{ "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 le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente seulement si il n'existe pas déjà de copie locale." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", "local_copy = \"syndrome_gripal_local_copy.csv\"\n", "import urllib.request\n", "import os.path\n", "if not os.path.isfile(local_copy):\n", " urllib.request.urlretrieve(data_url, local_copy)" ] }, { "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": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02025023290810272949.0308671.0436409.0463.0FRFrance
12025013235405218196.0252614.0353327.0379.0FRFrance
22024523201726185870.0217582.0302278.0326.0FRFrance
32024513201697187843.0215551.0302281.0323.0FRFrance
42024503136694126369.0147019.0205190.0220.0FRFrance
5202449310848799037.0117937.0163149.0177.0FRFrance
620244838738178687.096075.0131118.0144.0FRFrance
720244737628667626.084946.0114101.0127.0FRFrance
820244635639949006.063792.08574.096.0FRFrance
920244534734740843.053851.07161.081.0FRFrance
1020244433603930122.041956.05445.063.0FRFrance
1120244334657239928.053216.07060.080.0FRFrance
1220244236778560009.075561.010290.0114.0FRFrance
1320244137943571386.087484.0119107.0131.0FRFrance
1420244038496576555.093375.0127114.0140.0FRFrance
1520243939166082937.0100383.0137124.0150.0FRFrance
1620243839178682903.0100669.0138125.0151.0FRFrance
1720243735646049319.063601.08574.096.0FRFrance
1820243633365727906.039408.05041.059.0FRFrance
1920243532740422036.032772.04133.049.0FRFrance
2020243432671721003.032431.04031.049.0FRFrance
2120243332062315349.025897.03123.039.0FRFrance
2220243232318717532.028842.03527.043.0FRFrance
2320243132603520267.031803.03930.048.0FRFrance
2420243033639328593.044193.05543.067.0FRFrance
2520242933956032592.046528.05949.069.0FRFrance
2620242835434245781.062903.08168.094.0FRFrance
2720242734736440234.054494.07160.082.0FRFrance
2820242634421936956.051482.06655.077.0FRFrance
2920242534720440300.054108.07161.081.0FRFrance
.................................
206819852132609619621.032571.04735.059.0FRFrance
206919852032789620885.034907.05138.064.0FRFrance
207019851934315432821.053487.07859.097.0FRFrance
207119851834055529935.051175.07455.093.0FRFrance
207219851733405324366.043740.06244.080.0FRFrance
207319851635036236451.064273.09166.0116.0FRFrance
207419851536388145538.082224.011683.0149.0FRFrance
20751985143134545114400.0154690.0244207.0281.0FRFrance
20761985133197206176080.0218332.0357319.0395.0FRFrance
20771985123245240223304.0267176.0445405.0485.0FRFrance
20781985113276205252399.0300011.0501458.0544.0FRFrance
20791985103353231326279.0380183.0640591.0689.0FRFrance
20801985093369895341109.0398681.0670618.0722.0FRFrance
20811985083389886359529.0420243.0707652.0762.0FRFrance
20821985073471852432599.0511105.0855784.0926.0FRFrance
20831985063565825518011.0613639.01026939.01113.0FRFrance
20841985053637302592795.0681809.011551074.01236.0FRFrance
20851985043424937390794.0459080.0770708.0832.0FRFrance
20861985033213901174689.0253113.0388317.0459.0FRFrance
208719850239758680949.0114223.0177147.0207.0FRFrance
208819850138548965918.0105060.0155120.0190.0FRFrance
208919845238483060602.0109058.0154110.0198.0FRFrance
2090198451310172680242.0123210.0185146.0224.0FRFrance
20911984503123680101401.0145959.0225184.0266.0FRFrance
2092198449310107381684.0120462.0184149.0219.0FRFrance
209319844837862060634.096606.0143110.0176.0FRFrance
209419844737202954274.089784.013199.0163.0FRFrance
209519844638733067686.0106974.0159123.0195.0FRFrance
20961984453135223101414.0169032.0246184.0308.0FRFrance
209719844436842220056.0116788.012537.0213.0FRFrance
\n", "

