diff --git a/module3/exo2/analyse-syndrome-grippal-jupyter.ipynb b/module3/exo2/analyse-syndrome-grippal-jupyter.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f91b4a5ab39c2bd81240ffe1037e26afa60f8a85 --- /dev/null +++ b/module3/exo2/analyse-syndrome-grippal-jupyter.ipynb @@ -0,0 +1,2511 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "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": 64, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://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": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020223935154743092.060002.07865.091.0FRFrance
120223833438428715.040053.05243.061.0FRFrance
220223732656421654.031474.04033.047.0FRFrance
320223631930315018.023588.02923.035.0FRFrance
42022353129149679.016149.01914.024.0FRFrance
52022343107087334.014082.01611.021.0FRFrance
62022333129268869.016983.01913.025.0FRFrance
720223232225716158.028356.03425.043.0FRFrance
820223132182816268.027388.03325.041.0FRFrance
920223031966314779.024547.03023.037.0FRFrance
1020222932426818906.029630.03729.045.0FRFrance
1120222832484519214.030476.03729.045.0FRFrance
1220222734074533994.047496.06151.071.0FRFrance
1320222633401028521.039499.05143.059.0FRFrance
1420222532337719042.027712.03528.042.0FRFrance
1520222432632821829.030827.04033.047.0FRFrance
1620222332343018950.027910.03528.042.0FRFrance
1720222231895115099.022803.02923.035.0FRFrance
1820222131363210251.017013.02116.026.0FRFrance
1920222031978715756.023818.03024.036.0FRFrance
2020221931788414079.021689.02721.033.0FRFrance
2120221833035325089.035617.04638.054.0FRFrance
2220221733600630373.041639.05446.062.0FRFrance
2320221634994942836.057062.07564.086.0FRFrance
24202215310080690824.0110788.0152137.0167.0FRFrance
252022143155441143891.0166991.0234217.0251.0FRFrance
262022133191914179558.0204270.0289270.0308.0FRFrance
272022123166224155035.0177413.0251234.0268.0FRFrance
282022113122849113306.0132392.0185171.0199.0FRFrance
2920221038790479741.096067.0133121.0145.0FRFrance
.................................
194919852132609619621.032571.04735.059.0FRFrance
195019852032789620885.034907.05138.064.0FRFrance
195119851934315432821.053487.07859.097.0FRFrance
195219851834055529935.051175.07455.093.0FRFrance
195319851733405324366.043740.06244.080.0FRFrance
195419851635036236451.064273.09166.0116.0FRFrance
195519851536388145538.082224.011683.0149.0FRFrance
19561985143134545114400.0154690.0244207.0281.0FRFrance
19571985133197206176080.0218332.0357319.0395.0FRFrance
19581985123245240223304.0267176.0445405.0485.0FRFrance
19591985113276205252399.0300011.0501458.0544.0FRFrance
19601985103353231326279.0380183.0640591.0689.0FRFrance
19611985093369895341109.0398681.0670618.0722.0FRFrance
19621985083389886359529.0420243.0707652.0762.0FRFrance
19631985073471852432599.0511105.0855784.0926.0FRFrance
19641985063565825518011.0613639.01026939.01113.0FRFrance
19651985053637302592795.0681809.011551074.01236.0FRFrance
19661985043424937390794.0459080.0770708.0832.0FRFrance
19671985033213901174689.0253113.0388317.0459.0FRFrance
196819850239758680949.0114223.0177147.0207.0FRFrance
196919850138548965918.0105060.0155120.0190.0FRFrance
197019845238483060602.0109058.0154110.0198.0FRFrance
1971198451310172680242.0123210.0185146.0224.0FRFrance
19721984503123680101401.0145959.0225184.0266.0FRFrance
1973198449310107381684.0120462.0184149.0219.0FRFrance
197419844837862060634.096606.0143110.0176.0FRFrance
197519844737202954274.089784.013199.0163.0FRFrance
197619844638733067686.0106974.0159123.0195.0FRFrance
19771984453135223101414.0169032.0246184.0308.0FRFrance
197819844436842220056.0116788.012537.0213.0FRFrance
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1979 rows × 10 columns

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10251.0 17013.0 21 16.0 \n", + "19 202220 3 19787 15756.0 23818.0 30 24.0 \n", + "20 202219 3 17884 14079.0 21689.0 27 21.0 \n", + "21 202218 3 30353 25089.0 35617.0 46 38.0 \n", + "22 202217 3 36006 30373.0 41639.0 54 46.0 \n", + "23 202216 3 49949 42836.0 57062.0 75 64.0 \n", + "24 202215 3 100806 90824.0 110788.0 152 137.0 \n", + "25 202214 3 155441 143891.0 166991.0 234 217.0 \n", + "26 202213 3 191914 179558.0 204270.0 289 270.0 \n", + "27 202212 3 166224 155035.0 177413.0 251 234.0 \n", + "28 202211 3 122849 113306.0 132392.0 185 171.0 \n", + "29 202210 3 87904 79741.0 96067.0 133 121.0 \n", + "... ... ... ... ... ... ... ... \n", + "1949 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1950 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1951 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1952 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1953 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1954 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1955 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1956 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1957 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1958 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1959 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1960 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1961 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1962 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1963 