{ "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": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"syndrome-grippal.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": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020254435107243188.058956.07664.088.0FRFrance
120254335606748168.063966.08472.096.0FRFrance
220254237523966515.083963.011299.0125.0FRFrance
320254138686077648.096072.0130116.0144.0FRFrance
420254037916971180.087158.0118106.0130.0FRFrance
520253937293064872.080988.010997.0121.0FRFrance
620253836143554131.068739.09281.0103.0FRFrance
720253734637339689.053057.06959.079.0FRFrance
820253632558120702.030460.03831.045.0FRFrance
920253532271717480.027954.03426.042.0FRFrance
1020253432142916177.026681.03224.040.0FRFrance
1120253331676612022.021510.02518.032.0FRFrance
1220253231990014303.025497.03022.038.0FRFrance
1320253131847012625.024315.02819.037.0FRFrance
1420253031916614283.024049.02922.036.0FRFrance
1520252931867313815.023531.02821.035.0FRFrance
1620252832328518131.028439.03527.043.0FRFrance
1720252732145317129.025777.03226.038.0FRFrance
1820252632194517422.026468.03326.040.0FRFrance
1920252532332318546.028100.03528.042.0FRFrance
2020252432315418577.027731.03528.042.0FRFrance
2120252332439119307.029475.03628.044.0FRFrance
2220252231875514333.023177.02821.035.0FRFrance
2320252132376018671.028849.03527.043.0FRFrance
2420252032026515814.024716.03023.037.0FRFrance
2520251931626412394.020134.02418.030.0FRFrance
2620251831811513975.022255.02721.033.0FRFrance
2720251732215017291.027009.03326.040.0FRFrance
2820251632856422550.034578.04334.052.0FRFrance
2920251533572129592.041850.05344.062.0FRFrance
.................................
211019852132609619621.032571.04735.059.0FRFrance
211119852032789620885.034907.05138.064.0FRFrance
211219851934315432821.053487.07859.097.0FRFrance
211319851834055529935.051175.07455.093.0FRFrance
211419851733405324366.043740.06244.080.0FRFrance
211519851635036236451.064273.09166.0116.0FRFrance
211619851536388145538.082224.011683.0149.0FRFrance
21171985143134545114400.0154690.0244207.0281.0FRFrance
21181985133197206176080.0218332.0357319.0395.0FRFrance
21191985123245240223304.0267176.0445405.0485.0FRFrance
21201985113276205252399.0300011.0501458.0544.0FRFrance
21211985103353231326279.0380183.0640591.0689.0FRFrance
21221985093369895341109.0398681.0670618.0722.0FRFrance
21231985083389886359529.0420243.0707652.0762.0FRFrance
21241985073471852432599.0511105.0855784.0926.0FRFrance
21251985063565825518011.0613639.01026939.01113.0FRFrance
21261985053637302592795.0681809.011551074.01236.0FRFrance
21271985043424937390794.0459080.0770708.0832.0FRFrance
21281985033213901174689.0253113.0388317.0459.0FRFrance
212919850239758680949.0114223.0177147.0207.0FRFrance
213019850138548965918.0105060.0155120.0190.0FRFrance
213119845238483060602.0109058.0154110.0198.0FRFrance
2132198451310172680242.0123210.0185146.0224.0FRFrance
21331984503123680101401.0145959.0225184.0266.0FRFrance
2134198449310107381684.0120462.0184149.0219.0FRFrance
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2140 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202544 3 51072 43188.0 58956.0 76 64.0 \n", "1 202543 3 56067 48168.0 63966.0 84 72.0 \n", "2 202542 3 75239 66515.0 83963.0 112 99.0 \n", "3 202541 3 86860 77648.0 96072.0 130 116.0 \n", "4 202540 3 79169 71180.0 87158.0 118 106.0 \n", "5 202539 3 72930 64872.0 80988.0 109 97.0 \n", "6 202538 3 61435 54131.0 68739.0 92 81.0 \n", "7 202537 3 46373 39689.0 53057.0 69 59.0 \n", "8 202536 3 25581 20702.0 30460.0 38 31.0 \n", "9 202535 3 22717 17480.0 27954.0 34 26.0 \n", "10 202534 3 21429 16177.0 26681.0 32 24.0 \n", "11 202533 3 16766 12022.0 21510.0 25 18.0 \n", "12 202532 3 19900 14303.0 25497.0 30 22.0 \n", "13 202531 3 18470 12625.0 24315.0 28 19.0 \n", "14 202530 3 19166 14283.0 24049.0 29 22.0 \n", "15 202529 3 18673 13815.0 23531.0 28 21.0 \n", "16 202528 3 23285 18131.0 28439.0 35 27.0 \n", "17 202527 3 21453 17129.0 25777.0 32 26.0 \n", "18 202526 3 21945 17422.0 26468.0 33 26.0 \n", "19 202525 3 23323 18546.0 28100.0 35 28.0 \n", "20 202524 3 23154 18577.0 27731.0 35 28.0 \n", "21 202523 3 24391 19307.0 29475.0 36 28.0 \n", "22 202522 3 18755 14333.0 23177.0 28 21.0 \n", "23 202521 3 