{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 3, "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": 4, "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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020224637579666253.085339.0114100.0128.0FRFrance
120224534573039260.052200.06959.079.0FRFrance
220224433471328880.040546.05243.061.0FRFrance
320224334476936884.052654.06856.080.0FRFrance
420224234746240773.054151.07262.082.0FRFrance
520224134858342388.054778.07364.082.0FRFrance
620224034192736115.047739.06354.072.0FRFrance
720223933990234168.045636.06051.069.0FRFrance
820223832878123733.033829.04335.051.0FRFrance
920223732139517076.025714.03225.039.0FRFrance
1020223631412010487.017753.02116.026.0FRFrance
11202235392836485.012081.01410.018.0FRFrance
12202234374984731.010265.0117.015.0FRFrance
13202233375864442.010730.0116.016.0FRFrance
142022323122227749.016695.01811.025.0FRFrance
152022313132578905.017609.02013.027.0FRFrance
1620223031500610738.019274.02317.029.0FRFrance
1720222932080115829.025773.03124.038.0FRFrance
1820222832338717970.028804.03527.043.0FRFrance
1920222733601529709.042321.05444.064.0FRFrance
2020222632942124314.034528.04436.052.0FRFrance
2120222532288718582.027192.03529.041.0FRFrance
2220222431929415406.023182.02923.035.0FRFrance
2320222331715913450.020868.02620.032.0FRFrance
2420222231423910930.017548.02116.026.0FRFrance
252022213118048686.014922.01813.023.0FRFrance
2620222031735513600.021110.02620.032.0FRFrance
2720221931717813462.020894.02620.032.0FRFrance
2820221832756922584.032554.04234.050.0FRFrance
2920221733595030255.041645.05445.063.0FRFrance
.................................
195619852132609619621.032571.04735.059.0FRFrance
195719852032789620885.034907.05138.064.0FRFrance
195819851934315432821.053487.07859.097.0FRFrance
195919851834055529935.051175.07455.093.0FRFrance
196019851733405324366.043740.06244.080.0FRFrance
196119851635036236451.064273.09166.0116.0FRFrance
196219851536388145538.082224.011683.0149.0FRFrance
19631985143134545114400.0154690.0244207.0281.0FRFrance
19641985133197206176080.0218332.0357319.0395.0FRFrance
19651985123245240223304.0267176.0445405.0485.0FRFrance
19661985113276205252399.0300011.0501458.0544.0FRFrance
19671985103353231326279.0380183.0640591.0689.0FRFrance
19681985093369895341109.0398681.0670618.0722.0FRFrance
19691985083389886359529.0420243.0707652.0762.0FRFrance
19701985073471852432599.0511105.0855784.0926.0FRFrance
19711985063565825518011.0613639.01026939.01113.0FRFrance
19721985053637302592795.0681809.011551074.01236.0FRFrance
19731985043424937390794.0459080.0770708.0832.0FRFrance
19741985033213901174689.0253113.0388317.0459.0FRFrance
197519850239758680949.0114223.0177147.0207.0FRFrance
197619850138548965918.0105060.0155120.0190.0FRFrance
197719845238483060602.0109058.0154110.0198.0FRFrance
1978198451310172680242.0123210.0185146.0224.0FRFrance
19791984503123680101401.0145959.0225184.0266.0FRFrance
1980198449310107381684.0120462.0184149.0219.0FRFrance
198119844837862060634.096606.0143110.0176.0FRFrance
198219844737202954274.089784.013199.0163.0FRFrance
198319844638733067686.0106974.0159123.0195.0FRFrance
19841984453135223101414.0169032.0246184.0308.0FRFrance
198519844436842220056.0116788.012537.0213.0FRFrance
\n", "

