{ "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\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." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/all/inc-3-PAY.csv\" # \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", "data_locale = r'./mod3_exo1_donnes_brutes.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": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "completeName : ./ , influenza-like-illness-analysis.org\n", "completeName : ./ , influenza-like-illness-analysis.Rmd\n", "completeName : ./ , mod3_exo1_donnes_brutes.csv\n", "completeName : ./ , influenza-like-illness-analysis.ipynb\n", "completeName : ./ , analyse-syndrome-grippal.Rmd\n", "completeName : ./ , analyse-syndrome-grippal.org\n", "completeName : ./ , analyse-syndrome-grippal.ipynb\n", "completeName : ./.ipynb_checkpoints , analyse-syndrome-grippal-checkpoint.ipynb\n" ] } ], "source": [ "for dirPath, dirNames, files in os.walk(r'./'):\n", " for name in files:\n", " print( f'completeName : {dirPath} , {name}')\n", " " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020253232183514291.029379.03322.044.0FRFrance
120253131861512735.024495.02819.037.0FRFrance
220253031916614283.024049.02922.036.0FRFrance
320252931867313815.023531.02821.035.0FRFrance
420252832328518131.028439.03527.043.0FRFrance
520252732145317129.025777.03226.038.0FRFrance
620252632194517422.026468.03326.040.0FRFrance
720252532332318546.028100.03528.042.0FRFrance
820252432315418577.027731.03528.042.0FRFrance
920252332439119307.029475.03628.044.0FRFrance
1020252231875514333.023177.02821.035.0FRFrance
1120252132376018671.028849.03527.043.0FRFrance
1220252032026515814.024716.03023.037.0FRFrance
1320251931626412394.020134.02418.030.0FRFrance
1420251831811513975.022255.02721.033.0FRFrance
1520251732215017291.027009.03326.040.0FRFrance
1620251632856422550.034578.04334.052.0FRFrance
1720251533572129592.041850.05344.062.0FRFrance
1820251433757931232.043926.05647.065.0FRFrance
1920251333967333686.045660.05950.068.0FRFrance
2020251235254345627.059459.07868.088.0FRFrance
2120251135946952154.066784.08978.0100.0FRFrance
2220251036033453048.067620.09079.0101.0FRFrance
2320250938453174994.094068.0126112.0140.0FRFrance
242025083136020124824.0147216.0203186.0220.0FRFrance
252025073208952195988.0221916.0312293.0331.0FRFrance
262025063273519258159.0288879.0408385.0431.0FRFrance
272025053334395318416.0350374.0499475.0523.0FRFrance
282025043350043332885.0367201.0522496.0548.0FRFrance
292025033252772238917.0266627.0377356.0398.0FRFrance
.................................
209819852132609619621.032571.04735.059.0FRFrance
209919852032789620885.034907.05138.064.0FRFrance
210019851934315432821.053487.07859.097.0FRFrance
210119851834055529935.051175.07455.093.0FRFrance
210219851733405324366.043740.06244.080.0FRFrance
210319851635036236451.064273.09166.0116.0FRFrance
210419851536388145538.082224.011683.0149.0FRFrance
21051985143134545114400.0154690.0244207.0281.0FRFrance
21061985133197206176080.0218332.0357319.0395.0FRFrance
21071985123245240223304.0267176.0445405.0485.0FRFrance
21081985113276205252399.0300011.0501458.0544.0FRFrance
21091985103353231326279.0380183.0640591.0689.0FRFrance
21101985093369895341109.0398681.0670618.0722.0FRFrance
21111985083389886359529.0420243.0707652.0762.0FRFrance
21121985073471852432599.0511105.0855784.0926.0FRFrance
21131985063565825518011.0613639.01026939.01113.0FRFrance
21141985053637302592795.0681809.011551074.01236.0FRFrance
21151985043424937390794.0459080.0770708.0832.0FRFrance
21161985033213901174689.0253113.0388317.0459.0FRFrance
211719850239758680949.0114223.0177147.0207.0FRFrance
211819850138548965918.0105060.0155120.0190.0FRFrance
211919845238483060602.0109058.0154110.0198.0FRFrance
2120198451310172680242.0123210.0185146.0224.0FRFrance
21211984503123680101401.0145959.0225184.0266.0FRFrance
2122198449310107381684.0120462.0184149.0219.0FRFrance
212319844837862060634.096606.0143110.0176.0FRFrance
212419844737202954274.089784.013199.0163.0FRFrance
