diff --git a/module3/exo2/module3_exercice_2.ipynb b/module3/exo2/module3_exercice_2.ipynb
index 37cd6a20708d85f0ab0bae17966cc546bcd7d094..a366b6b7001469d99d0c1cd426911c57f2eecc4e 100644
--- a/module3/exo2/module3_exercice_2.ipynb
+++ b/module3/exo2/module3_exercice_2.ipynb
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,14 +81,1445 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "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",
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+       "    .dataframe thead th {\n",
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+       "</style>\n",
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+       "  <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",
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+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
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+       "      <td>FR</td>\n",
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+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
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+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
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+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "    <tr>\n",
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+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
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+       "      <td>5541</td>\n",
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+       "      <td>FR</td>\n",
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+       "    </tr>\n",
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+       "      <td>France</td>\n",
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+       "    <tr>\n",
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+       "      <td>3481</td>\n",
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+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
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+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "    <tr>\n",
+       "      <th>1556</th>\n",
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+       "      <td>11304</td>\n",
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+       "      <td>FR</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>199125</td>\n",
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+       "      <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>1558</th>\n",
+       "      <td>199124</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16171</td>\n",
+       "      <td>10071</td>\n",
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+       "      <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>1559</th>\n",
+       "      <td>199123</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11947</td>\n",
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+       "      <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>1560</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>1561</th>\n",
+       "      <td>199121</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14903</td>\n",
+       "      <td>8975</td>\n",
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+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1562</th>\n",
+       "      <td>199120</td>\n",
+       "      <td>7</td>\n",
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+       "      <td>12742</td>\n",
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+       "      <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>1563</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>1564</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>1565</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>1566</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>1567</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>1568</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>1569</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>1570</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>1571</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>1572</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>1573</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>1574</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>1575</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>1576</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>1577</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>1578</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>1579</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>1580</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>1581</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>1582</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>1583</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>1584</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>1585</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>1586 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202116          7   5472     2908    8036       8           4   \n",
+       "1     202115          7  11536     7684   15388      17          11   \n",
+       "2     202114          7  11197     7994   14400      17          12   \n",
+       "3     202113          7   9714     6289   13139      15          10   \n",
+       "4     202112          7  11520     8415   14625      17          12   \n",
+       "5     202111          7   9386     6678   12094      14          10   \n",
+       "6     202110          7   9056     6452   11660      14          10   \n",
+       "7     202109          7  10988     7938   14038      17          12   \n",
+       "8     202108          7  11281     8361   14201      17          13   \n",
+       "9     202107          7  13561    10315   16807      21          16   \n",
+       "10    202106          7  13401     9810   16992      20          15   \n",
+       "11    202105          7  12210     8988   15432      18          13   \n",
+       "12    202104          7  12026     8826   15226      18          13   \n",
+       "13    202103          7   8913     6375   11451      13           9   \n",
+       "14    202102          7   7795     5430   10160      12           8   \n",
+       "15    202101          7  10525     7750   13300      16          12   \n",
+       "16    202053          7  11978     8406   15550      18          13   \n",
+       "17    202052          7  12012     8285   15739      18          12   \n",
+       "18    202051          7  10564     7574   13554      16          11   \n",
