{ "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": [ "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": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020213431693812134.021742.02619.033.0FRFrance
12021333137869805.017767.02115.027.0FRFrance
220213231558611009.020163.02417.031.0FRFrance
320213131885513664.024046.02921.037.0FRFrance
42021303139919695.018287.02114.028.0FRFrance
52021293136269618.017634.02115.027.0FRFrance
6202128386365430.011842.0138.018.0FRFrance
72021273106936838.014548.01610.022.0FRFrance
8202126370864109.010063.0116.016.0FRFrance
9202125379425540.010344.0128.016.0FRFrance
10202124348553011.06699.074.010.0FRFrance
11202123367104455.08965.0107.013.0FRFrance
12202122378795495.010263.0128.016.0FRFrance
13202121378275403.010251.0128.016.0FRFrance
142021203102787540.013016.01612.020.0FRFrance
15202119395396860.012218.01410.018.0FRFrance
162021183121359165.015105.01814.022.0FRFrance
172021173120588891.015225.01813.023.0FRFrance
1820211631650512735.020275.02519.031.0FRFrance
1920211531930615398.023214.02923.035.0FRFrance
2020211432107317099.025047.03226.038.0FRFrance
2120211332641322094.030732.04033.047.0FRFrance
2220211233065825919.035397.04639.053.0FRFrance
2320211132498820718.029258.03832.044.0FRFrance
2420211031953915951.023127.03025.035.0FRFrance
2520210931757213926.021218.02721.033.0FRFrance
2620210832088216907.024857.03226.038.0FRFrance
2720210732239318303.026483.03428.040.0FRFrance
2820210632318319134.027232.03529.041.0FRFrance
2920210532242618445.026407.03428.040.0FRFrance
.................................
189219852132609619621.032571.04735.059.0FRFrance
189319852032789620885.034907.05138.064.0FRFrance
189419851934315432821.053487.07859.097.0FRFrance
189519851834055529935.051175.07455.093.0FRFrance
189619851733405324366.043740.06244.080.0FRFrance
189719851635036236451.064273.09166.0116.0FRFrance
189819851536388145538.082224.011683.0149.0FRFrance
18991985143134545114400.0154690.0244207.0281.0FRFrance
19001985133197206176080.0218332.0357319.0395.0FRFrance
19011985123245240223304.0267176.0445405.0485.0FRFrance
19021985113276205252399.0300011.0501458.0544.0FRFrance
19031985103353231326279.0380183.0640591.0689.0FRFrance
19041985093369895341109.0398681.0670618.0722.0FRFrance
19051985083389886359529.0420243.0707652.0762.0FRFrance
19061985073471852432599.0511105.0855784.0926.0FRFrance
19071985063565825518011.0613639.01026939.01113.0FRFrance
19081985053637302592795.0681809.011551074.01236.0FRFrance
19091985043424937390794.0459080.0770708.0832.0FRFrance
19101985033213901174689.0253113.0388317.0459.0FRFrance
191119850239758680949.0114223.0177147.0207.0FRFrance
191219850138548965918.0105060.0155120.0190.0FRFrance
191319845238483060602.0109058.0154110.0198.0FRFrance
1914198451310172680242.0123210.0185146.0224.0FRFrance
19151984503123680101401.0145959.0225184.0266.0FRFrance
1916198449310107381684.0120462.0184149.0219.0FRFrance
191719844837862060634.096606.0143110.0176.0FRFrance
191819844737202954274.089784.013199.0163.0FRFrance
191919844638733067686.0106974.0159123.0195.0FRFrance
19201984453135223101414.0169032.0246184.0308.0FRFrance
192119844436842220056.0116788.012537.0213.0FRFrance
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1922 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202134 3 16938 12134.0 21742.0 26 19.0 \n", "1 202133 3 13786 9805.0 17767.0 21 15.0 \n", "2 202132 3 15586 11009.0 20163.0 24 17.0 \n", "3 202131 3 18855 13664.0 24046.0 29 21.0 \n", "4 202130 3 13991 9695.0 18287.0 21 14.0 \n", "5 202129 3 13626 9618.0 17634.0 21 15.0 \n", "6 202128 3 8636 5430.0 11842.0 13 8.0 \n", "7 202127 3 10693 6838.0 14548.0 16 10.0 \n", "8 202126 3 7086 4109.0 10063.0 11 6.0 \n", "9 202125 3 7942 5540.0 10344.0 12 8.0 \n", "10 202124 3 4855 3011.0 6699.0 7 4.0 \n", "11 202123 3 6710 4455.0 8965.0 10 7.0 \n", "12 202122 3 7879 5495.0 10263.0 12 8.0 \n", "13 202121 3 7827 5403.0 10251.0 12 8.0 \n", "14 202120 3 10278 7540.0 13016.0 16 12.0 \n", "15 202119 3 9539 6860.0 12218.0 14 10.0 \n", "16 202118 3 12135 9165.0 15105.0 18 14.0 \n", "17 202117 3 12058 8891.0 15225.0 18 13.0 \n", "18 