212519844638733067686.0106974.0159123.0195.0FRFrance
21261984453135223101414.0169032.0246184.0308.0FRFrance
212719844436842220056.0116788.012537.0213.0FRFrance
\n", "

2128 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202532 3 21835 14291.0 29379.0 33 22.0 \n", "1 202531 3 18615 12735.0 24495.0 28 19.0 \n", "2 202530 3 19166 14283.0 24049.0 29 22.0 \n", "3 202529 3 18673 13815.0 23531.0 28 21.0 \n", "4 202528 3 23285 18131.0 28439.0 35 27.0 \n", "5 202527 3 21453 17129.0 25777.0 32 26.0 \n", "6 202526 3 21945 17422.0 26468.0 33 26.0 \n", "7 202525 3 23323 18546.0 28100.0 35 28.0 \n", "8 202524 3 23154 18577.0 27731.0 35 28.0 \n", "9 202523 3 24391 19307.0 29475.0 36 28.0 \n", "10 202522 3 18755 14333.0 23177.0 28 21.0 \n", "11 202521 3 23760 18671.0 28849.0 35 27.0 \n", "12 202520 3 20265 15814.0 24716.0 30 23.0 \n", "13 202519 3 16264 12394.0 20134.0 24 18.0 \n", "14 202518 3 18115 13975.0 22255.0 27 21.0 \n", "15 202517 3 22150 17291.0 27009.0 33 26.0 \n", "16 202516 3 28564 22550.0 34578.0 43 34.0 \n", "17 202515 3 35721 29592.0 41850.0 53 44.0 \n", "18 202514 3 37579 31232.0 43926.0 56 47.0 \n", "19 202513 3 39673 33686.0 45660.0 59 50.0 \n", "20 202512 3 52543 45627.0 59459.0 78 68.0 \n", "21 202511 3 59469 52154.0 66784.0 89 78.0 \n", "22 202510 3 60334 53048.0 67620.0 90 79.0 \n", "23 202509 3 84531 74994.0 94068.0 126 112.0 \n", "24 202508 3 136020 124824.0 147216.0 203 186.0 \n", "25 202507 3 208952 195988.0 221916.0 312 293.0 \n", "26 202506 3 273519 258159.0 288879.0 408 385.0 \n", "27 202505 3 334395 318416.0 350374.0 499 475.0 \n", "28 202504 3 350043 332885.0 367201.0 522 496.0 \n", "29 202503 3 252772 238917.0 266627.0 377 356.0 \n", "... ... ... ... ... ... ... ... \n", "2098 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2099 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2100 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2101 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2102 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2103 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2104 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2105 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2106 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2107 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2108 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2109 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2110 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2111 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2112 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2113 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2114 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2115 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2116 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2117 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2118 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2119 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2120 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2121 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2122 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2123 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2124 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2125 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2126 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2127 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 44.0 FR France \n", "1 37.0 FR France \n", "2 36.0 FR France \n", "3 35.0 FR France \n", "4 43.0 FR France \n", "5 38.0 FR France \n", "6 40.0 FR France \n", "7 42.0 FR France \n", "8 42.0 FR France \n", "9 44.0 FR France \n", "10 35.0 FR France \n", "11 43.0 FR France \n", "12 37.0 FR France \n", "13 30.0 FR France \n", "14 33.0 FR France \n", "15 40.0 FR France \n", "16 52.0 FR France \n", "17 62.0 FR France \n", "18 65.0 FR France \n", "19 68.0 FR France \n", "20 88.0 FR France \n", "21 100.0 FR France \n", "22 101.0 FR France \n", "23 140.0 FR France \n", "24 220.0 FR France \n", "25 331.0 FR France \n", "26 431.0 FR France \n", "27 523.0 FR France \n", "28 548.0 FR France \n", "29 398.0 FR France \n", "... ... ... ... \n", "2098 59.0 FR France \n", "2099 64.0 FR France \n", "2100 97.0 FR France \n", "2101 93.0 FR France \n", "2102 80.0 FR France \n", "2103 116.0 FR France \n", "2104 149.0 FR France \n", "2105 281.0 FR France \n", "2106 395.0 FR France \n", "2107 485.0 FR France \n", "2108 544.0 FR France \n", "2109 689.0 FR France \n", "2110 722.0 FR France \n", "2111 762.0 FR France \n", "2112 926.0 FR France \n", "2113 1113.0 FR France \n", "2114 1236.0 FR France \n", "2115 832.0 FR France \n", "2116 459.0 FR France \n", "2117 207.0 FR France \n", "2118 190.0 FR France \n", "2119 198.0 FR France \n", "2120 224.0 FR France \n", "2121 266.0 FR France \n", "2122 219.0 FR France \n", "2123 176.0 FR France \n", "2124 163.0 FR France \n", "2125 195.0 FR France \n", "2126 308.0 FR France \n", "2127 213.0 FR France \n", "\n", "[2128 rows x 10 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_locale, skiprows=0) # était data_url avec skiprows= 1\n", "# exécuté une fois puis commenté \n", "# raw_data.to_csv('./mod3_exo1_donnes_brutes.csv', index = False)\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": 22, "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
18911989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1891 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1891 FR France " ] }, "execution_count": 22, "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": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020253232183514291.029379.03322.044.0FRFrance
120253131861512735.024495.02819.037.0FRFrance
220253031916614283.024049.02922.036.0FRFrance
320252931867313815.023531.02821.035.0FRFrance
420252832328518131.028439.03527.043.0FRFrance
520252732145317129.025777.03226.038.0FRFrance
620252632194517422.026468.03326.040.0FRFrance
720252532332318546.028100.03528.042.0FRFrance
820252432315418577.027731.03528.042.0FRFrance
920252332439119307.029475.03628.044.0FRFrance
1020252231875514333.023177.02821.035.0FRFrance
1120252132376018671.028849.03527.043.0FRFrance
1220252032026515814.024716.03023.037.0FRFrance
1320251931626412394.020134.02418.030.0FRFrance
1420251831811513975.022255.02721.033.0FRFrance
1520251732215017291.027009.03326.040.0FRFrance
1620251632856422550.034578.04334.052.0FRFrance
1720251533572129592.041850.05344.062.0FRFrance
1820251433757931232.043926.05647.065.0FRFrance
1920251333967333686.045660.05950.068.0FRFrance
2020251235254345627.059459.07868.088.0FRFrance
2120251135946952154.066784.08978.0100.0FRFrance
2220251036033453048.067620.09079.0101.0FRFrance
2320250938453174994.094068.0126112.0140.0FRFrance
242025083136020124824.0147216.0203186.0220.0FRFrance
252025073208952195988.0221916.0312293.0331.0FRFrance
262025063273519258159.0288879.0408385.0431.0FRFrance
272025053334395318416.0350374.0499475.0523.0FRFrance
282025043350043332885.0367201.0522496.0548.0FRFrance
292025033252772238917.0266627.0377356.0398.0FRFrance
.................................
209819852132609619621.032571.04735.059.0FRFrance
209919852032789620885.034907.05138.064.0FRFrance
210019851934315432821.053487.07859.097.0FRFrance
210119851834055529935.051175.07455.093.0FRFrance
210219851733405324366.043740.06244.080.0FRFrance
210319851635036236451.064273.09166.0116.0FRFrance
210419851536388145538.082224.011683.0149.0FRFrance
21051985143134545114400.0154690.0244207.0281.0FRFrance
21061985133197206176080.0218332.0357319.0395.0FRFrance
21071985123245240223304.0267176.0445405.0485.0FRFrance
21081985113276205252399.0300011.0501458.0544.0FRFrance
21091985103353231326279.0380183.0640591.0689.0FRFrance
21101985093369895341109.0398681.0670618.0722.0FRFrance
21111985083389886359529.0420243.0707652.0762.0FRFrance
21121985073471852432599.0511105.0855784.0926.0FRFrance
21131985063565825518011.0613639.01026939.01113.0FRFrance
