{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os" ] }, { "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.\n", "Une copie local des données est crée pour le cas que les données ne soient pas disponible dans le futur. Il est testé si le fichier local existe et sinon il est téléchargé de la site web." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'incidence-PAY-3.csv'" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "if os.path.exists(\"incidence-PAY-3.csv\"):\n", " data_path = \"incidence-PAY-3.csv\"\n", "else:\n", " data_path = \"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": 13, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02023523133672119008.0148336.0201179.0223.0FRFrance
12023513150155137846.0162464.0226207.0245.0FRFrance
22023503147971136787.0159155.0223206.0240.0FRFrance
32023493147552136422.0158682.0222205.0239.0FRFrance
42023483124204113479.0134929.0187171.0203.0FRFrance
52023473110910100658.0121162.0167152.0182.0FRFrance
620234638385375096.092610.0126113.0139.0FRFrance
720234537200363178.080828.010895.0121.0FRFrance
820234434995242813.057091.07564.086.0FRFrance
920234334498238170.051794.06858.078.0FRFrance
1020234235684249277.064407.08675.097.0FRFrance
1120234135835751032.065682.08877.099.0FRFrance
1220234036889460069.077719.010491.0117.0FRFrance
1320233937200363452.080554.010895.0121.0FRFrance
1420233836321855227.071209.09583.0107.0FRFrance
1520233734908542079.056091.07463.085.0FRFrance
1620233633824732237.044257.05849.067.0FRFrance
1720233533169526013.037377.04839.057.0FRFrance
1820233432666321057.032269.04032.048.0FRFrance
1920233331914413161.025127.02920.038.0FRFrance
2020233231464110285.018997.02215.029.0FRFrance
2120233131528610705.019867.02316.030.0FRFrance
222023303132058647.017763.02013.027.0FRFrance
232023293111227113.015131.01711.023.0FRFrance
24202328391795703.012655.0149.019.0FRFrance
25202327389995763.012235.0149.019.0FRFrance
26202326390235934.012112.0149.019.0FRFrance
272023253100906739.013441.01510.020.0FRFrance
282023243113087639.014977.01711.023.0FRFrance
2920232331430010661.017939.02217.027.0FRFrance
.................................
201419852132609619621.032571.04735.059.0FRFrance
201519852032789620885.034907.05138.064.0FRFrance
201619851934315432821.053487.07859.097.0FRFrance
201719851834055529935.051175.07455.093.0FRFrance
201819851733405324366.043740.06244.080.0FRFrance
201919851635036236451.064273.09166.0116.0FRFrance
202019851536388145538.082224.011683.0149.0FRFrance
20211985143134545114400.0154690.0244207.0281.0FRFrance
20221985133197206176080.0218332.0357319.0395.0FRFrance
20231985123245240223304.0267176.0445405.0485.0FRFrance
20241985113276205252399.0300011.0501458.0544.0FRFrance
20251985103353231326279.0380183.0640591.0689.0FRFrance
20261985093369895341109.0398681.0670618.0722.0FRFrance
20271985083389886359529.0420243.0707652.0762.0FRFrance
20281985073471852432599.0511105.0855784.0926.0FRFrance
20291985063565825518011.0613639.01026939.01113.0FRFrance
20301985053637302592795.0681809.011551074.01236.0FRFrance
20311985043424937390794.0459080.0770708.0832.0FRFrance
20321985033213901174689.0253113.0388317.0459.0FRFrance
203319850239758680949.0114223.0177147.0207.0FRFrance
203419850138548965918.0105060.0155120.0190.0FRFrance
203519845238483060602.0109058.0154110.0198.0FRFrance
2036198451310172680242.0123210.0185146.0224.0FRFrance
20371984503123680101401.0145959.0225184.0266.0FRFrance
2038198449310107381684.0120462.0184149.0219.0FRFrance
203919844837862060634.096606.0143110.0176.0FRFrance
204019844737202954274.089784.013199.0163.0FRFrance
204119844638733067686.0106974.0159123.0195.0FRFrance
20421984453135223101414.0169032.0246184.0308.0FRFrance
204319844436842220056.0116788.012537.0213.0FRFrance
\n", "

