{ "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(raw_data)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "got here\n" ] }, { "data": { "text/html": [ "
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1970 rows × 11 columns

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267176.0 445 \n", "1950 1950 198511 3 276205 252399.0 300011.0 501 \n", "1951 1951 198510 3 353231 326279.0 380183.0 640 \n", "1952 1952 198509 3 369895 341109.0 398681.0 670 \n", "1953 1953 198508 3 389886 359529.0 420243.0 707 \n", "1954 1954 198507 3 471852 432599.0 511105.0 855 \n", "1955 1955 198506 3 565825 518011.0 613639.0 1026 \n", "1956 1956 198505 3 637302 592795.0 681809.0 1155 \n", "1957 1957 198504 3 424937 390794.0 459080.0 770 \n", "1958 1958 198503 3 213901 174689.0 253113.0 388 \n", "1959 1959 198502 3 97586 80949.0 114223.0 177 \n", "1960 1960 198501 3 85489 65918.0 105060.0 155 \n", "1961 1961 198452 3 84830 60602.0 109058.0 154 \n", "1962 1962 198451 3 101726 80242.0 123210.0 185 \n", "1963 1963 198450 3 123680 101401.0 145959.0 225 \n", "1964 1964 198449 3 101073 81684.0 120462.0 184 \n", "1965 1965 198448 3 78620 60634.0 96606.0 143 \n", "1966 1966 198447 3 72029 54274.0 89784.0 131 \n", "1967 1967 198446 3 87330 67686.0 106974.0 159 \n", "1968 1968 198445 3 135223 101414.0 169032.0 246 \n", "1969 1969 198444 3 68422 20056.0 116788.0 125 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 23.0 39.0 FR France \n", "1 29.0 45.0 FR France \n", "2 29.0 45.0 FR France \n", "3 51.0 71.0 FR France \n", "4 43.0 59.0 FR France \n", "5 28.0 42.0 FR France \n", "6 33.0 47.0 FR France \n", "7 28.0 42.0 FR France \n", "8 23.0 35.0 FR France \n", "9 16.0 26.0 FR France \n", "10 24.0 36.0 FR France \n", "11 21.0 33.0 FR France \n", "12 38.0 54.0 FR France \n", "13 46.0 62.0 FR France \n", "14 64.0 86.0 FR France \n", "15 137.0 167.0 FR France \n", "16 217.0 251.0 FR France \n", "17 270.0 308.0 FR France \n", "18 234.0 268.0 FR France \n", "19 171.0 199.0 FR France \n", "20 121.0 145.0 FR France \n", "21 67.0 85.0 FR France \n", "22 39.0 55.0 FR France \n", "23 45.0 61.0 FR France \n", "24 61.0 79.0 FR France \n", "25 85.0 105.0 FR France \n", "26 98.0 120.0 FR France \n", "27 102.0 124.0 FR France \n", "28 74.0 94.0 FR France \n", "29 77.0 97.0 FR France \n", "... ... ... ... ... \n", "1940 35.0 59.0 FR France \n", "1941 38.0 64.0 FR France \n", "1942 59.0 97.0 FR France \n", "1943 55.0 93.0 FR France \n", "1944 44.0 80.0 FR France \n", "1945 66.0 116.0 FR France \n", "1946 83.0 149.0 FR France \n", "1947 207.0 281.0 FR France \n", "1948 319.0 395.0 FR France \n", "1949 405.0 485.0 FR France \n", "1950 458.0 544.0 FR France \n", "1951 591.0 689.0 FR France \n", "1952 618.0 722.0 FR France \n", "1953 652.0 762.0 FR France \n", "1954 784.0 926.0 FR France \n", "1955 939.0 1113.0 FR France \n", "1956 1074.0 1236.0 FR France \n", "1957 708.0 832.0 FR France \n", "1958 317.0 459.0 FR France \n", "1959 147.0 207.0 FR France \n", "1960 120.0 190.0 FR France \n", "1961 110.0 198.0 FR France \n", "1962 146.0 224.0 FR France \n", "1963 184.0 266.0 FR France \n", "1964 149.0 219.0 FR France \n", "1965 110.0 176.0 FR France \n", "1966 99.0 163.0 FR France \n", "1967 123.0 195.0 FR France \n", "1968 184.0 308.0 FR France \n", "1969 37.0 213.0 FR France \n", "\n", "[1970 rows x 11 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from os.path import exists\n", "\n", "if not exists(\"data_csv.csv\"):\n", " raw_data = pd.read_csv(data_url, skiprows=1)\n", " raw_data.to_csv(\"data_csv.csv\")\n", "else:\n", " raw_data = pd.read_csv(\"data_csv.csv\")\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": 4, "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
