From f38bf9420d05f8e9bf1ca8edf7954ccc88528611 Mon Sep 17 00:00:00 2001 From: 0ac65f62d21d982ba39b93c80cd9db24 <0ac65f62d21d982ba39b93c80cd9db24@app-learninglab.inria.fr> Date: Tue, 8 Aug 2023 13:48:15 +0000 Subject: [PATCH] Adaptation de l'exercice syndrome grippaux aux syndrome de varicelle --- module3/exo1/analyse-syndrome-grippal.ipynb | 487 +-- module3/exo1/syndromes_grippaux.csv | 2023 +++++++++++++ module3/exo2/analyse-syndrome-varicelle.ipynb | 2612 +++++++++++++++++ module3/exo2/exercice.ipynb | 25 - module3/exo2/syndrome-varicelle.csv | 1705 +++++++++++ 5 files changed, 6615 insertions(+), 237 deletions(-) create mode 100644 module3/exo1/syndromes_grippaux.csv create mode 100644 module3/exo2/analyse-syndrome-varicelle.ipynb delete mode 100644 module3/exo2/exercice.ipynb create mode 100644 module3/exo2/syndrome-varicelle.csv diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index fd11360..5adbc92 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -33,7 +33,7 @@ "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\" #adresse URL du fichier \n", - "data_local = \"incidence-PAY-3.csv\" #Fichier local" + "data_local = \"syndromes_grippaux.csv\" #Fichier local" ] }, { @@ -62,12 +62,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "On test si un fichier local de données existe déjà. Si ce n'est pas le cas, on l'écrit avec les données téléchargées." + "On test si un fichier local de données existe déjà. Si ce n'est pas le cas, on l'écrit avec les données téléchargées.\n", + "Cela nous permet de toujours avoir le jeu de données sous la main, même sans accès à internet ou au site Sentinelle, et de garder en mémoire le jeu de données utilisé, au cas où celui-ci venait à être mis à jour, modifié ou supprimé." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -91,34 +92,23 @@ " \n", " \n", " \n", - " week\n", - " indicator\n", - " inc\n", - " inc_low\n", - " inc_up\n", - " inc100\n", - " inc100_low\n", - " inc100_up\n", - " geo_insee\n", - " geo_name\n", + " 0\n", + " 202330\n", + " 3\n", + " 15489\n", + " 9236.0\n", + " 21742.0\n", + " 23\n", + " 14.0\n", + " 32.0\n", + " FR\n", + " France\n", " \n", " \n", " \n", " \n", " 0\n", - " 202330\n", - " 3\n", - " 15489\n", - " 9236.0\n", - " 21742.0\n", - " 23\n", - " 14.0\n", - " 32.0\n", - " FR\n", - " France\n", - " \n", - " \n", - " 1\n", + " 1\n", " 202329\n", " 3\n", " 11347\n", @@ -131,7 +121,8 @@ " France\n", " \n", " \n", - " 2\n", + " 1\n", + " 2\n", " 202328\n", " 3\n", " 9179\n", @@ -144,7 +135,8 @@ " France\n", " \n", " \n", - " 3\n", + " 2\n", + " 3\n", " 202327\n", " 3\n", " 8999\n", @@ -157,7 +149,8 @@ " France\n", " \n", " \n", - " 4\n", + " 3\n", + " 4\n", " 202326\n", " 3\n", " 9023\n", @@ -170,7 +163,8 @@ " France\n", " \n", " \n", - " 5\n", + " 4\n", + " 5\n", " 202325\n", " 3\n", " 10090\n", @@ -183,7 +177,8 @@ " France\n", " \n", " \n", - " 6\n", + " 5\n", + " 6\n", " 202324\n", " 3\n", " 11308\n", @@ -196,7 +191,8 @@ " France\n", " \n", " \n", - " 7\n", + " 6\n", + " 7\n", " 202323\n", " 3\n", " 14300\n", @@ -209,7 +205,8 @@ " France\n", " \n", " \n", - " 8\n", + " 7\n", + " 8\n", " 202322\n", " 3\n", " 18303\n", @@ -222,7 +219,8 @@ " France\n", " \n", " \n", - " 9\n", + " 8\n", + " 9\n", " 202321\n", " 3\n", " 16460\n", @@ -235,7 +233,8 @@ " France\n", " \n", " \n", - " 10\n", + " 9\n", + " 10\n", " 202320\n", " 3\n", " 16162\n", @@ -248,7 +247,8 @@ " France\n", " \n", " \n", - " 11\n", + " 10\n", + " 11\n", " 202319\n", " 3\n", " 16901\n", @@ -261,7 +261,8 @@ " France\n", " \n", " \n", - " 12\n", + " 11\n", + " 12\n", " 202318\n", " 3\n", " 19929\n", @@ -274,7 +275,8 @@ " France\n", " \n", " \n", - " 13\n", + " 12\n", + " 13\n", " 202317\n", " 3\n", " 27007\n", @@ -287,7 +289,8 @@ " France\n", " \n", " \n", - " 14\n", + " 13\n", + " 14\n", " 202316\n", " 3\n", " 27875\n", @@ -300,7 +303,8 @@ " France\n", " \n", " \n", - " 15\n", + " 14\n", + " 15\n", " 202315\n", " 3\n", " 37455\n", @@ -313,7 +317,8 @@ " France\n", " \n", " \n", - " 16\n", + " 15\n", + " 16\n", " 202314\n", " 3\n", " 48060\n", @@ -326,7 +331,8 @@ " France\n", " \n", " \n", - " 17\n", + " 16\n", + " 17\n", " 202313\n", " 3\n", " 64859\n", @@ -339,7 +345,8 @@ " France\n", " \n", " \n", - " 18\n", + " 17\n", + " 18\n", " 202312\n", " 3\n", " 72750\n", @@ -352,7 +359,8 @@ " France\n", " \n", " \n", - " 19\n", + " 18\n", + " 19\n", " 202311\n", " 3\n", " 74638\n", @@ -365,7 +373,8 @@ " France\n", " \n", " \n", - " 20\n", + " 