{ "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": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_file = \"incidence-PAY-3.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", "\n", "| Nom de colonne | Libellé de colonne |\n", "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", "| week | Semaine calendaire (ISO 8601) |\n", "| indicator | Code de l'indicateur de surveillance |\n", "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", "\n", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020221733951332478.046548.06049.071.0FRFrance
120221635005042854.057246.07564.086.0FRFrance
2202215310080690824.0110788.0152137.0167.0FRFrance
32022143155441143891.0166991.0234217.0251.0FRFrance
42022133191914179558.0204270.0289270.0308.0FRFrance
52022123166224155035.0177413.0251234.0268.0FRFrance
62022113122849113306.0132392.0185171.0199.0FRFrance
720221038790479741.096067.0133121.0145.0FRFrance
820220935018243958.056406.07667.085.0FRFrance
920220833096325942.035984.04739.055.0FRFrance
1020220733488229446.040318.05345.061.0FRFrance
1120220634662340398.052848.07061.079.0FRFrance
1220220536297056043.069897.09585.0105.0FRFrance
1320220437220964804.079614.010998.0120.0FRFrance
1420220337461367144.082082.0113102.0124.0FRFrance
1520220235592049511.062329.08474.094.0FRFrance
1620220135762950699.064559.08777.097.0FRFrance
1720215235434947029.061669.08271.093.0FRFrance
1820215134169835359.048037.06353.073.0FRFrance
1920215033811732497.043737.05849.067.0FRFrance
2020214934016834716.045620.06153.069.0FRFrance
2120214834184236364.047320.06355.071.0FRFrance
2220214733659831338.041858.05547.063.0FRFrance
2320214633005925302.034816.04639.053.0FRFrance
2420214532036416564.024164.03125.037.0FRFrance
2520214431899915042.022956.02923.035.0FRFrance
2620214332704021935.032145.04133.049.0FRFrance
2720214232834323382.033304.04335.051.0FRFrance
2820214132504320586.029500.03831.045.0FRFrance
2920214032628621842.030730.04033.047.0FRFrance
.................................
192719852132609619621.032571.04735.059.0FRFrance
192819852032789620885.034907.05138.064.0FRFrance
192919851934315432821.053487.07859.097.0FRFrance
193019851834055529935.051175.07455.093.0FRFrance
193119851733405324366.043740.06244.080.0FRFrance
193219851635036236451.064273.09166.0116.0FRFrance
193319851536388145538.082224.011683.0149.0FRFrance
19341985143134545114400.0154690.0244207.0281.0FRFrance
19351985133197206176080.0218332.0357319.0395.0FRFrance
19361985123245240223304.0267176.0445405.0485.0FRFrance
19371985113276205252399.0300011.0501458.0544.0FRFrance
19381985103353231326279.0380183.0640591.0689.0FRFrance
19391985093369895341109.0398681.0670618.0722.0FRFrance
19401985083389886359529.0420243.0707652.0762.0FRFrance
19411985073471852432599.0511105.0855784.0926.0FRFrance
19421985063565825518011.0613639.01026939.01113.0FRFrance
19431985053637302592795.0681809.011551074.01236.0FRFrance
19441985043424937390794.0459080.0770708.0832.0FRFrance
19451985033213901174689.0253113.0388317.0459.0FRFrance
194619850239758680949.0114223.0177147.0207.0FRFrance
194719850138548965918.0105060.0155120.0190.0FRFrance
194819845238483060602.0109058.0154110.0198.0FRFrance
1949198451310172680242.0123210.0185146.0224.0FRFrance
19501984503123680101401.0145959.0225184.0266.0FRFrance
1951198449310107381684.0120462.0184149.0219.0FRFrance
195219844837862060634.096606.0143110.0176.0FRFrance
195319844737202954274.089784.013199.0163.0FRFrance
195419844638733067686.0106974.0159123.0195.0FRFrance
19551984453135223101414.0169032.0246184.0308.0FRFrance
195619844436842220056.0116788.012537.0213.0FRFrance
\n", "

1957 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202217 3 39513 32478.0 46548.0 60 49.0 \n", "1 202216 3 50050 42854.0 57246.0 75 64.0 \n", "2 202215 3 100806 90824.0 110788.0 152 137.0 \n", "3 202214 3 155441 143891.0 166991.0 234 217.0 \n", "4 202213 3 191914 179558.0 204270.0 289 270.0 \n", "5 202212 3 166224 155035.0 177413.0 251 234.0 \n", "6 202211 3 122849 113306.0 132392.0 185 171.0 \n", "7 202210 3 87904 79741.0 96067.0 133 121.0 \n", "8 202209 3 50182 43958.0 56406.0 76 67.0 \n", "9 202208 3 30963 25942.0 35984.0 47 39.0 \n", "10 202207 3 34882 29446.0 40318.0 53 45.0 \n", "11 202206 3 46623 40398.0 52848.0 70 61.0 \n", "12 202205 3 62970 56043.0 69897.0 95 85.0 \n", "13 202204 3 72209 64804.0 79614.0 109 98.0 \n", "14 202203 3 74613 67144.0 82082.0 113 102.0 \n", "15 202202 3 55920 