{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 1, "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": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "data_file = \"syndrome-grippal.csv\"\n", "#on vérifie si on a pas le document, et dans ce cas on le télécharge, comme ça, on garde le document dans le cas où l'url ne fonctionnerait plus ou si le document à télécharger était modifié\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "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": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020252932012214389.025855.03021.039.0FRFrance
120252832354318336.028750.03527.043.0FRFrance
220252732145317129.025777.03226.038.0FRFrance
320252632194517422.026468.03326.040.0FRFrance
420252532332318546.028100.03528.042.0FRFrance
520252432315418577.027731.03528.042.0FRFrance
620252332439119307.029475.03628.044.0FRFrance
720252231875514333.023177.02821.035.0FRFrance
820252132376018671.028849.03527.043.0FRFrance
920252032026515814.024716.03023.037.0FRFrance
1020251931626412394.020134.02418.030.0FRFrance
1120251831811513975.022255.02721.033.0FRFrance
1220251732215017291.027009.03326.040.0FRFrance
1320251632856422550.034578.04334.052.0FRFrance
1420251533572129592.041850.05344.062.0FRFrance
1520251433757931232.043926.05647.065.0FRFrance
1620251333967333686.045660.05950.068.0FRFrance
1720251235254345627.059459.07868.088.0FRFrance
1820251135946952154.066784.08978.0100.0FRFrance
1920251036033453048.067620.09079.0101.0FRFrance
2020250938453174994.094068.0126112.0140.0FRFrance
212025083136020124824.0147216.0203186.0220.0FRFrance
222025073208952195988.0221916.0312293.0331.0FRFrance
232025063273519258159.0288879.0408385.0431.0FRFrance
242025053334395318416.0350374.0499475.0523.0FRFrance
252025043350043332885.0367201.0522496.0548.0FRFrance
262025033252772238917.0266627.0377356.0398.0FRFrance
272025023257247242991.0271503.0384363.0405.0FRFrance
282025013231549214627.0248471.0345320.0370.0FRFrance
292024523201726185870.0217582.0302278.0326.0FRFrance
.................................
209519852132609619621.032571.04735.059.0FRFrance
209619852032789620885.034907.05138.064.0FRFrance
209719851934315432821.053487.07859.097.0FRFrance
209819851834055529935.051175.07455.093.0FRFrance
209919851733405324366.043740.06244.080.0FRFrance
210019851635036236451.064273.09166.0116.0FRFrance
210119851536388145538.082224.011683.0149.0FRFrance
21021985143134545114400.0154690.0244207.0281.0FRFrance
21031985133197206176080.0218332.0357319.0395.0FRFrance
21041985123245240223304.0267176.0445405.0485.0FRFrance
21051985113276205252399.0300011.0501458.0544.0FRFrance
21061985103353231326279.0380183.0640591.0689.0FRFrance
21071985093369895341109.0398681.0670618.0722.0FRFrance
21081985083389886359529.0420243.0707652.0762.0FRFrance
21091985073471852432599.0511105.0855784.0926.0FRFrance
21101985063565825518011.0613639.01026939.01113.0FRFrance
21111985053637302592795.0681809.011551074.01236.0FRFrance
21121985043424937390794.0459080.0770708.0832.0FRFrance
21131985033213901174689.0253113.0388317.0459.0FRFrance
211419850239758680949.0114223.0177147.0207.0FRFrance
211519850138548965918.0105060.0155120.0190.0FRFrance
211619845238483060602.0109058.0154110.0198.0FRFrance
2117198451310172680242.0123210.0185146.0224.0FRFrance
21181984503123680101401.0145959.0225184.0266.0FRFrance
2119198449310107381684.0120462.0184149.0219.0FRFrance
212019844837862060634.096606.0143110.0176.0FRFrance
212119844737202954274.089784.013199.0163.0FRFrance
212219844638733067686.0106974.0159123.0195.0FRFrance
21231984453135223101414.0169032.0246184.0308.0FRFrance
212419844436842220056.0116788.012537.0213.0FRFrance
\n", "