19\n", + " 20\n", " 202310\n", " 3\n", " 76368\n", @@ -378,7 +387,8 @@ " France\n", " \n", " \n", - " 21\n", + " 20\n", + " 21\n", " 202309\n", " 3\n", " 62062\n", @@ -391,7 +401,8 @@ " France\n", " \n", " \n", - " 22\n", + " 21\n", + " 22\n", " 202308\n", " 3\n", " 76391\n", @@ -404,7 +415,8 @@ " France\n", " \n", " \n", - " 23\n", + " 22\n", + " 23\n", " 202307\n", " 3\n", " 89851\n", @@ -417,7 +429,8 @@ " France\n", " \n", " \n", - " 24\n", + " 23\n", + " 24\n", " 202306\n", " 3\n", " 97368\n", @@ -430,7 +443,8 @@ " France\n", " \n", " \n", - " 25\n", + " 24\n", + " 25\n", " 202305\n", " 3\n", " 95469\n", @@ -443,7 +457,8 @@ " France\n", " \n", " \n", - " 26\n", + " 25\n", + " 26\n", " 202304\n", " 3\n", " 74901\n", @@ -456,7 +471,8 @@ " France\n", " \n", " \n", - " 27\n", + " 26\n", + " 27\n", " 202303\n", " 3\n", " 69570\n", @@ -469,7 +485,8 @@ " France\n", " \n", " \n", - " 28\n", + " 27\n", + " 28\n", " 202302\n", " 3\n", " 78260\n", @@ -482,7 +499,8 @@ " France\n", " \n", " \n", - " 29\n", + " 28\n", + " 29\n", " 202301\n", " 3\n", " 121773\n", @@ -495,6 +513,20 @@ " France\n", " \n", " \n", + " 29\n", + " 30\n", + " 202252\n", + " 3\n", + " 155371\n", + " 142004.0\n", + " 168738.0\n", + " 234\n", + " 214.0\n", + " 254.0\n", + " FR\n", + " France\n", + " \n", + " \n", " ...\n", " ...\n", " ...\n", @@ -506,9 +538,11 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", - " 1992\n", + " 1991\n", + " 1992\n", " 198521\n", " 3\n", " 26096\n", @@ -521,7 +555,8 @@ " France\n", " \n", " \n", - " 1993\n", + " 1992\n", + " 1993\n", " 198520\n", " 3\n", " 27896\n", @@ -534,7 +569,8 @@ " France\n", " \n", " \n", - " 1994\n", + " 1993\n", + " 1994\n", " 198519\n", " 3\n", " 43154\n", @@ -547,7 +583,8 @@ " France\n", " \n", " \n", - " 1995\n", + " 1994\n", + " 1995\n", " 198518\n", " 3\n", " 40555\n", @@ -560,7 +597,8 @@ " France\n", " \n", " \n", - " 1996\n", + " 1995\n", + " 1996\n", " 198517\n", " 3\n", " 34053\n", @@ -573,7 +611,8 @@ " France\n", " \n", " \n", - " 1997\n", + " 1996\n", + " 1997\n", " 198516\n", " 3\n", " 50362\n", @@ -586,7 +625,8 @@ " France\n", " \n", " \n", - " 1998\n", + " 1997\n", + " 1998\n", " 198515\n", " 3\n", " 63881\n", @@ -599,7 +639,8 @@ " France\n", " \n", " \n", - " 1999\n", + " 1998\n", + " 1999\n", " 198514\n", " 3\n", " 134545\n", @@ -612,7 +653,8 @@ " France\n", " \n", " \n", - " 2000\n", + " 1999\n", + " 2000\n", " 198513\n", " 3\n", " 197206\n", @@ -625,7 +667,8 @@ " France\n", " \n", " \n", - " 2001\n", + " 2000\n", + " 2001\n", " 198512\n", " 3\n", " 245240\n", @@ -638,7 +681,8 @@ " France\n", " \n", " \n", - " 2002\n", + " 2001\n", + " 2002\n", " 198511\n", " 3\n", " 276205\n", @@ -651,7 +695,8 @@ " France\n", " \n", " \n", - " 2003\n", + " 2002\n", + " 2003\n", " 198510\n", " 3\n", " 353231\n", @@ -664,7 +709,8 @@ " France\n", " \n", " \n", - " 2004\n", + " 2003\n", + " 2004\n", " 198509\n", " 3\n", " 369895\n", @@ -677,7 +723,8 @@ " France\n", " \n", " \n", - " 2005\n", + " 2004\n", + " 2005\n", " 198508\n", " 3\n", " 389886\n", @@ -690,7 +737,8 @@ " France\n", " \n", " \n", - " 2006\n", + " 2005\n", + " 2006\n", " 198507\n", " 3\n", " 471852\n", @@ -703,7 +751,8 @@ " France\n", " \n", " \n", - " 2007\n", + " 2006\n", + " 2007\n", " 198506\n", " 3\n", " 565825\n", @@ -716,7 +765,8 @@ " France\n", " \n", " \n", - " 2008\n", + " 2007\n", + " 2008\n", " 198505\n", " 3\n", " 637302\n", @@ -729,7 +779,8 @@ " France\n", " \n", " \n", - " 2009\n", + " 2008\n", + " 2009\n", " 198504\n", " 3\n", " 424937\n", @@ -742,7 +793,8 @@ " France\n", " \n", " \n", - " 2010\n", + " 2009\n", + " 2010\n", " 198503\n", " 3\n", " 213901\n", @@ -755,7 +807,8 @@ " France\n", " \n", " \n", - " 2011\n", + " 2010\n", + " 2011\n", " 198502\n", " 3\n", " 97586\n", @@ -768,7 +821,8 @@ " France\n", " \n", " \n", - " 2012\n", + " 2011\n", + " 2012\n", " 198501\n", " 3\n", " 85489\n", @@ -781,7 +835,8 @@ " France\n", " \n", " \n", - " 2013\n", + " 2012\n", + " 2013\n", " 198452\n", " 3\n", " 84830\n", @@ -794,7 +849,8 @@ " France\n", " \n", " \n", - " 2014\n", + " 2013\n", + " 2014\n", " 198451\n", " 3\n", " 101726\n", @@ -807,7 +863,8 @@ " France\n", " \n", " \n", - " 2015\n", + " 2014\n", + " 2015\n", " 198450\n", " 3\n", " 123680\n", @@ -820,7 +877,8 @@ " France\n", " \n", " \n", - " 2016\n", + " 2015\n", + " 2016\n", " 198449\n", " 3\n", " 101073\n", @@ -833,7 +891,8 @@ " France\n", " \n", " \n", - " 2017\n", + " 2016\n", + " 2017\n", " 198448\n", " 3\n", " 78620\n", @@ -846,7 +905,8 @@ " France\n", " \n", " \n", - " 2018\n", + " 2017\n", + " 2018\n", " 198447\n", " 3\n", " 72029\n", @@ -859,7 +919,8 @@ " France\n", " \n", " \n", - " 2019\n", + " 2018\n", + " 2019\n", " 198446\n", " 3\n", " 87330\n", @@ -872,7 +933,8 @@ " France\n", " \n", " \n", - " 2020\n", + " 2019\n", + " 2020\n", " 198445\n", " 3\n", " 135223\n", @@ -885,7 +947,8 @@ " France\n", " \n", " \n", - " 2021\n", + " 2020\n", + " 2021\n", " 198444\n", " 3\n", " 68422\n", @@ -899,140 +962,140 @@ " \n", " \n", "\n", - "