2098 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202502 3 290810 272949.0 308671.0 436 409.0 \n", "1 202501 3 235405 218196.0 252614.0 353 327.0 \n", "2 202452 3 201726 185870.0 217582.0 302 278.0 \n", "3 202451 3 201697 187843.0 215551.0 302 281.0 \n", "4 202450 3 136694 126369.0 147019.0 205 190.0 \n", "5 202449 3 108487 99037.0 117937.0 163 149.0 \n", "6 202448 3 87381 78687.0 96075.0 131 118.0 \n", "7 202447 3 76286 67626.0 84946.0 114 101.0 \n", "8 202446 3 56399 49006.0 63792.0 85 74.0 \n", "9 202445 3 47347 40843.0 53851.0 71 61.0 \n", "10 202444 3 36039 30122.0 41956.0 54 45.0 \n", "11 202443 3 46572 39928.0 53216.0 70 60.0 \n", "12 202442 3 67785 60009.0 75561.0 102 90.0 \n", "13 202441 3 79435 71386.0 87484.0 119 107.0 \n", "14 202440 3 84965 76555.0 93375.0 127 114.0 \n", "15 202439 3 91660 82937.0 100383.0 137 124.0 \n", "16 202438 3 91786 82903.0 100669.0 138 125.0 \n", "17 202437 3 56460 49319.0 63601.0 85 74.0 \n", "18 202436 3 33657 27906.0 39408.0 50 41.0 \n", "19 202435 3 27404 22036.0 32772.0 41 33.0 \n", "20 202434 3 26717 21003.0 32431.0 40 31.0 \n", "21 202433 3 20623 15349.0 25897.0 31 23.0 \n", "22 202432 3 23187 17532.0 28842.0 35 27.0 \n", "23 202431 3 26035 20267.0 31803.0 39 30.0 \n", "24 202430 3 36393 28593.0 44193.0 55 43.0 \n", "25 202429 3 39560 32592.0 46528.0 59 49.0 \n", "26 202428 3 54342 45781.0 62903.0 81 68.0 \n", "27 202427 3 47364 40234.0 54494.0 71 60.0 \n", "28 202426 3 44219 36956.0 51482.0 66 55.0 \n", "29 202425 3 47204 40300.0 54108.0 71 61.0 \n", "... ... ... ... ... ... ... ... \n", "2068 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2069 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2070 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2071 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2072 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2073 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2074 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2075 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2076 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2077 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2078 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2079 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2080 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2081 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2082 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2083 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2084 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2085 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2086 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2087 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2088 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2089 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2090 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2091 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2092 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2093 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2094 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2095 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2096 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2097 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 463.0 FR France \n", "1 379.0 FR France \n", "2 326.0 FR France \n", "3 323.0 FR France \n", "4 220.0 FR France \n", "5 177.0 FR France \n", "6 144.0 FR France \n", "7 127.0 FR France \n", "8 96.0 FR France \n", "9 81.0 FR France \n", "10 63.0 FR France \n", "11 80.0 FR France \n", "12 114.0 FR France \n", "13 131.0 FR France \n", "14 140.0 FR France \n", "15 150.0 FR France \n", "16 151.0 FR France \n", "17 96.0 FR France \n", "18 59.0 FR France \n", "19 49.0 FR France \n", "20 49.0 FR France \n", "21 39.0 FR France \n", "22 43.0 FR France \n", "23 48.0 FR France \n", "24 67.0 FR France \n", "25 69.0 FR France \n", "26 94.0 FR France \n", "27 82.0 FR France \n", "28 77.0 FR France \n", "29 81.0 FR France \n", "... ... ... ... \n", "2068 59.0 FR France \n", "2069 64.0 FR France \n", "2070 97.0 FR France \n", "2071 93.0 FR France \n", "2072 80.0 FR France \n", "2073 116.0 FR France \n", "2074 149.0 FR France \n", "2075 281.0 FR France \n", "2076 395.0 FR France \n", "2077 485.0 FR France \n", "2078 544.0 FR France \n", "2079 689.0 FR France \n", "2080 722.0 FR France \n", "2081 762.0 FR France \n", "2082 926.0 FR France \n", "2083 1113.0 FR France \n", "2084 