diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index a145700d2ee9e397369f8f74d58fcc3d77658ba5..8e378b72440c0ac72eebe92b31fdf4f253169424 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1029,7 +1029,7 @@ "[2098 rows x 10 columns]" ] }, - "execution_count": 6, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1048,9 +1048,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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5202449310848799037.0117937.0163149.0177.0FRFrance
620244838738178687.096075.0131118.0144.0FRFrance
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1020244433603930122.041956.05445.063.0FRFrance
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2220243232318717532.028842.03527.043.0FRFrance
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2097 rows × 10 columns

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63601.0 85 74.0 \n", + "18 202436 3 33657 27906.0 39408.0 50 41.0 \n", + "19 202435 3 27404 22036.0 32772.0 41 33.0 \n", + "20 202434 3 26717 21003.0 32431.0 40 31.0 \n", + "21 202433 3 20623 15349.0 25897.0 31 23.0 \n", + "22 202432 3 23187 17532.0 28842.0 35 27.0 \n", + "23 202431 3 26035 20267.0 31803.0 39 30.0 \n", + "24 202430 3 36393 28593.0 44193.0 55 43.0 \n", + "25 202429 3 39560 32592.0 46528.0 59 49.0 \n", + "26 202428 3 54342 45781.0 62903.0 81 68.0 \n", + "27 202427 3 47364 40234.0 54494.0 71 60.0 \n", + "28 202426 3 44219 36956.0 51482.0 66 55.0 \n", + "29 202425 3 47204 40300.0 54108.0 71 61.0 \n", + "... ... ... ... ... ... ... ... \n", + "2068 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2069 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2070 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2071 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2072 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2073 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2074 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2075 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2076 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2077 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2078 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2079 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2080 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2081 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2082 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2083 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2084 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2085 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2086 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2087 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2088 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2089 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2090 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2091 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2092 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2093 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2094 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2095 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2096 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2097 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 463.0 FR France \n", + "1 379.0 FR France \n", + "2 326.0 FR France \n", + "3 323.0 FR France \n", + "4 220.0 FR France \n", + "5 177.0 FR France \n", + "6 144.0 FR France \n", + "7 127.0 FR France \n", + "8 96.0 FR France \n", + "9 81.0 FR France \n", + "10 63.0 FR France \n", + "11 80.0 FR France \n", + "12 114.0 FR France \n", + "13 131.0 FR France \n", + "14 140.0 FR France \n", + "15 150.0 FR France \n", + "16 151.0 FR France \n", + "17 96.0 FR France \n", + "18 59.0 FR France \n", + "19 49.0 FR France \n", + "20 49.0 FR France \n", + "21 39.0 FR France \n", + "22 43.0 FR France \n", + "23 48.0 FR France \n", + "24 67.0 FR France \n", + "25 69.0 FR France \n", + "26 94.0 FR France \n", + "27 82.0 FR France \n", + "28 77.0 FR France \n", + "29 81.0 FR France \n", + "... ... ... ... \n", + "2068 59.0 FR France \n", + "2069 64.0 FR France \n", + "2070 97.0 FR France \n", + "2071 93.0 FR France \n", + "2072 80.0 FR France \n", + "2073 116.0 FR France \n", + "2074 149.0 FR France \n", + "2075 281.0 FR France \n", + "2076 395.0 FR France \n", + "2077 485.0 FR France \n", + "2078 544.0 FR France \n", + "2079 689.0 FR France \n", + "2080 722.0 FR France \n", + "2081 762.0 FR France \n", + "2082 926.0 FR France \n", + "2083 1113.0 FR France \n", + "2084 1236.0 FR France \n", + "2085 832.0 FR France \n", + "2086 459.0 FR France \n", + "2087 207.0 FR France \n", + "2088 190.0 FR France \n", + "2089 198.0 FR France \n", + "2090 224.0 FR France \n", + "2091 266.0 FR France \n", + "2092 219.0 FR France \n", + "2093 176.0 FR France \n", + "2094 163.0 FR France \n", + "2095 195.0 FR France \n", + "2096 308.0 FR France \n", + "2097 213.0 FR France \n", + "\n", + "[2097 rows x 10 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -1092,7 +2123,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1122,10 +2153,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -1149,9 +2178,99 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "period\n", + "1984-10-29/1984-11-04 