{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incidence de la varicelle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données de l'incidence du syndrome de varicelle sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = 'https://www.sentiweb.fr/datasets/incidence-PAY-7.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
    "\n",
    "```json\n",
    "{\n",
    "\t\"profile\": \"tabular-data-resource\",\n",
    "\t\"name\": \"sentiweb-incidence-{$id}\",\n",
    "\t\"path\": \"http://www.sentiweb.fr/datasets/{$file}\",\n",
    "\t\"title\": \"Sentiweb Incidence Data file\",\n",
    "\t\"description\": \"\",\n",
    "\t\"format\": \"csv\",\n",
    "\t\"mediatype\": \"text/csv\",\n",
    "\t\"encoding\": \"iso-8859-1\",\n",
    "\t\"schema\": {\n",
    "\t\t\"fields\": [\n",
    "\n",
    "\t\t\t{\n",
    "\t\t\t\t\"name\": \"week\",\n",
    "\t\t\t\t\"type\": \"integer\",\n",
    "\t\t\t\t\"description\": \"ISO8601 Yearweek number as numeric (year*100 + week nubmer)\"\n",
    "\t\t\t},\n",
    "\t\t\t{\n",
    "\t\t\t\t\"name\": \"geo_insee\",\n",
    "\t\t\t\t\"type\": \"string\",\n",
    "\t\t\t\t\"title\": \"Geographic area\",\n",
    "\t\t\t\t\"description\": \"Identifier of the geographic area, from INSEE https://www.insee.fr\"\n",
    "\t\t\t},\n",
    "\t\t\t{\n",
    "\t\t\t\t\"name\": \"geo_name\",\n",
    "\t\t\t\t\"type\": \"string\",\n",
    "\t\t\t\t\"title\": \"Geographic area label\",\n",
    "\t\t\t\t\"description\": \"Geographic label of the area, corresponding to INSEE code. This label is not an id and is only provided for human reading\"\n",
    "\t\t\t},\n",
    "\t\t\t{\n",
    "\t\t\t\t\"name\": \"indicator\",\n",
    "\t\t\t\t\"type\": \"integer\",\n",
    "\t\t\t\t\"title\": \"Indicator id\",\n",
    "\t\t\t\t\"description\": \"Unique identifier of the indicator, see metadata document https://www.sentiweb.fr/meta.json\"\n",
    "\t\t\t},\n",
    "\t\t\t{\n",
    "\t\t\t\t\"name\": \"inc\",\n",
    "\t\t\t\t\"type\": \"integer\",\n",
    "\t\t\t\t\"title\": \"Estimated incidence\",\n",
    "\t\t\t\t\"description\": \"Estimated incidence value for the time step, in the geographic level\"\n",
    "\t\t\t},\n",
    "\t\t\t{\n",
    "\t\t\t\t\"name\": \"inc_low\",\n",
    "\t\t\t\t\"type\": \"integer\",\n",
    "\t\t\t\t\"title\": \"Lower bound of Estimated incidence 95% CI\",\n",
    "\t\t\t\t\"description\": \"Lower bound of the estimated incidence 95% Confidence Interval\"\n",
    "\t\t\t},\n",
    "\t\t\t{\n",
    "\t\t\t\t\"name\": \"inc_up\",\n",
    "\t\t\t\t\"type\": \"integer\",\n",
    "\t\t\t\t\"title\": \"Upper bound of Estimated incidence 95% CI\",\n",
    "\t\t\t\t\"description\": \"Upper bound of the estimated incidence 95% Confidence Interval\"\n",
    "\t\t\t},\n",
    "\t\t\t{\n",
    "\t\t\t\t\"name\": \"inc100\",\n",
    "\t\t\t\t\"type\": \"integer\",\n",
    "\t\t\t\t\"title\": \"Estimated rate incidence\",\n",
    "\t\t\t\t\"description\": \"Estimated rate incidence per 100,000 inhabitants\"\n",
    "\t\t\t},\n",
    "\t\t\t{\n",
    "\t\t\t\t\"name\": \"inc100_low\",\n",
    "\t\t\t\t\"type\": \"integer\",\n",
    "\t\t\t\t\"title\": \"Lower bound of estimated rate incidence 95% CI\",\n",
    "\t\t\t\t\"description\": \"Lower bound of the estimated incidence 95% Confidence Interval\"\n",
    "\t\t\t},\n",
    "\t\t\t{\n",
    "\t\t\t\t\"name\": \"inc100_up\",\n",
    "\t\t\t\t\"type\": \"integer\",\n",
    "\t\t\t\t\"title\": \"Upper bound of rate incidence 95% CI\",\n",
    "\t\t\t\t\"description\": \"Upper bound of the estimated rate incidence 95% Confidence Interval\"\n",
    "\t\t\t}\n",
    "\n",
    "\t\t],\n",
    "\t\t\"primaryKey\": [\n",
    "\n",
    "\t\t\t\"week\",\n",
    "\t\t\t\"indicator\",\n",
    "\t\t\t\"geo_insee\"\n",
    "\n",
    "\t\t],\n",
    "\n",
    "\t\t\"missingValues\": [\"-\"]\n",
    "\t},\n",
    "\t\"dialect\": {\n",
    "\t\t\"csvddfVersion\": \"1.0\",\n",
    "\t\t\"delimiter\": \",\",\n",
    "\t\t\"doubleQuote\": false,\n",
    "\t\t\"lineTerminator\": \"\\r\\n\",\n",
    "\t\t\"quoteChar\": \"\\\"\",\n",
    "\t\t\"skipInitialSpace\": true,\n",
    "\t\t\"header\": true,\n",
    "\n",
    "\t\t\"commentChar\": \"#\"\n",
    "\t}\n",
    "}\n",
    "```\n",
    "\n",
    "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save source data so that even if URL is not available, we still have a copy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File 'varicelle_data.csv' does not exist, downloading...\n",
