From 09ac7fd8fbf94faff6830ef2a094da154dddfa2c Mon Sep 17 00:00:00 2001 From: 7eba932125d7468e05c00632ef18215f <7eba932125d7468e05c00632ef18215f@app-learninglab.inria.fr> Date: Tue, 1 Jun 2021 16:08:57 +0000 Subject: [PATCH] =?UTF-8?q?Importation=20des=20donn=C3=A9es=20d=C3=A9penda?= =?UTF-8?q?ntes=20de=20l'existence=20du=20fichier=20CSV?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- module3/exo1/analyse-syndrome-grippal.ipynb | 2116 ++++++++++++++++++- 1 file changed, 2091 insertions(+), 25 deletions(-) diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5..c6620c3 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": [ @@ -28,10 +28,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" @@ -59,14 +57,999 @@ "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On va tenter de télécharger les données du réseau Sentinelles. Si le fichier CSV est à l'url indiqué, on remplace les données. S'il nes'y trouve plus, on garde les données locales:" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data not found, let's keep local data\n" + ] + }, + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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.................................
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134545 114400.0 154690.0 244 207.0 \n", + "1886 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1887 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1888 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1889 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1890 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1891 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1892 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1893 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1894 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1895 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1896 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1897 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1898 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1899 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1900 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1901 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1902 198449 3 101073 81684.0 120462.0 184 149.0 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FR France \n", + "27 45.0 FR France \n", + "28 74.0 FR France \n", + "29 77.0 FR France \n", + "... ... ... ... \n", + "1878 59.0 FR France \n", + "1879 64.0 FR France \n", + "1880 97.0 FR France \n", + "1881 93.0 FR France \n", + "1882 80.0 FR France \n", + "1883 116.0 FR France \n", + "1884 149.0 FR France \n", + "1885 281.0 FR France \n", + "1886 395.0 FR France \n", + "1887 485.0 FR France \n", + "1888 544.0 FR France \n", + "1889 689.0 FR France \n", + "1890 722.0 FR France \n", + "1891 762.0 FR France \n", + "1892 926.0 FR France \n", + "1893 1113.0 FR France \n", + "1894 1236.0 FR France \n", + "1895 832.0 FR France \n", + "1896 459.0 FR France \n", + "1897 207.0 FR France \n", + "1898 190.0 FR France \n", + "1899 198.0 FR France \n", + "1900 224.0 FR France \n", + "1901 266.0 FR France \n", + "1902 219.0 FR France \n", + "1903 176.0 FR France \n", + "1904 163.0 FR France \n", + "1905 195.0 FR France \n", + "1906 308.0 FR France \n", + "1907 213.0 FR France \n", + "\n", + "[1908 rows x 10 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", - "raw_data" + "try:\n", + " raw_data = pd.read_csv(data_url_false, skiprows=1)\n", + "except pd.errors.ParserError:\n", + " print(\"Data not found, let's keep local data:\")\n", + "\n", + "raw_data\n" ] }, { @@ -78,9 +1061,73 @@ }, { "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
167119891930NaNNaN0NaNNaNFRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1671 198919 3 0 NaN NaN 0 NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1671 FR France " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] @@ -94,9 +1141,976 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02021203103777601.013153.01612.020.0FRFrance
1202119395856901.012269.01511.019.0FRFrance
22021183121359165.015105.01814.022.0FRFrance
32021173120588891.015225.01813.023.0FRFrance
420211631650512735.020275.02519.031.0FRFrance
520211531930615398.023214.02923.035.0FRFrance
620211432107317099.025047.03226.038.0FRFrance
720211332641322094.030732.04033.047.0FRFrance
820211233065825919.035397.04639.053.0FRFrance
920211132498820718.029258.03832.044.0FRFrance
1020211031953915951.023127.03025.035.0FRFrance
1120210931757213926.021218.02721.033.0FRFrance
1220210832088216907.024857.03226.038.0FRFrance
1320210732239318303.026483.03428.040.0FRFrance
1420210632318319134.027232.03529.041.0FRFrance
1520210532242618445.026407.03428.040.0FRFrance
1620210432580421491.030117.03932.046.0FRFrance
1720210332181017894.025726.03327.039.0FRFrance
1820210231732013906.020734.02621.031.0FRFrance
1920210132179917778.025820.03327.039.0FRFrance
2020205332122016498.025942.03225.039.0FRFrance
2120205231642812285.020571.02519.031.0FRFrance
2220205132161917370.025868.03327.039.0FRFrance
2320205031684513220.020470.02620.032.0FRFrance
242020493129399923.015955.02015.025.0FRFrance
2520204831380410641.016967.02116.026.0FRFrance
2620204731908515285.022885.02923.035.0FRFrance
2720204632480120503.029099.03831.045.0FRFrance
2820204534251636857.048175.06556.074.0FRFrance
2920204434456738521.050613.06859.077.0FRFrance
.................................
