From 307f9d77db9ee39b00ec548d3a5483bbb585d730 Mon Sep 17 00:00:00 2001 From: 4eabd7b2a36de6272dc6b0eb6646565f <4eabd7b2a36de6272dc6b0eb6646565f@app-learninglab.inria.fr> Date: Thu, 23 Nov 2023 23:06:50 +0000 Subject: [PATCH] =?UTF-8?q?Ajout=20de=20code=20permettant=20d'effectuer=20?= =?UTF-8?q?une=20copie=20locale=20des=20donn=C3=A9es=20r=C3=A9cup=C3=A9r?= =?UTF-8?q?=C3=A9es=20depuis=20le=20site=20Web=20du=20R=C3=A9seau=20Sentin?= =?UTF-8?q?elles.=20Correction=20du=20code=20g=C3=A9n=C3=A9rant=20une=20er?= =?UTF-8?q?reur=20lors=20de=20l'affichage=20du=20premier=20plot.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- module3/exo1/analyse-syndrome-grippal.ipynb | 2275 ++++++++++++++++++- module3/exo1/syndrome-grippal.csv | 2040 +++++++++++++++++ 2 files changed, 4278 insertions(+), 37 deletions(-) create mode 100644 module3/exo1/syndrome-grippal.csv diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5..ba1bc8b 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,15 +28,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_file = \"syndrome-grippal.csv\"\n", + "\n", + "import os\n", + "import urllib.request\n", + "if not os.path.exists(data_file):\n", + " urllib.request.urlretrieve(data_url, data_file)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -61,11 +80,978 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020234639293481854.0104014.0140123.0157.0FRFrance
120234537184562813.080877.010894.0122.0FRFrance
220234434995242813.057091.07564.086.0FRFrance
320234334498238170.051794.06858.078.0FRFrance
420234235684249277.064407.08675.097.0FRFrance
520234135835751032.065682.08877.099.0FRFrance
620234036889460069.077719.010491.0117.0FRFrance
720233937200363452.080554.010895.0121.0FRFrance
820233836321855227.071209.09583.0107.0FRFrance
920233734908542079.056091.07463.085.0FRFrance
1020233633824732237.044257.05849.067.0FRFrance
1120233533169526013.037377.04839.057.0FRFrance
1220233432666321057.032269.04032.048.0FRFrance
1320233331914413161.025127.02920.038.0FRFrance
1420233231464110285.018997.02215.029.0FRFrance
1520233131528610705.019867.02316.030.0FRFrance
162023303132058647.017763.02013.027.0FRFrance
172023293111227113.015131.01711.023.0FRFrance
18202328391795703.012655.0149.019.0FRFrance
19202327389995763.012235.0149.019.0FRFrance
20202326390235934.012112.0149.019.0FRFrance
212023253100906739.013441.01510.020.0FRFrance
222023243113087639.014977.01711.023.0FRFrance
2320232331430010661.017939.02217.027.0FRFrance
2420232231830313822.022784.02821.035.0FRFrance
2520232131646012188.020732.02519.031.0FRFrance
2620232031616211963.020361.02418.030.0FRFrance
2720231931690112577.021225.02518.032.0FRFrance
2820231831992915402.024456.03023.037.0FRFrance
2920231732700721779.032235.04133.049.0FRFrance
.................................
200819852132609619621.032571.04735.059.0FRFrance
200919852032789620885.034907.05138.064.0FRFrance
201019851934315432821.053487.07859.097.0FRFrance
201119851834055529935.051175.07455.093.0FRFrance
201219851733405324366.043740.06244.080.0FRFrance
201319851635036236451.064273.09166.0116.0FRFrance
201419851536388145538.082224.011683.0149.0FRFrance
20151985143134545114400.0154690.0244207.0281.0FRFrance
20161985133197206176080.0218332.0357319.0395.0FRFrance
20171985123245240223304.0267176.0445405.0485.0FRFrance
20181985113276205252399.0300011.0501458.0544.0FRFrance
20191985103353231326279.0380183.0640591.0689.0FRFrance
20201985093369895341109.0398681.0670618.0722.0FRFrance
20211985083389886359529.0420243.0707652.0762.0FRFrance
20221985073471852432599.0511105.0855784.0926.0FRFrance
20231985063565825518011.0613639.01026939.01113.0FRFrance
20241985053637302592795.0681809.011551074.01236.0FRFrance
20251985043424937390794.0459080.0770708.0832.0FRFrance
20261985033213901174689.0253113.0388317.0459.0FRFrance
202719850239758680949.0114223.0177147.0207.0FRFrance
202819850138548965918.0105060.0155120.0190.0FRFrance
202919845238483060602.0109058.0154110.0198.0FRFrance
2030198451310172680242.0123210.0185146.0224.0FRFrance
20311984503123680101401.0145959.0225184.0266.0FRFrance
2032198449310107381684.0120462.0184149.0219.0FRFrance
203319844837862060634.096606.0143110.0176.0FRFrance
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203519844638733067686.0106974.0159123.0195.0FRFrance
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2038 rows × 10 columns