      "File 'varicelle_data.csv' downloaded on 2020-08-22.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "from datetime import date\n",
    "\n",
    "filename = 'varicelle_data.csv'\n",
    "\n",
    "if os.path.isfile(filename):\n",
    "    print(\"File '{}' exists\".format(filename))\n",
    "else:\n",
    "    print(\"File '{}' does not exist, downloading...\".format(filename))\n",
    "    urllib.request.urlretrieve(data_url, filename)\n",
    "    download_time = date.today()\n",
    "    print(\"File '{}' downloaded on {}.\".format(filename, download_time))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202033</td>\n",
       "      <td>7</td>\n",
       "      <td>888</td>\n",
       "      <td>0</td>\n",
       "      <td>1841</td>\n",
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       "      <th>1</th>\n",
       "      <td>202032</td>\n",
       "      <td>7</td>\n",
       "      <td>2559</td>\n",
       "      <td>624</td>\n",
       "      <td>4494</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202031</td>\n",
       "      <td>7</td>\n",
       "      <td>1303</td>\n",
       "      <td>100</td>\n",
       "      <td>2506</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202030</td>\n",
       "      <td>7</td>\n",
       "      <td>1385</td>\n",
       "      <td>75</td>\n",
       "      <td>2695</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202029</td>\n",
       "      <td>7</td>\n",
       "      <td>841</td>\n",
       "      <td>10</td>\n",
       "      <td>1672</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>202028</td>\n",
       "      <td>7</td>\n",
       "      <td>728</td>\n",
       "      <td>0</td>\n",
       "      <td>1515</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>202027</td>\n",
       "      <td>7</td>\n",
       "      <td>986</td>\n",
       "      <td>149</td>\n",
       "      <td>1823</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>202026</td>\n",
       "      <td>7</td>\n",
       "      <td>694</td>\n",
       "      <td>0</td>\n",
       "      <td>1454</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>202025</td>\n",
       "      <td>7</td>\n",
       "      <td>228</td>\n",
       "      <td>0</td>\n",
       "      <td>597</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>202024</td>\n",
       "      <td>7</td>\n",
       "      <td>388</td>\n",
       "      <td>0</td>\n",
       "      <td>959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>202023</td>\n",
       "      <td>7</td>\n",
       "      <td>558</td>\n",
       "      <td>1</td>\n",
       "      <td>1115</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>202022</td>\n",
       "      <td>7</td>\n",
       "      <td>277</td>\n",
       "      <td>0</td>\n",
       "      <td>633</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>202021</td>\n",
       "      <td>7</td>\n",
       "      <td>602</td>\n",
       "      <td>36</td>\n",
       "      <td>1168</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>202020</td>\n",
       "      <td>7</td>\n",
       "      <td>824</td>\n",
       "      <td>20</td>\n",
       "      <td>1628</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>202019</td>\n",
       "      <td>7</td>\n",
       "      <td>310</td>\n",
       "      <td>0</td>\n",
       "      <td>753</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>202018</td>\n",
       "      <td>7</td>\n",
       "      <td>849</td>\n",
       "      <td>98</td>\n",
       "      <td>1600</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>202017</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>658</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>202016</td>\n",
       "      <td>7</td>\n",
       "      <td>758</td>\n",
       "      <td>78</td>\n",
       "      <td>1438</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>202015</td>\n",
       "      <td>7</td>\n",
       "      <td>1918</td>\n",
       "      <td>675</td>\n",
       "      <td>3161</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>202014</td>\n",
       "      <td>7</td>\n",
       "      <td>3879</td>\n",
       "      <td>2227</td>\n",
       "      <td>5531</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>202013</td>\n",
       "      <td>7</td>\n",
       "      <td>7326</td>\n",
       "      <td>5236</td>\n",
       "      <td>9416</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>202012</td>\n",
       "      <td>7</td>\n",
       "      <td>8123</td>\n",
