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@@ -0,0 +1,958 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyse de l'incidence de la varicelle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek\n",
+    "\n",
+    "# set diagram sizes\n",
+    "# print(plt.rcParams['figure.dpi'])  # default = 72\n",
+    "# print(plt.rcParams['figure.figsize'])  # default = 6.0, 4.0\n",
+    "# plt.rcParams['figure.dpi'] = 100\n",
+    "plt.rcParams['figure.figsize'] = [12.0, 4.0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Les données de l'incidence du syndrome grippal sont disponibles du site Web du Réseau Sentinelles. Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voici l'explication des colonnes données sur le [site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
+    "\n",
+    "| Nom de colonne | Libellé de colonne                                                                                                                   |\n",
+    "|----------------|-------------------------------------------------------------------------------------------------------------------------------------|\n",
+    "| week           | Semaine calendaire (ISO 8601)                                                                                                       |\n",
+    "| geo_insee      | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/                               |\n",
+    "| geo_name       | Libellé de la zone géographique (ce libellé peut être modifié sans préavis)                                                         |\n",
+    "| inc            | Estimation de l'incidence de consultations en nombre de cas                                                                         |\n",
+    "| inc_low        | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation                                                       |\n",
+    "| inc_up         | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation                                                       |\n",
+    "| inc100         | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants)                                     |\n",
+    "| inc100_low     | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| inc100_up      | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>202305</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6439</td>\n",
+       "      <td>3806</td>\n",
+       "      <td>9072</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>1</th>\n",
+       "      <td>202304</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6300</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>8653</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>2</th>\n",
+       "      <td>202303</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6063</td>\n",
+       "      <td>3798</td>\n",
+       "      <td>8328</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202302</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6576</td>\n",
+       "      <td>3060</td>\n",
+       "      <td>10092</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202301</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8153</td>\n",
+       "      <td>5470</td>\n",
+       "      <td>10836</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>5</th>\n",
+       "      <td>202252</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5171</td>\n",
+       "      <td>2717</td>\n",
+       "      <td>7625</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>202251</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6226</td>\n",
+       "      <td>3822</td>\n",
+       "      <td>8630</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>7</th>\n",
+       "      <td>202250</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6590</td>\n",
+       "      <td>3100</td>\n",
+       "      <td>10080</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>202249</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5095</td>\n",
+       "      <td>3212</td>\n",
+       "      <td>6978</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>9</th>\n",
+       "      <td>202248</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4985</td>\n",
+       "      <td>3043</td>\n",
+       "      <td>6927</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     week  indicator   inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
+       "0  202305          7  6439     3806    9072      10           6         14   \n",
+       "1  202304          7  6300     3947    8653       9           5         13   \n",
+       "2  202303          7  6063     3798    8328       9           6         12   \n",
+       "3  202302          7  6576     3060   10092      10           5         15   \n",
+       "4  202301          7  8153     5470   10836      12           8         16   \n",
+       "5  202252          7  5171     2717    7625       8           4         12   \n",
+       "6  202251          7  6226     3822    8630       9           5         13   \n",
+       "7  202250          7  6590     3100   10080      10           5         15   \n",
+       "8  202249          7  5095     3212    6978       8           5         11   \n",
+       "9  202248          7  4985     3043    6927       8           5         11   \n",
+       "\n",
+       "  geo_insee geo_name  \n",
+       "0        FR   France  \n",
+       "1        FR   France  \n",
+       "2        FR   France  \n",
+       "3        FR   France  \n",
+       "4        FR   France  \n",
+       "5        FR   France  \n",
+       "6        FR   France  \n",
+       "7        FR   France  \n",
+       "8        FR   France  \n",
+       "9        FR   France  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n",
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Contrôle Qualité\n",
+    "### Recherche des lignes sans donnée"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# recherche des lignes sans données\n",
+    "data[data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aucune donnée ne manque.\n",
+    "\n",
+    "### Format des dates\n",
+    "Nos données utilisent une convention inhabituelle: le numéro de semaine est collé à l'année, donnant l'impression qu'il s'agit de nombre entier. C'est comme ça que Pandas les interprète.  \n",
+    "Un deuxième problème est que Pandas ne comprend pas les numéros de semaine.  Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque isoweek.\n",
+    "\n",
+    "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés.  \n",
+    "Les résultats vont dans une nouvelle colonne 'period'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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",
+       "      <th>period</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202305</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6439</td>\n",
