diff --git a/module3/exo2/exercice_fr.ipynb b/module3/exo2/exercice_fr.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..aaca008a30d79acd04315b9d8f2e2d40b4d3a173 100644
--- a/module3/exo2/exercice_fr.ipynb
+++ b/module3/exo2/exercice_fr.ipynb
@@ -1,8 +1,988 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Incidence du syndrome grippal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import requests\n",
+    "import isoweek"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Les données de l'incidence de la 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 1991 et se termine avec une semaine récente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.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",
+    "| Nom de colonne | Libellé de colonne                                                                                                                |\n",
+    "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
+    "| week           | Semaine calendaire (ISO 8601)                                                                                                     |\n",
+    "| indicator      | Code de l'indicateur de surveillance                                                                                              |\n",
+    "| inc            | Estimation de l'incidence de consultations en nombre de cas                                                                       |\n",
+    "| inc_low        | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation                                                     |\n",
+    "| inc_up         | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation                                                     |\n",
+    "| inc100         | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants)                                   |\n",
+    "| inc100_low     | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| inc100_up      | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| geo_insee      | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/                            |\n",
+    "| geo_name       | Libellé de la zone géographique (ce libellé peut être modifié sans préavis)                                                       |\n",
+    "\n",
+    "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "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>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>202504</td>\n",
+       "      <td>3</td>\n",
+       "      <td>375118</td>\n",
+       "      <td>356288.0</td>\n",
+       "      <td>393948.0</td>\n",
+       "      <td>560</td>\n",
+       "      <td>532.0</td>\n",
+       "      <td>588.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202503</td>\n",
+       "      <td>3</td>\n",
+       "      <td>253215</td>\n",
+       "      <td>239337.0</td>\n",
+       "      <td>267093.0</td>\n",
+       "      <td>378</td>\n",
+       "      <td>357.0</td>\n",
+       "      <td>399.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202502</td>\n",
+       "      <td>3</td>\n",
+       "      <td>257247</td>\n",
+       "      <td>242991.0</td>\n",
+       "      <td>271503.0</td>\n",
+       "      <td>384</td>\n",
+       "      <td>363.0</td>\n",
+       "      <td>405.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202501</td>\n",
+       "      <td>3</td>\n",
+       "      <td>231549</td>\n",
+       "      <td>214627.0</td>\n",
+       "      <td>248471.0</td>\n",
+       "      <td>345</td>\n",
+       "      <td>320.0</td>\n",
+       "      <td>370.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202452</td>\n",
+       "      <td>3</td>\n",
+       "      <td>201726</td>\n",
+       "      <td>185870.0</td>\n",
+       "      <td>217582.0</td>\n",
+       "      <td>302</td>\n",
+       "      <td>278.0</td>\n",
+       "      <td>326.0</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>2095</th>\n",
+       "      <td>198448</td>\n",
+       "      <td>3</td>\n",
+       "      <td>78620</td>\n",
+       "      <td>60634.0</td>\n",
+       "      <td>96606.0</td>\n",
+       "      <td>143</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2096</th>\n",
+       "      <td>198447</td>\n",
+       "      <td>3</td>\n",
+       "      <td>72029</td>\n",
+       "      <td>54274.0</td>\n",
+       "      <td>89784.0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2097</th>\n",
+       "      <td>198446</td>\n",
+       "      <td>3</td>\n",
+       "      <td>87330</td>\n",
+       "      <td>67686.0</td>\n",
+       "      <td>106974.0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>123.0</td>\n",
+       "      <td>195.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2098</th>\n",
+       "      <td>198445</td>\n",
+       "      <td>3</td>\n",
+       "      <td>135223</td>\n",
+       "      <td>101414.0</td>\n",
+       "      <td>169032.0</td>\n",
+       "      <td>246</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>308.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2099</th>\n",
+       "      <td>198444</td>\n",
+       "      <td>3</td>\n",
+       "      <td>68422</td>\n",
+       "      <td>20056.0</td>\n",
+       "      <td>116788.0</td>\n",
+       "      <td>125</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2100 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator     inc   inc_low    inc_up inc100  inc100_low  \\\n",
+       "0     202504          3  375118  356288.0  393948.0    560       532.0   \n",
+       "1     202503          3  253215  239337.0  267093.0    378       357.0   \n",
+       "2     202502          3  257247  242991.0  271503.0    384       363.0   \n",
+       "3     202501          3  231549  214627.0  248471.0    345       320.0   \n",
+       "4     202452          3  201726  185870.0  217582.0    302       278.0   \n",
+       "...      ...        ...     ...       ...       ...    ...         ...   \n",
+       "2095  198448          3   78620   60634.0   96606.0    143       110.0   \n",
+       "2096  198447          3   72029   54274.0   89784.0    131        99.0   \n",
+       "2097  198446          3   87330   67686.0  106974.0    159       123.0   \n",
+       "2098  198445          3  135223  101414.0  169032.0    246       184.0   \n",
+       "2099  198444          3   68422   20056.0  116788.0    125        37.0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0         588.0        FR   France  \n",
+       "1         399.0        FR   France  \n",
+       "2         405.0        FR   France  \n",
+       "3         370.0        FR   France  \n",
+       "4         326.0        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "2095      176.0        FR   France  \n",
+       "2096      163.0        FR   France  \n",
+       "2097      195.0        FR   France  \n",
+       "2098      308.0        FR   France  \n",
+       "2099      213.0        FR   France  \n",
+       "\n",
+       "[2100 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Modification du code pour utiliser le fichier local contenant les données :\n",