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1964 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1965 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1966 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1967 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1968 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1969 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1970 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1971 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1972 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1973 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1974 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1975 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1976 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1977 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1978 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 91.0 FR France \n", + "1 61.0 FR France \n", + "2 47.0 FR France \n", + "3 35.0 FR France \n", + "4 24.0 FR France \n", + "5 21.0 FR France \n", + "6 25.0 FR France \n", + "7 43.0 FR France \n", + "8 41.0 FR France \n", + "9 37.0 FR France \n", + "10 45.0 FR France \n", + "11 45.0 FR France \n", + "12 71.0 FR France \n", + "13 59.0 FR France \n", + "14 42.0 FR France \n", + "15 47.0 FR France \n", + "16 42.0 FR France \n", + "17 35.0 FR France \n", + "18 26.0 FR France \n", + "19 36.0 FR France \n", + "20 33.0 FR France \n", + "21 54.0 FR France \n", + "22 62.0 FR France \n", + "23 86.0 FR France \n", + "24 167.0 FR France \n", + "25 251.0 FR France \n", + "26 308.0 FR France \n", + "27 268.0 FR France \n", + "28 199.0 FR France \n", + "29 145.0 FR France \n", + "... ... ... ... \n", + "1949 59.0 FR France \n", + "1950 64.0 FR France \n", + "1951 97.0 FR France \n", + "1952 93.0 FR France \n", + "1953 80.0 FR France \n", + "1954 116.0 FR France \n", + "1955 149.0 FR France \n", + "1956 281.0 FR France \n", + "1957 395.0 FR France \n", + "1958 485.0 FR France \n", + "1959 544.0 FR France \n", + "1960 689.0 FR France \n", + "1961 722.0 FR France \n", + "1962 762.0 FR France \n", + "1963 926.0 FR France \n", + "1964 1113.0 FR France \n", + "1965 1236.0 FR France \n", + "1966 832.0 FR France \n", + "1967 459.0 FR France \n", + "1968 207.0 FR France \n", + "1969 190.0 FR France \n", + "1970 198.0 FR France \n", + "1971 224.0 FR France \n", + "1972 266.0 FR France \n", + "1973 219.0 FR France \n", + "1974 176.0 FR France \n", + "1975 163.0 FR France \n", + "1976 195.0 FR France \n", + "1977 308.0 FR France \n", + "1978 213.0 FR France \n", + "\n", + "[1979 rows x 10 columns]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', 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": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
174219891930NaNNaN0NaNNaNFRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1742 198919 3 0 NaN NaN 0 NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1742 FR France " + ] + }, + "execution_count": 66, + "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": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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.................................
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1978 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202239 3 51547 43092.0 60002.0 78 65.0 \n", + "1 202238 3 34384 28715.0 40053.0 52 43.0 \n", + "2 202237 3 26564 21654.0 31474.0 40 33.0 \n", + "3 202236 3 19303 15018.0 23588.0 29 23.0 \n", + "4 202235 3 12914 9679.0 16149.0 19 14.0 \n", + "5 202234 3 10708 7334.0 14082.0 16 11.0 \n", + "6 202233 3 12926 8869.0 16983.0 19 13.0 \n", + "7 202232 3 22257 16158.0 28356.0 34 25.0 \n", + "8 202231 3 21828 16268.0 27388.0 33 25.0 \n", + "9 202230 3 19663 14779.0 24547.0 30 23.0 \n", + "10 202229 3 24268 18906.0 29630.0 37 29.0 \n", + "11 202228 3 24845 19214.0 30476.0 37 29.0 \n", + "12 202227 3 40745 33994.0 47496.0 61 51.0 \n", + "13 202226 3 34010 28521.0 39499.0 51 43.0 \n", + "14 202225 3 23377 19042.0 27712.0 35 28.0 \n", + "15 202224 3 26328 21829.0 30827.0 40 33.0 \n", + "16 202223 3 23430 18950.0 27910.0 35 28.0 \n", + "17 202222 3 18951 15099.0 22803.0 29 23.0 \n", + "18 202221 3 13632 10251.0 17013.0 21 16.0 \n", + "19 202220 3 19787 15756.0 23818.0 30 24.0 \n", + "20 202219 3 17884 14079.0 21689.0 27 21.0 \n", + "21 202218 3 30353 25089.0 35617.0 46 38.0 \n", + "22 202217 3 36006 30373.0 41639.0 54 46.0 \n", + "23 202216 3 49949 42836.0 57062.0 75 64.0 \n", + "24 202215 3 100806 90824.0 110788.0 152 137.0 \n", + "25 202214 3 155441 143891.0 166991.0 234 217.0 \n", + "26 202213 3 191914 179558.0 204270.0 289 270.0 \n", + "27 202212 3 166224 155035.0 177413.0 251 234.0 \n", + "28 202211 3 122849 113306.0 132392.0 185 171.0 \n", + "29 202210 3 87904 79741.0 96067.0 133 121.0 \n", + "... ... ... ... ... ... ... ... \n", + "1949 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1950 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1951 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1952 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1953 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1954 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1955 