2022 rows × 10 columns

\n", + "

2021 rows × 11 columns

\n", "" ], "text/plain": [ - " week indicator inc inc_low inc_up inc100 inc100_low \\\n", - "0 202330 3 15489 9236.0 21742.0 23 14.0 \n", - "1 202329 3 11347 7199.0 15495.0 17 11.0 \n", - "2 202328 3 9179 5703.0 12655.0 14 9.0 \n", - "3 202327 3 8999 5763.0 12235.0 14 9.0 \n", - "4 202326 3 9023 5934.0 12112.0 14 9.0 \n", - "5 202325 3 10090 6739.0 13441.0 15 10.0 \n", - "6 202324 3 11308 7639.0 14977.0 17 11.0 \n", - "7 202323 3 14300 10661.0 17939.0 22 17.0 \n", - "8 202322 3 18303 13822.0 22784.0 28 21.0 \n", - "9 202321 3 16460 12188.0 20732.0 25 19.0 \n", - "10 202320 3 16162 11963.0 20361.0 24 18.0 \n", - "11 202319 3 16901 12577.0 21225.0 25 18.0 \n", - "12 202318 3 19929 15402.0 24456.0 30 23.0 \n", - "13 202317 3 27007 21779.0 32235.0 41 33.0 \n", - "14 202316 3 27875 22767.0 32983.0 42 34.0 \n", - "15 202315 3 37455 30993.0 43917.0 56 46.0 \n", - "16 202314 3 48060 40671.0 55449.0 72 61.0 \n", - "17 202313 3 64859 56800.0 72918.0 98 86.0 \n", - "18 202312 3 72750 64499.0 81001.0 109 97.0 \n", - "19 202311 3 74638 66420.0 82856.0 112 100.0 \n", - "20 202310 3 76368 68243.0 84493.0 115 103.0 \n", - "21 202309 3 62062 54778.0 69346.0 93 82.0 \n", - "22 202308 3 76391 68065.0 84717.0 115 102.0 \n", - "23 202307 3 89851 80397.0 99305.0 135 121.0 \n", - "24 202306 3 97368 87636.0 107100.0 146 131.0 \n", - "25 202305 3 95469 86268.0 104670.0 144 130.0 \n", - "26 202304 3 74901 66916.0 82886.0 113 101.0 \n", - "27 202303 3 69570 61893.0 77247.0 105 93.0 \n", - "28 202302 3 78260 70090.0 86430.0 118 106.0 \n", - "29 202301 3 121773 111024.0 132522.0 183 167.0 \n", - "... ... ... ... ... ... ... ... \n", - "1992 198521 3 26096 19621.0 32571.0 47 35.0 \n", - "1993 198520 3 27896 20885.0 34907.0 51 38.0 \n", - "1994 198519 3 43154 32821.0 53487.0 78 59.0 \n", - "1995 198518 3 40555 29935.0 51175.0 74 55.0 \n", - "1996 198517 3 34053 24366.0 43740.0 62 44.0 \n", - "1997 198516 3 50362 36451.0 64273.0 91 66.0 \n", - "1998 198515 3 63881 45538.0 82224.0 116 83.0 \n", - "1999 198514 3 134545 114400.0 154690.0 244 207.0 \n", - "2000 198513 3 197206 176080.0 218332.0 357 319.0 \n", - "2001 198512 3 245240 223304.0 267176.0 445 405.0 \n", - "2002 198511 3 276205 252399.0 300011.0 501 458.0 \n", - "2003 198510 3 353231 326279.0 380183.0 640 591.0 \n", - "2004 198509 3 369895 341109.0 398681.0 670 618.0 \n", - "2005 198508 3 389886 359529.0 420243.0 707 652.0 \n", - "2006 198507 3 471852 432599.0 511105.0 855 784.0 \n", - "2007 198506 3 565825 518011.0 613639.0 1026 939.0 \n", - "2008 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", - "2009 198504 3 424937 390794.0 459080.0 770 708.0 \n", - "2010 198503 3 213901 174689.0 253113.0 388 317.0 \n", - "2011 198502 3 97586 80949.0 114223.0 177 147.0 \n", - "2012 198501 3 85489 65918.0 105060.0 155 120.0 \n", - "2013 198452 3 84830 60602.0 109058.0 154 110.0 \n", - "2014 198451 3 101726 80242.0 123210.0 185 146.0 \n", - "2015 198450 3 123680 101401.0 145959.0 225 184.0 \n", - "2016 198449 3 101073 81684.0 120462.0 184 149.0 \n", - "2017 198448 3 78620 60634.0 96606.0 143 110.0 \n", - "2018 198447 3 72029 54274.0 89784.0 131 99.0 \n", - "2019 198446 3 87330 67686.0 106974.0 159 123.0 \n", - "2020 198445 3 135223 101414.0 169032.0 246 184.0 \n", - "2021 198444 3 68422 20056.0 116788.0 125 37.0 \n", + " 0 202330 3 15489 9236.0 21742.0 23 14.0 32.0 FR \\\n", + "0 1 202329 3 11347 7199.0 15495.0 17 11.0 23.0 FR \n", + "1 2 202328 3 9179 5703.0 12655.0 14 9.0 19.0 FR \n", + "2 3 202327 3 8999 5763.0 12235.0 14 9.0 19.0 FR \n", + "3 4 202326 3 9023 5934.0 12112.0 14 9.0 19.0 FR \n", + "4 5 202325 3 10090 6739.0 13441.0 15 10.0 20.0 FR \n", + "5 6 202324 3 11308 7639.0 14977.0 17 11.0 23.0 FR \n", + "6 7 202323 3 14300 10661.0 17939.0 22 17.0 27.0 FR \n", + "7 8 202322 3 18303 13822.0 22784.0 28 21.0 35.0 FR \n", + "8 9 202321 3 16460 12188.0 