202116 3 16505 12735.0 20275.0 25 19.0 \n", "19 202115 3 19306 15398.0 23214.0 29 23.0 \n", "20 202114 3 21073 17099.0 25047.0 32 26.0 \n", "21 202113 3 26413 22094.0 30732.0 40 33.0 \n", "22 202112 3 30658 25919.0 35397.0 46 39.0 \n", "23 202111 3 24988 20718.0 29258.0 38 32.0 \n", "24 202110 3 19539 15951.0 23127.0 30 25.0 \n", "25 202109 3 17572 13926.0 21218.0 27 21.0 \n", "26 202108 3 20882 16907.0 24857.0 32 26.0 \n", "27 202107 3 22393 18303.0 26483.0 34 28.0 \n", "28 202106 3 23183 19134.0 27232.0 35 29.0 \n", "29 202105 3 22426 18445.0 26407.0 34 28.0 \n", "... ... ... ... ... ... ... ... \n", "1892 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1893 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1894 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1895 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1896 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1897 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1898 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1899 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1900 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1901 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1902 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1903 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1904 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1905 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1906 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1907 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1908 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1909 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1910 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1911 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1912 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1913 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1914 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1915 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1916 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1917 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1918 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1919 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1920 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1921 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 33.0 FR France \n", "1 27.0 FR France \n", "2 31.0 FR France \n", "3 37.0 FR France \n", "4 28.0 FR France \n", "5 27.0 FR France \n", "6 18.0 FR France \n", "7 22.0 FR France \n", "8 16.0 FR France \n", "9 16.0 FR France \n", "10 10.0 FR France \n", "11 13.0 FR France \n", "12 16.0 FR France \n", "13 16.0 FR France \n", "14 20.0 FR France \n", "15 18.0 FR France \n", "16 22.0 FR France \n", "17 23.0 FR France \n", "18 31.0 FR France \n", "19 35.0 FR France \n", "20 38.0 FR France \n", "21 47.0 FR France \n", "22 53.0 FR France \n", "23 44.0 FR France \n", "24 35.0 FR France \n", "25 33.0 FR France \n", "26 38.0 FR France \n", "27 40.0 FR France \n", "28 41.0 FR France \n", "29 40.0 FR France \n", "... ... ... ... \n", "1892 59.0 FR France \n", "1893 64.0 FR France \n", "1894 97.0 FR France \n", "1895 93.0 FR France \n", "1896 80.0 FR France \n", "1897 116.0 FR France \n", "1898 149.0 FR France \n", "1899 281.0 FR France \n", "1900 395.0 FR France \n", "1901 485.0 FR France \n", "1902 544.0 FR France \n", "1903 689.0 FR France \n", "1904 722.0 FR France \n", "1905 762.0 FR France \n", "1906 926.0 FR France \n", "1907 1113.0 FR France \n", "1908 1236.0 FR France \n", "1909 832.0 FR France \n", "1910 459.0 FR France \n", "1911 207.0 FR France \n", "1912 190.0 FR France \n", "1913 198.0 FR France \n", "1914 224.0 FR France \n", "1915 266.0 FR France \n", "1916 219.0 FR France \n", "1917 176.0 FR France \n", "1918 163.0 FR France \n", "1919 195.0 FR France \n", "1920 308.0 FR France \n", "1921 213.0 FR France \n", "\n", "[1922 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data = pd.read_csv(\"incidence-PAY-3.csv\", skiprows=1)\n", "raw_data\n", "# Telechargement des donnees au format csv dans un fichier local au notebook" ] }, { "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": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
