From d8eb26becd7b2a12c90354c70f1b50bfcbb793fc Mon Sep 17 00:00:00 2001
From: 25beebaf69fecb0be5d335e6142fa270
 <25beebaf69fecb0be5d335e6142fa270@app-learninglab.inria.fr>
Date: Fri, 7 Aug 2026 19:12:57 +0000
Subject: [PATCH] =?UTF-8?q?Lecture=20des=20donn=C3=A9es,=20nettoyage,=20tr?=
 =?UTF-8?q?aitements=20et=20visualisations?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 module2/exo5/exo5_fr.ipynb                  |  52 +-
 module3/exo1/analyse-syndrome-grippal.ipynb | 727 ++++++++++++++++++--
 2 files changed, 705 insertions(+), 74 deletions(-)

diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb
index b2d63d3..402c51d 100644
--- a/module2/exo5/exo5_fr.ipynb
+++ b/module2/exo5/exo5_fr.ipynb
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -177,7 +177,7 @@
        "22   1/12/86      6           58       200            1  14.444444"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -253,13 +253,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "# GW: Cherry picking! Laisse (trop) peu d'observations pour bien déterminer la relation. Je commente.\n",
-    "#data = data[data.Malfunction>0]\n",
-    "#data"
+    "# data = data[data.Malfunction>0]\n",
+    "# data"
    ]
   },
   {
@@ -275,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -328,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -355,7 +355,7 @@
        "  <th>Date:</th>           <td>Fri, 07 Aug 2026</td> <th>  Deviance:          </th> <td>  18.086</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>               <td>09:10:10</td>     <th>  Pearson chi2:      </th>  <td>  30.0</td>  \n",
+       "  <th>Time:</th>               <td>09:24:01</td>     <th>  Pearson chi2:      </th>  <td>  30.0</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Iterations:</th>         <td>6</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
@@ -384,7 +384,7 @@
        "Link Function:                  logit   Scale:                          1.0000\n",
        "Method:                          IRLS   Log-Likelihood:                -23.526\n",
        "Date:                Fri, 07 Aug 2026   Deviance:                       18.086\n",
-       "Time:                        09:10:10   Pearson chi2:                     30.0\n",
+       "Time:                        09:24:01   Pearson chi2:                     30.0\n",
        "No. Iterations:                     6   Covariance Type:             nonrobust\n",
        "===============================================================================\n",
        "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
@@ -395,7 +395,7 @@
        "\"\"\""
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -434,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -509,6 +509,16 @@
     "plt.show()\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Mon analyse (GW):\n",
+    "- Il n'est pas raisonnable d'extrapoler, autrement dit, ici de tirer une conclusion en n'ayant aucune donnée pour des températures aussi basses.\n",
+    "- Il faut absolument examiner les incertitudes sur les prédictions (cf. figure ci-dessus)\n",
+    "- Enfin, il ne faut écarter des données (_cherry picking_) à moins d'avoir de bonnes raisons (e.g., __outliers__) car la relation sera plus robuste si l'ensemble des données sont mobilisées pour établir la relation de causalité (cf. figure précédente)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -517,27 +527,19 @@
     "scrolled": true
    },
    "source": [
-    "~~Comme on pouvait s'attendre au vu des données initiales, la\n",
+    "Comme on pouvait s'attendre au vu des données initiales, la\n",
     "température n'a pas d'impact notable sur la probabilité d'échec des\n",
     "joints toriques. Elle sera d'environ 0.2, comme dans les essais\n",
-    "précédents où il y a eu défaillance d'au moins un joint.~~ Revenons\n",
+    "précédents où il y a eu défaillance d'au moins un joint. Revenons\n",
     "à l'ensemble des données initiales pour estimer la probabilité de\n",
     "défaillance d'un joint:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.06521739130434782\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = pd.read_csv(\"shuttle.csv\")\n",
     "print(np.sum(data.Malfunction)/np.sum(data.Count))"
diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb
index 59d72b5..be34c03 100644
--- a/module3/exo1/analyse-syndrome-grippal.ipynb
+++ b/module3/exo1/analyse-syndrome-grippal.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,15 +23,50 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
+    "- **Parenthèse :** (GW) Familiarisation avec `isoweek`. Voir [documentation](https://pypi.org/project/isoweek/) en ligne."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Week 2011W20 starts on 2011-05-16\n",
+      "Current week number is 32\n",
+      "Current year number is 2026\n",
+      "Week 2026W27 starts on 2026-06-29\n",
+      "Next week 2026W33 is\n"
+     ]
+    }
+   ],
+   "source": [
+    "from isoweek import Week\n",
+    "\n",
+    "w = Week(2011, 20)\n",