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9179 5703.0 12655.0 14 9.0 \n", + "19 202327 3 8999 5763.0 12235.0 14 9.0 \n", + "20 202326 3 9023 5934.0 12112.0 14 9.0 \n", + "21 202325 3 10090 6739.0 13441.0 15 10.0 \n", + "22 202324 3 11308 7639.0 14977.0 17 11.0 \n", + "23 202323 3 14300 10661.0 17939.0 22 17.0 \n", + "24 202322 3 18303 13822.0 22784.0 28 21.0 \n", + "25 202321 3 16460 12188.0 20732.0 25 19.0 \n", + "26 202320 3 16162 11963.0 20361.0 24 18.0 \n", + "27 202319 3 16901 12577.0 21225.0 25 18.0 \n", + "28 202318 3 19929 15402.0 24456.0 30 23.0 \n", + "29 202317 3 27007 21779.0 32235.0 41 33.0 \n", + "... ... ... ... ... ... ... ... \n", + "2008 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2009 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2010 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2011 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2012 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2013 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2014 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2015 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2016 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2017 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2018 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2019 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2020 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2021 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2022 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2023 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2024 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2025 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2026 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2027 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2028 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2029 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2030 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2031 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2032 198449 3 101073 81684.0 120462.0 184 149.0 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30.0 FR France \n", + "27 32.0 FR France \n", + "28 37.0 FR France \n", + "29 49.0 FR France \n", + "... ... ... ... \n", + "2008 59.0 FR France \n", + "2009 64.0 FR France \n", + "2010 97.0 FR France \n", + "2011 93.0 FR France \n", + "2012 80.0 FR France \n", + "2013 116.0 FR France \n", + "2014 149.0 FR France \n", + "2015 281.0 FR France \n", + "2016 395.0 FR France \n", + "2017 485.0 FR France \n", + "2018 544.0 FR France \n", + "2019 689.0 FR France \n", + "2020 722.0 FR France \n", + "2021 762.0 FR France \n", + "2022 926.0 FR France \n", + "2023 1113.0 FR France \n", + "2024 1236.0 FR France \n", + "2025 832.0 FR France \n", + "2026 459.0 FR France \n", + "2027 207.0 FR France \n", + "2028 190.0 FR France \n", + "2029 198.0 FR France \n", + "2030 224.0 FR France \n", + "2031 266.0 FR France \n", + "2032 219.0 FR France \n", + "2033 176.0 FR France \n", + "2034 163.0 FR France \n", + "2035 195.0 FR France \n", + "2036 308.0 FR France \n", + "2037 213.0 FR France \n", + "\n", + "[2038 rows x 10 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, @@ -78,9 +1064,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18011989193-NaNNaN-NaNNaNFRFrance
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2320232331430010661.017939.02217.027.0FRFrance
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2037 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202346 3 92934 81854.0 104014.0 140 123.0 \n", + "1 202345 3 71845 62813.0 80877.0 108 94.0 \n", + "2 202344 3 49952 42813.0 57091.0 75 64.0 \n", + "3 202343 3 44982 38170.0 51794.0 68 58.0 \n", + "4 202342 3 56842 49277.0 64407.0 86 75.0 \n", + "5 202341 3 58357 51032.0 65682.0 88 77.0 \n", + "6 202340 3 68894 60069.0 77719.0 104 91.0 \n", + "7 202339 3 72003 63452.0 80554.0 108 95.0 \n", + "8 202338 3 63218 55227.0 71209.0 95 83.0 \n", + "9 202337 3 49085 42079.0 56091.0 74 63.0 \n", + "10 202336 3 38247 32237.0 44257.0 58 49.0 \n", + "11 202335 3 31695 26013.0 37377.0 48 39.0 \n", + "12 202334 3 26663 21057.0 32269.0 40 32.0 \n", + "13 202333 3 19144 13161.0 25127.0 29 20.0 \n", + "14 202332 3 14641 10285.0 18997.0 22 15.0 \n", + "15 202331 3 15286 10705.0 19867.0 23 16.0 \n", + "16 202330 3 13205 8647.0 17763.0 20 13.0 \n", + "17 202329 3 11122 7113.0 15131.0 17 11.0 \n", + "18 202328 3 9179 5703.0 12655.0 14 9.0 \n", + "19 202327 3 8999 5763.0 12235.0 14 9.0 \n", + "20 202326 3 9023 5934.0 12112.0 14 9.0 \n", + "21 202325 3 10090 6739.0 13441.0 15 10.0 \n", + "22 202324 3 11308 7639.0 14977.0 17 11.0 \n", + "23 202323 3 14300 10661.0 17939.0 22 17.0 \n", + "24 202322 3 18303 13822.0 22784.0 28 21.0 \n", + "25 202321 3 16460 12188.0 20732.0 25 19.0 \n", + "26 202320 3 16162 11963.0 20361.0 24 18.0 \n", + "27 202319 3 16901 12577.0 21225.0 25 18.0 \n", + "28 202318 3 19929 15402.0 24456.0 30 23.0 \n", + "29 202317 3 27007 21779.0 32235.0 41 33.0 \n", + "... ... ... ... ... ... ... ... \n", + "2008 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2009 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2010 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2011 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2012 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2013 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2014 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2015 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2016 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2017 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2018 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2019 