diff --git a/module3/exo1/Analyse de l'incidence du syndrome grippal.ipynb b/module3/exo1/Analyse de l'incidence du syndrome grippal.ipynb
index cd640a208ad895d06e09f55efe25db97a821958b..546c65efc6b3c669f1772715e653a93f4fee5443 100644
--- a/module3/exo1/Analyse de l'incidence du syndrome grippal.ipynb	
+++ b/module3/exo1/Analyse de l'incidence du syndrome grippal.ipynb	
@@ -2064,23 +2064,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 23,
    "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",
+    "def convert_week(year_week_int):\n",
+    "    year_week_str = str(year_week_int)\n",
+    "    year = int(year_week_str[:4])\n",
+    "    week = int(year_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']]"
+    "data['periode'] = [convert_week(yw) for yw in data['week']]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -2114,9 +2114,11 @@
        "      <th>inc100_up</th>\n",
        "      <th>geo_insee</th>\n",
        "      <th>geo_name</th>\n",
+       "      <th>period</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>period</th>\n",
+       "      <th>periode</th>\n",
+       "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
@@ -2142,6 +2144,7 @@
        "      <td>213.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1984-10-29/1984-11-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1984-11-05/1984-11-11</th>\n",
@@ -2155,6 +2158,7 @@
        "      <td>308.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1984-11-05/1984-11-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1984-11-12/1984-11-18</th>\n",
@@ -2168,6 +2172,7 @@
        "      <td>195.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1984-11-12/1984-11-18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1984-11-19/1984-11-25</th>\n",
@@ -2181,6 +2186,7 @@
        "      <td>163.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1984-11-19/1984-11-25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1984-11-26/1984-12-02</th>\n",
@@ -2194,6 +2200,7 @@
        "      <td>176.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1984-11-26/1984-12-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1984-12-03/1984-12-09</th>\n",
@@ -2207,6 +2214,7 @@
        "      <td>219.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1984-12-03/1984-12-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1984-12-10/1984-12-16</th>\n",
@@ -2220,6 +2228,7 @@
        "      <td>266.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1984-12-10/1984-12-16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1984-12-17/1984-12-23</th>\n",
@@ -2233,6 +2242,7 @@
        "      <td>224.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1984-12-17/1984-12-23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1984-12-24/1984-12-30</th>\n",
@@ -2246,6 +2256,7 @@
        "      <td>198.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1984-12-24/1984-12-30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1984-12-31/1985-01-06</th>\n",
@@ -2259,6 +2270,7 @@
        "      <td>190.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1984-12-31/1985-01-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-01-07/1985-01-13</th>\n",
@@ -2272,6 +2284,7 @@
        "      <td>207.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-01-07/1985-01-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-01-14/1985-01-20</th>\n",
@@ -2285,6 +2298,7 @@
        "      <td>459.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-01-14/1985-01-20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-01-21/1985-01-27</th>\n",
@@ -2298,6 +2312,7 @@
        "      <td>832.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-01-21/1985-01-27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-01-28/1985-02-03</th>\n",
@@ -2311,6 +2326,7 @@
        "      <td>1236.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-01-28/1985-02-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-02-04/1985-02-10</th>\n",
@@ -2324,6 +2340,7 @@
        "      <td>1113.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-02-04/1985-02-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-02-11/1985-02-17</th>\n",
@@ -2337,6 +2354,7 @@
        "      <td>926.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-02-11/1985-02-17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-02-18/1985-02-24</th>\n",
@@ -2350,6 +2368,7 @@
        "      <td>762.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-02-18/1985-02-24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-02-25/1985-03-03</th>\n",
@@ -2363,6 +2382,7 @@
