diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5b58a3ae26346460dd39e62a39c55243d7..2dafad71d807048594fa8318ef8ec01d3605e2ba 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -2,15 +2,21 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -21,16 +27,20 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "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": null, + "execution_count": 2, "metadata": { - "collapsed": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ @@ -39,7 +49,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", "\n", @@ -61,9 +74,979 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020244433963932310.046968.05948.070.0FRFrance
120244334695140223.053679.07060.080.0FRFrance
220244236778560009.075561.010290.0114.0FRFrance
320244137943571386.087484.0119107.0131.0FRFrance
420244038496576555.093375.0127114.0140.0FRFrance
520243939166082937.0100383.0137124.0150.0FRFrance
620243839178682903.0100669.0138125.0151.0FRFrance
720243735646049319.063601.08574.096.0FRFrance
820243633365727906.039408.05041.059.0FRFrance
920243532740422036.032772.04133.049.0FRFrance
1020243432671721003.032431.04031.049.0FRFrance
1120243332062315349.025897.03123.039.0FRFrance
1220243232318717532.028842.03527.043.0FRFrance
1320243132603520267.031803.03930.048.0FRFrance
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1620242835434245781.062903.08168.094.0FRFrance
1720242734736440234.054494.07160.082.0FRFrance
1820242634421936956.051482.06655.077.0FRFrance
1920242534720440300.054108.07161.081.0FRFrance
2020242434111034671.047549.06252.072.0FRFrance
2120242333587530610.041140.05446.062.0FRFrance
2220242233377228274.039270.05143.059.0FRFrance
2320242132196317556.026370.03326.040.0FRFrance
2420242032005715780.024334.03024.036.0FRFrance
2520241931537511274.019476.02317.029.0FRFrance
2620241832240917653.027165.03427.041.0FRFrance
2720241732704221410.032674.04133.049.0FRFrance
2820241632888223305.034459.04335.051.0FRFrance
2920241533022924648.035810.04537.053.0FRFrance
.................................
205819852132609619621.032571.04735.059.0FRFrance
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2088 rows × 10 columns

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FR France \n", + "26 41.0 FR France \n", + "27 49.0 FR France \n", + "28 51.0 FR France \n", + "29 53.0 FR France \n", + "... ... ... ... \n", + "2058 59.0 FR France \n", + "2059 64.0 FR France \n", + "2060 97.0 FR France \n", + "2061 93.0 FR France \n", + "2062 80.0 FR France \n", + "2063 116.0 FR France \n", + "2064 149.0 FR France \n", + "2065 281.0 FR France \n", + "2066 395.0 FR France \n", + "2067 485.0 FR France \n", + "2068 544.0 FR France \n", + "2069 689.0 FR France \n", + "2070 722.0 FR France \n", + "2071 762.0 FR France \n", + "2072 926.0 FR France \n", + "2073 1113.0 FR France \n", + "2074 1236.0 FR France \n", + "2075 832.0 FR France \n", + "2076 459.0 FR France \n", + "2077 207.0 FR France \n", + "2078 190.0 FR France \n", + "2079 198.0 FR France \n", + "2080 224.0 FR France \n", + "2081 266.0 FR France \n", + "2082 219.0 FR France \n", + "2083 176.0 FR France \n", + "2084 163.0 FR France \n", + "2085 195.0 FR France \n", + "2086 308.0 FR France \n", + "2087 213.0 FR France \n", + "\n", + "[2088 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" @@ -71,32 +1054,1075 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "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": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18511989193-NaNNaN-NaNNaNFRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1851 198919 3 - NaN NaN - NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1851 FR France " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "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": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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2020242434111034671.047549.06252.072.0FRFrance
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.................................
