diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..f5d5803e2bfdc60161622c220b1a3709adf3079e 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,1358 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Incidence of chickenpox in France"
+   ]
+  },
+  {
+   "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 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. Only the complete dataset, starting in 1991 and ending with a recent week, is available for download."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Offline data available.\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>202238</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1759</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3539</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202237</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1735</td>\n",
+       "      <td>494</td>\n",
+       "      <td>2976</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>2</th>\n",
+       "      <td>202236</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1069</td>\n",
+       "      <td>178</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202235</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1581</td>\n",
+       "      <td>400</td>\n",
+       "      <td>2762</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>202234</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2266</td>\n",
+       "      <td>788</td>\n",
+       "      <td>3744</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>5</th>\n",
+       "      <td>202233</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7340</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17399</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>202232</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7801</td>\n",
+       "      <td>4086</td>\n",
+       "      <td>11516</td>\n",
+       "      <td>12</td>\n",
+       "      <td>6</td>\n",
+       "      <td>18</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>202231</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6896</td>\n",
+       "      <td>4170</td>\n",
+       "      <td>9622</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>8</th>\n",
+       "      <td>202230</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9039</td>\n",
+       "      <td>5770</td>\n",
+       "      <td>12308</td>\n",
+       "      <td>14</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>202229</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14851</td>\n",
+       "      <td>10060</td>\n",
+       "      <td>19642</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>10</th>\n",
+       "      <td>202228</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15471</td>\n",
+       "      <td>11028</td>\n",
+       "      <td>19914</td>\n",
+       "      <td>23</td>\n",
+       "      <td>16</td>\n",
+       "      <td>30</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>202227</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21191</td>\n",
+       "      <td>16198</td>\n",
+       "      <td>26184</td>\n",
+       "      <td>32</td>\n",
+       "      <td>24</td>\n",
+       "      <td>40</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>202226</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16854</td>\n",
+       "      <td>12806</td>\n",
+       "      <td>20902</td>\n",
+       "      <td>25</td>\n",
+       "      <td>19</td>\n",
+       "      <td>31</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>202225</td>\n",
+       "      <td>7</td>\n",
+       "      <td>22246</td>\n",
+       "      <td>18011</td>\n",
+       "      <td>26481</td>\n",
+       "      <td>34</td>\n",
+       "      <td>28</td>\n",
+       "      <td>40</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>202224</td>\n",
+       "      <td>7</td>\n",
+       "      <td>22458</td>\n",
+       "      <td>18105</td>\n",
+       "      <td>26811</td>\n",
+       "      <td>34</td>\n",
+       "      <td>27</td>\n",
+       "      <td>41</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>202223</td>\n",
+       "      <td>7</td>\n",
+       "      <td>18772</td>\n",
+       "      <td>14875</td>\n",
+       "      <td>22669</td>\n",
+       "      <td>28</td>\n",
+       "      <td>22</td>\n",
+       "      <td>34</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202222</td>\n",
+       "      <td>7</td>\n",
+       "      <td>18916</td>\n",
+       "      <td>14941</td>\n",
+       "      <td>22891</td>\n",
+       "      <td>29</td>\n",
+       "      <td>23</td>\n",
+       "      <td>35</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>202221</td>\n",
+       "      <td>7</td>\n",
+       "      <td>20310</td>\n",
+       "      <td>16307</td>\n",
+       "      <td>24313</td>\n",
+       "      <td>31</td>\n",
+       "      <td>25</td>\n",
+       "      <td>37</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>202220</td>\n",
+       "      <td>7</td>\n",
+       "      <td>23585</td>\n",
