diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..73a0159a301fdc1b9cd1ae9292389f3e63059642 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,1394 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek\n",
+    "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n",
+    "raw_data = pd.read_csv(data_url, skiprows=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202121</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6232</td>\n",
+       "      <td>3541</td>\n",
+       "      <td>8923</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202120</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7519</td>\n",
+       "      <td>4624</td>\n",
+       "      <td>10414</td>\n",
+       "      <td>11</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202119</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6654</td>\n",
+       "      <td>4370</td>\n",
+       "      <td>8938</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202118</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3912</td>\n",
+       "      <td>2110</td>\n",
+       "      <td>5714</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202117</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4686</td>\n",
+       "      <td>2878</td>\n",
+       "      <td>6494</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>202116</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4780</td>\n",
+       "      <td>2891</td>\n",
+       "      <td>6669</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>202115</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11215</td>\n",
+       "      <td>7627</td>\n",
+       "      <td>14803</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>22</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>202114</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11197</td>\n",
+       "      <td>7994</td>\n",
+       "      <td>14400</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>22</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>202113</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9714</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>13139</td>\n",
+       "      <td>15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>20</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>202112</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11520</td>\n",
+       "      <td>8415</td>\n",
+       "      <td>14625</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>22</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>202111</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9386</td>\n",
+       "      <td>6678</td>\n",
+       "      <td>12094</td>\n",
+       "      <td>14</td>\n",
+       "      <td>10</td>\n",
+       "      <td>18</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>202110</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9056</td>\n",
+       "      <td>6452</td>\n",
+       "      <td>11660</td>\n",
+       "      <td>14</td>\n",
+       "      <td>10</td>\n",
+       "      <td>18</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>202109</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10988</td>\n",
+       "      <td>7938</td>\n",
+       "      <td>14038</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>22</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>202108</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11281</td>\n",
+       "      <td>8361</td>\n",
+       "      <td>14201</td>\n",
+       "      <td>17</td>\n",
+       "      <td>13</td>\n",
+       "      <td>21</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>202107</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13561</td>\n",
+       "      <td>10315</td>\n",
+       "      <td>16807</td>\n",
+       "      <td>21</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>202106</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13401</td>\n",
+       "      <td>9810</td>\n",
+       "      <td>16992</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15</td>\n",
+       "      <td>25</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12210</td>\n",
+       "      <td>8988</td>\n",
+       "      <td>15432</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>202104</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12026</td>\n",
+       "      <td>8826</td>\n",
+       "      <td>15226</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>202103</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8913</td>\n",
+       "      <td>6375</td>\n",
+       "      <td>11451</td>\n",
+       "      <td>13</td>\n",
+       "      <td>9</td>\n",
+       "      <td>17</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>202102</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7795</td>\n",
+       "      <td>5430</td>\n",
+       "      <td>10160</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>202101</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10525</td>\n",
+       "      <td>7750</td>\n",
+       "      <td>13300</td>\n",
+       "      <td>16</td>\n",
+       "      <td>12</td>\n",
+       "      <td>20</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>202053</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11978</td>\n",
+       "      <td>8406</td>\n",