       "      <td>5790</td>\n",
       "      <td>10456</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>202011</td>\n",
       "      <td>7</td>\n",
       "      <td>10198</td>\n",
       "      <td>7568</td>\n",
       "      <td>12828</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>202010</td>\n",
       "      <td>7</td>\n",
       "      <td>9011</td>\n",
       "      <td>6691</td>\n",
       "      <td>11331</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>202009</td>\n",
       "      <td>7</td>\n",
       "      <td>13631</td>\n",
       "      <td>10544</td>\n",
       "      <td>16718</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>202008</td>\n",
       "      <td>7</td>\n",
       "      <td>10424</td>\n",
       "      <td>7708</td>\n",
       "      <td>13140</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>202007</td>\n",
       "      <td>7</td>\n",
       "      <td>8959</td>\n",
       "      <td>6574</td>\n",
       "      <td>11344</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>202006</td>\n",
       "      <td>7</td>\n",
       "      <td>9264</td>\n",
       "      <td>6925</td>\n",
       "      <td>11603</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>202005</td>\n",
       "      <td>7</td>\n",
       "      <td>8505</td>\n",
       "      <td>6314</td>\n",
       "      <td>10696</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>202004</td>\n",
       "      <td>7</td>\n",
       "      <td>7991</td>\n",
       "      <td>5831</td>\n",
       "      <td>10151</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1520</th>\n",
       "      <td>199126</td>\n",
       "      <td>7</td>\n",
       "      <td>17608</td>\n",
       "      <td>11304</td>\n",
       "      <td>23912</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>42</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1521</th>\n",
       "      <td>199125</td>\n",
       "      <td>7</td>\n",
       "      <td>16169</td>\n",
       "      <td>10700</td>\n",
       "      <td>21638</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1522</th>\n",
       "      <td>199124</td>\n",
       "      <td>7</td>\n",
       "      <td>16171</td>\n",
       "      <td>10071</td>\n",
       "      <td>22271</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>39</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>199123</td>\n",
       "      <td>7</td>\n",
       "      <td>11947</td>\n",
       "      <td>7671</td>\n",
       "      <td>16223</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1524</th>\n",
       "      <td>199122</td>\n",
       "      <td>7</td>\n",
       "      <td>15452</td>\n",
       "      <td>9953</td>\n",
       "      <td>20951</td>\n",
       "      <td>27</td>\n",
       "      <td>17</td>\n",
       "      <td>37</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>199121</td>\n",
       "      <td>7</td>\n",
       "      <td>14903</td>\n",
       "      <td>8975</td>\n",
       "      <td>20831</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>199120</td>\n",
       "      <td>7</td>\n",
       "      <td>19053</td>\n",
       "      <td>12742</td>\n",
       "      <td>25364</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1527</th>\n",
       "      <td>199119</td>\n",
       "      <td>7</td>\n",
       "      <td>16739</td>\n",
       "      <td>11246</td>\n",
       "      <td>22232</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>39</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528</th>\n",
       "      <td>199118</td>\n",
       "      <td>7</td>\n",
       "      <td>21385</td>\n",
       "      <td>13882</td>\n",
       "      <td>28888</td>\n",
       "      <td>38</td>\n",
       "      <td>25</td>\n",
       "      <td>51</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>199117</td>\n",
       "      <td>7</td>\n",
       "      <td>13462</td>\n",
       "      <td>8877</td>\n",
       "      <td>18047</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1530</th>\n",
       "      <td>199116</td>\n",
       "      <td>7</td>\n",
       "      <td>14857</td>\n",
       "      <td>10068</td>\n",
       "      <td>19646</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>34</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>199115</td>\n",
       "      <td>7</td>\n",
       "      <td>13975</td>\n",
       "      <td>9781</td>\n",