+       "19    202050          7   7063     4744    9382      11           7   \n",
+       "20    202049          7   5026     3145    6907       8           5   \n",
+       "21    202048          7   6683     4312    9054      10           6   \n",
+       "22    202047          7   4999     2963    7035       8           5   \n",
+       "23    202046          7   3752     1963    5541       6           3   \n",
+       "24    202045          7   3696     2016    5376       6           3   \n",
+       "25    202044          7   4391     2375    6407       7           4   \n",
+       "26    202043          7   4376     2505    6247       7           4   \n",
+       "27    202042          7   4000     1979    6021       6           3   \n",
+       "28    202041          7   3961     2099    5823       6           3   \n",
+       "29    202040          7   2078      675    3481       3           1   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1556  199126          7  17608    11304   23912      31          20   \n",
+       "1557  199125          7  16169    10700   21638      28          18   \n",
+       "1558  199124          7  16171    10071   22271      28          17   \n",
+       "1559  199123          7  11947     7671   16223      21          13   \n",
+       "1560  199122          7  15452     9953   20951      27          17   \n",
+       "1561  199121          7  14903     8975   20831      26          16   \n",
+       "1562  199120          7  19053    12742   25364      34          23   \n",
+       "1563  199119          7  16739    11246   22232      29          19   \n",
+       "1564  199118          7  21385    13882   28888      38          25   \n",
+       "1565  199117          7  13462     8877   18047      24          16   \n",
+       "1566  199116          7  14857    10068   19646      26          18   \n",
+       "1567  199115          7  13975     9781   18169      25          18   \n",
+       "1568  199114          7  12265     7684   16846      22          14   \n",
+       "1569  199113          7   9567     6041   13093      17          11   \n",
+       "1570  199112          7  10864     7331   14397      19          13   \n",
+       "1571  199111          7  15574    11184   19964      27          19   \n",
+       "1572  199110          7  16643    11372   21914      29          20   \n",
+       "1573  199109          7  13741     8780   18702      24          15   \n",
+       "1574  199108          7  13289     8813   17765      23          15   \n",
+       "1575  199107          7  12337     8077   16597      22          15   \n",
+       "1576  199106          7  10877     7013   14741      19          12   \n",
+       "1577  199105          7  10442     6544   14340      18          11   \n",
+       "1578  199104          7   7913     4563   11263      14           8   \n",
+       "1579  199103          7  15387    10484   20290      27          18   \n",
+       "1580  199102          7  16277    11046   21508      29          20   \n",
+       "1581  199101          7  15565    10271   20859      27          18   \n",
+       "1582  199052          7  19375    13295   25455      34          23   \n",
+       "1583  199051          7  19080    13807   24353      34          25   \n",
+       "1584  199050          7  11079     6660   15498      20          12   \n",
+       "1585  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0            12        FR   France  \n",
+       "1            23        FR   France  \n",
+       "2            22        FR   France  \n",
+       "3            20        FR   France  \n",
+       "4            22        FR   France  \n",
+       "5            18        FR   France  \n",
+       "6            18        FR   France  \n",
+       "7            22        FR   France  \n",
+       "8            21        FR   France  \n",
+       "9            26        FR   France  \n",
+       "10           25        FR   France  \n",
+       "11           23        FR   France  \n",
+       "12           23        FR   France  \n",
+       "13           17        FR   France  \n",
+       "14           16        FR   France  \n",
+       "15           20        FR   France  \n",
+       "16           23        FR   France  \n",
+       "17           24        FR   France  \n",
+       "18           21        FR   France  \n",
+       "19           15        FR   France  \n",
+       "20           11        FR   France  \n",
+       "21           14        FR   France  \n",
+       "22           11        FR   France  \n",
+       "23            9        FR   France  \n",
+       "24            9        FR   France  \n",
+       "25           10        FR   France  \n",
+       "26           10        FR   France  \n",
+       "27            9        FR   France  \n",
+       "28            9        FR   France  \n",
+       "29            5        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1556         42        FR   France  \n",
+       "1557         38        FR   France  \n",
+       "1558         39        FR   France  \n",
+       "1559         29        FR   France  \n",
+       "1560         37        FR   France  \n",
+       "1561         36        FR   France  \n",
+       "1562         45        FR   France  \n",
+       "1563         39        FR   France  \n",
+       "1564         51        FR   France  \n",
+       "1565         32        FR   France  \n",
+       "1566         34        FR   France  \n",
+       "1567         32        FR   France  \n",
+       "1568         30        FR   France  \n",
+       "1569         23        FR   France  \n",
+       "1570         25        FR   France  \n",
+       "1571         35        FR   France  \n",
+       "1572         38        FR   France  \n",
+       "1573         33        FR   France  \n",
+       "1574         31        FR   France  \n",
+       "1575         29        FR   France  \n",
+       "1576         26        FR   France  \n",
+       "1577         25        FR   France  \n",
+       "1578         20        FR   France  \n",
+       "1579         36        FR   France  \n",
+       "1580         38        FR   France  \n",
+       "1581         36        FR   France  \n",
+       "1582         45        FR   France  \n",
+       "1583         43        FR   France  \n",
+       "1584         28        FR   France  \n",
+       "1585          5        FR   France  \n",
+       "\n",
+       "[1586 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "raw_data = pd.read_csv(data_df, skiprows=1)\n",