23760 18671.0 28849.0 35 27.0 \n", "24 202520 3 20265 15814.0 24716.0 30 23.0 \n", "25 202519 3 16264 12394.0 20134.0 24 18.0 \n", "26 202518 3 18115 13975.0 22255.0 27 21.0 \n", "27 202517 3 22150 17291.0 27009.0 33 26.0 \n", "28 202516 3 28564 22550.0 34578.0 43 34.0 \n", "29 202515 3 35721 29592.0 41850.0 53 44.0 \n", "... ... ... ... ... ... ... ... \n", "2110 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2111 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2112 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2113 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2114 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2115 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2116 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2117 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2118 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2119 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2120 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2121 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2122 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2123 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2124 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2125 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2126 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2127 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2128 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2129 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2130 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2131 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2132 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2133 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2134 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2135 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2136 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2137 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2138 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2139 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 88.0 FR France \n", "1 96.0 FR France \n", "2 125.0 FR France \n", "3 144.0 FR France \n", "4 130.0 FR France \n", "5 121.0 FR France \n", "6 103.0 FR France \n", "7 79.0 FR France \n", "8 45.0 FR France \n", "9 42.0 FR France \n", "10 40.0 FR France \n", "11 32.0 FR France \n", "12 38.0 FR France \n", "13 37.0 FR France \n", "14 36.0 FR France \n", "15 35.0 FR France \n", "16 43.0 FR France \n", "17 38.0 FR France \n", "18 40.0 FR France \n", "19 42.0 FR France \n", "20 42.0 FR France \n", "21 44.0 FR France \n", "22 35.0 FR France \n", "23 43.0 FR France \n", "24 37.0 FR France \n", "25 30.0 FR France \n", "26 33.0 FR France \n", "27 40.0 FR France \n", "28 52.0 FR France \n", "29 62.0 FR France \n", "... ... ... ... \n", "2110 59.0 FR France \n", "2111 64.0 FR France \n", "2112 97.0 FR France \n", "2113 93.0 FR France \n", "2114 80.0 FR France \n", "2115 116.0 FR France \n", "2116 149.0 FR France \n", "2117 281.0 FR France \n", "2118 395.0 FR France \n", "2119 485.0 FR France \n", "2120 544.0 FR France \n", "2121 689.0 FR France \n", "2122 722.0 FR France \n", "2123 762.0 FR France \n", "2124 926.0 FR France \n", "2125 1113.0 FR France \n", "2126 1236.0 FR France \n", "2127 832.0 FR France \n", "2128 459.0 FR France \n", "2129 207.0 FR France \n", "2130 190.0 FR France \n", "2131 198.0 FR France \n", "2132 224.0 FR France \n", "2133 266.0 FR France \n", "2134 219.0 FR France \n", "2135 176.0 FR France \n", "2136 163.0 FR France \n", "2137 195.0 FR France \n", "2138 308.0 FR France \n", "2139 213.0 FR France \n", "\n", "[2140 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, 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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
19031989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1903 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1903 FR France " ] }, "execution_count": 5, "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": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020254435107243188.058956.07664.088.0FRFrance
120254335606748168.063966.08472.096.0FRFrance
220254237523966515.083963.011299.0125.0FRFrance