+    "print(f\"Week {w} starts on {w.monday()}\")\n",
+    "print(f\"Current week number is {Week.thisweek().week}\")\n",
+    "print(f\"Current year number is {Week.thisweek().year}\")\n",
+    "print(f\"Week {Week(2026,27)} starts on {Week(2026,27).day(0)}\")\n",
+    "print(f\"Next week {Week.thisweek() + 1} is\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lecture des données\n",
+    "\n",
+    "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier **en format CSV** dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui **commence en 1984** et se termine avec une semaine récente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
@@ -61,26 +96,292 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "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",
+       "    }\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>202631</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6732</td>\n",
+       "      <td>3537.0</td>\n",
+       "      <td>9927.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202630</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8942</td>\n",
+       "      <td>5702.0</td>\n",
+       "      <td>12182.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202629</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7601</td>\n",
+       "      <td>4679.0</td>\n",
+       "      <td>10523.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202628</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9217</td>\n",
+       "      <td>6138.0</td>\n",
+       "      <td>12296.0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202627</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11019</td>\n",
+       "      <td>7871.0</td>\n",
+       "      <td>14167.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2174</th>\n",
+       "      <td>198448</td>\n",
+       "      <td>3</td>\n",
+       "      <td>78620</td>\n",
+       "      <td>60634.0</td>\n",
+       "      <td>96606.0</td>\n",
+       "      <td>143</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2175</th>\n",
+       "      <td>198447</td>\n",
+       "      <td>3</td>\n",
+       "      <td>72029</td>\n",
+       "      <td>54274.0</td>\n",
+       "      <td>89784.0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2176</th>\n",
+       "      <td>198446</td>\n",
+       "      <td>3</td>\n",
+       "      <td>87330</td>\n",
+       "      <td>67686.0</td>\n",
+       "      <td>106974.0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>123.0</td>\n",
+       "      <td>195.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2177</th>\n",
+       "      <td>198445</td>\n",
+       "      <td>3</td>\n",
+       "      <td>135223</td>\n",
+       "      <td>101414.0</td>\n",
+       "      <td>169032.0</td>\n",
+       "      <td>246</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>308.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2178</th>\n",
+       "      <td>198444</td>\n",
+       "      <td>3</td>\n",
+       "      <td>68422</td>\n",
+       "      <td>20056.0</td>\n",
+       "      <td>116788.0</td>\n",
+       "      <td>125</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>213.0</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  \\\n",
+       "0     202631          3    6732    3537.0    9927.0     10         5.0   \n",
+       "1     202630          3    8942    5702.0   12182.0     13         8.0   \n",
+       "2     202629          3    7601    4679.0   10523.0     11         7.0   \n",
+       "3     202628          3    9217    6138.0   12296.0     14         9.0   \n",
+       "4     202627          3   11019    7871.0   14167.0     16        11.0   \n",
+       "2174  198448          3   78620   60634.0   96606.0    143       110.0   \n",
+       "2175  198447          3   72029   54274.0   89784.0    131        99.0   \n",
+       "2176  198446          3   87330   67686.0  106974.0    159       123.0   \n",
+       "2177  198445          3  135223  101414.0  169032.0    246       184.0   \n",
+       "2178  198444          3   68422   20056.0  116788.0    125        37.0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0          15.0        FR   France  \n",
+       "1          18.0        FR   France  \n",
+       "2          15.0        FR   France  \n",
+       "3          19.0        FR   France  \n",
+       "4          21.0        FR   France  \n",
+       "2174      176.0        FR   France  \n",
+       "2175      163.0        FR   France  \n",
+       "2176      195.0        FR   France  \n",
+       "2177      308.0        FR   France  \n",
+       "2178      213.0        FR   France  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
-    "raw_data"
+    "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)  \n",
+    "raw_data   # GW: codage latin1 des caractères accentués, sinon par défaut utf-8\n",
+    "pd.concat([raw_data.head(), raw_data.tail()])  #GW"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées."