+       "      <td>19004</td>\n",
+       "      <td>28166</td>\n",
+       "      <td>36</td>\n",
+       "      <td>29</td>\n",
+       "      <td>43</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>202219</td>\n",
+       "      <td>7</td>\n",
+       "      <td>18593</td>\n",
+       "      <td>14181</td>\n",
+       "      <td>23005</td>\n",
+       "      <td>28</td>\n",
+       "      <td>21</td>\n",
+       "      <td>35</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>202218</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17851</td>\n",
+       "      <td>13963</td>\n",
+       "      <td>21739</td>\n",
+       "      <td>27</td>\n",
+       "      <td>21</td>\n",
+       "      <td>33</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>202217</td>\n",
+       "      <td>7</td>\n",
+       "      <td>20314</td>\n",
+       "      <td>16001</td>\n",
+       "      <td>24627</td>\n",
+       "      <td>31</td>\n",
+       "      <td>24</td>\n",
+       "      <td>38</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>202216</td>\n",
+       "      <td>7</td>\n",
+       "      <td>19660</td>\n",
+       "      <td>14860</td>\n",
+       "      <td>24460</td>\n",
+       "      <td>30</td>\n",
+       "      <td>23</td>\n",
+       "      <td>37</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>202215</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17799</td>\n",
+       "      <td>13715</td>\n",
+       "      <td>21883</td>\n",
+       "      <td>27</td>\n",
+       "      <td>21</td>\n",
+       "      <td>33</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>202214</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17005</td>\n",
+       "      <td>13162</td>\n",
+       "      <td>20848</td>\n",
+       "      <td>26</td>\n",
+       "      <td>20</td>\n",
+       "      <td>32</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>202213</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15448</td>\n",
+       "      <td>11659</td>\n",
+       "      <td>19237</td>\n",
+       "      <td>23</td>\n",
+       "      <td>17</td>\n",
+       "      <td>29</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>202212</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14702</td>\n",
+       "      <td>10794</td>\n",
+       "      <td>18610</td>\n",
+       "      <td>22</td>\n",
+       "      <td>16</td>\n",
+       "      <td>28</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>202211</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11729</td>\n",
+       "      <td>8347</td>\n",
+       "      <td>15111</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>202210</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13314</td>\n",
+       "      <td>10036</td>\n",
+       "      <td>16592</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15</td>\n",
+       "      <td>25</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>202209</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10485</td>\n",
+       "      <td>7600</td>\n",
+       "      <td>13370</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>...</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>1630</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>1631</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>1632</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>1633</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>1634</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>1635</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>1636</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>1637</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>1638</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>1639</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>1640</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>1641</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>1642</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>1643</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>1644</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>1645</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>1646</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>1647</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>1648</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>1649</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>1650</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>1651</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>1652</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>1653</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>1654</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>1655</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>1656</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>1657</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>1658</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>1659</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>1660 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202238          7   1759        0    3539       3           0   \n",