173319891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1733 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1733 FR France " ] }, "execution_count": 4, "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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020223032046815330.025606.03123.039.0FRFrance
120222932427918914.029644.03729.045.0FRFrance
220222832484519214.030476.03729.045.0FRFrance
320222734074533994.047496.06151.071.0FRFrance
420222633401028521.039499.05143.059.0FRFrance
520222532337719042.027712.03528.042.0FRFrance
620222432632821829.030827.04033.047.0FRFrance
720222332343018950.027910.03528.042.0FRFrance
820222231895115099.022803.02923.035.0FRFrance
920222131363210251.017013.02116.026.0FRFrance
1020222031978715756.023818.03024.036.0FRFrance
1120221931788414079.021689.02721.033.0FRFrance
1220221833035325089.035617.04638.054.0FRFrance
1320221733600630373.041639.05446.062.0FRFrance
1420221634994942836.057062.07564.086.0FRFrance
15202215310080690824.0110788.0152137.0167.0FRFrance
162022143155441143891.0166991.0234217.0251.0FRFrance
172022133191914179558.0204270.0289270.0308.0FRFrance
182022123166224155035.0177413.0251234.0268.0FRFrance
192022113122849113306.0132392.0185171.0199.0FRFrance
2020221038790479741.096067.0133121.0145.0FRFrance
2120220935018243958.056406.07667.085.0FRFrance
2220220833096325942.035984.04739.055.0FRFrance
2320220733488229446.040318.05345.061.0FRFrance
2420220634662340398.052848.07061.079.0FRFrance
2520220536297056043.069897.09585.0105.0FRFrance
2620220437220964804.079614.010998.0120.0FRFrance
2720220337461367144.082082.0113102.0124.0FRFrance
2820220235592049511.062329.08474.094.0FRFrance
2920220135762950699.064559.08777.097.0FRFrance
.................................
194019852132609619621.032571.04735.059.0FRFrance
194119852032789620885.034907.05138.064.0FRFrance
194219851934315432821.053487.07859.097.0FRFrance
194319851834055529935.051175.07455.093.0FRFrance
194419851733405324366.043740.06244.080.0FRFrance
194519851635036236451.064273.09166.0116.0FRFrance
194619851536388145538.082224.011683.0149.0FRFrance
19471985143134545114400.0154690.0244207.0281.0FRFrance
19481985133197206176080.0218332.0357319.0395.0FRFrance
19491985123245240223304.0267176.0445405.0485.0FRFrance
19501985113276205252399.0300011.0501458.0544.0FRFrance
19511985103353231326279.0380183.0640591.0689.0FRFrance
19521985093369895341109.0398681.0670618.0722.0FRFrance
19531985083389886359529.0420243.0707652.0762.0FRFrance
19541985073471852432599.0511105.0855784.0926.0FRFrance
19551985063565825518011.0613639.01026939.01113.0FRFrance
19561985053637302592795.0681809.011551074.01236.0FRFrance
19571985043424937390794.0459080.0770708.0832.0FRFrance
19581985033213901174689.0253113.0388317.0459.0FRFrance
195919850239758680949.0114223.0177147.0207.0FRFrance
196019850138548965918.0105060.0155120.0190.0FRFrance
196119845238483060602.0109058.0154110.0198.0FRFrance
1962198451310172680242.0123210.0185146.0224.0FRFrance
19631984503123680101401.0145959.0225184.0266.0FRFrance
1964198449310107381684.0120462.0184149.0219.0FRFrance
196519844837862060634.096606.0143110.0176.0FRFrance
196619844737202954274.089784.013199.0163.0FRFrance
196719844638733067686.0106974.0159123.0195.0FRFrance
19681984453135223101414.0169032.0246184.0308.0FRFrance
196919844436842220056.0116788.012537.0213.0FRFrance
\n", "

1969 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202230 3 20468 15330.0 25606.0 31 23.0 \n", "1 202229 3 24279 18914.0 29644.0 37 29.0 \n", "2 202228 3 24845 19214.0 30476.0 37 29.0 \n", "3 202227 3 40745 33994.0 47496.0 61 51.0 \n", "4 202226 3 34010 28521.0 39499.0 51 43.0 \n", "5 202225 3 23377 19042.0 27712.0 35 28.0 \n", "6 202224 3 26328 21829.0 30827.0 40 33.0 \n", "7 202223 3 23430 18950.0 27910.0 35 28.0 \n", "8 202222 3 18951 15099.0 22803.0 29 23.0 \n", "9 202221 3 13632 10251.0 17013.0 21 16.0 \n", "10 202220 3 19787 15756.0 23818.0 30 24.0 \n", "11 202219 3 