{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os\n", "import urllib" ] }, { "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": 62, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous conservons les données dans un fichier local, afin que la conservation du fichier sur lequel nous effectuons notre analyse soit robuste aux défaillances du site Sentiweb. À cette fin également, nous ne retéléchargeons pas automatiquement une nouvelle version à chaque exécution, mais seulement si le fichier local est manquant." ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "if not os.path.exists(\"incidence-PAY-3.csv\"):\n", " urllib.request.urlretrieve(data_url, \"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": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02022343142029453.018951.02114.028.0FRFrance
12022333130518947.017155.02014.026.0FRFrance
220223232225716158.028356.03425.043.0FRFrance
320223132182816268.027388.03325.041.0FRFrance
420223031966314779.024547.03023.037.0FRFrance
520222932426818906.029630.03729.045.0FRFrance
620222832484519214.030476.03729.045.0FRFrance
720222734074533994.047496.06151.071.0FRFrance
820222633401028521.039499.05143.059.0FRFrance
920222532337719042.027712.03528.042.0FRFrance
1020222432632821829.030827.04033.047.0FRFrance
1120222332343018950.027910.03528.042.0FRFrance
1220222231895115099.022803.02923.035.0FRFrance
1320222131363210251.017013.02116.026.0FRFrance
1420222031978715756.023818.03024.036.0FRFrance
1520221931788414079.021689.02721.033.0FRFrance
1620221833035325089.035617.04638.054.0FRFrance
1720221733600630373.041639.05446.062.0FRFrance
1820221634994942836.057062.07564.086.0FRFrance
19202215310080690824.0110788.0152137.0167.0FRFrance
202022143155441143891.0166991.0234217.0251.0FRFrance
212022133191914179558.0204270.0289270.0308.0FRFrance
222022123166224155035.0177413.0251234.0268.0FRFrance
232022113122849113306.0132392.0185171.0199.0FRFrance
2420221038790479741.096067.0133121.0145.0FRFrance
2520220935018243958.056406.07667.085.0FRFrance
2620220833096325942.035984.04739.055.0FRFrance
2720220733488229446.040318.05345.061.0FRFrance
2820220634662340398.052848.07061.079.0FRFrance
2920220536297056043.069897.09585.0105.0FRFrance
.................................
194419852132609619621.032571.04735.059.0FRFrance
194519852032789620885.034907.05138.064.0FRFrance
194619851934315432821.053487.07859.097.0FRFrance
194719851834055529935.051175.07455.093.0FRFrance
194819851733405324366.043740.06244.080.0FRFrance
194919851635036236451.064273.09166.0116.0FRFrance
195019851536388145538.082224.011683.0149.0FRFrance
19511985143134545114400.0154690.0244207.0281.0FRFrance
19521985133197206176080.0218332.0357319.0395.0FRFrance
19531985123245240223304.0267176.0445405.0485.0FRFrance
19541985113276205252399.0300011.0501458.0544.0FRFrance
19551985103353231326279.0380183.0640591.0689.0FRFrance
19561985093369895341109.0398681.0670618.0722.0FRFrance
19571985083389886359529.0420243.0707652.0762.0FRFrance
19581985073471852432599.0511105.0855784.0926.0FRFrance
19591985063565825518011.0613639.01026939.01113.0FRFrance
19601985053637302592795.0681809.011551074.01236.0FRFrance
19611985043424937390794.0459080.0770708.0832.0FRFrance
19621985033213901174689.0253113.0388317.0459.0FRFrance