49511.0 62329.0 84 74.0 \n", "16 202201 3 57629 50699.0 64559.0 87 77.0 \n", "17 202152 3 54349 47029.0 61669.0 82 71.0 \n", "18 202151 3 41698 35359.0 48037.0 63 53.0 \n", "19 202150 3 38117 32497.0 43737.0 58 49.0 \n", "20 202149 3 40168 34716.0 45620.0 61 53.0 \n", "21 202148 3 41842 36364.0 47320.0 63 55.0 \n", "22 202147 3 36598 31338.0 41858.0 55 47.0 \n", "23 202146 3 30059 25302.0 34816.0 46 39.0 \n", "24 202145 3 20364 16564.0 24164.0 31 25.0 \n", "25 202144 3 18999 15042.0 22956.0 29 23.0 \n", "26 202143 3 27040 21935.0 32145.0 41 33.0 \n", "27 202142 3 28343 23382.0 33304.0 43 35.0 \n", "28 202141 3 25043 20586.0 29500.0 38 31.0 \n", "29 202140 3 26286 21842.0 30730.0 40 33.0 \n", "... ... ... ... ... ... ... ... \n", "1927 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1928 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1929 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1930 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1931 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1932 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1933 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1934 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1935 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1936 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1937 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1938 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1939 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1940 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1941 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1942 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1943 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1944 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1945 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1946 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1947 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1948 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1949 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1950 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1951 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1952 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1953 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1954 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1955 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1956 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 71.0 FR France \n", "1 86.0 FR France \n", "2 167.0 FR France \n", "3 251.0 FR France \n", "4 308.0 FR France \n", "5 268.0 FR France \n", "6 199.0 FR France \n", "7 145.0 FR France \n", "8 85.0 FR France \n", "9 55.0 FR France \n", "10 61.0 FR France \n", "11 79.0 FR France \n", "12 105.0 FR France \n", "13 120.0 FR France \n", "14 124.0 FR France \n", "15 94.0 FR France \n", "16 97.0 FR France \n", "17 93.0 FR France \n", "18 73.0 FR France \n", "19 67.0 FR France \n", "20 69.0 FR France \n", "21 71.0 FR France \n", "22 63.0 FR France \n", "23 53.0 FR France \n", "24 37.0 FR France \n", "25 35.0 FR France \n", "26 49.0 FR France \n", "27 51.0 FR France \n", "28 45.0 FR France \n", "29 47.0 FR France \n", "... ... ... ... \n", "1927 59.0 FR France \n", "1928 64.0 FR France \n", "1929 97.0 FR France \n", "1930 93.0 FR France \n", "1931 80.0 FR France \n", "1932 116.0 FR France \n", "1933 149.0 FR France \n", "1934 281.0 FR France \n", "1935 395.0 FR France \n", "1936 485.0 FR France \n", "1937 544.0 FR France \n", "1938 689.0 FR France \n", "1939 722.0 FR France \n", "1940 762.0 FR France \n", "1941 926.0 FR France \n", "1942 1113.0 FR France \n", "1943 1236.0 FR France \n", "1944 832.0 FR France \n", "1945 459.0 FR France \n", "1946 207.0 FR France \n", "1947 190.0 FR France \n", "1948 198.0 FR France \n", "1949 224.0 FR France \n", "1950 266.0 FR France \n", "1951 219.0 FR France \n", "1952 176.0 FR France \n", "1953 163.0 FR France \n", "1954 195.0 FR France \n", "1955 308.0 FR France \n", "1956 213.0 FR France \n", "\n", "[1957 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, { "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": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
172019891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1720 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1720 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