2044 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202352 3 133672 119008.0 148336.0 201 179.0 \n", "1 202351 3 150155 137846.0 162464.0 226 207.0 \n", "2 202350 3 147971 136787.0 159155.0 223 206.0 \n", "3 202349 3 147552 136422.0 158682.0 222 205.0 \n", "4 202348 3 124204 113479.0 134929.0 187 171.0 \n", "5 202347 3 110910 100658.0 121162.0 167 152.0 \n", "6 202346 3 83853 75096.0 92610.0 126 113.0 \n", "7 202345 3 72003 63178.0 80828.0 108 95.0 \n", "8 202344 3 49952 42813.0 57091.0 75 64.0 \n", "9 202343 3 44982 38170.0 51794.0 68 58.0 \n", "10 202342 3 56842 49277.0 64407.0 86 75.0 \n", "11 202341 3 58357 51032.0 65682.0 88 77.0 \n", "12 202340 3 68894 60069.0 77719.0 104 91.0 \n", "13 202339 3 72003 63452.0 80554.0 108 95.0 \n", "14 202338 3 63218 55227.0 71209.0 95 83.0 \n", "15 202337 3 49085 42079.0 56091.0 74 63.0 \n", "16 202336 3 38247 32237.0 44257.0 58 49.0 \n", "17 202335 3 31695 26013.0 37377.0 48 39.0 \n", "18 202334 3 26663 21057.0 32269.0 40 32.0 \n", "19 202333 3 19144 13161.0 25127.0 29 20.0 \n", "20 202332 3 14641 10285.0 18997.0 22 15.0 \n", "21 202331 3 15286 10705.0 19867.0 23 16.0 \n", "22 202330 3 13205 8647.0 17763.0 20 13.0 \n", "23 202329 3 11122 7113.0 15131.0 17 11.0 \n", "24 202328 3 9179 5703.0 12655.0 14 9.0 \n", "25 202327 3 8999 5763.0 12235.0 14 9.0 \n", "26 202326 3 9023 5934.0 12112.0 14 9.0 \n", "27 202325 3 10090 6739.0 13441.0 15 10.0 \n", "28 202324 3 11308 7639.0 14977.0 17 11.0 \n", "29 202323 3 14300 10661.0 17939.0 22 17.0 \n", "... ... ... ... ... ... ... ... \n", "2014 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2015 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2016 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2017 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2018 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2019 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2020 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2021 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2022 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2023 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2024 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2025 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2026 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2027 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2028 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2029 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2030 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2031 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2032 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2033 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2034 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2035 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2036 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2037 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2038 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2039 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2040 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2041 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2042 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2043 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 223.0 FR France \n", "1 245.0 FR France \n", "2 240.0 FR France \n", "3 239.0 FR France \n", "4 203.0 FR France \n", "5 182.0 FR France \n", "6 139.0 FR France \n", "7 121.0 FR France \n", "8 86.0 FR France \n", "9 78.0 FR France \n", "10 97.0 FR France \n", "11 99.0 FR France \n", "12 117.0 FR France \n", "13 121.0 FR France \n", "14 107.0 FR France \n", "15 85.0 FR France \n", "16 67.0 FR France \n", "17 57.0 FR France \n", "18 48.0 FR France \n", "19 38.0 FR France \n", "20 29.0 FR France \n", "21 30.0 FR France \n", "22 27.0 FR France \n", "23 23.0 FR France \n", "24 19.0 FR France \n", "25 19.0 FR France \n", "26 19.0 FR France \n", "27 20.0 FR France \n", "28 23.0 FR France \n", "29 27.0 FR France \n", "... ... ... ... \n", "2014 59.0 FR France \n", "2015 64.0 FR France \n", "2016 97.0 FR France \n", "2017 93.0 FR France \n", "2018 80.0 FR France \n", "2019 116.0 FR France \n", "2020 149.0 FR France \n", "2021 281.0 FR France \n", "2022 395.0 FR France \n", "2023 485.0 FR France \n", "2024 544.0 FR France \n", "2025 689.0 FR France \n", "2026 722.0 FR France \n", "2027 762.0 FR France \n", "2028 926.0 FR France \n", "2029 1113.0 FR France \n", "2030 1236.0 FR France \n", "2031 832.0 FR France \n", "2032 459.0 FR France \n", "2033 207.0 FR France \n", "2034 190.0 FR France \n", "2035 198.0 FR France \n", "2036 224.0 FR France \n", "2037 266.0 FR France \n", "2038 219.0 FR France \n", "2039 176.0 FR France \n", "2040 163.0 FR France \n", "2041 195.0 FR France \n", "2042 308.0 FR France \n", "2043 213.0 FR France \n", "\n", "[2044 rows x 10 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_path, 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": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18071989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1807 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1807 FR France " ] }, "execution_count": 14, "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": 15, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02023523133672119008.0148336.0201179.0223.0FRFrance
