{ "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": 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": [ "Si le fichier local n'existe pas, téléchargez les données et déposez-les dans le fichier local." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import os\n", "import requests\n", "\n", "if not os.path.exists('incidence-PAY-3.csv'):\n", " r = requests.get(data_url, allow_redirects=True)\n", " open('incidence-PAY-3.csv', 'wb').write(r.content)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lisez le fichier CSV local." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202119398807103.012657.01511.019.0FRFrance
12021183121359165.015105.01814.022.0FRFrance
22021173120588891.015225.01813.023.0FRFrance
320211631650512735.020275.02519.031.0FRFrance
420211531930615398.023214.02923.035.0FRFrance
520211432107317099.025047.03226.038.0FRFrance
620211332641322094.030732.04033.047.0FRFrance
720211233065825919.035397.04639.053.0FRFrance
820211132498820718.029258.03832.044.0FRFrance
920211031953915951.023127.03025.035.0FRFrance
1020210931757213926.021218.02721.033.0FRFrance
1120210832088216907.024857.03226.038.0FRFrance
1220210732239318303.026483.03428.040.0FRFrance
1320210632318319134.027232.03529.041.0FRFrance
1420210532242618445.026407.03428.040.0FRFrance
1520210432580421491.030117.03932.046.0FRFrance
1620210332181017894.025726.03327.039.0FRFrance
1720210231732013906.020734.02621.031.0FRFrance
1820210132179917778.025820.03327.039.0FRFrance
1920205332122016498.025942.03225.039.0FRFrance
2020205231642812285.020571.02519.031.0FRFrance
2120205132161917370.025868.03327.039.0FRFrance
2220205031684513220.020470.02620.032.0FRFrance
232020493129399923.015955.02015.025.0FRFrance
2420204831380410641.016967.02116.026.0FRFrance
2520204731908515285.022885.02923.035.0FRFrance
2620204632480120503.029099.03831.045.0FRFrance
2720204534251636857.048175.06556.074.0FRFrance
2820204434456738521.050613.06859.077.0FRFrance
2920204334373737523.049951.06657.075.0FRFrance
.................................
187719852132609619621.032571.04735.059.0FRFrance
187819852032789620885.034907.05138.064.0FRFrance
187919851934315432821.053487.07859.097.0FRFrance
188019851834055529935.051175.07455.093.0FRFrance
188119851733405324366.043740.06244.080.0FRFrance
188219851635036236451.064273.09166.0116.0FRFrance
188319851536388145538.082224.011683.0149.0FRFrance
18841985143134545114400.0154690.0244207.0281.0FRFrance
18851985133197206176080.0218332.0357319.0395.0FRFrance
18861985123245240223304.0267176.0445405.0485.0FRFrance
18871985113276205252399.0300011.0501458.0544.0FRFrance
18881985103353231326279.0380183.0640591.0689.0FRFrance
18891985093369895341109.0398681.0670618.0722.0FRFrance
18901985083389886359529.0420243.0707652.0762.0FRFrance
18911985073471852432599.0511105.0855784.0926.0FRFrance
18921985063565825518011.0613639.01026939.01113.0FRFrance
18931985053637302592795.0681809.011551074.01236.0FRFrance
18941985043424937390794.0459080.0770708.0832.0FRFrance
18951985033213901174689.0253113.0388317.0459.0FRFrance
189619850239758680949.0114223.0177147.0207.0FRFrance
189719850138548965918.0105060.0155120.0190.0FRFrance
189819845238483060602.0109058.0154110.0198.0FRFrance
1899198451310172680242.0123210.0185146.0224.0FRFrance
19001984503123680101401.0145959.0225184.0266.0FRFrance
1901198449310107381684.0120462.0184149.0219.0FRFrance
190219844837862060634.096606.0143110.0176.0FRFrance
190319844737202954274.089784.013199.0163.0FRFrance
190419844638733067686.0106974.0159123.0195.0FRFrance
19051984453135223101414.0169032.0246184.0308.0FRFrance
190619844436842220056.0116788.012537.0213.0FRFrance
\n", "

