From 1850b81b73efef1a1384f0858250774a143f48d2 Mon Sep 17 00:00:00 2001
From: d3ffe1dda057aedb6d37daa14d4dce86
 <d3ffe1dda057aedb6d37daa14d4dce86@app-learninglab.inria.fr>
Date: Thu, 16 Apr 2020 18:59:12 +0000
Subject: [PATCH] Download and read local file

---
 module3/exo1/analyse-syndrome-grippal.ipynb | 869 ++++++++++----------
 module3/exo1/incidence-PAY-3.csv            |   3 +-
 2 files changed, 440 insertions(+), 432 deletions(-)

diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb
index 40b4edc..5075795 100644
--- a/module3/exo1/analyse-syndrome-grippal.ipynb
+++ b/module3/exo1/analyse-syndrome-grippal.ipynb
@@ -16,14 +16,17 @@
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
-    "import isoweek"
+    "import isoweek\n",
+    "import os\n",
+    "import requests"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
+    "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente.\n",
+    "Nous avons préféré utiliser une copie locale des données (disponible dans le fichier incidence-PAY-3.csv) pour éviter les problemes de connexion Internet et s'assurer que les données retournées seront toujours les memes. "
    ]
   },
   {
@@ -32,8 +35,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n",
-    "data_source = \"/home/jovyan/work/module3/exo1/incidence-PAY-3.csv\""
+    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n",
+    "data_file = \"/home/jovyan/work/module3/exo1/incidence-PAY-3.csv\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Verification que le fichier incidence-PAY-3.csv n'existe. Si ce n'est pas le cas, les données vont etre téléchargées et insérées dans le fichier nouvellement crée."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if os.path.exists(data_file) == False :\n",
+    "    cvs_data = requests.get(data_url,allow_redirects=True).content\n",
+    "    open(data_file, \"wb\").write(cvs_data)"
    ]
   },
   {
@@ -60,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -99,7 +120,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>202014</td>\n",
+       "      <td>202015</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>0.0</td>\n",
@@ -112,7 +133,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>202013</td>\n",
+       "      <td>202014</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>0.0</td>\n",
@@ -125,6 +146,19 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
+       "      <td>202013</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
        "      <td>202012</td>\n",
        "      <td>3</td>\n",
        "      <td>8321</td>\n",
@@ -137,7 +171,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>4</th>\n",
        "      <td>202011</td>\n",
        "      <td>3</td>\n",
        "      <td>101704</td>\n",
@@ -150,7 +184,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>5</th>\n",
        "      <td>202010</td>\n",
        "      <td>3</td>\n",
        "      <td>104977</td>\n",
@@ -163,7 +197,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>6</th>\n",
        "      <td>202009</td>\n",
        "      <td>3</td>\n",
        "      <td>110696</td>\n",
@@ -176,7 +210,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>7</th>\n",
        "      <td>202008</td>\n",
        "      <td>3</td>\n",
        "      <td>143753</td>\n",
@@ -189,7 +223,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>8</th>\n",
        "      <td>202007</td>\n",
        "      <td>3</td>\n",
        "      <td>183610</td>\n",
@@ -202,7 +236,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
+       "      <th>9</th>\n",
        "      <td>202006</td>\n",
        "      <td>3</td>\n",
        "      <td>206669</td>\n",
@@ -215,7 +249,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>10</th>\n",
        "      <td>202005</td>\n",
        "      <td>3</td>\n",
        "      <td>187957</td>\n",
@@ -228,7 +262,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>11</th>\n",
        "      <td>202004</td>\n",
        "      <td>3</td>\n",
        "      <td>122331</td>\n",
@@ -241,7 +275,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
+       "      <th>12</th>\n",
        "      <td>202003</td>\n",
        "      <td>3</td>\n",
        "      <td>78413</td>\n",
@@ -254,7 +288,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>13</th>\n",
        "      <td>202002</td>\n",
        "      <td>3</td>\n",
        "      <td>53614</td>\n",
@@ -267,7 +301,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>14</th>\n",
        "      <td>202001</td>\n",
        "      <td>3</td>\n",
        "      <td>36850</td>\n",
@@ -280,7 +314,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>15</th>\n",
        "      <td>201952</td>\n",
        "      <td>3</td>\n",
        "      <td>28135</td>\n",
@@ -293,7 +327,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>16</th>\n",
        "      <td>201951</td>\n",
        "      <td>3</td>\n",
        "      <td>29786</td>\n",
@@ -306,7 +340,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>17</th>\n",
        "      <td>201950</td>\n",
        "      <td>3</td>\n",
        "      <td>34223</td>\n",
@@ -319,7 +353,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
+       "      <th>18</th>\n",
        "      <td>201949</td>\n",
        "      <td>3</td>\n",
        "      <td>25662</td>\n",
@@ -332,7 +366,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>19</th>\n",
        "      <td>201948</td>\n",
        "      <td>3</td>\n",
        "      <td>22367</td>\n",
@@ -345,7 +379,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>20</th>\n",
        "      <td>201947</td>\n",
        "      <td>3</td>\n",
        "      <td>18669</td>\n",
@@ -358,7 +392,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
+       "      <th>21</th>\n",
        "      <td>201946</td>\n",
        "      <td>3</td>\n",
        "      <td>16030</td>\n",
@@ -371,7 +405,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>22</th>\n",
        "      <td>201945</td>\n",
        "      <td>3</td>\n",
        "      <td>10138</td>\n",
@@ -384,7 +418,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>22</th>\n",
+       "      <th>23</th>\n",
        "      <td>201944</td>\n",
        "      <td>3</td>\n",
        "      <td>7822</td>\n",
@@ -397,7 +431,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
+       "      <th>24</th>\n",
        "      <td>201943</td>\n",
        "      <td>3</td>\n",
        "      <td>9487</td>\n",
@@ -410,7 +444,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>24</th>\n",
+       "      <th>25</th>\n",
        "      <td>201942</td>\n",
        "      <td>3</td>\n",
        "      <td>7747</td>\n",
@@ -423,7 +457,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25</th>\n",
+       "      <th>26</th>\n",
        "      <td>201941</td>\n",
        "      <td>3</td>\n",
        "      <td>7122</td>\n",
@@ -436,7 +470,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>27</th>\n",
        "      <td>201940</td>\n",
        "      <td>3</td>\n",
        "      <td>8505</td>\n",
@@ -449,7 +483,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
+       "      <th>28</th>\n",
        "      <td>201939</td>\n",
        "      <td>3</td>\n",
        "      <td>7091</td>\n",
@@ -462,7 +496,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28</th>\n",
+       "      <th>29</th>\n",
        "      <td>201938</td>\n",
        "      <td>3</td>\n",
        "      <td>4897</td>\n",
@@ -475,19 +509,6 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>201937</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3172</td>\n",
-       "      <td>1367.0</td>\n",
-       "      <td>4977.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
@@ -501,7 +522,7 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1819</th>\n",
+       "      <th>1820</th>\n",
        "      <td>198521</td>\n",
        "      <td>3</td>\n",
        "      <td>26096</td>\n",
@@ -514,7 +535,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1820</th>\n",
+       "      <th>1821</th>\n",
        "      <td>198520</td>\n",
        "      <td>3</td>\n",
        "      <td>27896</td>\n",
@@ -527,7 +548,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1821</th>\n",
+       "      <th>1822</th>\n",
        "      <td>198519</td>\n",
        "      <td>3</td>\n",
        "      <td>43154</td>\n",
@@ -540,7 +561,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1822</th>\n",
+       "      <th>1823</th>\n",
        "      <td>198518</td>\n",
        "      <td>3</td>\n",
        "      <td>40555</td>\n",
@@ -553,7 +574,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1823</th>\n",
+       "      <th>1824</th>\n",
        "      <td>198517</td>\n",
        "      <td>3</td>\n",
        "      <td>34053</td>\n",
@@ -566,7 +587,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1824</th>\n",
+       "      <th>1825</th>\n",
        "      <td>198516</td>\n",
        "      <td>3</td>\n",
        "      <td>50362</td>\n",
@@ -579,7 +600,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1825</th>\n",
+       "      <th>1826</th>\n",
        "      <td>198515</td>\n",
        "      <td>3</td>\n",
        "      <td>63881</td>\n",
@@ -592,7 +613,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1826</th>\n",
+       "      <th>1827</th>\n",
        "      <td>198514</td>\n",
        "      <td>3</td>\n",
        "      <td>134545</td>\n",
@@ -605,7 +626,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1827</th>\n",
+       "      <th>1828</th>\n",
        "      <td>198513</td>\n",
        "      <td>3</td>\n",
        "      <td>197206</td>\n",
@@ -618,7 +639,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1828</th>\n",
+       "      <th>1829</th>\n",
        "      <td>198512</td>\n",
        "      <td>3</td>\n",
        "      <td>245240</td>\n",
@@ -631,7 +652,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1829</th>\n",
+       "      <th>1830</th>\n",
        "      <td>198511</td>\n",
        "      <td>3</td>\n",
        "      <td>276205</td>\n",
@@ -644,7 +665,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1830</th>\n",
+       "      <th>1831</th>\n",
        "      <td>198510</td>\n",
        "      <td>3</td>\n",
