{ "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": 3, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.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", "| 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": "markdown", "metadata": {}, "source": [ "Téléchargement du fichier en local si il n'est pas déjà présent en local. Cela est là pour limiter le temps d'exécution et continuer à travailler s'il y a un problème sur le serveur." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Téléchargement du fichier depuis : http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\n", "Fichier téléchargé et enregistré : fichier.txt\n" ] } ], "source": [ "import os\n", "import requests\n", "\n", "def télécharger_fichier_si_nécessaire(url, chemin_local):\n", " # Vérifier si le fichier existe déjà en local\n", " if os.path.exists(chemin_local):\n", " print(f\"Le fichier existe déjà : {chemin_local}\")\n", " else:\n", " # Télécharger le fichier depuis l'URL\n", " print(f\"Téléchargement du fichier depuis : {url}\")\n", " réponse = requests.get(url)\n", "\n", " # Vérifier si la requête a réussi\n", " if réponse.status_code == 200:\n", " # Écrire le contenu dans le fichier local\n", " with open(chemin_local, 'wb') as fichier:\n", " fichier.write(réponse.content)\n", " print(f\"Fichier téléchargé et enregistré : {chemin_local}\")\n", " else:\n", " print(f\"Échec du téléchargement. Code d'état : {réponse.status_code}\")\n", "\n", "chemin_local = 'fichier.txt'\n", "télécharger_fichier_si_nécessaire(data_url, chemin_local)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020251036313354923.071343.09482.0106.0FRFrance
120250938460175044.094158.0126112.0140.0FRFrance
22025083136020124824.0147216.0203186.0220.0FRFrance
32025073208952195988.0221916.0312293.0331.0FRFrance
42025063273519258159.0288879.0408385.0431.0FRFrance
52025053334395318416.0350374.0499475.0523.0FRFrance
62025043350043332885.0367201.0522496.0548.0FRFrance
72025033252772238917.0266627.0377356.0398.0FRFrance
82025023257247242991.0271503.0384363.0405.0FRFrance
92025013231549214627.0248471.0345320.0370.0FRFrance
102024523201726185870.0217582.0302278.0326.0FRFrance
112024513201697187843.0215551.0302281.0323.0FRFrance
122024503136694126369.0147019.0205190.0220.0FRFrance
13202449310848799037.0117937.0163149.0177.0FRFrance
1420244838738178687.096075.0131118.0144.0FRFrance
1520244737628667626.084946.0114101.0127.0FRFrance
1620244635639949006.063792.08574.096.0FRFrance
1720244534734740843.053851.07161.081.0FRFrance
1820244433603930122.041956.05445.063.0FRFrance
1920244334657239928.053216.07060.080.0FRFrance
2020244236778560009.075561.010290.0114.0FRFrance
2120244137943571386.087484.0119107.0131.0FRFrance
2220244038496576555.093375.0127114.0140.0FRFrance
2320243939166082937.0100383.0137124.0150.0FRFrance
2420243839178682903.0100669.0138125.0151.0FRFrance
2520243735646049319.063601.08574.096.0FRFrance
2620243633365727906.039408.05041.059.0FRFrance
2720243532745422069.032839.04133.049.0FRFrance
2820243432671721003.032431.04031.049.0FRFrance
2920243332062315349.025897.03123.039.0FRFrance
.................................