        "      <td>722.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-02-25/1985-03-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-03-04/1985-03-10</th>\n",
@@ -2376,6 +2396,7 @@
        "      <td>689.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-03-04/1985-03-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-03-11/1985-03-17</th>\n",
@@ -2389,6 +2410,7 @@
        "      <td>544.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-03-11/1985-03-17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-03-18/1985-03-24</th>\n",
@@ -2402,6 +2424,7 @@
        "      <td>485.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-03-18/1985-03-24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-03-25/1985-03-31</th>\n",
@@ -2415,6 +2438,7 @@
        "      <td>395.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-03-25/1985-03-31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-04-01/1985-04-07</th>\n",
@@ -2428,6 +2452,7 @@
        "      <td>281.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-04-01/1985-04-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-04-08/1985-04-14</th>\n",
@@ -2441,6 +2466,7 @@
        "      <td>149.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-04-08/1985-04-14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-04-15/1985-04-21</th>\n",
@@ -2454,6 +2480,7 @@
        "      <td>116.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-04-15/1985-04-21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-04-22/1985-04-28</th>\n",
@@ -2467,6 +2494,7 @@
        "      <td>80.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-04-22/1985-04-28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-04-29/1985-05-05</th>\n",
@@ -2480,6 +2508,7 @@
        "      <td>93.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-04-29/1985-05-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-05-06/1985-05-12</th>\n",
@@ -2493,6 +2522,7 @@
        "      <td>97.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-05-06/1985-05-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-05-13/1985-05-19</th>\n",
@@ -2506,6 +2536,7 @@
        "      <td>64.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-05-13/1985-05-19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1985-05-20/1985-05-26</th>\n",
@@ -2519,6 +2550,7 @@
        "      <td>59.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>1985-05-20/1985-05-26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2532,6 +2564,7 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-03-06/2023-03-12</th>\n",
@@ -2545,6 +2578,7 @@
        "      <td>127.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-03-06/2023-03-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-03-13/2023-03-19</th>\n",
@@ -2558,6 +2592,7 @@
        "      <td>124.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-03-13/2023-03-19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-03-20/2023-03-26</th>\n",
@@ -2571,6 +2606,7 @@
        "      <td>121.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-03-20/2023-03-26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-03-27/2023-04-02</th>\n",
@@ -2584,6 +2620,7 @@
        "      <td>110.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-03-27/2023-04-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-04-03/2023-04-09</th>\n",
@@ -2597,6 +2634,7 @@
        "      <td>83.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-04-03/2023-04-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-04-10/2023-04-16</th>\n",
@@ -2610,6 +2648,7 @@
        "      <td>66.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-04-10/2023-04-16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-04-17/2023-04-23</th>\n",
@@ -2623,6 +2662,7 @@
        "      <td>50.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-04-17/2023-04-23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-04-24/2023-04-30</th>\n",
@@ -2636,6 +2676,7 @@
        "      <td>49.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-04-24/2023-04-30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-05-01/2023-05-07</th>\n",
@@ -2649,6 +2690,7 @@
        "      <td>37.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-05-01/2023-05-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-05-08/2023-05-14</th>\n",
@@ -2662,6 +2704,7 @@
        "      <td>32.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-05-08/2023-05-14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-05-15/2023-05-21</th>\n",
@@ -2675,6 +2718,7 @@