17884 14079.0 21689.0 27 21.0 \n", "12 202218 3 30353 25089.0 35617.0 46 38.0 \n", "13 202217 3 36006 30373.0 41639.0 54 46.0 \n", "14 202216 3 49949 42836.0 57062.0 75 64.0 \n", "15 202215 3 100806 90824.0 110788.0 152 137.0 \n", "16 202214 3 155441 143891.0 166991.0 234 217.0 \n", "17 202213 3 191914 179558.0 204270.0 289 270.0 \n", "18 202212 3 166224 155035.0 177413.0 251 234.0 \n", "19 202211 3 122849 113306.0 132392.0 185 171.0 \n", "20 202210 3 87904 79741.0 96067.0 133 121.0 \n", "21 202209 3 50182 43958.0 56406.0 76 67.0 \n", "22 202208 3 30963 25942.0 35984.0 47 39.0 \n", "23 202207 3 34882 29446.0 40318.0 53 45.0 \n", "24 202206 3 46623 40398.0 52848.0 70 61.0 \n", "25 202205 3 62970 56043.0 69897.0 95 85.0 \n", "26 202204 3 72209 64804.0 79614.0 109 98.0 \n", "27 202203 3 74613 67144.0 82082.0 113 102.0 \n", "28 202202 3 55920 49511.0 62329.0 84 74.0 \n", "29 202201 3 57629 50699.0 64559.0 87 77.0 \n", "... ... ... ... ... ... ... ... \n", "1940 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1941 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1942 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1943 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1944 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1945 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1946 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1947 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1948 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1949 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1950 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1951 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1952 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1953 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1954 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1955 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1956 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1957 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1958 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1959 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1960 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1961 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1962 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1963 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1964 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1965 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1966 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1967 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1968 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1969 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 39.0 FR France \n", "1 45.0 FR France \n", "2 45.0 FR France \n", "3 71.0 FR France \n", "4 59.0 FR France \n", "5 42.0 FR France \n", "6 47.0 FR France \n", "7 42.0 FR France \n", "8 35.0 FR France \n", "9 26.0 FR France \n", "10 36.0 FR France \n", "11 33.0 FR France \n", "12 54.0 FR France \n", "13 62.0 FR France \n", "14 86.0 FR France \n", "15 167.0 FR France \n", "16 251.0 FR France \n", "17 308.0 FR France \n", "18 268.0 FR France \n", "19 199.0 FR France \n", "20 145.0 FR France \n", "21 85.0 FR France \n", "22 55.0 FR France \n", "23 61.0 FR France \n", "24 79.0 FR France \n", "25 105.0 FR France \n", "26 120.0 FR France \n", "27 124.0 FR France \n", "28 94.0 FR France \n", "29 97.0 FR France \n", "... ... ... ... \n", "1940 59.0 FR France \n", "1941 64.0 FR France \n", "1942 97.0 FR France \n", "1943 93.0 FR France \n", "1944 80.0 FR France \n", "1945 116.0 FR France \n", "1946 149.0 FR France \n", "1947 281.0 FR France \n", "1948 395.0 FR France \n", "1949 485.0 FR France \n", "1950 544.0 FR France \n", "1951 689.0 FR France \n", "1952 722.0 FR France \n", "1953 762.0 FR France \n", "1954 