21141985053637302592795.0681809.011551074.01236.0FRFrance
21151985043424937390794.0459080.0770708.0832.0FRFrance
21161985033213901174689.0253113.0388317.0459.0FRFrance
211719850239758680949.0114223.0177147.0207.0FRFrance
211819850138548965918.0105060.0155120.0190.0FRFrance
211919845238483060602.0109058.0154110.0198.0FRFrance
2120198451310172680242.0123210.0185146.0224.0FRFrance
21211984503123680101401.0145959.0225184.0266.0FRFrance
2122198449310107381684.0120462.0184149.0219.0FRFrance
212319844837862060634.096606.0143110.0176.0FRFrance
212419844737202954274.089784.013199.0163.0FRFrance
212519844638733067686.0106974.0159123.0195.0FRFrance
21261984453135223101414.0169032.0246184.0308.0FRFrance
212719844436842220056.0116788.012537.0213.0FRFrance
\n", "

2127 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202532 3 21835 14291.0 29379.0 33 22.0 \n", "1 202531 3 18615 12735.0 24495.0 28 19.0 \n", "2 202530 3 19166 14283.0 24049.0 29 22.0 \n", "3 202529 3 18673 13815.0 23531.0 28 21.0 \n", "4 202528 3 23285 18131.0 28439.0 35 27.0 \n", "5 202527 3 21453 17129.0 25777.0 32 26.0 \n", "6 202526 3 21945 17422.0 26468.0 33 26.0 \n", "7 202525 3 23323 18546.0 28100.0 35 28.0 \n", "8 202524 3 23154 18577.0 27731.0 35 28.0 \n", "9 202523 3 24391 19307.0 29475.0 36 28.0 \n", "10 202522 3 18755 14333.0 23177.0 28 21.0 \n", "11 202521 3 23760 18671.0 28849.0 35 27.0 \n", "12 202520 3 20265 15814.0 24716.0 30 23.0 \n", "13 202519 3 16264 12394.0 20134.0 24 18.0 \n", "14 202518 3 18115 13975.0 22255.0 27 21.0 \n", "15 202517 3 22150 17291.0 27009.0 33 26.0 \n", "16 202516 3 28564 22550.0 34578.0 43 34.0 \n", "17 202515 3 35721 29592.0 41850.0 53 44.0 \n", "18 202514 3 37579 31232.0 43926.0 56 47.0 \n", "19 202513 3 39673 33686.0 45660.0 59 50.0 \n", "20 202512 3 52543 45627.0 59459.0 78 68.0 \n", "21 202511 3 59469 52154.0 66784.0 89 78.0 \n", "22 202510 3 60334 53048.0 67620.0 90 79.0 \n", "23 202509 3 84531 74994.0 94068.0 126 112.0 \n", "24 202508 3 136020 124824.0 147216.0 203 186.0 \n", "25 202507 3 208952 195988.0 221916.0 312 293.0 \n", "26 202506 3 273519 258159.0 288879.0 408 385.0 \n", "27 202505 3 334395 318416.0 350374.0 499 475.0 \n", "28 202504 3 350043 332885.0 367201.0 522 496.0 \n", "29 202503 3 252772 238917.0 266627.0 377 356.0 \n", "... ... ... ... ... ... ... ... \n", "2098 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2099 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2100 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2101 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2102 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2103 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2104 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2105 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2106 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2107 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2108 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2109 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2110 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2111 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2112 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2113 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2114 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2115 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2116 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2117 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2118 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2119 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2120 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2121 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2122 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2123 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2124 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2125 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2126 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2127 