320254138686077648.096072.0130116.0144.0FRFrance
420254037916971180.087158.0118106.0130.0FRFrance
520253937293064872.080988.010997.0121.0FRFrance
620253836143554131.068739.09281.0103.0FRFrance
720253734637339689.053057.06959.079.0FRFrance
820253632558120702.030460.03831.045.0FRFrance
920253532271717480.027954.03426.042.0FRFrance
1020253432142916177.026681.03224.040.0FRFrance
1120253331676612022.021510.02518.032.0FRFrance
1220253231990014303.025497.03022.038.0FRFrance
1320253131847012625.024315.02819.037.0FRFrance
1420253031916614283.024049.02922.036.0FRFrance
1520252931867313815.023531.02821.035.0FRFrance
1620252832328518131.028439.03527.043.0FRFrance
1720252732145317129.025777.03226.038.0FRFrance
1820252632194517422.026468.03326.040.0FRFrance
1920252532332318546.028100.03528.042.0FRFrance
2020252432315418577.027731.03528.042.0FRFrance
2120252332439119307.029475.03628.044.0FRFrance
2220252231875514333.023177.02821.035.0FRFrance
2320252132376018671.028849.03527.043.0FRFrance
2420252032026515814.024716.03023.037.0FRFrance
2520251931626412394.020134.02418.030.0FRFrance
2620251831811513975.022255.02721.033.0FRFrance
2720251732215017291.027009.03326.040.0FRFrance
2820251632856422550.034578.04334.052.0FRFrance
2920251533572129592.041850.05344.062.0FRFrance
.................................
211019852132609619621.032571.04735.059.0FRFrance
211119852032789620885.034907.05138.064.0FRFrance
211219851934315432821.053487.07859.097.0FRFrance
211319851834055529935.051175.07455.093.0FRFrance
211419851733405324366.043740.06244.080.0FRFrance
211519851635036236451.064273.09166.0116.0FRFrance
211619851536388145538.082224.011683.0149.0FRFrance
21171985143134545114400.0154690.0244207.0281.0FRFrance
21181985133197206176080.0218332.0357319.0395.0FRFrance
21191985123245240223304.0267176.0445405.0485.0FRFrance
21201985113276205252399.0300011.0501458.0544.0FRFrance
21211985103353231326279.0380183.0640591.0689.0FRFrance
21221985093369895341109.0398681.0670618.0722.0FRFrance
21231985083389886359529.0420243.0707652.0762.0FRFrance
21241985073471852432599.0511105.0855784.0926.0FRFrance
21251985063565825518011.0613639.01026939.01113.0FRFrance
21261985053637302592795.0681809.011551074.01236.0FRFrance
21271985043424937390794.0459080.0770708.0832.0FRFrance
21281985033213901174689.0253113.0388317.0459.0FRFrance
212919850239758680949.0114223.0177147.0207.0FRFrance
213019850138548965918.0105060.0155120.0190.0FRFrance
213119845238483060602.0109058.0154110.0198.0FRFrance
2132198451310172680242.0123210.0185146.0224.0FRFrance
21331984503123680101401.0145959.0225184.0266.0FRFrance
2134198449310107381684.0120462.0184149.0219.0FRFrance
213519844837862060634.096606.0143110.0176.0FRFrance
213619844737202954274.089784.013199.0163.0FRFrance
213719844638733067686.0106974.0159123.0195.0FRFrance
21381984453135223101414.0169032.0246184.0308.0FRFrance
213919844436842220056.0116788.012537.0213.0FRFrance
\n", "

2139 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202544 3 51072 43188.0 58956.0 76 64.0 \n", "1 202543 3 56067 48168.0 63966.0 84 72.0 \n", "2 202542 3 75239 66515.0 83963.0 112 99.0 \n", "3 202541 3 86860 77648.0 96072.0 130 116.0 \n", "4 202540 3 79169 71180.0 87158.0 118 106.0 \n", "5 202539 3 72930 64872.0 80988.0 109 97.0 \n", "6 202538 3 61435 54131.0 68739.0 92 81.0 \n", "7 202537 3 46373 39689.0 53057.0 69 59.0 \n", "8 202536 3 25581 20702.0 30460.0 38 31.0 \n", "9 202535 3 22717 17480.0 27954.0 34 26.0 \n", "10 202534 3 21429 16177.0 26681.0 32 24.0 \n", "11 202533 3 16766 12022.0 21510.0 25 18.0 \n", "12 202532 3 19900 14303.0 25497.0 30 22.0 \n", "13 202531 3 18470 12625.0 24315.0 28 19.0 \n", "14 202530 3 19166 14283.0 24049.0 29 22.0 \n", "15 202529 3 18673 13815.0 23531.0 28 21.0 \n", "16 202528 3 23285 18131.0 28439.0 35 27.0 \n", "17 202527 3 21453 17129.0 25777.0 32 26.0 \n", "18 202526 3 21945 17422.0 26468.0 33 26.0 \n", "19 202525 3 23323 18546.0 28100.0 35 28.0 \n", "20 202524 3 23154 18577.0 27731.0 35 28.0 \n", "21 202523 3 24391 19307.0 29475.0 36 28.0 \n", "22 202522 3 18755 14333.0 23177.0 28 21.0 \n", "23 202521 3 23760 18671.0 28849.0 35 27.0 \n", "24 202520 3 20265 15814.0 24716.0 30 23.0 \n", "25 202519 3 16264 12394.0 20134.0 24 18.0 \n", "26 202518 3 18115 13975.0 22255.0 27 