926.0 FR France \n", "1955 1113.0 FR France \n", "1956 1236.0 FR France \n", "1957 832.0 FR France \n", "1958 459.0 FR France \n", "1959 207.0 FR France \n", "1960 190.0 FR France \n", "1961 198.0 FR France \n", "1962 224.0 FR France \n", "1963 266.0 FR France \n", "1964 219.0 FR France \n", "1965 176.0 FR France \n", "1966 163.0 FR France \n", "1967 195.0 FR France \n", "1968 308.0 FR France \n", "1969 213.0 FR France \n", "\n", "[1969 rows x 10 columns]" ] }, "execution_count": 5, "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": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
020223032046815330.025606.03123.039.0FRFrance2022-07-25/2022-07-31
120222932427918914.029644.03729.045.0FRFrance2022-07-18/2022-07-24
220222832484519214.030476.03729.045.0FRFrance2022-07-11/2022-07-17
320222734074533994.047496.06151.071.0FRFrance2022-07-04/2022-07-10
420222633401028521.039499.05143.059.0FRFrance2022-06-27/2022-07-03
520222532337719042.027712.03528.042.0FRFrance2022-06-20/2022-06-26
620222432632821829.030827.04033.047.0FRFrance2022-06-13/2022-06-19
720222332343018950.027910.03528.042.0FRFrance2022-06-06/2022-06-12
820222231895115099.022803.02923.035.0FRFrance2022-05-30/2022-06-05
920222131363210251.017013.02116.026.0FRFrance2022-05-23/2022-05-29
1020222031978715756.023818.03024.036.0FRFrance2022-05-16/2022-05-22
1120221931788414079.021689.02721.033.0FRFrance2022-05-09/2022-05-15
1220221833035325089.035617.04638.054.0FRFrance2022-05-02/2022-05-08
1320221733600630373.041639.05446.062.0FRFrance2022-04-25/2022-05-01
1420221634994942836.057062.07564.086.0FRFrance2022-04-18/2022-04-24
15202215310080690824.0110788.0152137.0167.0FRFrance2022-04-11/2022-04-17
162022143155441143891.0166991.0234217.0251.0FRFrance2022-04-04/2022-04-10
172022133191914179558.0204270.0289270.0308.0FRFrance2022-03-28/2022-04-03
182022123166224155035.0177413.0251234.0268.0FRFrance2022-03-21/2022-03-27
192022113122849113306.0132392.0185171.0199.0FRFrance2022-03-14/2022-03-20
2020221038790479741.096067.0133121.0145.0FRFrance2022-03-07/2022-03-13
2120220935018243958.056406.07667.085.0FRFrance2022-02-28/2022-03-06
2220220833096325942.035984.04739.055.0FRFrance2022-02-21/2022-02-27
2320220733488229446.040318.05345.061.0FRFrance2022-02-14/2022-02-20
2420220634662340398.052848.07061.079.0FRFrance2022-02-07/2022-02-13
2520220536297056043.069897.09585.0105.0FRFrance2022-01-31/2022-02-06
2620220437220964804.079614.010998.0120.0FRFrance2022-01-24/2022-01-30
2720220337461367144.082082.0113102.0124.0FRFrance2022-01-17/2022-01-23
2820220235592049511.062329.08474.094.0FRFrance2022-01-10/2022-01-16
2920220135762950699.064559.08777.097.0FRFrance2022-01-03/2022-01-09
....................................
194019852132609619621.032571.04735.059.0FRFrance1985-05-20/1985-05-26
194119852032789620885.034907.05138.064.0FRFrance1985-05-13/1985-05-19
194219851934315432821.053487.07859.097.0FRFrance1985-05-06/1985-05-12
194319851834055529935.051175.07455.093.0FRFrance1985-04-29/1985-05-05
194419851733405324366.043740.06244.080.0FRFrance1985-04-22/1985-04-28
194519851635036236451.064273.09166.0116.0FRFrance1985-04-15/1985-04-21
194619851536388145538.082224.011683.0149.0FRFrance1985-04-08/1985-04-14
19471985143134545114400.0154690.0244207.0281.0FRFrance1985-04-01/1985-04-07
19481985133197206176080.0218332.0357319.0395.0FRFrance1985-03-25/1985-03-31
19491985123245240223304.0267176.0445405.0485.0FRFrance1985-03-18/1985-03-24
19501985113276205252399.0300011.0501458.0544.0FRFrance1985-03-11/1985-03-17
19511985103353231326279.0380183.0640591.0689.0FRFrance1985-03-04/1985-03-10
19521985093369895341109.0398681.0670618.0722.0FRFrance1985-02-25/1985-03-03
19531985083389886359529.0420243.0707652.0762.0FRFrance1985-02-18/1985-02-24
19541985073471852432599.0511105.0855784.0926.0FRFrance1985-02-11/1985-02-17