2125 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202529 3 20122 14389.0 25855.0 30 21.0 \n", "1 202528 3 23543 18336.0 28750.0 35 27.0 \n", "2 202527 3 21453 17129.0 25777.0 32 26.0 \n", "3 202526 3 21945 17422.0 26468.0 33 26.0 \n", "4 202525 3 23323 18546.0 28100.0 35 28.0 \n", "5 202524 3 23154 18577.0 27731.0 35 28.0 \n", "6 202523 3 24391 19307.0 29475.0 36 28.0 \n", "7 202522 3 18755 14333.0 23177.0 28 21.0 \n", "8 202521 3 23760 18671.0 28849.0 35 27.0 \n", "9 202520 3 20265 15814.0 24716.0 30 23.0 \n", "10 202519 3 16264 12394.0 20134.0 24 18.0 \n", "11 202518 3 18115 13975.0 22255.0 27 21.0 \n", "12 202517 3 22150 17291.0 27009.0 33 26.0 \n", "13 202516 3 28564 22550.0 34578.0 43 34.0 \n", "14 202515 3 35721 29592.0 41850.0 53 44.0 \n", "15 202514 3 37579 31232.0 43926.0 56 47.0 \n", "16 202513 3 39673 33686.0 45660.0 59 50.0 \n", "17 202512 3 52543 45627.0 59459.0 78 68.0 \n", "18 202511 3 59469 52154.0 66784.0 89 78.0 \n", "19 202510 3 60334 53048.0 67620.0 90 79.0 \n", "20 202509 3 84531 74994.0 94068.0 126 112.0 \n", "21 202508 3 136020 124824.0 147216.0 203 186.0 \n", "22 202507 3 208952 195988.0 221916.0 312 293.0 \n", "23 202506 3 273519 258159.0 288879.0 408 385.0 \n", "24 202505 3 334395 318416.0 350374.0 499 475.0 \n", "25 202504 3 350043 332885.0 367201.0 522 496.0 \n", "26 202503 3 252772 238917.0 266627.0 377 356.0 \n", "27 202502 3 257247 242991.0 271503.0 384 363.0 \n", "28 202501 3 231549 214627.0 248471.0 345 320.0 \n", "29 202452 3 201726 185870.0 217582.0 302 278.0 \n", "... ... ... ... ... ... ... ... \n", "2095 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2096 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2097 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2098 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2099 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2100 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2101 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2102 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2103 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2104 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2105 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2106 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2107 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2108 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2109 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2110 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2111 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2112 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2113 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2114 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2115 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2116 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2117 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2118 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2119 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2120 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2121 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2122 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2123 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2124 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 39.0 FR France \n", "1 43.0 FR France \n", "2 38.0 FR France \n", "3 40.0 FR France \n", "4 42.0 FR France \n", "5 42.0 FR France \n", "6 44.0 FR France \n", "7 35.0 FR France \n", "8 43.0 FR France \n", "9 37.0 FR France \n", "10 30.0 FR France \n", "11 33.0 FR France \n", "12 40.0 FR France \n", "13 52.0 FR France \n", "14 62.0 FR France \n", "15 65.0 FR France \n", "16 68.0 FR France \n", "17 88.0 FR France \n", "18 100.0 FR France \n", "19 101.0 FR France \n", "20 140.0 FR France \n", "21 220.0 FR France \n", "22 331.0 FR France \n", "23 431.0 FR France \n", "24 523.0 FR France \n", "25 548.0 FR France \n", "26 398.0 FR France \n", "27 405.0 FR France \n", "28 370.0 FR France \n", "29 326.0 FR France \n", "... ... ... ... \n", "2095 59.0 FR France \n", "2096 64.0 FR France \n", "2097 97.0 FR France \n", "2098 93.0 FR France \n", "2099 80.0 FR France \n", "2100 116.0 FR France \n", "2101 149.0 FR France \n", "2102 281.0 FR France \n", "2103 395.0 FR France \n", "2104 485.0 FR France \n", "2105 544.0 FR France \n", "2106 689.0 FR France \n", "2107 722.0 FR France \n", "2108 762.0 FR France \n", "2109 926.0 FR France \n", "2110 1113.0 FR France \n", "2111 1236.0 FR France \n", "2112 832.0 FR France \n", "2113 459.0 FR France \n", "2114 207.0 FR France \n", "2115 190.0 FR France \n", "2116 198.0 FR France \n", "2117 224.0 FR France \n", "2118 266.0 FR France \n", "2119 219.0 FR France \n", "2120 176.0 FR France \n", "2121 163.0 FR France \n", "2122 195.0 FR France \n", "2123 308.0 FR France \n", "2124 213.0 FR France \n", "\n", "[2125 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, 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