207619852132609619621.032571.04735.059.0FRFrance
207719852032789620885.034907.05138.064.0FRFrance
207819851934315432821.053487.07859.097.0FRFrance
207919851834055529935.051175.07455.093.0FRFrance
208019851733405324366.043740.06244.080.0FRFrance
208119851635036236451.064273.09166.0116.0FRFrance
208219851536388145538.082224.011683.0149.0FRFrance
20831985143134545114400.0154690.0244207.0281.0FRFrance
20841985133197206176080.0218332.0357319.0395.0FRFrance
20851985123245240223304.0267176.0445405.0485.0FRFrance
20861985113276205252399.0300011.0501458.0544.0FRFrance
20871985103353231326279.0380183.0640591.0689.0FRFrance
20881985093369895341109.0398681.0670618.0722.0FRFrance
20891985083389886359529.0420243.0707652.0762.0FRFrance
20901985073471852432599.0511105.0855784.0926.0FRFrance
20911985063565825518011.0613639.01026939.01113.0FRFrance
20921985053637302592795.0681809.011551074.01236.0FRFrance
20931985043424937390794.0459080.0770708.0832.0FRFrance
20941985033213901174689.0253113.0388317.0459.0FRFrance
209519850239758680949.0114223.0177147.0207.0FRFrance
209619850138548965918.0105060.0155120.0190.0FRFrance
209719845238483060602.0109058.0154110.0198.0FRFrance
2098198451310172680242.0123210.0185146.0224.0FRFrance
20991984503123680101401.0145959.0225184.0266.0FRFrance
2100198449310107381684.0120462.0184149.0219.0FRFrance
210119844837862060634.096606.0143110.0176.0FRFrance
210219844737202954274.089784.013199.0163.0FRFrance
210319844638733067686.0106974.0159123.0195.0FRFrance
21041984453135223101414.0169032.0246184.0308.0FRFrance
210519844436842220056.0116788.012537.0213.0FRFrance
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2106 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202510 3 63133 54923.0 71343.0 94 82.0 \n", "1 202509 3 84601 75044.0 94158.0 126 112.0 \n", "2 202508 3 136020 124824.0 147216.0 203 186.0 \n", "3 202507 3 208952 195988.0 221916.0 312 293.0 \n", "4 202506 3 273519 258159.0 288879.0 408 385.0 \n", "5 202505 3 334395 318416.0 350374.0 499 475.0 \n", "6 202504 3 350043 332885.0 367201.0 522 496.0 \n", "7 202503 3 252772 238917.0 266627.0 377 356.0 \n", "8 202502 3 257247 242991.0 271503.0 384 363.0 \n", "9 202501 3 231549 214627.0 248471.0 345 320.0 \n", "10 202452 3 201726 185870.0 217582.0 302 278.0 \n", "11 202451 3 201697 187843.0 215551.0 302 281.0 \n", "12 202450 3 136694 126369.0 147019.0 205 190.0 \n", "13 202449 3 108487 99037.0 117937.0 163 149.0 \n", "14 202448 3 87381 78687.0 96075.0 131 118.0 \n", "15 202447 3 76286 67626.0 84946.0 114 101.0 \n", "16 202446 3 56399 49006.0 63792.0 85 74.0 \n", "17 202445 3 47347 40843.0 53851.0 71 61.0 \n", "18 202444 3 36039 30122.0 41956.0 54 45.0 \n", "19 202443 3 46572 39928.0 53216.0 70 60.0 \n", "20 202442 3 67785 60009.0 75561.0 102 90.0 \n", "21 202441 3 79435 71386.0 87484.0 119 107.0 \n", "22 202440 3 84965 76555.0 93375.0 127 114.0 \n", "23 202439 3 91660 82937.0 100383.0 137 124.0 \n", "24 202438 3 91786 82903.0 100669.0 138 125.0 \n", "25 202437 3 56460 49319.0 63601.0 85 74.0 \n", "26 202436 3 33657 27906.0 39408.0 50 41.0 \n", "27 202435 3 27454 22069.0 32839.0 41 33.0 \n", "28 202434 3 26717 21003.0 32431.0 40 31.0 \n", "29 202433 3 20623 15349.0 25897.0 31 23.0 \n", "... ... ... ... ... ... ... ... \n", "2076 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2077 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2078 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2079 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2080 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2081 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2082 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2083 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2084 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2085 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2086 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2087 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2088 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2089 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2090 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2091 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2092 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2093 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2094 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2095 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2096 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2097 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2098 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2099 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2100 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2101 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2102 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2103 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2104 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2105 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 106.0 FR France \n", "1 140.0 FR France \n", "2 220.0 FR France \n", "3 331.0 FR France \n", "4 431.0 FR France \n", "5 523.0 FR France \n", "6 548.0 FR France \n", "7 398.0 FR France \n", "8 405.0 FR France \n", "9 370.0 FR France \n", "10 326.0 FR France \n", "11 323.0 FR France \n", "12 220.0 FR France \n", "13 177.0 FR France \n", "14 144.0 