From 0ca26dc7125b992339d558bf193bd236e9a621b5 Mon Sep 17 00:00:00 2001 From: 7404ea6678ce6fbf3a726e36f2bf2079 <7404ea6678ce6fbf3a726e36f2bf2079@app-learninglab.inria.fr> Date: Mon, 23 Sep 2024 20:15:16 +0000 Subject: [PATCH] Update jupyter notebook flue_syndrome_incidence_analysis with work of exo1 of module 3 + correction of 'inc' column data type --- module3/exo1/analyse-syndrome-grippal.ipynb | 2290 ++++++++++++++++++- 1 file changed, 2255 insertions(+), 35 deletions(-) diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5..2d187df 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": 12, "metadata": {}, "outputs": [], "source": [ @@ -28,13 +28,11 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "data_url = \"https://www.sentiweb.fr/datasets/all/inc-3-PAY.csv\"" ] }, { @@ -61,11 +59,995 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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520243232318717532.028842.03527.043.0FRFrance
620243132603520267.031803.03930.048.0FRFrance
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820242933956032592.046528.05949.069.0FRFrance
920242835434245781.062903.08168.094.0FRFrance
1020242734736440234.054494.07160.082.0FRFrance
1120242634421936956.051482.06655.077.0FRFrance
1220242534720440300.054108.07161.081.0FRFrance
1320242434111034671.047549.06252.072.0FRFrance
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1820241931537511274.019476.02317.029.0FRFrance
1920241832240917653.027165.03427.041.0FRFrance
2020241732704221410.032674.04133.049.0FRFrance
2120241632888223305.034459.04335.051.0FRFrance
2220241533022924648.035810.04537.053.0FRFrance
2320241433181326529.037097.04840.056.0FRFrance
2420241333509029607.040573.05345.061.0FRFrance
2520241234063934582.046696.06152.070.0FRFrance
2620241135026843331.057205.07565.085.0FRFrance
2720241036010752623.067591.09079.0101.0FRFrance
2820240937112162920.079322.010795.0119.0FRFrance
29202408310456694520.0114612.0157142.0172.0FRFrance
.................................
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2081 rows × 10 columns

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+ "26 85.0 FR France \n", + "27 101.0 FR France \n", + "28 119.0 FR France \n", + "29 172.0 FR France \n", + "... ... ... ... \n", + "2051 59.0 FR France \n", + "2052 64.0 FR France \n", + "2053 97.0 FR France \n", + "2054 93.0 FR France \n", + "2055 80.0 FR France \n", + "2056 116.0 FR France \n", + "2057 149.0 FR France \n", + "2058 281.0 FR France \n", + "2059 395.0 FR France \n", + "2060 485.0 FR France \n", + "2061 544.0 FR France \n", + "2062 689.0 FR France \n", + "2063 722.0 FR France \n", + "2064 762.0 FR France \n", + "2065 926.0 FR France \n", + "2066 1113.0 FR France \n", + "2067 1236.0 FR France \n", + "2068 832.0 FR France \n", + "2069 459.0 FR France \n", + "2070 207.0 FR France \n", + "2071 190.0 FR France \n", + "2072 198.0 FR France \n", + "2073 224.0 FR France \n", + "2074 266.0 FR France \n", + "2075 219.0 FR France \n", + "2076 176.0 FR France \n", + "2077 163.0 FR France \n", + "2078 195.0 FR France \n", + "2079 308.0 FR France \n", + "2080 213.0 FR France \n", + "\n", + "[2081 rows x 10 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "from os import path as pth\n", + "import requests\n", + "# Si le fichier csv des données d'incidence existe en local\n", + "# il n'est pas nécessaire de le télécharger par l'URL\n", + "local_filename = \"incidence-PAY-3.csv\"\n", + "if pth.exists(local_filename):\n", + " # Le fichier existe en local dans le dossier courant\n", + " raw_data = pd.read_csv(local_filename, skiprows=1)\n", + "else:\n", + " # le fichier de données n'existe pas en local,\n", + " # nous allons télécharger les données et les écrire\n", + " # dans un fichier en local\n", + " # Téléchargement des données\n", + " response = requests.get(data_url)\n", + " # Ecriture des données téléchargées dans le fichier local\n", + " with open(local_filename, \"wb\") as f:\n", + " f.write(response.content)\n", + " raw_data = pd.read_csv(local_filename, skiprows=1)\n", "raw_data" ] }, @@ -78,9 +1060,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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2080 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202437 3 59356 50585.0 68127.0 89 76.0 \n", + "1 202436 3 33435 27654.0 39216.0 50 41.0 \n", + "2 202435 3 27404 22036.0 32772.0 41 33.0 \n", + "3 202434 3 26717 21003.0 32431.0 40 31.0 \n", + "4 202433 3 20623 15349.0 25897.0 31 23.0 \n", + "5 202432 3 23187 17532.0 28842.0 35 27.0 \n", + "6 202431 3 26035 20267.0 31803.0 39 30.0 \n", + "7 202430 3 36393 28593.0 44193.0 55 43.0 \n", + "8 202429 3 39560 32592.0 46528.0 59 49.0 \n", + "9 202428 3 54342 45781.0 62903.0 81 68.0 \n", + "10 202427 3 47364 40234.0 54494.0 71 60.0 \n", + "11 202426 3 44219 36956.0 51482.0 66 55.0 \n", + "12 202425 3 47204 40300.0 54108.0 71 61.0 \n", + "13 202424 3 41110 34671.0 47549.0 62 52.0 \n", + "14 202423 3 35875 30610.0 41140.0 54 46.0 \n", + "15 202422 3 33772 28274.0 39270.0 51 43.0 \n", + "16 202421 3 21963 17556.0 26370.0 33 26.0 \n", + "17 202420 3 20057 15780.0 24334.0 30 24.0 \n", + "18 202419 3 15375 11274.0 19476.0 23 17.0 \n", + "19 202418 3 22409 17653.0 27165.0 34 27.0 \n", + "20 202417 3 27042 21410.0 32674.0 41 33.0 \n", + "21 202416 3 28882 23305.0 34459.0 43 35.0 \n", + "22 202415 3 30229 24648.0 35810.0 45 37.0 \n", + "23 202414 3 31813 26529.0 37097.0 48 40.0 \n", + "24 202413 3 35090 29607.0 40573.0 53 45.0 \n", + "25 202412 3 40639 34582.0 46696.0 61 52.0 \n", + "26 202411 3 50268 43331.0 57205.0 75 65.0 \n", + "27 202410 3 60107 52623.0 67591.0 90 79.0 \n", + "28 202409 3 71121 62920.0 79322.0 107 95.0 \n", + "29 202408 3 104566 94520.0 114612.0 157 142.0 \n", + "... ... ... ... ... ... ... ... \n", + "2051 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2052 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2053 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2054 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2055 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2056 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2057 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2058 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2059 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2060 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2061 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2062 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2063 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2064 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2065 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2066 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2067 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2068 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2069 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2070 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2071 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2072 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2073 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2074 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2075 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2076 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2077 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2078 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2079 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2080 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 102.0 FR France \n", + "1 59.0 FR France \n", + "2 49.0 FR France \n", + "3 49.0 FR France \n", + "4 39.0 FR France \n", + "5 43.0 FR France \n", + "6 48.0 FR France \n", + "7 67.0 FR France \n", + "8 69.0 FR France \n", + "9 94.0 FR France \n", + "10 82.0 FR France \n", + "11 77.0 FR France \n", + "12 81.0 FR France \n", + "13 72.0 FR France \n", + "14 62.0 FR France \n", + "15 59.0 FR France \n", + "16 40.0 FR France \n", + "17 36.0 FR France \n", + "18 29.0 FR France \n", + "19 41.0 FR France \n", + "20 49.0 FR France \n", + "21 51.0 FR France \n", + "22 53.0 FR France \n", + "23 56.0 FR France \n", + "24 61.0 FR France \n", + "25 70.0 FR France \n", + "26 85.0 FR France \n", + "27 101.0 FR France \n", + "28 119.0 FR France \n", + "29 172.0 FR France \n", + "... ... ... ... \n", + "2051 59.0 FR France \n", + "2052 64.0 FR France \n", + "2053 97.0 FR France \n", + "2054 93.0 FR France \n", + "2055 80.0 FR France \n", + "2056 116.0 FR France \n", + "2057 149.0 FR France \n", + "2058 281.0 FR France \n", + "2059 395.0 FR France \n", + "2060 485.0 FR France \n", + "2061 544.0 FR France \n", + "2062 689.0 FR France \n", + "2063 722.0 FR France \n", + "2064 762.0 FR France \n", + "2065 926.0 FR France \n", + "2066 1113.0 FR France \n", + "2067 1236.0 FR France \n", + "2068 832.0 FR France \n", + "2069 459.0 FR France \n", + "2070 207.0 FR France \n", + "2071 190.0 FR France \n", + "2072 198.0 FR France \n", + "2073 224.0 FR France \n", + "2074 266.0 FR France \n", + "2075 219.0 FR France \n", + "2076 176.0 FR France \n", + "2077 163.0 FR France \n", + "2078 195.0 FR France \n", + "2079 308.0 FR France \n", + "2080 213.0 FR France \n", + "\n", + "[2080 rows x 10 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2135,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2165,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,9 +2190,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "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 +2218,94 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, + "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 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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": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'68422'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data['inc'][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous constatons que les données de la colonne 'inc' ne sont pas numériques mais du texte. Il faut donc convertir ses données." + ] + }, + { + "cell_type": "code", + "execution_count": 26, "metadata": {}, "outputs": [], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].astype('int')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -331,9 +2528,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
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