1236.0 FR France \n", "2085 832.0 FR France \n", "2086 459.0 FR France \n", "2087 207.0 FR France \n", "2088 190.0 FR France \n", "2089 198.0 FR France \n", "2090 224.0 FR France \n", "2091 266.0 FR France \n", "2092 219.0 FR France \n", "2093 176.0 FR France \n", "2094 163.0 FR France \n", "2095 195.0 FR France \n", "2096 308.0 FR France \n", "2097 213.0 FR France \n", "\n", "[2098 rows x 10 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(local_copy, 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": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18611989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1861 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1861 FR France " ] }, "execution_count": 16, "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": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02025023290810272949.0308671.0436409.0463.0FRFrance
12025013235405218196.0252614.0353327.0379.0FRFrance
22024523201726185870.0217582.0302278.0326.0FRFrance
32024513201697187843.0215551.0302281.0323.0FRFrance
42024503136694126369.0147019.0205190.0220.0FRFrance
5202449310848799037.0117937.0163149.0177.0FRFrance
620244838738178687.096075.0131118.0144.0FRFrance
720244737628667626.084946.0114101.0127.0FRFrance
820244635639949006.063792.08574.096.0FRFrance
920244534734740843.053851.07161.081.0FRFrance
1020244433603930122.041956.05445.063.0FRFrance
1120244334657239928.053216.07060.080.0FRFrance
1220244236778560009.075561.010290.0114.0FRFrance
1320244137943571386.087484.0119107.0131.0FRFrance
1420244038496576555.093375.0127114.0140.0FRFrance
1520243939166082937.0100383.0137124.0150.0FRFrance
1620243839178682903.0100669.0138125.0151.0FRFrance
1720243735646049319.063601.08574.096.0FRFrance
1820243633365727906.039408.05041.059.0FRFrance
1920243532740422036.032772.04133.049.0FRFrance
2020243432671721003.032431.04031.049.0FRFrance
2120243332062315349.025897.03123.039.0FRFrance
2220243232318717532.028842.03527.043.0FRFrance
2320243132603520267.031803.03930.048.0FRFrance
2420243033639328593.044193.05543.067.0FRFrance
2520242933956032592.046528.05949.069.0FRFrance
2620242835434245781.062903.08168.094.0FRFrance
2720242734736440234.054494.07160.082.0FRFrance
2820242634421936956.051482.06655.077.0FRFrance
2920242534720440300.054108.07161.081.0FRFrance
.................................
206819852132609619621.032571.04735.059.0FRFrance
206919852032789620885.034907.05138.064.0FRFrance
207019851934315432821.053487.07859.097.0FRFrance
207119851834055529935.051175.07455.093.0FRFrance
207219851733405324366.043740.06244.080.0FRFrance
207319851635036236451.064273.09166.0116.0FRFrance
207419851536388145538.082224.011683.0149.0FRFrance
20751985143134545114400.0154690.0244207.0281.0FRFrance
20761985133197206176080.0218332.0357319.0395.0FRFrance
20771985123245240223304.0267176.0445405.0485.0FRFrance
20781985113276205252399.0300011.0501458.0544.0FRFrance
20791985103353231326279.0380183.0640591.0689.0FRFrance
20801985093369895341109.0398681.0670618.0722.0FRFrance
20811985083389886359529.0420243.0707652.0762.0FRFrance
20821985073471852432599.0511105.0855784.0926.0FRFrance
20831985063565825518011.0613639.01026939.01113.0FRFrance
20841985053637302592795.0681809.011551074.01236.0FRFrance
20851985043424937390794.0459080.0770708.0832.0FRFrance
20861985033213901174689.0253113.0388317.0459.0FRFrance
208719850239758680949.0114223.0177147.0207.0FRFrance
208819850138548965918.0105060.0155120.0190.0FRFrance
208919845238483060602.0109058.0154110.0198.0FRFrance
2090198451310172680242.0123210.0185146.0224.0FRFrance
20911984503123680101401.0145959.0225184.0266.0FRFrance
2092198449310107381684.0120462.0184149.0219.0FRFrance
209319844837862060634.096606.0143110.0176.0FRFrance
209419844737202954274.089784.013199.0163.0FRFrance
209519844638733067686.0106974.0159123.0195.0FRFrance
20961984453135223101414.0169032.0246184.0308.0FRFrance
209719844436842220056.0116788.012537.0213.0FRFrance
\n", "