+       "1     202237          7   1735      494    2976       3           1   \n",
+       "2     202236          7   1069      178    1960       2           1   \n",
+       "3     202235          7   1581      400    2762       2           0   \n",
+       "4     202234          7   2266      788    3744       3           1   \n",
+       "5     202233          7   7340        0   17399      11           0   \n",
+       "6     202232          7   7801     4086   11516      12           6   \n",
+       "7     202231          7   6896     4170    9622      10           6   \n",
+       "8     202230          7   9039     5770   12308      14           9   \n",
+       "9     202229          7  14851    10060   19642      22          15   \n",
+       "10    202228          7  15471    11028   19914      23          16   \n",
+       "11    202227          7  21191    16198   26184      32          24   \n",
+       "12    202226          7  16854    12806   20902      25          19   \n",
+       "13    202225          7  22246    18011   26481      34          28   \n",
+       "14    202224          7  22458    18105   26811      34          27   \n",
+       "15    202223          7  18772    14875   22669      28          22   \n",
+       "16    202222          7  18916    14941   22891      29          23   \n",
+       "17    202221          7  20310    16307   24313      31          25   \n",
+       "18    202220          7  23585    19004   28166      36          29   \n",
+       "19    202219          7  18593    14181   23005      28          21   \n",
+       "20    202218          7  17851    13963   21739      27          21   \n",
+       "21    202217          7  20314    16001   24627      31          24   \n",
+       "22    202216          7  19660    14860   24460      30          23   \n",
+       "23    202215          7  17799    13715   21883      27          21   \n",
+       "24    202214          7  17005    13162   20848      26          20   \n",
+       "25    202213          7  15448    11659   19237      23          17   \n",
+       "26    202212          7  14702    10794   18610      22          16   \n",
+       "27    202211          7  11729     8347   15111      18          13   \n",
+       "28    202210          7  13314    10036   16592      20          15   \n",
+       "29    202209          7  10485     7600   13370      16          12   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1630  199126          7  17608    11304   23912      31          20   \n",
+       "1631  199125          7  16169    10700   21638      28          18   \n",
+       "1632  199124          7  16171    10071   22271      28          17   \n",
+       "1633  199123          7  11947     7671   16223      21          13   \n",
+       "1634  199122          7  15452     9953   20951      27          17   \n",
+       "1635  199121          7  14903     8975   20831      26          16   \n",
+       "1636  199120          7  19053    12742   25364      34          23   \n",
+       "1637  199119          7  16739    11246   22232      29          19   \n",
+       "1638  199118          7  21385    13882   28888      38          25   \n",
+       "1639  199117          7  13462     8877   18047      24          16   \n",
+       "1640  199116          7  14857    10068   19646      26          18   \n",
+       "1641  199115          7  13975     9781   18169      25          18   \n",
+       "1642  199114          7  12265     7684   16846      22          14   \n",
+       "1643  199113          7   9567     6041   13093      17          11   \n",
+       "1644  199112          7  10864     7331   14397      19          13   \n",
+       "1645  199111          7  15574    11184   19964      27          19   \n",
+       "1646  199110          7  16643    11372   21914      29          20   \n",
+       "1647  199109          7  13741     8780   18702      24          15   \n",
+       "1648  199108          7  13289     8813   17765      23          15   \n",
+       "1649  199107          7  12337     8077   16597      22          15   \n",
+       "1650  199106          7  10877     7013   14741      19          12   \n",
+       "1651  199105          7  10442     6544   14340      18          11   \n",
+       "1652  199104          7   7913     4563   11263      14           8   \n",
+       "1653  199103          7  15387    10484   20290      27          18   \n",
+       "1654  199102          7  16277    11046   21508      29          20   \n",
+       "1655  199101          7  15565    10271   20859      27          18   \n",
+       "1656  199052          7  19375    13295   25455      34          23   \n",
+       "1657  199051          7  19080    13807   24353      34          25   \n",
+       "1658  199050          7  11079     6660   15498      20          12   \n",