1986 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202246 3 75796 66253.0 85339.0 114 100.0 \n", "1 202245 3 45730 39260.0 52200.0 69 59.0 \n", "2 202244 3 34713 28880.0 40546.0 52 43.0 \n", "3 202243 3 44769 36884.0 52654.0 68 56.0 \n", "4 202242 3 47462 40773.0 54151.0 72 62.0 \n", "5 202241 3 48583 42388.0 54778.0 73 64.0 \n", "6 202240 3 41927 36115.0 47739.0 63 54.0 \n", "7 202239 3 39902 34168.0 45636.0 60 51.0 \n", "8 202238 3 28781 23733.0 33829.0 43 35.0 \n", "9 202237 3 21395 17076.0 25714.0 32 25.0 \n", "10 202236 3 14120 10487.0 17753.0 21 16.0 \n", "11 202235 3 9283 6485.0 12081.0 14 10.0 \n", "12 202234 3 7498 4731.0 10265.0 11 7.0 \n", "13 202233 3 7586 4442.0 10730.0 11 6.0 \n", "14 202232 3 12222 7749.0 16695.0 18 11.0 \n", "15 202231 3 13257 8905.0 17609.0 20 13.0 \n", "16 202230 3 15006 10738.0 19274.0 23 17.0 \n", "17 202229 3 20801 15829.0 25773.0 31 24.0 \n", "18 202228 3 23387 17970.0 28804.0 35 27.0 \n", "19 202227 3 36015 29709.0 42321.0 54 44.0 \n", "20 202226 3 29421 24314.0 34528.0 44 36.0 \n", "21 202225 3 22887 18582.0 27192.0 35 29.0 \n", "22 202224 3 19294 15406.0 23182.0 29 23.0 \n", "23 202223 3 17159 13450.0 20868.0 26 20.0 \n", "24 202222 3 14239 10930.0 17548.0 21 16.0 \n", "25 202221 3 11804 8686.0 14922.0 18 13.0 \n", "26 202220 3 17355 13600.0 21110.0 26 20.0 \n", "27 202219 3 17178 13462.0 20894.0 26 20.0 \n", "28 202218 3 27569 22584.0 32554.0 42 34.0 \n", "29 202217 3 35950 30255.0 41645.0 54 45.0 \n", "... ... ... ... ... ... ... ... \n", "1956 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1957 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1958 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1959 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1960 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1961 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1962 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1963 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1964 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1965 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1966 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1967 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1968 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1969 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1970 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1971 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1972 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1973 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1974 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1975 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1976 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1977 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1978 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1979 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1980 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1981 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1982 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1983 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1984 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1985 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 128.0 FR France \n", "1 79.0 FR France \n", "2 61.0 FR France \n", "3 80.0 FR France \n", "4 82.0 FR France \n", "5 82.0 FR France \n", "6 72.0 FR France \n", "7 69.0 FR