+    "1. Vérifie si une copie locale des données existe déjà\n",
+    "2. Si elle n'existe pas, télécharge les données depuis le Réseau Sentinelles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Données locales déjà existantes.\n"
+     ]
+    },
+    {
+     "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>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>202504</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8673</td>\n",
+       "      <td>5508</td>\n",
+       "      <td>11838</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "      <td>18</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202503</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2471</td>\n",
+       "      <td>1167</td>\n",
+       "      <td>3775</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202502</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5966</td>\n",
+       "      <td>2757</td>\n",
+       "      <td>9175</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202501</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6059</td>\n",
+       "      <td>2451</td>\n",
+       "      <td>9667</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202452</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>1776</td>\n",
+       "      <td>6936</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11</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>1777</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>1778</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>1779</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>1780</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>1781</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>1782 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202504          7   8673     5508   11838      13           8   \n",
+       "1     202503          7   2471     1167    3775       4           2   \n",
+       "2     202502          7   5966     2757    9175       9           4   \n",
+       "3     202501          7   6059     2451    9667       9           4   \n",
+       "4     202452          7   4356     1776    6936       7           3   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1777  199101          7  15565    10271   20859      27          18   \n",
+       "1778  199052          7  19375    13295   25455      34          23   \n",
+       "1779  199051          7  19080    13807   24353      34          25   \n",
+       "1780  199050          7  11079     6660   15498      20          12   \n",
+       "1781  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0            18        FR   France  \n",
+       "1             6        FR   France  \n",
+       "2            14        FR   France  \n",
+       "3            14        FR   France  \n",
+       "4            11        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1777         36        FR   France  \n",
+       "1778         45        FR   France  \n",
+       "1779         43        FR   France  \n",
+       "1780         28        FR   France  \n",
+       "1781          5        FR   France  \n",
+       "\n",
+       "[1782 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file_path = r\"C:\\Users\\33612\\Downloads\\inc-7-PAY.csv\"\n",
+    "\n",
+    "if not os.path.exists(file_path):\n",
+    "    response = requests.get(data_url)\n",
+    "    with open(file_path, \"wb\") as file:\n",
+    "        file.write(response.content)\n",
+    "    print(\"Téléchargement des données et sauvegarde locale.\")\n",
+    "else:\n",
+    "    print(\"Données locales déjà existantes.\")\n",
+    "\n",
+    "raw_data = pd.read_csv(file_path, encoding = 'iso-8859-1', 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": 11,
+   "metadata": {},
+   "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>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",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pas besoin de modifier les données comme dans le cas du syndrome grippal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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": 29,
+   "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": 30,
+   "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": 31,
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "Un premier regard sur les données !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='period'>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc_up'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='period'>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc_up'][-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 hiver, à cheval\n",
+    "entre deux années civiles, nous définissons la période de référence\n",
+    "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n",
+    "1er août 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 août de chaque année, nous utilisons le\n",
+    "premier jour de la semaine qui contient le 1er août.\n",
+    "\n",
+    "Comme l'incidence de syndrome grippal est très faible en été, cette\n",
+    "modification ne risque pas de fausser nos conclusions.\n",
+    "\n",
+    "Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
+    "rend la première année incomplète. Nous commençons donc l'analyse en 1985."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
+    "                     for y in range(1991,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
+    "\n",
+    "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_august_week[:-1],\n",
+    "                        first_august_week[1:]):\n",
+    "    one_year = sorted_data['inc_up'][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": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "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": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020     315551\n",
+       "2021     498218\n",
+       "2023     529684\n",
+       "2024     635489\n",
+       "2018     711241\n",
+       "1996     726373\n",
+       "2017     748586\n",
+       "2019     750000\n",
+       "2002     785909\n",
+       "2022     808973\n",
+       "2015     818594\n",
+       "1995     823747\n",
+       "1993     830385\n",
+       "2000     866282\n",
+       "1994     880108\n",
+       "2011     881779\n",
+       "2012     884146\n",
+       "1997     884271\n",
+       "1998     892351\n",
+       "2014     924636\n",
+       "2001     945023\n",
+       "2006     949742\n",
+       "2005     962511\n",
+       "2013     972671\n",
+       "2007     988668\n",
+       "2016    1008333\n",
+       "1999    1055734\n",
+       "2004    1069991\n",
+       "2008    1070668\n",
+       "1992    1084008\n",
+       "2009    1135565\n",
+       "2010    1155636\n",
+       "2003    1159039\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.hist(xrot=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -16,10 +996,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 1
 }
-