+       "1659  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0             6        FR   France  \n",
+       "1             5        FR   France  \n",
+       "2             3        FR   France  \n",
+       "3             4        FR   France  \n",
+       "4             5        FR   France  \n",
+       "5            26        FR   France  \n",
+       "6            18        FR   France  \n",
+       "7            14        FR   France  \n",
+       "8            19        FR   France  \n",
+       "9            29        FR   France  \n",
+       "10           30        FR   France  \n",
+       "11           40        FR   France  \n",
+       "12           31        FR   France  \n",
+       "13           40        FR   France  \n",
+       "14           41        FR   France  \n",
+       "15           34        FR   France  \n",
+       "16           35        FR   France  \n",
+       "17           37        FR   France  \n",
+       "18           43        FR   France  \n",
+       "19           35        FR   France  \n",
+       "20           33        FR   France  \n",
+       "21           38        FR   France  \n",
+       "22           37        FR   France  \n",
+       "23           33        FR   France  \n",
+       "24           32        FR   France  \n",
+       "25           29        FR   France  \n",
+       "26           28        FR   France  \n",
+       "27           23        FR   France  \n",
+       "28           25        FR   France  \n",
+       "29           20        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1630         42        FR   France  \n",
+       "1631         38        FR   France  \n",
+       "1632         39        FR   France  \n",
+       "1633         29        FR   France  \n",
+       "1634         37        FR   France  \n",
+       "1635         36        FR   France  \n",
+       "1636         45        FR   France  \n",
+       "1637         39        FR   France  \n",
+       "1638         51        FR   France  \n",
+       "1639         32        FR   France  \n",
+       "1640         34        FR   France  \n",
+       "1641         32        FR   France  \n",
+       "1642         30        FR   France  \n",
+       "1643         23        FR   France  \n",
+       "1644         25        FR   France  \n",
+       "1645         35        FR   France  \n",
+       "1646         38        FR   France  \n",
+       "1647         33        FR   France  \n",
+       "1648         31        FR   France  \n",
+       "1649         29        FR   France  \n",
+       "1650         26        FR   France  \n",
+       "1651         25        FR   France  \n",
+       "1652         20        FR   France  \n",
+       "1653         36        FR   France  \n",
+       "1654         38        FR   France  \n",
+       "1655         36        FR   France  \n",
+       "1656         45        FR   France  \n",
+       "1657         43        FR   France  \n",
+       "1658         28        FR   France  \n",
+       "1659          5        FR   France  \n",
+       "\n",
+       "[1660 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_url = 'https://www.sentiweb.fr/datasets/incidence-PAY-7.csv'\n",
+    "data_file = \"./incidence-PAY-7.csv\"\n",
+    "\n",
+    "import os\n",
+    "from urllib import request\n",
+    "if not os.path.exists(data_file):\n",
+    "    print('Offline data not available: attempt to retrieve database online')\n",
+    "    request.urlretrieve(data_url, data_file)\n",
+    "else:\n",
+    "    print('Offline data available.')\n",
+    "\n",
+    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Are there missing data points? No, the dataset is complete."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]\n",
+    "\n",
+    "data = raw_data"
+   ]
+  },
+  {
+   "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": 4,
+   "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": 5,
+   "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c78e47b70>"
+      ]
+     },
+     "execution_count": 7,
+     "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": [
+    "And a  zoom on the last few years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c76d08828>"
+      ]
+     },
+     "execution_count": 8,
+     "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": [
+    "Since the peaks of the epidemic happen in the first half of the year, we define the reference period for the annual incidence from September 1st of year $N$ to September 1st of year $N+1$.\n",
+    "\n",
+    "Our task is a bit complicated by the fact that a year does not have an integer number of weeks. Therefore we modify our reference period a bit: instead of August 1st, we use the first day of the week containing September 1st.\n",
+    "\n",
+    "A final detail: the dataset starts in week 49 of 1990 and it ends in week 38 of 2022, the first and last peaks are thus incomplete."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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": 10,