168519891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1685 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1685 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
020213431693812134.021742.02619.033.0FRFrance
12021333137869805.017767.02115.027.0FRFrance
220213231558611009.020163.02417.031.0FRFrance
320213131885513664.024046.02921.037.0FRFrance
42021303139919695.018287.02114.028.0FRFrance
52021293136269618.017634.02115.027.0FRFrance
6202128386365430.011842.0138.018.0FRFrance
72021273106936838.014548.01610.022.0FRFrance
8202126370864109.010063.0116.016.0FRFrance
9202125379425540.010344.0128.016.0FRFrance
10202124348553011.06699.074.010.0FRFrance
11202123367104455.08965.0107.013.0FRFrance
12202122378795495.010263.0128.016.0FRFrance
13202121378275403.010251.0128.016.0FRFrance
142021203102787540.013016.01612.020.0FRFrance
15202119395396860.012218.01410.018.0FRFrance
162021183121359165.015105.01814.022.0FRFrance
172021173120588891.015225.01813.023.0FRFrance
1820211631650512735.020275.02519.031.0FRFrance
1920211531930615398.023214.02923.035.0FRFrance
2020211432107317099.025047.03226.038.0FRFrance
2120211332641322094.030732.04033.047.0FRFrance
2220211233065825919.035397.04639.053.0FRFrance
2320211132498820718.029258.03832.044.0FRFrance
2420211031953915951.023127.03025.035.0FRFrance
2520210931757213926.021218.02721.033.0FRFrance
2620210832088216907.024857.03226.038.0FRFrance
2720210732239318303.026483.03428.040.0FRFrance
2820210632318319134.027232.03529.041.0FRFrance
2920210532242618445.026407.03428.040.0FRFrance
.................................
189219852132609619621.032571.04735.059.0FRFrance
189319852032789620885.034907.05138.064.0FRFrance
189419851934315432821.053487.07859.097.0FRFrance
189519851834055529935.051175.07455.093.0FRFrance
189619851733405324366.043740.06244.080.0FRFrance
189719851635036236451.064273.09166.0116.0FRFrance
189819851536388145538.082224.011683.0149.0FRFrance
18991985143134545114400.0154690.0244207.0281.0FRFrance
19001985133197206176080.0218332.0357319.0395.0FRFrance
19011985123245240223304.0267176.0445405.0485.0FRFrance
19021985113276205252399.0300011.0501458.0544.0FRFrance
19031985103353231326279.0380183.0640591.0689.0FRFrance
19041985093369895341109.0398681.0670618.0722.0FRFrance
19051985083389886359529.0420243.0707652.0762.0FRFrance
19061985073471852432599.0511105.0855784.0926.0FRFrance
19071985063565825518011.0613639.01026939.01113.0FRFrance
19081985053637302592795.0681809.011551074.01236.0FRFrance
19091985043424937390794.0459080.0770708.0832.0FRFrance
19101985033213901174689.0253113.0388317.0459.0FRFrance
191119850239758680949.0114223.0177147.0207.0FRFrance
191219850138548965918.0105060.0155120.0190.0FRFrance
191319845238483060602.0109058.0154110.0198.0FRFrance
1914198451310172680242.0123210.0185146.0224.0FRFrance
19151984503123680101401.0145959.0225184.0266.0FRFrance
1916198449310107381684.0120462.0184149.0219.0FRFrance
191719844837862060634.096606.0143110.0176.0FRFrance
191819844737202954274.089784.013199.0163.0FRFrance
191919844638733067686.0106974.0159123.0195.0FRFrance
19201984453135223101414.0169032.0246184.0308.0FRFrance
192119844436842220056.0116788.012537.0213.0FRFrance
\n", "

1921 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202134 3 16938 12134.0 21742.0 26 19.0 \n", "1 202133 3 13786 9805.0 17767.0 21 15.0 \n", "2 202132 3 15586 11009.0 20163.0 24 17.0 \n", "3 202131 3 18855 13664.0 24046.0 29 21.0 \n", "4 202130 3 13991 9695.0 18287.0 21 14.0 \n", "5 202129 3 13626 9618.0 17634.0 21 15.0 \n", "6 202128 3 8636 5430.0 11842.0 13 8.0 \n", "7 202127 3 10693 6838.0 14548.0 16 10.0 \n", "8 202126 3 7086 4109.0 10063.0 11 6.0 \n", "9 202125 3 7942 5540.0 10344.0 12 8.0 \n", "10 202124 3 4855 3011.0 6699.0 7 4.0 \n", "11 202123 3 6710 4455.0 8965.0 10 7.0 \n", "12 202122 3 7879 5495.0 10263.0 12 8.0 \n", "13 202121 3 7827 5403.0 10251.0 12 8.0 \n", "14 202120 3 10278 7540.0 13016.0 16 12.0 \n", "15 202119 3 9539 6860.0 12218.0 14 10.0 \n", "16 202118 