        "      <td>353231</td>\n",
@@ -657,7 +678,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1831</th>\n",
+       "      <th>1832</th>\n",
        "      <td>198509</td>\n",
        "      <td>3</td>\n",
        "      <td>369895</td>\n",
@@ -670,7 +691,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1832</th>\n",
+       "      <th>1833</th>\n",
        "      <td>198508</td>\n",
        "      <td>3</td>\n",
        "      <td>389886</td>\n",
@@ -683,7 +704,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1833</th>\n",
+       "      <th>1834</th>\n",
        "      <td>198507</td>\n",
        "      <td>3</td>\n",
        "      <td>471852</td>\n",
@@ -696,7 +717,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1834</th>\n",
+       "      <th>1835</th>\n",
        "      <td>198506</td>\n",
        "      <td>3</td>\n",
        "      <td>565825</td>\n",
@@ -709,7 +730,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1835</th>\n",
+       "      <th>1836</th>\n",
        "      <td>198505</td>\n",
        "      <td>3</td>\n",
        "      <td>637302</td>\n",
@@ -722,7 +743,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1836</th>\n",
+       "      <th>1837</th>\n",
        "      <td>198504</td>\n",
        "      <td>3</td>\n",
        "      <td>424937</td>\n",
@@ -735,7 +756,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1837</th>\n",
+       "      <th>1838</th>\n",
        "      <td>198503</td>\n",
        "      <td>3</td>\n",
        "      <td>213901</td>\n",
@@ -748,7 +769,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1838</th>\n",
+       "      <th>1839</th>\n",
        "      <td>198502</td>\n",
        "      <td>3</td>\n",
        "      <td>97586</td>\n",
@@ -761,7 +782,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1839</th>\n",
+       "      <th>1840</th>\n",
        "      <td>198501</td>\n",
        "      <td>3</td>\n",
        "      <td>85489</td>\n",
@@ -774,7 +795,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1840</th>\n",
+       "      <th>1841</th>\n",
        "      <td>198452</td>\n",
        "      <td>3</td>\n",
        "      <td>84830</td>\n",
@@ -787,7 +808,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1841</th>\n",
+       "      <th>1842</th>\n",
        "      <td>198451</td>\n",
        "      <td>3</td>\n",
        "      <td>101726</td>\n",
@@ -800,7 +821,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1842</th>\n",
+       "      <th>1843</th>\n",
        "      <td>198450</td>\n",
        "      <td>3</td>\n",
        "      <td>123680</td>\n",
@@ -813,7 +834,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1843</th>\n",
+       "      <th>1844</th>\n",
        "      <td>198449</td>\n",
        "      <td>3</td>\n",
        "      <td>101073</td>\n",
@@ -826,7 +847,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1844</th>\n",
+       "      <th>1845</th>\n",
        "      <td>198448</td>\n",
        "      <td>3</td>\n",
        "      <td>78620</td>\n",
@@ -839,7 +860,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1845</th>\n",
+       "      <th>1846</th>\n",
        "      <td>198447</td>\n",
        "      <td>3</td>\n",
        "      <td>72029</td>\n",
@@ -852,7 +873,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1846</th>\n",
+       "      <th>1847</th>\n",
        "      <td>198446</td>\n",
        "      <td>3</td>\n",
        "      <td>87330</td>\n",
@@ -865,7 +886,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1847</th>\n",
+       "      <th>1848</th>\n",
        "      <td>198445</td>\n",
        "      <td>3</td>\n",
        "      <td>135223</td>\n",
@@ -878,7 +899,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1848</th>\n",
+       "      <th>1849</th>\n",
        "      <td>198444</td>\n",
        "      <td>3</td>\n",
        "      <td>68422</td>\n",
@@ -892,146 +913,146 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1849 rows × 10 columns</p>\n",
+       "<p>1850 rows × 10 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
-       "0     202014          3       0       0.0       0.0       0         0.0   \n",
-       "1     202013          3       0       0.0       0.0       0         0.0   \n",
-       "2     202012          3    8321    5873.0   10769.0      13         9.0   \n",
-       "3     202011          3  101704   93652.0  109756.0     154       142.0   \n",
-       "4     202010          3  104977   96650.0  113304.0     159       146.0   \n",
-       "5     202009          3  110696  102066.0  119326.0     168       155.0   \n",
-       "6     202008          3  143753  133984.0  153522.0     218       203.0   \n",
-       "7     202007          3  183610  172812.0  194408.0     279       263.0   \n",
-       "8     202006          3  206669  195481.0  217857.0     314       297.0   \n",
-       "9     202005          3  187957  177445.0  198469.0     285       269.0   \n",
-       "10    202004          3  122331  113492.0  131170.0     186       173.0   \n",
-       "11    202003          3   78413   71330.0   85496.0     119       108.0   \n",
-       "12    202002          3   53614   47654.0   59574.0      81        72.0   \n",
-       "13    202001          3   36850   31608.0   42092.0      56        48.0   \n",
-       "14    201952          3   28135   23220.0   33050.0      43        36.0   \n",
-       "15    201951          3   29786   25042.0   34530.0      45        38.0   \n",
-       "16    201950          3   34223   29156.0   39290.0      52        44.0   \n",
-       "17    201949          3   25662   21414.0   29910.0      39        33.0   \n",
-       "18    201948          3   22367   18055.0   26679.0      34        27.0   \n",
-       "19    201947          3   18669   14759.0   22579.0      28        22.0   \n",
-       "20    201946          3   16030   12567.0   19493.0      24        19.0   \n",
-       "21    201945          3   10138    7160.0   13116.0      15        10.0   \n",
-       "22    201944          3    7822    5010.0   10634.0      12         8.0   \n",
-       "23    201943          3    9487    6448.0   12526.0      14         9.0   \n",
-       "24    201942          3    7747    5243.0   10251.0      12         8.0   \n",
-       "25    201941          3    7122    4720.0    9524.0      11         7.0   \n",
-       "26    201940          3    8505    5784.0   11226.0      13         9.0   \n",
-       "27    201939          3    7091    4462.0    9720.0      11         7.0   \n",
-       "28    201938          3    4897    2891.0    6903.0       7         4.0   \n",
-       "29    201937          3    3172    1367.0    4977.0       5         2.0   \n",
+       "0     202015          3       0       0.0       0.0       0         0.0   \n",
+       "1     202014          3       0       0.0       0.0       0         0.0   \n",
+       "2     202013          3       0       0.0       0.0       0         0.0   \n",
+       "3     202012          3    8321    5873.0   10769.0      13         9.0   \n",
+       "4     202011          3  101704   93652.0  109756.0     154       142.0   \n",
+       "5     202010          3  104977   96650.0  113304.0     159       146.0   \n",
+       "6     202009          3  110696  102066.0  119326.0     168       155.0   \n",
+       "7     202008          3  143753  133984.0  153522.0     218       203.0   \n",
+       "8     202007          3  183610  172812.0  194408.0     279       263.0   \n",
+       "9     202006          3  206669  195481.0  217857.0     314       297.0   \n",
+       "10    202005          3  187957  177445.0  198469.0     285       269.0   \n",
+       "11    202004          3  122331  113492.0  131170.0     186       173.0   \n",
+       "12    202003          3   78413   71330.0   85496.0     119       108.0   \n",
+       "13    202002          3   53614   47654.0   59574.0      81        72.0   \n",
+       "14    202001          3   36850   31608.0   42092.0      56        48.0   \n",
+       "15    201952          3   28135   23220.0   33050.0      43        36.0   \n",
+       "16    201951          3   29786   25042.0   34530.0      45        38.0   \n",
+       "17    201950          3   34223   29156.0   39290.0      52        44.0   \n",
+       "18    201949          3   25662   21414.0   29910.0      39        33.0   \n",
+       "19    201948          3   22367   18055.0   26679.0      34        27.0   \n",
+       "20    201947          3   18669   14759.0   22579.0      28        22.0   \n",
+       "21    201946          3   16030   12567.0   19493.0      24        19.0   \n",
+       "22    201945          3   10138    7160.0   13116.0      15        10.0   \n",
+       "23    201944          3    7822    5010.0   10634.0      12         8.0   \n",
+       "24    201943          3    9487    6448.0   12526.0      14         9.0   \n",
+       "25    201942          3    7747    5243.0   10251.0      12         8.0   \n",
+       "26    201941          3    7122    4720.0    9524.0      11         7.0   \n",
+       "27    201940          3    8505    5784.0   11226.0      13         9.0   \n",
+       "28    201939          3    7091    4462.0    9720.0      11         7.0   \n",
+       "29    201938          3    4897    2891.0    6903.0       7         4.0   \n",
        "...      ...        ...     ...       ...       ...     ...         ...   \n",
-       "1819  198521          3   26096   19621.0   32571.0      47        35.0   \n",
-       "1820  198520          3   27896   20885.0   34907.0      51        38.0   \n",
-       "1821  198519          3   43154   32821.0   53487.0      78        59.0   \n",
-       "1822  198518          3   40555   29935.0   51175.0      74        55.0   \n",
-       "1823  198517          3   34053   24366.0   43740.0      62        44.0   \n",
-       "1824  198516          3   50362   36451.0   64273.0      91        66.0   \n",
-       "1825  198515          3   63881   45538.0   82224.0     116        83.0   \n",
-       "1826  198514          3  134545  114400.0  154690.0     244       207.0   \n",
-       "1827  198513          3  197206  176080.0  218332.0     357       319.0   \n",