20732.0 25 19.0 31.0 FR \n", + "9 10 202320 3 16162 11963.0 20361.0 24 18.0 30.0 FR \n", + "10 11 202319 3 16901 12577.0 21225.0 25 18.0 32.0 FR \n", + "11 12 202318 3 19929 15402.0 24456.0 30 23.0 37.0 FR \n", + "12 13 202317 3 27007 21779.0 32235.0 41 33.0 49.0 FR \n", + "13 14 202316 3 27875 22767.0 32983.0 42 34.0 50.0 FR \n", + "14 15 202315 3 37455 30993.0 43917.0 56 46.0 66.0 FR \n", + "15 16 202314 3 48060 40671.0 55449.0 72 61.0 83.0 FR \n", + "16 17 202313 3 64859 56800.0 72918.0 98 86.0 110.0 FR \n", + "17 18 202312 3 72750 64499.0 81001.0 109 97.0 121.0 FR \n", + "18 19 202311 3 74638 66420.0 82856.0 112 100.0 124.0 FR \n", + "19 20 202310 3 76368 68243.0 84493.0 115 103.0 127.0 FR \n", + "20 21 202309 3 62062 54778.0 69346.0 93 82.0 104.0 FR \n", + "21 22 202308 3 76391 68065.0 84717.0 115 102.0 128.0 FR \n", + "22 23 202307 3 89851 80397.0 99305.0 135 121.0 149.0 FR \n", + "23 24 202306 3 97368 87636.0 107100.0 146 131.0 161.0 FR \n", + "24 25 202305 3 95469 86268.0 104670.0 144 130.0 158.0 FR \n", + "25 26 202304 3 74901 66916.0 82886.0 113 101.0 125.0 FR \n", + "26 27 202303 3 69570 61893.0 77247.0 105 93.0 117.0 FR \n", + "27 28 202302 3 78260 70090.0 86430.0 118 106.0 130.0 FR \n", + "28 29 202301 3 121773 111024.0 132522.0 183 167.0 199.0 FR \n", + "29 30 202252 3 155371 142004.0 168738.0 234 214.0 254.0 FR \n", + "... ... ... .. ... ... ... ... ... ... .. \n", + "1991 1992 198521 3 26096 19621.0 32571.0 47 35.0 59.0 FR \n", + "1992 1993 198520 3 27896 20885.0 34907.0 51 38.0 64.0 FR \n", + "1993 1994 198519 3 43154 32821.0 53487.0 78 59.0 97.0 FR \n", + "1994 1995 198518 3 40555 29935.0 51175.0 74 55.0 93.0 FR \n", + "1995 1996 198517 3 34053 24366.0 43740.0 62 44.0 80.0 FR \n", + "1996 1997 198516 3 50362 36451.0 64273.0 91 66.0 116.0 FR \n", + "1997 1998 198515 3 63881 45538.0 82224.0 116 83.0 149.0 FR \n", + "1998 1999 198514 3 134545 114400.0 154690.0 244 207.0 281.0 FR \n", + "1999 2000 198513 3 197206 176080.0 218332.0 357 319.0 395.0 FR \n", + "2000 2001 198512 3 245240 223304.0 267176.0 445 405.0 485.0 FR \n", + "2001 2002 198511 3 276205 252399.0 300011.0 501 458.0 544.0 FR \n", + "2002 2003 198510 3 353231 326279.0 380183.0 640 591.0 689.0 FR \n", + "2003 2004 198509 3 369895 341109.0 398681.0 670 618.0 722.0 FR \n", + "2004 2005 198508 3 389886 359529.0 420243.0 707 652.0 762.0 FR \n", + "2005 2006 198507 3 471852 432599.0 511105.0 855 784.0 926.0 FR \n", + "2006 2007 198506 3 565825 518011.0 613639.0 1026 939.0 1113.0 FR \n", + "2007 2008 198505 3 637302 592795.0 681809.0 1155 1074.0 1236.0 FR \n", + "2008 2009 198504 3 424937 390794.0 459080.0 770 708.0 832.0 FR \n", + "2009 2010 198503 3 213901 174689.0 253113.0 388 317.0 459.0 FR \n", + "2010 2011 198502 3 97586 80949.0 114223.0 177 147.0 207.0 FR \n", + "2011 2012 198501 3 85489 65918.0 105060.0 155 120.0 190.0 FR \n", + "2012 2013 198452 3 84830 60602.0 109058.0 154 110.0 198.0 FR \n", + "2013 2014 198451 3 101726 80242.0 123210.0 185 146.0 224.0 FR \n", + "2014 2015 198450 3 123680 101401.0 145959.0 225 184.0 266.0 FR \n", + "2015 2016 198449 3 101073 81684.0 120462.0 184 149.0 219.0 FR \n", + "2016 2017 198448 3 78620 60634.0 96606.0 143 110.0 176.0 FR \n", + "2017 2018 198447 3 72029 54274.0 89784.0 131 99.0 163.0 FR \n", + "2018 2019 198446 3 87330 67686.0 106974.0 159 123.0 195.0 FR \n", + "2019 2020 198445 3 135223 101414.0 169032.0 246 184.0 308.0 FR \n", + "2020 2021 198444 3 68422 20056.0 116788.0 125 37.0 213.0 FR \n", "\n", - " inc100_up geo_insee geo_name \n", - "0 32.0 FR France \n", - "1 23.0 FR France \n", - "2 19.0 FR France \n", - "3 19.0 FR France \n", - "4 19.0 FR France \n", - "5 20.0 FR France \n", - "6 23.0 FR France \n", - "7 27.0 FR France \n", - "8 35.0 FR France \n", - "9 31.0 FR France \n", - "10 30.0 FR France \n", - "11 32.0 FR France \n", - "12 37.0 FR France \n", - "13 49.0 FR France \n", - "14 50.0 FR France \n", - "15 66.0 FR France \n", - "16 83.0 FR France \n", - "17 110.0 FR France \n", - "18 121.0 FR France \n", - "19 124.0 FR France \n", - "20 127.0 FR France \n", - "21 104.0 FR France \n", - "22 128.0 FR France \n", - "23 149.0 FR France \n", - "24 161.0 FR France \n", - "25 158.0 FR France \n", - "26 125.0 FR France \n", - "27 117.0 FR France \n", - "28 130.0 FR France \n", - "29 199.0 FR France \n", - "... ... ... ... \n", - "1992 59.0 FR France \n", - "1993 64.0 FR France \n", - "1994 97.0 FR France \n", - "1995 93.0 FR France \n", - "1996 