{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "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." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# before MTE changes\n", "#data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fichier local 'incidence_grippal.csv' déjà présent. Lecture directe...\n", "Aperçu des données :\n", " # {\"source\":\"ré \\\n", "0 week,indicator,inc,inc_low,inc_up,inc100,inc10... \n", "1 202541,3,93620,82948,104292,140,124,156,FR,France \n", "2 202540,3,79213,71213,87213,118,106,130,FR,France \n", "3 202539,3,72930,64872,80988,109,97,121,FR,France \n", "4 202538,3,61435,54131,68739,92,81,103,FR,France \n", "\n", " seau Sentinelles, INSERM, Sorbonne Université \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " , https:\\/\\/www.sentiweb.fr\",\"meta\":{\"period\":[198444,202541],\"geo\":[\"PAY\",0],\"geo_ref\":\"insee\",\"indicator\":\"3\",\"type\":\"all\",\"conf_int\":true,\"compact\":false,\"age_group\":false,\"span\":\"all\"},\"date\":\"2025-10-15T21:27:28+02:00\",\"bundle\":\"1760556448\"} \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n" ] } ], "source": [ "# ajout par MTE avec les consignes de l'exercice\n", "\n", "import os\n", "import pandas as pd\n", "\n", "# URL d'origine (par exemple celle du Réseau Sentinelles)\n", "url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", "\n", "# Nom du fichier local\n", "local_file = \"incidence_grippal.csv\"\n", "\n", "# Étape 1 : Si le fichier n'existe pas, on télécharge les données\n", "if not os.path.exists(local_file):\n", " print(\"Fichier local introuvable. Téléchargement en cours...\")\n", " try:\n", " df = pd.read_csv(url, sep=';') # ajuster le séparateur selon le format réel\n", " df.to_csv(local_file, index=False)\n", " print(f\"Données téléchargées et sauvegardées dans '{local_file}'.\")\n", " except Exception as e:\n", " print(\"Erreur lors du téléchargement :\", e)\n", " raise\n", "else:\n", " print(f\"Fichier local '{local_file}' déjà présent. Lecture directe...\")\n", "\n", "# Étape 2 : Lecture du fichier local\n", "df = pd.read_csv(local_file)\n", "\n", "# Affichage de vérification\n", "print(\"Aperçu des données :\")\n", "print(df.head())\n", "\n", "data_url = local_file\n" ] }, { "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", "| indicator | Code de l'indicateur de surveillance |\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) |\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", "\n", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_nameUnnamed: 1Unnamed: 2
0202541,3,93620,82948,104292,140,124,156,FR,FranceNaNNaN
1202540,3,79213,71213,87213,118,106,130,FR,FranceNaNNaN
2202539,3,72930,64872,80988,109,97,121,FR,FranceNaNNaN
3202538,3,61435,54131,68739,92,81,103,FR,FranceNaNNaN
4202537,3,46373,39689,53057,69,59,79,FR,FranceNaNNaN
5202536,3,25581,20702,30460,38,31,45,FR,FranceNaNNaN
6202535,3,22717,17480,27954,34,26,42,FR,FranceNaNNaN
7202534,3,21429,16177,26681,32,24,40,FR,FranceNaNNaN
8202533,3,16766,12022,21510,25,18,32,FR,FranceNaNNaN
9202532,3,19900,14303,25497,30,22,38,FR,FranceNaNNaN
10202531,3,18470,12625,24315,28,19,37,FR,FranceNaNNaN
11202530,3,19166,14283,24049,29,22,36,FR,FranceNaNNaN
12202529,3,18673,13815,23531,28,21,35,FR,FranceNaNNaN
13202528,3,23285,18131,28439,35,27,43,FR,FranceNaNNaN
14202527,3,21453,17129,25777,32,26,38,FR,FranceNaNNaN
15202526,3,21945,17422,26468,33,26,40,FR,FranceNaNNaN
16202525,3,23323,18546,28100,35,28,42,FR,FranceNaNNaN
17202524,3,23154,18577,27731,35,28,42,FR,FranceNaNNaN
18202523,3,24391,19307,29475,36,28,44,FR,FranceNaNNaN
19202522,3,18755,14333,23177,28,21,35,FR,FranceNaNNaN
20202521,3,23760,18671,28849,35,27,43,FR,FranceNaNNaN
21202520,3,20265,15814,24716,30,23,37,FR,FranceNaNNaN
22202519,3,16264,12394,20134,24,18,30,FR,FranceNaNNaN
23202518,3,18115,13975,22255,27,21,33,FR,FranceNaNNaN
24202517,3,22150,17291,27009,33,26,40,FR,FranceNaNNaN
25202516,3,28564,22550,34578,43,34,52,FR,FranceNaNNaN
26202515,3,35721,29592,41850,53,44,62,FR,FranceNaNNaN
27202514,3,37579,31232,43926,56,47,65,FR,FranceNaNNaN
28202513,3,39673,33686,45660,59,50,68,FR,FranceNaNNaN
29202512,3,52543,45627,59459,78,68,88,FR,FranceNaNNaN
............