-       "1828  198512          3  245240  223304.0  267176.0     445       405.0   \n",
-       "1829  198511          3  276205  252399.0  300011.0     501       458.0   \n",
-       "1830  198510          3  353231  326279.0  380183.0     640       591.0   \n",
-       "1831  198509          3  369895  341109.0  398681.0     670       618.0   \n",
-       "1832  198508          3  389886  359529.0  420243.0     707       652.0   \n",
-       "1833  198507          3  471852  432599.0  511105.0     855       784.0   \n",
-       "1834  198506          3  565825  518011.0  613639.0    1026       939.0   \n",
-       "1835  198505          3  637302  592795.0  681809.0    1155      1074.0   \n",
-       "1836  198504          3  424937  390794.0  459080.0     770       708.0   \n",
-       "1837  198503          3  213901  174689.0  253113.0     388       317.0   \n",
-       "1838  198502          3   97586   80949.0  114223.0     177       147.0   \n",
-       "1839  198501          3   85489   65918.0  105060.0     155       120.0   \n",
-       "1840  198452          3   84830   60602.0  109058.0     154       110.0   \n",
-       "1841  198451          3  101726   80242.0  123210.0     185       146.0   \n",
-       "1842  198450          3  123680  101401.0  145959.0     225       184.0   \n",
-       "1843  198449          3  101073   81684.0  120462.0     184       149.0   \n",
-       "1844  198448          3   78620   60634.0   96606.0     143       110.0   \n",
-       "1845  198447          3   72029   54274.0   89784.0     131        99.0   \n",
-       "1846  198446          3   87330   67686.0  106974.0     159       123.0   \n",
-       "1847  198445          3  135223  101414.0  169032.0     246       184.0   \n",
-       "1848  198444          3   68422   20056.0  116788.0     125        37.0   \n",
+       "1820  198521          3   26096   19621.0   32571.0      47        35.0   \n",
+       "1821  198520          3   27896   20885.0   34907.0      51        38.0   \n",
+       "1822  198519          3   43154   32821.0   53487.0      78        59.0   \n",
+       "1823  198518          3   40555   29935.0   51175.0      74        55.0   \n",
+       "1824  198517          3   34053   24366.0   43740.0      62        44.0   \n",
+       "1825  198516          3   50362   36451.0   64273.0      91        66.0   \n",
+       "1826  198515          3   63881   45538.0   82224.0     116        83.0   \n",
+       "1827  198514          3  134545  114400.0  154690.0     244       207.0   \n",
+       "1828  198513          3  197206  176080.0  218332.0     357       319.0   \n",
+       "1829  198512          3  245240  223304.0  267176.0     445       405.0   \n",
+       "1830  198511          3  276205  252399.0  300011.0     501       458.0   \n",
+       "1831  198510          3  353231  326279.0  380183.0     640       591.0   \n",
+       "1832  198509          3  369895  341109.0  398681.0     670       618.0   \n",
+       "1833  198508          3  389886  359529.0  420243.0     707       652.0   \n",
+       "1834  198507          3  471852  432599.0  511105.0     855       784.0   \n",
+       "1835  198506          3  565825  518011.0  613639.0    1026       939.0   \n",
+       "1836  198505          3  637302  592795.0  681809.0    1155      1074.0   \n",
+       "1837  198504          3  424937  390794.0  459080.0     770       708.0   \n",
+       "1838  198503          3  213901  174689.0  253113.0     388       317.0   \n",
+       "1839  198502          3   97586   80949.0  114223.0     177       147.0   \n",
+       "1840  198501          3   85489   65918.0  105060.0     155       120.0   \n",
+       "1841  198452          3   84830   60602.0  109058.0     154       110.0   \n",
+       "1842  198451          3  101726   80242.0  123210.0     185       146.0   \n",
+       "1843  198450          3  123680  101401.0  145959.0     225       184.0   \n",
+       "1844  198449          3  101073   81684.0  120462.0     184       149.0   \n",
+       "1845  198448          3   78620   60634.0   96606.0     143       110.0   \n",
+       "1846  198447          3   72029   54274.0   89784.0     131        99.0   \n",
+       "1847  198446          3   87330   67686.0  106974.0     159       123.0   \n",
+       "1848  198445          3  135223  101414.0  169032.0     246       184.0   \n",
+       "1849  198444          3   68422   20056.0  116788.0     125        37.0   \n",
        "\n",
        "      inc100_up geo_insee geo_name  \n",
        "0           0.0        FR   France  \n",
        "1           0.0        FR   France  \n",
-       "2          17.0        FR   France  \n",
-       "3         166.0        FR   France  \n",
-       "4         172.0        FR   France  \n",
-       "5         181.0        FR   France  \n",
-       "6         233.0        FR   France  \n",
-       "7         295.0        FR   France  \n",
-       "8         331.0        FR   France  \n",
-       "9         301.0        FR   France  \n",
-       "10        199.0        FR   France  \n",
-       "11        130.0        FR   France  \n",
-       "12         90.0        FR   France  \n",
-       "13         64.0        FR   France  \n",
-       "14         50.0        FR   France  \n",
-       "15         52.0        FR   France  \n",
-       "16         60.0        FR   France  \n",
-       "17         45.0        FR   France  \n",
-       "18         41.0        FR   France  \n",
-       "19         34.0        FR   France  \n",
-       "20         29.0        FR   France  \n",
-       "21         20.0        FR   France  \n",
-       "22         16.0        FR   France  \n",
-       "23         19.0        FR   France  \n",
-       "24         16.0        FR   France  \n",
-       "25         15.0        FR   France  \n",
-       "26         17.0        FR   France  \n",
-       "27         15.0        FR   France  \n",
-       "28         10.0        FR   France  \n",
-       "29          8.0        FR   France  \n",
+       "2           0.0        FR   France  \n",
+       "3          17.0        FR   France  \n",
+       "4         166.0        FR   France  \n",
+       "5         172.0        FR   France  \n",
+       "6         181.0        FR   France  \n",
+       "7         233.0        FR   France  \n",
+       "8         295.0        FR   France  \n",
+       "9         331.0        FR   France  \n",
+       "10        301.0        FR   France  \n",
+       "11        199.0        FR   France  \n",
+       "12        130.0        FR   France  \n",
+       "13         90.0        FR   France  \n",
+       "14         64.0        FR   France  \n",
+       "15         50.0        FR   France  \n",
+       "16         52.0        FR   France  \n",
+       "17         60.0        FR   France  \n",
+       "18         45.0        FR   France  \n",
+       "19         41.0        FR   France  \n",
+       "20         34.0        FR   France  \n",
+       "21         29.0        FR   France  \n",
+       "22         20.0        FR   France  \n",
+       "23         16.0        FR   France  \n",
+       "24         19.0        FR   France  \n",
+       "25         16.0        FR   France  \n",
+       "26         15.0        FR   France  \n",
+       "27         17.0        FR   France  \n",
+       "28         15.0        FR   France  \n",
+       "29         10.0        FR   France  \n",
        "...         ...       ...      ...  \n",
-       "1819       59.0        FR   France  \n",
-       "1820       64.0        FR   France  \n",
-       "1821       97.0        FR   France  \n",
-       "1822       93.0        FR   France  \n",
-       "1823       80.0        FR   France  \n",
-       "1824      116.0        FR   France  \n",
-       "1825      149.0        FR   France  \n",
-       "1826      281.0        FR   France  \n",
-       "1827      395.0        FR   France  \n",
-       "1828      485.0        FR   France  \n",
-       "1829      544.0        FR   France  \n",
-       "1830      689.0        FR   France  \n",
-       "1831      722.0        FR   France  \n",
-       "1832      762.0        FR   France  \n",
-       "1833      926.0        FR   France  \n",
-       "1834     1113.0        FR   France  \n",
-       "1835     1236.0        FR   France  \n",
-       "1836      832.0        FR   France  \n",
-       "1837      459.0        FR   France  \n",
-       "1838      207.0        FR   France  \n",
-       "1839      190.0        FR   France  \n",
-       "1840      198.0        FR   France  \n",
-       "1841      224.0        FR   France  \n",
-       "1842      266.0        FR   France  \n",
-       "1843      219.0        FR   France  \n",
-       "1844      176.0        FR   France  \n",
-       "1845      163.0        FR   France  \n",
-       "1846      195.0        FR   France  \n",
-       "1847      308.0        FR   France  \n",
-       "1848      213.0        FR   France  \n",
+       "1820       59.0        FR   France  \n",
+       "1821       64.0        FR   France  \n",
+       "1822       97.0        FR   France  \n",
+       "1823       93.0        FR   France  \n",
+       "1824       80.0        FR   France  \n",
+       "1825      116.0        FR   France  \n",
+       "1826      149.0        FR   France  \n",
+       "1827      281.0        FR   France  \n",
+       "1828      395.0        FR   France  \n",
+       "1829      485.0        FR   France  \n",
+       "1830      544.0        FR   France  \n",
+       "1831      689.0        FR   France  \n",
+       "1832      722.0        FR   France  \n",
+       "1833      762.0        FR   France  \n",
+       "1834      926.0        FR   France  \n",
+       "1835     1113.0        FR   France  \n",
+       "1836     1236.0        FR   France  \n",
+       "1837      832.0        FR   France  \n",
+       "1838      459.0        FR   France  \n",
+       "1839      207.0        FR   France  \n",
+       "1840      190.0        FR   France  \n",
+       "1841      198.0        FR   France  \n",
+       "1842      224.0        FR   France  \n",
+       "1843      266.0        FR   France  \n",
+       "1844      219.0        FR   France  \n",
+       "1845      176.0        FR   France  \n",