80.0 FR France \n", - "1997 116.0 FR France \n", - "1998 149.0 FR France \n", - "1999 281.0 FR France \n", - "2000 395.0 FR France \n", - "2001 485.0 FR France \n", - "2002 544.0 FR France \n", - "2003 689.0 FR France \n", - "2004 722.0 FR France \n", - "2005 762.0 FR France \n", - "2006 926.0 FR France \n", - "2007 1113.0 FR France \n", - "2008 1236.0 FR France \n", - "2009 832.0 FR France \n", - "2010 459.0 FR France \n", - "2011 207.0 FR France \n", - "2012 190.0 FR France \n", - "2013 198.0 FR France \n", - "2014 224.0 FR France \n", - "2015 266.0 FR France \n", - "2016 219.0 FR France \n", - "2017 176.0 FR France \n", - "2018 163.0 FR France \n", - "2019 195.0 FR France \n", - "2020 308.0 FR France \n", - "2021 213.0 FR France \n", + " France \n", + "0 France \n", + "1 France \n", + "2 France \n", + "3 France \n", + "4 France \n", + "5 France \n", + "6 France \n", + "7 France \n", + "8 France \n", + "9 France \n", + "10 France \n", + "11 France \n", + "12 France \n", + "13 France \n", + "14 France \n", + "15 France \n", + "16 France \n", + "17 France \n", + "18 France \n", + "19 France \n", + "20 France \n", + "21 France \n", + "22 France \n", + "23 France \n", + "24 France \n", + "25 France \n", + "26 France \n", + "27 France \n", + "28 France \n", + "29 France \n", + "... ... \n", + "1991 France \n", + "1992 France \n", + "1993 France \n", + "1994 France \n", + "1995 France \n", + "1996 France \n", + "1997 France \n", + "1998 France \n", + "1999 France \n", + "2000 France \n", + "2001 France \n", + "2002 France \n", + "2003 France \n", + "2004 France \n", + "2005 France \n", + "2006 France \n", + "2007 France \n", + "2008 France \n", + "2009 France \n", + "2010 France \n", + "2011 France \n", + "2012 France \n", + "2013 France \n", + "2014 France \n", + "2015 France \n", + "2016 France \n", + "2017 France \n", + "2018 France \n", + "2019 France \n", + "2020 France \n", "\n", - "[2022 rows x 10 columns]" + "[2021 rows x 11 columns]" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1040,8 +1103,8 @@ "source": [ "try:\n", " raw_data = pd.read_csv(data_local, skiprows=1)\n", - "\n", "except FileNotFoundError: \n", + " print(\"File does not exist, downloading the data and writing them in a file...\")\n", " raw_data = pd.read_csv(data_url, skiprows=1)\n", " raw_data.to_csv(data_local)\n", "raw_data" diff --git a/module3/exo1/syndromes_grippaux.csv b/module3/exo1/syndromes_grippaux.csv new file mode 100644 index 0000000..6cdebdb --- /dev/null +++ b/module3/exo1/syndromes_grippaux.csv @@ -0,0 +1,2023 @@ +,week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name 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as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence du syndrome de la varicelle 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 1991 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv?v=2vnb9\" # URL de téléchargement\n", + "data_local = \"syndrome-varicelle.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": "markdown", + "metadata": {}, + "source": [ + "On test si un fichier local de données existe déjà. Si ce n'est pas le cas, on l'écrit avec les données téléchargées.\n", + "Cela nous permet de toujours avoir le jeu de données sous la main, même sans accès à internet ou au site Sentinelle, et de garder en mémoire le jeu de données utilisé, au cas où celui-ci venait à être mis à jour, modifié ou supprimé." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File does not exist, downloading the data and writing them in a file...\n" + ] + }, + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02023307681934171022110515FRFrance
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420232679192622312161141018FRFrance
5202325711498825714739171222FRFrance
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112023197934460911259714919FRFrance
12202318710671729114051161121FRFrance
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17202313713322970016944201525FRFrance
18202312710374721813530161121FRFrance
1920231174919288069587410FRFrance
2020231074854273169777410FRFrance
21202309770044548946011715FRFrance
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23202307765953782940810614FRFrance
242023067959560171317314919FRFrance
2520230576237390785679513FRFrance
2620230476299397386259612FRFrance
2720230376063379883289612FRFrance
282023027657630601009210515FRFrance
292023017815354701083612816FRFrance
.................................