+    "## Examen des données & nettoyage\n",
+    "Y a-t-il des **points manquants** dans ce jeux de données ? \n",
+    "- Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées."
    ]
   },
   {
    "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",
+       "    }\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>1942</th>\n",
+       "      <td>198919</td>\n",
+       "      <td>3</td>\n",
+       "      <td>-</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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",
+       "1942  198919          3   -      NaN     NaN      -         NaN        NaN   \n",
+       "\n",
+       "     geo_insee geo_name  \n",
+       "1942        FR   France  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "raw_data[raw_data.isnull().any(axis=1)]"
    ]
@@ -94,24 +395,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2179, 10) (2178, 10)\n"
+     ]
+    }
+   ],
    "source": [
     "data = raw_data.dropna().copy()\n",
-    "data"
+    "print(raw_data.shape, data.shape)  #GW"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Premiers traitements sur les données\n",
+    "\n",
     "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",
+    "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",
@@ -122,70 +433,352 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "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",
+       "    }\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>202631</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6732</td>\n",
+       "      <td>3537.0</td>\n",
+       "      <td>9927.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2026-07-27/2026-08-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202630</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8942</td>\n",
+       "      <td>5702.0</td>\n",
+       "      <td>12182.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2026-07-20/2026-07-26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202629</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7601</td>\n",
+       "      <td>4679.0</td>\n",
+       "      <td>10523.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2026-07-13/2026-07-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202628</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9217</td>\n",
+       "      <td>6138.0</td>\n",
+       "      <td>12296.0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2026-07-06/2026-07-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202627</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11019</td>\n",
+       "      <td>7871.0</td>\n",
+       "      <td>14167.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "      <td>2026-06-29/2026-07-05</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  202631          3   6732   3537.0   9927.0     10         5.0       15.0   \n",
+       "1  202630          3   8942   5702.0  12182.0     13         8.0       18.0   \n",
+       "2  202629          3   7601   4679.0  10523.0     11         7.0       15.0   \n",
+       "3  202628          3   9217   6138.0  12296.0     14         9.0       19.0   \n",
+       "4  202627          3  11019   7871.0  14167.0     16        11.0       21.0   \n",
+       "\n",
+       "  geo_insee geo_name                period  \n",
+       "0        FR   France 2026-07-27/2026-08-02  \n",
+       "1        FR   France 2026-07-20/2026-07-26  \n",
+       "2        FR   France 2026-07-13/2026-07-19  \n",
+       "3        FR   France 2026-07-06/2026-07-12  \n",
+       "4        FR   France 2026-06-29/2026-07-05  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def convert_week(year_and_week_int):\n",
+    "    '''First and last dates (period) of a string in isoweek format YYYYWW\n",
+    "    '''\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']]"
+    "# Add column\n",
+    "data['period'] = [convert_week(yw) for yw in data['week']]\n",
+    "data.head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Il restent deux petites modifications à faire.\n",
+    "#### Il restent deux petites modifications à faire.\n",
     "\n",
-    "Premièrement, nous définissons les périodes d'observation\n",
+    "- Premièrement, nous définissons les périodes d'observation\n",
     "comme nouvel index de notre jeux de données. Ceci en fait\n",
     "une suite chronologique, ce qui sera pratique par la suite.\n",
     "\n",
-    "Deuxièmement, nous trions les points par période, dans\n",
+    "- Deuxièmement, nous trions les points par période, dans\n",
     "le sens chronologique."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 8,