+       "      <td>15550</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>202052</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12012</td>\n",
+       "      <td>8285</td>\n",
+       "      <td>15739</td>\n",
+       "      <td>18</td>\n",
+       "      <td>12</td>\n",
+       "      <td>24</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>202051</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10564</td>\n",
+       "      <td>7574</td>\n",
+       "      <td>13554</td>\n",
+       "      <td>16</td>\n",
+       "      <td>11</td>\n",
+       "      <td>21</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>202050</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7063</td>\n",
+       "      <td>4744</td>\n",
+       "      <td>9382</td>\n",
+       "      <td>11</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>202049</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5026</td>\n",
+       "      <td>3145</td>\n",
+       "      <td>6907</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>202048</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6683</td>\n",
+       "      <td>4312</td>\n",
+       "      <td>9054</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>202047</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4999</td>\n",
+       "      <td>2963</td>\n",
+       "      <td>7035</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>202046</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3752</td>\n",
+       "      <td>1963</td>\n",
+       "      <td>5541</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>202045</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3696</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>5376</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1561</th>\n",
+       "      <td>199126</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17608</td>\n",
+       "      <td>11304</td>\n",
+       "      <td>23912</td>\n",
+       "      <td>31</td>\n",
+       "      <td>20</td>\n",
+       "      <td>42</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1562</th>\n",
+       "      <td>199125</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16169</td>\n",
+       "      <td>10700</td>\n",
+       "      <td>21638</td>\n",
+       "      <td>28</td>\n",
+       "      <td>18</td>\n",
+       "      <td>38</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1563</th>\n",
+       "      <td>199124</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16171</td>\n",
+       "      <td>10071</td>\n",
+       "      <td>22271</td>\n",
+       "      <td>28</td>\n",
+       "      <td>17</td>\n",
+       "      <td>39</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1564</th>\n",
+       "      <td>199123</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11947</td>\n",
+       "      <td>7671</td>\n",
+       "      <td>16223</td>\n",
+       "      <td>21</td>\n",
+       "      <td>13</td>\n",
+       "      <td>29</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1565</th>\n",
+       "      <td>199122</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15452</td>\n",
+       "      <td>9953</td>\n",
+       "      <td>20951</td>\n",
+       "      <td>27</td>\n",
+       "      <td>17</td>\n",
+       "      <td>37</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1566</th>\n",
+       "      <td>199121</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14903</td>\n",
+       "      <td>8975</td>\n",
+       "      <td>20831</td>\n",
+       "      <td>26</td>\n",
+       "      <td>16</td>\n",
+       "      <td>36</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1567</th>\n",
+       "      <td>199120</td>\n",
+       "      <td>7</td>\n",
+       "      <td>19053</td>\n",
+       "      <td>12742</td>\n",
+       "      <td>25364</td>\n",
+       "      <td>34</td>\n",
+       "      <td>23</td>\n",
+       "      <td>45</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1568</th>\n",
+       "      <td>199119</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16739</td>\n",
+       "      <td>11246</td>\n",
+       "      <td>22232</td>\n",
+       "      <td>29</td>\n",
+       "      <td>19</td>\n",
+       "      <td>39</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1569</th>\n",
+       "      <td>199118</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21385</td>\n",
+       "      <td>13882</td>\n",
+       "      <td>28888</td>\n",
+       "      <td>38</td>\n",
+       "      <td>25</td>\n",
+       "      <td>51</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1570</th>\n",
+       "      <td>199117</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13462</td>\n",
+       "      <td>8877</td>\n",
+       "      <td>18047</td>\n",
+       "      <td>24</td>\n",
+       "      <td>16</td>\n",
+       "      <td>32</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1571</th>\n",
+       "      <td>199116</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14857</td>\n",
+       "      <td>10068</td>\n",
+       "      <td>19646</td>\n",
+       "      <td>26</td>\n",
+       "      <td>18</td>\n",
+       "      <td>34</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1572</th>\n",
+       "      <td>199115</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13975</td>\n",
+       "      <td>9781</td>\n",