12023513150155137846.0162464.0226207.0245.0FRFrance
22023503147971136787.0159155.0223206.0240.0FRFrance
32023493147552136422.0158682.0222205.0239.0FRFrance
42023483124204113479.0134929.0187171.0203.0FRFrance
52023473110910100658.0121162.0167152.0182.0FRFrance
620234638385375096.092610.0126113.0139.0FRFrance
720234537200363178.080828.010895.0121.0FRFrance
820234434995242813.057091.07564.086.0FRFrance
920234334498238170.051794.06858.078.0FRFrance
1020234235684249277.064407.08675.097.0FRFrance
1120234135835751032.065682.08877.099.0FRFrance
1220234036889460069.077719.010491.0117.0FRFrance
1320233937200363452.080554.010895.0121.0FRFrance
1420233836321855227.071209.09583.0107.0FRFrance
1520233734908542079.056091.07463.085.0FRFrance
1620233633824732237.044257.05849.067.0FRFrance
1720233533169526013.037377.04839.057.0FRFrance
1820233432666321057.032269.04032.048.0FRFrance
1920233331914413161.025127.02920.038.0FRFrance
2020233231464110285.018997.02215.029.0FRFrance
2120233131528610705.019867.02316.030.0FRFrance
222023303132058647.017763.02013.027.0FRFrance
232023293111227113.015131.01711.023.0FRFrance
24202328391795703.012655.0149.019.0FRFrance
25202327389995763.012235.0149.019.0FRFrance
26202326390235934.012112.0149.019.0FRFrance
272023253100906739.013441.01510.020.0FRFrance
282023243113087639.014977.01711.023.0FRFrance
2920232331430010661.017939.02217.027.0FRFrance
.................................
201419852132609619621.032571.04735.059.0FRFrance
201519852032789620885.034907.05138.064.0FRFrance
201619851934315432821.053487.07859.097.0FRFrance
201719851834055529935.051175.07455.093.0FRFrance
201819851733405324366.043740.06244.080.0FRFrance
201919851635036236451.064273.09166.0116.0FRFrance
202019851536388145538.082224.011683.0149.0FRFrance
20211985143134545114400.0154690.0244207.0281.0FRFrance
20221985133197206176080.0218332.0357319.0395.0FRFrance
20231985123245240223304.0267176.0445405.0485.0FRFrance
20241985113276205252399.0300011.0501458.0544.0FRFrance
20251985103353231326279.0380183.0640591.0689.0FRFrance
20261985093369895341109.0398681.0670618.0722.0FRFrance
20271985083389886359529.0420243.0707652.0762.0FRFrance
20281985073471852432599.0511105.0855784.0926.0FRFrance
20291985063565825518011.0613639.01026939.01113.0FRFrance
20301985053637302592795.0681809.011551074.01236.0FRFrance
20311985043424937390794.0459080.0770708.0832.0FRFrance
20321985033213901174689.0253113.0388317.0459.0FRFrance
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2043 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202352 3 133672 119008.0 148336.0 201 179.0 \n", "1 202351 3 150155 137846.0 162464.0 226 207.0 \n", "2 202350 3 147971 136787.0 159155.0 223 206.0 \n", "3 202349 3 147552 136422.0 158682.0 222 205.0 \n", "4 202348 3 124204 113479.0 134929.0 187 171.0 \n", "5 202347 3 110910 100658.0 121162.0 167 152.0 \n", "6 202346 3 83853 75096.0 92610.0 126 113.0 \n", "7 202345 3 72003 63178.0 80828.0 108 95.0 \n", "8 202344 3 49952 42813.0 57091.0 75 64.0 \n", "9 202343 3 44982 38170.0 51794.0 68 58.0 \n", "10 202342 3 56842 49277.0 64407.0 86 75.0 \n", "11 202341 3 58357 51032.0 65682.0 88 77.0 \n", "12 202340 3 68894 60069.0 77719.0 104 91.0 \n", "13 202339 3 72003 63452.0 80554.0 108 95.0 \n", "14 202338 3 63218 55227.0 71209.0 95 83.0 \n", "15 202337 3 49085 42079.0 56091.0 74 63.0 \n", "16 202336 3 38247 32237.0 44257.0 58 49.0 \n", "17 202335 3 31695 26013.0 37377.0 48 39.0 \n", "18 202334 3 26663 21057.0 32269.0 40 32.0 \n", "19 202333 3 19144 13161.0 25127.0 29 20.0 \n", "20 202332 3 14641 10285.0 18997.0 22 15.0 \n", "21 202331 3 15286 10705.0 19867.0 23 16.0 \n", "22 202330 3 13205 8647.0 17763.0 20 13.0 \n", "23 202329 3 11122 7113.0 15131.0 17 11.0 \n", "24 202328 3 9179 