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 44.0 FR France \n", "1 37.0 FR France \n", "2 36.0 FR France \n", "3 35.0 FR France \n", "4 43.0 FR France \n", "5 38.0 FR France \n", "6 40.0 FR France \n", "7 42.0 FR France \n", "8 42.0 FR France \n", "9 44.0 FR France \n", "10 35.0 FR France \n", "11 43.0 FR France \n", "12 37.0 FR France \n", "13 30.0 FR France \n", "14 33.0 FR France \n", "15 40.0 FR France \n", "16 52.0 FR France \n", "17 62.0 FR France \n", "18 65.0 FR France \n", "19 68.0 FR France \n", "20 88.0 FR France \n", "21 100.0 FR France \n", "22 101.0 FR France \n", "23 140.0 FR France \n", "24 220.0 FR France \n", "25 331.0 FR France \n", "26 431.0 FR France \n", "27 523.0 FR France \n", "28 548.0 FR France \n", "29 398.0 FR France \n", "... ... ... ... \n", "2098 59.0 FR France \n", "2099 64.0 FR France \n", "2100 97.0 FR France \n", "2101 93.0 FR France \n", "2102 80.0 FR France \n", "2103 116.0 FR France \n", "2104 149.0 FR France \n", "2105 281.0 FR France \n", "2106 395.0 FR France \n", "2107 485.0 FR France \n", "2108 544.0 FR France \n", "2109 689.0 FR France \n", "2110 722.0 FR France \n", "2111 762.0 FR France \n", "2112 926.0 FR France \n", "2113 1113.0 FR France \n", "2114 1236.0 FR France \n", "2115 832.0 FR France \n", "2116 459.0 FR France \n", "2117 207.0 FR France \n", "2118 190.0 FR France \n", "2119 198.0 FR France \n", "2120 224.0 FR France \n", "2121 266.0 FR France \n", "2122 219.0 FR France \n", "2123 176.0 FR France \n", "2124 163.0 FR France \n", "2125 195.0 FR France \n", "2126 308.0 FR France \n", "2127 213.0 FR France \n", "\n", "[2127 rows x 10 columns]" ] }, "execution_count": 23, "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": 24, "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": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1984-10-29/1984-11-0419844436842220056.0116788.012537.0213.0FRFrance
1984-11-05/1984-11-111984453135223101414.0169032.0246184.0308.0FRFrance
1984-11-12/1984-11-1819844638733067686.0106974.0159123.0195.0FRFrance
1984-11-19/1984-11-2519844737202954274.089784.013199.0163.0FRFrance
1984-11-26/1984-12-0219844837862060634.096606.0143110.0176.0FRFrance
1984-12-03/1984-12-09198449310107381684.0120462.0184149.0219.0FRFrance
1984-12-10/1984-12-161984503123680101401.0145959.0225184.0266.0FRFrance
1984-12-17/1984-12-23198451310172680242.0123210.0185146.0224.0FRFrance
1984-12-24/1984-12-3019845238483060602.0109058.0154110.0198.0FRFrance
1984-12-31/1985-01-0619850138548965918.0105060.0155120.0190.0FRFrance
1985-01-07/1985-01-1319850239758680949.0114223.0177147.0207.0FRFrance
1985-01-14/1985-01-201985033213901174689.0253113.0388317.0459.0FRFrance
1985-01-21/1985-01-271985043424937390794.0459080.0770708.0832.0FRFrance
1985-01-28/1985-02-031985053637302592795.0681809.011551074.01236.0FRFrance
1985-02-04/1985-02-101985063565825518011.0613639.01026939.01113.0FRFrance
1985-02-11/1985-02-171985073471852432599.0511105.0855784.0926.0FRFrance
1985-02-18/1985-02-241985083389886359529.0420243.0707652.0762.0FRFrance
1985-02-25/1985-03-031985093369895341109.0398681.0670618.0722.0FRFrance
1985-03-04/1985-03-101985103353231326279.0380183.0640591.0689.0FRFrance
1985-03-11/1985-03-171985113276205252399.0300011.0501458.0544.0FRFrance
1985-03-18/1985-03-241985123245240223304.0267176.0445405.0485.0FRFrance
1985-03-25/1985-03-311985133197206176080.0218332.0357319.0395.0FRFrance
1985-04-01/1985-04-071985143134545114400.0154690.0244207.0281.0FRFrance
1985-04-08/1985-04-1419851536388145538.082224.011683.0149.0FRFrance
1985-04-15/1985-04-2119851635036236451.064273.09166.0116.0FRFrance
1985-04-22/1985-04-2819851733405324366.043740.06244.080.0FRFrance
1985-04-29/1985-05-0519851834055529935.051175.07455.093.0FRFrance
1985-05-06/1985-05-1219851934315432821.053487.07859.097.0FRFrance
1985-05-13/1985-05-1919852032789620885.034907.05138.064.0FRFrance
1985-05-20/1985-05-2619852132609619621.032571.04735.059.0FRFrance
.................................