+       "      <td>18169</td>\n",
+       "      <td>25</td>\n",
+       "      <td>18</td>\n",
+       "      <td>32</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1573</th>\n",
+       "      <td>199114</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12265</td>\n",
+       "      <td>7684</td>\n",
+       "      <td>16846</td>\n",
+       "      <td>22</td>\n",
+       "      <td>14</td>\n",
+       "      <td>30</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1574</th>\n",
+       "      <td>199113</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9567</td>\n",
+       "      <td>6041</td>\n",
+       "      <td>13093</td>\n",
+       "      <td>17</td>\n",
+       "      <td>11</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1575</th>\n",
+       "      <td>199112</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10864</td>\n",
+       "      <td>7331</td>\n",
+       "      <td>14397</td>\n",
+       "      <td>19</td>\n",
+       "      <td>13</td>\n",
+       "      <td>25</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1576</th>\n",
+       "      <td>199111</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15574</td>\n",
+       "      <td>11184</td>\n",
+       "      <td>19964</td>\n",
+       "      <td>27</td>\n",
+       "      <td>19</td>\n",
+       "      <td>35</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1577</th>\n",
+       "      <td>199110</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16643</td>\n",
+       "      <td>11372</td>\n",
+       "      <td>21914</td>\n",
+       "      <td>29</td>\n",
+       "      <td>20</td>\n",
+       "      <td>38</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1578</th>\n",
+       "      <td>199109</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13741</td>\n",
+       "      <td>8780</td>\n",
+       "      <td>18702</td>\n",
+       "      <td>24</td>\n",
+       "      <td>15</td>\n",
+       "      <td>33</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1579</th>\n",
+       "      <td>199108</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13289</td>\n",
+       "      <td>8813</td>\n",
+       "      <td>17765</td>\n",
+       "      <td>23</td>\n",
+       "      <td>15</td>\n",
+       "      <td>31</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1580</th>\n",
+       "      <td>199107</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12337</td>\n",
+       "      <td>8077</td>\n",
+       "      <td>16597</td>\n",
+       "      <td>22</td>\n",
+       "      <td>15</td>\n",
+       "      <td>29</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1581</th>\n",
+       "      <td>199106</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10877</td>\n",
+       "      <td>7013</td>\n",
+       "      <td>14741</td>\n",
+       "      <td>19</td>\n",
+       "      <td>12</td>\n",
+       "      <td>26</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1582</th>\n",
+       "      <td>199105</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10442</td>\n",
+       "      <td>6544</td>\n",
+       "      <td>14340</td>\n",
+       "      <td>18</td>\n",
+       "      <td>11</td>\n",
+       "      <td>25</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1583</th>\n",
+       "      <td>199104</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7913</td>\n",
+       "      <td>4563</td>\n",
+       "      <td>11263</td>\n",
+       "      <td>14</td>\n",
+       "      <td>8</td>\n",
+       "      <td>20</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1584</th>\n",
+       "      <td>199103</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15387</td>\n",
+       "      <td>10484</td>\n",
+       "      <td>20290</td>\n",
+       "      <td>27</td>\n",
+       "      <td>18</td>\n",
+       "      <td>36</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1585</th>\n",
+       "      <td>199102</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16277</td>\n",
+       "      <td>11046</td>\n",
+       "      <td>21508</td>\n",
+       "      <td>29</td>\n",
+       "      <td>20</td>\n",
+       "      <td>38</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1586</th>\n",
+       "      <td>199101</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15565</td>\n",
+       "      <td>10271</td>\n",
+       "      <td>20859</td>\n",
+       "      <td>27</td>\n",
+       "      <td>18</td>\n",
+       "      <td>36</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1587</th>\n",
+       "      <td>199052</td>\n",
+       "      <td>7</td>\n",
+       "      <td>19375</td>\n",
+       "      <td>13295</td>\n",
+       "      <td>25455</td>\n",
+       "      <td>34</td>\n",
+       "      <td>23</td>\n",
+       "      <td>45</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1588</th>\n",
+       "      <td>199051</td>\n",
+       "      <td>7</td>\n",
+       "      <td>19080</td>\n",
+       "      <td>13807</td>\n",
+       "      <td>24353</td>\n",
+       "      <td>34</td>\n",
+       "      <td>25</td>\n",
+       "      <td>43</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1589</th>\n",
+       "      <td>199050</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11079</td>\n",
+       "      <td>6660</td>\n",
+       "      <td>15498</td>\n",