16741991267176081130423912312042FRFrance
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1704 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202330 7 6819 3417 10221 10 5 \n", + "1 202329 7 14037 8326 19748 21 12 \n", + "2 202328 7 6700 4043 9357 10 6 \n", + "3 202327 7 7253 4599 9907 11 7 \n", + "4 202326 7 9192 6223 12161 14 10 \n", + "5 202325 7 11498 8257 14739 17 12 \n", + "6 202324 7 11115 7968 14262 17 12 \n", + "7 202323 7 12563 6134 18992 19 9 \n", + "8 202322 7 12184 8125 16243 18 12 \n", + "9 202321 7 11349 7598 15100 17 11 \n", + "10 202320 7 9000 4615 13385 14 7 \n", + "11 202319 7 9344 6091 12597 14 9 \n", + "12 202318 7 10671 7291 14051 16 11 \n", + "13 202317 7 9184 6162 12206 14 9 \n", + "14 202316 7 11387 8014 14760 17 12 \n", + "15 202315 7 14040 7613 20467 21 11 \n", + "16 202314 7 15247 11032 19462 23 17 \n", + "17 202313 7 13322 9700 16944 20 15 \n", + "18 202312 7 10374 7218 13530 16 11 \n", + "19 202311 7 4919 2880 6958 7 4 \n", + "20 202310 7 4854 2731 6977 7 4 \n", + "21 202309 7 7004 4548 9460 11 7 \n", + "22 202308 7 8175 5316 11034 12 8 \n", + "23 202307 7 6595 3782 9408 10 6 \n", + "24 202306 7 9595 6017 13173 14 9 \n", + "25 202305 7 6237 3907 8567 9 5 \n", + "26 202304 7 6299 3973 8625 9 6 \n", + "27 202303 7 6063 3798 8328 9 6 \n", + "28 202302 7 6576 3060 10092 10 5 \n", + "29 202301 7 8153 5470 10836 12 8 \n", + "... ... ... ... ... ... ... ... \n", + "1674 199126 7 17608 11304 23912 31 20 \n", + "1675 199125 7 16169 10700 21638 28 18 \n", + "1676 199124 7 16171 10071 22271 28 17 \n", + "1677 199123 7 11947 7671 16223 21 13 \n", + "1678 199122 7 15452 9953 20951 27 17 \n", + "1679 199121 7 14903 8975 20831 26 16 \n", + "1680 199120 7 19053 12742 25364 34 23 \n", + "1681 199119 7 16739 11246 22232 29 19 \n", + "1682 199118 7 21385 13882 28888 38 25 \n", + "1683 199117 7 13462 8877 18047 24 16 \n", + "1684 199116 7 14857 10068 19646 26 18 \n", + "1685 199115 7 13975 9781 18169 25 18 \n", + "1686 199114 7 12265 7684 16846 22 14 \n", + "1687 199113 7 9567 6041 13093 17 11 \n", + "1688 199112 7 10864 7331 14397 19 13 \n", + "1689 199111 7 15574 11184 19964 27 19 \n", + "1690 199110 7 16643 11372 21914 29 20 \n", + "1691 199109 7 13741 8780 18702 24 15 \n", + "1692 199108 7 13289 8813 17765 23 15 \n", + "1693 199107 7 12337 8077 16597 22 15 \n", + "1694 199106 7 10877 7013 14741 19 12 \n", + "1695 199105 7 10442 6544 14340 18 11 \n", + "1696 199104 7 7913 4563 11263 14 8 \n", + "1697 199103 7 15387 10484 20290 27 18 \n", + "1698 199102 7 16277 11046 21508 29 20 \n", + "1699 199101 7 15565 10271 20859 27 18 \n", + "1700 199052 7 19375 13295 25455 34 23 \n", + "1701 199051 7 19080 13807 24353 34 25 \n", + "1702 199050 7 11079 6660 15498 20 12 \n", + "1703 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 15 FR France \n", + "1 30 FR France \n", + "2 14 FR France \n", + "3 15 FR France \n", + "4 18 FR France \n", + "5 22 FR France \n", + "6 22 FR France \n", + "7 29 FR France \n", + "8 24 FR France \n", + "9 23 FR France \n", + "10 21 FR France \n", + "11 19 FR France \n", + "12 21 FR France \n", + "13 19 FR France \n", + "14 22 FR France \n", + "15 31 FR France \n", + "16 29 FR France \n", + "17 25 FR France \n", + "18 21 FR France \n", + "19 10 FR France \n", + "20 10 FR France \n", + "21 15 FR France \n", + "22 16 FR France \n", + "23 14 FR France \n", + "24 19 FR France \n", + "25 13 FR France \n", + "26 12 FR France \n", + "27 12 FR France \n", + "28 15 FR France \n", + "29 16 FR France \n", + "... ... ... ... \n", + "1674 42 FR France \n", + "1675 38 FR France \n", + "1676 39 FR France \n", + "1677 29 FR France \n", + "1678 37 FR France \n", + "1679 36 FR France \n", + "1680 45 FR France \n", + "1681 39 FR France \n", + "1682 51 FR France \n", + "1683 32 FR France \n", + "1684 34 FR France \n", + "1685 32 FR France \n", + "1686 30 FR France \n", + "1687 23 FR France \n", + "1688 25 FR France \n", + "1689 35 FR France \n", + "1690 38 FR France \n", + "1691 33 FR France \n", + "1692 31 FR France \n", + "1693 29 FR France \n", + "1694 26 FR France \n", + "1695 25 FR France \n", + "1696 20 FR France \n", + "1697 36 FR France \n", + "1698 38 FR France \n", + "1699 36 FR France \n", + "1700 45 FR France \n", + "1701 43 FR France \n", + "1702 28 FR France \n", + "1703 5 FR France \n", + "\n", + "[1704 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "try:\n", + " raw_data = pd.read_csv(data_local, skiprows=1)\n", + "except FileNotFoundError: \n", + " print(\"File does not exist, downloading the data and writing them in a file...\")\n", + " raw_data = pd.read_csv(data_url, skiprows=1)\n", + " raw_data.to_csv(data_local)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Points manquants dans le jeu de données" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", + "Index: []" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pas de points manquants. Au cas où, ci-après la commande pour en retirer s'il y en avait. On renomme le dataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02023307681934171022110515FRFrance
1202329714037832619748211230FRFrance
2202328767004043935710614FRFrance
3202327772534599990711715FRFrance
420232679192622312161141018FRFrance
5202325711498825714739171222FRFrance
6202324711115796814262171222FRFrance
720232371256361341899219929FRFrance
8202322712184812516243181224FRFrance
9202321711349759815100171123FRFrance
102023207900046151338514721FRFrance
112023197934460911259714919FRFrance
12202318710671729114051161121FRFrance
132023177918461621220614919FRFrance
14202316711387801414760171222FRFrance
15202315714040761320467211131FRFrance
162023147152471103219462231729FRFrance
17202313713322970016944201525FRFrance
18202312710374721813530161121FRFrance
1920231174919288069587410FRFrance
2020231074854273169777410FRFrance
21202309770044548946011715FRFrance
222023087817553161103412816FRFrance
23202307765953782940810614FRFrance
242023067959560171317314919FRFrance
2520230576237390785679513FRFrance
2620230476299397386259612FRFrance
2720230376063379883289612FRFrance
282023027657630601009210515FRFrance
292023017815354701083612816FRFrance
.................................