        "      <td>30.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-05-15/2023-05-21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-05-22/2023-05-28</th>\n",
@@ -2688,6 +2732,7 @@
        "      <td>31.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-05-22/2023-05-28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-05-29/2023-06-04</th>\n",
@@ -2701,6 +2746,7 @@
        "      <td>35.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-05-29/2023-06-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-06-05/2023-06-11</th>\n",
@@ -2714,6 +2760,7 @@
        "      <td>27.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-06-05/2023-06-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-06-12/2023-06-18</th>\n",
@@ -2727,6 +2774,7 @@
        "      <td>23.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-06-12/2023-06-18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-06-19/2023-06-25</th>\n",
@@ -2740,6 +2788,7 @@
        "      <td>20.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-06-19/2023-06-25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-06-26/2023-07-02</th>\n",
@@ -2753,6 +2802,7 @@
        "      <td>19.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-06-26/2023-07-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-07-03/2023-07-09</th>\n",
@@ -2766,6 +2816,7 @@
        "      <td>19.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-07-03/2023-07-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-07-10/2023-07-16</th>\n",
@@ -2779,6 +2830,7 @@
        "      <td>19.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-07-10/2023-07-16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-07-17/2023-07-23</th>\n",
@@ -2792,6 +2844,7 @@
        "      <td>23.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-07-17/2023-07-23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-07-24/2023-07-30</th>\n",
@@ -2805,6 +2858,7 @@
        "      <td>27.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-07-24/2023-07-30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-07-31/2023-08-06</th>\n",
@@ -2818,6 +2872,7 @@
        "      <td>30.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-07-31/2023-08-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-08-07/2023-08-13</th>\n",
@@ -2831,6 +2886,7 @@
        "      <td>29.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-08-07/2023-08-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-08-14/2023-08-20</th>\n",
@@ -2844,6 +2900,7 @@
        "      <td>38.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-08-14/2023-08-20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-08-21/2023-08-27</th>\n",
@@ -2857,6 +2914,7 @@
        "      <td>48.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-08-21/2023-08-27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-08-28/2023-09-03</th>\n",
@@ -2870,6 +2928,7 @@
        "      <td>57.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-08-28/2023-09-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-04/2023-09-10</th>\n",
@@ -2883,6 +2942,7 @@
        "      <td>67.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-09-04/2023-09-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-11/2023-09-17</th>\n",
@@ -2896,6 +2956,7 @@
        "      <td>85.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-09-11/2023-09-17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-18/2023-09-24</th>\n",
@@ -2909,6 +2970,7 @@
        "      <td>108.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-09-18/2023-09-24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023-09-25/2023-10-01</th>\n",
@@ -2922,15 +2984,16 @@
        "      <td>141.0</td>\n",
        "      <td>FR</td>\n",
        "      <td>France</td>\n",
+       "      <td>2023-09-25/2023-10-01</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>2030 rows × 10 columns</p>\n",
+       "<p>2030 rows × 11 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                         week  indicator     inc   inc_low    inc_up inc100  \\\n",
-       "period                                                                        \n",
+       "periode                                                                       \n",
        "1984-10-29/1984-11-04  198444          3   68422   20056.0  116788.0    125   \n",
        "1984-11-05/1984-11-11  198445          3  135223  101414.0  169032.0    246   \n",
        "1984-11-12/1984-11-18  198446          3   87330   67686.0  106974.0    159   \n",
@@ -2993,124 +3056,186 @@
        "2023-09-18/2023-09-24  202338          3   63567   55525.0   71609.0     96   \n",