21.0 \n", "27 202517 3 22150 17291.0 27009.0 33 26.0 \n", "28 202516 3 28564 22550.0 34578.0 43 34.0 \n", "29 202515 3 35721 29592.0 41850.0 53 44.0 \n", "... ... ... ... ... ... ... ... \n", "2110 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2111 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2112 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2113 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2114 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2115 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2116 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2117 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2118 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2119 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2120 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2121 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2122 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2123 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2124 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2125 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2126 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2127 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2128 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2129 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2130 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2131 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2132 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2133 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2134 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2135 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2136 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2137 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2138 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2139 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 88.0 FR France \n", "1 96.0 FR France \n", "2 125.0 FR France \n", "3 144.0 FR France \n", "4 130.0 FR France \n", "5 121.0 FR France \n", "6 103.0 FR France \n", "7 79.0 FR France \n", "8 45.0 FR France \n", "9 42.0 FR France \n", "10 40.0 FR France \n", "11 32.0 FR France \n", "12 38.0 FR France \n", "13 37.0 FR France \n", "14 36.0 FR France \n", "15 35.0 FR France \n", "16 43.0 FR France \n", "17 38.0 FR France \n", "18 40.0 FR France \n", "19 42.0 FR France \n", "20 42.0 FR France \n", "21 44.0 FR France \n", "22 35.0 FR France \n", "23 43.0 FR France \n", "24 37.0 FR France \n", "25 30.0 FR France \n", "26 33.0 FR France \n", "27 40.0 FR France \n", "28 52.0 FR France \n", "29 62.0 FR France \n", "... ... ... ... \n", "2110 59.0 FR France \n", "2111 64.0 FR France \n", "2112 97.0 FR France \n", "2113 93.0 FR France \n", "2114 80.0 FR France \n", "2115 116.0 FR France \n", "2116 149.0 FR France \n", "2117 281.0 FR France \n", "2118 395.0 FR France \n", "2119 485.0 FR France \n", "2120 544.0 FR France \n", "2121 689.0 FR France \n", "2122 722.0 FR France \n", "2123 762.0 FR France \n", "2124 926.0 FR France \n", "2125 1113.0 FR France \n", "2126 1236.0 FR France \n", "2127 832.0 FR France \n", "2128 459.0 FR France \n", "2129 207.0 FR France \n", "2130 190.0 FR France \n", "2131 198.0 FR France \n", "2132 224.0 FR France \n", "2133 266.0 FR France \n", "2134 219.0 FR France \n", "2135 176.0 FR France \n", "2136 163.0 FR France \n", "2137 195.0 FR France \n", "2138 308.0 FR France \n", "2139 213.0 FR France \n", "\n", "[2139 rows x 10 columns]" ] }, "execution_count": 6, "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": 7, "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": 8, "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": 9, "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": 10, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "Empty 'DataFrame': no numeric data to plot", "output_type": "error", "traceback": [ 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Le creux des incidences se trouve en été." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "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": {}, "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": {}, "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 }