From 19ddcd7cea3d31c8c95344cd97dc2201572c441f Mon Sep 17 00:00:00 2001
From: 25beebaf69fecb0be5d335e6142fa270
<25beebaf69fecb0be5d335e6142fa270@app-learninglab.inria.fr>
Date: Sat, 8 Aug 2026 14:51:29 +0000
Subject: [PATCH] =?UTF-8?q?Premi=C3=A8re=20version=20compl=C3=A8te=20du=20?=
=?UTF-8?q?Notebook?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
module3/exo2/analyse-varicelle.ipynb | 171 ++++++++++++++++++++-------
1 file changed, 129 insertions(+), 42 deletions(-)
diff --git a/module3/exo2/analyse-varicelle.ipynb b/module3/exo2/analyse-varicelle.ipynb
index 6454583..b89a0e3 100644
--- a/module3/exo2/analyse-varicelle.ipynb
+++ b/module3/exo2/analyse-varicelle.ipynb
@@ -28,14 +28,14 @@
"source": [
"## Source des données\n",
"\n",
- "Les données de l'incidence de la varicelle 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 1991** et se termine avec une semaine récente."
+ "Les données de l'incidence de la varicelle 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 1990** et se termine avec une semaine récente."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "- Approche préférée, se prémunie d'un URL déplacé ou un fichier de format modifié."
+ "- Approche préférée, se prémunie d'un URL déplacé ou un format ou contenu de fichier modifié."
]
},
{
@@ -47,8 +47,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Fichier non trouvé, téléchargement en cours...\n",
- "Téléchargement terminé : 'inc-7-PAY.csv'\n"
+ "Le fichier 'inc-7-PAY.csv' existe déjà en local.\n"
]
}
],
@@ -393,7 +392,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -521,14 +520,14 @@
"4 FR France 2026-06-29/2026-07-05 "
]
},
- "execution_count": 6,
+ "execution_count": 5,
"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",
+ " '''First and last dates (period) of a YYYYWW string (isoweek format) \n",
" '''\n",
" year_and_week_str = str(year_and_week_int)\n",
" year = int(year_and_week_str[:4])\n",
@@ -536,7 +535,7 @@
" w = isoweek.Week(year, week)\n",
" return pd.Period(w.day(0), 'W')\n",
"\n",
- "# Copy and add column\n",
+ "# Copy and add column with period\n",
"data = raw_data.copy()\n",
"data['period'] = [convert_week(yw) for yw in data['week']]\n",
"data.head()"
@@ -558,7 +557,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -695,7 +694,7 @@
"1990-12-31/1991-01-06 18 36 FR France "
]
},
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -720,7 +719,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -736,14 +735,12 @@
"metadata": {},
"source": [
"## 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"
+ "Un premier regard sur les données !"
]
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -763,9 +760,12 @@
"# If needed, uncomment & 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",
+ "\n",
"fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,4))\n",
"fig.suptitle(\"Evolution de la Varicelle en France\")\n",
- "sorted_data['inc'].plot(ax=ax1)\n",
+ "sorted_data['inc'].plot(ax=ax1) #GW: ma 1ère solution, les deux suivantes marchent moins bien\n",
+ "#ax1.plot(sorted_data['week'], sorted_data['inc'])\n",
+ "#ax1.plot(sorted_data.index.to_timestamp(), sorted_data['inc'])\n",
"ax1.set_ylabel(\"Incidence (habitants)\")\n",
"ax1.grid()\n",
"\n",
@@ -784,7 +784,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -825,29 +825,60 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "1. Etant donné que **le pic de l'épidémie se situe en hiver, à cheval\n",
- "entre deux années civiles**, nous définissons la période de référence\n",
- "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n",
- "1er août de l'année $N+1$.\n",
+ "1. Nous définissons la période de référence du 1er septembre de l'année $N$ au 1er septembre de l'année $N+1$.\n",
"\n",
- "2. Notre tâche est un peu compliquée par le fait que **l'année ne comporte\n",
- "pas un nombre entier de semaines**. \n",
+ "2. Notre tâche est un peu compliquée par le fait que **l'année ne comporte pas un nombre entier de semaines**. \n",
" - Nous modifions donc un peu nos périodes de référence: à la place du 1er août de chaque année, nous utilisons **le premier jour de la semaine qui contient le 1er août**.Comme l'incidence de syndrome grippal est très faible en été, cette modification ne risque pas de fausser nos conclusions.\n",
" \n",
"\n",
- "Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
- "rend la première année incomplète. Nous commençons donc l'analyse en 1985."
+ "Encore un petit détail: les données commencent an décembre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "datetime.date(1990, 12, 3)"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from isoweek import Week\n",
+ "Week(1990,49).day(0) # Trouver la date de début de la première semaine de données"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Period('1991-08-26/1991-09-01', 'W-SUN'),\n",
+ " Period('1992-08-31/1992-09-06', 'W-SUN'),\n",
+ " Period('1993-08-30/1993-09-05', 'W-SUN'),\n",
+ " Period('1994-08-29/1994-09-04', 'W-SUN'),\n",
+ " Period('1995-08-28/1995-09-03', 'W-SUN')]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W') for y in range(1985,\n",
+ "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W') for y in range(1991,\n",
" sorted_data.index[-1].year)]\n",
- "first_august_week[:5]"
+ "first_sept_week[:5]"
]
},
{
@@ -855,22 +886,38 @@
"metadata": {},
"source": [
"#### Calcul annuel & Vérification\n",
- "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons **nos intervalles d'environ un an** comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
+ "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. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
"\n",
"Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1992 832939\n",
+ "1993 643387\n",
+ "1994 661409\n",
+ "1995 652478\n",
+ "1996 564901\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Initialisation\n",
"year = []\n",
"yearly_incidence = []\n",
"\n",
- "for week1, week2 in zip(first_august_week[:-1], first_august_week[1:]):\n",
+ "for week1, week2 in zip(first_sept_week[:-1], first_sept_week[1:]):\n",
" one_year = sorted_data['inc'][week1:week2-1]\n",
" assert abs(len(one_year)-52) < 2 # vérification du nombre de valeurs / semaines\n",
" yearly_incidence.append(one_year.sum()) # calcul des incidences sur une durée de 1 an\n",
@@ -890,12 +937,25 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"yearly_incidence.plot(style='*')\n",
- "plt.title(\"Syndromes Gripaux en France\")\n",
+ "plt.title(\"Varicelle en France\")\n",
"plt.ylabel(\"Incidence annuelle (habitants)\")\n",
"plt.grid()\n",
"plt.show()"
@@ -910,9 +970,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2009 842373\n",
+ "1992 832939\n",
+ "2010 829911\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"yearly_incidence.sort_values(ascending=False).head(3)"
]
@@ -921,15 +995,28 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
- " française, sont assez rares : il y en eu trois au cours des 40 dernières années."
+ "Enfin, un histogramme montre qu'il n'y a pas vraiment d'épidémies fortes, qui touchent environ 10% de la population\n",
+ " française. Une légère baisse s'observe dans la figure précédente, depuis 2020, tendance à confirmer."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"yearly_incidence.hist(bins=10, xrot=20, edgecolor='white', zorder=3)\n",
"plt.xlabel(\"Incidence annuelle (habitants)\")\n",
--
2.18.1