J98OvBrUopa6bvAd+x7+n9hmwWsfHvKenUVoCieYdw7+d2m0C7vjNqgFmw3cErzeSfhA9Fqw7gB8TfQ1zrl3Imz+Ofxx3iz4HrkEf3lARGb2R+DsoCzhTah8iv87/cZ8Ex1XB9M/DtYt9TvLObcLf7337cGxOBI4AV+LWqMUzqpQ8N/An/EH5Fr8D9OZYYu9hb8OZDb+h+qpYPrT+ANgGv4i3WzKaLDROZeJP6f/pfmq64PC5m/G/6f3W/yp1d8D451zmyrwvt7DX8D5Mf7U0cdhi/whmD7d/Omoj/DXzJXmG/xt05uC93BqyAfsPPypt3n4sPUqpZ8iAN+0yTdmthP/X9m1QZV1lb3/CB4IXusDM8vA/7iNCOa1Dcq8A3+68zMi/Jg653bj71i6AP8+z8B/KRTO/w7/BfVwMH9xsGxpnsDX/JyFv0EiC38dCPgvvkn4L60d+LusLnPOLYhQrnn42/2/xv9YDcTf0VqaXwBzzGwXPhC/jv8CxzmXGdRsrnPOrcMHr2znXGmndW7DXyC+Hf/ZeL2U5SLpiT/udgZl/4+L3KRHGn5fbcWfCtqMv0MTynkcB3+7X+L/a9+Evw71vEj7swr8DfgOf7fiXPzdkJVpqPgt/LG2FX98nOycy420oHNuFf6H6SZ84FmFD8x7/G7s7XguTQWO87K8jw8eP+P/ttns26Ugd+EDwjYzu4GyP8udKPtzcQr+5q+pQTm+DsYvDVnmz/jfiK34Y//Fwhnl3PfP4wPMOvzNUL8p57q/wgfNBfibMSYABMFvJ/44i+Rv+DNB31rxacZHQ+afCQwL3s/f8d/rhZ/13+IvEXgqZN3Q2qhb8NdXrsDv53uCGv7S3ImvLVxkYac8g2PxRPxvyTb8JR0nBtOhjO+swJX4mww24P85usI5V+M1Z4UX0EkNMDMH9HTOLY52WaLFzC7A36FaaiOoIiKxzMxm40/tlavhWzN7Fn9N4riQkBBTzOxc/Gn9P0a7LOIvOBcREZFycs4N3sdVLgaux9cMl3YaOaqcc+q9I4ZU22lNM+tkviuH+ebvors2mN7czD403/XCh2bWrJT1x5jZQjNbbKXceSUiIhLrgpsL7nbOxWQwk9hTbac1zawdvnHH74OLO2fizwNfgL+V9+9B6GrmnPtD2Lrx+OsGRuNv9f4WOCu4HkZERESkzqq2mjPn3Frn3PfBeAb+gsoO+AsVnw0WexYf2MINBxY73x7JbvzFzCdUV1lFREREYkWNXHNmZl3x3SJ8g++iYS34AFd423qYDpS8yyad4rvhwrd9KcEdMI0aNRrap0+fKiy5iIiISPWYOXPmJufcHo1dV3s4M99m12vABOfcDrNytQcYaaGI51+dc4/j21Ni2LBh7rvvvqtoUUVERERqjJlF7EmjWts5Cxp6fA2Y6JwrbPNmfXA9WuF1aRsirJpOydaPw1uxFhEREamTqvNuTcM3sDrfOXdfyKy38R2wEjy+Fb4u/gaAnuZby0/CN273dnWVVURERCRWVGfN2Uh8S8RHme9EebaZjcO3HDzazBbh78b8O4CZtTezKQBBl0VX41t8ng/8Nxot9IqIiIjUtGq75sw59wWldzi7R4fEzrk1wLiQ51PwXSuIiIhIPZSbm0t6ejrZ2dnRLkqlJCcn07FjRxITE8u1vHoIEBERkZiUnp5OamoqXbt2pZw3FMYc5xybN28mPT2dbt26lWsddXwuIiIiMSk7O5sWLVrU2mAGYGa0aNFin2r/FM5EREQkZtXmYFZoX9+DwpmIiIhIDFE4ExERESnFRRddROvWrRkwYECNvabCmYiIiEgpLrjgAqZOnVqjr6lwJiIiIlKKww8/nObNm9foa6opDREREYl5EybA7NlVu83Bg+H++6t2m1VBNWciIiIiMUQ1ZyIiIhLzYrGGq7qo5kxEREQkhiiciYiIiJTirLPO4uCDD2bhwoV07NiRp556qtpfU6c1RURERErx0ksv1fhrquZMREREJIYonImIiIjEEIUzERERkRiicCYiIiISQxTORERERGKIwpmIiIhIDFE4ExERESnFqlWrOPLII+nbty/9+/fngQceqPbXVDtnIiIiIqVISEjg3nvvZciQIWRkZDB06FBGjx5Nv379qu01VXMmIiIiUop27doxZMgQAFJTU+nbty+rV6+u1tdUzZmIiIjEvAlTJzB73ewq3ebgtoO5f8z95V5++fLlzJo1ixEjRlRpOcKp5kxERERkL3bu3Mkpp5zC/fffT1paWrW+lmrOREREJObtSw1XVcvNzeWUU07hnHPO4eSTT67216u2cGZmTwPjgQ3OuQHBtJeB3sEiTYFtzrnBEdZdDmQA+UCec25YdZVTREREpDTOOX7961/Tt29frr/++hp5zeo8rfkMMCZ0gnPuDOfc4CCQvQa8Xsb6RwbLKpiJiIhIVHz55Zc8//zzfPzxxwwePJjBgwczZcqUan3Naqs5c85NM7OukeaZmQGnA0dV1+uL1HXOwbp1MGcOzJ1b/Ni5M/zrX9C9e7RLKCJS+x166KE452r0NaN1zdlhwHrn3KJS5jvgAzNzwGPOucdL25CZXQpcCtC5c+cqL6hILMjMhJ9+KhnE5syBzZuLl+nQAfr3h08+gQED4PbbYcIESNCVpSIitUq0vrbPAl4qY/5I59waM2sNfGhmC5xz0yItGAS3xwGGDRtWrdH2N+/9BoDxvcZzRJcjaJDQoDpfTmpIXh5kZECzZtEuCRQUwNKlJQPY3LmweLGvKQNo2BAGDoSTT/aPgwb5x+bN/fz0dLjySvjd7+Cll+DJJ+GAA6L3nkREZN/UeDgzswTgZGBoacs459YEjxvM7A1gOBAxnNWkDbs28NbCt3hoxkM0SmzE0d2P5riexzGu5zg6pHWIdvGknHbtgm++gS++gM8/h6+/9tOOOALOOQdOPbVmg9r27fDmmzBpki/Prl1+uhn06OHD17nnFgexbt0groyrRTt2hLfegldfhWuugQMPhBtugFtugZSUGnlLIiJSCdGoOTsaWOCcS48008waAXHOuYxg/Bjg9posYGkmnTqJzNxMPln2CZMXTWbyosm8tfAtwDdkd1zP4ziu53EM7zCc+Lj4KJdWCm3YAF9+6YPPF1/A999Dfr4PP4MGwQUX+Fqn//4XLr0Urr4axo3zQW38eEhOrvoy7dwJ77zjA9nUqbB7N3TtChddBIMH+yDWv7+vJasIMzjtNDj6aF+DdvfdPqw9/jgcpSs9RURimlXXRW5m9hIwCmgJrAducc49ZWbPANOdc4+GLNseeNI5N87MugNvBLMSgBedc3eU5zWHDRvmvvvuuyp8F2VzzvHjhh+LgtpXq76iwBXQsmFLxvQYw3E9j+PY/Y6lWUoMnC+rJ5zzpwC/+KJ4+PlnP69BAxgxAg49FA47DA4+GJo0KbnuzJkwcaIPTevWQVoanHKKr7k64giIr0TmzsqCKVP8tidP9s87dIDTT4czzoDhw32oqg4ff+yD55IlPgD+85+xcRpXRKQs8+fPp2/fvtEuRpWI9F7MbGakVimqLZxFQ02Hs3Bbsrbw/uL3mbxoMu8tfo8tWVuIt3gO6XSIr1XrdRz9W/XHqusXuB7Ky4PZs0uGsfXr/bzmzWHkSB/EDj0UhgzxAa088vN9oJk4EV5/3V+T1r49nHWWr1EbPLh8QSonBz74