196319850239758680949.0114223.0177147.0207.0FRFrance
196419850138548965918.0105060.0155120.0190.0FRFrance
196519845238483060602.0109058.0154110.0198.0FRFrance
1966198451310172680242.0123210.0185146.0224.0FRFrance
19671984503123680101401.0145959.0225184.0266.0FRFrance
1968198449310107381684.0120462.0184149.0219.0FRFrance
196919844837862060634.096606.0143110.0176.0FRFrance
197019844737202954274.089784.013199.0163.0FRFrance
197119844638733067686.0106974.0159123.0195.0FRFrance
19721984453135223101414.0169032.0246184.0308.0FRFrance
197319844436842220056.0116788.012537.0213.0FRFrance
\n", "

1974 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202234 3 14202 9453.0 18951.0 21 14.0 \n", "1 202233 3 13051 8947.0 17155.0 20 14.0 \n", "2 202232 3 22257 16158.0 28356.0 34 25.0 \n", "3 202231 3 21828 16268.0 27388.0 33 25.0 \n", "4 202230 3 19663 14779.0 24547.0 30 23.0 \n", "5 202229 3 24268 18906.0 29630.0 37 29.0 \n", "6 202228 3 24845 19214.0 30476.0 37 29.0 \n", "7 202227 3 40745 33994.0 47496.0 61 51.0 \n", "8 202226 3 34010 28521.0 39499.0 51 43.0 \n", "9 202225 3 23377 19042.0 27712.0 35 28.0 \n", "10 202224 3 26328 21829.0 30827.0 40 33.0 \n", "11 202223 3 23430 18950.0 27910.0 35 28.0 \n", "12 202222 3 18951 15099.0 22803.0 29 23.0 \n", "13 202221 3 13632 10251.0 17013.0 21 16.0 \n", "14 202220 3 19787 15756.0 23818.0 30 24.0 \n", "15 202219 3 17884 14079.0 21689.0 27 21.0 \n", "16 202218 3 30353 25089.0 35617.0 46 38.0 \n", "17 202217 3 36006 30373.0 41639.0 54 46.0 \n", "18 202216 3 49949 42836.0 57062.0 75 64.0 \n", "19 202215 3 100806 90824.0 110788.0 152 137.0 \n", "20 202214 3 155441 143891.0 166991.0 234 217.0 \n", "21 202213 3 191914 179558.0 204270.0 289 270.0 \n", "22 202212 3 166224 155035.0 177413.0 251 234.0 \n", "23 202211 3 122849 113306.0 132392.0 185 171.0 \n", "24 202210 3 87904 79741.0 96067.0 133 121.0 \n", "25 202209 3 50182 43958.0 56406.0 76 67.0 \n", "26 202208 3 30963 25942.0 35984.0 47 39.0 \n", "27 202207 3 34882 29446.0 40318.0 53 45.0 \n", "28 202206 3 46623 40398.0 52848.0 70 61.0 \n", "29 202205 3 62970 56043.0 69897.0 95 85.0 \n", "... ... ... ... ... ... ... ... \n", "1944 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1945 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1946 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1947 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1948 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1949 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1950 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1951 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1952 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1953 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1954 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1955 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1956 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1957 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1958 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1959 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1960 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1961 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1962 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1963 