       "      <td>18169</td>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>32</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>199114</td>\n",
       "      <td>7</td>\n",
       "      <td>12265</td>\n",
       "      <td>7684</td>\n",
       "      <td>16846</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>30</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>199113</td>\n",
       "      <td>7</td>\n",
       "      <td>9567</td>\n",
       "      <td>6041</td>\n",
       "      <td>13093</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>199112</td>\n",
       "      <td>7</td>\n",
       "      <td>10864</td>\n",
       "      <td>7331</td>\n",
       "      <td>14397</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>199111</td>\n",
       "      <td>7</td>\n",
       "      <td>15574</td>\n",
       "      <td>11184</td>\n",
       "      <td>19964</td>\n",
       "      <td>27</td>\n",
       "      <td>19</td>\n",
       "      <td>35</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>199110</td>\n",
       "      <td>7</td>\n",
       "      <td>16643</td>\n",
       "      <td>11372</td>\n",
       "      <td>21914</td>\n",
       "      <td>29</td>\n",
       "      <td>20</td>\n",
       "      <td>38</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>199109</td>\n",
       "      <td>7</td>\n",
       "      <td>13741</td>\n",
       "      <td>8780</td>\n",
       "      <td>18702</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>33</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>199108</td>\n",
       "      <td>7</td>\n",
       "      <td>13289</td>\n",
       "      <td>8813</td>\n",
       "      <td>17765</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1539</th>\n",
       "      <td>199107</td>\n",
       "      <td>7</td>\n",
       "      <td>12337</td>\n",
       "      <td>8077</td>\n",
       "      <td>16597</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540</th>\n",
       "      <td>199106</td>\n",
       "      <td>7</td>\n",
       "      <td>10877</td>\n",
       "      <td>7013</td>\n",
       "      <td>14741</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>199105</td>\n",
       "      <td>7</td>\n",
       "      <td>10442</td>\n",
       "      <td>6544</td>\n",
       "      <td>14340</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1542</th>\n",
       "      <td>199104</td>\n",
       "      <td>7</td>\n",
       "      <td>7913</td>\n",
       "      <td>4563</td>\n",
       "      <td>11263</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>199103</td>\n",
       "      <td>7</td>\n",
       "      <td>15387</td>\n",
       "      <td>10484</td>\n",
       "      <td>20290</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>199102</td>\n",
       "      <td>7</td>\n",
       "      <td>16277</td>\n",
       "      <td>11046</td>\n",
       "      <td>21508</td>\n",
       "      <td>29</td>\n",
       "      <td>20</td>\n",
       "      <td>38</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>199101</td>\n",
       "      <td>7</td>\n",
       "      <td>15565</td>\n",
       "      <td>10271</td>\n",
       "      <td>20859</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1546</th>\n",
       "      <td>199052</td>\n",
       "      <td>7</td>\n",
       "      <td>19375</td>\n",
       "      <td>13295</td>\n",
       "      <td>25455</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>199051</td>\n",
       "      <td>7</td>\n",
       "      <td>19080</td>\n",
       "      <td>13807</td>\n",
       "      <td>24353</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>43</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548</th>\n",
       "      <td>199050</td>\n",
       "      <td>7</td>\n",
       "      <td>11079</td>\n",
       "      <td>6660</td>\n",
       "      <td>15498</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1549</th>\n",
       "      <td>199049</td>\n",
       "      <td>7</td>\n",
       "      <td>1143</td>\n",
       "      <td>0</td>\n",
       "      <td>2610</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1550 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
       "0     202033          7    888        0    1841       1           0   \n",
       "1     202032          7   2559      624    4494       4           1   \n",
       "2     202031          7   1303      100    2506       2           0   \n",
       "3     202030          7   1385       75    2695       2           0   \n",