     "raw_data"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y a-t-il des points manquants dans ce jeux de données ? Non !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nos données utilisent une convention inhabituelle: le numéro de\n",
+    "semaine est collé à l'année, donnant l'impression qu'il s'agit\n",
+    "de nombre entier. C'est comme ça que Pandas les interprète.\n",
+    "  \n",
+    "Un deuxième problème est que Pandas ne comprend pas les numéros de\n",
+    "semaine.  Il faut lui fournir les dates de début et de fin de\n",
+    "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n",
+    "\n",
+    "Comme la conversion des semaines est devenu assez complexe, nous\n",
+    "écrivons une petite fonction Python pour cela. Ensuite, nous\n",
+    "l'appliquons à tous les points de nos donnés. Les résultats vont\n",
+    "dans une nouvelle colonne 'period'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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",
+    "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il restent deux petites modifications à faire.\n",
+    "\n",
+    "Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.\n",
+    "\n",
+    "Deuxièmement, nous trions les points par période, dans le sens chronologique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = raw_data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous vérifions la cohérence des données. Entre la fin d'une période et le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n",
+    "\n",
+    "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives entre lesquelles il manque une semaine.\n",
+    "\n",
+    "Nous reconnaissons ces dates: c'est la semaine sans observations que nous avions supprimées !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "periods = sorted_data.index\n",
+    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
+    "    delta = p2.to_timestamp() - p1.end_time\n",
+    "    if delta > pd.Timedelta('1s'):\n",
+    "        print(p1, p2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un premier regard sur les données !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a37371470>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a351c43c8>"
+      ]
+     },
+     "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": [
+    "sorted_data['inc'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Etude de l'incidence annuelle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Etant donné que le pic de l'épidémie se situe en fin d'hiver / début de printemps, à cheval entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er septembre de l'année  𝑁  au 1er septembre de l'année  𝑁+1 .\n",
+    "\n",
+    "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er août de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er août.\n",
+    "\n",
+    "Comme l'incidence de la varicelle est très faible en été, cette modification ne risque pas de fausser nos conclusions.\n",
+    "\n",
+    "Encore un petit détail: les données commencent en décembre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Period('1991-08-26/1991-09-01', 'W-SUN'),\n",
+       " Period('1992-08-31/1992-09-06', 'W-SUN'),\n",
+       " Period('1993-08-30/1993-09-05', 'W-SUN'),\n",
+       " Period('1994-08-29/1994-09-04', 'W-SUN'),\n",
+       " Period('1995-08-28/1995-09-03', 'W-SUN'),\n",
+       " Period('1996-08-26/1996-09-01', 'W-SUN'),\n",
+       " Period('1997-09-01/1997-09-07', 'W-SUN'),\n",
+       " Period('1998-08-31/1998-09-06', 'W-SUN'),\n",
+       " Period('1999-08-30/1999-09-05', 'W-SUN'),\n",
+       " Period('2000-08-28/2000-09-03', 'W-SUN'),\n",
+       " Period('2001-08-27/2001-09-02', 'W-SUN'),\n",
+       " Period('2002-08-26/2002-09-01', 'W-SUN'),\n",
+       " Period('2003-09-01/2003-09-07', 'W-SUN'),\n",
+       " Period('2004-08-30/2004-09-05', 'W-SUN'),\n",
+       " Period('2005-08-29/2005-09-04', 'W-SUN'),\n",
+       " Period('2006-08-28/2006-09-03', 'W-SUN'),\n",
+       " Period('2007-08-27/2007-09-02', 'W-SUN'),\n",
+       " Period('2008-09-01/2008-09-07', 'W-SUN'),\n",
+       " Period('2009-08-31/2009-09-06', 'W-SUN'),\n",
+       " Period('2010-08-30/2010-09-05', 'W-SUN'),\n",
+       " Period('2011-08-29/2011-09-04', 'W-SUN'),\n",
+       " Period('2012-08-27/2012-09-02', 'W-SUN'),\n",
+       " Period('2013-08-26/2013-09-01', 'W-SUN'),\n",
+       " Period('2014-09-01/2014-09-07', 'W-SUN'),\n",
+       " Period('2015-08-31/2015-09-06', 'W-SUN'),\n",
+       " Period('2016-08-29/2016-09-04', 'W-SUN'),\n",
+       " Period('2017-08-28/2017-09-03', 'W-SUN'),\n",
+       " Period('2018-08-27/2018-09-02', 'W-SUN'),\n",
+       " Period('2019-08-26/2019-09-01', 'W-SUN'),\n",
+       " Period('2020-08-31/2020-09-06', 'W-SUN')]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1991,\n",
+    "                                    sorted_data.index[-1].year)]\n",
+    "\n",
+    "first_september_week"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
+    "\n",
+    "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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)\n",
+    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voici les incidences annuelles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a35003ba8>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.plot(style='*')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020    221186\n",
+       "2002    516689\n",
+       "2018    542312\n",
+       "2017    551041\n",
+       "1996    564901\n",
+       "2019    584066\n",
+       "2015    604382\n",
+       "2000    617597\n",
+       "2001    619041\n",
+       "2012    624573\n",
+       "2005    628464\n",
+       "2006    632833\n",
+       "2011    642368\n",
+       "1993    643387\n",
+       "1995    652478\n",
+       "1994    661409\n",
+       "1998    677775\n",
+       "1997    683434\n",
+       "2014    685769\n",
+       "2013    698332\n",
+       "2007    717352\n",
+       "2008    749478\n",
+       "1999    756456\n",
+       "2003    758363\n",
+       "2004    777388\n",
+       "2016    782114\n",
+       "2010    829911\n",
+       "1992    832939\n",
+       "2009    842373\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Enfin, un histogramme montre bien que le nombre de cas de varicelle est assez constant d'une année sur l'autre (entre 1 et 1,5% de la population française).\n",
+    "Seule l'année 2020 se distingue par son très faible nombre de cas de varicelle.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a34fd8ef0>"
+      ]
+     },
+     "execution_count": 31,
+     "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,