+       "1846      163.0        FR   France  \n",
+       "1847      195.0        FR   France  \n",
+       "1848      308.0        FR   France  \n",
+       "1849      213.0        FR   France  \n",
        "\n",
-       "[1849 rows x 10 columns]"
+       "[1850 rows x 10 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "raw_data = pd.read_csv(data_source, skiprows=1)\n",
+    "raw_data = pd.read_csv(data_file, skiprows=1)\n",
     "raw_data"
    ]
   },
@@ -1044,7 +1065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -1082,7 +1103,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1612</th>\n",
+       "      <th>1613</th>\n",
        "      <td>198919</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
@@ -1100,13 +1121,13 @@
       ],
       "text/plain": [
        "        week  indicator  inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
-       "1612  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
+       "1613  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
        "\n",
        "     geo_insee geo_name  \n",
-       "1612        FR   France  "
+       "1613        FR   France  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1124,7 +1145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -1163,7 +1184,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>202014</td>\n",
+       "      <td>202015</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>0.0</td>\n",
@@ -1176,7 +1197,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>202013</td>\n",
+       "      <td>202014</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>0.0</td>\n",
@@ -1189,6 +1210,19 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
+       "      <td>202013</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
        "      <td>202012</td>\n",
        "      <td>3</td>\n",
        "      <td>8321</td>\n",
@@ -1201,7 +1235,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>4</th>\n",
        "      <td>202011</td>\n",
        "      <td>3</td>\n",
        "      <td>101704</td>\n",
@@ -1214,7 +1248,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>5</th>\n",
        "      <td>202010</td>\n",
        "      <td>3</td>\n",
        "      <td>104977</td>\n",
@@ -1227,7 +1261,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>6</th>\n",
        "      <td>202009</td>\n",
        "      <td>3</td>\n",
        "      <td>110696</td>\n",
@@ -1240,7 +1274,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>7</th>\n",
        "      <td>202008</td>\n",
        "      <td>3</td>\n",
        "      <td>143753</td>\n",
@@ -1253,7 +1287,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>8</th>\n",
        "      <td>202007</td>\n",
        "      <td>3</td>\n",
        "      <td>183610</td>\n",
@@ -1266,7 +1300,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
+       "      <th>9</th>\n",
        "      <td>202006</td>\n",
        "      <td>3</td>\n",
        "      <td>206669</td>\n",
@@ -1279,7 +1313,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>10</th>\n",
        "      <td>202005</td>\n",
        "      <td>3</td>\n",
        "      <td>187957</td>\n",
@@ -1292,7 +1326,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>11</th>\n",
        "      <td>202004</td>\n",
        "      <td>3</td>\n",
        "      <td>122331</td>\n",
@@ -1305,7 +1339,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
+       "      <th>12</th>\n",
        "      <td>202003</td>\n",
        "      <td>3</td>\n",
        "      <td>78413</td>\n",
@@ -1318,7 +1352,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>13</th>\n",
        "      <td>202002</td>\n",
        "      <td>3</td>\n",
        "      <td>53614</td>\n",
@@ -1331,7 +1365,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>14</th>\n",
        "      <td>202001</td>\n",
        "      <td>3</td>\n",
        "      <td>36850</td>\n",
@@ -1344,7 +1378,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>15</th>\n",
        "      <td>201952</td>\n",
        "      <td>3</td>\n",
        "      <td>28135</td>\n",
@@ -1357,7 +1391,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>16</th>\n",
        "      <td>201951</td>\n",
        "      <td>3</td>\n",
        "      <td>29786</td>\n",
@@ -1370,7 +1404,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>17</th>\n",
        "      <td>201950</td>\n",
        "      <td>3</td>\n",
        "      <td>34223</td>\n",
@@ -1383,7 +1417,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
+       "      <th>18</th>\n",
        "      <td>201949</td>\n",
        "      <td>3</td>\n",
        "      <td>25662</td>\n",
@@ -1396,7 +1430,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>19</th>\n",
        "      <td>201948</td>\n",
        "      <td>3</td>\n",
        "      <td>22367</td>\n",
@@ -1409,7 +1443,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>20</th>\n",
        "      <td>201947</td>\n",
        "      <td>3</td>\n",
        "      <td>18669</td>\n",
@@ -1422,7 +1456,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
+       "      <th>21</th>\n",
        "      <td>201946</td>\n",
        "      <td>3</td>\n",
        "      <td>16030</td>\n",
@@ -1435,7 +1469,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>22</th>\n",
        "      <td>201945</td>\n",
        "      <td>3</td>\n",
        "      <td>10138</td>\n",
@@ -1448,7 +1482,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>22</th>\n",
+       "      <th>23</th>\n",
        "      <td>201944</td>\n",
        "      <td>3</td>\n",
        "      <td>7822</td>\n",
@@ -1461,7 +1495,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
+       "      <th>24</th>\n",
        "      <td>201943</td>\n",
        "      <td>3</td>\n",
        "      <td>9487</td>\n",
@@ -1474,7 +1508,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>24</th>\n",
+       "      <th>25</th>\n",
        "      <td>201942</td>\n",
        "      <td>3</td>\n",
        "      <td>7747</td>\n",
@@ -1487,7 +1521,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25</th>\n",
+       "      <th>26</th>\n",
        "      <td>201941</td>\n",
        "      <td>3</td>\n",
        "      <td>7122</td>\n",
@@ -1500,7 +1534,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>27</th>\n",
        "      <td>201940</td>\n",
        "      <td>3</td>\n",
        "      <td>8505</td>\n",
@@ -1513,7 +1547,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
+       "      <th>28</th>\n",
        "      <td>201939</td>\n",
        "      <td>3</td>\n",
        "      <td>7091</td>\n",
@@ -1526,7 +1560,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28</th>\n",
+       "      <th>29</th>\n",
        "      <td>201938</td>\n",
        "      <td>3</td>\n",
        "      <td>4897</td>\n",
@@ -1539,19 +1573,6 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>201937</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3172</td>\n",
-       "      <td>1367.0</td>\n",
-       "      <td>4977.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
@@ -1565,7 +1586,7 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1819</th>\n",
+       "      <th>1820</th>\n",
        "      <td>198521</td>\n",
        "      <td>3</td>\n",
        "      <td>26096</td>\n",
@@ -1578,7 +1599,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1820</th>\n",
+       "      <th>1821</th>\n",
        "      <td>198520</td>\n",
        "      <td>3</td>\n",
        "      <td>27896</td>\n",
@@ -1591,7 +1612,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1821</th>\n",
+       "      <th>1822</th>\n",
        "      <td>198519</td>\n",
        "      <td>3</td>\n",
        "      <td>43154</td>\n",
@@ -1604,7 +1625,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1822</th>\n",
+       "      <th>1823</th>\n",
        "      <td>198518</td>\n",
        "      <td>3</td>\n",
        "      <td>40555</td>\n",
@@ -1617,7 +1638,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1823</th>\n",
+       "      <th>1824</th>\n",
        "      <td>198517</td>\n",
        "      <td>3</td>\n",
        "      <td>34053</td>\n",
@@ -1630,7 +1651,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1824</th>\n",
+       "      <th>1825</th>\n",
        "      <td>198516</td>\n",
        "      <td>3</td>\n",
        "      <td>50362</td>\n",
@@ -1643,7 +1664,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1825</th>\n",
+       "      <th>1826</th>\n",
        "      <td>198515</td>\n",
        "      <td>3</td>\n",
        "      <td>63881</td>\n",
@@ -1656,7 +1677,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1826</th>\n",
+       "      <th>1827</th>\n",
        "      <td>198514</td>\n",
        "      <td>3</td>\n",
        "      <td>134545</td>\n",
@@ -1669,7 +1690,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1827</th>\n",
+       "      <th>1828</th>\n",
        "      <td>198513</td>\n",
        "      <td>3</td>\n",
        "      <td>197206</td>\n",
@@ -1682,7 +1703,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1828</th>\n",
+       "      <th>1829</th>\n",
        "      <td>198512</td>\n",
        "      <td>3</td>\n",
        "      <td>245240</td>\n",
@@ -1695,7 +1716,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1829</th>\n",
+       "      <th>1830</th>\n",
        "      <td>198511</td>\n",
        "      <td>3</td>\n",
        "      <td>276205</td>\n",
@@ -1708,7 +1729,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1830</th>\n",
+       "      <th>1831</th>\n",
        "      <td>198510</td>\n",
        "      <td>3</td>\n",
        "      <td>353231</td>\n",
@@ -1721,7 +1742,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1831</th>\n",
+       "      <th>1832</th>\n",
        "      <td>198509</td>\n",
        "      <td>3</td>\n",
        "      <td>369895</td>\n",
@@ -1734,7 +1755,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1832</th>\n",
+       "      <th>1833</th>\n",
        "      <td>198508</td>\n",
        "      <td>3</td>\n",
        "      <td>389886</td>\n",
@@ -1747,7 +1768,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1833</th>\n",
+       "      <th>1834</th>\n",
        "      <td>198507</td>\n",