3 12135 9165.0 15105.0 18 14.0 \n", "17 202117 3 12058 8891.0 15225.0 18 13.0 \n", "18 202116 3 16505 12735.0 20275.0 25 19.0 \n", "19 202115 3 19306 15398.0 23214.0 29 23.0 \n", "20 202114 3 21073 17099.0 25047.0 32 26.0 \n", "21 202113 3 26413 22094.0 30732.0 40 33.0 \n", "22 202112 3 30658 25919.0 35397.0 46 39.0 \n", "23 202111 3 24988 20718.0 29258.0 38 32.0 \n", "24 202110 3 19539 15951.0 23127.0 30 25.0 \n", "25 202109 3 17572 13926.0 21218.0 27 21.0 \n", "26 202108 3 20882 16907.0 24857.0 32 26.0 \n", "27 202107 3 22393 18303.0 26483.0 34 28.0 \n", "28 202106 3 23183 19134.0 27232.0 35 29.0 \n", "29 202105 3 22426 18445.0 26407.0 34 28.0 \n", "... ... ... ... ... ... ... ... \n", "1892 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1893 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1894 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1895 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1896 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1897 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1898 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1899 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1900 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1901 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1902 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1903 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1904 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1905 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1906 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1907 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1908 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1909 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1910 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1911 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1912 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1913 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1914 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1915 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1916 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1917 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1918 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1919 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1920 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1921 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 33.0 FR France \n", "1 27.0 FR France \n", "2 31.0 FR France \n", "3 37.0 FR France \n", "4 28.0 FR France \n", "5 27.0 FR France \n", "6 18.0 FR France \n", "7 22.0 FR France \n", "8 16.0 FR France \n", "9 16.0 FR France \n", "10 10.0 FR France \n", "11 13.0 FR France \n", "12 16.0 FR France \n", "13 16.0 FR France \n", "14 20.0 FR France \n", "15 18.0 FR France \n", "16 22.0 FR France \n", "17 23.0 FR France \n", "18 31.0 FR France \n", "19 35.0 FR France \n", "20 38.0 FR France \n", "21 47.0 FR France \n", "22 53.0 FR France \n", "23 44.0 FR France \n", "24 35.0 FR France \n", "25 33.0 FR France \n", "26 38.0 FR France \n", "27 40.0 FR France \n", "28 41.0 FR France \n", "29 40.0 FR France \n", "... ... ... ... \n", "1892 59.0 FR France \n", "1893 64.0 FR France \n", "1894 97.0 FR France \n", "1895 93.0 FR France \n", "1896 80.0 FR France \n", "1897 116.0 FR France \n", "1898 149.0 FR France \n", "1899 281.0 FR France \n", "1900 395.0 FR France \n", "1901 485.0 FR France \n", "1902 544.0 FR France \n", "1903 689.0 FR France \n", "1904 722.0 FR France \n", "1905 762.0 FR France \n", "1906 926.0 FR France \n", "1907 1113.0 FR France \n", "1908 1236.0 FR France \n", "1909 832.0 FR France \n", "1910 459.0 FR France \n", "1911 207.0 FR France \n", "1912 190.0 FR France \n", "1913 198.0 FR France \n", "1914 224.0 FR France \n", "1915 266.0 FR France \n", "1916 219.0 FR France \n", "1917 176.0 FR France \n", "1918 163.0 FR France \n", "1919 195.0 FR France \n", "1920 308.0 FR France \n", "1921 213.0 FR France \n", "\n", "[1921 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": 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 }