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1956 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1957 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1958 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1959 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1960 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1961 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1962 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1963 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1964 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1965 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1966 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1967 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1968 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1969 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1970 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1971 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1972 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1973 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1974 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1975 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1976 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1977 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1978 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 91.0 FR France \n", + "1 61.0 FR France \n", + "2 47.0 FR France \n", + "3 35.0 FR France \n", + "4 24.0 FR France \n", + "5 21.0 FR France \n", + "6 25.0 FR France \n", + "7 43.0 FR France \n", + "8 41.0 FR France \n", + "9 37.0 FR France \n", + "10 45.0 FR France \n", + "11 45.0 FR France \n", + "12 71.0 FR France \n", + "13 59.0 FR France \n", + "14 42.0 FR France \n", + "15 47.0 FR France \n", + "16 42.0 FR France \n", + "17 35.0 FR France \n", + "18 26.0 FR France \n", + "19 36.0 FR France \n", + "20 33.0 FR France \n", + "21 54.0 FR France \n", + "22 62.0 FR France \n", + "23 86.0 FR France \n", + "24 167.0 FR France \n", + "25 251.0 FR France \n", + "26 308.0 FR France \n", + "27 268.0 FR France \n", + "28 199.0 FR France \n", + "29 145.0 FR France \n", + "... ... ... ... \n", + "1949 59.0 FR France \n", + "1950 64.0 FR France \n", + "1951 97.0 FR France \n", + "1952 93.0 FR France \n", + "1953 80.0 FR France \n", + "1954 116.0 FR France \n", + "1955 149.0 FR France \n", + "1956 281.0 FR France \n", + "1957 395.0 FR France \n", + "1958 485.0 FR France \n", + "1959 544.0 FR France \n", + "1960 689.0 FR France \n", + "1961 722.0 FR France \n", + "1962 762.0 FR France \n", + "1963 926.0 FR France \n", + "1964 1113.0 FR France \n", + "1965 1236.0 FR France \n", + "1966 832.0 FR France \n", + "1967 459.0 FR France \n", + "1968 207.0 FR France \n", + "1969 190.0 FR France \n", + "1970 198.0 FR France \n", + "1971 224.0 FR France \n", + "1972 266.0 FR France \n", + "1973 219.0 FR France \n", + "1974 176.0 FR France \n", + "1975 163.0 FR France \n", + "1976 195.0 FR France \n", + "1977 308.0 FR France \n", + "1978 213.0 FR France \n", + "\n", + "[1978 rows x 10 columns]" + ] + }, + "execution_count": 67, + "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": 68, + "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": 69, + "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": 70, + "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": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 71, + "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": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 72, + "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": 73, + "metadata": {}, + "outputs": [], + "source": [ + "first_september_week = [pd.Period(pd.Timestamp(y, 9, 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 september, 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": 74, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_september_week[:-1],\n", + " first_september_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) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici les incidences annuelles." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 75, + "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": [ + "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": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2021 978259\n", + "2014 1601698\n", + "1991 1663610\n", + "1995 1828304\n", + "2020 2078964\n", + "2012 2183912\n", + "2003 2234514\n", + "2019 2254363\n", + "2006 2297262\n", + "2017 2322818\n", + "2001 2540826\n", + "1992 2590314\n", + "1993 2699482\n", + "2018 2701716\n", + "1988 2759663\n", + "2007 2786458\n", + "2011 2852504\n", + "2016 2859019\n", + "1987 2867464\n", + "2008 2984311\n", + "1998 3047298\n", + "2002 3115484\n", + "1994 3514133\n", + "1996 3540251\n", + "2009 3558474\n", + "2004 3572810\n", + "1997 3624129\n", + "2015 3647492\n", + "2000 3808190\n", + "2005 3831409\n", + "1999 3914003\n", + "2010 3992174\n", + "2013 4176872\n", + "1986 5050543\n", + "1990 5214494\n", + "1989 5461328\n", + "dtype: int64" + ] + }, + "execution_count": 76, + "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": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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