France \n", "8 51.0 FR France \n", "9 39.0 FR France \n", "10 26.0 FR France \n", "11 18.0 FR France \n", "12 15.0 FR France \n", "13 16.0 FR France \n", "14 25.0 FR France \n", "15 27.0 FR France \n", "16 29.0 FR France \n", "17 38.0 FR France \n", "18 43.0 FR France \n", "19 64.0 FR France \n", "20 52.0 FR France \n", "21 41.0 FR France \n", "22 35.0 FR France \n", "23 32.0 FR France \n", "24 26.0 FR France \n", "25 23.0 FR France \n", "26 32.0 FR France \n", "27 32.0 FR France \n", "28 50.0 FR France \n", "29 63.0 FR France \n", "... ... ... ... \n", "1956 59.0 FR France \n", "1957 64.0 FR France \n", "1958 97.0 FR France \n", "1959 93.0 FR France \n", "1960 80.0 FR France \n", "1961 116.0 FR France \n", "1962 149.0 FR France \n", "1963 281.0 FR France \n", "1964 395.0 FR France \n", "1965 485.0 FR France \n", "1966 544.0 FR France \n", "1967 689.0 FR France \n", "1968 722.0 FR France \n", "1969 762.0 FR France \n", "1970 926.0 FR France \n", "1971 1113.0 FR France \n", "1972 1236.0 FR France \n", "1973 832.0 FR France \n", "1974 459.0 FR France \n", "1975 207.0 FR France \n", "1976 190.0 FR France \n", "1977 198.0 FR France \n", "1978 224.0 FR France \n", "1979 266.0 FR France \n", "1980 219.0 FR France \n", "1981 176.0 FR France \n", "1982 163.0 FR France \n", "1983 195.0 FR France \n", "1984 308.0 FR France \n", "1985 213.0 FR France \n", "\n", "[1986 rows x 10 columns]" ] }, "execution_count": 5, "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": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
174919891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1749 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1749 FR France " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020224637579666253.085339.0114100.0128.0FRFrance
120224534573039260.052200.06959.079.0FRFrance
220224433471328880.040546.05243.061.0FRFrance
320224334476936884.052654.06856.080.0FRFrance
420224234746240773.054151.07262.082.0FRFrance
520224134858342388.054778.07364.082.0FRFrance
620224034192736115.047739.06354.072.0FRFrance
720223933990234168.045636.06051.069.0FRFrance
820223832878123733.033829.04335.051.0FRFrance
920223732139517076.025714.03225.039.0FRFrance
1020223631412010487.017753.02116.026.0FRFrance
11202235392836485.012081.01410.018.0FRFrance
12202234374984731.010265.0117.015.0FRFrance
13202233375864442.010730.0116.016.0FRFrance
142022323122227749.016695.01811.025.0FRFrance
152022313132578905.017609.02013.027.0FRFrance
1620223031500610738.019274.02317.029.0FRFrance
1720222932080115829.025773.03124.038.0FRFrance
1820222832338717970.028804.03527.043.0FRFrance
1920222733601529709.042321.05444.064.0FRFrance
2020222632942124314.034528.04436.052.0FRFrance
2120222532288718582.027192.03529.041.0FRFrance
2220222431929415406.023182.02923.035.0FRFrance
2320222331715913450.020868.02620.032.0FRFrance
2420222231423910930.017548.02116.026.0FRFrance
252022213118048686.014922.01813.023.0FRFrance
2620222031735513600.021110.02620.032.0FRFrance
2720221931717813462.020894.02620.032.0FRFrance
2820221832756922584.032554.04234.050.0FRFrance
2920221733595030255.041645.05445.063.0FRFrance
.................................
195619852132609619621.032571.04735.059.0FRFrance
195719852032789620885.034907.05138.064.0FRFrance
195819851934315432821.053487.07859.097.0FRFrance
195919851834055529935.051175.07455.093.0FRFrance