2107198521,3,26096,19621,32571,47,35,59,FR,FranceNaNNaN
2108198520,3,27896,20885,34907,51,38,64,FR,FranceNaNNaN
2109198519,3,43154,32821,53487,78,59,97,FR,FranceNaNNaN
2110198518,3,40555,29935,51175,74,55,93,FR,FranceNaNNaN
2111198517,3,34053,24366,43740,62,44,80,FR,FranceNaNNaN
2112198516,3,50362,36451,64273,91,66,116,FR,FranceNaNNaN
2113198515,3,63881,45538,82224,116,83,149,FR,FranceNaNNaN
2114198514,3,134545,114400,154690,244,207,281,FR,F...NaNNaN
2115198513,3,197206,176080,218332,357,319,395,FR,F...NaNNaN
2116198512,3,245240,223304,267176,445,405,485,FR,F...NaNNaN
2117198511,3,276205,252399,300011,501,458,544,FR,F...NaNNaN
2118198510,3,353231,326279,380183,640,591,689,FR,F...NaNNaN
2119198509,3,369895,341109,398681,670,618,722,FR,F...NaNNaN
2120198508,3,389886,359529,420243,707,652,762,FR,F...NaNNaN
2121198507,3,471852,432599,511105,855,784,926,FR,F...NaNNaN
2122198506,3,565825,518011,613639,1026,939,1113,FR...NaNNaN
2123198505,3,637302,592795,681809,1155,1074,1236,F...NaNNaN
2124198504,3,424937,390794,459080,770,708,832,FR,F...NaNNaN
2125198503,3,213901,174689,253113,388,317,459,FR,F...NaNNaN
2126198502,3,97586,80949,114223,177,147,207,FR,FranceNaNNaN
2127198501,3,85489,65918,105060,155,120,190,FR,FranceNaNNaN
2128198452,3,84830,60602,109058,154,110,198,FR,FranceNaNNaN
2129198451,3,101726,80242,123210,185,146,224,FR,Fr...NaNNaN
2130198450,3,123680,101401,145959,225,184,266,FR,F...NaNNaN
2131198449,3,101073,81684,120462,184,149,219,FR,Fr...NaNNaN
2132198448,3,78620,60634,96606,143,110,176,FR,FranceNaNNaN
2133198447,3,72029,54274,89784,131,99,163,FR,FranceNaNNaN
2134198446,3,87330,67686,106974,159,123,195,FR,FranceNaNNaN
2135198445,3,135223,101414,169032,246,184,308,FR,F...NaNNaN
2136198444,3,68422,20056,116788,125,37,213,FR,FranceNaNNaN
\n", "

2137 rows × 3 columns

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" ], "text/plain": [ " week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name \\\n", "0 202541,3,93620,82948,104292,140,124,156,FR,France \n", "1 202540,3,79213,71213,87213,118,106,130,FR,France \n", "2 202539,3,72930,64872,80988,109,97,121,FR,France \n", "3 202538,3,61435,54131,68739,92,81,103,FR,France \n", "4 202537,3,46373,39689,53057,69,59,79,FR,France \n", "5 202536,3,25581,20702,30460,38,31,45,FR,France \n", "6 202535,3,22717,17480,27954,34,26,42,FR,France \n", "7 202534,3,21429,16177,26681,32,24,40,FR,France \n", "8 202533,3,16766,12022,21510,25,18,32,FR,France \n", "9 202532,3,19900,14303,25497,30,22,38,FR,France \n", "10 202531,3,18470,12625,24315,28,19,37,FR,France \n", "11 202530,3,19166,14283,24049,29,22,36,FR,France \n", "12 202529,3,18673,13815,23531,28,21,35,FR,France \n", "13 202528,3,23285,18131,28439,35,27,43,FR,France \n", "14 202527,3,21453,17129,25777,32,26,38,FR,France \n", "15 202526,3,21945,17422,26468,33,26,40,FR,France \n", "16 202525,3,23323,18546,28100,35,28,42,FR,France \n", "17 202524,3,23154,18577,27731,35,28,42,FR,France \n", "18 202523,3,24391,19307,29475,36,28,44,FR,France \n", "19 202522,3,18755,14333,23177,28,21,35,FR,France \n", "20 202521,3,23760,18671,28849,35,27,43,FR,France \n", "21 202520,3,20265,15814,24716,30,23,37,FR,France \n", "22 202519,3,16264,12394,20134,24,18,30,FR,France \n", "23 202518,3,18115,13975,22255,27,21,33,FR,France \n", "24 202517,3,22150,17291,27009,33,26,40,FR,France \n", "25 202516,3,28564,22550,34578,43,34,52,FR,France \n", "26 202515,3,35721,29592,41850,53,44,62,FR,France \n", "27 202514,3,37579,31232,43926,56,47,65,FR,France \n", "28 202513,3,39673,33686,45660,59,50,68,FR,France \n", "29 202512,3,52543,45627,59459,78,68,88,FR,France \n", "... ... \n", "2107 198521,3,26096,19621,32571,47,35,59,FR,France \n", "2108 198520,3,27896,20885,34907,51,38,64,FR,France \n", "2109 198519,3,43154,32821,53487,78,59,97,FR,France \n", "2110 198518,3,40555,29935,51175,74,55,93,FR,France \n", "2111 198517,3,34053,24366,43740,62,44,80,FR,France \n", "2112 198516,3,50362,36451,64273,91,66,116,FR,France \n", "2113 198515,3,63881,45538,82224,116,83,149,FR,France \n", "2114 198514,3,134545,114400,154690,244,207,281,FR,F... \n", "2115 198513,3,197206,176080,218332,357,319,395,FR,F... \n", "2116 198512,3,245240,223304,267176,445,405,485,FR,F... \n", "2117 198511,3,276205,252399,300011,501,458,544,FR,F... \n", "2118 198510,3,353231,326279,380183,640,591,689,FR,F... \n", "2119 198509,3,369895,341109,398681,670,618,722,FR,F... \n", "2120 198508,3,389886,359529,420243,707,652,762,FR,F... \n", "2121 198507,3,471852,432599,511105,855,784,926,FR,F... \n", "2122 198506,3,565825,518011,613639,1026,939,1113,FR... \n", "2123 198505,3,637302,592795,681809,1155,1074,1236,F... \n", "2124 198504,3,424937,390794,459080,770,708,832,FR,F... \n", "2125 198503,3,213901,174689,253113,388,317,459,FR,F... \n", "2126 198502,3,97586,80949,114223,177,147,207,FR,France \n", "2127 198501,3,85489,65918,105060,155,120,190,FR,France \n", "2128 198452,3,84830,60602,109058,154,110,198,FR,France \n", "2129 198451,3,101726,80242,123210,185,146,224,FR,Fr... \n", "2130 198450,3,123680,101401,145959,225,184,266,FR,F... \n", "2131 198449,3,101073,81684,120462,184,149,219,FR,Fr... \n", "2132 198448,3,78620,60634,96606,143,110,176,FR,France \n", "2133 198447,3,72029,54274,89784,131,99,163,FR,France \n", "2134 198446,3,87330,67686,106974,159,123,195,FR,France \n", "2135 198445,3,135223,101414,169032,246,184,308,FR,F... \n", "2136 198444,3,68422,20056,116788,125,37,213,FR,France \n", "\n", " Unnamed: 1 Unnamed: 2 \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "10 