diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..66380f57d4b749bc72be3cae0619790bcdbb6b75 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,1424 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analysis of the incidence of chickenpox"
+   ]
+  },
+  {
+   "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": [
+    "The data on the incidence of chickenpox illness are available from the Web site of the [Réseau Sentinelles](http://www.sentiweb.fr/). We download them as a file in CSV format, in which each line corresponds to a week in the observation period."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is preferable to first make a copy of the data, and then use that copy in the computational document. To ensure traceability of the data, the computational document must nevertheless contain the URL from which the data was obtained (which we have above). A reader can then download the data again and compare with the version used in the analysis. When publishing a computational document, the copy of the data is published as well, after verifying that republishing the data is legally possible. Below we make the local file with the data if it doesn't yet exist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>202519</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4799</td>\n",
+       "      <td>1358</td>\n",
+       "      <td>8240</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202518</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4795</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>7090</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202517</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6246</td>\n",
+       "      <td>3424</td>\n",
+       "      <td>9068</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202516</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6151</td>\n",
+       "      <td>3193</td>\n",
+       "      <td>9109</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202515</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5557</td>\n",
+       "      <td>3262</td>\n",
+       "      <td>7852</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>202514</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4984</td>\n",
+       "      <td>2858</td>\n",
+       "      <td>7110</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>202513</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5964</td>\n",
+       "      <td>3608</td>\n",
+       "      <td>8320</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>202512</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3855</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>5715</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>8</th>\n",
+       "      <td>202511</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5878</td>\n",
+       "      <td>2747</td>\n",
+       "      <td>9009</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>9</th>\n",
+       "      <td>202510</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2921</td>\n",
+       "      <td>1421</td>\n",
+       "      <td>4421</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>10</th>\n",
+       "      <td>202509</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3381</td>\n",
+       "      <td>1468</td>\n",
+       "      <td>5294</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>202508</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2835</td>\n",
+       "      <td>1286</td>\n",
+       "      <td>4384</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>12</th>\n",
+       "      <td>202507</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4502</td>\n",
+       "      <td>2382</td>\n",
+       "      <td>6622</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>202506</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3455</td>\n",
+       "      <td>1958</td>\n",
+       "      <td>4952</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>202505</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2087</td>\n",
+       "      <td>1056</td>\n",
+       "      <td>3118</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>15</th>\n",
+       "      <td>202504</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6895</td>\n",
+       "      <td>4466</td>\n",
+       "      <td>9324</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202503</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2462</td>\n",
+       "      <td>1161</td>\n",
+       "      <td>3763</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>17</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>18</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>19</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>20</th>\n",
+       "      <td>202451</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4670</td>\n",
+       "      <td>2239</td>\n",
+       "      <td>7101</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>21</th>\n",
+       "      <td>202450</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7363</td>\n",
+       "      <td>4438</td>\n",
+       "      <td>10288</td>\n",
+       "      <td>11</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>202449</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6077</td>\n",
+       "      <td>3631</td>\n",
+       "      <td>8523</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>202448</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4189</td>\n",
+       "      <td>1454</td>\n",
+       "      <td>6924</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>202447</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1931</td>\n",
+       "      <td>726</td>\n",
+       "      <td>3136</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>25</th>\n",
+       "      <td>202446</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2260</td>\n",
+       "      <td>863</td>\n",
+       "      <td>3657</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>26</th>\n",
+       "      <td>202445</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2713</td>\n",
+       "      <td>1216</td>\n",
+       "      <td>4210</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>27</th>\n",
+       "      <td>202444</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2135</td>\n",
+       "      <td>676</td>\n",
+       "      <td>3594</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>28</th>\n",
+       "      <td>202443</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2124</td>\n",
+       "      <td>641</td>\n",
+       "      <td>3607</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>29</th>\n",
+       "      <td>202442</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2621</td>\n",
+       "      <td>1246</td>\n",