FR France \n", "15 127.0 FR France \n", "16 96.0 FR France \n", "17 81.0 FR France \n", "18 63.0 FR France \n", "19 80.0 FR France \n", "20 114.0 FR France \n", "21 131.0 FR France \n", "22 140.0 FR France \n", "23 150.0 FR France \n", "24 151.0 FR France \n", "25 96.0 FR France \n", "26 59.0 FR France \n", "27 49.0 FR France \n", "28 49.0 FR France \n", "29 39.0 FR France \n", "... ... ... ... \n", "2076 59.0 FR France \n", "2077 64.0 FR France \n", "2078 97.0 FR France \n", "2079 93.0 FR France \n", "2080 80.0 FR France \n", "2081 116.0 FR France \n", "2082 149.0 FR France \n", "2083 281.0 FR France \n", "2084 395.0 FR France \n", "2085 485.0 FR France \n", "2086 544.0 FR France \n", "2087 689.0 FR France \n", "2088 722.0 FR France \n", "2089 762.0 FR France \n", "2090 926.0 FR France \n", "2091 1113.0 FR France \n", "2092 1236.0 FR France \n", "2093 832.0 FR France \n", "2094 459.0 FR France \n", "2095 207.0 FR France \n", "2096 190.0 FR France \n", "2097 198.0 FR France \n", "2098 224.0 FR France \n", "2099 266.0 FR France \n", "2100 219.0 FR France \n", "2101 176.0 FR France \n", "2102 163.0 FR France \n", "2103 195.0 FR France \n", "2104 308.0 FR France \n", "2105 213.0 FR France \n", "\n", "[2106 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(chemin_local, 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": 6, "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
18691989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1869 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1869 FR France " ] }, "execution_count": 6, "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": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020251036313354923.071343.09482.0106.0FRFrance
120250938460175044.094158.0126112.0140.0FRFrance
22025083136020124824.0147216.0203186.0220.0FRFrance
32025073208952195988.0221916.0312293.0331.0FRFrance
42025063273519258159.0288879.0408385.0431.0FRFrance
52025053334395318416.0350374.0499475.0523.0FRFrance
62025043350043332885.0367201.0522496.0548.0FRFrance
72025033252772238917.0266627.0377356.0398.0FRFrance
82025023257247242991.0271503.0384363.0405.0FRFrance
92025013231549214627.0248471.0345320.0370.0FRFrance
102024523201726185870.0217582.0302278.0326.0FRFrance
112024513201697187843.0215551.0302281.0323.0FRFrance
122024503136694126369.0147019.0205190.0220.0FRFrance
13202449310848799037.0117937.0163149.0177.0FRFrance
1420244838738178687.096075.0131118.0144.0FRFrance
1520244737628667626.084946.0114101.0127.0FRFrance
1620244635639949006.063792.08574.096.0FRFrance
1720244534734740843.053851.07161.081.0FRFrance
1820244433603930122.041956.05445.063.0FRFrance
1920244334657239928.053216.07060.080.0FRFrance
2020244236778560009.075561.010290.0114.0FRFrance
2120244137943571386.087484.0119107.0131.0FRFrance
2220244038496576555.093375.0127114.0140.0FRFrance
2320243939166082937.0100383.0137124.0150.0FRFrance
2420243839178682903.0100669.0138125.0151.0FRFrance
2520243735646049319.063601.08574.096.0FRFrance
2620243633365727906.039408.05041.059.0FRFrance
2720243532745422069.032839.04133.049.0FRFrance
2820243432671721003.032431.04031.049.0FRFrance
2920243332062315349.025897.03123.039.0FRFrance
.................................
207619852132609619621.032571.04735.059.0FRFrance
207719852032789620885.034907.05138.064.0FRFrance
207819851934315432821.053487.07859.097.0FRFrance
207919851834055529935.051175.07455.093.0FRFrance
208019851733405324366.043740.06244.080.0FRFrance
208119851635036236451.064273.09166.0116.0FRFrance
208219851536388145538.082224.011683.0149.0FRFrance
20831985143134545114400.0154690.0244207.0281.0FRFrance
20841985133197206176080.0218332.0357319.0395.0FRFrance
20851985123245240223304.0267176.0445405.0485.0FRFrance
20861985113276205252399.0300011.0501458.0544.0FRFrance
20871985103353231326279.0380183.0640591.0689.0FRFrance
20881985093369895341109.0398681.0670618.0722.0FRFrance
20891985083389886359529.0420243.0707652.0762.0FRFrance
20901985073471852432599.0511105.0855784.0926.0FRFrance
20911985063565825518011.0613639.01026939.01113.0FRFrance
20921985053637302592795.0681809.011551074.01236.0FRFrance
20931985043424937390794.0459080.0770708.0832.0FRFrance
20941985033213901174689.0253113.0388317.0459.0FRFrance
209519850239758680949.0114223.0177147.0207.0FRFrance
209619850138548965918.0105060.0155120.0190.0FRFrance
209719845238483060602.0109058.0154110.0198.0FRFrance
2098198451310172680242.0123210.0185146.0224.0FRFrance
20991984503123680101401.0145959.0225184.0266.0FRFrance
2100198449310107381684.0120462.0184149.0219.0FRFrance
210119844837862060634.096606.0143110.0176.0FRFrance
210219844737202954274.089784.013199.0163.0FRFrance
210319844638733067686.0106974.0159123.0195.0FRFrance
21041984453135223101414.0169032.0246184.0308.0FRFrance
210519844436842220056.0116788.012537.0213.0FRFrance
\n", "