+       "      <td>20</td>\n",
+       "      <td>12</td>\n",
+       "      <td>28</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1590</th>\n",
+       "      <td>199049</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2610</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1591 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202121          7   6232     3541    8923       9           5   \n",
+       "1     202120          7   7519     4624   10414      11           7   \n",
+       "2     202119          7   6654     4370    8938      10           7   \n",
+       "3     202118          7   3912     2110    5714       6           3   \n",
+       "4     202117          7   4686     2878    6494       7           4   \n",
+       "5     202116          7   4780     2891    6669       7           4   \n",
+       "6     202115          7  11215     7627   14803      17          12   \n",
+       "7     202114          7  11197     7994   14400      17          12   \n",
+       "8     202113          7   9714     6289   13139      15          10   \n",
+       "9     202112          7  11520     8415   14625      17          12   \n",
+       "10    202111          7   9386     6678   12094      14          10   \n",
+       "11    202110          7   9056     6452   11660      14          10   \n",
+       "12    202109          7  10988     7938   14038      17          12   \n",
+       "13    202108          7  11281     8361   14201      17          13   \n",
+       "14    202107          7  13561    10315   16807      21          16   \n",
+       "15    202106          7  13401     9810   16992      20          15   \n",
+       "16    202105          7  12210     8988   15432      18          13   \n",
+       "17    202104          7  12026     8826   15226      18          13   \n",
+       "18    202103          7   8913     6375   11451      13           9   \n",
+       "19    202102          7   7795     5430   10160      12           8   \n",
+       "20    202101          7  10525     7750   13300      16          12   \n",
+       "21    202053          7  11978     8406   15550      18          13   \n",
+       "22    202052          7  12012     8285   15739      18          12   \n",
+       "23    202051          7  10564     7574   13554      16          11   \n",
+       "24    202050          7   7063     4744    9382      11           7   \n",
+       "25    202049          7   5026     3145    6907       8           5   \n",
+       "26    202048          7   6683     4312    9054      10           6   \n",
+       "27    202047          7   4999     2963    7035       8           5   \n",
+       "28    202046          7   3752     1963    5541       6           3   \n",
+       "29    202045          7   3696     2016    5376       6           3   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1561  199126          7  17608    11304   23912      31          20   \n",
+       "1562  199125          7  16169    10700   21638      28          18   \n",
+       "1563  199124          7  16171    10071   22271      28          17   \n",
+       "1564  199123          7  11947     7671   16223      21          13   \n",
+       "1565  199122          7  15452     9953   20951      27          17   \n",
+       "1566  199121          7  14903     8975   20831      26          16   \n",
+       "1567  199120          7  19053    12742   25364      34          23   \n",
+       "1568  199119          7  16739    11246   22232      29          19   \n",
+       "1569  199118          7  21385    13882   28888      38          25   \n",
+       "1570  199117          7  13462     8877   18047      24          16   \n",
+       "1571  199116          7  14857    10068   19646      26          18   \n",
+       "1572  199115          7  13975     9781   18169      25          18   \n",
+       "1573  199114          7  12265     7684   16846      22          14   \n",
+       "1574  199113          7   9567     6041   13093      17          11   \n",
+       "1575  199112          7  10864     7331   14397      19          13   \n",
+       "1576  199111          7  15574    11184   19964      27          19   \n",
+       "1577  199110          7  16643    11372   21914      29          20   \n",
+       "1578  199109          7  13741     8780   18702      24          15   \n",
+       "1579  199108          7  13289     8813   17765      23          15   \n",
+       "1580  199107          7  12337     8077   16597      22          15   \n",
+       "1581  199106          7  10877     7013   14741      19          12   \n",
+       "1582  199105          7  10442     6544   14340      18          11   \n",
+       "1583  199104          7   7913     4563   11263      14           8   \n",
+       "1584  199103          7  15387    10484   20290      27          18   \n",
+       "1585  199102          7  16277    11046   21508      29          20   \n",
+       "1586  199101          7  15565    10271   20859      27          18   \n",
+       "1587  199052          7  19375    13295   25455      34          23   \n",
+       "1588  199051          7  19080    13807   24353      34          25   \n",
+       "1589  199050          7  11079     6660   15498      20          12   \n",