196019851733405324366.043740.06244.080.0FRFrance
196119851635036236451.064273.09166.0116.0FRFrance
196219851536388145538.082224.011683.0149.0FRFrance
19631985143134545114400.0154690.0244207.0281.0FRFrance
19641985133197206176080.0218332.0357319.0395.0FRFrance
19651985123245240223304.0267176.0445405.0485.0FRFrance
19661985113276205252399.0300011.0501458.0544.0FRFrance
19671985103353231326279.0380183.0640591.0689.0FRFrance
19681985093369895341109.0398681.0670618.0722.0FRFrance
19691985083389886359529.0420243.0707652.0762.0FRFrance
19701985073471852432599.0511105.0855784.0926.0FRFrance
19711985063565825518011.0613639.01026939.01113.0FRFrance
19721985053637302592795.0681809.011551074.01236.0FRFrance
19731985043424937390794.0459080.0770708.0832.0FRFrance
19741985033213901174689.0253113.0388317.0459.0FRFrance
197519850239758680949.0114223.0177147.0207.0FRFrance
197619850138548965918.0105060.0155120.0190.0FRFrance
197719845238483060602.0109058.0154110.0198.0FRFrance
1978198451310172680242.0123210.0185146.0224.0FRFrance
19791984503123680101401.0145959.0225184.0266.0FRFrance
1980198449310107381684.0120462.0184149.0219.0FRFrance
198119844837862060634.096606.0143110.0176.0FRFrance
198219844737202954274.089784.013199.0163.0FRFrance
198319844638733067686.0106974.0159123.0195.0FRFrance
19841984453135223101414.0169032.0246184.0308.0FRFrance
198519844436842220056.0116788.012537.0213.0FRFrance
\n", "

1985 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202246 3 75796 66253.0 85339.0 114 100.0 \n", "1 202245 3 45730 39260.0 52200.0 69 59.0 \n", "2 202244 3 34713 28880.0 40546.0 52 43.0 \n", "3 202243 3 44769 36884.0 52654.0 68 56.0 \n", "4 202242 3 47462 40773.0 54151.0 72 62.0 \n", "5 202241 3 48583 42388.0 54778.0 73 64.0 \n", "6 202240 3 41927 36115.0 47739.0 63 54.0 \n", "7 202239 3 39902 34168.0 45636.0 60 51.0 \n", "8 202238 3 28781 23733.0 33829.0 43 35.0 \n", "9 202237 3 21395 17076.0 25714.0 32 25.0 \n", "10 202236 3 14120 10487.0 17753.0 21 16.0 \n", "11 202235 3 9283 6485.0 12081.0 14 10.0 \n", "12 202234 3 7498 4731.0 10265.0 11 7.0 \n", "13 202233 3 7586 4442.0 10730.0 11 6.0 \n", "14 202232 3 12222 7749.0 16695.0 18 11.0 \n", "15 202231 3 13257 8905.0 17609.0 20 13.0 \n", "16 202230 3 15006 10738.0 19274.0 23 17.0 \n", "17 202229 3 20801 15829.0 25773.0 31 24.0 \n", "18 202228 3 23387 17970.0 28804.0 35 27.0 \n", "19 202227 3 36015 29709.0 42321.0 54 44.0 \n", "20 202226 3 29421 24314.0 34528.0 44 36.0 \n", "21 202225 3 22887 18582.0 27192.0 35 29.0 \n", "22 202224 3 19294 15406.0 23182.0 29 23.0 \n", "23 202223 3 17159 13450.0 20868.0 26 20.0 \n", "24 202222 3 14239 10930.0 17548.0 21 16.0 \n", "25 202221 3 11804 8686.0 14922.0 18 13.0 \n", "26 202220 3 17355 13600.0 21110.0 26 20.0 \n", "27 202219 3 17178 13462.0 20894.0 26 20.0 \n", "28 202218 3 27569 22584.0 32554.0 42 34.0 \n", "29 202217 3 35950 30255.0 41645.0 54 45.0 \n", "... ... ... ... ... ... ... ... \n", "1956 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1957 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1958 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1959 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1960 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1961 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1962 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1963 