16741991267176081130423912312042FRFrance
16751991257161691070021638281838FRFrance
16761991247161711007122271281739FRFrance
1677199123711947767116223211329FRFrance
1678199122715452995320951271737FRFrance
1679199121714903897520831261636FRFrance
16801991207190531274225364342345FRFrance
16811991197167391124622232291939FRFrance
16821991187213851388228888382551FRFrance
1683199117713462887718047241632FRFrance
16841991167148571006819646261834FRFrance
1685199115713975978118169251832FRFrance
1686199114712265768416846221430FRFrance
168719911379567604113093171123FRFrance
1688199112710864733114397191325FRFrance
16891991117155741118419964271935FRFrance
16901991107166431137221914292038FRFrance
1691199109713741878018702241533FRFrance
1692199108713289881317765231531FRFrance
1693199107712337807716597221529FRFrance
1694199106710877701314741191226FRFrance
1695199105710442654414340181125FRFrance
16961991047791345631126314820FRFrance
16971991037153871048420290271836FRFrance
16981991027162771104621508292038FRFrance
16991991017155651027120859271836FRFrance
17001990527193751329525455342345FRFrance
17011990517190801380724353342543FRFrance
1702199050711079666015498201228FRFrance
17031990497114302610205FRFrance
\n", + "

1704 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202330 7 6819 3417 10221 10 5 \n", + "1 202329 7 14037 8326 19748 21 12 \n", + "2 202328 7 6700 4043 9357 10 6 \n", + "3 202327 7 7253 4599 9907 11 7 \n", + "4 202326 7 9192 6223 12161 14 10 \n", + "5 202325 7 11498 8257 14739 17 12 \n", + "6 202324 7 11115 7968 14262 17 12 \n", + "7 202323 7 12563 6134 18992 19 9 \n", + "8 202322 7 12184 8125 16243 18 12 \n", + "9 202321 7 11349 7598 15100 17 11 \n", + "10 202320 7 9000 4615 13385 14 7 \n", + "11 202319 7 9344 6091 12597 14 9 \n", + "12 202318 7 10671 7291 14051 16 11 \n", + "13 202317 7 9184 6162 12206 14 9 \n", + "14 202316 7 11387 8014 14760 17 12 \n", + "15 202315 7 14040 7613 20467 21 11 \n", + "16 202314 7 15247 11032 19462 23 17 \n", + "17 202313 7 13322 9700 16944 20 15 \n", + "18 202312 7 10374 7218 13530 16 11 \n", + "19 202311 7 4919 2880 6958 7 4 \n", + "20 202310 7 4854 2731 6977 7 4 \n", + "21 202309 7 7004 4548 9460 11 7 \n", + "22 202308 7 8175 5316 11034 12 8 \n", + "23 202307 7 6595 3782 9408 10 6 \n", + "24 202306 7 9595 6017 13173 14 9 \n", + "25 202305 7 6237 3907 8567 9 5 \n", + "26 202304 7 6299 3973 8625 9 6 \n", + "27 202303 7 6063 3798 8328 9 6 \n", + "28 202302 7 6576 3060 10092 10 5 \n", + "29 202301 7 8153 5470 10836 12 8 \n", + "... ... ... ... ... ... ... ... \n", + "1674 199126 7 17608 11304 23912 31 20 \n", + "1675 199125 7 16169 10700 21638 28 18 \n", + "1676 199124 7 16171 10071 22271 28 17 \n", + "1677 199123 7 11947 7671 16223 21 13 \n", + "1678 199122 7 15452 9953 20951 27 17 \n", + "1679 199121 7 14903 8975 20831 26 16 \n", + "1680 199120 7 19053 12742 25364 34 23 \n", + "1681 199119 7 16739 11246 22232 29 19 \n", + "1682 199118 7 21385 13882 28888 38 25 \n", + "1683 199117 7 13462 8877 18047 24 16 \n", + "1684 199116 7 14857 10068 19646 26 18 \n", + "1685 199115 7 13975 9781 18169 25 18 \n", + "1686 199114 7 12265 7684 16846 22 14 \n", + "1687 199113 7 9567 6041 13093 17 11 \n", + "1688 199112 7 10864 7331 14397 19 13 \n", + "1689 199111 7 15574 11184 19964 27 19 \n", + "1690 199110 7 16643 11372 21914 29 20 \n", + "1691 199109 7 13741 8780 18702 24 15 \n", + "1692 199108 7 13289 8813 17765 23 15 \n", + "1693 199107 7 12337 8077 16597 22 15 \n", + "1694 199106 7 10877 7013 14741 19 12 \n", + "1695 199105 7 10442 6544 14340 18 11 \n", + "1696 199104 7 7913 4563 11263 14 8 \n", + "1697 199103 7 15387 10484 20290 27 18 \n", + "1698 199102 7 16277 11046 21508 29 20 \n", + "1699 199101 7 15565 10271 20859 27 18 \n", + "1700 199052 7 19375 13295 25455 34 23 \n", + "1701 199051 7 19080 13807 24353 34 25 \n", + "1702 199050 7 11079 6660 15498 20 12 \n", + "1703 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 15 FR France \n", + "1 30 FR France \n", + "2 14 FR France \n", + "3 15 FR France \n", + "4 18 FR France \n", + "5 22 FR France \n", + "6 22 FR France \n", + "7 29 FR France \n", + "8 24 FR France \n", + "9 23 FR France \n", + "10 21 FR France \n", + "11 19 FR France \n", + "12 21 FR France \n", + "13 19 FR France \n", + "14 22 FR France \n", + "15 31 FR France \n", + "16 29 FR France \n", + "17 25 FR France \n", + "18 21 FR France \n", + "19 10 FR France \n", + "20 10 FR France \n", + "21 15 FR France \n", + "22 16 FR France \n", + "23 14 FR France \n", + "24 19 FR France \n", + "25 13 FR France \n", + "26 12 FR France \n", + "27 12 FR France \n", + "28 15 FR France \n", + "29 16 FR France \n", + "... ... ... ... \n", + "1674 42 FR France \n", + "1675 38 FR France \n", + "1676 39 FR France \n", + "1677 29 FR France \n", + "1678 37 FR France \n", + "1679 36 FR France \n", + "1680 45 FR France \n", + "1681 39 FR France \n", + "1682 51 FR France \n", + "1683 32 FR France \n", + "1684 34 FR France \n", + "1685 32 FR France \n", + "1686 30 FR France \n", + "1687 23 FR France \n", + "1688 25 FR France \n", + "1689 35 FR France \n", + "1690 38 FR France \n", + "1691 33 FR France \n", + "1692 31 FR France \n", + "1693 29 FR France \n", + "1694 26 FR France \n", + "1695 25 FR France \n", + "1696 20 FR France \n", + "1697 36 FR France \n", + "1698 38 FR France \n", + "1699 36 FR France \n", + "1700 45 FR France \n", + "1701 43 FR France \n", + "1702 28 FR France \n", + "1703 5 FR France \n", + "\n", + "[1704 rows x 10 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reformatage de la date\n", + "\n", + "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": 11, + "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": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2023-07-24/2023-07-30\n", + "1 2023-07-17/2023-07-23\n", + "2 2023-07-10/2023-07-16\n", + "3 2023-07-03/2023-07-09\n", + "4 2023-06-26/2023-07-02\n", + "5 2023-06-19/2023-06-25\n", + "6 2023-06-12/2023-06-18\n", + "7 2023-06-05/2023-06-11\n", + "8 2023-05-29/2023-06-04\n", + "9 2023-05-22/2023-05-28\n", + "10 