+       "1590  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0            13        FR   France  \n",
+       "1            15        FR   France  \n",
+       "2            13        FR   France  \n",
+       "3             9        FR   France  \n",
+       "4            10        FR   France  \n",
+       "5            10        FR   France  \n",
+       "6            22        FR   France  \n",
+       "7            22        FR   France  \n",
+       "8            20        FR   France  \n",
+       "9            22        FR   France  \n",
+       "10           18        FR   France  \n",
+       "11           18        FR   France  \n",
+       "12           22        FR   France  \n",
+       "13           21        FR   France  \n",
+       "14           26        FR   France  \n",
+       "15           25        FR   France  \n",
+       "16           23        FR   France  \n",
+       "17           23        FR   France  \n",
+       "18           17        FR   France  \n",
+       "19           16        FR   France  \n",
+       "20           20        FR   France  \n",
+       "21           23        FR   France  \n",
+       "22           24        FR   France  \n",
+       "23           21        FR   France  \n",
+       "24           15        FR   France  \n",
+       "25           11        FR   France  \n",
+       "26           14        FR   France  \n",
+       "27           11        FR   France  \n",
+       "28            9        FR   France  \n",
+       "29            9        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1561         42        FR   France  \n",
+       "1562         38        FR   France  \n",
+       "1563         39        FR   France  \n",
+       "1564         29        FR   France  \n",
+       "1565         37        FR   France  \n",
+       "1566         36        FR   France  \n",
+       "1567         45        FR   France  \n",
+       "1568         39        FR   France  \n",
+       "1569         51        FR   France  \n",
+       "1570         32        FR   France  \n",
+       "1571         34        FR   France  \n",
+       "1572         32        FR   France  \n",
+       "1573         30        FR   France  \n",
+       "1574         23        FR   France  \n",
+       "1575         25        FR   France  \n",
+       "1576         35        FR   France  \n",
+       "1577         38        FR   France  \n",
+       "1578         33        FR   France  \n",
+       "1579         31        FR   France  \n",
+       "1580         29        FR   France  \n",
+       "1581         26        FR   France  \n",
+       "1582         25        FR   France  \n",
+       "1583         20        FR   France  \n",
+       "1584         36        FR   France  \n",
+       "1585         38        FR   France  \n",
+       "1586         36        FR   France  \n",
+       "1587         45        FR   France  \n",
+       "1588         43        FR   France  \n",
+       "1589         28        FR   France  \n",
+       "1590          5        FR   France  \n",
+       "\n",
+       "[1591 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'data' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-4f9c04a6e476>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPeriod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'W'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'period'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mconvert_week\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myw\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0myw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'week'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "def convert_week(year_and_week_int):\n",
+    "    year_and_week_str = str(year_and_week_int)\n",
+    "    year = int(year_and_week_str[:4])\n",
+    "    week = int(year_and_week_str[4:])\n",
+    "    w = isoweek.Week(year, week)\n",
+    "    return pd.Period(w.day(0), 'W')\n",
+    "\n",
+    "data['period'] = [convert_week(yw) for yw in data['week']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.dropna().copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_week(year_and_week_int):\n",
+    "    year_and_week_str = str(year_and_week_int)\n",
+    "    year = int(year_and_week_str[:4])\n",
+    "    week = int(year_and_week_str[4:])\n",
+    "    w = isoweek.Week(year, week)\n",
+    "    return pd.Period(w.day(0), 'W')\n",
+    "\n",
+    "data['period'] = [convert_week(yw) for yw in data['week']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = data.set_index('period').sort_index() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25c67eb3c8>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25c45323c8>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1990,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-11-9a471f5827a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m                         first_august_week[1:]):\n\u001b[1;32m      5\u001b[0m     \u001b[0mone_year\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mweek1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m52\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0myear\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_august_week[:-1],\n",
+    "                        first_august_week[1:]):\n",
+    "    one_year = sorted_data['inc'][week1:week2-1]\n",