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1964 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1965 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1966 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1967 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1968 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1969 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1970 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1971 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1972 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1973 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1974 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1975 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1976 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1977 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1978 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1979 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1980 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1981 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1982 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1983 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1984 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1985 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 128.0 FR France \n", "1 79.0 FR France \n", "2 61.0 FR France \n", "3 80.0 FR France \n", "4 82.0 FR France \n", "5 82.0 FR France \n", "6 72.0 FR France \n", "7 69.0 FR France \n", "8 51.0 FR France \n", "9 39.0 FR France \n", "10 26.0 FR France \n", "11 18.0 FR France \n", "12 15.0 FR France \n", "13 16.0 FR France \n", "14 25.0 FR France \n", "15 27.0 FR France \n", "16 29.0 FR France \n", "17 38.0 FR France \n", "18 43.0 FR France \n", "19 64.0 FR France \n", "20 52.0 FR France \n", "21 41.0 FR France \n", "22 35.0 FR France \n", "23 32.0 FR France \n", "24 26.0 FR France \n", "25 23.0 FR France \n", "26 32.0 FR France \n", "27 32.0 FR France \n", "28 50.0 FR France \n", "29 63.0 FR France \n", "... ... ... ... \n", "1956 59.0 FR France \n", "1957 64.0 FR France \n", "1958 97.0 FR France \n", "1959 93.0 FR France \n", "1960 80.0 FR France \n", "1961 116.0 FR France \n", "1962 149.0 FR France \n", "1963 281.0 FR France \n", "1964 395.0 FR France \n", "1965 485.0 FR France \n", "1966 544.0 FR France \n", "1967 689.0 FR France \n", "1968 722.0 FR France \n", "1969 762.0 FR France \n", "1970 926.0 FR France \n", "1971 1113.0 FR France \n", "1972 1236.0 FR France \n", "1973 832.0 FR France \n", "1974 459.0 FR France \n", "1975 207.0 FR France \n", "1976 190.0 FR France \n", "1977 198.0 FR France \n", "1978 224.0 FR France \n", "1979 266.0 FR France \n", "1980 219.0 FR France \n", "1981 176.0 FR France \n", "1982 163.0 FR France \n", "1983 195.0 FR France \n", "1984 308.0 FR France \n", "1985 213.0 FR France \n", "\n", "[1985 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de\n", "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", "de nombre entier. C'est comme ça que Pandas les interprète.\n", " \n", "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", "semaine. Il faut lui fournir les dates de début et de fin de\n", "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous\n", "écrivons une petite fonction Python pour cela. Ensuite, nous\n", "l'appliquons à tous les points de nos donnés. Les résultats vont\n", "dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il restent deux petites modifications à faire.