2097 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202502 3 290810 272949.0 308671.0 436 409.0 \n", "1 202501 3 235405 218196.0 252614.0 353 327.0 \n", "2 202452 3 201726 185870.0 217582.0 302 278.0 \n", "3 202451 3 201697 187843.0 215551.0 302 281.0 \n", "4 202450 3 136694 126369.0 147019.0 205 190.0 \n", "5 202449 3 108487 99037.0 117937.0 163 149.0 \n", "6 202448 3 87381 78687.0 96075.0 131 118.0 \n", "7 202447 3 76286 67626.0 84946.0 114 101.0 \n", "8 202446 3 56399 49006.0 63792.0 85 74.0 \n", "9 202445 3 47347 40843.0 53851.0 71 61.0 \n", "10 202444 3 36039 30122.0 41956.0 54 45.0 \n", "11 202443 3 46572 39928.0 53216.0 70 60.0 \n", "12 202442 3 67785 60009.0 75561.0 102 90.0 \n", "13 202441 3 79435 71386.0 87484.0 119 107.0 \n", "14 202440 3 84965 76555.0 93375.0 127 114.0 \n", "15 202439 3 91660 82937.0 100383.0 137 124.0 \n", "16 202438 3 91786 82903.0 100669.0 138 125.0 \n", "17 202437 3 56460 49319.0 63601.0 85 74.0 \n", "18 202436 3 33657 27906.0 39408.0 50 41.0 \n", "19 202435 3 27404 22036.0 32772.0 41 33.0 \n", "20 202434 3 26717 21003.0 32431.0 40 31.0 \n", "21 202433 3 20623 15349.0 25897.0 31 23.0 \n", "22 202432 3 23187 17532.0 28842.0 35 27.0 \n", "23 202431 3 26035 20267.0 31803.0 39 30.0 \n", "24 202430 3 36393 28593.0 44193.0 55 43.0 \n", "25 202429 3 39560 32592.0 46528.0 59 49.0 \n", "26 202428 3 54342 45781.0 62903.0 81 68.0 \n", "27 202427 3 47364 40234.0 54494.0 71 60.0 \n", "28 202426 3 44219 36956.0 51482.0 66 55.0 \n", "29 202425 3 47204 40300.0 54108.0 71 61.0 \n", "... ... ... ... ... ... ... ... \n", "2068 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2069 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2070 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2071 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2072 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2073 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2074 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2075 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2076 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2077 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2078 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2079 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2080 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2081 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2082 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2083 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2084 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2085 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2086 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2087 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2088 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2089 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2090 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2091 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2092 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2093 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2094 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2095 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2096 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2097 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 463.0 FR France \n", "1 379.0 FR France \n", "2 326.0 FR France \n", "3 323.0 FR France \n", "4 220.0 FR France \n", "5 177.0 FR France \n", "6 144.0 FR France \n", "7 127.0 FR France \n", "8 96.0 FR France \n", "9 81.0 FR France \n", "10 63.0 FR France \n", "11 80.0 FR France \n", "12 114.0 FR France \n", "13 131.0 FR France \n", "14 140.0 FR France \n", "15 150.0 FR France \n", "16 151.0 FR France \n", "17 96.0 FR France \n", "18 59.0 FR France \n", "19 49.0 FR France \n", "20 49.0 FR France \n", "21 39.0 FR France \n", "22 43.0 FR France \n", "23 48.0 FR France \n", "24 67.0 FR France \n", "25 69.0 FR France \n", "26 94.0 FR France \n", "27 82.0 FR France \n", "28 77.0 FR France \n", "29 81.0 FR France \n", "... ... ... ... \n", "2068 59.0 FR France \n", "2069 64.0 FR France \n", "2070 97.0 FR France \n", "2071 93.0 FR France \n", "2072 80.0 FR