2025-01-13/2025-01-192025033252772238917.0266627.0377356.0398.0FRFrance
2025-01-20/2025-01-262025043350043332885.0367201.0522496.0548.0FRFrance
2025-01-27/2025-02-022025053334395318416.0350374.0499475.0523.0FRFrance
2025-02-03/2025-02-092025063273519258159.0288879.0408385.0431.0FRFrance
2025-02-10/2025-02-162025073208952195988.0221916.0312293.0331.0FRFrance
2025-02-17/2025-02-232025083136020124824.0147216.0203186.0220.0FRFrance
2025-02-24/2025-03-0220250938453174994.094068.0126112.0140.0FRFrance
2025-03-03/2025-03-0920251036033453048.067620.09079.0101.0FRFrance
2025-03-10/2025-03-1620251135946952154.066784.08978.0100.0FRFrance
2025-03-17/2025-03-2320251235254345627.059459.07868.088.0FRFrance
2025-03-24/2025-03-3020251333967333686.045660.05950.068.0FRFrance
2025-03-31/2025-04-0620251433757931232.043926.05647.065.0FRFrance
2025-04-07/2025-04-1320251533572129592.041850.05344.062.0FRFrance
2025-04-14/2025-04-2020251632856422550.034578.04334.052.0FRFrance
2025-04-21/2025-04-2720251732215017291.027009.03326.040.0FRFrance
2025-04-28/2025-05-0420251831811513975.022255.02721.033.0FRFrance
2025-05-05/2025-05-1120251931626412394.020134.02418.030.0FRFrance
2025-05-12/2025-05-1820252032026515814.024716.03023.037.0FRFrance
2025-05-19/2025-05-2520252132376018671.028849.03527.043.0FRFrance
2025-05-26/2025-06-0120252231875514333.023177.02821.035.0FRFrance
2025-06-02/2025-06-0820252332439119307.029475.03628.044.0FRFrance
2025-06-09/2025-06-1520252432315418577.027731.03528.042.0FRFrance
2025-06-16/2025-06-2220252532332318546.028100.03528.042.0FRFrance
2025-06-23/2025-06-2920252632194517422.026468.03326.040.0FRFrance
2025-06-30/2025-07-0620252732145317129.025777.03226.038.0FRFrance
2025-07-07/2025-07-1320252832328518131.028439.03527.043.0FRFrance
2025-07-14/2025-07-2020252931867313815.023531.02821.035.0FRFrance
2025-07-21/2025-07-2720253031916614283.024049.02922.036.0FRFrance
2025-07-28/2025-08-0320253131861512735.024495.02819.037.0FRFrance
2025-08-04/2025-08-1020253232183514291.029379.03322.044.0FRFrance
\n", "

2127 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 \\\n", "period \n", "1984-10-29/1984-11-04 198444 3 68422 20056.0 116788.0 125 \n", "1984-11-05/1984-11-11 198445 3 135223 101414.0 169032.0 246 \n", "1984-11-12/1984-11-18 198446 3 87330 67686.0 106974.0 159 \n", "1984-11-19/1984-11-25 198447 3 72029 54274.0 89784.0 131 \n", "1984-11-26/1984-12-02 198448 3 78620 60634.0 96606.0 143 \n", "1984-12-03/1984-12-09 198449 3 101073 81684.0 120462.0 184 \n", "1984-12-10/1984-12-16 198450 3 123680 101401.0 145959.0 225 \n", "1984-12-17/1984-12-23 198451 3 101726 80242.0 123210.0 185 \n", "1984-12-24/1984-12-30 198452 3 84830 60602.0 109058.0 154 \n", "1984-12-31/1985-01-06 198501 3 85489 65918.0 105060.0 155 \n", "1985-01-07/1985-01-13 198502 3 97586 80949.0 114223.0 177 \n", "1985-01-14/1985-01-20 198503 3 213901 174689.0 253113.0 388 \n", "1985-01-21/1985-01-27 198504 3 424937 390794.0 459080.0 770 \n", "1985-01-28/1985-02-03 198505 3 637302 592795.0 681809.0 1155 \n", "1985-02-04/1985-02-10 198506 3 565825 518011.0 613639.0 1026 \n", "1985-02-11/1985-02-17 198507 3 471852 432599.0 511105.0 855 \n", "1985-02-18/1985-02-24 198508 3 389886 359529.0 420243.0 707 \n", "1985-02-25/1985-03-03 198509 3 369895 