19551985063565825518011.0613639.01026939.01113.0FRFrance1985-02-04/1985-02-10
19561985053637302592795.0681809.011551074.01236.0FRFrance1985-01-28/1985-02-03
19571985043424937390794.0459080.0770708.0832.0FRFrance1985-01-21/1985-01-27
19581985033213901174689.0253113.0388317.0459.0FRFrance1985-01-14/1985-01-20
195919850239758680949.0114223.0177147.0207.0FRFrance1985-01-07/1985-01-13
196019850138548965918.0105060.0155120.0190.0FRFrance1984-12-31/1985-01-06
196119845238483060602.0109058.0154110.0198.0FRFrance1984-12-24/1984-12-30
1962198451310172680242.0123210.0185146.0224.0FRFrance1984-12-17/1984-12-23
19631984503123680101401.0145959.0225184.0266.0FRFrance1984-12-10/1984-12-16
1964198449310107381684.0120462.0184149.0219.0FRFrance1984-12-03/1984-12-09
196519844837862060634.096606.0143110.0176.0FRFrance1984-11-26/1984-12-02
196619844737202954274.089784.013199.0163.0FRFrance1984-11-19/1984-11-25
196719844638733067686.0106974.0159123.0195.0FRFrance1984-11-12/1984-11-18
19681984453135223101414.0169032.0246184.0308.0FRFrance1984-11-05/1984-11-11
196919844436842220056.0116788.012537.0213.0FRFrance1984-10-29/1984-11-04
\n", "

1969 rows × 11 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202230 3 20468 15330.0 25606.0 31 23.0 \n", "1 202229 3 24279 18914.0 29644.0 37 29.0 \n", "2 202228 3 24845 19214.0 30476.0 37 29.0 \n", "3 202227 3 40745 33994.0 47496.0 61 51.0 \n", "4 202226 3 34010 28521.0 39499.0 51 43.0 \n", "5 202225 3 23377 19042.0 27712.0 35 28.0 \n", "6 202224 3 26328 21829.0 30827.0 40 33.0 \n", "7 202223 3 23430 18950.0 27910.0 35 28.0 \n", "8 202222 3 18951 15099.0 22803.0 29 23.0 \n", "9 202221 3 13632 10251.0 17013.0 21 16.0 \n", "10 202220 3 19787 15756.0 23818.0 30 24.0 \n", "11 202219 3 17884 14079.0 21689.0 27 21.0 \n", "12 202218 3 30353 25089.0 35617.0 46 38.0 \n", "13 202217 3 36006 30373.0 41639.0 54 46.0 \n", "14 202216 3 49949 42836.0 57062.0 75 64.0 \n", "15 202215 3 100806 90824.0 110788.0 152 137.0 \n", "16 202214 3 155441 143891.0 166991.0 234 217.0 \n", "17 202213 3 191914 179558.0 204270.0 289 270.0 \n", "18 202212 3 166224 155035.0 177413.0 251 234.0 \n", "19 202211 3 122849 113306.0 132392.0 185 171.0 \n", "20 202210 3 87904 79741.0 96067.0 133 121.0 \n", "21 202209 3 50182 43958.0 56406.0 76 67.0 \n", "22 202208 3 30963 25942.0 35984.0 47 39.0 \n", "23 202207 3 34882 29446.0 40318.0 53 45.0 \n", "24 202206 3 46623 40398.0 52848.0 70 61.0 \n", "25 202205 3 62970 56043.0 69897.0 95 85.0 \n", "26 202204 3 72209 64804.0 79614.0 109 98.0 \n", "27 202203 3 74613 67144.0 82082.0 113 102.0 \n", "28 202202 3 55920 49511.0 62329.0 84 74.0 \n", "29 202201 3 57629 50699.0 64559.0 87 77.0 \n", "... ... ... ... ... ... ... ... \n", "1940 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1941 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1942 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1943 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1944 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1945 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1946 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1947 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1948 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1949 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1950 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1951 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1952 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1953 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1954 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1955 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1956 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1957 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1958 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1959 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1960 