        "      <td>3</td>\n",
        "      <td>471852</td>\n",
@@ -1760,7 +1781,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1834</th>\n",
+       "      <th>1835</th>\n",
        "      <td>198506</td>\n",
        "      <td>3</td>\n",
        "      <td>565825</td>\n",
@@ -1773,7 +1794,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1835</th>\n",
+       "      <th>1836</th>\n",
        "      <td>198505</td>\n",
        "      <td>3</td>\n",
        "      <td>637302</td>\n",
@@ -1786,7 +1807,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1836</th>\n",
+       "      <th>1837</th>\n",
        "      <td>198504</td>\n",
        "      <td>3</td>\n",
        "      <td>424937</td>\n",
@@ -1799,7 +1820,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1837</th>\n",
+       "      <th>1838</th>\n",
        "      <td>198503</td>\n",
        "      <td>3</td>\n",
        "      <td>213901</td>\n",
@@ -1812,7 +1833,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1838</th>\n",
+       "      <th>1839</th>\n",
        "      <td>198502</td>\n",
        "      <td>3</td>\n",
        "      <td>97586</td>\n",
@@ -1825,7 +1846,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1839</th>\n",
+       "      <th>1840</th>\n",
        "      <td>198501</td>\n",
        "      <td>3</td>\n",
        "      <td>85489</td>\n",
@@ -1838,7 +1859,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1840</th>\n",
+       "      <th>1841</th>\n",
        "      <td>198452</td>\n",
        "      <td>3</td>\n",
        "      <td>84830</td>\n",
@@ -1851,7 +1872,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1841</th>\n",
+       "      <th>1842</th>\n",
        "      <td>198451</td>\n",
        "      <td>3</td>\n",
        "      <td>101726</td>\n",
@@ -1864,7 +1885,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1842</th>\n",
+       "      <th>1843</th>\n",
        "      <td>198450</td>\n",
        "      <td>3</td>\n",
        "      <td>123680</td>\n",
@@ -1877,7 +1898,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1843</th>\n",
+       "      <th>1844</th>\n",
        "      <td>198449</td>\n",
        "      <td>3</td>\n",
        "      <td>101073</td>\n",
@@ -1890,7 +1911,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1844</th>\n",
+       "      <th>1845</th>\n",
        "      <td>198448</td>\n",
        "      <td>3</td>\n",
        "      <td>78620</td>\n",
@@ -1903,7 +1924,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1845</th>\n",
+       "      <th>1846</th>\n",
        "      <td>198447</td>\n",
        "      <td>3</td>\n",
        "      <td>72029</td>\n",
@@ -1916,7 +1937,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1846</th>\n",
+       "      <th>1847</th>\n",
        "      <td>198446</td>\n",
        "      <td>3</td>\n",
        "      <td>87330</td>\n",
@@ -1929,7 +1950,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1847</th>\n",
+       "      <th>1848</th>\n",
        "      <td>198445</td>\n",
        "      <td>3</td>\n",
        "      <td>135223</td>\n",
@@ -1942,7 +1963,7 @@
        "      <td>France</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1848</th>\n",
+       "      <th>1849</th>\n",
        "      <td>198444</td>\n",
        "      <td>3</td>\n",
        "      <td>68422</td>\n",
@@ -1956,140 +1977,140 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1848 rows × 10 columns</p>\n",
+       "<p>1849 rows × 10 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
-       "0     202014          3       0       0.0       0.0       0         0.0   \n",
-       "1     202013          3       0       0.0       0.0       0         0.0   \n",
-       "2     202012          3    8321    5873.0   10769.0      13         9.0   \n",
-       "3     202011          3  101704   93652.0  109756.0     154       142.0   \n",
-       "4     202010          3  104977   96650.0  113304.0     159       146.0   \n",
-       "5     202009          3  110696  102066.0  119326.0     168       155.0   \n",
-       "6     202008          3  143753  133984.0  153522.0     218       203.0   \n",
-       "7     202007          3  183610  172812.0  194408.0     279       263.0   \n",
-       "8     202006          3  206669  195481.0  217857.0     314       297.0   \n",
-       "9     202005          3  187957  177445.0  198469.0     285       269.0   \n",
-       "10    202004          3  122331  113492.0  131170.0     186       173.0   \n",
-       "11    202003          3   78413   71330.0   85496.0     119       108.0   \n",
-       "12    202002          3   53614   47654.0   59574.0      81        72.0   \n",
-       "13    202001          3   36850   31608.0   42092.0      56        48.0   \n",
-       "14    201952          3   28135   23220.0   33050.0      43        36.0   \n",
-       "15    201951          3   29786   25042.0   34530.0      45        38.0   \n",
-       "16    201950          3   34223   29156.0   39290.0      52        44.0   \n",
-       "17    201949          3   25662   21414.0   29910.0      39        33.0   \n",
-       "18    201948          3   22367   18055.0   26679.0      34        27.0   \n",
-       "19    201947          3   18669   14759.0   22579.0      28        22.0   \n",
-       "20    201946          3   16030   12567.0   19493.0      24        19.0   \n",
-       "21    201945          3   10138    7160.0   13116.0      15        10.0   \n",
-       "22    201944          3    7822    5010.0   10634.0      12         8.0   \n",
-       "23    201943          3    9487    6448.0   12526.0      14         9.0   \n",
-       "24    201942          3    7747    5243.0   10251.0      12         8.0   \n",
-       "25    201941          3    7122    4720.0    9524.0      11         7.0   \n",
-       "26    201940          3    8505    5784.0   11226.0      13         9.0   \n",
-       "27    201939          3    7091    4462.0    9720.0      11         7.0   \n",
-       "28    201938          3    4897    2891.0    6903.0       7         4.0   \n",
-       "29    201937          3    3172    1367.0    4977.0       5         2.0   \n",
+       "0     202015          3       0       0.0       0.0       0         0.0   \n",
+       "1     202014          3       0       0.0       0.0       0         0.0   \n",
+       "2     202013          3       0       0.0       0.0       0         0.0   \n",
+       "3     202012          3    8321    5873.0   10769.0      13         9.0   \n",
+       "4     202011          3  101704   93652.0  109756.0     154       142.0   \n",
+       "5     202010          3  104977   96650.0  113304.0     159       146.0   \n",
+       "6     202009          3  110696  102066.0  119326.0     168       155.0   \n",
+       "7     202008          3  143753  133984.0  153522.0     218       203.0   \n",
+       "8     202007          3  183610  172812.0  194408.0     279       263.0   \n",
+       "9     202006          3  206669  195481.0  217857.0     314       297.0   \n",
+       "10    202005          3  187957  177445.0  198469.0     285       269.0   \n",
+       "11    202004          3  122331  113492.0  131170.0     186       173.0   \n",
+       "12    202003          3   78413   71330.0   85496.0     119       108.0   \n",
+       "13    202002          3   53614   47654.0   59574.0      81        72.0   \n",
+       "14    202001          3   36850   31608.0   42092.0      56        48.0   \n",
+       "15    201952          3   28135   23220.0   33050.0      43        36.0   \n",
+       "16    201951          3   29786   25042.0   34530.0      45        38.0   \n",
+       "17    201950          3   34223   29156.0   39290.0      52        44.0   \n",
+       "18    201949          3   25662   21414.0   29910.0      39        33.0   \n",
+       "19    201948          3   22367   18055.0   26679.0      34        27.0   \n",
+       "20    201947          3   18669   14759.0   22579.0      28        22.0   \n",
+       "21    201946          3   16030   12567.0   19493.0      24        19.0   \n",
+       "22    201945          3   10138    7160.0   13116.0      15        10.0   \n",
+       "23    201944          3    7822    5010.0   10634.0      12         8.0   \n",
+       "24    201943          3    9487    6448.0   12526.0      14         9.0   \n",
+       "25    201942          3    7747    5243.0   10251.0      12         8.0   \n",
+       "26    201941          3    7122    4720.0    9524.0      11         7.0   \n",
+       "27    201940          3    8505    5784.0   11226.0      13         9.0   \n",
+       "28    201939          3    7091    4462.0    9720.0      11         7.0   \n",
+       "29    201938          3    4897    2891.0    6903.0       7         4.0   \n",
        "...      ...        ...     ...       ...       ...     ...         ...   \n",
-       "1819  198521          3   26096   19621.0   32571.0      47        35.0   \n",
-       "1820  198520          3   27896   20885.0   34907.0      51        38.0   \n",
-       "1821  198519          3   43154   32821.0   53487.0      78        59.0   \n",
-       "1822  198518          3   40555   29935.0   51175.0      74        55.0   \n",
-       "1823  198517          3   34053   24366.0   43740.0      62        44.0   \n",
-       "1824  198516          3   50362   36451.0   64273.0      91        66.0   \n",
-       "1825  198515          3   63881   45538.0   82224.0     116        83.0   \n",
-       "1826  198514          3  134545  114400.0  154690.0     244       207.0   \n",
-       "1827  198513          3  197206  176080.0  218332.0     357       319.0   \n",
-       "1828  198512          3  245240  223304.0  267176.0     445       405.0   \n",
-       "1829  198511          3  276205  252399.0  300011.0     501       458.0   \n",
-       "1830  198510          3  353231  326279.0  380183.0     640       591.0   \n",
-       "1831  198509          3  369895  341109.0  398681.0     670       618.0   \n",
-       "1832  198508          3  389886  359529.0  420243.0     707       652.0   \n",
-       "1833  198507          3  471852  432599.0  511105.0     855       784.0   \n",
-       "1834  198506          3  565825  518011.0  613639.0    1026       939.0   \n",