NaN NaN \n", "11 NaN NaN \n", "12 NaN NaN \n", "13 NaN NaN \n", "14 NaN NaN \n", "15 NaN NaN \n", "16 NaN NaN \n", "17 NaN NaN \n", "18 NaN NaN \n", "19 NaN NaN \n", "20 NaN NaN \n", "21 NaN NaN \n", "22 NaN NaN \n", "23 NaN NaN \n", "24 NaN NaN \n", "25 NaN NaN \n", "26 NaN NaN \n", "27 NaN NaN \n", "28 NaN NaN \n", "29 NaN NaN \n", "... ... ... \n", "2107 NaN NaN \n", "2108 NaN NaN \n", "2109 NaN NaN \n", "2110 NaN NaN \n", "2111 NaN NaN \n", "2112 NaN NaN \n", "2113 NaN NaN \n", "2114 NaN NaN \n", "2115 NaN NaN \n", "2116 NaN NaN \n", "2117 NaN NaN \n", "2118 NaN NaN \n", "2119 NaN NaN \n", "2120 NaN NaN \n", "2121 NaN NaN \n", "2122 NaN NaN \n", "2123 NaN NaN \n", "2124 NaN NaN \n", "2125 NaN NaN \n", "2126 NaN NaN \n", "2127 NaN NaN \n", "2128 NaN NaN \n", "2129 NaN NaN \n", "2130 NaN NaN \n", "2131 NaN NaN \n", "2132 NaN NaN \n", "2133 NaN NaN \n", "2134 NaN NaN \n", "2135 NaN NaN \n", "2136 NaN NaN \n", "\n", "[2137 rows x 3 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, { "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." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
19001989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1900 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1900 FR France " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020254139362082948.0104292.0140124.0156.0FRFrance
120254037921371213.087213.0118106.0130.0FRFrance
220253937293064872.080988.010997.0121.0FRFrance
320253836143554131.068739.09281.0103.0FRFrance
420253734637339689.053057.06959.079.0FRFrance
520253632558120702.030460.03831.045.0FRFrance
620253532271717480.027954.03426.042.0FRFrance
720253432142916177.026681.03224.040.0FRFrance
820253331676612022.021510.02518.032.0FRFrance
920253231990014303.025497.03022.038.0FRFrance
1020253131847012625.024315.02819.037.0FRFrance
1120253031916614283.024049.02922.036.0FRFrance
1220252931867313815.023531.02821.035.0FRFrance
1320252832328518131.028439.03527.043.0FRFrance
1420252732145317129.025777.03226.038.0FRFrance
1520252632194517422.026468.03326.040.0FRFrance
1620252532332318546.028100.03528.042.0FRFrance
1720252432315418577.027731.03528.042.0FRFrance
1820252332439119307.029475.03628.044.0FRFrance
1920252231875514333.023177.02821.035.0FRFrance
2020252132376018671.028849.03527.043.0FRFrance
2120252032026515814.024716.03023.037.0FRFrance
2220251931626412394.020134.02418.030.0FRFrance
2320251831811513975.022255.02721.033.0FRFrance
2420251732215017291.027009.03326.040.0FRFrance
2520251632856422550.034578.04334.052.0FRFrance
2620251533572129592.041850.05344.062.0FRFrance
2720251433757931232.043926.05647.065.0FRFrance
2820251333967333686.045660.05950.068.0FRFrance
2920251235254345627.059459.07868.088.0FRFrance
.................................