187819852132609619621.032571.04735.059.0FRFrance
187919852032789620885.034907.05138.064.0FRFrance
188019851934315432821.053487.07859.097.0FRFrance
188119851834055529935.051175.07455.093.0FRFrance
188219851733405324366.043740.06244.080.0FRFrance
188319851635036236451.064273.09166.0116.0FRFrance
188419851536388145538.082224.011683.0149.0FRFrance
18851985143134545114400.0154690.0244207.0281.0FRFrance
18861985133197206176080.0218332.0357319.0395.0FRFrance
18871985123245240223304.0267176.0445405.0485.0FRFrance
18881985113276205252399.0300011.0501458.0544.0FRFrance
18891985103353231326279.0380183.0640591.0689.0FRFrance
18901985093369895341109.0398681.0670618.0722.0FRFrance
18911985083389886359529.0420243.0707652.0762.0FRFrance
18921985073471852432599.0511105.0855784.0926.0FRFrance
18931985063565825518011.0613639.01026939.01113.0FRFrance
18941985053637302592795.0681809.011551074.01236.0FRFrance
18951985043424937390794.0459080.0770708.0832.0FRFrance
18961985033213901174689.0253113.0388317.0459.0FRFrance
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189919845238483060602.0109058.0154110.0198.0FRFrance
1900198451310172680242.0123210.0185146.0224.0FRFrance
19011984503123680101401.0145959.0225184.0266.0FRFrance
1902198449310107381684.0120462.0184149.0219.0FRFrance
190319844837862060634.096606.0143110.0176.0FRFrance
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1907 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202120 3 10377 7601.0 13153.0 16 12.0 \n", + "1 202119 3 9585 6901.0 12269.0 15 11.0 \n", + "2 202118 3 12135 9165.0 15105.0 18 14.0 \n", + "3 202117 3 12058 8891.0 15225.0 18 13.0 \n", + "4 202116 3 16505 12735.0 20275.0 25 19.0 \n", + "5 202115 3 19306 15398.0 23214.0 29 23.0 \n", + "6 202114 3 21073 17099.0 25047.0 32 26.0 \n", + "7 202113 3 26413 22094.0 30732.0 40 33.0 \n", + "8 202112 3 30658 25919.0 35397.0 46 39.0 \n", + "9 202111 3 24988 20718.0 29258.0 38 32.0 \n", + "10 202110 3 19539 15951.0 23127.0 30 25.0 \n", + "11 202109 3 17572 13926.0 21218.0 27 21.0 \n", + "12 202108 3 20882 16907.0 24857.0 32 26.0 \n", + "13 202107 3 22393 18303.0 26483.0 34 28.0 \n", + "14 202106 3 23183 19134.0 27232.0 35 29.0 \n", + "15 202105 3 22426 18445.0 26407.0 34 28.0 \n", + "16 202104 3 25804 21491.0 30117.0 39 32.0 \n", + "17 202103 3 21810 17894.0 25726.0 33 27.0 \n", + "18 202102 3 17320 13906.0 20734.0 26 21.0 \n", + "19 202101 3 21799 17778.0 25820.0 33 27.0 \n", + "20 202053 3 21220 16498.0 25942.0 32 25.0 \n", + "21 202052 3 16428 12285.0 20571.0 25 19.0 \n", + "22 202051 3 21619 17370.0 25868.0 33 27.0 \n", + "23 202050 3 16845 13220.0 20470.0 26 20.0 \n", + "24 202049 3 12939 9923.0 15955.0 20 15.0 \n", + "25 202048 3 13804 10641.0 16967.0 21 16.0 \n", + "26 202047 3 19085 15285.0 22885.0 29 23.0 \n", + "27 202046 3 24801 20503.0 29099.0 38 31.0 \n", + "28 202045 3 42516 36857.0 48175.0 65 56.0 \n", + "29 202044 3 44567 38521.0 50613.0 68 59.0 \n", + "... ... ... ... ... ... ... ... \n", + "1878 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1879 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1880 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1881 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1882 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1883 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1884 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1885 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1886 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1887 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1888 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1889 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1890 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1891 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1892 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1893 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1894 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1895 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1896 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1897 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1898 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1899 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1900 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1901 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1902 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1903 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1904 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1905 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1906 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1907 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 20.0 FR France \n", + "1 19.0 FR France \n", + "2 22.0 FR France \n", + "3 23.0 FR France \n", + "4 31.0 FR France \n", + "5 35.0 FR France \n", + "6 38.0 FR France \n", + "7 47.0 FR France \n", + "8 53.0 FR France \n", + "9 44.0 FR France \n", + "10 35.0 FR France \n", + "11 33.0 FR France \n", + "12 38.0 FR France \n", + "13 40.0 FR France \n", + "14 41.0 FR France \n", + "15 40.0 FR France \n", + "16 46.0 FR France \n", + "17 39.0 FR France \n", + "18 31.0 FR France \n", + "19 39.0 FR France \n", + "20 39.0 FR France \n", + "21 31.0 FR France \n", + "22 39.0 FR France \n", + "23 32.0 FR France \n", + "24 25.0 FR France \n", + "25 26.0 FR France \n", + "26 35.0 FR France \n", + "27 45.0 FR France \n", + "28 74.0 FR France \n", + "29 77.0 FR France \n", + "... ... ... ... \n", + "1878 59.0 FR France \n", + "1879 64.0 FR France \n", + "1880 97.0 FR France \n", + "1881 93.0 FR France \n", + "1882 80.0 FR France \n", + "1883 116.0 FR France \n", + "1884 149.0 FR France \n", + "1885 281.0 FR France \n", + "1886 395.0 FR France \n", + "1887 485.0 FR France \n", + "1888 544.0 FR France \n", + "1889 689.0 FR France \n", + "1890 722.0 FR France \n", + "1891 762.0 FR France \n", + "1892 926.0 FR France \n", + "1893 1113.0 FR France \n", + "1894 1236.0 FR France \n", + "1895 832.0 FR France \n", + "1896 459.0 FR France \n", + "1897 207.0 FR France \n", + "1898 190.0 FR France \n", + "1899 198.0 FR France \n", + "1900 224.0 FR France \n", + "1901 266.0 FR France \n", + "1902 219.0 FR France \n", + "1903 176.0 FR France \n", + "1904 163.0 FR France \n", + "1905 195.0 FR France \n", + "1906 308.0 FR France \n", + "1907 213.0 FR France \n", + "\n", + "[1907 rows x 10 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2136,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2166,8 @@ }, { "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 +2191,17 @@ }, { "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 +2219,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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