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Commit bb6ebdfc authored by a12161f2df0e1aabd768453ba47e6d46's avatar a12161f2df0e1aabd768453ba47e6d46
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fin adaptation

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module3/exo2/exercice.ipynb
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...@@ -12,7 +12,7 @@ ...@@ -12,7 +12,7 @@
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...@@ -37,7 +37,7 @@ ...@@ -37,7 +37,7 @@
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...@@ -84,7 +84,7 @@ ...@@ -84,7 +84,7 @@
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...@@ -94,8 +94,7 @@ ...@@ -94,8 +94,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Téléchargement du fichier depuis : http://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\n", "Le fichier existe déjà : fichiervaricelle.txt\n"
"Fichier téléchargé et enregistré : fichiervaricelle.txt\n"
] ]
} }
], ],
...@@ -127,7 +126,7 @@ ...@@ -127,7 +126,7 @@
}, },
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...@@ -1095,7 +1094,7 @@ ...@@ -1095,7 +1094,7 @@
"[1788 rows x 10 columns]" "[1788 rows x 10 columns]"
] ]
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"execution_count": 15, "execution_count": 28,
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...@@ -1117,7 +1116,7 @@ ...@@ -1117,7 +1116,7 @@
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...@@ -1167,7 +1166,7 @@ ...@@ -1167,7 +1166,7 @@
"Index: []" "Index: []"
] ]
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...@@ -1188,7 +1187,7 @@ ...@@ -1188,7 +1187,7 @@
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...@@ -2156,7 +2155,7 @@ ...@@ -2156,7 +2155,7 @@
"[1788 rows x 10 columns]" "[1788 rows x 10 columns]"
] ]
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...@@ -2189,7 +2188,7 @@ ...@@ -2189,7 +2188,7 @@
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...@@ -2225,7 +2224,7 @@ ...@@ -2225,7 +2224,7 @@
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...@@ -2256,7 +2255,7 @@ ...@@ -2256,7 +2255,7 @@
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...@@ -2282,7 +2281,7 @@ ...@@ -2282,7 +2281,7 @@
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...@@ -2291,10 +2290,10 @@ ...@@ -2291,10 +2290,10 @@
{ {
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"<matplotlib.axes._subplots.AxesSubplot at 0x7f0d0d8cae80>" "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d0b477f98>"
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...@@ -2327,7 +2326,7 @@ ...@@ -2327,7 +2326,7 @@
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...@@ -2336,10 +2335,10 @@ ...@@ -2336,10 +2335,10 @@
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...@@ -2379,32 +2378,32 @@ ...@@ -2379,32 +2378,32 @@
"source": [ "source": [
"Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", "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 années civiles, nous définissons la période de référence\n",
"entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", "entre deux minima de l'incidence, du 1er septembre de l'année $N$ au\n",
"1er août de l'année $N+1$.\n", "1er septembre de l'année $N+1$.\n",
"\n", "\n",
"Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
"pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
"de référence: à la place du 1er août de chaque année, nous utilisons le\n", "de référence: à la place du 1er spetembre de chaque année, nous utilisons le\n",
"premier jour de la semaine qui contient le 1er août.\n", "premier jour de la semaine qui contient le 1er septembre.\n",
"\n", "\n",
"Comme l'incidence de syndrome grippal est très faible en été, cette\n", "Comme l'incidence de la varicelle est très faible en été, cette\n",
"modification ne risque pas de fausser nos conclusions.\n", "modification ne risque pas de fausser nos conclusions.\n",
"\n", "\n",
"Encore un petit détail: les données commencent an octobre 1984, ce qui\n", "Encore un petit détail: les données commencent an octobre 1990, ce qui\n",
"rend la première année incomplète. Nous commençons donc l'analyse en 1985." "rend la première année incomplète. Nous commençons donc l'analyse en 1991. Comme l'année en cours n'est pas terminée, on arrête l'année précédente."
] ]
}, },
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"outputs": [], "outputs": [],
"source": [ "source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", "first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
" for y in range(1985,\n", " for y in range(1991,\n",
" sorted_data.index[-1].year)]" " sorted_data.index[-1].year)]"
] ]
}, },
...@@ -2415,31 +2414,19 @@ ...@@ -2415,31 +2414,19 @@
"hidePrompt": false "hidePrompt": false
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"source": [ "source": [
"En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", "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", "\n",
"Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
] ]
}, },
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"outputs": [ "outputs": [],
{
"ename": "AssertionError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-24-9a471f5827a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m first_august_week[1:]):\n\u001b[1;32m 5\u001b[0m \u001b[0mone_year\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted_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[0mweek1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m52\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0myear\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: "
]
}
],
"source": [ "source": [
"year = []\n", "year = []\n",
"yearly_incidence = []\n", "yearly_incidence = []\n",
...@@ -2464,12 +2451,35 @@ ...@@ -2464,12 +2451,35 @@
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{
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"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": [ "source": [
"yearly_incidence.plot(style='*')" "yearly_incidence.plot(style='*')"
] ]
...@@ -2481,21 +2491,102 @@ ...@@ -2481,21 +2491,102 @@
"hidePrompt": false "hidePrompt": false
}, },
"source": [ "source": [
"Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin) et les plus faibles (début)."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 45,
"metadata": { "metadata": {
"hideCode": false, "hideCode": false,
"hidePrompt": false "hidePrompt": false
}, },
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"2020 221186\n",
"2023 366227\n",
"2021 376290\n",
"2024 479258\n",
"2002 516689\n",
"2018 542312\n",
"2017 551041\n",
"1996 564901\n",
"2019 584066\n",
"2015 604382\n",
"2000 617597\n",
"2001 619041\n",
"2012 624573\n",
"2005 628464\n",
"2006 632833\n",
"2022 641397\n",
"2011 642368\n",
"1993 643387\n",
"1995 652478\n",
"1994 661409\n",
"1998 677775\n",
"1997 683434\n",
"2014 685769\n",
"2013 698332\n",
"2007 717352\n",
"2008 749478\n",
"1999 756456\n",
"2003 758363\n",
"2004 777388\n",
"2016 782114\n",
"2010 829911\n",
"1992 832939\n",
"2009 842373\n",
"dtype: int64"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"yearly_incidence.sort_values()" "yearly_incidence_sort= yearly_incidence.sort_values()\n",
"yearly_incidence_sort"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Voici les années où l'incidence est la plus faible et la plus forte. "
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'numpy.int64' object has no attribute 'index'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-51-3d550e11d8e0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"L'année avec la plus faible incidence\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myearly_incidence_sort\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"L'année avec la plus forte incidence\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myearly_incidence_sort\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'numpy.int64' object has no attribute 'index'"
]
}
],
"source": [
"print(\"L'année avec la plus faible incidence\",yearly_incidence_sort.index[-1].index[0])\n",
"print(\"L'année avec la plus forte incidence\",yearly_incidence_sort.index[0].year)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
...@@ -2509,12 +2600,35 @@ ...@@ -2509,12 +2600,35 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 40,
"metadata": { "metadata": {
"hideCode": false, "hideCode": false,
"hidePrompt": false "hidePrompt": false
}, },
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0d0b2ecc88>"
]
},
"execution_count": 40,
"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": [ "source": [
"yearly_incidence.hist(xrot=20)" "yearly_incidence.hist(xrot=20)"
] ]
......
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