1907 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202119 3 9880 7103.0 12657.0 15 11.0 \n", "1 202118 3 12135 9165.0 15105.0 18 14.0 \n", "2 202117 3 12058 8891.0 15225.0 18 13.0 \n", "3 202116 3 16505 12735.0 20275.0 25 19.0 \n", "4 202115 3 19306 15398.0 23214.0 29 23.0 \n", "5 202114 3 21073 17099.0 25047.0 32 26.0 \n", "6 202113 3 26413 22094.0 30732.0 40 33.0 \n", "7 202112 3 30658 25919.0 35397.0 46 39.0 \n", "8 202111 3 24988 20718.0 29258.0 38 32.0 \n", "9 202110 3 19539 15951.0 23127.0 30 25.0 \n", "10 202109 3 17572 13926.0 21218.0 27 21.0 \n", "11 202108 3 20882 16907.0 24857.0 32 26.0 \n", "12 202107 3 22393 18303.0 26483.0 34 28.0 \n", "13 202106 3 23183 19134.0 27232.0 35 29.0 \n", "14 202105 3 22426 18445.0 26407.0 34 28.0 \n", "15 202104 3 25804 21491.0 30117.0 39 32.0 \n", "16 202103 3 21810 17894.0 25726.0 33 27.0 \n", "17 202102 3 17320 13906.0 20734.0 26 21.0 \n", "18 202101 3 21799 17778.0 25820.0 33 27.0 \n", "19 202053 3 21220 16498.0 25942.0 32 25.0 \n", "20 202052 3 16428 12285.0 20571.0 25 19.0 \n", "21 202051 3 21619 17370.0 25868.0 33 27.0 \n", "22 202050 3 16845 13220.0 20470.0 26 20.0 \n", "23 202049 3 12939 9923.0 15955.0 20 15.0 \n", "24 202048 3 13804 10641.0 16967.0 21 16.0 \n", "25 202047 3 19085 15285.0 22885.0 29 23.0 \n", "26 202046 3 24801 20503.0 29099.0 38 31.0 \n", "27 202045 3 42516 36857.0 48175.0 65 56.0 \n", "28 202044 3 44567 38521.0 50613.0 68 59.0 \n", "29 202043 3 43737 37523.0 49951.0 66 57.0 \n", "... ... ... ... ... ... ... ... \n", "1877 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1878 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1879 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1880 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1881 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1882 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1883 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1884 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1885 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1886 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1887 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1888 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1889 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1890 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1891 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1892 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1893 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1894 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1895 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1896 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1897 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1898 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1899 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1900 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1901 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1902 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1903 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1904 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1905 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1906 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 19.0 FR France \n", "1 22.0 FR France \n", "2 23.0 FR France \n", "3 31.0 FR France \n", "4 35.0 FR France \n", "5 38.0 FR France \n", "6 47.0 FR France \n", "7 53.0 FR France \n", "8 44.0 FR France \n", "9 35.0 FR France \n", "10 33.0 FR France \n", "11 38.0 FR France \n", "12 40.0 FR France \n", "13 41.0 FR France \n", "14 40.0 FR France \n", "15 46.0 FR France \n", "16 39.0 FR France \n", "17 31.0 FR France \n", "18 39.0 FR France \n", "19 39.0 FR France \n", "20 31.0 FR France \n", "21 39.0 FR France \n", "22 32.0 FR France \n", "23 25.0 FR France \n", "24 26.0 FR France \n", "25 35.0 FR France \n", "26 45.0 FR France \n", "27 74.0 FR France \n", "28 77.0 FR France \n", "29 75.0 FR France \n", "... ... ... ... \n", "1877 59.0 FR France \n", "1878 64.0 FR France \n", "1879 97.0 FR France \n", "1880 93.0 FR France \n", "1881 80.0 FR France \n", "1882 116.0 FR France \n", "1883 149.0 FR France \n", "1884 281.0 FR France \n", "1885 395.0 FR France \n", "1886 485.0 FR France \n", "1887 544.0 FR France \n", "1888 689.0 FR France \n", "1889 722.0 FR France \n", "1890 762.0 FR France \n", "1891 926.0 FR France \n", "1892 1113.0 FR France \n", "1893 1236.0 FR France \n", "1894 832.0 FR France \n", "1895 459.0 FR France \n", "1896 207.0 FR France \n", "1897 190.0 FR France \n", "1898 198.0 FR France \n", "1899 224.0 FR France \n", "1900 266.0 FR France \n", "1901 219.0 FR France \n", "1902 176.0 FR France \n", "1903 163.0 FR France \n", "1904 195.0 FR France \n", "1905 308.0 FR France \n", "1906 213.0 FR France \n", "\n", "[1907 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv('incidence-PAY-3.csv', skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202119398807103.012657.01511.019.0FRFrance