+   "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": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c76c99668>"
+      ]
+     },
+     "execution_count": 11,
+     "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": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020    221186\n",
+       "2021    376290\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",
+       "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": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +1369,9 @@
    "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/incidence-PAY-7.csv b/module3/exo2/incidence-PAY-7.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1f5082606a312b11cf45652626e309a3fcbbb7bb
--- /dev/null
+++ b/module3/exo2/incidence-PAY-7.csv
@@ -0,0 +1,1662 @@
+# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[199049,202238],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2022-10-03T11:39:40+02:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
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+202237,7,1735,494,2976,3,1,5,FR,France
+202236,7,1069,178,1960,2,1,3,FR,France
+202235,7,1581,400,2762,2,0,4,FR,France
+202234,7,2266,788,3744,3,1,5,FR,France
+202233,7,7340,0,17399,11,0,26,FR,France
+202232,7,7801,4086,11516,12,6,18,FR,France
+202231,7,6896,4170,9622,10,6,14,FR,France
+202230,7,9039,5770,12308,14,9,19,FR,France
+202229,7,14851,10060,19642,22,15,29,FR,France
+202228,7,15471,11028,19914,23,16,30,FR,France
+202227,7,21191,16198,26184,32,24,40,FR,France
+202226,7,16854,12806,20902,25,19,31,FR,France
+202225,7,22246,18011,26481,34,28,40,FR,France
+202224,7,22458,18105,26811,34,27,41,FR,France
+202223,7,18772,14875,22669,28,22,34,FR,France
+202222,7,18916,14941,22891,29,23,35,FR,France
+202221,7,20310,16307,24313,31,25,37,FR,France
+202220,7,23585,19004,28166,36,29,43,FR,France
+202219,7,18593,14181,23005,28,21,35,FR,France
+202218,7,17851,13963,21739,27,21,33,FR,France
+202217,7,20314,16001,24627,31,24,38,FR,France
+202216,7,19660,14860,24460,30,23,37,FR,France
+202215,7,17799,13715,21883,27,21,33,FR,France
+202214,7,17005,13162,20848,26,20,32,FR,France
+202213,7,15448,11659,19237,23,17,29,FR,France
+202212,7,14702,10794,18610,22,16,28,FR,France
+202211,7,11729,8347,15111,18,13,23,FR,France
+202210,7,13314,10036,16592,20,15,25,FR,France
+202209,7,10485,7600,13370,16,12,20,FR,France
+202208,7,12088,8741,15435,18,13,23,FR,France
+202207,7,14003,10789,17217,21,16,26,FR,France
+202206,7,9798,7048,12548,15,11,19,FR,France
+202205,7,10851,7797,13905,16,11,21,FR,France
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+202151,7,13326,9629,17023,20,14,26,FR,France
+202150,7,14128,10312,17944,21,15,27,FR,France
+202149,7,13674,10369,16979,21,16,26,FR,France
+202148,7,11549,8503,14595,17,12,22,FR,France
+202147,7,11419,8376,14462,17,12,22,FR,France
+202146,7,8216,5724,10708,12,8,16,FR,France
+202145,7,8965,6468,11462,14,10,18,FR,France
+202144,7,8736,5636,11836,13,8,18,FR,France
+202143,7,8145,5164,11126,12,7,17,FR,France
+202142,7,9443,6037,12849,14,9,19,FR,France
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+202135,7,2562,1107,4017,4,2,6,FR,France
+202134,7,1429,378,2480,2,0,4,FR,France
+202133,7,3829,1830,5828,6,3,9,FR,France
+202132,7,4108,1895,6321,6,3,9,FR,France
+202131,7,4793,2301,7285,7,3,11,FR,France
+202130,7,7190,4191,10189,11,6,16,FR,France
+202129,7,6800,4109,9491,10,6,14,FR,France
+202128,7,9734,0,21731,15,0,33,FR,France
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+202119,7,6654,4370,8938,10,7,13,FR,France
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+202115,7,11215,7627,14803,17,12,22,FR,France
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+202113,7,9714,6289,13139,15,10,20,FR,France
+202112,7,11520,8415,14625,17,12,22,FR,France
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+202110,7,9056,6452,11660,14,10,18,FR,France
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+202106,7,13401,9810,16992,20,15,25,FR,France
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+202102,7,7795,5430,10160,12,8,16,FR,France
+202101,7,10525,7750,13300,16,12,20,FR,France
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+202043,7,4376,2505,6247,7,4,10,FR,France
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+202041,7,3961,2099,5823,6,3,9,FR,France
+202040,7,2078,675,3481,3,1,5,FR,France
+202039,7,1049,237,1861,2,1,3,FR,France
+202038,7,2251,781,3721,3,1,5,FR,France
+202037,7,1584,405,2763,2,0,4,FR,France
+202036,7,919,100,1738,1,0,2,FR,France
+202035,7,828,0,1694,1,0,2,FR,France
+202034,7,2272,371,4173,3,0,6,FR,France
+202033,7,1284,177,2391,2,0,4,FR,France
+202032,7,2650,689,4611,4,1,7,FR,France
+202031,7,1303,100,2506,2,0,4,FR,France
+202030,7,1385,75,2695,2,0,4,FR,France
+202029,7,841,10,1672,1,0,2,FR,France
+202028,7,728,0,1515,1,0,2,FR,France
+202027,7,986,149,1823,1,0,2,FR,France
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+202023,7,558,1,1115,1,0,2,FR,France
+202022,7,277,0,633,0,0,1,FR,France