210719852132609619621.032571.04735.059.0FRFrance
210819852032789620885.034907.05138.064.0FRFrance
210919851934315432821.053487.07859.097.0FRFrance
211019851834055529935.051175.07455.093.0FRFrance
211119851733405324366.043740.06244.080.0FRFrance
211219851635036236451.064273.09166.0116.0FRFrance
211319851536388145538.082224.011683.0149.0FRFrance
21141985143134545114400.0154690.0244207.0281.0FRFrance
21151985133197206176080.0218332.0357319.0395.0FRFrance
21161985123245240223304.0267176.0445405.0485.0FRFrance
21171985113276205252399.0300011.0501458.0544.0FRFrance
21181985103353231326279.0380183.0640591.0689.0FRFrance
21191985093369895341109.0398681.0670618.0722.0FRFrance
21201985083389886359529.0420243.0707652.0762.0FRFrance
21211985073471852432599.0511105.0855784.0926.0FRFrance
21221985063565825518011.0613639.01026939.01113.0FRFrance
21231985053637302592795.0681809.011551074.01236.0FRFrance
21241985043424937390794.0459080.0770708.0832.0FRFrance
21251985033213901174689.0253113.0388317.0459.0FRFrance
212619850239758680949.0114223.0177147.0207.0FRFrance
212719850138548965918.0105060.0155120.0190.0FRFrance
212819845238483060602.0109058.0154110.0198.0FRFrance
2129198451310172680242.0123210.0185146.0224.0FRFrance
21301984503123680101401.0145959.0225184.0266.0FRFrance
2131198449310107381684.0120462.0184149.0219.0FRFrance
213219844837862060634.096606.0143110.0176.0FRFrance
213319844737202954274.089784.013199.0163.0FRFrance
213419844638733067686.0106974.0159123.0195.0FRFrance
21351984453135223101414.0169032.0246184.0308.0FRFrance
213619844436842220056.0116788.012537.0213.0FRFrance
\n", "

2136 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202541 3 93620 82948.0 104292.0 140 124.0 \n", "1 202540 3 79213 71213.0 87213.0 118 106.0 \n", "2 202539 3 72930 64872.0 80988.0 109 97.0 \n", "3 202538 3 61435 54131.0 68739.0 92 81.0 \n", "4 202537 3 46373 39689.0 53057.0 69 59.0 \n", "5 202536 3 25581 20702.0 30460.0 38 31.0 \n", "6 202535 3 22717 17480.0 27954.0 34 26.0 \n", "7 202534 3 21429 16177.0 26681.0 32 24.0 \n", "8 202533 3 16766 12022.0 21510.0 25 18.0 \n", "9 202532 3 19900 14303.0 25497.0 30 22.0 \n", "10 202531 3 18470 12625.0 24315.0 28 19.0 \n", "11 202530 3 19166 14283.0 24049.0 29 22.0 \n", "12 202529 3 18673 13815.0 23531.0 28 21.0 \n", "13 202528 3 23285 18131.0 28439.0 35 27.0 \n", "14 202527 3 21453 17129.0 25777.0 32 26.0 \n", "15 202526 3 21945 17422.0 26468.0 33 26.0 \n", "16 202525 3 23323 18546.0 28100.0 35 28.0 \n", "17 202524 3 23154 18577.0 27731.0 35 28.0 \n", "18 202523 3 24391 19307.0 29475.0 36 28.0 \n", "19 202522 3 18755 14333.0 23177.0 28 21.0 \n", "20 202521 3 23760 18671.0 28849.0 35 27.0 \n", "21 202520 3 20265 15814.0 24716.0 30 23.0 \n", "22 202519 3 16264 12394.0 20134.0 24 18.0 \n", "23 202518 3 18115 13975.0 22255.0 27 21.0 \n", "24 202517 3 22150 17291.0 27009.0 33 26.0 \n", "25 202516 3 28564 22550.0 34578.0 43 34.0 \n", "26 202515 3 35721 29592.0 41850.0 53 44.0 \n", "27 202514 3 37579 31232.0 43926.0 56 47.0 \n", "28 202513 3 39673 33686.0 45660.0 59 50.0 \n", "29 202512 3 52543 45627.0 59459.0 78 68.0 \n", "... ... ... ... ... ... ... ... \n", "2107 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2108 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2109 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2110 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2111 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2112 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2113 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2114 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2115 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2116 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2117 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2118 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2119 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2120 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2121 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2122 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2123 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2124 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2125 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2126 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2127 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2128 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2129 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2130 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2131 