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1961 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1962 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1963 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1964 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1965 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1966 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1967 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1968 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1969 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name period \n", "0 39.0 FR France 2022-07-25/2022-07-31 \n", "1 45.0 FR France 2022-07-18/2022-07-24 \n", "2 45.0 FR France 2022-07-11/2022-07-17 \n", "3 71.0 FR France 2022-07-04/2022-07-10 \n", "4 59.0 FR France 2022-06-27/2022-07-03 \n", "5 42.0 FR France 2022-06-20/2022-06-26 \n", "6 47.0 FR France 2022-06-13/2022-06-19 \n", "7 42.0 FR France 2022-06-06/2022-06-12 \n", "8 35.0 FR France 2022-05-30/2022-06-05 \n", "9 26.0 FR France 2022-05-23/2022-05-29 \n", "10 36.0 FR France 2022-05-16/2022-05-22 \n", "11 33.0 FR France 2022-05-09/2022-05-15 \n", "12 54.0 FR France 2022-05-02/2022-05-08 \n", "13 62.0 FR France 2022-04-25/2022-05-01 \n", "14 86.0 FR France 2022-04-18/2022-04-24 \n", "15 167.0 FR France 2022-04-11/2022-04-17 \n", "16 251.0 FR France 2022-04-04/2022-04-10 \n", "17 308.0 FR France 2022-03-28/2022-04-03 \n", "18 268.0 FR France 2022-03-21/2022-03-27 \n", "19 199.0 FR France 2022-03-14/2022-03-20 \n", "20 145.0 FR France 2022-03-07/2022-03-13 \n", "21 85.0 FR France 2022-02-28/2022-03-06 \n", "22 55.0 FR France 2022-02-21/2022-02-27 \n", "23 61.0 FR France 2022-02-14/2022-02-20 \n", "24 79.0 FR France 2022-02-07/2022-02-13 \n", "25 105.0 FR France 2022-01-31/2022-02-06 \n", "26 120.0 FR France 2022-01-24/2022-01-30 \n", "27 124.0 FR France 2022-01-17/2022-01-23 \n", "28 94.0 FR France 2022-01-10/2022-01-16 \n", "29 97.0 FR France 2022-01-03/2022-01-09 \n", "... ... ... ... ... \n", "1940 59.0 FR France 1985-05-20/1985-05-26 \n", "1941 64.0 FR France 1985-05-13/1985-05-19 \n", "1942 97.0 FR France 1985-05-06/1985-05-12 \n", "1943 93.0 FR France 1985-04-29/1985-05-05 \n", "1944 80.0 FR France 1985-04-22/1985-04-28 \n", "1945 116.0 FR France 1985-04-15/1985-04-21 \n", "1946 149.0 FR France 1985-04-08/1985-04-14 \n", "1947 281.0 FR France 1985-04-01/1985-04-07 \n", "1948 395.0 FR France 1985-03-25/1985-03-31 \n", "1949 485.0 FR France 1985-03-18/1985-03-24 \n", "1950 544.0 FR France 1985-03-11/1985-03-17 \n", "1951 689.0 FR France 1985-03-04/1985-03-10 \n", "1952 722.0 FR France 1985-02-25/1985-03-03 \n", "1953 762.0 FR France 1985-02-18/1985-02-24 \n", "1954 926.0 FR France 1985-02-11/1985-02-17 \n", "1955 1113.0 FR France 1985-02-04/1985-02-10 \n", "1956 1236.0 FR France 1985-01-28/1985-02-03 \n", "1957 832.0 FR France 1985-01-21/1985-01-27 \n", "1958 459.0 FR France 1985-01-14/1985-01-20 \n", "1959 207.0 FR France 1985-01-07/1985-01-13 \n", "1960 190.0 FR France 1984-12-31/1985-01-06 \n", "1961 198.0 FR France 1984-12-24/1984-12-30 \n", "1962 224.0 FR France 1984-12-17/1984-12-23 \n", "1963 266.0 FR France 1984-12-10/1984-12-16 \n", "1964 219.0 FR France 1984-12-03/1984-12-09 \n", "1965 176.0 FR France 1984-11-26/1984-12-02 \n", "1966 163.0 FR France 1984-11-19/1984-11-25 \n", "1967 195.0 FR France 1984-11-12/1984-11-18 \n", "1968 308.0 FR France 1984-11-05/1984-11-11 \n", "1969 213.0 FR France 1984-10-29/1984-11-04 \n", "\n", "[1969 rows x 11 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "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']]\n", "data" ] }, { "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", "text/plain": [ "
" ] }, "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": 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 }