5703.0 12655.0 14 9.0 \n", "25 202327 3 8999 5763.0 12235.0 14 9.0 \n", "26 202326 3 9023 5934.0 12112.0 14 9.0 \n", "27 202325 3 10090 6739.0 13441.0 15 10.0 \n", "28 202324 3 11308 7639.0 14977.0 17 11.0 \n", "29 202323 3 14300 10661.0 17939.0 22 17.0 \n", "... ... ... ... ... ... ... ... \n", "2014 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2015 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2016 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2017 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2018 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2019 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2020 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2021 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2022 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2023 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2024 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2025 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2026 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2027 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2028 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2029 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2030 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2031 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2032 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2033 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2034 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2035 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2036 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2037 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2038 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2039 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2040 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2041 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2042 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2043 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 223.0 FR France \n", "1 245.0 FR France \n", "2 240.0 FR France \n", "3 239.0 FR France \n", "4 203.0 FR France \n", "5 182.0 FR France \n", "6 139.0 FR France \n", "7 121.0 FR France \n", "8 86.0 FR France \n", "9 78.0 FR France \n", "10 97.0 FR France \n", "11 99.0 FR France \n", "12 117.0 FR France \n", "13 121.0 FR France \n", "14 107.0 FR France \n", "15 85.0 FR France \n", "16 67.0 FR France \n", "17 57.0 FR France \n", "18 48.0 FR France \n", "19 38.0 FR France \n", "20 29.0 FR France \n", "21 30.0 FR France \n", "22 27.0 FR France \n", "23 23.0 FR France \n", "24 19.0 FR France \n", "25 19.0 FR France \n", "26 19.0 FR France \n", "27 20.0 FR France \n", "28 23.0 FR France \n", "29 27.0 FR France \n", "... ... ... ... \n", "2014 59.0 FR France \n", "2015 64.0 FR France \n", "2016 97.0 FR France \n", "2017 93.0 FR France \n", "2018 80.0 FR France \n", "2019 116.0 FR France \n", "2020 149.0 FR France \n", "2021 281.0 FR France \n", "2022 395.0 FR France \n", "2023 485.0 FR France \n", "2024 544.0 FR France \n", "2025 689.0 FR France \n", "2026 722.0 FR France \n", "2027 762.0 FR France \n", "2028 926.0 FR France \n", "2029 1113.0 FR France \n", "2030 1236.0 FR France \n", "2031 832.0 FR France \n", "2032 459.0 FR France \n", "2033 207.0 FR France \n", "2034 190.0 FR France \n", "2035 198.0 FR France \n", "2036 224.0 FR France \n", "2037 266.0 FR France \n", "2038 219.0 FR France \n", "2039 176.0 FR France \n", "2040 163.0 FR France \n", "2041 195.0 FR France \n", "2042 308.0 FR France \n", "2043 213.0 FR France \n", "\n", "[2043 rows x 10 columns]" ] }, "execution_count": 15, "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": 16, "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": 17, "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": 31, "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": 19, "metadata": { "collapsed": true }, "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": 32, "metadata": { "collapsed": true }, "outputs": [ { "ename": "TypeError", "evalue": "Empty 'DataFrame': no numeric data to plot", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, 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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 }