341109.0 398681.0 670 \n", "1985-03-04/1985-03-10 198510 3 353231 326279.0 380183.0 640 \n", "1985-03-11/1985-03-17 198511 3 276205 252399.0 300011.0 501 \n", "1985-03-18/1985-03-24 198512 3 245240 223304.0 267176.0 445 \n", "1985-03-25/1985-03-31 198513 3 197206 176080.0 218332.0 357 \n", "1985-04-01/1985-04-07 198514 3 134545 114400.0 154690.0 244 \n", "1985-04-08/1985-04-14 198515 3 63881 45538.0 82224.0 116 \n", "1985-04-15/1985-04-21 198516 3 50362 36451.0 64273.0 91 \n", "1985-04-22/1985-04-28 198517 3 34053 24366.0 43740.0 62 \n", "1985-04-29/1985-05-05 198518 3 40555 29935.0 51175.0 74 \n", "1985-05-06/1985-05-12 198519 3 43154 32821.0 53487.0 78 \n", "1985-05-13/1985-05-19 198520 3 27896 20885.0 34907.0 51 \n", "1985-05-20/1985-05-26 198521 3 26096 19621.0 32571.0 47 \n", "... ... ... ... ... ... ... \n", "2025-01-13/2025-01-19 202503 3 252772 238917.0 266627.0 377 \n", "2025-01-20/2025-01-26 202504 3 350043 332885.0 367201.0 522 \n", "2025-01-27/2025-02-02 202505 3 334395 318416.0 350374.0 499 \n", "2025-02-03/2025-02-09 202506 3 273519 258159.0 288879.0 408 \n", "2025-02-10/2025-02-16 202507 3 208952 195988.0 221916.0 312 \n", "2025-02-17/2025-02-23 202508 3 136020 124824.0 147216.0 203 \n", "2025-02-24/2025-03-02 202509 3 84531 74994.0 94068.0 126 \n", "2025-03-03/2025-03-09 202510 3 60334 53048.0 67620.0 90 \n", "2025-03-10/2025-03-16 202511 3 59469 52154.0 66784.0 89 \n", "2025-03-17/2025-03-23 202512 3 52543 45627.0 59459.0 78 \n", "2025-03-24/2025-03-30 202513 3 39673 33686.0 45660.0 59 \n", "2025-03-31/2025-04-06 202514 3 37579 31232.0 43926.0 56 \n", "2025-04-07/2025-04-13 202515 3 35721 29592.0 41850.0 53 \n", "2025-04-14/2025-04-20 202516 3 28564 22550.0 34578.0 43 \n", "2025-04-21/2025-04-27 202517 3 22150 17291.0 27009.0 33 \n", "2025-04-28/2025-05-04 202518 3 18115 13975.0 22255.0 27 \n", "2025-05-05/2025-05-11 202519 3 16264 12394.0 20134.0 24 \n", "2025-05-12/2025-05-18 202520 3 20265 15814.0 24716.0 30 \n", "2025-05-19/2025-05-25 202521 3 23760 18671.0 28849.0 35 \n", "2025-05-26/2025-06-01 202522 3 18755 14333.0 23177.0 28 \n", "2025-06-02/2025-06-08 202523 3 24391 19307.0 29475.0 36 \n", "2025-06-09/2025-06-15 202524 3 23154 18577.0 27731.0 35 \n", "2025-06-16/2025-06-22 202525 3 23323 18546.0 28100.0 35 \n", "2025-06-23/2025-06-29 202526 3 21945 17422.0 26468.0 33 \n", "2025-06-30/2025-07-06 202527 3 21453 17129.0 25777.0 32 \n", "2025-07-07/2025-07-13 202528 3 23285 18131.0 28439.0 35 \n", "2025-07-14/2025-07-20 202529 3 18673 13815.0 23531.0 28 \n", "2025-07-21/2025-07-27 202530 3 19166 14283.0 24049.0 29 \n", "2025-07-28/2025-08-03 202531 3 18615 12735.0 24495.0 28 \n", "2025-08-04/2025-08-10 202532 3 21835 14291.0 29379.0 33 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "period \n", "1984-10-29/1984-11-04 37.0 213.0 FR France \n", "1984-11-05/1984-11-11 184.0 308.0 FR France \n", "1984-11-12/1984-11-18 123.0 195.0 FR France \n", "1984-11-19/1984-11-25 99.0 163.0 FR France \n", "1984-11-26/1984-12-02 110.0 176.0 FR France \n", "1984-12-03/1984-12-09 149.0 219.0 FR France \n", "1984-12-10/1984-12-16 184.0 266.0 FR France \n", "1984-12-17/1984-12-23 146.0 224.0 FR France \n", "1984-12-24/1984-12-30 110.0 198.0 FR France \n", "1984-12-31/1985-01-06 120.0 190.0 FR