+    "    assert abs(len(one_year)-52) < 2\n",
+    "    yearly_incidence.append(one_year.sum())\n",
+    "    year.append(week2.year)\n",
+    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-6f6ae0d8eb0d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m                         first_august_week[1:]):\n\u001b[1;32m      5\u001b[0m     \u001b[0mone_year\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mweek1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m52\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0myear\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_august_week[:-1],\n",
+    "                        first_august_week[1:]):\n",
+    "    one_year = sorted_data['inc'][week1:week2-1]\n",
+    "    assert abs(len(one_year)-52) < 2\n",
+    "    yearly_incidence.append(one_year.sum())\n",
+    "    year.append(week2.year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_august_week[:-1],\n",
+    "                        first_august_week[1:]):\n",
+    "    one_year = sorted_data['inc'][week1:week2-1]\n",
+    "    #assert abs(len(one_year)-52) < 2\n",
+    "    yearly_incidence.append(one_year.sum())\n",
+    "    year.append(week2.year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25c4521ef0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.hist(xrot=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020    221186\n",
+       "2002    516689\n",
+       "2018    542312\n",
+       "2017    551041\n",
+       "1991    553090\n",
+       "1996    564901\n",
+       "2019    584066\n",
+       "2015    604382\n",
+       "2000    617597\n",
+       "2001    619041\n",
+       "2012    624573\n",
+       "2005    628464\n",
+       "2006    632833\n",
+       "2011    642368\n",
+       "1993    643387\n",
+       "1995    652478\n",
+       "1994    661409\n",
+       "1998    677775\n",
+       "1997    683434\n",
+       "2014    685769\n",
+       "2013    698332\n",
+       "2007    717352\n",
+       "2008    749478\n",
+       "1999    756456\n",
+       "2003    758363\n",
+       "2004    777388\n",
+       "2016    782114\n",
+       "2010    829911\n",
+       "1992    832939\n",
+       "2009    842373\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25c448d6d8>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.plot(style='*') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +1405,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/projet_7_evaluation_par_pairs.ipynb b/module3/projet_7_evaluation_par_pairs.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..748ac4ad8f8e78d95a403ec97d229418e151fa89
--- /dev/null
+++ b/module3/projet_7_evaluation_par_pairs.ipynb
@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sujet 7 : Autour du SARS-CoV-2 (Covid-19) *(Evaluation par pairs)*\n",
+    "\n",
+    "## Résumé de l'énoncé\n",
+    "\n",
+    "Le but est ici de reproduire des graphes semblables à ceux du South China Morning Post (SCMP), sur la page [The Coronavirus Pandemic](https://www.scmp.com/coronavirus?src=homepage_covid_widget) et qui montrent pour différents pays le nombre cumulé (c'est-à-dire le nombre total de cas depuis le début de l'épidémie) de personnes atteintes de la maladie à coronavirus 2019. Les données sont disponibles à https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv.\n",
+    "Nous créerons un graphe montrant l’évolution du nombre de cas cumulé au cours du temps pour les pays suivants (14 au total): \n",
+    "+ la Belgique (Belgium) \n",
+    "+ la Chine - toutes les provinces sauf Hong-Kong (China), \n",
+    "+ Hong Kong (China, Hong-Kong), \n",
+    "+ la France métropolitaine (France), \n",
+    "+ l’Allemagne (Germany), \n",
+    "+ l’Iran (Iran), \n",
+    "+ l’Italie (Italy), \n",
+    "+ le Japon (Japan), \n",
+    "+ la Corée du Sud (Korea, South), \n",
+    "+ la Hollande sans les colonies (Netherlands), \n",
+    "+ le Portugal (Portugal), \n",
+    "+ l’Espagne (Spain), \n",
+    "+ le Royaume-Unis sans les colonies (United Kingdom), \n",
+    "+ les États-Unis (US).\n",
+    "\n",
+    "Les graphes auront la date en abscisse et le nombre cumulé de cas à cette date en ordonnée. Nous aurons deux versions de ce graphe, une avec une échelle linéaire et une avec une échelle logarithmique."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chargement des données"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous importons les librairies necessaires pour l'analyse des données dans un premier temps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous chargeons dans un second temps les données dans une variable Python (utilisant la structure de données *panda*)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n",