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2132 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2133 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2134 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2135 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2136 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 156.0 FR France \n", "1 130.0 FR France \n", "2 121.0 FR France \n", "3 103.0 FR France \n", "4 79.0 FR France \n", "5 45.0 FR France \n", "6 42.0 FR France \n", "7 40.0 FR France \n", "8 32.0 FR France \n", "9 38.0 FR France \n", "10 37.0 FR France \n", "11 36.0 FR France \n", "12 35.0 FR France \n", "13 43.0 FR France \n", "14 38.0 FR France \n", "15 40.0 FR France \n", "16 42.0 FR France \n", "17 42.0 FR France \n", "18 44.0 FR France \n", "19 35.0 FR France \n", "20 43.0 FR France \n", "21 37.0 FR France \n", "22 30.0 FR France \n", "23 33.0 FR France \n", "24 40.0 FR France \n", "25 52.0 FR France \n", "26 62.0 FR France \n", "27 65.0 FR France \n", "28 68.0 FR France \n", "29 88.0 FR France \n", "... ... ... ... \n", "2107 59.0 FR France \n", "2108 64.0 FR France \n", "2109 97.0 FR France \n", "2110 93.0 FR France \n", "2111 80.0 FR France \n", "2112 116.0 FR France \n", "2113 149.0 FR France \n", "2114 281.0 FR France \n", "2115 395.0 FR France \n", "2116 485.0 FR France \n", "2117 544.0 FR France \n", "2118 689.0 FR France \n", "2119 722.0 FR France \n", "2120 762.0 FR France \n", "2121 926.0 FR France \n", "2122 1113.0 FR France \n", "2123 1236.0 FR France \n", "2124 832.0 FR France \n", "2125 459.0 FR France \n", "2126 207.0 FR France \n", "2127 190.0 FR France \n", "2128 198.0 FR France \n", "2129 224.0 FR France \n", "2130 266.0 FR France \n", "2131 219.0 FR France \n", "2132 176.0 FR France \n", "2133 163.0 FR France \n", "2134 195.0 FR France \n", "2135 308.0 FR France \n", "2136 213.0 FR France \n", "\n", "[2136 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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", "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous\n", "écrivons une petite fonction Python pour cela. Ensuite, nous\n", "l'appliquons à tous les points de nos donnés. Les résultats vont\n", "dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 7, "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", " 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']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il restent deux petites modifications à faire.\n", "\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", "le sens chronologique." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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", "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 !" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "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", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "Empty 'DataFrame': no numeric data to plot", "output_type": "error", "traceback": [ 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Le creux des incidences se trouve en été." ] }, { "cell_type": "code", "execution_count": 11, "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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \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[0;34m-\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m:\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, 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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", "\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", "de référence: à la place du 1er août de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er août.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en été, cette\n", "modification ne risque pas de fausser nos conclusions.\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." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "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", "\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, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_august_week[:-1],\n", " first_august_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": "markdown", "metadata": {}, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "yearly_incidence.plot(style='*')" ] }, { "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": null, "metadata": {}, "outputs": [], "source": [ "yearly_incidence.sort_values()" ] }, { "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 35 dernières années." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": 1 }