020221733951332478.046548.06049.071.0FRFrance
120221635005042854.057246.07564.086.0FRFrance
2202215310080690824.0110788.0152137.0167.0FRFrance
32022143155441143891.0166991.0234217.0251.0FRFrance
42022133191914179558.0204270.0289270.0308.0FRFrance
52022123166224155035.0177413.0251234.0268.0FRFrance
62022113122849113306.0132392.0185171.0199.0FRFrance
720221038790479741.096067.0133121.0145.0FRFrance
820220935018243958.056406.07667.085.0FRFrance
920220833096325942.035984.04739.055.0FRFrance
1020220733488229446.040318.05345.061.0FRFrance
1120220634662340398.052848.07061.079.0FRFrance
1220220536297056043.069897.09585.0105.0FRFrance
1320220437220964804.079614.010998.0120.0FRFrance
1420220337461367144.082082.0113102.0124.0FRFrance
1520220235592049511.062329.08474.094.0FRFrance
1620220135762950699.064559.08777.097.0FRFrance
1720215235434947029.061669.08271.093.0FRFrance
1820215134169835359.048037.06353.073.0FRFrance
1920215033811732497.043737.05849.067.0FRFrance
2020214934016834716.045620.06153.069.0FRFrance
2120214834184236364.047320.06355.071.0FRFrance
2220214733659831338.041858.05547.063.0FRFrance
2320214633005925302.034816.04639.053.0FRFrance
2420214532036416564.024164.03125.037.0FRFrance
2520214431899915042.022956.02923.035.0FRFrance
2620214332704021935.032145.04133.049.0FRFrance
2720214232834323382.033304.04335.051.0FRFrance
2820214132504320586.029500.03831.045.0FRFrance
2920214032628621842.030730.04033.047.0FRFrance
.................................
192719852132609619621.032571.04735.059.0FRFrance
192819852032789620885.034907.05138.064.0FRFrance
192919851934315432821.053487.07859.097.0FRFrance
193019851834055529935.051175.07455.093.0FRFrance
193119851733405324366.043740.06244.080.0FRFrance
193219851635036236451.064273.09166.0116.0FRFrance
193319851536388145538.082224.011683.0149.0FRFrance
19341985143134545114400.0154690.0244207.0281.0FRFrance
19351985133197206176080.0218332.0357319.0395.0FRFrance
19361985123245240223304.0267176.0445405.0485.0FRFrance
19371985113276205252399.0300011.0501458.0544.0FRFrance
19381985103353231326279.0380183.0640591.0689.0FRFrance
19391985093369895341109.0398681.0670618.0722.0FRFrance
19401985083389886359529.0420243.0707652.0762.0FRFrance
19411985073471852432599.0511105.0855784.0926.0FRFrance
19421985063565825518011.0613639.01026939.01113.0FRFrance
19431985053637302592795.0681809.011551074.01236.0FRFrance
19441985043424937390794.0459080.0770708.0832.0FRFrance
19451985033213901174689.0253113.0388317.0459.0FRFrance
194619850239758680949.0114223.0177147.0207.0FRFrance
194719850138548965918.0105060.0155120.0190.0FRFrance
194819845238483060602.0109058.0154110.0198.0FRFrance
1949198451310172680242.0123210.0185146.0224.0FRFrance
19501984503123680101401.0145959.0225184.0266.0FRFrance
1951198449310107381684.0120462.0184149.0219.0FRFrance
195219844837862060634.096606.0143110.0176.0FRFrance
195319844737202954274.089784.013199.0163.0FRFrance
195419844638733067686.0106974.0159123.0195.0FRFrance
19551984453135223101414.0169032.0246184.0308.0FRFrance
195619844436842220056.0116788.012537.0213.0FRFrance
\n", "

1956 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202217 3 39513 32478.0 46548.0 60 49.0 \n", "1 202216 3 50050 42854.0 57246.0 75 64.0 \n", "2 202215 3 100806 90824.0 110788.0 152 137.0 \n", "3 202214 3 155441 143891.0 166991.0 234 217.0 \n", "4 202213 3 191914 179558.0 204270.0 289 270.0 \n", "5 202212 3 166224 155035.0 177413.0 251 234.0 \n", "6 202211 3 122849 113306.0 132392.0 185 171.0 \n", "7 202210 3 87904 79741.0 96067.0 133 121.0 \n", "8 202209 3 50182 43958.0 56406.0 76 67.0 \n", "9 202208 3 30963 25942.0 35984.0 47 39.0 \n", "10 202207 3 34882 29446.0 40318.0 53 45.0 \n", "11 202206 3 46623 40398.0 52848.0 70 61.0 \n", "12 202205 3 62970 56043.0 69897.0 95 85.0 \n", "13 202204 3 72209 64804.0 79614.0 109 98.0 \n", "14 202203 3 74613 67144.0 82082.0 113 102.0 \n", "15 202202 3 55920 49511.0 62329.0 84 74.0 \n", "16 202201 3 57629 50699.0 64559.0 87 77.0 \n", "17 202152 3 54349 47029.0 61669.0 82 71.0 \n", "18 202151 3 41698 35359.0 48037.0 63 53.0 \n", "19 202150 3 38117 32497.0 43737.0 58 49.0 \n", "20 202149 3 40168 34716.0 45620.0 61 53.0 \n", "21 202148 3 41842 36364.0 47320.0 63 55.0 \n", "22 202147 3 36598 31338.0 41858.0 55 47.0 \n", "23 202146 3 30059 25302.0 34816.0 46 39.0 \n", "24 202145 3 20364 16564.0 24164.0 31 25.0 \n", "25 202144 3 18999 15042.0 22956.0 29 23.0 \n", "26 202143 3 27040 21935.0 32145.0 