France \n", "1985-01-07/1985-01-13 147.0 207.0 FR France \n", "1985-01-14/1985-01-20 317.0 459.0 FR France \n", "1985-01-21/1985-01-27 708.0 832.0 FR France \n", "1985-01-28/1985-02-03 1074.0 1236.0 FR France \n", "1985-02-04/1985-02-10 939.0 1113.0 FR France \n", "1985-02-11/1985-02-17 784.0 926.0 FR France \n", "1985-02-18/1985-02-24 652.0 762.0 FR France \n", "1985-02-25/1985-03-03 618.0 722.0 FR France \n", "1985-03-04/1985-03-10 591.0 689.0 FR France \n", "1985-03-11/1985-03-17 458.0 544.0 FR France \n", "1985-03-18/1985-03-24 405.0 485.0 FR France \n", "1985-03-25/1985-03-31 319.0 395.0 FR France \n", "1985-04-01/1985-04-07 207.0 281.0 FR France \n", "1985-04-08/1985-04-14 83.0 149.0 FR France \n", "1985-04-15/1985-04-21 66.0 116.0 FR France \n", "1985-04-22/1985-04-28 44.0 80.0 FR France \n", "1985-04-29/1985-05-05 55.0 93.0 FR France \n", "1985-05-06/1985-05-12 59.0 97.0 FR France \n", "1985-05-13/1985-05-19 38.0 64.0 FR France \n", "1985-05-20/1985-05-26 35.0 59.0 FR France \n", "... ... ... ... ... \n", "2025-01-13/2025-01-19 356.0 398.0 FR France \n", "2025-01-20/2025-01-26 496.0 548.0 FR France \n", "2025-01-27/2025-02-02 475.0 523.0 FR France \n", "2025-02-03/2025-02-09 385.0 431.0 FR France \n", "2025-02-10/2025-02-16 293.0 331.0 FR France \n", "2025-02-17/2025-02-23 186.0 220.0 FR France \n", "2025-02-24/2025-03-02 112.0 140.0 FR France \n", "2025-03-03/2025-03-09 79.0 101.0 FR France \n", "2025-03-10/2025-03-16 78.0 100.0 FR France \n", "2025-03-17/2025-03-23 68.0 88.0 FR France \n", "2025-03-24/2025-03-30 50.0 68.0 FR France \n", "2025-03-31/2025-04-06 47.0 65.0 FR France \n", "2025-04-07/2025-04-13 44.0 62.0 FR France \n", "2025-04-14/2025-04-20 34.0 52.0 FR France \n", "2025-04-21/2025-04-27 26.0 40.0 FR France \n", "2025-04-28/2025-05-04 21.0 33.0 FR France \n", "2025-05-05/2025-05-11 18.0 30.0 FR France \n", "2025-05-12/2025-05-18 23.0 37.0 FR France \n", "2025-05-19/2025-05-25 27.0 43.0 FR France \n", "2025-05-26/2025-06-01 21.0 35.0 FR France \n", "2025-06-02/2025-06-08 28.0 44.0 FR France \n", "2025-06-09/2025-06-15 28.0 42.0 FR France \n", "2025-06-16/2025-06-22 28.0 42.0 FR France \n", "2025-06-23/2025-06-29 26.0 40.0 FR France \n", "2025-06-30/2025-07-06 26.0 38.0 FR France \n", "2025-07-07/2025-07-13 27.0 43.0 FR France \n", "2025-07-14/2025-07-20 21.0 35.0 FR France \n", "2025-07-21/2025-07-27 22.0 36.0 FR France \n", "2025-07-28/2025-08-03 19.0 37.0 FR France \n", "2025-08-04/2025-08-10 22.0 44.0 FR France \n", "\n", "[2127 rows x 10 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = data.set_index('period').sort_index()\n", "sorted_data" ] }, { "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": 26, "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": 27, "metadata": {}, "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[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, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" ] } ], "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": 28, "metadata": {}, "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, ax, figsize, 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\u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" ] } ], "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 }