        "2023-09-25/2023-10-01  202339          3   82112   70891.0   93333.0    124   \n",
        "\n",
-       "                       inc100_low  inc100_up geo_insee geo_name  \n",
-       "period                                                           \n",
-       "1984-10-29/1984-11-04        37.0      213.0        FR   France  \n",
-       "1984-11-05/1984-11-11       184.0      308.0        FR   France  \n",
-       "1984-11-12/1984-11-18       123.0      195.0        FR   France  \n",
-       "1984-11-19/1984-11-25        99.0      163.0        FR   France  \n",
-       "1984-11-26/1984-12-02       110.0      176.0        FR   France  \n",
-       "1984-12-03/1984-12-09       149.0      219.0        FR   France  \n",
-       "1984-12-10/1984-12-16       184.0      266.0        FR   France  \n",
-       "1984-12-17/1984-12-23       146.0      224.0        FR   France  \n",
-       "1984-12-24/1984-12-30       110.0      198.0        FR   France  \n",
-       "1984-12-31/1985-01-06       120.0      190.0        FR   France  \n",
-       "1985-01-07/1985-01-13       147.0      207.0        FR   France  \n",
-       "1985-01-14/1985-01-20       317.0      459.0        FR   France  \n",
-       "1985-01-21/1985-01-27       708.0      832.0        FR   France  \n",
-       "1985-01-28/1985-02-03      1074.0     1236.0        FR   France  \n",
-       "1985-02-04/1985-02-10       939.0     1113.0        FR   France  \n",
-       "1985-02-11/1985-02-17       784.0      926.0        FR   France  \n",
-       "1985-02-18/1985-02-24       652.0      762.0        FR   France  \n",
-       "1985-02-25/1985-03-03       618.0      722.0        FR   France  \n",
-       "1985-03-04/1985-03-10       591.0      689.0        FR   France  \n",
-       "1985-03-11/1985-03-17       458.0      544.0        FR   France  \n",
-       "1985-03-18/1985-03-24       405.0      485.0        FR   France  \n",
-       "1985-03-25/1985-03-31       319.0      395.0        FR   France  \n",
-       "1985-04-01/1985-04-07       207.0      281.0        FR   France  \n",
-       "1985-04-08/1985-04-14        83.0      149.0        FR   France  \n",
-       "1985-04-15/1985-04-21        66.0      116.0        FR   France  \n",
-       "1985-04-22/1985-04-28        44.0       80.0        FR   France  \n",
-       "1985-04-29/1985-05-05        55.0       93.0        FR   France  \n",
-       "1985-05-06/1985-05-12        59.0       97.0        FR   France  \n",
-       "1985-05-13/1985-05-19        38.0       64.0        FR   France  \n",
-       "1985-05-20/1985-05-26        35.0       59.0        FR   France  \n",
-       "...                           ...        ...       ...      ...  \n",
-       "2023-03-06/2023-03-12       103.0      127.0        FR   France  \n",
-       "2023-03-13/2023-03-19       100.0      124.0        FR   France  \n",
-       "2023-03-20/2023-03-26        97.0      121.0        FR   France  \n",
-       "2023-03-27/2023-04-02        86.0      110.0        FR   France  \n",
-       "2023-04-03/2023-04-09        61.0       83.0        FR   France  \n",
-       "2023-04-10/2023-04-16        46.0       66.0        FR   France  \n",
-       "2023-04-17/2023-04-23        34.0       50.0        FR   France  \n",
-       "2023-04-24/2023-04-30        33.0       49.0        FR   France  \n",
-       "2023-05-01/2023-05-07        23.0       37.0        FR   France  \n",
-       "2023-05-08/2023-05-14        18.0       32.0        FR   France  \n",
-       "2023-05-15/2023-05-21        18.0       30.0        FR   France  \n",
-       "2023-05-22/2023-05-28        19.0       31.0        FR   France  \n",
-       "2023-05-29/2023-06-04        21.0       35.0        FR   France  \n",
-       "2023-06-05/2023-06-11        17.0       27.0        FR   France  \n",
-       "2023-06-12/2023-06-18        11.0       23.0        FR   France  \n",
-       "2023-06-19/2023-06-25        10.0       20.0        FR   France  \n",
-       "2023-06-26/2023-07-02         9.0       19.0        FR   France  \n",
-       "2023-07-03/2023-07-09         9.0       19.0        FR   France  \n",
-       "2023-07-10/2023-07-16         9.0       19.0        FR   France  \n",
-       "2023-07-17/2023-07-23        11.0       23.0        FR   France  \n",
-       "2023-07-24/2023-07-30        13.0       27.0        FR   France  \n",
-       "2023-07-31/2023-08-06        16.0       30.0        FR   France  \n",
-       "2023-08-07/2023-08-13        15.0       29.0        FR   France  \n",