wAeyt9/2NWatW/tarTPO8OUr6xRlVcrKgttu88GsZUt46CF/CleHo4jEKoWzOiDa4SxUfkE+36z+hsk/+1q1H9b/AECXJl24ZMglXH/w9aQk6gKgfZWfD7Nmwaef+uHzz2HHDj+vWzcfwgqHPn2qJvhkZflTkBMnwnvvQW4u9OvnQ9rZZ/vTkaFyc32wmzQJ3njDX1PWvLmvgTvjDF8DF807KGfNgosv9qd3f/lL+Pe//XVqIiKxJhbCWXZ2Nocffjg5OTnk5eVx6qmnctttt+3zdhTOYlD6jnSmLJrCa/Nf44MlH9ClSRf+MfofnNbvNNWklSE/H374wQexTz6BadOKw1jv3jBqlB8OPbRmAsbmzfDKKz6offGFnzZypA9qPXrAa6/5a7s2b/anRE88Ec4801/7lZhY/eUrr7w8eOAB+POffVC8+2647LKaq8UTESmPWAhnzjl27dpF48aNyc3N5dBDD+WBBx7goIMOKlqma9euLF++vMztKJzFuE+WfcKE9ycwZ/0cDut8GPePuZ8h7YZEu1gxoaDANx/xySc+kE2bBtu2+Xk9e8KRRxYHsnbtoldOgOXLfVMVEyf6NsjAX8D/y1/6QHbssdVzM0FVWrrUh7KPPvIh84knoI6cQRCROiAWwlmozMxMDj30UB555BFGjBhRNF3hrAy1JZyBP+355PdPcvMnN7M5czMXDr6QO35xB20bt4120WpUQYFvxyu0ZmzrVj+vR4/iIDZqlL94PhY55wPlypX+TshGjaJdon3jHDz3HFx3nW/G409/ghtvhKSkaJdMROq7EoFm5gTYOrtqX6DZYBh6/14Xy8/PZ+jQoSxevJirrrqKu+++u8T8qg5nOokRJfFx8Vw27DIWXbOI6w66jufmPEevh3px9xd3k5OXE+3i1YhXX4W2bf3F9RMm+JB20knw/PM+6Cxa5GtyzjkndoMZ+Avq998fjj++9gUz8OU//3yYP983bHvLLdCmjb9p4Ykn/N9CRKQ+i4+PZ/bs2aSnpzNjxgx+/PFH7rjjjqK+NtesWVM0ftVVV1X69VRzFiN+3vwzN3xwA+/8/A7dm3Xn3mPu5YTeJ9TJ69Hy8/21Tnfd5ZuPuOoqXzOm3rdiw0cf+dO1778Pq1f7ab17+9O0xxzj/1a1MYSKSO0Ta6c1AW677TYaNWrEDTfcUDRNNWd1VK8WvXj7rLd5/9z3SU5I5qSXT+Lo549m7vq50S5aldq61dcw3XWXb3dr2jQ47zwFs1hy9NHw1FOwapW/lu5f//J3wj7xhG+Ut3lzf/r27rt9MyYFBdEusYhI9dm4cSPbgoufs7Ky+Oijj+jTp0+1vqbCWYw5Zr9j+OHyH3h47MPMXjebwY8N5op3r2Djro3RLlql/fST70roo4/g0UfhscfK3+6Y1Dwz32TIhAm+CZEtW+DDD+E3v/F3o954o++zs107+NWv/OnowjbmRETqirVr13LkkUcyaNAgDjzwQEaPHs348eOr9TV1WjOGbcnawq2f3sp/vv0PjZMac8sRt3DV8KtIiq99V2q//rqvIUtN9deajRwZ7RJJZa1d6xvYLRw2bfLT99/fnwK9+GJ/h62ISEXF4mnNitJpzTqieUpzHhz7IHOumMNBHQ/i+g+uZ+AjA3n353epLaG6oABuvtk3wDpgAHz3nYJZXdGunb+RYOJEX2M2cybceSc0bQr33edv9HjuuWiXUkSk9lHNWS3hnGPKoilc/8H1/Lz5ZwyjUVIjGic1Ln1ILH1eWoM0RnQcUa21cNu2+Tstp0yBX//at0Sv05j1w+rV/m//2We+Y/mHH9ZNBCKy7+przVkUO5GRfWFmHNfrOEbvN5oX577Isq3L2Ll7px9ydxaNb8nawsrtK9m5eycZORlk7M4gryAv4ja7Nu3K7aNu5+yBZxMfV4kevSOYN8+3jr9sGfznP3D55erDsT7p0MFfW/jXv/ph+nT4739h4MBol0xEahvnXK1vuWBfK8JUc1YP7M7fXRzkgmHFthXc9cVdzFo3iwGtB3DnUXcyvtf4KvkAvPmmv0C8YUN/fdlhh1X+PUjt9fHHvhZt2zZ48EF/LVot/54VkRqybNkyUlNTadGiRa0NaM45Nm/eTEZGBt26dSsxTz0EyB4KXAGv/PQKf/7kzyzasohDOh3CXb+4i8O7HF6x7RXAbbfB7bf7uzJff10daou3fr0P7B9+6Lu2euwx3/eoiEhZcnNzSU9PJzs7O9pFqZTk5GQ6duxIYlgnywpnUqrc/Fz+b/b/cdtnt7EmYw1je4zlzl/cyeC2g8u9je3b/Y/vO+/AhRf6U5mx3q+k1KyCAt822p//DF27+tOcQ9SlrIjUY7pbU0qVGJ/IpUMvZdE1i7j76LuZnj6dAx47gLNfO5vFWxbvdf0FC2DECN8W1sMP+wZMFcwkXFwc/PGPvh/VnBw4+GB46CHft6eIiBRTOJMiDRMb8vuRv2fptUu56dCbeGvhW/T9d1+unHwlazPWRlzn7bd9F0xbtsD//ue7YqqllwVIDTn0UN+zwDHH+AZtTz65uLN7ERFROJMImiY35Y5f3MHiaxZz6ZBLeeL7J9jvwf246X83sS17GwAZGb6D7BNO8P0uzpwJh1fsUjWph1q08MH+vvtg8mTf08D06dEulYhIbNA1Z7JXS7Ys4ZZPb+HFuS/SML4JPdbeyMLnryE7oyHnnee7YkpJiXYppbaaMQPOOAPS032fq9df70+BiojUdbrmTCrEOVg3fz/SPnqBtJdmsWv+SH5ofSNxE3pww6THeOrpfAUzqZThw2HWLF8L+7vfwfHHF3cFJSJSHymc1UI5OdV/EfW8efCnP0H37v4aoWeegbEH7M87Z73Lx+d+zgFdu/PPBZdz+qunkZWbVb2FkTqvaVN45RXfi8RHH/n+OadNi3apRESiQ6c1a4Ft2+CLL/yP1Wef+eu7kpOhb9/ioV8//9i9OyRUsN+H1avhpZd8X4mzZ/tTS6NH+wZETzzRd1peyDnHQzMeYsLUCYzsPJK3z3ybZinNquDdSn03ezacfjosWeKva7zppoof0yIisUztnNUimzbB55/7IDZtmv+xcg6SkvwpoJEjITvb127Nn++v1SmUlAS9epUMbH37+mmRmrfYtg1ee80Hsk8/9a8zfLgPZGecAW3alF3WV356hXPfOJcezXsw9ZypdGrSqQr3hNRXGRlw5ZXwwgv+eJ84Ebp0iXapRESqlsJZDFu7trhWbNo0+OknPz0lxbcFdfjhcMQRvi2xSNd37djh2xqbP784sM2b5/u1LCjwy8TF+Vq1wtDWpYs/fTR5sj9N2rOnD2Rnn+3H98Wnyz/lhEknkJqUytRzpzKg9YDK7RCRwMSJcMUV/vh99FHfu4CISF2hcBZDVqwoGcYWLfLTGzf213cVhrFhw3xNWEVlZ8PPP5cMbPPn+2m5ub5W7MwzfSgbNqxy7ZPNWT+HsRPHkpmbyVtnvlXhLqBEwi1d6o/R6dPh/PN9w7Whp9hFRGqrGg9nZvY0MB7Y4JwbEEy7FbgE2BgsdpNzbkqEdccADwDxwJPOub+X5zWjHc5yc2HdOl8TtnYtrFmz5+OaNbBhg1++aVPfKfgRR/hAdsABNXNtTW6uv76sY8eqfb2V21cy5oUxLN26lIknT+SUfqdU3calXsvL83223nEHdOsGL77oT7+LiNRm0QhnhwM7gefCwtlO59w/y1gvHvgZGA2kA98CZznn5u3tNas7nH3/va/1Ki14bdq0512UcXHQujW0bw/t2vlh4EAfyAYOrHvtOW3J2sLxLx3P16u+5sGxD3L18KujXSSpQz7/HM4913/ebr8dfv97iI+PdqlERCqmtHBWbfU0zrlpZta1AqsOBxY755YCmNkk4ARgr+Gsul1wAcyd68fj4/1pwXbtoHNnfz1YYQALDWKtW9evO82apzTno199xFmvncU1713D2oy1/O2ov2Hq00mqwGGHwQ8/wGWX+bs4P/gAnn/e1wKLiNQV0YgNV5vZecB3wG+dc+G96nUAVoU8TwdGlLYxM7sUuBSgc+fOVVzUkp58EhITfehq1Ur/sZcmJTGFV09/launXM2dX9zJmp1reHz84yTGJ0a7aFIHNG0KkybB2LFw9dUwaJD/bJ58crRLJiJSNWr6pNojwH7AYGAtcG+EZSJVsZR67tU597hzbphzblirVq2qpJClGT7cXxfWtq2C2d4kxCXwyHGPcPuo23lm9jOcMOkEdu7eGe1iSR1h5muyZ82C/faDU06BSy+FXbuiXTIRkcqr0XDmnFvvnMt3zhUAT+BPYYZLB0Iby+oIrKmJ8knVMjP+fMSfeeL4J3h/yfsc9exRbNy1ce8ripRTz57w5Zdw442+9mzoUH9tqIhIbVaj4czM2oU8PQn4McJi3wI9zaybmSUBZwJv10T5pHpcPORi3jzjTX7c8COHPH0IS7cujXaRpA5JSvIdpv/vf7BzJxx0ENx7b3EbfyIitU21hTMzewn4GuhtZulm9mvgH2Y218zmAEcC1wXLtjezKQDOuTzgauB9YD7wX+fcT9VVTqkZx/c+no/P/5itWVs5+KmDmblmZrSLJHXMkUf6mwXGj4cbboAxY/yd1CIitY0aoZUatXDTQo594Vg2Z23mtdNf45j9jol2kaSOcQ6eeAImTICGDeEvf4ELL1TDtSISe0prSqOOtbIlsa53y9589euv2K/Zfhz34nE898Nz1KV/ECT6zPzNAd9/77sru/Za39TG9df7Ls1ERGKdas4kKrZnb+fk/57Mx8s+Jq1BGoPaDGJQ60H+sc0gBrQeQGoDVXVI5X3zDTzwALzyir8O7YQTfK3aYYdVrssyEZHKUt+aEnNy8nKYOHci36/9njnr5zBn/Ry252wvmt+9Wfei0DawzUAGtRnEfs32Iz5O7ZjIvlu9Gv7zH9+B+pYtvlmca6/1/cs2aBDt0olIfaRwJjHPOceqHauKglrhsHDzQgqcv/UuJSGFAa0HFNWwDWoziP3b7E+zlGZRLr3UFpmZMHEi3H8/zJvne/q44gq4/HI/LiJSUxTOpNbKys1i/qb5JQLbD+t/YFPmJgCS4pO475j7uPLAK9VNlJSbc/DRRz6kTZnim+Q4+2xfmzZ4cLRLJyL1gcKZ1CnOOdbvWs+c9XN48JsHmbxoMucMPIfHxj9Go6RG0S6e1DILF8KDD8Izz/iatVGj/HVp48erNxARqT4KZ1JnFbgC7vr8Lv78yZ/p37o/r53+Gr1a9Ip2saQW2roVnnoKHnoIVq6E7t3hyiuhXz9ISysemjTxTXMouIlIZSicSZ334ZIPOeu1s9idv5tnTnyGk/uqJ2ypmLw8ePNNf8rzyy9LX65Ro5KhLTS8hU9r3NgHutDH0PGEhJp6dyISKxTOpF5YuX0lp71yGjNWz+B3h/yOO39xJwlx+tWTilu6FDZsgB07/LB9e/F4+BBpXnm/YpOT9wxsoSGuSRPo0MG32dapk3/s0EF3morUZgpnUm/k5OVw3fvX8ch3j3BElyOYdOok2jZuG+1iST3kHOza5UPbzp3FQ0ZGyce9Tdu5059y3bFjz9do1ao4rIUGt8LxDh188BOR2KNwJvXO8z88z2XvXkbT5Ka8ctorjOw8MtpFEqmUjAzfXlt6uh9WrSoeL3y+deue67Vs6cNaz57+Jofjj4dman1GJOoUzqRemrN+Dqf89xSWb1vOPaPv4doR16q5DanTdu0qDnDh4W32bP+YkAC/+AWccgqceKKvfRORmqdwJvXW9uztXPDWBby54E1O7386Tx7/pLqGknrJOfj2W3jtNT8sWQJxcb4rq1NOgZNP9qdBRaRmKJxJveac456v7uGP//sjvVv05rXTX6Nvq77RLpZI1DgHc+YUB7V58/z0gw7yQe2UU6Bbt+iWUaSuUzgTAT5d/ilnvHoGmbmZPPXLpzi9/+nRLpJITFiwoDiozZrlpx1wQHFQ69MnuuUTqYsUzkQCq3es5vRXT+erVV8xYcQE/jH6HyTGJ0a7WCIxY+lSeP11H9SmT/fT+vb1Ie3002HgwOiWT6SuUDgTCZGbn8vvPvwdD3zzACM7jeS/p/2X9qnto10skZizejW88YYPatOmQUEBDBsGl1wCZ53l22ITkYopLZzFRaMwItGWGJ/I/WPu56VTXmL2utns/+j+PDHzCfIL8qNdNJGY0qEDXH01fPIJrF3re03IyoLLLoN27eDii+Gbb8rf2K6I7J3CmdRrZw44kxmXzKB3i95c+u6lDHl8CJ8s+yTaxRKJSa1bw7XXwty58PXXcMYZ8NJL/iaCQYN85/FbtkS7lCK1n8KZ1Hv9WvXj8ws/5+VTX2Z79naOeu4oTnr5JBZvWRztoonEJDMfyJ56ytemPfaY74Xg2muhfXs491z49FPVpolUlK45EwmRnZfNv77+F3d+cSc5eTn8ZsRvuPnwm2ma3DTaRROJebNnw5NPwgsv+C6revb0pz3PPx/atIl26URij24IENkH63au4+aPb+bpWU/TomELbh91O5cMvUSdqIuUQ2YmvPqqD2qff+57JPjlL/1NBKNHQ3x8tEsoEhsUzkQqYNbaWVz3/nV8tuIz+rfqz33H3scx+x0T7WKJ1BoLFviQ9uyzsGkTdO7su4zq2tXfbFDYSXu7dpCoFm2knlE4E6kg5xxvLniTGz68gaVbl3Jcz+P45zH/pE9LtcopUl67d8Nbb8ETT8AXX/g7PkOZ+VOfoYEt0njDhtEpv0h1UDgTqaScvBwemvEQf532VzJzM7ly2JXcMuoWmqc0j3bRRGoV52DbNt8Je2En7ZHGt27dc91mzXxQO/hguOEG6NWrxosvUmVqPJyZ2dPAeGCDc25AMO0e4HhgN7AEuNA5ty3CusuBDCAfyItU8EgUzqQmbNi1gb988hee+P4JmjRowm2jbuPyYZerlwGRKrZrlw9p4aFt5Ur48EPIyYHTToM//hEGD452aUX2XTTC2eHATuC5kHB2DPCxcy7PzO4GcM79IcK6y4FhzrlN+/KaCmdSk+aun8t171/H/5b9jz4t+3DXL+5ifK/xumlApAZs2OAbxP33v2HHDhg3Dv70JzjkkGiXTKT8aryHAOfcNGBL2LQPnHN5wdPpQMfqen2R6jawzUA+/NWHvH3m2+QX5HPSyyfR/t72XDPlGr5J/4a6dMmASKxp3RruvBNWrIC//Q1mzICRI2HUKPjgA7WxJrVbtV5zZmZdgXcLa87C5r0DvOyceyHCvGXAVsABjznnHi/jNS4FLgXo3Lnz0BUrVlRR6UXKLzc/lymLpjBx7kTeXvg2Ofk57NdsP84ZeA7nDDqHXi10YYxIddq1y98Ves89/tTn0KFw003+ztA4NbcuMSoqNwSUFs7M7E/AMOBkF6EAZtbeObfGzFoDHwLXBDVxZdJpTYkF27O38/r815k4dyIfL/sYh2NY+2GcM/AczhxwJm0bt412EUXqrJwc3wju3/8OixdD377+mrQzz1RTHRJ7YqbjczM7H3+jwDmRghmAc25N8LgBeAMYXnMlFKmcJslNuPCAC/novI9Ivz6de4+5lwJXwHXvX0eH+zpwzPPH8OzsZ8nIyYh2UUXqnAYN4Ne/hvnzfb+fCQlw3nn+rs5HHoHs7GiXUGTvarTmzMzGAPcBRzjnNpayTiMgzjmXEYx/CNzunJu6t9dTzZnEsvkb5/Pi3BeZOHciy7YtIyUhhV/2/iXnDDyHY3scS1J8UrSLKFLnOAfvvgt33AHffANt28L118Pll0NqarRLJ/VdNO7WfAkYBbQE1gO3AH8EGgCbg8WmO+cuN7P2wJPOuXFm1h1fWwaQALzonLujPK+pcCa1gXOOr9O/ZuKcibz808tsztpM85TmnN7vdC4ZeglD2g2JdhFF6hznfGfsd94JH33k20s780w49FB/I0Hnzr4hXJGapEZoRWJQbn4uHyz5gIlzJ/LmgjfJyc/h9lG388fD/kic6SpmkeowYwbcfTe8/76/kQB8w7YjR/qmOEaOhP331zVqUv0UzkRi3Pbs7Vwx+Qpe+vElRncfzfMnPU+bxm2iXSyROisvD+bOhS+/9MNXX/kGbsF3EzV8uA9qI0f6HgmaNo1qcaUOUjgTqQWcczz5/ZP8ZupvaJrclBdPfpEjux0Z7WKJ1Bvp6cVB7csvYfZsyM/3pzz79SsOayNHQvfuOhUqlVPhcGZmyfi7Kw8D2gNZwI/AZOfcT9VQ1gpTOJO6Ys76OZz+yuks2rKIvxz+F24+/Gbi4+KjXSyRemfnTvj22+Lata+/hu3b/bw2bXx7agMHFg99+kCS7u2RcqpQODOzW/F9YX4KzAQ2AMlAL+DIYPy3zrk5VV/kfadwJnXJzt07uXLylTw/53mO7HokE0+eSLvUdtEulki9VlAA8+YV167Nnu2b7cjN9fMTEqB37+KwNmiQf9QNBxJJRcPZcc65yWXMbw10ds7FRCJSOJO66JnZz3Dl5CtJbZDKCye9wOj9Rke7SCISIjcXfv7ZX79WOMyZ47uWKpSWBgMGlKxlGzjQ3zUaSV6eb5MtK8s/hg7h03bvhnbtoGdP6NhRPSLUJlV2zVlwmjPJObejqgpXVRTOpK6at3Eep71yGvM3zuemw27i1lG3qoN1kRi3Ywf8+GPJ0DZ3LmzdWrxM+/b+5oPw0JWfX7HXTE6G/fbzQS18aN9etXexpkrCmZldDPwK37PA5865m6quiJWncCZ1WWZuJtdMuYanZz/NYZ0P46VTXqJDWodoF0tE9oFzsGZNcVD76Sdf85acDCkp/jF0KM+0xER/I8OiRX74+Wf/uGSJr1Ur1LAh9OgRObi1aaPgFg0VPa15vHPunZDnk5xzZwbjPzjn9q+W0laQwpnUBy/MeYHL372clMQUnj/pecb0GBPtIolIDMrPh1WrikNb6LB0qT91Wig11bftNnRo8dC7N8TrPqRqVdFwdjNwIPAX59wPZnYT0B9w+C6Wzq6uAleEwpnUFws2LeCMV89gzvo5/GHkH/jrkX8lMV4tZopI+eTl+WviCsPawoXw/ff+BoesLL9Mo0YweHDJwNanT9UFth07fBmWL/fDihWwbp2/Zi4hwQ/x8cXjZQ2hy7VoAWPHQuPGVVPO6lSZpjTaArcHT/8CNAYaxsodmqEUzqQ+ycrN4rr3r+OxmY9xSKdDeOmUl+jcpHO0iyUitVheHixYADNnFg+zZ0Nmpp/fsGHkwJYQ4RLYbdtKBq/w8dBr78Cfom3Xrrgc+fn+MdJQUFD2+2jUCE45Bc4/H0aNit2bJCoTzlKBfKAn8FfgW+Ae51x2dRS0MhTOpD6a9OMkLn3nUhLjE3nmhGc4vvfx0S6SiNQh+fl7BrZZs4oDW0qKD2z9+sHGjcUBrLA9uEING0LXrn7o0qV4vPB569blv+6toMCXK1KAW7wYnnsOXn7Z18516gS/+hWcd54/VRtLKnpa82/A4UAi8LJz7n4z+yVwLfCMc+756ipwRSicSX21aPMiznj1DGatm8WJfU5k/zb707tFb/q07EOvFr1olNQo2kUUkTokP9+fCg0NbAsXQtu2JYNX6HiLFjV700FWFrz9Njz7rO9HtaAARozwtWlnnAHNm9dcWUpT0XA22zk32MwMmOmcGxJMTwCucs49UG0lrgCFM6nPsvOyufnjm3l9/uss37YcR/Fnu2NaR/q07EPvFr2LQlvvlr3pmNZRHayLSJ23di1MnOiD2o8/+l4cfvlLX5s2Zkz0OrmvaDh7AX/xfwqwyjl3XfUVsfIUzkS87LxsFm1exMLNC1m4aSELNy9kwaYFLNy8kB05xU0UpiSk0KtFr+Lg1tIHtwGtB5AUrz5oRKRucc5fQ/fss/Dii/40bKtWcPbZvkZt8OCard2rzDVnA4Fc59yC6ipcVVE4Eymbc471u9b7oBYW2pZvW06B81fZdm3alYfHPsxxvY6LcolFRKpHbi68956/Pu2dd3ybcAMH+tq0c84pvjmhOlVZDwGxTOFMpOKy87JZvGUxc9bP4W/T/sb8TfM5qc9JPDDmATo16RTt4omIVJstW2DSJF+jNmOGv8lh/Xrf/lt1UjgTkXLbnb+be7+6l79O+ytxFsctR9zChIMmqC01EanzFiyAb7/1d3hWN4UzEdlny7ct5zfv/YZ3fn6HAa0H8Mhxj3Bo50OjXSwRkTqhtHBW7tu0zKyDmR1iZocXDlVbRBGJNV2bduXts97mzTPeZHv2dg77v8O46K2L2JS5KdpFExGps8pVc2ZmdwNnAPPwDdICOOfcL6uxbPtMNWci1WfX7l3c/tnt3Df9PtIapHH30Xdz0QEXqSkOEZEKqtRpTTNbCAxyzuVUR+GqisKZSPX7ccOPXDn5Sj5f+TkHdzyYR457hP3b7h/tYomI1DqVPa25FN9LgIjUcwNaD+CzCz7j/074PxZtWcTQx4dy/fvXk5GTEe2iiYjUCeUNZ5nAbDN7zMweLByqs2AiErvMjAsGX8CCqxZw0QEX8a/p/6Lvv/vy6rxXqUs3GYmIREN5w9nb+E7PvwJmhgwiUo+1aNiCx49/nK9//TUtG7bktFdOY9yL41iyZUm0iyYiUmuVuykNM0sBOjvnFlZvkSpO15yJRE9eQR4Pz3iYP3/yZ/IK8rhs6GVcOvRS+rXqF+2iiYjEpEpdc2ZmxwOzganB88Fm9vZe1nnazDaY2Y8h05qb2Ydmtih4bFbKumPMbKGZLTazG8tTRhGJroS4BCYcNIEFVy3g1H6n8p9v/0P///Rn5NMjeWb2M+zavSvaRRQRqRXKe1rzVmA4sA3AOTcb6LaXdZ4BxoRNuxH4n3OuJ/C/4HkJZhYP/BsYC/QDzjIz/estUkt0SOvA8yc9z+rrV/PP0f9kc+ZmLnzrQtrf154r3r2C79d+H+0iiojEtPKGszzn3PawaWWeD3XOTQO2hE0+AXg2GH8WODHCqsOBxc65pc653cCkYD0RqUVaNWrFbw/5LfOvms+0C6ZxQu8TeOaHZxj6+FCGPj6UR797lO3Z4V8rIiJS3nD2o5mdDcSbWU8zewh/c8C+auOcWwsQPLaOsEwHYFXI