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2087 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202444 3 39639 32310.0 46968.0 59 48.0 \n", + "1 202443 3 46951 40223.0 53679.0 70 60.0 \n", + "2 202442 3 67785 60009.0 75561.0 102 90.0 \n", + "3 202441 3 79435 71386.0 87484.0 119 107.0 \n", + "4 202440 3 84965 76555.0 93375.0 127 114.0 \n", + "5 202439 3 91660 82937.0 100383.0 137 124.0 \n", + "6 202438 3 91786 82903.0 100669.0 138 125.0 \n", + "7 202437 3 56460 49319.0 63601.0 85 74.0 \n", + "8 202436 3 33657 27906.0 39408.0 50 41.0 \n", + "9 202435 3 27404 22036.0 32772.0 41 33.0 \n", + "10 202434 3 26717 21003.0 32431.0 40 31.0 \n", + "11 202433 3 20623 15349.0 25897.0 31 23.0 \n", + "12 202432 3 23187 17532.0 28842.0 35 27.0 \n", + "13 202431 3 26035 20267.0 31803.0 39 30.0 \n", + "14 202430 3 36393 28593.0 44193.0 55 43.0 \n", + "15 202429 3 39560 32592.0 46528.0 59 49.0 \n", + "16 202428 3 54342 45781.0 62903.0 81 68.0 \n", + "17 202427 3 47364 40234.0 54494.0 71 60.0 \n", + "18 202426 3 44219 36956.0 51482.0 66 55.0 \n", + "19 202425 3 47204 40300.0 54108.0 71 61.0 \n", + "20 202424 3 41110 34671.0 47549.0 62 52.0 \n", + "21 202423 3 35875 30610.0 41140.0 54 46.0 \n", + "22 202422 3 33772 28274.0 39270.0 51 43.0 \n", + "23 202421 3 21963 17556.0 26370.0 33 26.0 \n", + "24 202420 3 20057 15780.0 24334.0 30 24.0 \n", + "25 202419 3 15375 11274.0 19476.0 23 17.0 \n", + "26 202418 3 22409 17653.0 27165.0 34 27.0 \n", + "27 202417 3 27042 21410.0 32674.0 41 33.0 \n", + "28 202416 3 28882 23305.0 34459.0 43 35.0 \n", + "29 202415 3 30229 24648.0 35810.0 45 37.0 \n", + "... ... ... ... ... ... ... ... \n", + "2058 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2059 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2060 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2061 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2062 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2063 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2064 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2065 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2066 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2067 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2068 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2069 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2070 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2071 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2072 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2073 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2074 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2075 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2076 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2077 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2078 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2079 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2080 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2081 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2082 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2083 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2084 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2085 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2086 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2087 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 70.0 FR France \n", + "1 80.0 FR France \n", + "2 114.0 FR France \n", + "3 131.0 FR France \n", + "4 140.0 FR France \n", + "5 150.0 FR France \n", + "6 151.0 FR France \n", + "7 96.0 FR France \n", + "8 59.0 FR France \n", + "9 49.0 FR France \n", + "10 49.0 FR France \n", + "11 39.0 FR France \n", + "12 43.0 FR France \n", + "13 48.0 FR France \n", + "14 67.0 FR France \n", + "15 69.0 FR France \n", + "16 94.0 FR France \n", + "17 82.0 FR France \n", + "18 77.0 FR France \n", + "19 81.0 FR France \n", + "20 72.0 FR France \n", + "21 62.0 FR France \n", + "22 59.0 FR France \n", + "23 40.0 FR France \n", + "24 36.0 FR France \n", + "25 29.0 FR France \n", + "26 41.0 FR France \n", + "27 49.0 FR France \n", + "28 51.0 FR France \n", + "29 53.0 FR France \n", + "... ... ... ... \n", + "2058 59.0 FR France \n", + "2059 64.0 FR France \n", + "2060 97.0 FR France \n", + "2061 93.0 FR France \n", + "2062 80.0 FR France \n", + "2063 116.0 FR France \n", + "2064 149.0 FR France \n", + "2065 281.0 FR France \n", + "2066 395.0 FR France \n", + "2067 485.0 FR France \n", + "2068 544.0 FR France \n", + "2069 689.0 FR France \n", + "2070 722.0 FR France \n", + "2071 762.0 FR France \n", + "2072 926.0 FR France \n", + "2073 1113.0 FR France \n", + "2074 1236.0 FR France \n", + "2075 832.0 FR France \n", + "2076 459.0 FR France \n", + "2077 207.0 FR France \n", + "2078 190.0 FR France \n", + "2079 198.0 FR France \n", + "2080 224.0 FR France \n", + "2081 266.0 FR France \n", + "2082 219.0 FR France \n", + "2083 176.0 FR France \n", + "2084 163.0 FR France \n", + "2085 195.0 FR France \n", + "2086 308.0 FR France \n", + "2087 213.0 FR France \n", + "\n", + "[2087 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -104,7 +2130,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "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", @@ -122,8 +2151,11 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 6, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", @@ -138,7 +2170,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "Il restent deux petites modifications à faire.\n", "\n", @@ -152,9 +2187,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { - "collapsed": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ @@ -163,7 +2199,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "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", @@ -179,9 +2218,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "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", @@ -192,46 +2242,117 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'68422'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "sorted_data['inc'].plot()" + "sorted_data['inc'][0]" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Toute la colonne est représenté en string à cause du trait de la ligne de la semaine 19 de l'année 1989" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "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, + "execution_count": 13, "metadata": {}, "outputs": [], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "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", @@ -252,9 +2373,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { - "collapsed": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ @@ -265,7 +2387,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "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", @@ -274,8 +2399,11 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 17, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [], "source": [ "year = []\n", @@ -291,39 +2419,126 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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"2023 2873501\n", + "2008 2973918\n", + "1998 3034904\n", + "2002 3125418\n", + "2009 3444020\n", + "1994 3514763\n", + "1996 3539413\n", + "2004 3567744\n", + "1997 3620066\n", + "2015 3654892\n", + "2000 3826372\n", + "2005 3835025\n", + "1999 3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "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." @@ -331,9 +2546,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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