41 33.0 \n", "27 202142 3 28343 23382.0 33304.0 43 35.0 \n", "28 202141 3 25043 20586.0 29500.0 38 31.0 \n", "29 202140 3 26286 21842.0 30730.0 40 33.0 \n", "... ... ... ... ... ... ... ... \n", "1927 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1928 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1929 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1930 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1931 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1932 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1933 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1934 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1935 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1936 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1937 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1938 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1939 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1940 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1941 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1942 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1943 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1944 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1945 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1946 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1947 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1948 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1949 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1950 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1951 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1952 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1953 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1954 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1955 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1956 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 71.0 FR France \n", "1 86.0 FR France \n", "2 167.0 FR France \n", "3 251.0 FR France \n", "4 308.0 FR France \n", "5 268.0 FR France \n", "6 199.0 FR France \n", "7 145.0 FR France \n", "8 85.0 FR France \n", "9 55.0 FR France \n", "10 61.0 FR France \n", "11 79.0 FR France \n", "12 105.0 FR France \n", "13 120.0 FR France \n", "14 124.0 FR France \n", "15 94.0 FR France \n", "16 97.0 FR France \n", "17 93.0 FR France \n", "18 73.0 FR France \n", "19 67.0 FR France \n", "20 69.0 FR France \n", "21 71.0 FR France \n", "22 63.0 FR France \n", "23 53.0 FR France \n", "24 37.0 FR France \n", "25 35.0 FR France \n", "26 49.0 FR France \n", "27 51.0 FR France \n", "28 45.0 FR France \n", "29 47.0 FR France \n", "... ... ... ... \n", "1927 59.0 FR France \n", "1928 64.0 FR France \n", "1929 97.0 FR France \n", "1930 93.0 FR France \n", "1931 80.0 FR France \n", "1932 116.0 FR France \n", "1933 149.0 FR France \n", "1934 281.0 FR France \n", "1935 395.0 FR France \n", "1936 485.0 FR France \n", "1937 544.0 FR France \n", "1938 689.0 FR France \n", "1939 722.0 FR France \n", "1940 762.0 FR France \n", "1941 926.0 FR France \n", "1942 1113.0 FR France \n", "1943 1236.0 FR France \n", "1944 832.0 FR France \n", "1945 459.0 FR France \n", "1946 207.0 FR France \n", "1947 190.0 FR France \n", "1948 198.0 FR France \n", "1949 224.0 FR France \n", "1950 266.0 FR France \n", "1951 219.0 FR France \n", "1952 176.0 FR France \n", "1953 163.0 FR France \n", "1954 195.0 FR France \n", "1955 308.0 FR France \n", "1956 213.0 FR France \n", "\n", "[1956 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": 6, "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": 7, "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": 8, "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": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "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": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "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": 11, "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": 12, "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": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "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": 14, "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": 14, "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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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