+       "      <td>3996</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>...</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>1767</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>1768</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>1769</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>1770</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>1771</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>1772</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>1773</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>1774</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>1775</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>1776</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>1777</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>1778</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>1779</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>1780</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>1781</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>1782</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>1783</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>1784</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>1785</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>1786</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>1787</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>1788</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>1789</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>1790</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>1791</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>1792</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>1793</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>1794</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>1795</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>1796</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>1797 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202519          7   4799     1358    8240       7           2   \n",
+       "1     202518          7   4795     2500    7090       7           4   \n",
+       "2     202517          7   6246     3424    9068       9           5   \n",
+       "3     202516          7   6151     3193    9109       9           5   \n",
+       "4     202515          7   5557     3262    7852       8           5   \n",
+       "5     202514          7   4984     2858    7110       7           4   \n",
+       "6     202513          7   5964     3608    8320       9           5   \n",
+       "7     202512          7   3855     1995    5715       6           3   \n",
+       "8     202511          7   5878     2747    9009       9           4   \n",
+       "9     202510          7   2921     1421    4421       4           2   \n",
+       "10    202509          7   3381     1468    5294       5           2   \n",
+       "11    202508          7   2835     1286    4384       4           2   \n",
+       "12    202507          7   4502     2382    6622       7           4   \n",
+       "13    202506          7   3455     1958    4952       5           3   \n",
+       "14    202505          7   2087     1056    3118       3           1   \n",
+       "15    202504          7   6895     4466    9324      10           6   \n",
+       "16    202503          7   2462     1161    3763       4           2   \n",
+       "17    202502          7   5966     2757    9175       9           4   \n",
+       "18    202501          7   6059     2451    9667       9           4   \n",
+       "19    202452          7   4356     1776    6936       7           3   \n",
+       "20    202451          7   4670     2239    7101       7           3   \n",
+       "21    202450          7   7363     4438   10288      11           7   \n",
+       "22    202449          7   6077     3631    8523       9           5   \n",
+       "23    202448          7   4189     1454    6924       6           2   \n",
+       "24    202447          7   1931      726    3136       3           1   \n",
+       "25    202446          7   2260      863    3657       3           1   \n",
+       "26    202445          7   2713     1216    4210       4           2   \n",
+       "27    202444          7   2135      676    3594       3           1   \n",
+       "28    202443          7   2124      641    3607       3           1   \n",
+       "29    202442          7   2621     1246    3996       4           2   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1767  199126          7  17608    11304   23912      31          20   \n",
+       "1768  199125          7  16169    10700   21638      28          18   \n",
+       "1769  199124          7  16171    10071   22271      28          17   \n",
+       "1770  199123          7  11947     7671   16223      21          13   \n",
+       "1771  199122          7  15452     9953   20951      27          17   \n",
+       "1772  199121          7  14903     8975   20831      26          16   \n",
+       "1773  199120          7  19053    12742   25364      34          23   \n",
+       "1774  199119          7  16739    11246   22232      29          19   \n",
+       "1775  199118          7  21385    13882   28888      38          25   \n",
+       "1776  199117          7  13462     8877   18047      24          16   \n",
+       "1777  199116          7  14857    10068   19646      26          18   \n",
+       "1778  199115          7  13975     9781   18169      25          18   \n",
+       "1779  199114          7  12265     7684   16846      22          14   \n",
+       "1780  199113          7   9567     6041   13093      17          11   \n",
+       "1781  199112          7  10864     7331   14397      19          13   \n",
+       "1782  199111          7  15574    11184   19964      27          19   \n",
+       "1783  199110          7  16643    11372   21914      29          20   \n",
+       "1784  199109          7  13741     8780   18702      24          15   \n",
+       "1785  199108          7  13289     8813   17765      23          15   \n",
+       "1786  199107          7  12337     8077   16597      22          15   \n",
+       "1787  199106          7  10877     7013   14741      19          12   \n",
+       "1788  199105          7  10442     6544   14340      18          11   \n",
+       "1789  199104          7   7913     4563   11263      14           8   \n",
+       "1790  199103          7  15387    10484   20290      27          18   \n",
+       "1791  199102          7  16277    11046   21508      29          20   \n",
+       "1792  199101          7  15565    10271   20859      27          18   \n",
+       "1793  199052          7  19375    13295   25455      34          23   \n",
+       "1794  199051          7  19080    13807   24353      34          25   \n",
+       "1795  199050          7  11079     6660   15498      20          12   \n",
+       "1796  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0            12        FR   France  \n",
+       "1            10        FR   France  \n",
+       "2            13        FR   France  \n",
+       "3            13        FR   France  \n",
+       "4            11        FR   France  \n",
+       "5            10        FR   France  \n",
+       "6            13        FR   France  \n",
+       "7             9        FR   France  \n",
+       "8            14        FR   France  \n",
+       "9             6        FR   France  \n",
+       "10            8        FR   France  \n",
+       "11            6        FR   France  \n",
+       "12           10        FR   France  \n",
+       "13            7        FR   France  \n",