18881989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1888 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1888 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
020252932012214389.025855.03021.039.0FRFrance
120252832354318336.028750.03527.043.0FRFrance
220252732145317129.025777.03226.038.0FRFrance
320252632194517422.026468.03326.040.0FRFrance
420252532332318546.028100.03528.042.0FRFrance
520252432315418577.027731.03528.042.0FRFrance
620252332439119307.029475.03628.044.0FRFrance
720252231875514333.023177.02821.035.0FRFrance
820252132376018671.028849.03527.043.0FRFrance
920252032026515814.024716.03023.037.0FRFrance
1020251931626412394.020134.02418.030.0FRFrance
1120251831811513975.022255.02721.033.0FRFrance
1220251732215017291.027009.03326.040.0FRFrance
1320251632856422550.034578.04334.052.0FRFrance
1420251533572129592.041850.05344.062.0FRFrance
1520251433757931232.043926.05647.065.0FRFrance
1620251333967333686.045660.05950.068.0FRFrance
1720251235254345627.059459.07868.088.0FRFrance
1820251135946952154.066784.08978.0100.0FRFrance
1920251036033453048.067620.09079.0101.0FRFrance
2020250938453174994.094068.0126112.0140.0FRFrance
212025083136020124824.0147216.0203186.0220.0FRFrance
222025073208952195988.0221916.0312293.0331.0FRFrance
232025063273519258159.0288879.0408385.0431.0FRFrance
242025053334395318416.0350374.0499475.0523.0FRFrance
252025043350043332885.0367201.0522496.0548.0FRFrance
262025033252772238917.0266627.0377356.0398.0FRFrance
272025023257247242991.0271503.0384363.0405.0FRFrance
282025013231549214627.0248471.0345320.0370.0FRFrance
292024523201726185870.0217582.0302278.0326.0FRFrance
.................................
209519852132609619621.032571.04735.059.0FRFrance
209619852032789620885.034907.05138.064.0FRFrance
209719851934315432821.053487.07859.097.0FRFrance
209819851834055529935.051175.07455.093.0FRFrance
209919851733405324366.043740.06244.080.0FRFrance
210019851635036236451.064273.09166.0116.0FRFrance
210119851536388145538.082224.011683.0149.0FRFrance
21021985143134545114400.0154690.0244207.0281.0FRFrance
21031985133197206176080.0218332.0357319.0395.0FRFrance
21041985123245240223304.0267176.0445405.0485.0FRFrance
21051985113276205252399.0300011.0501458.0544.0FRFrance
21061985103353231326279.0380183.0640591.0689.0FRFrance
21071985093369895341109.0398681.0670618.0722.0FRFrance
21081985083389886359529.0420243.0707652.0762.0FRFrance
21091985073471852432599.0511105.0855784.0926.0FRFrance
21101985063565825518011.0613639.01026939.01113.0FRFrance
21111985053637302592795.0681809.011551074.01236.0FRFrance
21121985043424937390794.0459080.0770708.0832.0FRFrance
21131985033213901174689.0253113.0388317.0459.0FRFrance
211419850239758680949.0114223.0177147.0207.0FRFrance
211519850138548965918.0105060.0155120.0190.0FRFrance
211619845238483060602.0109058.0154110.0198.0FRFrance
2117198451310172680242.0123210.0185146.0224.0FRFrance
21181984503123680101401.0145959.0225184.0266.0FRFrance
2119198449310107381684.0120462.0184149.0219.0FRFrance
212019844837862060634.096606.0143110.0176.0FRFrance
212119844737202954274.089784.013199.0163.0FRFrance
212219844638733067686.0106974.0159123.0195.0FRFrance
21231984453135223101414.0169032.0246184.0308.0FRFrance
212419844436842220056.0116788.012537.0213.0FRFrance
\n", "

2124 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202529 3 20122 14389.0 25855.0 30 21.0 \n", "1 202528 3 23543 18336.0 28750.0 35 27.0 \n", "2 202527 3 21453 17129.0 25777.0 32 26.0 \n", "3 202526 3 21945 17422.0 26468.0 33 26.0 \n", "4 202525 3 23323 18546.0 28100.0 35 28.0 \n", "5 202524 3 23154 18577.0 27731.0 35 28.0 \n", "6 202523 3 24391 19307.0 29475.0 36 28.0 \n", "7 202522 3 18755 14333.0 23177.0 28 21.0 \n", "8 202521 3 23760 18671.0 28849.0 35 27.0 \n", "9 202520 3 20265 15814.0 24716.0 30 23.0 \n", "10 202519 3 16264 12394.0 20134.0 24 18.0 \n", "11 202518 3 18115 13975.0 22255.0 27 21.0 \n", "12 202517 3 22150 17291.0 27009.0 33 26.0 \n", "13 202516 3 28564 22550.0 34578.0 43 34.0 \n", "14 202515 3 35721 29592.0 41850.0 53 44.0 \n", "15 202514 3 37579 31232.0 43926.0 56 47.0 \n", "16 202513 3 39673 33686.0 45660.0 59 50.0 \n", "17 202512 3 52543 45627.0 59459.0 78 68.0 \n", "18 202511 3 59469 52154.0 66784.0 89 78.0 \n", "19 202510 3 60334 53048.0 67620.0 90 79.0 \n", "20 202509 3 84531 74994.0 94068.0 126 112.0 \n", "21 202508 3 136020 124824.0 147216.0 203 186.0 \n", "22 202507 3 208952 195988.0 221916.0 312 293.0 \n", "23 202506 3 273519 258159.0 288879.0 408 385.0 \n", "24 202505 3 334395 318416.0 350374.0 499 475.0 \n", "25 202504 3 350043 332885.0 367201.0 522 496.0 \n", "26 202503 3 252772 238917.0 266627.0 377 356.0 \n", "27 202502 3 257247 242991.0 271503.0 384 363.0 \n", "28 202501 3 231549 214627.0 248471.0 345 320.0 \n", "29 202452 3 201726 185870.0 217582.0 302 278.0 \n", "... ... ... ... ... ... ... ... \n", "2095 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2096 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2097 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2098 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2099 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2100 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2101 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2102 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2103 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2104 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2105 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2106 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2107 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2108 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2109 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2110 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2111 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2112 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2113 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2114 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2115 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2116 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2117 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2118 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2119 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2120 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2121 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2122 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2123 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2124 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 39.0 FR France \n", "1 43.0 FR France \n", "2 38.0 FR France \n", "3 40.0 FR France \n", "4 42.0 FR France \n", "5 42.0 FR France \n", "6 44.0 FR France \n", "7 35.0 FR France \n", "8 43.0 FR France \n", "9 37.0 FR France \n", "10 30.0 FR France \n", "11 33.0 FR France \n", "12 40.0 FR France \n", "13 52.0 FR France \n", "14 62.0 FR France \n", "15 65.0 FR France \n", "16 68.0 FR France \n", "17 88.0 FR France \n", "18 100.0 FR France \n", "19 101.0 FR France \n", "20 140.0 FR France \n", "21 220.0 FR France \n", "22 331.0 FR France \n", "23 431.0 FR France \n", "24 523.0 FR France \n", "25 548.0 FR France \n", "26 398.0 FR France \n", "27 405.0 FR France \n", "28 370.0 FR France \n", "29 326.0 FR France \n", "... ... ... ... \n", "2095 59.0 FR France \n", "2096 64.0 FR France \n", "2097 97.0 FR France \n", "2098 93.0 FR France \n", "2099 80.0 FR France \n", "2100 116.0 FR France \n", "2101 149.0 FR France \n", "2102 281.0 FR France \n", "2103 395.0 FR France \n", "2104 485.0 FR France \n", "2105 544.0 FR France \n", "2106 689.0 FR France \n", "2107 722.0 FR France \n", "2108 762.0 FR France \n", "2109 926.0 FR France \n", "2110 1113.0 FR France \n", "2111 1236.0 FR France \n", "2112 832.0 FR France \n", "2113 459.0 FR France \n", "2114 207.0 FR France \n", "2115 190.0 FR France \n", "2116 198.0 FR France \n", "2117 224.0 FR France \n", "2118 266.0 FR France \n", "2119 219.0 FR France \n", "2120 176.0 FR France \n", "2121 163.0 FR France \n", "2122 195.0 FR France \n", "2123 308.0 FR France \n", "2124 213.0 FR France \n", "\n", "[2124 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": 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 }