       "4     202029          7    841       10    1672       1           0   \n",
       "5     202028          7    728        0    1515       1           0   \n",
       "6     202027          7    986      149    1823       1           0   \n",
       "7     202026          7    694        0    1454       1           0   \n",
       "8     202025          7    228        0     597       0           0   \n",
       "9     202024          7    388        0     959       1           0   \n",
       "10    202023          7    558        1    1115       1           0   \n",
       "11    202022          7    277        0     633       0           0   \n",
       "12    202021          7    602       36    1168       1           0   \n",
       "13    202020          7    824       20    1628       1           0   \n",
       "14    202019          7    310        0     753       0           0   \n",
       "15    202018          7    849       98    1600       1           0   \n",
       "16    202017          7    272        0     658       0           0   \n",
       "17    202016          7    758       78    1438       1           0   \n",
       "18    202015          7   1918      675    3161       3           1   \n",
       "19    202014          7   3879     2227    5531       6           3   \n",
       "20    202013          7   7326     5236    9416      11           8   \n",
       "21    202012          7   8123     5790   10456      12           8   \n",
       "22    202011          7  10198     7568   12828      15          11   \n",
       "23    202010          7   9011     6691   11331      14          10   \n",
       "24    202009          7  13631    10544   16718      21          16   \n",
       "25    202008          7  10424     7708   13140      16          12   \n",
       "26    202007          7   8959     6574   11344      14          10   \n",
       "27    202006          7   9264     6925   11603      14          10   \n",
       "28    202005          7   8505     6314   10696      13          10   \n",
       "29    202004          7   7991     5831   10151      12           9   \n",
       "...      ...        ...    ...      ...     ...     ...         ...   \n",
       "1520  199126          7  17608    11304   23912      31          20   \n",
       "1521  199125          7  16169    10700   21638      28          18   \n",
       "1522  199124          7  16171    10071   22271      28          17   \n",
       "1523  199123          7  11947     7671   16223      21          13   \n",
       "1524  199122          7  15452     9953   20951      27          17   \n",
       "1525  199121          7  14903     8975   20831      26          16   \n",
       "1526  199120          7  19053    12742   25364      34          23   \n",
       "1527  199119          7  16739    11246   22232      29          19   \n",
       "1528  199118          7  21385    13882   28888      38          25   \n",
       "1529  199117          7  13462     8877   18047      24          16   \n",
       "1530  199116          7  14857    10068   19646      26          18   \n",
       "1531  199115          7  13975     9781   18169      25          18   \n",
       "1532  199114          7  12265     7684   16846      22          14   \n",
       "1533  199113          7   9567     6041   13093      17          11   \n",
       "1534  199112          7  10864     7331   14397      19          13   \n",
       "1535  199111          7  15574    11184   19964      27          19   \n",
       "1536  199110          7  16643    11372   21914      29          20   \n",
       "1537  199109          7  13741     8780   18702      24          15   \n",
       "1538  199108          7  13289     8813   17765      23          15   \n",
       "1539  199107          7  12337     8077   16597      22          15   \n",
       "1540  199106          7  10877     7013   14741      19          12   \n",
       "1541  199105          7  10442     6544   14340      18          11   \n",
       "1542  199104          7   7913     4563   11263      14           8   \n",
       "1543  199103          7  15387    10484   20290      27          18   \n",
       "1544  199102          7  16277    11046   21508      29          20   \n",
       "1545  199101          7  15565    10271   20859      27          18   \n",
       "1546  199052          7  19375    13295   25455      34          23   \n",
       "1547  199051          7  19080    13807   24353      34          25   \n",