12021183121359165.015105.01814.022.0FRFrance
22021173120588891.015225.01813.023.0FRFrance
320211631650512735.020275.02519.031.0FRFrance
420211531930615398.023214.02923.035.0FRFrance
520211432107317099.025047.03226.038.0FRFrance
620211332641322094.030732.04033.047.0FRFrance
720211233065825919.035397.04639.053.0FRFrance
820211132498820718.029258.03832.044.0FRFrance
920211031953915951.023127.03025.035.0FRFrance
1020210931757213926.021218.02721.033.0FRFrance
1120210832088216907.024857.03226.038.0FRFrance
1220210732239318303.026483.03428.040.0FRFrance
1320210632318319134.027232.03529.041.0FRFrance
1420210532242618445.026407.03428.040.0FRFrance
1520210432580421491.030117.03932.046.0FRFrance
1620210332181017894.025726.03327.039.0FRFrance
1720210231732013906.020734.02621.031.0FRFrance
1820210132179917778.025820.03327.039.0FRFrance
1920205332122016498.025942.03225.039.0FRFrance
2020205231642812285.020571.02519.031.0FRFrance
2120205132161917370.025868.03327.039.0FRFrance
2220205031684513220.020470.02620.032.0FRFrance
232020493129399923.015955.02015.025.0FRFrance
2420204831380410641.016967.02116.026.0FRFrance
2520204731908515285.022885.02923.035.0FRFrance
2620204632480120503.029099.03831.045.0FRFrance
2720204534251636857.048175.06556.074.0FRFrance
2820204434456738521.050613.06859.077.0FRFrance
2920204334373737523.049951.06657.075.0FRFrance
.................................
187719852132609619621.032571.04735.059.0FRFrance
187819852032789620885.034907.05138.064.0FRFrance
187919851934315432821.053487.07859.097.0FRFrance
188019851834055529935.051175.07455.093.0FRFrance
188119851733405324366.043740.06244.080.0FRFrance
188219851635036236451.064273.09166.0116.0FRFrance
188319851536388145538.082224.011683.0149.0FRFrance
18841985143134545114400.0154690.0244207.0281.0FRFrance
18851985133197206176080.0218332.0357319.0395.0FRFrance
18861985123245240223304.0267176.0445405.0485.0FRFrance
18871985113276205252399.0300011.0501458.0544.0FRFrance
18881985103353231326279.0380183.0640591.0689.0FRFrance
18891985093369895341109.0398681.0670618.0722.0FRFrance
18901985083389886359529.0420243.0707652.0762.0FRFrance
18911985073471852432599.0511105.0855784.0926.0FRFrance
18921985063565825518011.0613639.01026939.01113.0FRFrance
18931985053637302592795.0681809.011551074.01236.0FRFrance
18941985043424937390794.0459080.0770708.0832.0FRFrance
18951985033213901174689.0253113.0388317.0459.0FRFrance
189619850239758680949.0114223.0177147.0207.0FRFrance
189719850138548965918.0105060.0155120.0190.0FRFrance
189819845238483060602.0109058.0154110.0198.0FRFrance
1899198451310172680242.0123210.0185146.0224.0FRFrance
19001984503123680101401.0145959.0225184.0266.0FRFrance
1901198449310107381684.0120462.0184149.0219.0FRFrance
190219844837862060634.096606.0143110.0176.0FRFrance
190319844737202954274.089784.013199.0163.0FRFrance
190419844638733067686.0106974.0159123.0195.0FRFrance
19051984453135223101414.0169032.0246184.0308.0FRFrance
190619844436842220056.0116788.012537.0213.0FRFrance
\n", "

1907 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202119 3 9880 7103.0 12657.0 15 11.0 \n", "1 202118 3 12135 9165.0 15105.0 18 14.0 \n", "2 202117 3 12058 8891.0 15225.0 18 13.0 \n", "3 202116 3 16505 12735.0 20275.0 25 19.0 \n", "4 202115 3 19306 15398.0 23214.0 29 23.0 \n", "5 202114 3 21073 17099.0 25047.0 32 26.0 \n", "6 202113 3 26413 22094.0 30732.0 40 33.0 \n", "7 202112 3 30658 25919.0 35397.0 46 39.0 \n", "8 202111 3 24988 20718.0 29258.0 38 32.0 \n", "9 202110 3 19539 15951.0 23127.0 30 25.0 \n", "10 202109 3 17572 13926.0 21218.0 27 21.0 \n", "11 202108 3 20882 16907.0 24857.0 32 26.0 \n", "12 202107 3 22393 18303.0 26483.0 34 28.0 \n", "13 202106 3 23183 19134.0 27232.0 35 29.0 \n", "14 202105 3 22426 18445.0 26407.0 34 28.0 \n", "15 202104 3 25804 21491.0 30117.0 39 32.0 \n", "16 202103 3 21810 17894.0 25726.0 33 27.0 \n", "17 202102 3 17320 13906.0 20734.0 26 21.0 \n", "18 202101 3 21799 17778.0 25820.0 33 27.0 \n", "19 202053 3 21220 16498.0 25942.0 32 25.0 \n", "20 202052 3 16428 12285.0 20571.0 25 19.0 \n", "21 202051 3 21619 17370.0 25868.0 33 27.0 \n", "22 202050 3 16845 13220.0 20470.0 26 20.0 \n", "23 202049 3 12939 9923.0 15955.0 20 15.0 \n", "24 202048 3 13804 10641.0 16967.0 21 16.0 \n", "25 202047 3 19085 15285.0 22885.0 29 23.0 \n", "26 202046 3 24801 20503.0 29099.0 38 31.0 \n", "27 202045 3 42516 36857.0 48175.0 65 56.0 \n", "28 202044 3 44567 38521.0 50613.0 68 59.0 \n", "29 202043 3 43737 37523.0 49951.0 66 57.0 \n", "... ... ... ... ... ... ... ... \n", "1877 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1878 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1879 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1880 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1881 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1882 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1883 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1884 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1885 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1886 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1887 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1888 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1889 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1890 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1891 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1892 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1893 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1894 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1895 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1896 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1897 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1898 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1899 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1900 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1901 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1902 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1903 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1904 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1905 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1906 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 19.0 FR France \n", "1 22.0 FR France \n", "2 23.0 FR France \n", "3 31.0 FR France \n", "4 35.0 FR France \n", "5 38.0 FR France \n", "6 47.0 FR France \n", "7 53.0 FR France \n", "8 44.0 FR France \n", "9 35.0 FR France \n", "10 33.0 FR France \n", "11 38.0 FR France \n", "12 40.0 FR France \n", "13 41.0 FR France \n", "14 40.0 FR France \n", "15 46.0 FR France \n", "16 39.0 FR France \n", "17 31.0 FR France \n", "18 39.0 FR France \n", "19 39.0 FR France \n", "20 31.0 FR France \n", "21 39.0 FR France \n", "22 32.0 FR France \n", "23 25.0 FR France \n", "24 26.0 FR France \n", "25 35.0 FR France \n", "26 45.0 FR France \n", "27 74.0 FR France \n", "28 77.0 FR France \n", "29 75.0 FR France \n", "... ... ... ... \n", "1877 59.0 FR France \n", "1878 64.0 FR France \n", "1879 97.0 FR France \n", "1880 93.0 FR France \n", "1881 80.0 FR France \n", "1882 116.0 FR France \n", "1883 149.0 FR France \n", "1884 281.0 FR France \n", "1885 395.0 FR France \n", "1886 485.0 FR France \n", "1887 544.0 FR France \n", "1888 689.0 FR France \n", "1889 722.0 FR France \n", "1890 762.0 FR France \n", "1891 926.0 FR France \n", "1892 1113.0 FR France \n", "1893 1236.0 FR France \n", "1894 832.0 FR France \n", "1895 459.0 FR France \n", "1896 207.0 FR France \n", "1897 190.0 FR France \n", "1898 198.0 FR France \n", "1899 224.0 FR France \n", "1900 266.0 FR France \n", "1901 219.0 FR France \n", "1902 176.0 FR France \n", "1903 163.0 FR France \n", "1904 195.0 FR France \n", "1905 308.0 FR France \n", "1906 213.0 FR France \n", "\n", "[1907 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": null, "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": null, "metadata": { "collapsed": true }, "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "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": { "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 }