From a30ac5137970d7354f0b8881709e11b0a1711e6f Mon Sep 17 00:00:00 2001 From: 28e1f4de90ce29a7aec6f2ae83fdfffc <28e1f4de90ce29a7aec6f2ae83fdfffc@app-learninglab.inria.fr> Date: Mon, 18 Aug 2025 17:13:11 +0000 Subject: [PATCH] premier test --- module3/exo1/analyse-syndrome-grippal.ipynb | 1437 ++++++++++++++++--- 1 file changed, 1234 insertions(+), 203 deletions(-) diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index b56a867..472593a 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -16,7 +16,8 @@ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import isoweek" + "import isoweek\n", + "import os" ] }, { @@ -28,11 +29,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "data_url = \"https://www.sentiweb.fr/datasets/all/inc-3-PAY.csv\" # \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "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_exo3_donnes_brutes.csv'" ] }, { @@ -59,7 +61,34 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "completeName : ./ , influenza-like-illness-analysis.org\n", + "completeName : ./ , influenza-like-illness-analysis.Rmd\n", + "completeName : ./ , influenza-like-illness-analysis.ipynb\n", + "completeName : ./ , mod3_exo3_donnes_brutes.csv\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": 11, "metadata": {}, "outputs": [ { @@ -1024,15 +1053,15 @@ "[2128 rows x 10 columns]" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data = pd.read_csv(data_url, skiprows=1) # était data_url avec skiprows= 1\n", "# exécuté une fois puis commenté \n", - "# raw_data.to_csv('./mod3_exo3_donnes_brutes.csv', index = False)\n", + "# raw_data.to_csv('./mod3_exo1_donnes_brutes.csv', index = False)\n", "raw_data" ] }, @@ -1045,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1107,7 +1136,7 @@ "1891 FR France " ] }, - "execution_count": 7, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1125,7 +1154,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -2090,7 +2119,7 @@ "[2127 rows x 10 columns]" ] }, - "execution_count": 8, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -2120,7 +2149,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -2150,198 +2179,1200 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "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": null, - "metadata": {}, - "outputs": [], - "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": null, - "metadata": {}, - "outputs": [], - "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": 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": { - "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, + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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
.................................
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\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": 15, + "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": 16, + "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": 17, + "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 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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": [ - "yearly_incidence.hist(xrot=20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, "outputs": [], "source": [] } -- 2.18.1