       "1548  199050          7  11079     6660   15498      20          12   \n",
       "1549  199049          7   1143        0    2610       2           0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0             2        FR   France  \n",
       "1             7        FR   France  \n",
       "2             4        FR   France  \n",
       "3             4        FR   France  \n",
       "4             2        FR   France  \n",
       "5             2        FR   France  \n",
       "6             2        FR   France  \n",
       "7             2        FR   France  \n",
       "8             1        FR   France  \n",
       "9             2        FR   France  \n",
       "10            2        FR   France  \n",
       "11            1        FR   France  \n",
       "12            2        FR   France  \n",
       "13            2        FR   France  \n",
       "14            1        FR   France  \n",
       "15            2        FR   France  \n",
       "16            1        FR   France  \n",
       "17            2        FR   France  \n",
       "18            5        FR   France  \n",
       "19            9        FR   France  \n",
       "20           14        FR   France  \n",
       "21           16        FR   France  \n",
       "22           19        FR   France  \n",
       "23           18        FR   France  \n",
       "24           26        FR   France  \n",
       "25           20        FR   France  \n",
       "26           18        FR   France  \n",
       "27           18        FR   France  \n",
       "28           16        FR   France  \n",
       "29           15        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1520         42        FR   France  \n",
       "1521         38        FR   France  \n",
       "1522         39        FR   France  \n",
       "1523         29        FR   France  \n",
       "1524         37        FR   France  \n",
       "1525         36        FR   France  \n",
       "1526         45        FR   France  \n",
       "1527         39        FR   France  \n",
       "1528         51        FR   France  \n",
       "1529         32        FR   France  \n",
       "1530         34        FR   France  \n",
       "1531         32        FR   France  \n",
       "1532         30        FR   France  \n",
       "1533         23        FR   France  \n",
       "1534         25        FR   France  \n",
       "1535         35        FR   France  \n",
       "1536         38        FR   France  \n",
       "1537         33        FR   France  \n",
       "1538         31        FR   France  \n",
       "1539         29        FR   France  \n",
       "1540         26        FR   France  \n",
       "1541         25        FR   France  \n",
       "1542         20        FR   France  \n",
       "1543         36        FR   France  \n",
       "1544         38        FR   France  \n",
       "1545         36        FR   France  \n",
       "1546         45        FR   France  \n",
       "1547         43        FR   France  \n",
       "1548         28        FR   France  \n",
       "1549          5        FR   France  \n",
       "\n",
       "[1550 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(filename, skiprows=1)\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y a-t-il des points manquants dans ce jeux de données ? Non"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]\n",
    "data = raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nos données utilisent une convention inhabituelle: le numéro de\n",
    "semaine est collé à l'année, donnant l'impression qu'il s'agit\n",
    "de nombre entier. C'est comme ça que Pandas les interprète.\n",
    "  \n",
    "Un deuxième problème est que Pandas ne comprend pas les numéros de\n",
    "semaine.  Il faut lui fournir les dates de début et de fin de\n",
    "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n",
    "\n",
    "Comme la conversion des semaines est devenu assez complexe, nous\n",
    "écrivons une petite fonction Python pour cela. Ensuite, nous\n",
    "l'appliquons à tous les points de nos donnés. Les résultats vont\n",
    "dans une nouvelle colonne 'period'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_week(year_and_week_int):\n",
    "    year_and_week_str = str(year_and_week_int)\n",
    "    year = int(year_and_week_str[:4])\n",
    "    week = int(year_and_week_str[4:])\n",
    "    w = isoweek.Week(year, week)\n",
    "    return pd.Period(w.day(0), 'W')\n",