\n", "\n", "Premièrement, nous définissons les périodes d'observation\n", "comme nouvel index de notre jeux de données. Ceci en fait\n", "une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans\n", "le sens chronologique." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", "le début de la période qui suit, la différence temporelle doit être\n", "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", "d'une seconde.\n", "\n", "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", "entre lesquelles il manque une semaine.\n", "\n", "Nous reconnaissons ces dates: c'est la semaine sans observations\n", "que nous avions supprimées !" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" ] } ], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "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": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "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": 13, "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": 14, "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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dx0/xtadf5sEbL891O210ivq+bCZF+hvzGkpOFHXONQ9TIVY7RX1fNpNm/BvzLzbWQZEuD+JLbrSOIr0vm0kz/415ysv68VSIWW0V8W/MU142Kp4KMautZv4bc0BpQcOde+BtkWa11ax/Y57yakG+TIyZjUS1U15elG8hzbwYWA9F2uZp1gie8moh/sncsWnGbZ5mWfIIpYU082JgLXlkZ1Ydj1BaTLMuBtaSR3Zm1fEIpcX4J3NHziM7s+o4oJhVwWeVmw3P24bNzGxIPlPezMzqygHFzMwy4YBi1qQa/fO+ra4V+98BxaxJ+UTMxmrF/veivFmT8c/7NlYz9r8X5c1alE/EbKws+79o02YOKGZ1VusPCZ+I2VhZ9n/Rps18YqNZnZV/SNTq5wN8ImZjjbX/i3r9OK+hmNVJM86t11Mr/XxA3n4m2GsoZjnjtY2xKdr0z1gUddrSU15mdVLUD4lGK+r0z1gVcdrSAcWsjor4IdFoL925cNDpn2ZWxCuDO6CY1VERPyQazSO74nBAMbPc88iuGLzLy8zMhpTZLi9JF0jaJulNSXsl3Z7Sp0jaIqkz3U8uK7Na0n5J+yQtLku/QtJr6bV1kpTSJ0h6JqXvlDSnrMyKdIxOSSvK0uemvJ2p7JnVdo6ZWbPI09n01Wwb7gX+JCI+BVwJ3CbpEmAVsDUi5gFb03PSa8uAS4ElwLcknZHqeghYCcxLtyUp/WbgWER8EngAWJvqmgLcAywA5gP3lAWutcAD6fjHUh1mZi0lT9uph11DiYjDwOH0+ISkN4FZwFLg6pTtcWA7cFdKXx8RPcDPJe0H5ks6AJwdETsAJH0XuB7YlMp8I9X1feDBNHpZDGyJiKOpzBZgiaT1wDXAjWXH/walgGVm1vTyuJ16RCc2pqmoy4GdwPkp2PQFnekp2yzg7bJih1LarPS4Mr1fmYjoBd4Hpg5R11TgvZS3sq7KNq+U1CGpo7u7eyT/XDOz3MrjibJVBxRJHwf+GvjjiDg+VNYB0mKI9NGUGaqu/okRj0REe0S0T5s2baAsZmaFk8ft1FUFFEnjKQWTJyPiByn5XUkz0uszgK6Ufgi4oKz4bOCdlD57gPR+ZSS1AecAR4eo6whwbspbWZeZWUvo20694atXsXzBRXSf7Gloe4ZdQ0lrGY8Cb0bEN8te2gisAO5P98+VpT8l6ZvATEqL77si4gNJJyRdSWnK7Cbgf1TUtQO4AXgxIkLSZuC/lC3ELwJWp9e2pbzrK45vZtYS8naibDUjlKuArwDXSHol3X6XUiC5VlIncG16TkTsBZ4F3gCeB26LiA9SXbcC3wb2A29RWpCHUsCamhbw7yDtGEuL8fcBu9Pt3r4FekobAO5IZaamOixH8rSd0cxqzyc2Ws3cveE1ntx1kOXzL6zZ736YWe1Ve2KjL71imcvjdkYzqz3/HoplLo/bGc2s9hxQLHN53M5oZrXnKS+rCV8d1qz1eFHezMyG5N+UNzOzunJAaUI+/8PMGsEBpQnl6XLWZtY6vCjfRHz+h9nwuo6f4mtPv8yDN17unYcZ8wilifj8D7PheQRfOx6hNBGf/2E2OI/ga88jlCaTt8tZm+WFR/C15xFKk8nb5azN8sIj+NpzQDGzluErONSWz5Q3M7Mh+Ux5MzOrKwcUMzPLhAOKmZllwgHFzMwy4YBiZmaZcEAxM7NMOKCYmVkmHFDMzCwTDihmZpYJBxQzM8uEA4qZmWXCAcXMzDLhgGJmZplwQDEzs0w4oJiZWSYcUMzMLBMOKGZmlgkHFDMzy8SwAUXSY5K6JL1eljZF0hZJnel+ctlrqyXtl7RP0uKy9CskvZZeWydJKX2CpGdS+k5Jc8rKrEjH6JS0oix9bsrbmcqeOfauMDOzsahmhPKXwJKKtFXA1oiYB2xNz5F0CbAMuDSV+ZakM1KZh4CVwLx066vzZuBYRHwSeABYm+qaAtwDLADmA/eUBa61wAPp+MdSHWZm1kDDBpSI+AlwtCJ5KfB4evw4cH1Z+vqI6ImInwP7gfmSZgBnR8SOiAjguxVl+ur6PvC5NHpZDGyJiKMRcQzYAixJr12T8lYe38zMGmS0ayjnR8RhgHQ/PaXPAt4uy3copc1KjyvT+5WJiF7gfWDqEHVNBd5LeSvr+jWSVkrqkNTR3d09wn+mmZlVK+tFeQ2QFkOkj6bMUHX9+gsRj0REe0S0T5s2bbBsZmY2RqMNKO+maSzSfVdKPwRcUJZvNvBOSp89QHq/MpLagHMoTbENVtcR4NyUt7IuMzNrkNEGlI1A366rFcBzZenL0s6tuZQW33elabETkq5MayA3VZTpq+sG4MW0zrIZWCRpclqMXwRsTq9tS3krj29mZg3SNlwGSU8DVwPnSTpEaefV/cCzkm4GDgJfAoiIvZKeBd4AeoHbIuKDVNWtlHaMfQzYlG4AjwLfk7Sf0shkWarrqKT7gN0p370R0bc54C5gvaQ1wMupDjMzayCVvvC3hvb29ujo6Gh0M8zMCkXSnohoHy6fz5Q3M7NMOKCYmVkmHFDMzJpY1/FTfPnhHXSdOFXzYzmgmJk1sXVbO9l94CjrXuis+bGG3eVlZmbFc/Hdm+jpPf3h8yd2HuSJnQeZ0DaOfWs+X5NjeoRiZtaEXrpzIdddNpOJ40sf8xPHj2PpZTN56a6FNTumA4qZWROafvZEJk1oo6f3NBPaxtHTe5pJE9qYPmlizY7pKS8zsyZ15GQPyxdcxI3zL+SpXQfprvHCvE9sNDOzIfnERjMzqysHFDMzy4QDipmZZcIBxczMMuGAYmZmmXBAMTOzTLTUtmFJ3cA/DPDSeZR+WrgoitTeIrUVitXeIrUVitXeIrUVat/eiyJi2nCZWiqgDEZSRzV7rPOiSO0tUluhWO0tUluhWO0tUlshP+31lJeZmWXCAcXMzDLhgFLySKMbMEJFam+R2grFam+R2grFam+R2go5aa/XUMzMLBMeoZiZWSaaNqBIekxSl6TXy9L+haQdkl6T9D8lnZ3Sx0t6PKW/KWl1WZntkvZJeiXdpje4rWdK+k5Kf1XS1WVlrkjp+yWtk6Ss25pxe+vRtxdI2pb+X/dKuj2lT5G0RVJnup9cVmZ16sN9khaXpde0fzNua+76VtLUlP+kpAcr6spV3w7T1jz27bWS9qQ+3CPpmrK66vK5AEBENOUN+FfAbwOvl6XtBv51evyHwH3p8Y3A+vT4N4ADwJz0fDvQnqO23gZ8Jz2eDuwBxqXnu4DPAgI2AZ/PeXvr0bczgN9OjycBfw9cAvxXYFVKXwWsTY8vAV4FJgBzgbeAM+rRvxm3NY99exbwO8AfAQ9W1JW3vh2qrXns28uBmenxp4F/rFfflt+adoQSET8BjlYkXwz8JD3eAvx+X3bgLEltwMeAfwKO16OdMOK2XgJsTeW6gPeAdkkzgLMjYkeU3kXfBa7Pa3tr0a6BRMThiPg/6fEJ4E1gFrAUeDxle5yP+moppS8XPRHxc2A/ML8e/ZtVW7NsU5btjYhfRsTfAv1+5SmPfTtYW+tlFO19OSLeSel7gYmSJtTzcwGaeMprEK8D16XHXwIuSI+/D/wSOAwcBP5bRJR/YH4nDW3/U02Hi9W19VVgqaQ2SXOBK9Jrs4BDZeUPpbR6GWl7+9StbyXNofRNbidwfkQchtIfL6XRE5T67O2yYn39WNf+HWNb++StbweTx74dTp779veBlyOihzr3basFlD8EbpO0h9Iw8p9S+nzgA2AmpamDP5H0z9JryyPiM8C/TLevNLitj1F6U3QAfw78L6CX0nC2Uj238I20vVDHvpX0ceCvgT+OiKFGn4P1Y936N4O2Qj77dtAqBkhrdN8OJbd9K+lSYC1wS1/SANlq9rnQUgElIn4WEYsi4grgaUpzzlBaQ3k+In6VpmX+jjQtExH/mO5PAE9RpymFwdoaEb0R8R8i4rKIWAqcC3RS+tCeXVbFbOCdynpz1N669a2k8ZT+KJ+MiB+k5HfTdEDflEtXSj9E/xFUXz/WpX8zamte+3YweezbQeW1byXNBjYAN0VE32dbXT8XWiqg9O3GkDQOuBv4i/TSQeAalZwFXAn8LE3TnJfKjAe+QGlqp2FtlfQbqY1IuhbojYg30vD3hKQr0xD8JuC5erR1NO2tV9+mvngUeDMivln20kZgRXq8go/6aiOwLM0/zwXmAbvq0b9ZtTXHfTugnPbtYPXksm8lnQv8EFgdEX/Xl7nunwtZr/Ln5UbpW/Jh4FeUovTNwO2Udkv8PXA/H53Y+XHgrygtZr0B/Mf4aKfHHuCn6bX/TtpF08C2zgH2UVqke4HSVUD76mmn9OZ+C3iwr0we21vHvv0dSkP8nwKvpNvvAlMpbRboTPdTysp8PfXhPsp2xNS6f7Nqa8779gClDR0n03vnkhz37a+1Na99S+lL3C/L8r4CTK9H35bffKa8mZlloqWmvMzMrHYcUMzMLBMOKGZmlgkHFDMzy4QDipmZZcIBxczMMuGAYmZmmXBAMTOzTPx/yCCfx6w8frMAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2021 743449\n", "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2010315\n", "2012 2175217\n", "2003 2234584\n", "2019 2254386\n", "2006 2307352\n", "2017 2321583\n", "2001 2529279\n", "1992 2574578\n", "1993 2703886\n", "2018 2705325\n", "1988 2765617\n", "2007 2780164\n", "1987 2855570\n", "2016 2856393\n", "2011 2857040\n", "2008 2973918\n", "1998 3034904\n", "2002 3125418\n", "2009 3444020\n", "1994 3514763\n", "1996 3539413\n", "2004 3567744\n", "1997 3620066\n", "2015 3654892\n", "2000 3826372\n", "2005 3835025\n", "1999 3908112\n", "2010 4111392\n", "2013 4182691\n", "1986 5115251\n", "1990 5235827\n", "1989 5466192\n", "dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "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": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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