2105 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202510 3 63133 54923.0 71343.0 94 82.0 \n", "1 202509 3 84601 75044.0 94158.0 126 112.0 \n", "2 202508 3 136020 124824.0 147216.0 203 186.0 \n", "3 202507 3 208952 195988.0 221916.0 312 293.0 \n", "4 202506 3 273519 258159.0 288879.0 408 385.0 \n", "5 202505 3 334395 318416.0 350374.0 499 475.0 \n", "6 202504 3 350043 332885.0 367201.0 522 496.0 \n", "7 202503 3 252772 238917.0 266627.0 377 356.0 \n", "8 202502 3 257247 242991.0 271503.0 384 363.0 \n", "9 202501 3 231549 214627.0 248471.0 345 320.0 \n", "10 202452 3 201726 185870.0 217582.0 302 278.0 \n", "11 202451 3 201697 187843.0 215551.0 302 281.0 \n", "12 202450 3 136694 126369.0 147019.0 205 190.0 \n", "13 202449 3 108487 99037.0 117937.0 163 149.0 \n", "14 202448 3 87381 78687.0 96075.0 131 118.0 \n", "15 202447 3 76286 67626.0 84946.0 114 101.0 \n", "16 202446 3 56399 49006.0 63792.0 85 74.0 \n", "17 202445 3 47347 40843.0 53851.0 71 61.0 \n", "18 202444 3 36039 30122.0 41956.0 54 45.0 \n", "19 202443 3 46572 39928.0 53216.0 70 60.0 \n", "20 202442 3 67785 60009.0 75561.0 102 90.0 \n", "21 202441 3 79435 71386.0 87484.0 119 107.0 \n", "22 202440 3 84965 76555.0 93375.0 127 114.0 \n", "23 202439 3 91660 82937.0 100383.0 137 124.0 \n", "24 202438 3 91786 82903.0 100669.0 138 125.0 \n", "25 202437 3 56460 49319.0 63601.0 85 74.0 \n", "26 202436 3 33657 27906.0 39408.0 50 41.0 \n", "27 202435 3 27454 22069.0 32839.0 41 33.0 \n", "28 202434 3 26717 21003.0 32431.0 40 31.0 \n", "29 202433 3 20623 15349.0 25897.0 31 23.0 \n", "... ... ... ... ... ... ... ... \n", "2076 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2077 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2078 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2079 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2080 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2081 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2082 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2083 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2084 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2085 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2086 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2087 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2088 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2089 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2090 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2091 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2092 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2093 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2094 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2095 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2096 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2097 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2098 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2099 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2100 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2101 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2102 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2103 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2104 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2105 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 106.0 FR France \n", "1 140.0 FR France \n", "2 220.0 FR France \n", "3 331.0 FR France \n", "4 431.0 FR France \n", "5 523.0 FR France \n", "6 548.0 FR France \n", "7 398.0 FR France \n", "8 405.0 FR France \n", "9 370.0 FR France \n", "10 326.0 FR France \n", "11 323.0 FR France \n", "12 220.0 FR France \n", "13 177.0 FR France \n", "14 144.0 FR France \n", "15 127.0 FR France \n", "16 96.0 FR France \n", "17 81.0 FR France \n", "18 63.0 FR France \n", "19 80.0 FR France \n", "20 114.0 FR France \n", "21 131.0 FR France \n", "22 140.0 FR France \n", "23 150.0 FR France \n", "24 151.0 FR France \n", "25 96.0 FR France \n", "26 59.0 FR France \n", "27 49.0 FR France \n", "28 49.0 FR France \n", "29 39.0 FR France \n", "... ... ... ... \n", "2076 59.0 FR France \n", "2077 64.0 FR France \n", "2078 97.0 FR France \n", "2079 93.0 FR France \n", "2080 80.0 FR France \n", "2081 116.0 FR France \n", "2082 149.0 FR France \n", "2083 281.0 FR France \n", "2084 395.0 FR France \n", "2085 485.0 FR France \n", "2086 544.0 FR France \n", "2087 689.0 FR France \n", "2088 722.0 FR France \n", "2089 762.0 FR France \n", "2090 926.0 FR France \n", "2091 1113.0 FR France \n", "2092 1236.0 FR France \n", "2093 832.0 FR France \n", "2094 459.0 FR France \n", "2095 207.0 FR France \n", "2096 190.0 FR France \n", "2097 198.0 FR France \n", "2098 224.0 FR France \n", "2099 266.0 FR France \n", "2100 219.0 FR France \n", "2101 176.0 FR France \n", "2102 163.0 FR France \n", "2103 195.0 FR France \n", "2104 308.0 FR France \n", "2105 213.0 FR France \n", "\n", "[2105 rows x 10 columns]" ] }, "execution_count": 7, "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": 8, "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": 9, "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": 10, "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": 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[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, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\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 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\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 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" ] } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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", "\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": {}, "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": {}, "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 }