-       "2023-08-14/2023-08-20        20.0       38.0        FR   France  \n",
-       "2023-08-21/2023-08-27        32.0       48.0        FR   France  \n",
-       "2023-08-28/2023-09-03        39.0       57.0        FR   France  \n",
-       "2023-09-04/2023-09-10        49.0       67.0        FR   France  \n",
-       "2023-09-11/2023-09-17        63.0       85.0        FR   France  \n",
-       "2023-09-18/2023-09-24        84.0      108.0        FR   France  \n",
-       "2023-09-25/2023-10-01       107.0      141.0        FR   France  \n",
+       "                       inc100_low  inc100_up geo_insee geo_name  \\\n",
+       "periode                                                           \n",
+       "1984-10-29/1984-11-04        37.0      213.0        FR   France   \n",
+       "1984-11-05/1984-11-11       184.0      308.0        FR   France   \n",
+       "1984-11-12/1984-11-18       123.0      195.0        FR   France   \n",
+       "1984-11-19/1984-11-25        99.0      163.0        FR   France   \n",
+       "1984-11-26/1984-12-02       110.0      176.0        FR   France   \n",
+       "1984-12-03/1984-12-09       149.0      219.0        FR   France   \n",
+       "1984-12-10/1984-12-16       184.0      266.0        FR   France   \n",
+       "1984-12-17/1984-12-23       146.0      224.0        FR   France   \n",
+       "1984-12-24/1984-12-30       110.0      198.0        FR   France   \n",
+       "1984-12-31/1985-01-06       120.0      190.0        FR   France   \n",
+       "1985-01-07/1985-01-13       147.0      207.0        FR   France   \n",
+       "1985-01-14/1985-01-20       317.0      459.0        FR   France   \n",
+       "1985-01-21/1985-01-27       708.0      832.0        FR   France   \n",
+       "1985-01-28/1985-02-03      1074.0     1236.0        FR   France   \n",
+       "1985-02-04/1985-02-10       939.0     1113.0        FR   France   \n",
+       "1985-02-11/1985-02-17       784.0      926.0        FR   France   \n",
+       "1985-02-18/1985-02-24       652.0      762.0        FR   France   \n",
+       "1985-02-25/1985-03-03       618.0      722.0        FR   France   \n",
+       "1985-03-04/1985-03-10       591.0      689.0        FR   France   \n",
+       "1985-03-11/1985-03-17       458.0      544.0        FR   France   \n",
+       "1985-03-18/1985-03-24       405.0      485.0        FR   France   \n",
+       "1985-03-25/1985-03-31       319.0      395.0        FR   France   \n",
+       "1985-04-01/1985-04-07       207.0      281.0        FR   France   \n",
+       "1985-04-08/1985-04-14        83.0      149.0        FR   France   \n",
+       "1985-04-15/1985-04-21        66.0      116.0        FR   France   \n",
+       "1985-04-22/1985-04-28        44.0       80.0        FR   France   \n",
+       "1985-04-29/1985-05-05        55.0       93.0        FR   France   \n",
+       "1985-05-06/1985-05-12        59.0       97.0        FR   France   \n",
+       "1985-05-13/1985-05-19        38.0       64.0        FR   France   \n",
+       "1985-05-20/1985-05-26        35.0       59.0        FR   France   \n",
+       "...                           ...        ...       ...      ...   \n",
+       "2023-03-06/2023-03-12       103.0      127.0        FR   France   \n",
+       "2023-03-13/2023-03-19       100.0      124.0        FR   France   \n",
+       "2023-03-20/2023-03-26        97.0      121.0        FR   France   \n",
+       "2023-03-27/2023-04-02        86.0      110.0        FR   France   \n",
+       "2023-04-03/2023-04-09        61.0       83.0        FR   France   \n",
+       "2023-04-10/2023-04-16        46.0       66.0        FR   France   \n",
+       "2023-04-17/2023-04-23        34.0       50.0        FR   France   \n",
+       "2023-04-24/2023-04-30        33.0       49.0        FR   France   \n",
+       "2023-05-01/2023-05-07        23.0       37.0        FR   France   \n",
+       "2023-05-08/2023-05-14        18.0       32.0        FR   France   \n",
+       "2023-05-15/2023-05-21        18.0       30.0        FR   France   \n",
+       "2023-05-22/2023-05-28        19.0       31.0        FR   France   \n",
+       "2023-05-29/2023-06-04        21.0       35.0        FR   France   \n",
+       "2023-06-05/2023-06-11        17.0       27.0        FR   France   \n",
+       "2023-06-12/2023-06-18        11.0       23.0        FR   France   \n",
+       "2023-06-19/2023-06-25        10.0       20.0        FR   France   \n",
+       "2023-06-26/2023-07-02         9.0       19.0        FR   France   \n",