-       "1835  198505          3  637302  592795.0  681809.0    1155      1074.0   \n",
-       "1836  198504          3  424937  390794.0  459080.0     770       708.0   \n",
-       "1837  198503          3  213901  174689.0  253113.0     388       317.0   \n",
-       "1838  198502          3   97586   80949.0  114223.0     177       147.0   \n",
-       "1839  198501          3   85489   65918.0  105060.0     155       120.0   \n",
-       "1840  198452          3   84830   60602.0  109058.0     154       110.0   \n",
-       "1841  198451          3  101726   80242.0  123210.0     185       146.0   \n",
-       "1842  198450          3  123680  101401.0  145959.0     225       184.0   \n",
-       "1843  198449          3  101073   81684.0  120462.0     184       149.0   \n",
-       "1844  198448          3   78620   60634.0   96606.0     143       110.0   \n",
-       "1845  198447          3   72029   54274.0   89784.0     131        99.0   \n",
-       "1846  198446          3   87330   67686.0  106974.0     159       123.0   \n",
-       "1847  198445          3  135223  101414.0  169032.0     246       184.0   \n",
-       "1848  198444          3   68422   20056.0  116788.0     125        37.0   \n",
+       "1820  198521          3   26096   19621.0   32571.0      47        35.0   \n",
+       "1821  198520          3   27896   20885.0   34907.0      51        38.0   \n",
+       "1822  198519          3   43154   32821.0   53487.0      78        59.0   \n",
+       "1823  198518          3   40555   29935.0   51175.0      74        55.0   \n",
+       "1824  198517          3   34053   24366.0   43740.0      62        44.0   \n",
+       "1825  198516          3   50362   36451.0   64273.0      91        66.0   \n",
+       "1826  198515          3   63881   45538.0   82224.0     116        83.0   \n",
+       "1827  198514          3  134545  114400.0  154690.0     244       207.0   \n",
+       "1828  198513          3  197206  176080.0  218332.0     357       319.0   \n",
+       "1829  198512          3  245240  223304.0  267176.0     445       405.0   \n",
+       "1830  198511          3  276205  252399.0  300011.0     501       458.0   \n",
+       "1831  198510          3  353231  326279.0  380183.0     640       591.0   \n",
+       "1832  198509          3  369895  341109.0  398681.0     670       618.0   \n",
+       "1833  198508          3  389886  359529.0  420243.0     707       652.0   \n",
+       "1834  198507          3  471852  432599.0  511105.0     855       784.0   \n",
+       "1835  198506          3  565825  518011.0  613639.0    1026       939.0   \n",
+       "1836  198505          3  637302  592795.0  681809.0    1155      1074.0   \n",
+       "1837  198504          3  424937  390794.0  459080.0     770       708.0   \n",
+       "1838  198503          3  213901  174689.0  253113.0     388       317.0   \n",
+       "1839  198502          3   97586   80949.0  114223.0     177       147.0   \n",
+       "1840  198501          3   85489   65918.0  105060.0     155       120.0   \n",
+       "1841  198452          3   84830   60602.0  109058.0     154       110.0   \n",
+       "1842  198451          3  101726   80242.0  123210.0     185       146.0   \n",
+       "1843  198450          3  123680  101401.0  145959.0     225       184.0   \n",
+       "1844  198449          3  101073   81684.0  120462.0     184       149.0   \n",
+       "1845  198448          3   78620   60634.0   96606.0     143       110.0   \n",
+       "1846  198447          3   72029   54274.0   89784.0     131        99.0   \n",
+       "1847  198446          3   87330   67686.0  106974.0     159       123.0   \n",
+       "1848  198445          3  135223  101414.0  169032.0     246       184.0   \n",
+       "1849  198444          3   68422   20056.0  116788.0     125        37.0   \n",
        "\n",
        "      inc100_up geo_insee geo_name  \n",
        "0           0.0        FR   France  \n",
        "1           0.0        FR   France  \n",
-       "2          17.0        FR   France  \n",
-       "3         166.0        FR   France  \n",
-       "4         172.0        FR   France  \n",
-       "5         181.0        FR   France  \n",
-       "6         233.0        FR   France  \n",
-       "7         295.0        FR   France  \n",
-       "8         331.0        FR   France  \n",
-       "9         301.0        FR   France  \n",
-       "10        199.0        FR   France  \n",
-       "11        130.0        FR   France  \n",
-       "12         90.0        FR   France  \n",
-       "13         64.0        FR   France  \n",
-       "14         50.0        FR   France  \n",
-       "15         52.0        FR   France  \n",
-       "16         60.0        FR   France  \n",
-       "17         45.0        FR   France  \n",
-       "18         41.0        FR   France  \n",
-       "19         34.0        FR   France  \n",
-       "20         29.0        FR   France  \n",
-       "21         20.0        FR   France  \n",
-       "22         16.0        FR   France  \n",
-       "23         19.0        FR   France  \n",
-       "24         16.0        FR   France  \n",
-       "25         15.0        FR   France  \n",
-       "26         17.0        FR   France  \n",
-       "27         15.0        FR   France  \n",
-       "28         10.0        FR   France  \n",
-       "29          8.0        FR   France  \n",
+       "2           0.0        FR   France  \n",
+       "3          17.0        FR   France  \n",
+       "4         166.0        FR   France  \n",
+       "5         172.0        FR   France  \n",
+       "6         181.0        FR   France  \n",
+       "7         233.0        FR   France  \n",
+       "8         295.0        FR   France  \n",
+       "9         331.0        FR   France  \n",
+       "10        301.0        FR   France  \n",
+       "11        199.0        FR   France  \n",
+       "12        130.0        FR   France  \n",
+       "13         90.0        FR   France  \n",
+       "14         64.0        FR   France  \n",
+       "15         50.0        FR   France  \n",
+       "16         52.0        FR   France  \n",
+       "17         60.0        FR   France  \n",
+       "18         45.0        FR   France  \n",
+       "19         41.0        FR   France  \n",
+       "20         34.0        FR   France  \n",
+       "21         29.0        FR   France  \n",
+       "22         20.0        FR   France  \n",
+       "23         16.0        FR   France  \n",
+       "24         19.0        FR   France  \n",
+       "25         16.0        FR   France  \n",
+       "26         15.0        FR   France  \n",
+       "27         17.0        FR   France  \n",
+       "28         15.0        FR   France  \n",
+       "29         10.0        FR   France  \n",
        "...         ...       ...      ...  \n",
-       "1819       59.0        FR   France  \n",
-       "1820       64.0        FR   France  \n",
-       "1821       97.0        FR   France  \n",
-       "1822       93.0        FR   France  \n",
-       "1823       80.0        FR   France  \n",
-       "1824      116.0        FR   France  \n",
-       "1825      149.0        FR   France  \n",
-       "1826      281.0        FR   France  \n",
-       "1827      395.0        FR   France  \n",
-       "1828      485.0        FR   France  \n",
-       "1829      544.0        FR   France  \n",
-       "1830      689.0        FR   France  \n",
-       "1831      722.0        FR   France  \n",
-       "1832      762.0        FR   France  \n",
-       "1833      926.0        FR   France  \n",
-       "1834     1113.0        FR   France  \n",
-       "1835     1236.0        FR   France  \n",
-       "1836      832.0        FR   France  \n",
-       "1837      459.0        FR   France  \n",
-       "1838      207.0        FR   France  \n",
-       "1839      190.0        FR   France  \n",
-       "1840      198.0        FR   France  \n",
-       "1841      224.0        FR   France  \n",
-       "1842      266.0        FR   France  \n",
-       "1843      219.0        FR   France  \n",
-       "1844      176.0        FR   France  \n",
-       "1845      163.0        FR   France  \n",
-       "1846      195.0        FR   France  \n",
-       "1847      308.0        FR   France  \n",
-       "1848      213.0        FR   France  \n",
+       "1820       59.0        FR   France  \n",
+       "1821       64.0        FR   France  \n",
+       "1822       97.0        FR   France  \n",
+       "1823       93.0        FR   France  \n",
+       "1824       80.0        FR   France  \n",
+       "1825      116.0        FR   France  \n",
+       "1826      149.0        FR   France  \n",
+       "1827      281.0        FR   France  \n",
+       "1828      395.0        FR   France  \n",
+       "1829      485.0        FR   France  \n",
+       "1830      544.0        FR   France  \n",
+       "1831      689.0        FR   France  \n",
+       "1832      722.0        FR   France  \n",
+       "1833      762.0        FR   France  \n",
+       "1834      926.0        FR   France  \n",
+       "1835     1113.0        FR   France  \n",
+       "1836     1236.0        FR   France  \n",
+       "1837      832.0        FR   France  \n",
+       "1838      459.0        FR   France  \n",
+       "1839      207.0        FR   France  \n",
+       "1840      190.0        FR   France  \n",
+       "1841      198.0        FR   France  \n",
+       "1842      224.0        FR   France  \n",
+       "1843      266.0        FR   France  \n",
+       "1844      219.0        FR   France  \n",
+       "1845      176.0        FR   France  \n",
+       "1846      163.0        FR   France  \n",
+       "1847      195.0        FR   France  \n",
+       "1848      308.0        FR   France  \n",
+       "1849      213.0        FR   France  \n",
        "\n",
-       "[1848 rows x 10 columns]"