+       "14            5        FR   France  \n",
+       "15           14        FR   France  \n",
+       "16            6        FR   France  \n",
+       "17           14        FR   France  \n",
+       "18           14        FR   France  \n",
+       "19           11        FR   France  \n",
+       "20           11        FR   France  \n",
+       "21           15        FR   France  \n",
+       "22           13        FR   France  \n",
+       "23           10        FR   France  \n",
+       "24            5        FR   France  \n",
+       "25            5        FR   France  \n",
+       "26            6        FR   France  \n",
+       "27            5        FR   France  \n",
+       "28            5        FR   France  \n",
+       "29            6        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1767         42        FR   France  \n",
+       "1768         38        FR   France  \n",
+       "1769         39        FR   France  \n",
+       "1770         29        FR   France  \n",
+       "1771         37        FR   France  \n",
+       "1772         36        FR   France  \n",
+       "1773         45        FR   France  \n",
+       "1774         39        FR   France  \n",
+       "1775         51        FR   France  \n",
+       "1776         32        FR   France  \n",
+       "1777         34        FR   France  \n",
+       "1778         32        FR   France  \n",
+       "1779         30        FR   France  \n",
+       "1780         23        FR   France  \n",
+       "1781         25        FR   France  \n",
+       "1782         35        FR   France  \n",
+       "1783         38        FR   France  \n",
+       "1784         33        FR   France  \n",
+       "1785         31        FR   France  \n",
+       "1786         29        FR   France  \n",
+       "1787         26        FR   France  \n",
+       "1788         25        FR   France  \n",
+       "1789         20        FR   France  \n",
+       "1790         36        FR   France  \n",
+       "1791         38        FR   France  \n",
+       "1792         36        FR   France  \n",
+       "1793         45        FR   France  \n",
+       "1794         43        FR   France  \n",
+       "1795         28        FR   France  \n",
+       "1796          5        FR   France  \n",
+       "\n",
+       "[1797 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import urllib.request\n",
+    "import os\n",
+    "local_filename = \"local_data.csv\"\n",
+    "\n",
+    "if not os.path.exists(local_filename):\n",
+    "    urllib.request.urlretrieve(data_url, local_filename)\n",
+    "\n",
+    "data = pd.read_csv(local_filename, skiprows=1)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Are there missing data points?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideOutput": true
+   },
+   "source": [
+    "Fortunately no!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our dataset uses an uncommon encoding; the week number is attached\n",
+    "to the year number, leaving the impression of a six-digit integer.\n",
+    "That is how Pandas interprets it.\n",
+    "\n",
+    "A second problem is that Pandas does not know about week numbers.\n",
+    "It needs to be given the dates of the beginning and end of the week.\n",
+    "We use the library `isoweek` for that.\n",
+    "\n",
+    "Since the conversion is a bit lengthy, we write a small Python \n",
+    "function for doing it. Then we apply it to all points in our dataset. \n",
+    "The results go into a new column 'period'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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": [
+    "There are two more small changes to make.\n",
+    "\n",
+    "First, we define the observation periods as the new index of\n",
+    "our dataset. That turns it into a time series, which will be\n",
+    "convenient later on.\n",
+    "\n",
+    "Second, we sort the points chronologically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We check the consistency of the data. Between the end of a period and\n",
+    "the beginning of the next one, the difference should be zero, or very small.\n",
+    "We tolerate an error of one second.\n",
+    "\n",
+    "This is OK except for one pair of consecutive periods between which\n",
+    "a whole week is missing.\n",
+    "\n",
+    "We recognize the dates: it's the week without observations that we\n",
+    "have deleted earlier!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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": [
+    "A first look at the data!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe37a2445f8>"
+      ]
+     },
+     "execution_count": 12,
+     "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": [
+    "A zoom on the last few years shows more clearly that the peaks are situated in winter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3781252e8>"
+      ]
+     },
+     "execution_count": 13,
+     "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": [
+    "## Study of the annual incidence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As required by the exercise choose September 1st as the beginning of each annual period."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1991,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Starting from this list of weeks that contain September 1st, we obtain intervals of approximately one year as the periods between two adjacent weeks in this list. We compute the sums of weekly incidences for all these periods.\n",
+    "\n",
+    "We also check that our periods contain between 51 and 52 weeks, as a safeguard against potential mistakes in our code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_sept_week[:-1],\n",
+    "                        first_sept_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": [
+    "And here are the annual incidences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3780294a8>"
+      ]
+     },
+     "execution_count": 17,
+     "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": [
+    "A sorted list makes it easier to find the highest values (at the end)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020    221186\n",
+       "2023    366227\n",
+       "2021    376290\n",
+       "2024    479258\n",
+       "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",
+       "2022    641397\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": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So the strongest epidemic was in 2009 and the weakest in 2020."
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +1435,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo2/local_data.csv b/module3/exo2/local_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..94c0872d52ac60519432a4c9af936afabdbe2d23
--- /dev/null
+++ b/module3/exo2/local_data.csv
@@ -0,0 +1,1799 @@
+# {"source":"r&#233;seau Sentinelles, INSERM, Sorbonne Universit&#233;, https:\/\/www.sentiweb.fr","meta":{"period":[199049,202519],"geo":["PAY",0],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false,"age_group":false,"span":"all"},"date":"2025-05-14T16:34:31+02:00","bundle":"1747233258"}
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