+       "      <td>3806</td>\n",
+       "      <td>9072</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2023-01-30/2023-02-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202304</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6300</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>8653</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2023-01-23/2023-01-29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202303</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6063</td>\n",
+       "      <td>3798</td>\n",
+       "      <td>8328</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2023-01-16/2023-01-22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202302</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6576</td>\n",
+       "      <td>3060</td>\n",
+       "      <td>10092</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2023-01-09/2023-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202301</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8153</td>\n",
+       "      <td>5470</td>\n",
+       "      <td>10836</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2023-01-02/2023-01-08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     week  indicator   inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
+       "0  202305          7  6439     3806    9072      10           6         14   \n",
+       "1  202304          7  6300     3947    8653       9           5         13   \n",
+       "2  202303          7  6063     3798    8328       9           6         12   \n",
+       "3  202302          7  6576     3060   10092      10           5         15   \n",
+       "4  202301          7  8153     5470   10836      12           8         16   \n",
+       "\n",
+       "  geo_insee geo_name                period  \n",
+       "0        FR   France 2023-01-30/2023-02-05  \n",
+       "1        FR   France 2023-01-23/2023-01-29  \n",
+       "2        FR   France 2023-01-16/2023-01-22  \n",
+       "3        FR   France 2023-01-09/2023-01-15  \n",
+       "4        FR   France 2023-01-02/2023-01-08  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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']]\n",
+    "data.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il restent deux petites modifications à faire.  \n",
+    " - Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données. Ceci en fait\n",
+    "une suite chronologique, ce qui sera pratique par la suite.  \n",
+    " - Deuxièmement, nous trions les points par période, dans\n",
+    "le sens chronologique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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",
+       "    <tr>\n",
+       "      <th>period</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1990-12-03/1990-12-09</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",
+       "    <tr>\n",
+       "      <th>1990-12-10/1990-12-16</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>1990-12-17/1990-12-23</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>1990-12-24/1990-12-30</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>1990-12-31/1991-01-06</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",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         week  indicator    inc  inc_low  inc_up  inc100  \\\n",
+       "period                                                                     \n",
+       "1990-12-03/1990-12-09  199049          7   1143        0    2610       2   \n",
+       "1990-12-10/1990-12-16  199050          7  11079     6660   15498      20   \n",
+       "1990-12-17/1990-12-23  199051          7  19080    13807   24353      34   \n",
+       "1990-12-24/1990-12-30  199052          7  19375    13295   25455      34   \n",
+       "1990-12-31/1991-01-06  199101          7  15565    10271   20859      27   \n",
+       "\n",
+       "                       inc100_low  inc100_up geo_insee geo_name  \n",
+       "period                                                           \n",
+       "1990-12-03/1990-12-09           0          5        FR   France  \n",
+       "1990-12-10/1990-12-16          12         28        FR   France  \n",
+       "1990-12-17/1990-12-23          25         43        FR   France  \n",
+       "1990-12-24/1990-12-30          23         45        FR   France  \n",
+       "1990-12-31/1991-01-06          18         36        FR   France  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_data = data.set_index('period').sort_index()\n",
+    "sorted_data.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Continuité des données\n",
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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": [
+    "Il n'y a pas de correction à apporter.\n",
+    "\n",
+    "## Inspection visuelle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f692ef60fd0>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f692c271e10>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Étude de l'incidence annuelle\n",
+    "### reformattage des données\n",
+    "Les pics d'incidence commence avec l'hiver et peuvent durer jusqu'à la fin de l'été, 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 septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.\n",
+    "\n",
+    "Cette modification ne risque pas de fausser nos conclusions car la date a été choisie lors du minimum d'incidence.\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": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1991, sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En partant de cette liste des semaines qui contiennent un 1er septembre, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste.\n",
+    "\n",
+    "Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.  \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": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_september_week[:-1],\n",
+    "                        first_september_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": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6926dd00f0>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.plot(style='*')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "221186"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence[2020]"
+   ]
+  },
+  {
+   "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": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2004    777388\n",
+       "2016    782114\n",
+       "2010    829911\n",
+       "1992    832939\n",
+       "2009    842373\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values().tail(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6926a53438>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x288 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": []
+  }
+ ],
+ "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": 2
+}