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2020 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2021 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2022 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2023 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2024 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2025 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2026 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2027 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2028 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2029 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2030 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2031 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2032 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2033 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2034 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2035 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2036 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2037 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 157.0 FR France \n", + "1 122.0 FR France \n", + "2 86.0 FR France \n", + "3 78.0 FR France \n", + "4 97.0 FR France \n", + "5 99.0 FR France \n", + "6 117.0 FR France \n", + "7 121.0 FR France \n", + "8 107.0 FR France \n", + "9 85.0 FR France \n", + "10 67.0 FR France \n", + "11 57.0 FR France \n", + "12 48.0 FR France \n", + "13 38.0 FR France \n", + "14 29.0 FR France \n", + "15 30.0 FR France \n", + "16 27.0 FR France \n", + "17 23.0 FR France \n", + "18 19.0 FR France \n", + "19 19.0 FR France \n", + "20 19.0 FR France \n", + "21 20.0 FR France \n", + "22 23.0 FR France \n", + "23 27.0 FR France \n", + "24 35.0 FR France \n", + "25 31.0 FR France \n", + "26 30.0 FR France \n", + "27 32.0 FR France \n", + "28 37.0 FR France \n", + "29 49.0 FR France \n", + "... ... ... ... \n", + "2008 59.0 FR France \n", + "2009 64.0 FR France \n", + "2010 97.0 FR France \n", + "2011 93.0 FR France \n", + "2012 80.0 FR France \n", + "2013 116.0 FR France \n", + "2014 149.0 FR France \n", + "2015 281.0 FR France \n", + "2016 395.0 FR France \n", + "2017 485.0 FR France \n", + "2018 544.0 FR France \n", + "2019 689.0 FR France \n", + "2020 722.0 FR France \n", + "2021 762.0 FR France \n", + "2022 926.0 FR France \n", + "2023 1113.0 FR France \n", + "2024 1236.0 FR France \n", + "2025 832.0 FR France \n", + "2026 459.0 FR France \n", + "2027 207.0 FR France \n", + "2028 190.0 FR France \n", + "2029 198.0 FR France \n", + "2030 224.0 FR France \n", + "2031 266.0 FR France \n", + "2032 219.0 FR France \n", + "2033 176.0 FR France \n", + "2034 163.0 FR France \n", + "2035 195.0 FR France \n", + "2036 308.0 FR France \n", + "2037 213.0 FR France \n", + "\n", + "[2037 rows x 10 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2139,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2169,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,9 +2194,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "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", @@ -199,9 +2222,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "Empty 'DataFrame': no numeric data to plot", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m 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364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" + ] + } + ], + "source": [ + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Toute la colonne 'inc' est représentée ici par des chaînes de caractères en raison du trait présent dans la ligne de la semaine 19 de l'année 1989. On convertit alors explicitement cette colonne en entiers pour corriger cette erreur tout en conservant l'intégralité des données déjà présentes." + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.plot(style='*')" ] @@ -314,9 +2446,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2021 743449\n", + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2010315\n", + "2022 2060304\n", + "2012 2175217\n", + "2003 2234584\n", + "2019 2254386\n", + "2006 2307352\n", + "2017 2321583\n", + "2001 2529279\n", + "1992 2574578\n", + "1993 2703886\n", + "2018 2705325\n", + "1988 2765617\n", + "2007 2780164\n", + "1987 2855570\n", + "2016 2856393\n", + "2011 2857040\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": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -331,9 +2511,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.hist(xrot=20)" ] @@ -341,9 +2544,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -364,7 +2565,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo1/syndrome-grippal.csv b/module3/exo1/syndrome-grippal.csv new file mode 100644 index 0000000..25375f9 --- /dev/null +++ b/module3/exo1/syndrome-grippal.csv @@ -0,0 +1,2040 @@ +# @source="réseau Sentinelles, INSERM, Sorbonne Université, https://www.sentiweb.fr", @meta={"period":[198444,202346],"geo":["PAY",1],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false,"age_group":false}, @date=2023-11-22T18:00:50+01:00, bundle=1700672450 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