+       "2023-07-03/2023-07-09         9.0       19.0        FR   France   \n",
+       "2023-07-10/2023-07-16         9.0       19.0        FR   France   \n",
+       "2023-07-17/2023-07-23        11.0       23.0        FR   France   \n",
+       "2023-07-24/2023-07-30        13.0       27.0        FR   France   \n",
+       "2023-07-31/2023-08-06        16.0       30.0        FR   France   \n",
+       "2023-08-07/2023-08-13        15.0       29.0        FR   France   \n",
+       "2023-08-14/2023-08-20        20.0       38.0        FR   France   \n",
+       "2023-08-21/2023-08-27        32.0       48.0        FR   France   \n",
+       "2023-08-28/2023-09-03        39.0       57.0        FR   France   \n",
+       "2023-09-04/2023-09-10        49.0       67.0        FR   France   \n",
+       "2023-09-11/2023-09-17        63.0       85.0        FR   France   \n",
+       "2023-09-18/2023-09-24        84.0      108.0        FR   France   \n",
+       "2023-09-25/2023-10-01       107.0      141.0        FR   France   \n",
        "\n",
-       "[2030 rows x 10 columns]"
+       "                                     period  \n",
+       "periode                                      \n",
+       "1984-10-29/1984-11-04 1984-10-29/1984-11-04  \n",
+       "1984-11-05/1984-11-11 1984-11-05/1984-11-11  \n",
+       "1984-11-12/1984-11-18 1984-11-12/1984-11-18  \n",
+       "1984-11-19/1984-11-25 1984-11-19/1984-11-25  \n",
+       "1984-11-26/1984-12-02 1984-11-26/1984-12-02  \n",
+       "1984-12-03/1984-12-09 1984-12-03/1984-12-09  \n",
+       "1984-12-10/1984-12-16 1984-12-10/1984-12-16  \n",
+       "1984-12-17/1984-12-23 1984-12-17/1984-12-23  \n",
+       "1984-12-24/1984-12-30 1984-12-24/1984-12-30  \n",
+       "1984-12-31/1985-01-06 1984-12-31/1985-01-06  \n",
+       "1985-01-07/1985-01-13 1985-01-07/1985-01-13  \n",
+       "1985-01-14/1985-01-20 1985-01-14/1985-01-20  \n",
+       "1985-01-21/1985-01-27 1985-01-21/1985-01-27  \n",
+       "1985-01-28/1985-02-03 1985-01-28/1985-02-03  \n",
+       "1985-02-04/1985-02-10 1985-02-04/1985-02-10  \n",
+       "1985-02-11/1985-02-17 1985-02-11/1985-02-17  \n",
+       "1985-02-18/1985-02-24 1985-02-18/1985-02-24  \n",
+       "1985-02-25/1985-03-03 1985-02-25/1985-03-03  \n",
+       "1985-03-04/1985-03-10 1985-03-04/1985-03-10  \n",
+       "1985-03-11/1985-03-17 1985-03-11/1985-03-17  \n",
+       "1985-03-18/1985-03-24 1985-03-18/1985-03-24  \n",
+       "1985-03-25/1985-03-31 1985-03-25/1985-03-31  \n",
+       "1985-04-01/1985-04-07 1985-04-01/1985-04-07  \n",
+       "1985-04-08/1985-04-14 1985-04-08/1985-04-14  \n",
+       "1985-04-15/1985-04-21 1985-04-15/1985-04-21  \n",
+       "1985-04-22/1985-04-28 1985-04-22/1985-04-28  \n",
+       "1985-04-29/1985-05-05 1985-04-29/1985-05-05  \n",
+       "1985-05-06/1985-05-12 1985-05-06/1985-05-12  \n",
+       "1985-05-13/1985-05-19 1985-05-13/1985-05-19  \n",
+       "1985-05-20/1985-05-26 1985-05-20/1985-05-26  \n",
+       "...                                     ...  \n",
+       "2023-03-06/2023-03-12 2023-03-06/2023-03-12  \n",
+       "2023-03-13/2023-03-19 2023-03-13/2023-03-19  \n",
+       "2023-03-20/2023-03-26 2023-03-20/2023-03-26  \n",
+       "2023-03-27/2023-04-02 2023-03-27/2023-04-02  \n",
+       "2023-04-03/2023-04-09 2023-04-03/2023-04-09  \n",
+       "2023-04-10/2023-04-16 2023-04-10/2023-04-16  \n",
+       "2023-04-17/2023-04-23 2023-04-17/2023-04-23  \n",
+       "2023-04-24/2023-04-30 2023-04-24/2023-04-30  \n",
+       "2023-05-01/2023-05-07 2023-05-01/2023-05-07  \n",
+       "2023-05-08/2023-05-14 2023-05-08/2023-05-14  \n",
+       "2023-05-15/2023-05-21 2023-05-15/2023-05-21  \n",
+       "2023-05-22/2023-05-28 2023-05-22/2023-05-28  \n",
+       "2023-05-29/2023-06-04 2023-05-29/2023-06-04  \n",
+       "2023-06-05/2023-06-11 2023-06-05/2023-06-11  \n",
+       "2023-06-12/2023-06-18 2023-06-12/2023-06-18  \n",
+       "2023-06-19/2023-06-25 2023-06-19/2023-06-25  \n",
+       "2023-06-26/2023-07-02 2023-06-26/2023-07-02  \n",
+       "2023-07-03/2023-07-09 2023-07-03/2023-07-09  \n",
+       "2023-07-10/2023-07-16 2023-07-10/2023-07-16  \n",
+       "2023-07-17/2023-07-23 2023-07-17/2023-07-23  \n",
+       "2023-07-24/2023-07-30 2023-07-24/2023-07-30  \n",
+       "2023-07-31/2023-08-06 2023-07-31/2023-08-06  \n",
+       "2023-08-07/2023-08-13 2023-08-07/2023-08-13  \n",
+       "2023-08-14/2023-08-20 2023-08-14/2023-08-20  \n",
+       "2023-08-21/2023-08-27 2023-08-21/2023-08-27  \n",
+       "2023-08-28/2023-09-03 2023-08-28/2023-09-03  \n",