8/RgWkRmdqmZfWdm323cuLECRRKR6mRmHNblMJ476TnWXL+Gh8c+TH5BPldMvoL297Xnwrcu5KtVX+kuTxGRQHnD2TVAfyAHeBHYDkyopjJZhGmlfms75x53zg1zzg1r1apVNRVJRKpCs5RmXDX8KmZdNosZF8/gnIHn8Oq8Vxn59EgGPDKA+6ffz+bMzdEupohIVJUrnDnnMp1zf3LOHRgMNzvnsivweuvNrB1A8LghwjLpQKeQ5x2BNRV4LRGJUWbGgR0O5PHjH2ftb9fyxPFPkJqUynXvX0f7+9pz1mtn8fGyjylwBdEuqohIjSsznJnZ42Y2sJR5jczsIjM7Zx9e723g/GD8fOCtCMt8C/Q0s25mlgScGawnInVQ46TGXDzkYqZfPJ0fLv+By4ZextTFU/nFc79gwH8G8OT3T5KdV5H/BUVEaqcy2zkzs8HATcBA4EdgI5AM9ATSgKeBRyP1uWlmLwGjgJbAeuAW4E3gv0BnYCVwmnNui5m1B550zo0L1h0H3A/EA0875+4oz5tRO2cidUNWbhavzHuFf03/F7PXzaZ1o9ZcdeBVXDHsClo10uULIlI3VLbj88bAMKAdkAXMj8XGaBXOROoW5xyfLP+Ee7++lymLppCckMx5g87juoOvo0/LPtEunohIpVQqnNUWCmciddf8jfP51/R/8dwPz5GTn8P4XuO5/qDrGdV1FGaR7iMSEYltleohQEQk2vq26svjxz/OyutWcssRtzA9fTpHPXcUw54YxsQ5E8nNz412EUVEqoTCmYjUKq0btebWUbeycsJKHh//OJm5mZz7xrl0f7A793x5D9uyt0W7iCIilVLevjVPK880EZGakpKYwiVDL+GnK3/i3bPepWfznvz+o9/T6V+dmDB1Asu2Lot2EUVEKqS8NwR875wbsrdp0aZrzkTqt1lrZ3Hf9PuY9OMkClwBJ/c9mUM7HUqHtA50SO1Ax7SOtG3clsT4xGgXVUSkYjcEmNlYYBxwOvByyKw0oJ9zbnhVF7QyFM5EBCB9RzoPz3iYx2c+ztbsrSXmGUabxm3omNaxKLB1SO1Ah7SS442TGkep9CJSX1Q0nO0PDAZuB/4SMisD+MQ5tzXSetGicCYioZxzbM7azOodq1mdsZr0HeklxzNWs3rH6j0CHECTBk2KatyapzSnaXJTmiU3o2ly0zKHBgkNovBORaQ2Ki2cJZS1knPuBzP7ETjGOfdstZVORKQamBktG7akZcOW7N92/1KXy8zNLAptq3eEBLfg+YrtK9iWvY2tWVvJLSj7rtDkhOQ9QlzzlOb0bN6Tvq360rdlX3q26ElyQnJVv10RqSPKDGcAzrl8M2thZknOud01USgRkZrUMLEhPVv0pGeLnmUu55wjOy+bbdnbfFjL3lo0HjpszdrKthw/vjFzI/M2zuPFuS/i8Gcq4iyObk27FYW1vi37Fo03SW5SE29ZRGLYXsNZYAXwpZm9DewqnOicu69aSiUiEoPMjJTEFFISU2iX2m6f1s3MzeTnzT8zf+N85m/yw4JNC/hgyQfszi/+v7dd43b0admnRGDr26ov7Rq3U2O7IvVEecPZmmCIA1KrrzgiInVTw8SGDG47mMFtB5eYnleQx7Kty3xgCwluz895nozdGUXLNU1uyrD2wxjRYQQjOoxgeIfhtGncpobfhYjUBHXfJCISg5xzrN25tiiwzV0/lxlrZjB3/VzyXT4AXZp0YUTHEQxvP5wRHUcwpN0QGiY2jHLJRaS8KtvxeSvg90B/oOgqVufcUVVZyMpSOBORui4zN5Pv137PN+nf8M3qb5ixegYrtq8AIN7iGdhmYInatb6t+hJn5e8MJr8gn+0529mStYXNmZv9Y5Z/3JK1hR05O9idv5ucvBx2F+xmd/6+Dy1SWjCmxxjG9RzHkV2PJCUxpbp2l0hMq2w4+wDfztkNwOXA+cBG59wfqrqglaFwJiL10bqd65ixegYzVs8oCmw7cnYAkJqUyoEdDmR4++H0admHHTk7ioJWYegKDV9bs7YW3bgQSeOkxjSIb0BSfFKFh2XblvHxso/JzM0kOSGZo7odxbge4xjbcyzdm3Wvqd0mEnWVDWcznXNDzWyOc25QMO0z59wR1VDWClM4ExGBAlfAz5t/LlG79sP6H8gryCtapkmDJjRPaU6Lhi38Y4p/3GO8YfF4s+RmxMfFV0kZs/OymbZiGlMWTWHKoiks2rIIgD4t+zCuxzjG9RzHoZ0PrZJ245xzrM5YzfyN85m3cZ4fNs0jzuLo0awHPZoXD/s134+0BmmVfk2R8qhsOJvunDvIzN4HHsTfHPCqc26/qi9qxSmciYhElpWbRfqOdN+YbkozEuLKez9YzVi0eRHvLX6PKYum8OnyT8nJz6FxUmOO7n50Ua1ax7SOZW6jwBWwcvvK4gAWMoTeXNE8pTn9WvXDOcfiLYtZv2t9ie20btS6OLCFhbdmKc2q5f1L/VTZcDYe+BzoBDyE777pNufc21Vd0MpQOBMRqf127d7FJ8s/YfLPk5myeAort68EYFCbQUW1am0bt2X+pvklAtj8TfPJzM0s2k6bRm3o16rfHkOrhq1KNEuSkZPBkq1LWLxl8R7D6ozVJcrWPKV5ieDWvVl3ujTtQtemXemY1jHmQq/Etop235SMv8asBzAXeMo5l1fqClGmcCYiUrc455i3cZ4//bl4Cl+s/KLE6VmAjmkdffBqWRzA+rbqS/OU5pV+/czcTJZuXRoxuK3cvrLE9XlxFkfHtI50aeLDWtemXYvGuzTtQqe0TureS0qoaDh7GcjF15qNBVY4566ttlJWksKZiEjdtj17Ox8t/YgdOTuKQli0rhHLycth1Y5VLN+2nBXbVvjH7cWP6TvSKXAFRcsbRrvUdiVDW0iQ69yks+5crWcqGs7mOucGBuMJwAzn3JDqK2blKJyJiEisyM3PZXXGapZvW14c4LYXB7lVO1btUQvYtnHborDWtUnXolq3wiCn8Fa3VKjjc3ytGQDOuTx1HSIiIlI+ifGJRUErkvyCfNZkrCmqbQsdvl39La/Ne43cgtwS67Rp1KY4vIWcOu2Y1pF2qe1okdJC3XzVAXurOcunuC9NA1KAzGDcOedi6n5j1ZyJiEhdkV+Qz7qd6/YIboW1byu2ryjRLytAUnwS7Rq3o31qe9qltqN94/a0T21f/DwYb5bcTCEuBlTqbs3aQuFMRETqiwJXUBTe1mSsKTGs3bm2aHxb9rY91m0Q36BEWGvTqA1xFkd+QT75Lp/8gnwKXIEfD57v7TEhLoGRnUYytudYhrYbWmVt4tVlCmciIiL1UFZuVomwFinArd+5Hocj3uKJj4vf62Ocxe0xbdfuXcxeNxuHo0VKC47tcSxj9hvDsT2OpXWj1tHeDTGpoteciYiISC2WkphC92bda6RrrE2Zm/hwyYe8t/g93l/yPi/OfRGAoe2GMrbHWMb0GMOIjiPUHtxe1HjNmZn1xvfTWag78Bfn3P0hy4wC3gKWBZNed87dvrdtq+ZMREQkNhS4AmatncXUxVN5b/F7fJ3+NQWugKbJTRndfTRje4zl2B7H0j61fbSLGjUxeVrTzOKB1cAI59yKkOmjgBucc+P3ZXsKZyIiIrFpa9ZWPlr6EVMXT2XqkqmsyVgDwP5t9mdMjzGM7TGWQzodQmJ8YpRLWnNiNZwdA9zinBsZNn0UCmciIiJ1knOOuRvm8t6i95i6ZGpRzw8pCSlFDfJ2adKFLk27lHhsn9q+Tt1oEKvh7Gnge+fcw2HTRwGvAen4TtZvcM79VMo2LgUuBejcufPQFStWRFpMREREYtSOnB18vOxjPl/xeYmmQjZlbiqxXLzF+y6yCkNbWIDrlNapqKHe3fm72bV7Fzt372RX7i527d5V9FjmtNxdOOd44eQXqv19x1w4M7MkfPDq75xbHzYvDShwzu00s3HAA865nnvbpmrORERE6o5du3exaseqorBW9BiMr85YXaKLLIDUpFSy8rL26H1hbxolNqJRUiMaJTaiaXJTvr/s+6p8KxHF4t2aY/G1ZuvDZzjndoSMTzGz/5hZS+fcpvBlRUREpG5qlNSIPi370Kdln4jzc/Nzi3pZCK1ta5jYkEaJjWic1LgocBU+hk9rnNSYlISUmGqUN5rh7CzgpUgzzKwtsN4558xsOBAHbK7JwomIiEhsS4xP9Kc1m3aBLtEuTdWJSjgzs4bAaOCykGmXAzjnHgVOBa4wszwgCzjT1aXWckVERERKEZVw5pzLBFqETXs0ZPxh4OHw9URERETqurhoF0BEREREiimciYiIiMQQhTMRERGRGKJwJiIiIhJDFM5EREREYojCmYiIiEgMUTgTERERiSEKZyIiIiIxROFMREREJIYonImIiIjEEIUzERERkRiicCYiIiISQxTORERERGKIwpmIiIhIDFE4ExEREYkhCmciIiIiMUThTERERCSGKJyJiIiIxBCFMxEREZEYonAmIiIiEkMUzkRERERiiMKZiIiISAxROBMRERGJIQpnIiIiIjFE4UxEREQkhkQlnJnZcjOba2azzey7CPPNzB40s8VmNsfMhkSjnCIiIiI1LSGKr32kc25TKfPGAj2DYQTwSPAoIiIiUqfF6mnNE4DnnDcdaGpm7aJdKBEREZHqFq1w5oAPzGymmV0aYX4HYFXI8/Rg2h7M7FIz+87Mvtu4cWM1FFVERESk5kQrnI10zg3Bn768yswOD5tvEdZxkTbknHvcOTfMOTesVatWVV1OERERkRoVlXDmnFsTPG4A3gCGhy2SDnQKed4RWFMzpRMRERGJnhq/IcDMGgFxzrmMYPwY4Pawxd4GrjazSfgbAbY759bWcFErLj8HstdB1rrgcW0wvhay10NiE0jrC036+sfG3SEumvdmiIiISKyIRiJoA7xhZoWv/6JzbqqZXQ7gnHsUmAKMAxYDmcCFUSjnnnZvDYJWSNgKDWCFj7u3RljZILkVJLeBnM2w7LniWXFJkNoTmvTzYa0wuKX2goSUGnt7IiIiEn3mXMRLuWqlYcOGue++26PZtKozuT9sn1dyWnwKpLSD5LYhj233nJbcumTt2O7tsGMB7JgP2+cXP+5aCq4gWMigcbeStWyF40lNq+99ioiISLUzs5nOuWHh03UubV8MvB1cXskAlpAKFun+hb1IagItR/ghVH427Pi5ZGjbMR/WfQQFOcXLxSVBYiokpPnHssYT03w5S4ynQcOOEN+gcvtEREREqpTC2b7ofEr1v0Z8MjQb5IdQBfmwa1kQ2BbA7s2QuwNyMyAvw4/nbPQ1b0XTd5b9WhYHDbtAWi9/CjW1lz+9mtYLGnaGuPjqe58iIiISkcJZbREXD6k9/MDx5VvHFfiAlpsRDDuCIJcBudtg5zLI+NnX1G38smSYi0sKXi8IbaEBLrl1xWoLRUREZK8Uzuoyi/OnLxPT9r6sc/6GhoxFPqxlBMOOhbBmMhTkFi+bkFoc1tL6+hsZmvTzYS4usfrej4iISD2gcCaemb+GLqUdtA5rE7ggDzJXBqFtUXFt26avYMVLIdtIKHnXaVFo012nIiIi5aVwJnsXl+DbYmvcHRhTcl7eLn8N3Pb5/k7WHfNg2xxIfyPsrtPuxWGtMLil9fE3KYiIiEgRhTOpnIRG0HyoH0LlZ/tatu3zgpsY5vnxtVNLniJt2MnXtqX28I+Ng+vqGu+n2jYREamXFM6kesQnQ9OBfghVkAs7lwa1bEFzIRmLYNVrvnHeUA07Foe1wgDXuAek7udDoYiISB2kcCY1Ky4R0nr7gZNKztu9FTKWBNe1LYadi/1j+lu+mZBQKe2Lw1qTvtBurD9VqrtIRUSkllMPAVI77N4OO5f4sJaxqDi4ZSz2d5mCr13reBJ0OglaDPd3q4qIiMQo9RAgtVtSE2g+xA/hMtfA6rdg1Ruw4D6Y/w9fs9bxBB/W2oxSEx8iIlJrqOZM6pbdW2H1ZH+36JqpkJ8JiU2hw3hfo9buWF2vJiIiMUE1Z1I/JDWDbuf6IS8L1n3ga9RWvwPLX/Ad1bc7xteodTgeGjSPdolFRERKUDiTuishJTi1eYJvSHfDNF+jlv6mv8nA4qH1EUFQGw+NuuiGAhERiTqd1pT6xznYMtMHtVVv+CY9wNe6NR0UNvTXaVAREakWOq0pUsgMWgzzw/53+P5D130E2+b63g2W/l9IJ/DmG8RtFhbaGnfT3aAiIlItFM5EitpdC7gC2LUcts7xYa0wtK16AwhqmhMaQZMBxWGt2SBour+/q1RERKQSFM5EwllccV+inU4snp6XCdt/8kFt6xzYPtf3bLDkieL1Wozwd4S2OxaaHwhx8VF5CyIiUnvpmjORynAOstb6wLbpK1j7Pmz+FnD+Gra2RxeHtYYdo11aERGJIbrmTKQ6mEHD9n5oPwYG3e77CF33kQ9qa9+Hla/4ZZv0g7bH+uVaHaaO3UVEJCLVnIlUJ+f8qdDCoLZhGhTk+I7hWx9RXKuW1lfNeIiI1DOqOROJBjNoOsAPfX/rr1vb8FlxWPv+er9cw44+pLUcCY06Q8NO0LCDmvEQEamHFM5EalJCQ2g/1g8Au1aGnP58FZY8VXL5pGY+uKV09I8NOwWPIUNias2/DxERqTYKZyLR1Kgz9LjEDwV5vgmPzPSSQ1Y67FoFW2dC9oY9t5GYFhbaukBqDz803k9dVImI1DIKZyKxIi6hOFSVJj8HstaEhLdVJYPc1h8ge13JdZKaQeMeIYGtB6Tu5x+TW+taNxGRGFPj4czMOgHPAW2BAuBx59wDYcuMAt4ClgWTXnfO3V6DxRSJTfENfO8EjbuVvkxeFuxcCjuXQMZi2LnYP26aDitf9o3sFkpovGdgS+3hG9ZVjZuISFREo+YsD/itc+57M0sFZprZh865eWHLfe6cGx+F8onUbgkpvk/Qpv33nJe/G3atKA5sheFt+1xY/RYU5BYv26Q/tD7cN/vR+jC10yYiUkNqPJw559YCa4PxDDObD3QAwsOZiFS1+CRI6+mHcAX5/jRpxiLY8i1s+ByWvQCLHvHzG3XzIa3VYT60pfbUKVERkWoQ1WvOzKwrcADwTYTZB5vZD8Aa4Abn3E+lbONS4FKAzp07V1NJReqBuHho3NUP7UZDf/xNCtvm+KC2cRqseQ+WPeeXT24DrQ4trl1rOkjdVYmIVIGoNUJrZo2Bz4A7nHOvh81LAwqcczvNbBzwgHMuwr/6JakRWpFq5hxk/Owb093wOWz83N9hCv6u0ZYji2vXGnYMatbigkfz/Y+GPhaOh88vmp7gb5SwuKi8XRGR6lRaI7RRCWdmlgi8C7zvnLuvHMsvB4Y55zaVtZzCmUgU7FrlQ1ph7dr2arhCweLAEoOglghxZYxbQjAtEeKSIKV9cUf2hUNyG52SFZGoi5keAszMgKeA+aUFMzNrC6x3zjkzGw7EAZtrsJgiUl6NOkGjs6Hr2f559ibfCXzOZsD5wRWEPYaMh0+nIOQx39+kUJALLi/yeGnzXC7k7vBhMWt1yTLHNwzueu2+59Com/o9FZGoisY1ZyOBXwFzzWx2MO0moDOAc+5R4FTgCjPLA7KAM11d6gRUpC5LbgkdfxntUpSUnw07lwdNjATDruBx/ceQt6vk8intgqDW3Ye4hh0gua0fUtr6mrf4BlF5KyJS96njcxGp35yDnI0lg1vokJmOr+ELk9Q8CGqhoa2tD3ah0xs0j941cwW5vgYzZzPkbPIhNaWdvx4wqZlO7YpEWcyc1hQRiSlmvqeE5NbQ8qA95+fvhpwNkLXO976QtQ6y1vrxwuebp/tp+VkRtp/gt52YBgmpvi/UxNTi8fDH0ubFN4DdW33IKgxcu0OCV6TneRmlv+/4lKDf1g7FXX+FjjfsGPQgoZsxRGqawpmISFnik4rDSlmcg7ydxcEtNMxlr/fXv+VlQG6Gr6nLzQie74SCnMqVMbEJNGgBSS2gQStI6xPyvAU0aOkf4xpA9tqQLr9W+75bN37uuwULbYQYfLBMaR+8/w6Q0hFS2vhgF58C8ckh42HTEsKexyVW7j2K1CMKZyIiVcGsuOYrrde+rZu/2we7wvBWFNxCHvOz/anIwqBVFL6aV03wcQWQvdGHtczVxQEua3Vxv62rJ0N+ZsW2b/HFAS4uyT+3+OBO3PiwIXxahGUSGvvTxyntQk4tB6eUG7RWm3tSqymciYhEW3wSxDePbn+mFudrxVLaQPOhkZdxzp+6LRqy/WNe2PNIy4QuV7Db34nr8n0oLBrPBwp8bxXh01y+r2EsnLdrOaz7EHK3R34vDVqFXQPYruR1gcltIbmVP91c30/d5u70tbnxDSGpaf2+2cWF3EEeF72IpHAmIiLlYwYJDf0QK/Ky/GnjotPJa/e8NnD7j/65y4uwAfMBLamZDyaJTSOPJwXjiSHjSU19oInFGyucg9xtfh+ED9mF42v8Y97OkuvGJxe/z6L3W8p46P5IaOyDd35mEMbDHvMzg4Be2vRgcAX4QF6w53hZ80osFxL8KYjwz0BpyxQE+6AhnBF2F3cNUjgTEZHaKyGluNuxsrgCyNkSBJMguO3eDLu3+Rstdm/zYWb3Vt+/bOF4eDMrkcQnQ1xycH1d6JASYV5KyWXikstRc7eXVhVcga/5Cg9g+dkRytowqE1sB80OgPbj/HiDVn753G3BPtlWPJ6zGXYuKZ4Wfm1iRcU1CK5PbFj8GNfAX+toccUDcSE9hYRND1+uqBeS8FPicXtOIy44/R2+TJwvRxQpnImISN1ncb4NvuSW0HRg+dcryIXd231QKwxsoUEuLzM4dZtdfBq3IDtkWjbkbiweD58XsTavAhKbFIeuVocEp27bFU8rHBJSK1fTV3hqOzS8FY7nZvhTovEpPgQW3RTSMEIIS9Z1gWVQOBMRESlNXGJxqKsOBXnstWYM8H3OlqGmro8qcWq7fc28Zj2kcCYiIhItUbzoXGJXPb9FRURERCS2KJyJiIiIxBCFMxEREZEYonAmIiIiEkMUzkRERERiiMKZiIiISAxROBMRERGJIQpnIiIiIjFE4UxEREQkhiiciYiIiMQQhTMRERGRGKJwJiIiIhJDFM5EREREYojCmYiIiEgMUTgTERERiSEKZyIiIiIxJCrhzMzGmNlCM1tsZjdGmG9m9mAwf46ZDYlGOUVERERqWo2HMzOLB/4NjAX6AWeZWb+wxcYCPYPhUuCRGi2kiIiISJREo+ZsOLDYObfUObcbmAScELbMCcBzzpsONDWzdjVdUBEREZGalhCF1+wArAp5ng6MKMcyHYC14Rszs0vxtWsAO81sYdUVNaKWwKZqfo36RPuz6mmfVj3t06ql/Vn1tE+rVk3tzy6RJkYjnFmEaa4Cy/iJzj0OPF7ZQpWXmX3nnBtWU69X12l/Vj3t06qnfVq1tD+rnvZp1Yr2/ozGac10oFPI847AmgosIyIiIlLnRCOcfQv0NLNuZpYEnAm8HbbM28B5wV2bBwHbnXN7nNIUERERqWtq/LSmcy7PzK4G3gfigaedcz+Z2eXB/EeBKcA4YDGQCVxY0+UsQ42dQq0ntD+rnvZp1dM+rVran1VP+7RqRXV/mnMRL+USERERkShQDwEiIiIiMaTehzMz62Rmn5jZfDP7ycyuDaY3N7MPzWxR8NgsmD7azGaa2dzg8aiQbQ0Npi8OejiIdNdpnVbF+/PToCeJ2cHQOlrvK5oqsE+Hh+yzH8zspJBt1ftjFKp8n9b743Rf92fIep3NbKeZ3RAyTccoVb5PdYzu+2e+q5llheyzR0O2Vf3HqHOuXg9AO2BIMJ4K/IzvueAfwI3B9BuBu4PxA4D2wfgAYHXItmYAB+ObAnkPGBvt91fL9+enwLBov6doDxXYpw2BhJB1N4Q8r/fHaDXs03p/nO7r/gxZ7zXgFeCGkGk6Rqt+n+oY3ffPfFfgx1K2Ve3HaL2vOXPOrXXOfR+MZwDz8Q3engA8Gyz2LHBisMws51xhsx4/Aclm1sB8DwZpzrmvnf/rPVe4Tn1SVfuzRgsd4yqwTzOdc3nB9GSCNgJ1jBarqn0q3r7uTwAzOxFYiv/cF07TMRqoqn0qXkX2ZyQ1dYzW+3AWysy64mtyvgHauKD5juAxUjXwKcAs51wO/o+cHjKvsFeDequS+7PQ/wVVyn+ur6c3QpV3n5rZCDP7CZgLXB4ECx2jEVRynxbScRooz/40s0bAH4DbwlbXMRpBJfdpIR2jgX34bepmZrPM7DMzOyyYViPHqMJZwMwa46uDJzjndpRj+f7A3cBlhZMiLFZv/7uugv0JcI5zbiBwWDD8qjrKWlvsyz51zn3jnOsPHAj80cyS0TG6hyrYp6DjtMg+7M/bgH8553aGbyLCsjpGK7dPQcdokX3Yn2uBzs65A4DrgRfNLI0aOkYVzgAzS8T/sSY6514PJq8Pqi8LqzE3hCzfEXgDOM85tySYnI7vyaBQve3VoIr2J8651cFjBvAiMLxm3kHs2dd9Wsg5Nx/Yhb+eT8doiCrapzpOA/u4P0cA/zCz5cAE4Cbz7V/qGA1RRftUx2hgX/ancy7HObc5GJ8JLAF6UUPHaL0PZ0H17lPAfOfcfSGz3gbOD8bPB94Klm8KTAb+6Jz7snDhoDo0w8wOCrZ5XuE69UlV7U8zSzCzlsF4IjAe+LHa30AMqsA+7WZmCcF4F6A3sFzHaLGq2qc6Tr193Z/OucOcc12dc12B+4E7nXMP6xgtVlX7VMeoV4HPfCsziw/GuwM9gaU1doxGukugPg3AofgqyTnA7GAYB7QA/gcsCh6bB8vfjP+veXbI0DqYNwx/0C8BHiZo5Lc+DVW1P4FGwMxgOz8BDwDx0X5/tWSf/irYZ7OB74ETQ7ZV74/RqtynOk4rtj/D1r2VkncW6hitwn2qY7Ri+xN/DfRPwA/BZ/74kG1V+zGqHgJEREREYki9P60pIiIiEksUzkRERERiiMKZiIiISAxROBMRERGJIQpnIiIVZGaXWVjH0yIilaVwJiL1hpmdZGbOzPpUwbb+Amxxzm2tgqKJiBRRUxoiUm+Y2X+BdsD/nHO3Rrk4IiIRqeZMROqFoE+9kcCvgTODaaPM7FMze9XMFpjZxMJOoc1suZndZmbfm9ncwto2M2tkZk+b2bdBp8gnBNPjzeyeYPocM7uslKKIiJRJ4UxE6osTganOuZ+BLWY2JJh+AL4vwn5Ad3yAK7TJOTcEeAS4IZj2J+Bj59yBwJHAPWbWCB/6tgfTDwQuMbNu1fuWRKQuUjgTkfriLGBSMD4peA4wwzmX7pwrwHfp0jVkncLOkWeGTD8GuNHMZgOfAslA52D6ecH0b/DdwvSs8nchInVeQrQLICJS3cysBXAUMMDMHBCP72dvCpATsmg+Jb8XcyJMN+AU59zCsNcw4Brn3PtV/w5EpD5RzZmI1AenAs8557o457o65zoBy/CdIe+r94FrQq5NOyBk+hVmlhhM7xWc7hQR2ScKZyJSH5wFvBE27TXg7Aps669AIjDHzH4MngM8CcwDvg+mP4bOTohIBagpDREREZEYopozERERkRiicCYiIiISQxTORERERGKIwpmIiIhIDFE4ExEREYkhCmciIiIiMUThTERERCSGKJyJiIiIxJD/B27xAK8jBnOIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(prop_enfant.loc[annee_debut_affi:,1]*100, label='1', color='blue')\n",
    "plt.plot(prop_enfant.loc[annee_debut_affi:,2]*100, label='2', color='green')\n",
    "plt.plot(prop_enfant.loc[annee_debut_affi:,3]*100, label='3+', color='orange')\n",
    "plt.ylim(0, 20)\n",
    "plt.ylabel('Part (en %)')\n",
    "plt.xlabel('Année')\n",
    "plt.legend()\n",
    "plt.title('Proportion de personnes de 18 à 54 ans selon nombre d`enfants, Québec, '+str(annee_debut_affi)+'-'+str(annee_fin))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hykxCq_1NRA_"
   },
   "source": [
    "Production du tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "F2QSs-zsNRA_"
   },
   "outputs": [],
   "source": [
    "saut = int((len(pop.loc[annee_debut_affi:])-1)/3)\n",
    "\n",
    "if len(pop)>=5:\n",
    "  table_annee = {1:annee_debut_affi,2:(annee_debut_affi+saut),3:(annee_debut_affi+2*saut),4:(annee_debut_affi+3*saut)}\n",
    "\n",
    "  table = pd.DataFrame(index=[str(table_annee[1]),str(table_annee[2]),str(table_annee[3]), str(table_annee[4])], columns=['1 enfant', '2 enfants','3 enfants'])\n",
    "\n",
    "  for i in range(0,4):\n",
    "    j = annee_debut_affi + i*saut\n",
    "    \n",
    "    table.iloc[i,0] = round(prop_enfant.loc[j,1]*100,1)\n",
    "    table.iloc[i,1] = round(prop_enfant.loc[j,2]*100,1)\n",
    "    table.iloc[i,2] = round(prop_enfant.loc[j,3]*100,1)\n",
    "else:\n",
    "  table = pd.DataFrame(index=[str(annee_debut_affi),str(annee_debut_affi+1),str(annee_debut_affi+2), str(annee_debut_affi+3), str(annee_debut_affi+4)], columns=['1 enfant', '2 enfants','3 enfants'])\n",
    "\n",
    "  for i in range(0,(len(pop))):\n",
    "    j = annee_debut_affi + i\n",
    "\n",
    "    table.iloc[i,0] = round(prop_enfant.loc[j,1]*100,1)\n",
    "    table.iloc[i,1] = round(prop_enfant.loc[j,2]*100,1)\n",
    "    table.iloc[i,2] = round(prop_enfant.loc[j,3]*100,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HVdlaZYkNRA_"
   },
   "source": [
    "Affichage tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "NpaVBk_TNRA_",
    "outputId": "77c12b86-222a-4b8b-bae9-1601bb36e225"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1 enfant</th>\n",
       "      <th>2 enfants</th>\n",
       "      <th>3 enfants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>15</td>\n",
       "      <td>17.3</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>17.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>12.9</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>10.9</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     1 enfant 2 enfants 3 enfants\n",
       "2020       15      17.3       4.6\n",
       "2030     17.5      11.2       2.8\n",
       "2040     12.9       8.6       1.8\n",
       "2050     10.9       7.4       1.7"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oncJWcF2yLPJ"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Exemple - utilisation SIMGEN",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
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