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1964 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1965 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1966 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1967 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1968 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1969 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1970 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1971 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1972 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1973 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 28.0 FR France \n", "1 26.0 FR France \n", "2 43.0 FR France \n", "3 41.0 FR France \n", "4 37.0 FR France \n", "5 45.0 FR France \n", "6 45.0 FR France \n", "7 71.0 FR France \n", "8 59.0 FR France \n", "9 42.0 FR France \n", "10 47.0 FR France \n", "11 42.0 FR France \n", "12 35.0 FR France \n", "13 26.0 FR France \n", "14 36.0 FR France \n", "15 33.0 FR France \n", "16 54.0 FR France \n", "17 62.0 FR France \n", "18 86.0 FR France \n", "19 167.0 FR France \n", "20 251.0 FR France \n", "21 308.0 FR France \n", "22 268.0 FR France \n", "23 199.0 FR France \n", "24 145.0 FR France \n", "25 85.0 FR France \n", "26 55.0 FR France \n", "27 61.0 FR France \n", "28 79.0 FR France \n", "29 105.0 FR France \n", "... ... ... ... \n", "1944 59.0 FR France \n", "1945 64.0 FR France \n", "1946 97.0 FR France \n", "1947 93.0 FR France \n", "1948 80.0 FR France \n", "1949 116.0 FR France \n", "1950 149.0 FR France \n", "1951 281.0 FR France \n", "1952 395.0 FR France \n", "1953 485.0 FR France \n", "1954 544.0 FR France \n", "1955 689.0 FR France \n", "1956 722.0 FR France \n", "1957 762.0 FR France \n", "1958 926.0 FR France \n", "1959 1113.0 FR France \n", "1960 1236.0 FR France \n", "1961 832.0 FR France \n", "1962 459.0 FR France \n", "1963 207.0 FR France \n", "1964 190.0 FR France \n", "1965 198.0 FR France \n", "1966 224.0 FR France \n", "1967 266.0 FR France \n", "1968 219.0 FR France \n", "1969 176.0 FR France \n", "1970 163.0 FR France \n", "1971 195.0 FR France \n", "1972 308.0 FR France \n", "1973 213.0 FR France \n", "\n", "[1974 rows x 10 columns]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(\"incidence-PAY-3.csv\", 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": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
173719891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1737 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1737 FR France " ] }, "execution_count": 65, "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": 66, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02022343142029453.018951.02114.028.0FRFrance
12022333130518947.017155.02014.026.0FRFrance
220223232225716158.028356.03425.043.0FRFrance
320223132182816268.027388.03325.041.0FRFrance
420223031966314779.024547.03023.037.0FRFrance
520222932426818906.029630.03729.045.0FRFrance
620222832484519214.030476.03729.045.0FRFrance
720222734074533994.047496.06151.071.0FRFrance
820222633401028521.039499.05143.059.0FRFrance
920222532337719042.027712.03528.042.0FRFrance
1020222432632821829.030827.04033.047.0FRFrance
1120222332343018950.027910.03528.042.0FRFrance
1220222231895115099.022803.02923.035.0FRFrance