+202021,7,602,36,1168,1,0,2,FR,France
+202020,7,824,20,1628,1,0,2,FR,France
+202019,7,310,0,753,0,0,1,FR,France
+202018,7,849,98,1600,1,0,2,FR,France
+202017,7,272,0,658,0,0,1,FR,France
+202016,7,758,78,1438,1,0,2,FR,France
+202015,7,1918,675,3161,3,1,5,FR,France
+202014,7,3879,2227,5531,6,3,9,FR,France
+202013,7,7326,5236,9416,11,8,14,FR,France
+202012,7,8123,5790,10456,12,8,16,FR,France
+202011,7,10198,7568,12828,15,11,19,FR,France
+202010,7,9011,6691,11331,14,10,18,FR,France
+202009,7,13631,10544,16718,21,16,26,FR,France
+202008,7,10424,7708,13140,16,12,20,FR,France
+202007,7,8959,6574,11344,14,10,18,FR,France
+202006,7,9264,6925,11603,14,10,18,FR,France
+202005,7,8505,6314,10696,13,10,16,FR,France
+202004,7,7991,5831,10151,12,9,15,FR,France
+202003,7,5968,4100,7836,9,6,12,FR,France
+202002,7,6534,4530,8538,10,7,13,FR,France
+202001,7,9835,7019,12651,15,11,19,FR,France
+201952,7,7941,5246,10636,12,8,16,FR,France
+201951,7,5823,3675,7971,9,6,12,FR,France
+201950,7,6424,4276,8572,10,7,13,FR,France
+201949,7,6621,4540,8702,10,7,13,FR,France
+201948,7,5542,3383,7701,8,5,11,FR,France
+201947,7,7536,5058,10014,11,7,15,FR,France
+201946,7,2638,1316,3960,4,2,6,FR,France
+201945,7,4492,2615,6369,7,4,10,FR,France
+201944,7,5728,3627,7829,9,6,12,FR,France
+201943,7,4834,2751,6917,7,4,10,FR,France
+201942,7,6279,3989,8569,10,7,13,FR,France
+201941,7,4130,2030,6230,6,3,9,FR,France
+201940,7,4211,2218,6204,6,3,9,FR,France
+201939,7,3137,1310,4964,5,2,8,FR,France
+201938,7,3078,1416,4740,5,2,8,FR,France
+201937,7,970,162,1778,1,0,2,FR,France
+201936,7,1277,263,2291,2,0,4,FR,France
+201935,7,922,0,1857,1,0,2,FR,France
+201934,7,1997,605,3389,3,1,5,FR,France
+201933,7,3279,1103,5455,5,2,8,FR,France
+201932,7,3627,1576,5678,6,3,9,FR,France
+201931,7,7611,4605,10617,12,7,17,FR,France
+201930,7,8875,5629,12121,13,8,18,FR,France
+201929,7,10658,7151,14165,16,11,21,FR,France
+201928,7,11251,7604,14898,17,11,23,FR,France
+201927,7,16602,12349,20855,25,19,31,FR,France
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+201923,7,19264,15027,23501,29,23,35,FR,France
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+201921,7,17572,13140,22004,27,20,34,FR,France
+201920,7,16180,12310,20050,25,19,31,FR,France
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+201917,7,15868,11815,19921,24,18,30,FR,France
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+201915,7,21449,16525,26373,33,26,40,FR,France
+201914,7,16131,12254,20008,25,19,31,FR,France
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+201912,7,14133,10499,17767,21,15,27,FR,France
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+201850,7,11554,8405,14703,18,13,23,FR,France
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+201845,7,7479,4820,10138,11,7,15,FR,France
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+201715,7,16400,11762,21038,25,18,32,FR,France
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+201713,7,21702,16488,26916,33,25,41,FR,France
+201712,7,18534,14161,22907,28,21,35,FR,France
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+201705,7,8885,6064,11706,14,10,18,FR,France
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+201702,7,9034,6097,11971,14,9,19,FR,France
+201701,7,7803,5142,10464,12,8,16,FR,France
+201652,7,7665,4768,10562,12,8,16,FR,France
+201651,7,9916,5966,13866,15,9,21,FR,France
+201650,7,8866,5849,11883,14,9,19,FR,France
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+199844,7,14145,9390,18900,24,16,32,FR,France
+199843,7,16707,11447,21967,29,20,38,FR,France
+199842,7,12475,7806,17144,21,13,29,FR,France
+199841,7,13018,8151,17885,22,14,30,FR,France
+199840,7,7396,3653,11139,13,7,19,FR,France
+199839,7,4467,1896,7038,8,4,12,FR,France
+199838,7,3421,1176,5666,6,2,10,FR,France
+199837,7,4489,2136,6842,8,4,12,FR,France
+199836,7,6355,2914,9796,11,5,17,FR,France
+199835,7,7689,3867,11511,13,6,20,FR,France
+199834,7,7235,3191,11279,12,5,19,FR,France
+199833,7,11431,6018,16844,20,11,29,FR,France
+199832,7,20493,9237,31749,35,16,54,FR,France
+199831,7,15619,9125,22113,27,16,38,FR,France
+199830,7,19360,12617,26103,33,21,45,FR,France
+199829,7,17816,11414,24218,31,20,42,FR,France
+199828,7,20757,14385,27129,36,25,47,FR,France
+199827,7,20714,14671,26757,36,26,46,FR,France
+199826,7,24479,17611,31347,42,30,54,FR,France
+199825,7,24462,17924,31000,42,31,53,FR,France
+199824,7,18928,13945,23911,32,23,41,FR,France
+199823,7,13478,8876,18080,23,15,31,FR,France
+199822,7,13213,8997,17429,23,16,30,FR,France
+199821,7,10741,6627,14855,18,11,25,FR,France
+199820,7,23223,17217,29229,40,30,50,FR,France
+199819,7,16130,10667,21593,28,19,37,FR,France
+199818,7,15802,10374,21230,27,18,36,FR,France
+199817,7,14119,8452,19786,24,14,34,FR,France
+199816,7,11744,7632,15856,20,13,27,FR,France
+199815,7,17422,12372,22472,30,21,39,FR,France
+199814,7,20962,15699,26225,36,27,45,FR,France
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+199733,7,11167,5245,17089,19,9,29,FR,France
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