+       "2023-09-04/2023-09-10 2023-09-04/2023-09-10  \n",
+       "2023-09-11/2023-09-17 2023-09-11/2023-09-17  \n",
+       "2023-09-18/2023-09-24 2023-09-18/2023-09-24  \n",
+       "2023-09-25/2023-10-01 2023-09-25/2023-10-01  \n",
+       "\n",
+       "[2030 rows x 11 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sortie_data = data.set_index('period').sort_index()\n",
+    "sortie_data = data.set_index('periode').sort_index()\n",
     "sortie_data "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "periods = sortie_data.index\n",
-    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
+    "periodes = sortie_data.index\n",
+    "for pa, pb in zip(periodes[:-1], periodes[1:]):\n",
     "    delta = p2.to_timestamp() - p1.end_time\n",
     "    if delta > pd.Timedelta('1s'):\n",
-    "        print(p1, p2)"
+    "        print(pa, pb)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "Empty 'DataFrame': no numeric data to plot",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-15046ab663d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msortie_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[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m   2501\u001b[0m                            \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2502\u001b[0m                            \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m                            **kwds)\n\u001b[0m\u001b[1;32m   2504\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m   1925\u001b[0m                  \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1926\u001b[0m                  \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m                  **kwds)\n\u001b[0m\u001b[1;32m   1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m   1727\u001b[0m         \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1730\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1731\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    248\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    363\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m             raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m                             'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m    366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot"
-     ]
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57a7abc5c0>"
+      ]
+     },
+     "execution_count": 47,
+     "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": [
@@ -3119,16 +3244,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'68422'"
+       "68422"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3137,14 +3262,9 @@
     "sortie_data['inc'][0]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3153,22 +3273,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57aa2ef358>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57a79d6c88>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3185,22 +3305,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57a8075f60>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57a7abcda0>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3217,25 +3337,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
-    "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
+    "aout_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
     "                     for y in range(1985,\n",
     "                                    sortie_data.index[-1].year)]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
     "year = []\n",
     "yearly_incidence = []\n",
-    "for week1, week2 in zip(first_august_week[:-1],\n",
-    "                        first_august_week[1:]):\n",
+    "for week1, week2 in zip(aout_week[:-1],\n",
+    "                        aout_week[1:]):\n",
     "    one_year = sortie_data['inc'][week1:week2-1]\n",
     "    assert abs(len(one_year)-52) < 2\n",
     "    yearly_incidence.append(one_year.sum())\n",
@@ -3245,16 +3365,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57a7fd36a0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57a79e3278>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3277,7 +3397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -3323,7 +3443,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3334,16 +3454,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57a8064e80>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57a7b46b38>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3364,6 +3484,27 @@
     "yearly_incidence.hist(xrot=20)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,