+       "[1849 rows x 10 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2119,7 +2140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2149,7 +2170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2174,7 +2195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -2202,22 +2223,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b7fb40208>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5fadb68320>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2241,22 +2262,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b7d962240>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5fab9b1898>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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kAVCvqk+r43S/F3h7Sp+N7uOHgKtcK+QaYJOqdrpCsQlHaAygvTfC/LrK4b9rgs41QL9dQWdlMJqgyrU07DwVlkyWRsDcU2VHXjEN1210EfCM2/RhEXlBRDaISIPbtgg4lNLtsNu2yH2c3j6qj6rGgR6gaZyxZj2JpHKyP8K8+tBwW2WFD5/YorXxGIjGqQkGqDFLo+BkDoSLuafKjJxFQ0Rqge8DH1PVXhxX01nAauAY8O/eoRm66zjtk+2TOrdbRGSLiGzp6OgY932UC6f6IyQV5tWNiIaIUBMM2AK/cfAsjeqQk3KbTNpVcKFIXxEObiDcthEpK3ISDRGpwBGM76rqDwBU9YSqJlQ1CXwDWOsefhhYnNK9FTjqtrdmaB/VR0QCwBygc5yxRqGqd6vqGlVd09LSkstbKnna+yIAtKS4pwBqQgHC5nbJihfTqAk6WT6pW6UbkyeRVBJJJegfnT0VDFjKbbmRS/aUAPcAu1T1CyntC1IOewfwovv4EWCdmxG1DFgJPKuqx4A+EbnMHfMm4OGUPl5m1PXAE27c4zHgahFpcN1fV7tts572viEA5qe4pwCqQ376ze2SlXA0TnUwQHXIif9Y0kBhiHr1wdMC4RV+H3Gz5sqKXLKnrgDeA2wXkW1u2z8AN4rIahx30X7ggwCqukNEHgR24mRe3epmTgF8CPg2UIWTNfWo234PcJ+ItOFYGOvcsTpF5NPAZve4T6lq5+TeannR3utYGvPqR1sataEAYfshzErYdU/VhpwrYtvptjB4cYsxgXCfDAuKUR5MKBqq+hSZYws/HafPHcAdGdq3ABdkaB8Cbsgy1gZgw0TznG2ccEWjpTbN0gja+oNsRONJ4kmlxk25BbM0CkUk7nzmQmkxjWDAZ+e4zLAV4SVKe98QDdUVYwKPNUHbHiMbg65VUeVmT4GJRqGIxBxrIlRhK8LLHRONEqW9L8K8tCA4OIFw+yHMTDjmnJfqoJ/qYfeUnatCEHFdUJVjRMM2LCw3TDRKlPa+0Ws0PGpCfvPTZ8Hb1bY66GduVQXg1CIxpk4295RtI1J+mGiUKB29Q5ktjaAFwrPhpSJXVfhprHG2XDnVH53JKZUNQ557KoNoxM09VVaYaJQgyaTS0Z/Z0qgOBRiwRWsZ8VaAVwcD1FdW4PcJnQMmGoVgxNIw91S5Y6JRgnSFo8QSOmo1uIeXSmqL1sYy5Prdq4I+fD6hoTpoolEgRmIaVk+j3DHRKEG81eCZ3FOWSpodb72AdzXcVBPklIlGQYjEslkaPlunUWaYaJQgHcNbiGQOhIMtWstEerC2scYsjULhWRqhMZaGWMptmWGiUYJ4P3TNtcExz9n6g+wMryVwr4Yba4N0mWgUBO/cjk259RFPmqVRTpholCAn+x1Lo6kmk6VhopEN72rYWxBp7qnCMTROym0soVa3voww0ShBOgeiBHxCfdXYXWCGRcMWrY0hk3uqZzBmgdoCEMmScusJtLmoygcTjRKkcyBKQ00Qt8T6KLwtv23/qbFE0/zuTe5aja6wWRtTxRPkTCvCARPmMsJEowQ5NRAd/sFLx9xT2Rl2T/k9S8Nx71kwfOpE4kl84uxqm0rA51kaJhrlgolGCXKqP0JThiA4pATCLXtqDJF4goBPCPhH3FMAnbYqfMoMxRKEAv4x1m+FuafKDhONEqRzIDp8lZyOtxGfbSUylkhsdDnSYdEw99SUicSTYxb2AQTNPVV2mGiUIOO5pyr8PoIBn1Xvy0AknhwVqB0WDXNPTZlILDlmYR84n0cw0ciHzoEov3q5Y6ankZVcyr0uFpFfiMguEdkhIh912xtFZJOI7HHvG1L63CYibSKyW0SuSWm/RES2u8/d6ZZ9xS0N+4Db/oyILE3ps959jT0isp5ZTjSepG8oPvyDl4maoN/qhGcgGh/9w9ZQ7ex0a5sWTp2heGLMwj4w0ZgM9zy1l/d+61n6i9RbkIulEQf+WlXPAy4DbhWRVcAngMdVdSXwuPs37nPrgPOBa4Gvioj3Tb0LuAWnbvhK93mAm4EuVV0BfBH4nDtWI3A7cCmwFrg9VZxmI16mT7aYBlhNjWxE0n7YAn4fc6srzNIoAJFYksqMlobjnorGLaaRK7uP95NUONQZnumpZGRC0VDVY6r6nPu4D9gFLAKuAza6h20E3u4+vg64X1UjqroPaAPWisgCoF5Vn1Znpc+9aX28sR4CrnKtkGuATaraqapdwCZGhGZWMrKwbzxLw6r3ZSLdPQW2lUihSBdkD8/SsFXhufNKRz8AB0tVNFJx3UYXAc8A81X1GDjCAsxzD1sEHErpdthtW+Q+Tm8f1UdV40AP0DTOWLMW7wcuWyAc3EJM5p4aQyQ+1u/urAqPzNCMyoeh2FhBBnNP5ctQLMGBUwNACVsaHiJSC3wf+Jiq9o53aIY2Had9sn1S53aLiGwRkS0dHcUbQCoEI6IxgXvKLI0xROKJMTXVm2pCnLSYxpSJxBNjFvbBiGiYeyo39p0cwCuFU9KWhohU4AjGd1X1B27zCdflhHvf7rYfBhandG8FjrrtrRnaR/URkQAwB+gcZ6xRqOrdqrpGVde0tLTk8pZKFi9om2mzQo+aoMU0MhHJcDV8ZlM1BzvDJKxo1ZTI5PoDWxGeL3vaHddUddBfuqLhxhbuAXap6hdSnnoE8LKZ1gMPp7SvczOiluEEvJ91XVh9InKZO+ZNaX28sa4HnnDjHo8BV4tIgxsAv9ptm7WcGojg9wn1lRVZj6k291RGoomxP2zLW2qIxpMc6RqcoVmVB5lcf2DuqXxpO9GHT+C1y5uKVjTG7ng3liuA9wDbRWSb2/YPwGeBB0XkZuAgcAOAqu4QkQeBnTiZV7eqqvcL9iHg20AV8Kh7A0eU7hORNhwLY507VqeIfBrY7B73KVXtnOR7LQs6B6I0VAfx+TJ57hyqKvzDewEZI2RaS3BWSy3gBB+XNFXPxLTKAmdF+HgxDbPkcqGto58zm2pYMa+WX7edJJnUcb/rM8GEoqGqT5E5tgBwVZY+dwB3ZGjfAlyQoX0IV3QyPLcB2DDRPGcLp/qzL+zzCAZ8w7uOGiNkyvBZniIabzx3XqZuRg5E4klCGWIawYC5p/Jhz4l+VsyrZXFjNdF4kva+CGfMGVuhcyaxFeElhrOFyPiiEQr4hzfnM0bIlnLbUF3BKx0DMzSr8iAyoaVhn8eJSCSVfScHOKulliWNjtVbjC4qE40So28onrGORiqhgI9oIknSgrujiMSTY7KnwLE29rq58cbkGIonM67TCJho5Ew4GieeVJpqgiYaRuHoj8SHtz/PhvfljdoXdRTp24h4nNVSY5bGFFBVovEJVoRbTGNCwu7O1NUhPwvnVuETEw2jAAxE49ROJBrul9dcVKOJxDO7UJa31HKyP0LPYGwGZlX6RNKKW6Xi1S6J2wXMhHiiURMMEAz4aKwJ0tFXfAtPTTRKjIEcLA3PBWMZVCMkkkosoVksDScYbi6qyTEsGpZyOyW8tVXVbvXNhuog3UW4bb+JRgkRjSeJJXS4pGs2vKtpy6AaIb3UayrnnlEHwFeffMWEdhJEYl6pV0u5nQrDloZ7UdhQXZz7oplolBDelciEMY1hS8NEw8MTA89dksrixmr++Y9WsWnnCT7+wLYxzxvjM76l4e1ya5/FifC2/hm2NGoqirJ+fS6L+4wioT9n0XA+dPZFHWE8SwPg/Vcu42BnmO/87gDRLFlWRmY8Qc4ULxIRAj4x91QOeDVwvO93Y02Q5w52z+SUMmLfjBLCuxKZMBBeYTGNdMa7Gva4aMlc4m6uvJE7Q64bNNOGheC4qOwCZmLSLY251UG6BqI4OyoVDyYaJUR6oCwbIb+5p9IZ72rY4+z5TmzjpePjbeJspDPRua2s8NlnMQfCw99v19KoDhJPatFV8DPRKCG8TQhztzTsi+rhXQ2PJxpntdQS8Akvn+g7XdMqCyITnNuqCj+DMbN6J2LAW6cxbGk4m5J2DRRXKriJRgmReyDcXadhX9RhRtYSZLfSggEfy5pr2H3cUm/zYSjuZU9lPreVFX6G7LM4IeFoHL9PhsXX2y6os8iC4SYaJYRnpk68uM9WhKfj+dQzZU+lcvYZdew+Ye6pfBi2NLIkGYQq/MOWnpGdgUiC6qAfp3KEE9MAii6DykSjhMg5pjFsadgX1WPY757lh83j3Pl1HOoctCJWeTBRkoET0zBLYyLC0Tg1wZELQs/S6CqytRomGiXEQNrin2xYTGMsIz9sE1saMFJBzZiYSDz74j5wYxpRE42JGIgmqA6NCG/jsKVhMQ1jkgxE4gRSfJ7ZCPot5TadXFJuAc5xM6h2WwZVzowkGYwT07DP4oQMRhOjLI26ygA+KUFLQ0Q2iEi7iLyY0vZJETkiItvc29tSnrtNRNpEZLeIXJPSfomIbHefu9Mt+YpbFvYBt/0ZEVma0me9iOxxb1452FmLt++U5/PMhlkaY/GSAiYS3MWN1QQDPtrM0siZXFJuLaYxMQOR+CjXs88nzlYiJRjT+DZwbYb2L6rqavf2UwARWYVTqvV8t89XRcQ7C3cBt+DUDF+ZMubNQJeqrgC+CHzOHasRuB24FFgL3O7WCZ+19EcSE+47BSOWhi2oGsFLCpgopuH3Ccuba0w08mCilNvKgLmnciEcTYxxPc+trii6TQsnFA1V/RVO3e5cuA64X1UjqroPaAPWisgCoF5Vn1ZneeO9wNtT+mx0Hz8EXOVaIdcAm1S1U1W7gE1kFq9ZQy473IJT+CbgE3NPpTD8w+afWHRXzKu1mEYeDMUTBHwyXHApncqg1azPhYFonKq0i8LGmuLbtHAqMY0Pi8gLrvvKswAWAYdSjjnsti1yH6e3j+qjqnGgB2gaZ6xZy0A0N9EA56rPsqdGGK/mQzor5tVypHvQro5zJBIbW0Y3lcqApdzmQjiDJ8HZHr08AuF3AWcBq4FjwL+77Zmc7TpO+2T7jEJEbhGRLSKypaOjY7x5lzQDkYkLMHkEA7Z1Qyrj7XKbzop5tajCK1ZfIyci8eS4iyadmIYJ8EQMROPDW4h4FOP26JMSDVU9oaoJVU0C38CJOYBjDSxOObQVOOq2t2ZoH9VHRALAHBx3WLaxMs3nblVdo6prWlpaJvOWSgJv8U8uhALmEkglEk8S9Pvw+cZPIgBHNMBEI1cGYwkqx7E0qir8xJNqO92Og6q6MY00S6MmSFe4uDYtnJRouDEKj3cAXmbVI8A6NyNqGU7A+1lVPQb0ichlbrziJuDhlD5eZtT1wBNu3OMx4GoRaXDdX1e7bbOW/jwsjVCF7SyailMfPLeP+7LmGnyCBcNzZDCaGOOLT8XbXsSsjexE4kkSSc1gaVQQSxTXpoUT/gKJyPeAPwCaReQwTkbTH4jIahx30X7ggwCqukNEHgR2AnHgVlX1PikfwsnEqgIedW8A9wD3iUgbjoWxzh2rU0Q+DWx2j/uUquYakC9L8o5pmGgME4kncopngGOlLWms5sUjPXz9l69w7QVncGZTzTTPsHQJZ3CrpOIt+huKJamrPF2zKi1G6oOPFt+m2hAAp/qj1FVWnPZ5ZWLCXyBVvTFD8z3jHH8HcEeG9i3ABRnah4Absoy1Adgw0RxnC+HI2JS8bDjuKRMNj0gsmVM8w2PFvFp+vqudX+zuYPfxPr7wrtXTOLvSJjyBpREyS2NChrcISvt+t9Q5onGyP8LS5uK4cLEV4SVCNJ4kmkhSG8otpuEEwu1L6jFRsDady89qprk2xEVL5rJp1wk7l+MwFBs/1lZlojEhI5ZGmmi4lkZHX+S0zykbJholwkBagZaJCAUsppFKJJ7IOaYB8L4rlrL5H6/if79pJX1DcX7bdmoaZ1fahKPji8ZITMM+j9kYrtqXdlHYXOfsP3Wy30TDyJNct0X3sJjGaCJ5BMLBqW0tIly+oom6ygA/2X5sGmdX2oSjCaoqcohpmLWWleH64GkXhU01IXxiloYxCbwrkbxiGnZlN4yTPZW7e8ojFPDzlvPms2nnCZLJ4kl7LCYGzT01ZcLRzGUP/D6hsSZIh1kaRr54pV7T87izEbIaBqOIxJME87A0Urn4zAZ6BmO0F9HVXjHhZE9N7J6yFfbZCaeVek0k+tV0AAAgAElEQVSluTZER1/xLPAz0SgRci316mHuqdEMRhNZ6z1MxOLGagAOdoYLOaWyIJlUhmLJrKVeIdU9ZZ/HbIznSWipC5mlYeTPsGjkGAgPWiB8FOE81riks8QVjUMmGmMYjGW/Qvbw3ILmnsqOF9PIdB5bakOcLCIr10SjROgdcjYtq6+ydRqTYSCayDnzLJ2FcysRMUsjE+O5VTy8NRwRE42sDGdPZfiMepZGsWwlYqJRIvQNOR+qXFeFhmydxijCkXhOtUgyEQr4WVBfyaEuE410vDhF1bgrwt2YholGVgYicSorfPgz7I3WUhciGk/SO1QcW4mYaJQIvYMxRKAuj+ypWEJJWMYPyaQSjiXGrLbNh9bGanNPZSAcy5z1k4q3maGt08hOz2CMOVWZLwibi2yBn4lGidA75GxWmMsurTBSN8LiGs76ANWx+/rkw5LGag51DhZwVuVBeNjSyH5uvaJgFtPITs9gjLlVwYzPpW4lUgyYaJQIvUMx6vPYsMxKvo7gpStPxdJY3FDN8d4h++FLw3NPVU+wRUtVhRViGo/ucHZLwxMNszSMvOgbilNXmfuPnmdpWFxj5IdtSpZGUxUAR7rN2khlJBA+/mczVOG3mMY49AzGmFNt7imjgPQO5mdpeGmOlkGVmpkyedFY3GBrNTLhrWQezz0FzloNy57KTnc4xtwslsbcqgoCPjH3lJEffUPxnNNtgeF9lszSSN2iYfLuKW+txmETjVEM5pByC657yj6LWekejDI3i6Xh8wmLGqrYvL+zKNJuTTRKhN6hWF5FWEKWsTJMvluwZKKlLkRVhZ+9JwcKNa2yIJd1GuCk3do2IpkZiiUYiiWZW505EA7wvsuXsnl/F798ueM0ziwzE4qGiGwQkXYReTGlrVFENonIHve+IeW520SkTUR2i8g1Ke2XiMh297k73bKvuKVhH3DbnxGRpSl91ruvsUdEvJKwsxLHPZX7lbK3z1LU6jIXxNIQEc5bUMeOI72FmlZZ4MUpcnFP2QVMZnoHvYW72S8K/+zSM1ncWMXnf7Z7xtPoc7E0vg1cm9b2CeBxVV0JPO7+jYiswinXer7b56si4n2a7gJuwakbvjJlzJuBLlVdAXwR+Jw7ViNOadlLgbXA7aniNJtIJp0aweN9qNIZjmnYF3XE0piCaAC8qnUuLx7tmfEvbTERjsbx+2TCqoiV5p7KSrcrGtliGuBcBP7dNeey81gv//jD7TPqpppQNFT1Vzi1u1O5DtjoPt4IvD2l/X5VjajqPqANWCsiC4B6VX1anXd7b1ofb6yHgKtcK+QaYJOqdqpqF7CJseI1KxiIxkkqlj01ScJZCtzky4WL5hCOJtjb0V+IaZUFg9EkVRV+XMdBViot5TYr3WFXNLLENDz++NUL+cibVnD/5kN889f7TsfUMjLZmMZ8VT0G4N7Pc9sXAYdSjjvsti1yH6e3j+qjqnGgB2gaZ6xZh7eFSH7ZU55o2Bd1IEspzXx5VescAF443DPlOZULg7H4hK4p8ETDLmAy0TNsaWSPaXj8n7eczfKWGjbvT7+OP30UOhCe6XJDx2mfbJ/RLypyi4hsEZEtHR0zHygqNN5mhfkFwi3l1iMciSPCpLdG91jeUkt10M/2IyYaHhOVevWoDPhMNLLQHXZqZWRb3JeKiFAXCszo93qy36ITrssJ977dbT8MLE45rhU46ra3Zmgf1UdEAsAcHHdYtrHGoKp3q+oaVV3T0tIyybdUvAxbGpNIubUV4e4Otzm4UCbC7xMuWDiHFw53F2hmpY9T6nVi0agKmqWRDc/SyLa4Lx1nB+uZO5eTFY1HAC+baT3wcEr7OjcjahlOwPtZ14XVJyKXufGKm9L6eGNdDzzhxj0eA64WkQY3AH612zbr8LIrJpdya1/UcDQ+pS1EUrmwdQ47j/USt6w0wFmnkZOlYTGNrHSHY/jy2Yx0hjPRJpyliHwP+AOgWUQO42Q0fRZ4UERuBg4CNwCo6g4ReRDYCcSBW1XV+9X6EE4mVhXwqHsDuAe4T0TacCyMde5YnSLyaWCze9ynVHXmHHkzyEhMI/cfPk9gPNfWbCYcTUxpC5FUzp5fy1AsybGeoeGKfrMZp9TrxJ/LyoCPwVgCVZ2yxVdudA9GmVNVkftmpAE/J+MzV/51wv+2qt6Y5amrshx/B3BHhvYtwAUZ2odwRSfDcxuADRPNsdwZKcCUu6VRFfRTE/RzsohqC88UA5HJF2BK54w5zh5UJ3pNNMAR5CZ3b6TxCFWMxNjGKw07G+kZjI+7sC+dUMXM1sqxFeElwEgBpvx++JrrQkWzX81M4pR6LcwP1Rn1lQAc6xkqyHilzmAsN/eUF/cwd+lYusPRvC4IKwP+GV1/ZaJRAvQOxggFfMMZUbnSXGuiAVMr9ZrOGXMc0TjRa6IBuWdPeWsQvDUJxghOLY084pVmaRgTke++Ux7NtUETDdxSrwWyNOorA1RV+M3ScBmMJqiqmFiQG2oc98upAXOXptMdjk24sC8VszSMCenNc4dbD8fSsC9puICWhoiwYE4lx83SQFXdQPjEgtzkikaXicYYJmNpzOSWLCYaJUDv4GQtjRBd4eisTw8dyPGHLVfm11dy3CwNIvEkSZ14s0KABjfQ2xk20UglkVR6h7JX7ctEKOAjltAZ2wPNRKME6BuK55Vu69FcF0IVOmf51V24gNlTgGNpmGjkXEsDoNEsjYz0DMZQhTl5ZE9VDmeizYy1YaJRAuRbH9yj2f2idsziuEY0niSaSBZsnQY4wfATvUMkZ/lut2FvW/QcUmirg36CAd+sv4BJ56hbPnihm2CRC8P7ys1QXMNEowToHIgOX6nlQ7NbkH42xzWGr4YLtCIcHNGIJ3XWB3U73c9VLmsMRITG6qCJRhqHuxzRaG3Ifc3PTO8rZ6JR5MQSSbrDMZpqJyEa7qKrk0VSkH4mCMecNS4FtTTctRqz3UV1pNspfdvaUJXT8Y01QbospjGKI66lsSjHcwgjG2/O1JoXE40ix/MB57LqNp3mWi/NcfaKhleAqdCWBsCxnsGCjVmKeFfJi+bmLhpmaYzmcFeY6qCfhjxSbs3SMMbFc4E0T8I9VRsKEAr4ZrV7yivAVOiYBtgCvyPdg1QH/TmvMWgw0RjDka5BFs2tyms/rsoZLrBmolHknHJ/8CcT0xARZ63GLHZPDVsaBcyeaq4JEfDJrF/gl+8PXmN1hYlGGoe7BnN273l4lsZM7XRrolHkeK6lybinwAmGz+bsqeFSrwW0NHw+obWhildmednXI92DefniG2qC9A7Fic3ydUOp5HsOYeZLOZtoFDmea6l5EoFwgJba4Kx2T3nbqEzGUhuPi5c0sPVAF07pl9nJ0e7BnOMZMLIq3PafcugbitEzGMsrcwqcbUTAUm6NLHQORAj4ZFLrNMA2LTzSPYTISByiUFyytIGT/VEOnAoXdNxSIRyN0xWO5W1pgC029RjOnMpDeGHE0piprURMNIqcU/1RGmqCORdoSWdeXYhT/ZEZ23JgpjnaPcj8ukoq/IX9qL9maSMAm/fPyrpgHMkzcwqgsdpEI5Ujw2s08hONkrY0RGS/iGwXkW0issVtaxSRTSKyx71vSDn+NhFpE5HdInJNSvsl7jhtInKnWxIWt2zsA277MyKydCrzLUVO9keHzfrJ0FJfSVLh1Cy1No71DLJwbmGtDIAVLbXUVwbYsr+r4GOXAocncZXsWRq2VsNhOGV50jGNEhQNlzeq6mpVXeP+/QngcVVdCTzu/o2IrMIp5Xo+cC3wVRHxopN3Abfg1BRf6T4PcDPQpaorgC8CnyvAfEuKUwOR4UV6k2G+uyr8RO/sFI2j3UMsyNP8zwWfT1iztJEtB2a5pZHHD16juadGcaR7kFDAR0ue329vG5FyWtx3HbDRfbwReHtK+/2qGlHVfUAbsFZEFgD1qvq0OlHFe9P6eGM9BFwl+SQ0lwGn+qOTWg3uMb9+9q4pUFUnO2UaRAPgkjMbeKVjYFZuwneke5CAT5hXl7sV12DuqVG0tfdzZlN13jXTKytKe3GfAv8jIltF5Ba3bb6qHgNw7+e57YuAQyl9D7tti9zH6e2j+qhqHOgBmqY455JisvtOeQyLRt/sE41TA1Gi8WRem8HlwwWL5gCw+0TftIxfzBzsDLNgbiX+PGJtwYCPulDARMPlpWO9nLegPu9+QX9pp9xeoaoXA28FbhWR149zbKZPl47TPl6f0QOL3CIiW0RkS0dHx0RzLhmGYgn6I/Epuaeaa4OIzE731LFuRygXTpOlcVZLDcCsW6+hqjx3oItXLZqbd9+Fc6s41Dk7M85S6Q5HOdozNCnR8PmEoN9Xmov7VPWoe98O/BBYC5xwXU649+3u4YeBxSndW4GjbntrhvZRfUQkAMwBxjiRVfVuVV2jqmtaWlqm8paKCm8LkakEwgN+H821IdpnoXvKS2mcLtFYOKeKqgo/r7QPTMv4xcqhzkGO9Qxx6fLGvPuumF/Ly+2zzzJL56XjzjmYjGjAzNYJn7RoiEiNiNR5j4GrgReBR4D17mHrgYfdx48A69yMqGU4Ae9nXRdWn4hc5sYrbkrr4411PfCEzqLVVKcKtDBtfn1oVsY0jk6zaPh8wvKWmllnafxu3ykALl2Wv6f47Hl1HOocHF6pP1vZdawXgPPOqJtU/1DAP2OWxlQ25JkP/NAN4gSA/1TVn4nIZuBBEbkZOAjcAKCqO0TkQWAnEAduVVVPKj8EfBuoAh51bwD3APeJSBuOhbFuCvMtOU5NYYfbVObXVc7KfZKOdg9SWeHLawfRfDmrpZatB2ZX2u0zeztpqK5g5bzavPuePd/p09bez6ta83dvlQu7jvXSVBOkpW5y3+3KGbQ0Ji0aqroXeHWG9lPAVVn63AHckaF9C3BBhvYhXNGZjXS4cYjJbiHiMa++km2HugsxpZLiaM8gC/PcQTRfVsyr5ZHfH2UwmsipVnYp8/zBLg6cCvO7vae4dFnTpBacrpzvXFm/fGJ2i8ZLx/s4d0HdpD+boYCvZLOnjGnklY5+gn7flFNG59eHhjOJZhNHu4emLd3W46wW58p578nyd1Hd9oPtfOyBbRzpHpxUPANgaVM1Qb+PPbMw48wjnkiy+3gf550xuXgGOO6pSBmt0zAKxMsn+ljeUkNgiltgeGm3s2m321gi6Zy/5pppfZ0V80bcLeVMe98QLx3v4+2rF/Ley5dy3epFE3fKQMDvY3lLDS/PYtHYcbSXSDzJ+YsmLxqOe6r0YhrGNLOnvZ+LljRMfOAEzK/3VoVP/5V3sbDjaC/haILXLJvcFXGunNlUjU/glY7yzqB6+hUn+H3zlcu5sHXOlMZaOb+O52ZRHOgff7ido92DfHP9a/D7hB/9/igVfuGN58ybuHMWHEvD3FNGCgOROIe7Bjl7EsHGdLxVu7Mp7XbzPicze+3S6RWNygo/ZzbVsPNo77S+zkzz1J6TzK2uYNXCyV8de5w9r5Yj3YP0R8o/g6pnMMZ/bTnML3Z3sOGpfSSSyo9eOMobzp7H3OrJxypDFT7b5dYYjefu8AKHU8FzTx2fRRlUz+zrZFlzDfPqp2c1eCpXrmjmN20nZ2wvoOlGVflN20kuP6sprxXg2Xj1YicA/jvXeilnHt1+jGgiyaoF9fzr/+zm8z97iRO9Ef5k9cIpjVtploaRjufzXTl/6pZGU02Qqgo/+2dJ7YdkUtm8v3ParQyPN6+az2AsMezCKTf2nhzgaM8QV6xoLsh4ly1vor4ywE9fPFaQ8YqZ/952hGXNNdx781rOPaOOr/9qL9VBP28+b/KuKZjZxX0W0yhS2tqdzKkzG/Or6pUJn084d0Hd8IKicufl9j56BmOsneZ4hsdlyxupCfrZtOsEbzx3aj8Gxcgj244iwpR88KkEAz7evGo+P995gmg8STBQnteu+04O8My+Tj521dk014b477+6gp/tOE6F3zflmvWVM7i4rzz/W2VAoTKnPM5bUM/OY72zojyp5/Y4XaIRCvh5wzkt/HznCZJlVuwqmVQe2nqYK1c0F3Rl/dsuWEDvUJyn95anddbRF+F933qWulCAG9Y4uyT5fMLbLlzAW1bNn/L4JbmNiDE9RONJvvnrvTy7r5NzJrnFQCZWLainbyg+vB9TOfNU2ymWNFazuABWWq68+bz5tPdFhrfYKBd+t/cUR7oHuf6S1okPzoMrVzZTE/Tz8PNHCjpuMTAYTfD+b2/mRG+Eb71v7bRsY2OL+4xhPv3jnfx/P9nFha1z+OhVKws2rrcxWrln+cQTSZ7Ze6pg/vdceduFC2iuDXLXk6+c1tedLk70DvGPP9zOPz38InWVAa45/4yCjl9Z4edPX7OY/952pKzWuKgqf/PQ73nxaA9f+bOLuOTMqafMZ6Kyws9QLDEjngMTjSJi64EuvvPMAd57+VLuv+W1LG+ZehDc49wz6hCBXcfKe1HVC0d66IvEuWLF6S27Ulnh5+Yrl/PrPSf5fYlv2dIfifPeb23moa2HqQz4+eu3nD1c+KeQfPiNK6gOBvjXx14q+NgzQTSe5G8feoGfvHCMv7/2XK46b+puqGyEAj6SCvEZcIeaaBQBnQNR/vWxl/jIfz7HGfWV/M015xT8NWpCAZY21ZRkMPxUf4SXjuc279+2nQTg8rNOr6UB8OeXLWFOVQXf+PXe0/7ahUJV+dj923j5RB9337SGn370dbz3imXT8lpNtSE++PrlPLbjBD/feWJaXuN08pHvPcdDWw/z0atW8sHXL5/W15rJ6n2WPXUa2Xqgk/ufPcS+kwOsWljPOy9u5dWL5/L333+BJ15qZ/Xiudz21nOpDU3Pv2XVgnq2HepmKJaYlivH6aBzIMr1X3ua/acG+Ks/OIsbLlnMgrmVhAKj5//k7nY+++hLnBqIsmpB/ZS3k58MdZUVXHv+GTz64jESSS3ImobTzQ+eO8LPd53gn/7wPN5w9vTXpvnA65fz2M7jfPyBbTz84SsKal2fTn7bdpLHdpzgb685h1vfuGLaXy+1Tvh0/V5kwyyN08Sj249x493P8D87T5BU5cEth3jnXb/l//2ijU07T/DxN6/k+x+6nDXTuLbgypXNHOke5IrPPsFjO45P2+sUiuM9Q/zFxs0c6R7kmlVn8P9+8Qp/8G9PcsVnn2Dz/pFaXJF4gn9+eAcn+yME/b6CB23z4YqVzfQOxXnxSM+MzWEyvHC4mw1P7eNTP97JmjMbeP80WRfpVFb4+dqfX0LAL3zi+9tPy2sWGlXl3/5nN2fUV3LzlafnvHkXTWZplClb9ndy638+x0VLGrhn/RrmVgfpCcf4068/zb8+tpvm2iDvOw1f0nWvWcySxmo+8+gu58ru1isIBnzMrQoyZxprTkyGh7cd4RPf305ClS+9azVvvXABzx/soq29n7uefIV3f+MZPvO/LuSdl7Ry39MHONgZZuP7156Wq+PxuPwsJ5byVNvJ4ZXPxc5Lx3t5512/JZZQmmqCfO76V01q2/PJ0tpQzUfetJJP/Xgnzx3s4uIC7LdWaGKJJAGfDG9l/uy+Tjbv7+T6S1r54fNHeO5gN3e844LTZsGHKtw64TOwC4GUW97+mjVrdMuWLTM9jWGGYgne9qVfE00k+dnHXj/KlDzSPcgHNm7hA69fxjsuOn1Xxyd6h/jDO39N72CcaMJZXHXdqxdyxzsuLIqFVkOxBFd89glaG6r4yp9dPCZ1ticc40Pf3cpvXznFpcsaee5gF5ctb+K+my+doRmP5q1f+jUN1RX85wcum9F5bDvUzZO726kO+hmIJDjUGWbH0V78PuHMpmreeXErZ8+v4y+/s5X2viF++FdXsHBu1Yy41fojcS7/zONcubKZr777ktP++tk40j3Ilx/fww+ec1Z2X33+fH5/uIdfvdwBgN8nJJLKNefP5yt/djEVBVpXNRGP7TjOB+/byn/fegWrC3RxIiJbVXXNRMeVhKUhItcCXwL8wDdV9bOFfo1kUnnk90dpbahicWM19ZUV3PPUXnYd7+Nf3nEhc6ryvxI/2R/hnx9+kb0nB/juX1w6xve4aG4VP/3o6wr1FnJmfn0ld/35JXzz13u5/KxmXjrex/eePciapQ286zVLCvpaLx7p4Xd7T1FfVcFrljayLIetyh/ZdpRTA1G+fONFGddazKmuYOP71/LJR3bw6IvHec9lS/mrN55V0HlPhStXNLHxtwfyLsyUSCr9kfikPmupqCr3Pn2AT/9456jsmpa6EBcumoMAWw508eiLIy7Kb9y05rSua0mnNhTg3Zedydd/+QpPvHSCN507fZlHuaCq/OiFY/zjD7cTjSf5k9ULefFID19+oo0zm6r5+JvP5toLzuCep/Zy3oJ63nv50mkt9pXORYvn4hP4+c4TBRONXCl6S0NE/MDLwFuAw8Bm4EZV3Znp+MlaGid6h7j0Xx5PeV1QBZ/A6sVz2fj+tdQEAzy24zib93fRH4nxnsuWDm8Trao8f6ib3+w5yZqljbx0vJcvbHqZwWiCj7/l7NMSHJssqsoff+UpBiIJfv5/3pDxSvOl4738dPtxljfX8LqVzeOWoD3aPcj9zx7kqbaTPHdwdPrp2mWNnL+wnuXNNSxrrmVpczV1oQoGonHC0QThaJy//a8XEIFHP/q60/pFLBS/aTvJu7/5DG8+bz7/6+JF7Dzay5+uWcySpmoOdYaJJ5W+oRi7j/fxupUttNSFuH/zQb7xq73sPxXm7Pm1vLp1Lhe2zuH6S1rz2nJiy/5OPv2TXfz+UDdXnTuPL/zpanw+qKrwj9pdIJZI8quXO+gciLK4sZrLlp/eFOVMnOqPcOM3fsfLJ/p507nzeO3yJm5Y00plhZ9Hfn8Uvwjz6kPMr69kXl2IOVUVWT8f6e6kXInEEzyzt5Ov/fIVfvvKKS5aMpcvvesiljRVo6oMxhJT3gKkUKzf8Cxt7f38+u/eWBB3Yq6WRimIxmuBT6rqNe7ftwGo6mcyHT9Z0Ygnkuw/FeZQV5hDnWGO9wzx+rNb6BqI8uHvPU9N0M/CuVW8dLyPqgo/fp/QH4lzwaJ6FsypYufR3jGrrV+3spnb/3gVK+YVbmX3dPGTF45x638+xz++7TzeeG4Lzx3s5rdtJ91sqyTHU7ZVr6rwc/OVy2iqDXKsZ4jjPUOIQFNNiMWNVXzp8T30Dsa4YNEc/vDCBbzjokUMRBP85IWjPPricfadHCAcHd8X++83vJp3zmBAeyqoKt/6zX4+8+guYgnn+zW3uoKLlzTwxEvto46tCwVY0lTNjqO9rF48lzec3cJzB7vYdayPk/0RmmtDnHtGHZ0DUZIZvqsBv7CipZaWuhB7OwZ4/KV25teH+Jurz+GdF7ee1thEIRiKJfjyE3v40e+PcbAzTEN1BTWhAIe7xu5kEAz4mF8f4vLlzaxZ2sCuY30c6xnkcNcgu471sqihijeeM4/nD3bRPRjD7xOO9wxRGwpw0ZK5hAJ+Eu45PWd+HR19EX74/BH6I3HmVlfw8TefzbsvXVKwrXwKzcPbjvDR+7fxvQ9cxmvPmrrol5NoXA9cq6p/4f79HuBSVf1wpuOnI6bx/MEuNvxmP3tO9PGB1y13fwTj3Pv0AZ5+5RRHewY574x6XreymTedN4/N+7qorQzw+pXNJXOlnEgqf/zlp9iZso6juTbImjMbqasMcNa8Wv50zWIOd4X5+i/38pPtzg6lwYCPM+orEYH23giDsQTnnlHHXX9+SVZXlKrS3hdhb8cAe0/2MxRLUhP0UxX0UxMMMKe6gjVnNpTMucvG3o5+2vucH/6//M5WJxvsdcs4s6maUMDPgjmV/Otju9l1rJdP/sn5/MmrF456z1sPdPKVJ9roHozRVBPEl+F8DMWTvHy8j+7BKHOqKviztWfygdcvK5qr4amw61gv//LTXfQOxfnbq89hUUMV7b1DnOiL0N47RHtfhMNdYZ7c3UE4mqCywkdrQzVn1FeyamE9zx/sYuuBLi5a0kBrQxXReJL59ZV0haNsP9yD4ngS4knlwKkwwYCPP3rVAv7oVQt47fLmoq/5PhhN8Jo7fk5lhY8GtzbHuQvq+fKNF01qvHISjRuAa9JEY62qfiTlmFuAWwCWLFlyyYEDB2ZkrqXOUCzBtkPdHOwM8+rWuZw9vzbrD/fR7kFCAR+NNcHhY2KJJHs7BljaXD1mHcVsJxJPEEtoxpz6eCJZtFezpUA46hQsW9ZcMyYQneu57R2KAVBfWVxZhBPx4JZDPLl7xHpd2lTD31177qTGKifROC3uKcMwjNlMrqJRCpc3m4GVIrJMRILAOuCRGZ6TYRjGrKToHZ+qGheRDwOP4aTcblDVHTM8LcMwjFlJ0YsGgKr+FPjpTM/DMAxjtlMK7inDMAyjSDDRMAzDMHLGRMMwDMPIGRMNwzAMI2dMNAzDMIycKfrFffkiIn3A7gINNwcoZDWdQo7XDJws0FhQ3O91OsbzKNR5LIX3W+gx7TM4NYrp/DUDNao6cUEaVS2rG7ClgGPdXeC5FWy8Qr7PYn+v0zFeoc9jKbzfaZijfQbL5PzlMxdzT43Pj4p8vEJS7O+1mM8dlMb7tXNYXOMVmtMyv3J0T23RHPZPKXVmy/ucbuw8Th47d1OjmM5fPnMpR0vj7pmewGlitrzP6cbO4+Sxczc1iun85TyXsrM0DMMwjOmjHC0NwzAMY5ow0SgSRGSxiPxCRHaJyA4R+ajb3igim0Rkj3vf4LY3ucf3i8hXUsapE5FtKbeTIvIfM/W+TjeFOo/uczeKyHYReUFEfiYizTPxnk4XBT5373LP2w4R+fxMvJ/TzSTO31tEZKv7GdsqIm9KGesSt71NRO6UYipjWciUL7tNKV1uAXCx+7gOeBlYBXwe+ITb/gngc+7jGuBK4C+Br4wz7lbg9TP9/krtPOLsAN0ONLt/fx6nGNiMv8cSOHdNwHd5hvYAAARESURBVEGgxf17I3DVTL+/Ijx/FwEL3ccXAEdSxnoWeC0gwKPAW2f6/Xk3szSKBFU9pqrPuY/7gF3AIuA6nC8d7v3b3WMGVPUpYCjbmCKyEpgH/Hoap15UFPA8inurca/y6oGj0/8OZo4CnrvlwMuq2uH+/XPgndM8/RlnEufveVX1PlM7gEoRCYnIAqBeVZ9WR0Hu9foUAyYaRYiILMW5CnkGmK+qx8D5UOKIQK7cCDzgfvBmHVM5j6oaAz4EbMcRi1XAPdM43aJiip/BNuBcEVkqIgGcH7zF0zfb4mMS5++dwPOqGsERmsMpzx1224oCE40iQ0Rqge8DH1PV3ikOtw743tRnVXpM9TyKSAWOaFwELAReAG4r6CSLlKmeO1Xtwjl3D+BYufuBeCHnWMzke/5E5Hzgc8AHvaYMhxXNhZ+JRhHh/lB9H/iuqv7AbT7hmqu49+05jvVqIKCqW6dlskVMgc7jagBVfcW11B4ELp+mKRcNhfoMquqPVPVSVX0tzl5we6ZrzsVEvudPRFqBHwI3qeorbvNhoDVl2FaKyDVqolEkuH7ze4BdqvqFlKceAda7j9cDD+c45I3MQiujgOfxCLBKRLwN3N6C46MuWwr5GRSRee59A/BXwDcLO9viI9/zJyJzgZ8At6nqb7yDXRdWn4hc5o55E7l/76efmY7E28254WShKI4bZJt7extOJsrjOFdqjwONKX32A51AP87VyaqU5/YC5870+yrl84iTFbTLHetHQNNMv78SOnffA3a6t3Uz/d6K8fwB/wQMpBy7DZjnPrcGeBF4BfgK7kLsYrjZinDDMAwjZ8w9ZRiGYeSMiYZhGIaRMyYahmEYRs6YaBiGYRg5Y6JhGIZh5IyJhmGcZkTkL0XkpjyOXyoiL07nnAwjVwIzPQHDmE2ISEBVvzbT8zCMyWKiYRh54m5G9zOczeguwtkC+ybgPOALQC1wEnivqh4TkSeB3wJXAI+ISB3Qr6r/JiKrga8B1TgLud6vql0icgmwAQgDT52+d2cY42PuKcOYHOcAd6vqq4Be4Fbgy8D1qur94N+RcvxcVX2Dqv572jj3An/vjrMduN1t/xbwv9XZu8kwigazNAxjchzSkf2CvgP8A04hnU1ukTU/cCzl+AfSBxCROThi8ku3aSPwXxna7wPeWvi3YBj5Y6JhGJMjff+dPmDHOJbBQB5jS4bxDaMoMPeUYUyOJSLiCcSNwO+AFq9NRCrcOglZUdUeoEtEXuc2vQf4pap2Az0icqXb/u7CT98wJodZGoYxOXYB60Xk6zi7l34ZeAy403UvBYD/wCnjOR7rga+JSDXOzsTvc9vfB2wQkbA7rmEUBbbLrWHkiZs99WNVvWCGp2IYpx1zTxmGYRg5Y5aGYRiGkTNmaRiGYRg5Y6JhGIZh5IyJhmEYhpEzJhqGYRhGzphoGIZhGDljomEYhmHkzP8PTTd+fTih+OcAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2301,7 +2322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2321,7 +2342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2345,16 +2366,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b7d889438>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5fab957ac8>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2384,7 +2405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -2427,7 +2448,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2446,16 +2467,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b7d85aeb8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5fab92f320>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2475,20 +2496,6 @@
    "source": [
     "yearly_incidence.hist(xrot=20)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/module3/exo1/incidence-PAY-3.csv b/module3/exo1/incidence-PAY-3.csv
index fa7a45b..ba6e935 100644
--- a/module3/exo1/incidence-PAY-3.csv
+++ b/module3/exo1/incidence-PAY-3.csv
@@ -1,5 +1,6 @@
-# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[198444,202014],"geo":["PAY","1"],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false}, @date=2020-04-16T17:35:09+02:00
+# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[198444,202015],"geo":["PAY","1"],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false}, @date=2020-04-16T20:07:16+02:00
 week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202015,3,0,0,0,0,0,0,FR,France
 202014,3,0,0,0,0,0,0,FR,France
 202013,3,0,0,0,0,0,0,FR,France
 202012,3,8321,5873,10769,13,9,17,FR,France
-- 
2.18.1