+   "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>1984-10-29/1984-11-04</th>\n",
+       "      <td>198444</td>\n",
+       "      <td>3</td>\n",
+       "      <td>68422</td>\n",
+       "      <td>20056.0</td>\n",
+       "      <td>116788.0</td>\n",
+       "      <td>125</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1984-11-05/1984-11-11</th>\n",
+       "      <td>198445</td>\n",
+       "      <td>3</td>\n",
+       "      <td>135223</td>\n",
+       "      <td>101414.0</td>\n",
+       "      <td>169032.0</td>\n",
+       "      <td>246</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>308.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1984-11-12/1984-11-18</th>\n",
+       "      <td>198446</td>\n",
+       "      <td>3</td>\n",
+       "      <td>87330</td>\n",
+       "      <td>67686.0</td>\n",
+       "      <td>106974.0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>123.0</td>\n",
+       "      <td>195.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1984-11-19/1984-11-25</th>\n",
+       "      <td>198447</td>\n",
+       "      <td>3</td>\n",
+       "      <td>72029</td>\n",
+       "      <td>54274.0</td>\n",
+       "      <td>89784.0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1984-11-26/1984-12-02</th>\n",
+       "      <td>198448</td>\n",
+       "      <td>3</td>\n",
+       "      <td>78620</td>\n",
+       "      <td>60634.0</td>\n",
+       "      <td>96606.0</td>\n",
+       "      <td>143</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>176.0</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",
+       "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",
+       "1984-11-19/1984-11-25  198447          3   72029   54274.0   89784.0    131   \n",
+       "1984-11-26/1984-12-02  198448          3   78620   60634.0   96606.0    143   \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  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "sorted_data = data.set_index('period').sort_index()"
+    "sorted_data = data.set_index('period').sort_index()\n",
+    "sorted_data.head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Nous vérifions la cohérence des données. Entre la fin d'une période et\n",
+    "#### Vérification\n",
+    "Nous vérifions la cohérence des données. **Entre la fin d'une période et\n",
     "le début de la période qui suit, la différence temporelle doit être\n",
-    "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n",
+    "zéro**, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n",
     "d'une seconde.\n",
     "\n",
     "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n",
     "entre lesquelles il manque une semaine.\n",
     "\n",
     "Nous reconnaissons ces dates: c'est la semaine sans observations\n",
-    "que nous avions supprimées !"
+    "que nous avions supprimée !"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
+     ]
+    }
+   ],
    "source": [
     "periods = sorted_data.index\n",
     "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
     "    delta = p2.to_timestamp() - p1.end_time\n",
+    "    delta = p2.start_time - p1.end_time      #GW: alternative\n",
     "    if delta > pd.Timedelta('1s'):\n",
     "        print(p1, p2)"
    ]
@@ -194,16 +787,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Un premier regard sur les données !"
+    "## Visualisation\n",
+    "Un premier regard sur les données !\n",
+    "\n",
+    "Et un premier problème en essayant de faire un `sorted_data['inc'].plot()` apparait : nos données sont des `str`, sans doute à cause du \"-\" dans la semaine 19 de l'année 1989.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "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()"
+    "# Convert str into numeric before plot\n",
+    "sorted_data['inc'] = sorted_data['inc'].astype(int)\n",
+    "sorted_data['inc100'] = sorted_data['inc100'].astype(int)\n",
+    "sorted_data['inc100'].plot()\n",
+    "plt.title(\"Incidence des Syndromes Gripaux en France\")\n",
+    "plt.ylabel(\"Taux pour 100 000 habitants\")\n",
+    "plt.grid()\n",
+    "plt.show()"
    ]
   },
   {
@@ -215,11 +831,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "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()"
+    "sorted_data['inc100'][-200:].plot()\n",
+    "plt.title(\"Incidence des Syndromes Gripaux en France\")\n",
+    "plt.ylabel(\"Taux pour 100 000 habitants\")\n",
+    "plt.grid()\n",
+    "plt.show()"
    ]
   },
   {
@@ -253,9 +886,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
@@ -341,9 +972,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -364,7 +993,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
-- 
2.18.1