+    "raw_data = pd.read_csv(data_url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb744d815f8>"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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LC+9doureZmb5ZrbWzMZFlY80s/fCuocsXBlkZmlm9mwoX2RmA6LaTArbyDOzSVHl2aFuXmjb5sT+KEREPrR1zyH+sHgzV5ydRf9M7Z00xLHsodwErI76fCsw390HA/PDZ8xsKDARGAaMBx4xs+TQ5lFgCjA4vMaH8snAbncfBDwA3Bf66gpMBc4BRgFTo4LrPuCBsP3doQ8RkRO2oegA10xbRJLBdy4aHOvhtBoNChQzywI+D/wuqngCMCMszwAujyqf6e5l7r4ByAdGmVlvoKO7v+XuDjxRq011X88DF4a9l3HAPHcvdvfdwDxgfFh3Qahbe/siIsetvLKK7zyzlB17S/nllWfqjsLHoKF7KA8C/wVURZX1dPftAOG9RyjvC2yJqlcQyvqG5drlNdq4ewWwF8iso69MYE+oW7svEZHj9pvX1/He1r38/IoRelb8Mao3UMzsEmCnuy9pYJ9Hu2Oa11F+PG3q6qvmYMymmFmumeUWFhYerYqICADv7yjhV/PzuOSM3gqT49CQPZRzgcvMbCMwE7jAzJ4CdoTDWIT3naF+AXBSVPssYFsozzpKeY02ZpYCdAKK6+irCOgc6tbuqwZ3f9zdc9w9p3v37g34uiKSiKqqnFv/uJwOaSncOWF4rIfTKtUbKO5+m7tnufsAIpPtr7r71cAcoPqsq0nA7LA8B5gYztzKJjL5vjgcFisxs9FhDuTaWm2q+7oibMOBucBYM+sSJuPHAnPDugWhbu3ti4gck6oq539fzeedzXv48SVD6dpeJ40ejxO5l9fPgFlmNhnYDFwJ4O4rzWwWsAqoAG5098rQ5npgOtAWeCm8AKYBT5pZPpE9k4mhr2Izuwt4O9S7092Lw/ItwEwzuxtYGvoQETkmlVXOt55awrxVO7h4eC++cJamY4+XRX7ZTww5OTmem5sb62GISAvywLz3+dX8PG66cDA3fmYQbVJ0vXdtZrbE3XPqq6e7DYtIwnrj/UIeejWPL52dxXcvGqynMJ4gRbGIJKQP9pby3Wff5ZQeGdx9+XCFSSNQoIhIwik+cJgb//AOpeWVPPzVs2nbJrn+RlIvHfISkYSyfe8hLv3ff1G0v4xfXjmCQT06xHpIcUOBIiIJ43BFFTc8/Q6HDlfwh2+cwycHdYv1kOKKAkVEEsZPX1zN0s17ePgrZytMmoDmUEQkIbyyagfT39zI5E9l67YqTUSBIiJxb80H+5g6ZyWDenTg1ouHxHo4cUuHvEQkrs1d+QHfemoJbVOTefiro0lN1u/RTUWBIiJxa/X2fXzv2Xc5vW8nfnPNSHp30rNNmpICRUTi0t5D5XzrqSVkpKfwu2tz6NExPdZDinsKFBGJO1VVzs3PLWPr7kPMnDJaYdJMdDBRROJKwe6D/McTufx91Q5uvXgIOQO6xnpICUN7KCISN7bvPcTExxdSWFLGTRcOZvKnsmM9pISiQBGRuLD7wGG++rtF7DlYznPfGsMZWZ1jPaSEo0ARkVavorKKbz+zlILdh3hq8jkKkxhRoIhIq1aw+yD3vrSGf+YX8fMvncGobM2ZxIoCRURarS3FB7nisTfZsS8yZ3LVJ06K9ZASmgJFRFold+ebTy6htLyKv33nUwzr0ynWQ0p4Om1YRFqdqirnv+esZNX2ffzwc0MUJi2EAkVEWp3H3ljHjLc20atjOpec0SfWw5FAh7xEpNWorHKmzlnBUws3c+mIPjw08Uw9C74F0R6KiLQa989by1MLN3P5mX346ReGK0xaGO2hiEir8MLybTy8YB1fHnUSP/3C6QqTFkh7KCLS4q35YB83P7eckf27cMdl2jNpqeoNFDNLN7PFZrbMzFaa2R2hvKuZzTOzvPDeJarNbWaWb2ZrzWxcVPlIM3svrHvIwr8KM0szs2dD+SIzGxDVZlLYRp6ZTYoqzw5180LbNo3zRyIiLcm+0nJufPodOqSn8OjVZ9MmRb8Ht1QN+ZspAy5w9xHAmcB4MxsN3ArMd/fBwPzwGTMbCkwEhgHjgUfMLDn09SgwBRgcXuND+WRgt7sPAh4A7gt9dQWmAucAo4CpUcF1H/BA2P7u0IeIxJE1H+zj0/ctYH3RAR789zPpkaHb0Ldk9QaKR+wPH1PDy4EJwIxQPgO4PCxPAGa6e5m7bwDygVFm1hvo6O5vubsDT9RqU93X88CFYe9lHDDP3YvdfTcwj0igGXBBqFt7+yISB/YeLOebTy4hNTmJJ64bxbmDusV6SFKPBu07mlmymb0L7CTyA34R0NPdtwOE9x6hel9gS1TzglDWNyzXLq/Rxt0rgL1AZh19ZQJ7Qt3afdUe+xQzyzWz3MLCwoZ8XRGJsaoq57vPLmXbnkP85pqRfHpw91gPSRqgQYHi7pXufiaQRWRvY3gd1Y82W+Z1lB9Pm7r6qlno/ri757h7Tvfu+kcp0ho8+Mr7LFhbyE8uHcbI/l3qbyAtwjHNbrn7HuA1InMfO8JhLML7zlCtAIi+Q1sWsC2UZx2lvEYbM0sBOgHFdfRVBHQOdWv3JSKtVEVlFU+8tZGHXs3nypFZXH1Ov1gPSY5BQ87y6m5mncNyW+AiYA0wB6g+62oSMDsszwEmhjO3solMvi8Oh8VKzGx0mAO5tlab6r6uAF4N8yxzgbFm1iVMxo8F5oZ1C0Ld2tsXkVbocEUVU55cwk9mr2REVifuulynB7c2DbmwsTcwI5yplQTMcvcXzOwtYJaZTQY2A1cCuPtKM5sFrAIqgBvdvTL0dT0wHWgLvBReANOAJ80sn8ieycTQV7GZ3QW8Herd6e7FYfkWYKaZ3Q0sDX2ISCvk7vzoL+/x6pqd/Ojzp3HNmP6kpSTX31BaFIv8sp8YcnJyPDc3N9bDEJFaHl6Qzy/mruU7Fw7m+589JdbDkVrMbIm759RXT1cIiUhM/XXZNn4xdy0TzuzD9y4aHOvhyAnQvbxEJCZKyyu5aeZS5q7cwdn9OnPfl87QnEkrp0ARkWZXWeV855mlzFu9gxs/M5Apnx5IeqrmTFo7BYqINCv3yNMW/75qB/996VC+dm52rIckjURzKCLSrJ5atJknF27im+edrDCJM9pDEZFmkbejhMffWM9zSwq4YEgPbhk/JNZDkkamQBGRJveXpVv53qx3SUkyrsrJ4vbPDyUpSRPw8UaBIiJN6q11u/jP55YxakBXfnnVCLK6tIv1kKSJKFBEpEms+WAfLy7fzlOLNtOvazumfe0TdEjTj5x4pr9dEWl0v5i7hocXrCM5yTjrpM7c+8XTFSYJQH/DItJo3J3HXl/PwwvWMeHMPtx28Wn06qSnLCYKBYqINIpF63fxs5fXsHTzHj53ei9+eeUIUpJ1ZUIiUaCIyAn7y9Kt/OC5ZfTqmM49XxjOV0b1021UEpACRUROyB8Wbeb2v7zHOdld+d0kTbwnMv3Ni8hxWbF1L0++tYlnc7fwmVO78+jVI3U/rgSnQBGRY1JV5fz2H+v5xdy1mMG1Y/rzo88PpU2K5ksSnQJFRBps1/4yfvDcMl5bW8jFw3vxsy+eQad2qbEelrQQChQRqdeBsgoee30dzyzewr7Scu6aMIyrR/fXxLvUoEARkTr9K7+I/3p+OVv3HOKTAzO5/fOnMaxPp1gPS1ogBYqIHNWGogM8vXAT0/61gezM9sz65hhGZXeN9bCkBVOgiEgNew+W89MXV/Ns7hYAvjzqJH5yyTDattEZXFI3BYqIHPHO5t1c/9QSivYf5pvnnczVo/tzUlfdHVgaRoEiIqz9oIRHX8vnb+9tp3entsy+8VyG99U8iRwbBYpIglu2ZQ8TH18YHn51EjePO5XO7drEeljSCilQRBLYxqIDXDf9bbpltOH5b32Snh11Z2A5fvVe2mpmJ5nZAjNbbWYrzeymUN7VzOaZWV547xLV5jYzyzeztWY2Lqp8pJm9F9Y9ZOEkdjNLM7NnQ/kiMxsQ1WZS2EaemU2KKs8OdfNCW/1KJXIMSssr+cYTuVS5M+ProxQmcsIacq+ECuAH7n4aMBq40cyGArcC8919MDA/fCasmwgMA8YDj5hZ9ekhjwJTgMHhNT6UTwZ2u/sg4AHgvtBXV2AqcA4wCpgaFVz3AQ+E7e8OfYhIAz21cBP5O/fzwL+fycndO8R6OBIH6g0Ud9/u7u+E5RJgNdAXmADMCNVmAJeH5QnATHcvc/cNQD4wysx6Ax3d/S13d+CJWm2q+3oeuDDsvYwD5rl7sbvvBuYB48O6C0Ld2tsXkXoUlpQx7Z8b+MSALpx/ao9YD0fixDHdzS0cijoLWAT0dPftEAkdoPpfZV9gS1SzglDWNyzXLq/Rxt0rgL1AZh19ZQJ7Qt3afdUe8xQzyzWz3MLCwmP5uiJxqbLK+fr0xewsKeP68wfGejgSRxocKGbWAfgj8F1331dX1aOUeR3lx9Omrr5qFro/7u457p7TvXv3o1URSShPLdzEiq37uP+qEVwwpGeshyNxpEGBYmapRMLkaXf/UyjeEQ5jEd53hvIC4KSo5lnAtlCedZTyGm3MLAXoBBTX0VcR0DnUrd2XiHyM9YX7+fnLa/j04G5cNqJPrIcjcaYhZ3kZMA1Y7e73R62aA1SfdTUJmB1VPjGcuZVNZPJ9cTgsVmJmo0Of19ZqU93XFcCrYZ5lLjDWzLqEyfixwNywbkGoW3v7IvIx7vjrKpKTjJ9fcYbuFCyNriHXoZwLXAO8Z2bvhrIfAj8DZpnZZGAzcCWAu680s1nAKiJniN3o7pWh3fXAdKAt8FJ4QSSwnjSzfCJ7JhNDX8Vmdhfwdqh3p7sXh+VbgJlmdjewNPQhIh9j0fpdvP5+IbddPITendrGejgShyzyy35iyMnJ8dzc3FgPQ6TZ7T1UzpcefZOS0nJev/kzelSvHBMzW+LuOfXV05XyInHO3fnOM0vZtOsAM64bpTCRJqOHQIvEuUUbinn9/UJuGT+ETw7sFuvhSBxToIjEuTnLtpGRlsLVo/vHeigS5xQoInFuecEeRpzUWYe6pMkpUETiWGl5JWu2l3BGlp5tIk1PgSISx3I37qaiyjm7X5f6K4ucIAWKSJzac/AwL63YTnpqEucO0mS8ND2dNiwSh8orqzjzznkAXHRaT9q20fyJND3toYjEofe27j2yfMEQ3Z5emocCRSQOLVy/C4C+ndsyfnivGI9GEoUOeYnEoRVb99I/sx2v3/yZWA9FEoj2UETi0NoPSji1Z0ashyEJRoEiEmdKyyvZuOsgp/ZSoEjzUqCIxJnlBXuprHKG9OoY66FIglGgiMSZ53K30L5NMuefqkdeS/NSoIjEkb0Hy/nr8m1cdmYf2qfpnBtpXgoUkTjy/DsFlJZX6c7CEhMKFJE44e48vXATZ/frzLA+uhmkND8FikiceHPdLtYXHdDeicSMAkUkTvz2H+vp0i6Vz53eO9ZDkQSlQBGJA7kbi3ltbSFTzhuoB2lJzChQROLAQ6/m061DGpM+qcNdEjsKFJFW7tDhSt5aV8SXzu5LuzY6VVhiR4Ei0sr9aWkB5ZXO6IGZsR6KJDgFikgrVrD7IFNnr6R7RhqfGNA11sORBFdvoJjZ781sp5mtiCrrambzzCwvvHeJWnebmeWb2VozGxdVPtLM3gvrHjIzC+VpZvZsKF9kZgOi2kwK28gzs0lR5dmhbl5o2+bE/yhEWp+nFm7Ggee+OYYOujJeYqwheyjTgfG1ym4F5rv7YGB++IyZDQUmAsNCm0fMrPqUk0eBKcDg8KruczKw290HAQ8A94W+ugJTgXOAUcDUqOC6D3ggbH936EMkoZSWVzLz7c2MHdqTAd3ax3o4IvUHiru/ARTXKp4AzAjLM4DLo8pnunuZu28A8oFRZtYb6Ojub7m7A0/UalPd1/PAhWHvZRwwz92L3X03MA8YH9ZdEOrW3r5IwpizbBt7DpZz7ZgBsR6KCHD8cyg93X07QHivfmh1X2BLVL2CUNY3LNcur9HG3SuAvUBmHX1lAntC3dp9fYSZTTGzXDPLLSwsPMavKdIylVdWMe0fGzilZwdGn6y5E2kZGntS3o5S5nWUH0+buvr66Ar3x909x91zunfX7byl9XN37vnbatbuKOH7nz2VMB0pEnPHGyg7wmEswvvOUF4AnBRVLwvYFsqzjlIu682yAAAHw0lEQVReo42ZpQCdiBxi+7i+ioDOoW7tvkTi3k9mr2T6mxv52icHMH54r1gPR+SI4w2UOUD1WVeTgNlR5RPDmVvZRCbfF4fDYiVmNjrMgVxbq011X1cAr4Z5lrnAWDPrEibjxwJzw7oFoW7t7YvEtUXrd/Hkwk1MGtOfqZcOjfVwRGqo9zxDM3sGOB/oZmYFRM68+hkwy8wmA5uBKwHcfaWZzQJWARXAje5eGbq6nsgZY22Bl8ILYBrwpJnlE9kzmRj6Kjazu4C3Q7073b365IBbgJlmdjewNPQhEtcOV1Txo7+sIKtLW269+DQd6pIWxyK/8CeGnJwcz83NjfUwRI7LI6/l8/OX1/L7r+VwwZCesR6OJBAzW+LuOfXV05XyIq3A2g9KeGh+HuOH9VKYSIulS2tFWjB358mFm7h/3vtkpKdy54RhsR6SyMfSHopIC/bgK3n8ZPZKTuvVkVnfHEOPjumxHpLIx9IeikgL9cqqHfx6QT5fOKsv9181QpPw0uJpD0WkBXrj/UJuePodhvfpyF2XD1eYSKugPRSRFqS0vJL7573PtH9uYHCPDsy4bpTuIiythv6lirQQb28s5r+eX86GogNM/MRJ3HrxEDq305MZpPVQoIjE2M59pTz6+jqmv7mRPp3a8vQ3zuHcQd1iPSyRY6ZAEYmRktJyfvjnFfx1WeRWdNeM7s8tFw/RIS5ptfQvVyQGthQf5BszcllXuJ8bzh/IF8/uy6AeGbEelsgJUaCINKO9h8r545ICHnzlfRyYcd0oHd6SuKFAEWkG7s6L733A1DkrKNp/mHMHZXL35aeTrUf3ShxRoIg0sfd3lHD331bzxvuFnN63E7+5Joez+3XWtSUSdxQoIk3A3Xl1zU4ee30db2/cTYe0FKZeOpRrRvcnJVnXE0t8UqCINKK9B8uZv2YHv/3HBlZv30ffzm354eeG8MWzs+jWIS3WwxNpUgoUkRN0uKKKN9cV8ad3tvLSiu2UVzond2/P/1w5ggln9iFVeySSIBQoIsehtLyS19YWMnflB7yyegclpRVkpKdw9ej+XHJGb846qQtJSZojkcSiQBFpgIOHK1i2ZS/ri/azbc8hZuUWUFhSRud2qYwf1ovxw3tx7qBupKcmx3qoIjGjQBGJsvvAYdYV7md94QHWFUXe1xfuZ+Oug1RWffi47DEnZ/I/V47gkwMzdUhLJFCgSKvj7lRWOeWVzuHKKsrDqyJ8rqh0yiurOHi4kpLSckpKKygpLWdfacWR5ZrvkeUDhyvZe6j8yHbaJCfRP7MdA7t34OLhvRnZvwtDemfQrUOaQkTkKBQoclTukR/YpRWV7NxXSvGBcsorqyI/wCuqPvxBXvHhD/XDFVVUeuS3+Koqp2j/YfaVllNaXkllleMOFVVeIwAiy05FVc1AqKiKlFdVOZUhQKqOvB//90pNNjLSU8lIT4m80lLpn9mOjPRU2rVJPhIgJ3dvT9/ObXWKr8gxUKAkoC3FB9lQdICi/WUUlpRFvR+msKSMwv1l7Dl4+IR+cANkpKfQqW0q6anJpIQJ6tTkJFKTjZTkJNJSk2iflkJqspGanERKchKpSUZKWJ+SZCQnGckWeU+KWk5OMtqkJJGanESb6PbJRkpSEinJRvs2KR8GRwiRtJQkXVAo0kQUKAlk064D/PTF1fx91Q48KizSU5PonpFG9w5p9M9sR86ALnRp14b01CTSU5Pp0q4NvTqlHwmD1OQk0sIP89SUSFmb5Mjn5BAcZpCWoglqkUSiQEkQc5Zt46aZS2mbmsykMQO4eHgvenRMp3tGGu3bJOu3dhE5Ya06UMxsPPArIBn4nbv/LMZDiqnKKmfNB/tYXrCXcwd2o19mO8orq9i25xD3/G0Vndum8qcbztUNCUWkSbTaQDGzZOBh4LNAAfC2mc1x91WxHVnzcHf2HipnzQclLC/Yw+INxSzeUMy+0gogMvmcZEZZRdWRNv996VCFiYg0mVYbKMAoIN/d1wOY2UxgAtDogbJ4QzE79pUC4ER+mEdzB8c/XA6ro+v6kf/UqvtxfYTyisoqdu0/fNQJ9MOVH4ZFdrf2fO703pxzcld6dWzLK6t3kJwUmZju3Tmdfl3bkdO/SyP+qYiI1NSaA6UvsCXqcwFwTu1KZjYFmALQr1+/49rQo6/ls2Bt4XG1bQzJSUbX9m3o3iGNbhlpDOqRQbeMyOdBPTowrE8numfUvPHgmIGZMRqtiCSq1hwoR5tF/siJru7+OPA4QE5OznGdCHv3F07nYFkFH85b25Hl6iIzi1qG6k/Rc91mHJn8/ri6UZvAiJwe27ltqu4LJSItXmsOlALgpKjPWcC2pthQ385tm6JbEZG40povA34bGGxm2WbWBpgIzInxmEREElar3UNx9woz+3/AXCKnDf/e3VfGeFgiIgmr1QYKgLu/CLwY63GIiEjrPuQlIiItiAJFREQahQJFREQahQJFREQahdW+jUg8M7NCYNNxNu8GFDXicFoDfef4l2jfF/Sdj0d/d+9eX6WECpQTYWa57p4T63E0J33n+Jdo3xf0nZuSDnmJiEijUKCIiEijUKA03OOxHkAM6DvHv0T7vqDv3GQ0hyIiIo1CeygiItIoFCgiItIoFCgiItIoFCgiItIoFCgiItIo/j82tYb5RANmVgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "raw_data.loc[(raw_data[\"Country/Region\"]==\"France\") & raw_data[\"Province/State\"].isna()].iloc[:,4:].isnull().values.any()\n",
+    "raw_data.loc[(raw_data[\"Country/Region\"]==\"France\") & raw_data[\"Province/State\"].isna()].iloc[:,4:].transpose().plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous notons la date de début et la date de fin. \n",
+    "Ensuite nous chargeons les données des 14 pays.\n",
+    "Nous vérifions qu'il n'existe pas d'entrées eronnées dans les données de ces pays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_fr = raw_data.loc[(raw_data[\"Country/Region\"]==\"France\") & raw_data[\"Province/State\"].isna()]\n",
+    "data_be = raw_data.loc[(raw_data[\"Country/Region\"]==\"Belgium\") & raw_data[\"Province/State\"].isna()]\n",
+    "data_de = raw_data.loc[(raw_data[\"Country/Region\"]==\"Germany\") & raw_data[\"Province/State\"].isna()]\n",
+    "data_ir = raw_data.loc[(raw_data[\"Country/Region\"]==\"Iran\") & raw_data[\"Province/State\"].isna()]\n",
+    "data_it = raw_data.loc[(raw_data[\"Country/Region\"]==\"Italy\") & raw_data[\"Province/State\"].isna()]\n",
+    "data_jp = raw_data.loc[(raw_data[\"Country/Region\"]==\"Japan\") & raw_data[\"Province/State\"].isna()]\n",
+    "data_kr = raw_data.loc[(raw_data[\"Country/Region\"]==\"Korea, South\") & raw_data[\"Province/State\"].isna()]\n",
+    "data_nl = raw_data.loc[(raw_data[\"Country/Region\"]==\"Netherlands\") & raw_data[\"Province/State\"].isna()]\n",
+    "data_pt = raw_data.loc[(raw_data[\"Country/Region\"]==\"Portugal\") & raw_data[\"Province/State\"].isna()]\n",
+    "data_es = raw_data.loc[(raw_data[\"Country/Region\"]==\"Spain\") & raw_data[\"Province/State\"].isna()]\n",
+    "data_uk = raw_data.loc[(raw_data[\"Country/Region\"]==\"United Kingdom\") & raw_data[\"Province/State\"].isna()]\n",
+    "data_us = raw_data.loc[(raw_data[\"Country/Region\"]==\"US\") & raw_data[\"Province/State\"].isna()]\n",
+    "\n",
+    "data_hg = raw_data.loc[(raw_data[\"Country/Region\"]==\"China\") & (raw_data[\"Province/State\"]==\"Hong Kong\")]\n",
+    "data_ch = pd.DataFrame([raw_data.loc[(raw_data[\"Country/Region\"]==\"China\") & (raw_data[\"Province/State\"]!=\"Hong Kong\")].sum(axis=0)])\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}