    "\n",
    "data['period'] = [convert_week(yw) for yw in data['week']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il restent deux petites modifications à faire.\n",
    "\n",
    "Premièrement, nous définissons les périodes d'observation\n",
    "comme nouvel index de notre jeux de données. Ceci en fait\n",
    "une suite chronologique, ce qui sera pratique par la suite.\n",
    "\n",
    "Deuxièmement, nous trions les points par période, dans\n",
    "le sens chronologique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_data = data.set_index('period').sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous vérifions la cohérence des données. Entre la fin d'une période et\n",
    "le début de la période qui suit, la différence temporelle doit être\n",
    "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n",
    "d'une seconde.\n",
    "\n",
    "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n",
    "entre lesquelles il manque une semaine.\n",
    "\n",
    "Nous reconnaissons ces dates: c'est la semaine sans observations\n",
    "que nous avions supprimées !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "periods = sorted_data.index\n",
    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
    "    delta = p2.to_timestamp() - p1.end_time\n",
    "    if delta > pd.Timedelta('1s'):\n",
    "        print(p1, p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce6bc0d048>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sorted_data['inc'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un zoom sur les dernières années montre mieux la situation des pics en fin d'hiver. Le creux des incidences se trouve en septembre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce699e8a90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sorted_data['inc'][-200:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etude de l'incidence annuelle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Etant donné que le pic de l'épidémie se situe en du début de l'automne à la fin du printemps, à cheval\n",
    "entre deux années civiles, nous définissons la période de référence\n",
    "entre deux minima de l'incidence, du 1er septembre de l'année $N$ au\n",
    "1er septembre de l'année $N+1$.\n",
    "\n",
    "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
    "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
    "de référence: à la place du 1er septembre de chaque année, nous utilisons le\n",
    "premier jour de la semaine qui contient le 1er septembre.\n",
    "\n",
    "Comme l'incidence de syndrome de la varicelle est très faible en été, cette\n",
    "modification ne risque pas de fausser nos conclusions.\n",
    "\n",
    "Encore un petit détail: les données commencent en décembre 1990, ce qui\n",
    "rend la première année incomplète. Nous commençons donc l'analyse en 1991."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
    "                     for y in range(1991,\n",
    "                                    sorted_data.index[-1].year)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_september_week[:-1],\n",
    "                        first_september_week[1:]):\n",
    "    one_year = sorted_data['inc'][week1:week2-1]\n",
    "    assert abs(len(one_year)-52) < 2\n",
    "    yearly_incidence.append(one_year.sum())\n",
    "    year.append(week2.year)\n",
    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici les incidences annuelles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce6995be80>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "yearly_incidence.plot(style='*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2002    516689\n",
       "2018    542312\n",
       "2017    551041\n",
       "1996    564901\n",
       "2019    584066\n",
       "2015    604382\n",
       "2000    617597\n",
       "2001    619041\n",
       "2012    624573\n",
       "2005    628464\n",
       "2006    632833\n",
       "2011    642368\n",
       "1993    643387\n",
       "1995    652478\n",
       "1994    661409\n",
       "1998    677775\n",
       "1997    683434\n",
       "2014    685769\n",
       "2013    698332\n",
       "2007    717352\n",
       "2008    749478\n",
       "1999    756456\n",
       "2003    758363\n",
       "2004    777388\n",
       "2016    782114\n",
       "2010    829911\n",
       "1992    832939\n",
       "2009    842373\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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