2023-05-15/2023-05-21\n", + "11 2023-05-08/2023-05-14\n", + "12 2023-05-01/2023-05-07\n", + "13 2023-04-24/2023-04-30\n", + "14 2023-04-17/2023-04-23\n", + "15 2023-04-10/2023-04-16\n", + "16 2023-04-03/2023-04-09\n", + "17 2023-03-27/2023-04-02\n", + "18 2023-03-20/2023-03-26\n", + "19 2023-03-13/2023-03-19\n", + "20 2023-03-06/2023-03-12\n", + "21 2023-02-27/2023-03-05\n", + "22 2023-02-20/2023-02-26\n", + "23 2023-02-13/2023-02-19\n", + "24 2023-02-06/2023-02-12\n", + "25 2023-01-30/2023-02-05\n", + "26 2023-01-23/2023-01-29\n", + "27 2023-01-16/2023-01-22\n", + "28 2023-01-09/2023-01-15\n", + "29 2023-01-02/2023-01-08\n", + " ... \n", + "1674 1991-06-24/1991-06-30\n", + "1675 1991-06-17/1991-06-23\n", + "1676 1991-06-10/1991-06-16\n", + "1677 1991-06-03/1991-06-09\n", + "1678 1991-05-27/1991-06-02\n", + "1679 1991-05-20/1991-05-26\n", + "1680 1991-05-13/1991-05-19\n", + "1681 1991-05-06/1991-05-12\n", + "1682 1991-04-29/1991-05-05\n", + "1683 1991-04-22/1991-04-28\n", + "1684 1991-04-15/1991-04-21\n", + "1685 1991-04-08/1991-04-14\n", + "1686 1991-04-01/1991-04-07\n", + "1687 1991-03-25/1991-03-31\n", + "1688 1991-03-18/1991-03-24\n", + "1689 1991-03-11/1991-03-17\n", + "1690 1991-03-04/1991-03-10\n", + "1691 1991-02-25/1991-03-03\n", + "1692 1991-02-18/1991-02-24\n", + "1693 1991-02-11/1991-02-17\n", + "1694 1991-02-04/1991-02-10\n", + "1695 1991-01-28/1991-02-03\n", + "1696 1991-01-21/1991-01-27\n", + "1697 1991-01-14/1991-01-20\n", + "1698 1991-01-07/1991-01-13\n", + "1699 1990-12-31/1991-01-06\n", + "1700 1990-12-24/1990-12-30\n", + "1701 1990-12-17/1990-12-23\n", + "1702 1990-12-10/1990-12-16\n", + "1703 1990-12-03/1990-12-09\n", + "Name: period, Length: 1704, dtype: object" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['period']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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", + "Nous trions ensuite les points par période, dans\n", + "le sens chronologique." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", + "le début de la période qui suit, la différence temporelle doit être\n", + "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", + "d'une seconde." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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": [ + "### Visualisation des données" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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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": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous remarquons que le creux de l'épidémie se situe autour de septembre, et nous définissons donc la période de référence\n", + "entre deux minima de l'incidence, du 1er septembre de l'année $N$ au 1er septembre de l'année $N+1$.\n", + "\n", + "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre. L'incidence faible autour de cette période devrait permettre de minimiser les incertitudes.\n", + "\n", + "Encore un petit détail: les données commencent an décembre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", + " for y in range(1991,\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": 20, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_september_week[:-1],\n", + " first_september_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": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "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": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\n", + "2021 376290\n", + "2002 516689\n", + "2018 542312\n", + "2017 551041\n", + "1996 564901\n", + "2019 584066\n", + "2015 604382\n", + "2000 617597\n", + "2001 619041\n", + "2012 624573\n", + "2005 628464\n", + "2006 632833\n", + "2022 641397\n", + "2011 642368\n", + "1993 643387\n", + "1995 652478\n", + "1994 661409\n", + "1998 677775\n", + "1997 683434\n", + "2014 685769\n", + "2013 698332\n", + "2007 717352\n", + "2008 749478\n", + "1999 756456\n", + "2003 758363\n", + "2004 777388\n", + "2016 782114\n", + "2010 829911\n", + "1992 832939\n", + "2009 842373\n", + "dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, un histogramme :" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.hist(xrot=20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Année avec un maximum et un minimum d'incidences :" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "année d'incidence maximum : 2009\n", + "Incidence maximum : 842373\n" + ] + } + ], + "source": [ + "print(\"année d'incidence maximum : \", yearly_incidence.idxmax())\n", + "print(\"Incidence maximum : \", yearly_incidence.max())" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "année d'incidence minimum : 2020\n", + "Incidence minimum : 221186\n" + ] + } + ], + "source": [ + "print(\"année d'incidence minimum : \", yearly_incidence.idxmin())\n", + "print(\"Incidence minimum : \", yearly_incidence.min())" + ] + }, + { + "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": 2 +} diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb deleted file mode 100644 index 0bbbe37..0000000 --- a/module3/exo2/exercice.ipynb +++ /dev/null @@ -1,25 +0,0 @@ -{ - "cells": [], - "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": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} - diff --git a/module3/exo2/syndrome-varicelle.csv b/module3/exo2/syndrome-varicelle.csv new file mode 100644 index 0000000..decae60 --- /dev/null +++ b/module3/exo2/syndrome-varicelle.csv @@ -0,0 +1,1705 @@ +,week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name +0,202330,7,6819,3417,10221,10,5,15,FR,France +1,202329,7,14037,8326,19748,21,12,30,FR,France +2,202328,7,6700,4043,9357,10,6,14,FR,France +3,202327,7,7253,4599,9907,11,7,15,FR,France +4,202326,7,9192,6223,12161,14,10,18,FR,France +5,202325,7,11498,8257,14739,17,12,22,FR,France +6,202324,7,11115,7968,14262,17,12,22,FR,France +7,202323,7,12563,6134,18992,19,9,29,FR,France +8,202322,7,12184,8125,16243,18,12,24,FR,France +9,202321,7,11349,7598,15100,17,11,23,FR,France 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