1320222131363210251.017013.02116.026.0FRFrance
1420222031978715756.023818.03024.036.0FRFrance
1520221931788414079.021689.02721.033.0FRFrance
1620221833035325089.035617.04638.054.0FRFrance
1720221733600630373.041639.05446.062.0FRFrance
1820221634994942836.057062.07564.086.0FRFrance
19202215310080690824.0110788.0152137.0167.0FRFrance
202022143155441143891.0166991.0234217.0251.0FRFrance
212022133191914179558.0204270.0289270.0308.0FRFrance
222022123166224155035.0177413.0251234.0268.0FRFrance
232022113122849113306.0132392.0185171.0199.0FRFrance
2420221038790479741.096067.0133121.0145.0FRFrance
2520220935018243958.056406.07667.085.0FRFrance
2620220833096325942.035984.04739.055.0FRFrance
2720220733488229446.040318.05345.061.0FRFrance
2820220634662340398.052848.07061.079.0FRFrance
2920220536297056043.069897.09585.0105.0FRFrance
.................................
194419852132609619621.032571.04735.059.0FRFrance
194519852032789620885.034907.05138.064.0FRFrance
194619851934315432821.053487.07859.097.0FRFrance
194719851834055529935.051175.07455.093.0FRFrance
194819851733405324366.043740.06244.080.0FRFrance
194919851635036236451.064273.09166.0116.0FRFrance
195019851536388145538.082224.011683.0149.0FRFrance
19511985143134545114400.0154690.0244207.0281.0FRFrance
19521985133197206176080.0218332.0357319.0395.0FRFrance
19531985123245240223304.0267176.0445405.0485.0FRFrance
19541985113276205252399.0300011.0501458.0544.0FRFrance
19551985103353231326279.0380183.0640591.0689.0FRFrance
19561985093369895341109.0398681.0670618.0722.0FRFrance
19571985083389886359529.0420243.0707652.0762.0FRFrance
19581985073471852432599.0511105.0855784.0926.0FRFrance
19591985063565825518011.0613639.01026939.01113.0FRFrance
19601985053637302592795.0681809.011551074.01236.0FRFrance
19611985043424937390794.0459080.0770708.0832.0FRFrance
19621985033213901174689.0253113.0388317.0459.0FRFrance
196319850239758680949.0114223.0177147.0207.0FRFrance
196419850138548965918.0105060.0155120.0190.0FRFrance
196519845238483060602.0109058.0154110.0198.0FRFrance
1966198451310172680242.0123210.0185146.0224.0FRFrance
19671984503123680101401.0145959.0225184.0266.0FRFrance
1968198449310107381684.0120462.0184149.0219.0FRFrance
196919844837862060634.096606.0143110.0176.0FRFrance
197019844737202954274.089784.013199.0163.0FRFrance
197119844638733067686.0106974.0159123.0195.0FRFrance
19721984453135223101414.0169032.0246184.0308.0FRFrance
197319844436842220056.0116788.012537.0213.0FRFrance
\n", "

1973 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202234 3 14202 9453.0 18951.0 21 14.0 \n", "1 202233 3 13051 8947.0 17155.0 20 14.0 \n", "2 202232 3 22257 16158.0 28356.0 34 25.0 \n", "3 202231 3 21828 16268.0 27388.0 33 25.0 \n", "4 202230 3 19663 14779.0 24547.0 30 23.0 \n", "5 202229 3 24268 18906.0 29630.0 37 29.0 \n", "6 202228 3 24845 19214.0 30476.0 37 29.0 \n", "7 202227 3 40745 33994.0 47496.0 61 51.0 \n", "8 202226 3 34010 28521.0 39499.0 51 43.0 \n", "9 202225 3 23377 19042.0 27712.0 35 28.0 \n", "10 202224 3 26328 21829.0 30827.0 40 33.0 \n", "11 202223 3 23430 18950.0 27910.0 35 28.0 \n", "12 202222 3 18951 15099.0 22803.0 29 23.0 \n", "13 202221 3 13632 10251.0 17013.0 21 16.0 \n", "14 202220 3 19787 15756.0 23818.0 30 24.0 \n", "15 202219 3 17884 14079.0 21689.0 27 21.0 \n", "16 202218 3 30353 25089.0 35617.0 46 38.0 \n", "17 202217 3 36006 30373.0 41639.0 54 46.0 \n", "18 202216 3 49949 42836.0 57062.0 75 64.0 \n", "19 202215 3 100806 90824.0 110788.0 152 137.0 \n", "20 202214 3 155441 143891.0 166991.0 234 217.0 \n", "21 202213 3 191914 179558.0 204270.0 289 270.0 \n", "22 202212 3 166224 155035.0 177413.0 251 234.0 \n", "23 202211 3 122849 113306.0 132392.0 185 171.0 \n", "24 202210 3 87904 79741.0 96067.0 133 121.0 \n", "25 202209 3 50182 43958.0 56406.0 76 67.0 \n", "26 202208 3 30963 25942.0 35984.0 47 39.0 \n", "27 202207 3 34882 29446.0 40318.0 53 45.0 \n", "28 202206 3 46623 40398.0 52848.0 70 61.0 \n", "29 202205 3 62970 56043.0 69897.0 95 85.0 \n", "... ... ... ... ... ... ... ... \n", "1944 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1945 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1946 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1947 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1948 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1949 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1950 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1951 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1952 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1953 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1954 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1955 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1956 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1957 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1958 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1959 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1960 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1961 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1962 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1963 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1964 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1965 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1966 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1967 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1968 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1969 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1970 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1971 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1972 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1973 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 28.0 FR France \n", "1 26.0 FR France \n", "2 43.0 FR France \n", "3 41.0 FR France \n", "4 37.0 FR France \n", "5 45.0 FR France \n", "6 45.0 FR France \n", "7 71.0 FR France \n", "8 59.0 FR France \n", "9 42.0 FR France \n", "10 47.0 FR France \n", "11 42.0 FR France \n", "12 35.0 FR France \n", "13 26.0 FR France \n", "14 36.0 FR France \n", "15 33.0 FR France \n", "16 54.0 FR France \n", "17 62.0 FR France \n", "18 86.0 FR France \n", "19 167.0 FR France \n", "20 251.0 FR France \n", "21 308.0 FR France \n", "22 268.0 FR France \n", "23 199.0 FR France \n", "24 145.0 FR France \n", "25 85.0 FR France \n", "26 55.0 FR France \n", "27 61.0 FR France \n", "28 79.0 FR France \n", "29 105.0 FR France \n", "... ... ... ... \n", "1944 59.0 FR France \n", "1945 64.0 FR France \n", "1946 97.0 FR France \n", "1947 93.0 FR France \n", "1948 80.0 FR France \n", "1949 116.0 FR France \n", "1950 149.0 FR France \n", "1951 281.0 FR France \n", "1952 395.0 FR France \n", "1953 485.0 FR France \n", "1954 544.0 FR France \n", "1955 689.0 FR France \n", "1956 722.0 FR France \n", "1957 762.0 FR France \n", "1958 926.0 FR France \n", "1959 1113.0 FR France \n", "1960 1236.0 FR France \n", "1961 832.0 FR France \n", "1962 459.0 FR France \n", "1963 207.0 FR France \n", "1964 190.0 FR France \n", "1965 198.0 FR France \n", "1966 224.0 FR France \n", "1967 266.0 FR France \n", "1968 219.0 FR France \n", "1969 176.0 FR France \n", "1970 163.0 FR France \n", "1971 195.0 FR France \n", "1972 308.0 FR France \n", "1973 213.0 FR France \n", "\n", "[1973 rows x 10 columns]" ] }, "execution_count": 66, "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": 67, "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": 68, "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": 69, "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": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 70, "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": 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'][-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": 52, "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": 53, "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": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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nXBs9FWK1VdTfy2bSjH9jDig1VtQ51zxMhVjtFPX3spk049+YvwK4Dop0vynfw6l1FOn3spk089+Y11DsGF2Hjhz3Hk7+9Go2ckX8G/Maig2Lp0LMaquZ/8YcUFrQYIuB3hZpVlvN+jfmKa8W1Gr3ITKzkal2ysuL8i2kmRcD66Hr0BG+8+Qr3H/NBU0xPWGWNU95tRBfezAyzbjN0yxLHqG0kGZeDKwlj+zMquMRSotp1sXAWvLIzqw6HqG0GH8H+9B5ZGdWHQcUsyr4qnKzwXnbsJmZDchXypuZWV05oJiZWSYcUMzMLBMOKGZNqhm/wKlIWrH/HVDMmpSv7G+sVux/7/IyazL+vvjGyrL/83L/OO/yMsupWk+F+Mr+xsqy/4s2yvGFjWZ1Vv4mUYuvD/CV/Y2VRf8X9f5xDihmdVLPNwlf2d9YI+3/l2+fe9yvCc4zr6GY1UkRv0s8T/KynlAvd6zbyRPb9nLiCaP4zSdHG/qFeF5DMcsZT0WNTNHWE0aqiHcG9wjFrI5ufKyDSePGHjMVUn4HaPs871prvGpHKA4oZpZrnipsPE95mVlT8FRhcTigmFnuFXE9oV7ydIsXT3mZmRXYinU7+eG2vTXdBZbZlJekMyW9KOlNSbsk3ZrSJ0jaJKkzPY8vK7Nc0h5JuyXNL0ufLWlnem2VJKX0MZKeSulbJc0sK7M4naNT0uKy9Fkpb2cqe2K1nWNmVnTnrNjAzGXP8vjWvUSUrmuauexZzlmxoWFtqmbKqxf404j4MnARcIukc4FlwOaIOBvYnH4mvbYIOA9YAHxX0gmprgeAJcDZ6bEgpd8AHIyILwH3AfemuiYAdwIXAnOAO8sC173Afen8B1MdZmYtIY+32Bk0oETE/oj4h3R8GHgTmAYsBFanbKuBK9PxQmBNRPRExFvAHmCOpCnAKRGxJUrzbI9WlOmr62ngsjR6mQ9siojuiDgIbAIWpNcuTXkrz29m1vTyuFlhSLdeSVNRFwBbgTMiYj+Ugo6kySnbNOBnZcX2pbSP03Flel+Zd1JdvZI+BCaWp1eUmQh8EBG9/dRlZtYS8naLnaoDiqQvAH8D/ElEHErLH/1m7SctBkgfTpmB6jq2MdISStNszJgxo78sZmaFVH5R7Morv9LAlpRUtW1Y0mhKweSHEfGjlPxemsYiPXel9H3AmWXFpwPvpvTp/aQfU0ZSG3Aq0D1AXe8Dp6W8lXUdIyIeioj2iGifNGlSNf9cMzMbhmp2eQl4GHgzIv6i7KX1QN+uq8XAM2Xpi9LOrVmUFt+3pemxw5IuSnVeX1Gmr66rgBfSOstGYJ6k8Wkxfh6wMb32YspbeX4zM2uAaqa8LgauA3ZKejWl/RfgHmCtpBuAvcDVABGxS9Ja4A1KO8RuiYhPUrmbgB8AJwEb0gNKAesxSXsojUwWpbq6Jd0NbE/57oqI7nS8FFgjaSXwSqrDzMwaxBc2Ws202u3GzZqV7+VlDddqtxs3a3X+xkbLXFG/vtTMRsYjFMtcHq/gNbPac0CxzOXxCl4zqz1PeVlN5O0KXjOrPe/yMjOzAXmXVwvL0xfumFnrcEBpQt6ua2aN4DWUJuLtumbWSB6hNBFv1zUbnKeEa8cBpYl4u67Z4DwlXDue8moy3q5r1j9PCdeetw2bWUvoOnSElc+9yU92/YojHx9l7OhRzD/vi9zx9S97FD8Ibxs2MyvjKeHa85SXmbUMTwnXlqe8zMxsQJ7yMjOzunJAMTOzTDigmJlZJhxQzMwsEw4oZmaWCQcUMzPLhAOKmZllwgHFzMwy4YBiZmaZcEAxM7NMOKCYmVkmHFDMzCwTDihmZpYJBxQzM8uEA4qZmWXCAcXMzDLhgGJmZplwQDEzs0w4oJiZWSYGDSiSHpHUJen1srQJkjZJ6kzP48teWy5pj6TdkuaXpc+WtDO9tkqSUvoYSU+l9K2SZpaVWZzO0SlpcVn6rJS3M5U9ceRdYWZmI1HNCOUHwIKKtGXA5og4G9icfkbSucAi4LxU5ruSTkhlHgCWAGenR1+dNwAHI+JLwH3AvamuCcCdwIXAHODOssB1L3BfOv/BVIeZmTXQoAElIn4KdFckLwRWp+PVwJVl6Wsioici3gL2AHMkTQFOiYgtERHAoxVl+up6GrgsjV7mA5siojsiDgKbgAXptUtT3srzm5lZgwx3DeWMiNgPkJ4np/RpwDtl+faltGnpuDL9mDIR0Qt8CEwcoK6JwAcpb2VdnyNpiaQOSR0HDhwY4j/TzKzYug4d4VsPbqHr8JGanyvrRXn1kxYDpA+nzEB1ff6FiIcioj0i2idNmnS8bGZmTWnV5k62v93Nquc7a36utmGWe0/SlIjYn6azulL6PuDMsnzTgXdT+vR+0svL7JPUBpxKaYptH3BJRZmXgPeB0yS1pVFKeV1mZgacs2IDPb1HP/358a17eXzrXsa0jWL3yq/V5JzDHaGsB/p2XS0GnilLX5R2bs2itPi+LU2LHZZ0UVoDub6iTF9dVwEvpHWWjcA8SePTYvw8YGN67cWUt/L8ZmYGvHz7XK44fypjR5fe5seOHsXC86fy8tK5NTvnoCMUSU9SGimcLmkfpZ1X9wBrJd0A7AWuBoiIXZLWAm8AvcAtEfFJquomSjvGTgI2pAfAw8BjkvZQGpksSnV1S7ob2J7y3RURfZsDlgJrJK0EXkl1mJlZMvmUsYwb00ZP71HGtI2ip/co48a0MXnc2JqdU6UP/K2hvb09Ojo6Gt0MM7O6uPGxDiaNG8s1c2bwxLa9HDh8hAevax9yPZJ2RMSgBR1QzMxsQNUGFN96xczMMuGAYmZmmXBAMTOzTDigmJlZJhxQzMwsEw4oZmaWiZbaNizpAPBP/bx0OqVbuhRFkdpbpLZCsdpbpLZCsdpbpLZC7dt7VkQMejPElgooxyOpo5o91nlRpPYWqa1QrPYWqa1QrPYWqa2Qn/Z6ysvMzDLhgGJmZplwQCl5qNENGKIitbdIbYVitbdIbYVitbdIbYWctNdrKGZmlgmPUMzMLBNNG1AkPSKpS9LrZWn/StIWSTsl/W9Jp6T00ZJWp/Q3JS0vK/OSpN2SXk2PyQ1u64mSvp/SX5N0SVmZ2Sl9j6RV6cvMMpdhe+vRt2dKejH9v+6SdGtKnyBpk6TO9Dy+rMzy1Ie7Jc0vS69p/2bc1tz1raSJKf9Hku6vqCtXfTtIW/PYt5dL2pH6cIekS8vqqsv7AgAR0ZQP4N8Avwu8Xpa2Hfi36fiPgbvT8TXAmnT8W8DbwMz080tAe47aegvw/XQ8GdgBjEo/bwN+DxClLzD7Ws7bW4++nQL8bjoeB/wjcC7w58CylL4MuDcdnwu8BowBZgG/BE6oR/9m3NY89u3JwO8D3wbur6grb307UFvz2LcXAFPT8VeAf65X35Y/mnaEEhE/pfQNkOXOAX6ajjcBf9iXHThZpe+0Pwn4DXCoHu2EIbf1XGBzKtcFfAC0S5oCnBIRW6L0W/QocGVe21uLdvUnIvZHxD+k48PAm8A0YCGwOmVbzWd9tZDSh4ueiHgL2APMqUf/ZtXWLNuUZXsj4tcR8XfAkfJ68ti3x2trvQyjva9ExLspfRcwVqWvYq/b+wI08ZTXcbwOXJGOrwbOTMdPA78G9lP6SuP/GZ993TDA99PQ9r/WdLhYXVtfAxZKapM0C5idXpsG7Csrvy+l1ctQ29unbn0raSalT3JbgTMiYj+U/ngpjZ6g1GfvlBXr68e69u8I29onb317PHns28HkuW//EHglInqoc9+2WkD5Y+AWSTsoDSN/k9LnAJ8AUylNHfyppH+RXrs2Ir4K/Ov0uK7BbX2E0i9FB/CXwP8BeikNZyvVcwvfUNsLdexbSV8A/gb4k4gYaPR5vH6sW/9m0FbIZ98et4p+0hrdtwPJbd9KOg+4F7ixL6mfbDV7X2ipgBIRv4iIeRExG3iS0pwzlNZQfhwRH6dpmb8nTctExD+n58PAE9RpSuF4bY2I3oj4TxFxfkQsBE4DOim9aU8vq2I68G5lvTlqb936VtJoSn+UP4yIH6Xk99J0QN+US1dK38exI6i+fqxL/2bU1rz27fHksW+PK699K2k6sA64PiL63tvq+r7QUgGlbzeGpFHACuB76aW9wKUqORm4CPhFmqY5PZUZDXyD0tROw9oq6bdSG5F0OdAbEW+k4e9hSRelIfj1wDP1aOtw2luvvk198TDwZkT8RdlL64HF6Xgxn/XVemBRmn+eBZwNbKtH/2bV1hz3bb9y2rfHqyeXfSvpNOBZYHlE/H1f5rq/L2S9yp+XB6VPyfuBjylF6RuAWyntlvhH4B4+u7DzC8BfU1rMegP4z/HZTo8dwM/Ta/+LtIumgW2dCeymtEj3PKW7gPbV007pl/uXwP19ZfLY3jr27e9TGuL/HHg1Pf49MJHSZoHO9DyhrMwdqQ93U7Yjptb9m1Vbc963b1Pa0PFR+t05N8d9+7m25rVvKX2I+3VZ3leByfXo2/KHr5Q3M7NMtNSUl5mZ1Y4DipmZZcIBxczMMuGAYmZmmXBAMTOzTDigmJlZJhxQzMwsEw4oZmaWif8P5AS727RchlQAAAAASUVORK5CYII=\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": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2021 938731\n", "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2053781\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": 56, "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": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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