France \n", "2073 116.0 FR France \n", "2074 149.0 FR France \n", "2075 281.0 FR France \n", "2076 395.0 FR France \n", "2077 485.0 FR France \n", "2078 544.0 FR France \n", "2079 689.0 FR France \n", "2080 722.0 FR France \n", "2081 762.0 FR France \n", "2082 926.0 FR France \n", "2083 1113.0 FR France \n", "2084 1236.0 FR France \n", "2085 832.0 FR France \n", "2086 459.0 FR France \n", "2087 207.0 FR France \n", "2088 190.0 FR France \n", "2089 198.0 FR France \n", "2090 224.0 FR France \n", "2091 266.0 FR France \n", "2092 219.0 FR France \n", "2093 176.0 FR France \n", "2094 163.0 FR France \n", "2095 195.0 FR France \n", "2096 308.0 FR France \n", "2097 213.0 FR France \n", "\n", "[2097 rows x 10 columns]" ] }, "execution_count": 17, "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": 18, "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": 19, "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": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "period\n", "1984-10-29/1984-11-04 68422\n", "1984-11-05/1984-11-11 135223\n", "1984-11-12/1984-11-18 87330\n", "1984-11-19/1984-11-25 72029\n", "1984-11-26/1984-12-02 78620\n", "1984-12-03/1984-12-09 101073\n", "1984-12-10/1984-12-16 123680\n", "1984-12-17/1984-12-23 101726\n", "1984-12-24/1984-12-30 84830\n", "1984-12-31/1985-01-06 85489\n", "1985-01-07/1985-01-13 97586\n", "1985-01-14/1985-01-20 213901\n", "1985-01-21/1985-01-27 424937\n", "1985-01-28/1985-02-03 637302\n", "1985-02-04/1985-02-10 565825\n", "1985-02-11/1985-02-17 471852\n", "1985-02-18/1985-02-24 389886\n", "1985-02-25/1985-03-03 369895\n", "1985-03-04/1985-03-10 353231\n", "1985-03-11/1985-03-17 276205\n", "1985-03-18/1985-03-24 245240\n", "1985-03-25/1985-03-31 197206\n", "1985-04-01/1985-04-07 134545\n", "1985-04-08/1985-04-14 63881\n", "1985-04-15/1985-04-21 50362\n", "1985-04-22/1985-04-28 34053\n", "1985-04-29/1985-05-05 40555\n", "1985-05-06/1985-05-12 43154\n", "1985-05-13/1985-05-19 27896\n", "1985-05-20/1985-05-26 26096\n", " ... \n", "2024-06-17/2024-06-23 47204\n", "2024-06-24/2024-06-30 44219\n", "2024-07-01/2024-07-07 47364\n", "2024-07-08/2024-07-14 54342\n", "2024-07-15/2024-07-21 39560\n", "2024-07-22/2024-07-28 36393\n", "2024-07-29/2024-08-04 26035\n", "2024-08-05/2024-08-11 23187\n", "2024-08-12/2024-08-18 20623\n", "2024-08-19/2024-08-25 26717\n", "2024-08-26/2024-09-01 27404\n", "2024-09-02/2024-09-08 33657\n", "2024-09-09/2024-09-15 56460\n", "2024-09-16/2024-09-22 91786\n", "2024-09-23/2024-09-29 91660\n", "2024-09-30/2024-10-06 84965\n", "2024-10-07/2024-10-13 79435\n", "2024-10-14/2024-10-20 67785\n", "2024-10-21/2024-10-27 46572\n", "2024-10-28/2024-11-03 36039\n", "2024-11-04/2024-11-10 47347\n", "2024-11-11/2024-11-17 56399\n", "2024-11-18/2024-11-24 76286\n", "2024-11-25/2024-12-01 87381\n", "2024-12-02/2024-12-08 108487\n", "2024-12-09/2024-12-15 136694\n", "2024-12-16/2024-12-22 201697\n", "2024-12-23/2024-12-29 201726\n", "2024-12-30/2025-01-05 235405\n", "2025-01-06/2025-01-12 290810\n", "Freq: W-SUN, Name: inc, Length: 2097, dtype: object" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data['inc']" ] }, { "cell_type": "code", "execution_count": 20, "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": 24, "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 \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\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-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m 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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 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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": 14, "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, 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 \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\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-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m 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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 }