68422\n", + "1984-11-05/1984-11-11 135223\n", + "1984-11-12/1984-11-18 87330\n", + "1984-11-19/1984-11-25 72029\n", + "1984-11-26/1984-12-02 78620\n", + "1984-12-03/1984-12-09 101073\n", + "1984-12-10/1984-12-16 123680\n", + "1984-12-17/1984-12-23 101726\n", + "1984-12-24/1984-12-30 84830\n", + "1984-12-31/1985-01-06 85489\n", + "1985-01-07/1985-01-13 97586\n", + "1985-01-14/1985-01-20 213901\n", + "1985-01-21/1985-01-27 424937\n", + "1985-01-28/1985-02-03 637302\n", + "1985-02-04/1985-02-10 565825\n", + "1985-02-11/1985-02-17 471852\n", + "1985-02-18/1985-02-24 389886\n", + "1985-02-25/1985-03-03 369895\n", + "1985-03-04/1985-03-10 353231\n", + "1985-03-11/1985-03-17 276205\n", + "1985-03-18/1985-03-24 245240\n", + "1985-03-25/1985-03-31 197206\n", + "1985-04-01/1985-04-07 134545\n", + "1985-04-08/1985-04-14 63881\n", + "1985-04-15/1985-04-21 50362\n", + "1985-04-22/1985-04-28 34053\n", + "1985-04-29/1985-05-05 40555\n", + "1985-05-06/1985-05-12 43154\n", + "1985-05-13/1985-05-19 27896\n", + "1985-05-20/1985-05-26 26096\n", + " ... \n", + "2024-06-17/2024-06-23 47204\n", + "2024-06-24/2024-06-30 44219\n", + "2024-07-01/2024-07-07 47364\n", + "2024-07-08/2024-07-14 54342\n", + "2024-07-15/2024-07-21 39560\n", + "2024-07-22/2024-07-28 36393\n", + "2024-07-29/2024-08-04 26035\n", + "2024-08-05/2024-08-11 23187\n", + "2024-08-12/2024-08-18 20623\n", + "2024-08-19/2024-08-25 26717\n", + "2024-08-26/2024-09-01 27404\n", + "2024-09-02/2024-09-08 33657\n", + "2024-09-09/2024-09-15 56460\n", + "2024-09-16/2024-09-22 91786\n", + "2024-09-23/2024-09-29 91660\n", + "2024-09-30/2024-10-06 84965\n", + "2024-10-07/2024-10-13 79435\n", + "2024-10-14/2024-10-20 67785\n", + "2024-10-21/2024-10-27 46572\n", + "2024-10-28/2024-11-03 36039\n", + "2024-11-04/2024-11-10 47347\n", + "2024-11-11/2024-11-17 56399\n", + "2024-11-18/2024-11-24 76286\n", + "2024-11-25/2024-12-01 87381\n", + "2024-12-02/2024-12-08 108487\n", + "2024-12-09/2024-12-15 136694\n", + "2024-12-16/2024-12-22 201697\n", + "2024-12-23/2024-12-29 201726\n", + "2024-12-30/2025-01-05 235405\n", + "2025-01-06/2025-01-12 290810\n", + "Freq: W-SUN, Name: inc, Length: 2097, dtype: object" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data['inc']" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "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", @@ -1169,9 +2288,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "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 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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 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\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 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 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()" ] @@ -1185,9 +2321,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "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[0;34m-\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m:\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 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\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 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 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'][-200:].plot()" ] diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..c5f11df3e9eba737febda9a6146e44094ecf5efe 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,1414 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence de la varicelle" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence de la varicelle sont disponibles du site Web du Réseau Sentinelles. Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente sauf si une copie locale est disponible." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"\n", + "local_copy = \"varicelle_local_copy.csv\"\n", + "import urllib.request\n", + "import os.path\n", + "if not os.path.isfile(local_copy):\n", + " urllib.request.urlretrieve(data_url, local_copy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1780 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202502 7 7332 2924 11740 11 4 \n", + "1 202501 7 6125 2464 9786 9 4 \n", + "2 202452 7 4356 1776 6936 7 3 \n", + "3 202451 7 4670 2239 7101 7 3 \n", + "4 202450 7 7363 4438 10288 11 7 \n", + "5 202449 7 6077 3631 8523 9 5 \n", + "6 202448 7 4189 1454 6924 6 2 \n", + "7 202447 7 1931 726 3136 3 1 \n", + "8 202446 7 2260 863 3657 3 1 \n", + "9 202445 7 2713 1216 4210 4 2 \n", + "10 202444 7 2135 676 3594 3 1 \n", + "11 202443 7 2124 641 3607 3 1 \n", + "12 202442 7 2621 1246 3996 4 2 \n", + "13 202441 7 2035 381 3689 3 1 \n", + "14 202440 7 2125 725 3525 3 1 \n", + "15 202439 7 2898 1333 4463 4 2 \n", + "16 202438 7 751 0 1513 1 0 \n", + "17 202437 7 916 28 1804 1 0 \n", + "18 202436 7 2235 870 3600 3 1 \n", + "19 202435 7 1620 285 2955 2 0 \n", + "20 202434 7 2560 622 4498 4 1 \n", + "21 202433 7 1971 536 3406 3 1 \n", + "22 202432 7 4399 1944 6854 7 3 \n", + "23 202431 7 4500 2213 6787 7 4 \n", + "24 202430 7 7004 4278 9730 11 7 \n", + "25 202429 7 9270 6303 12237 14 10 \n", + "26 202428 7 9364 6498 12230 14 10 \n", + "27 202427 7 10247 7090 13404 15 10 \n", + "28 202426 7 14368 10399 18337 22 16 \n", + "29 202425 7 11174 8039 14309 17 12 \n", + "... ... ... ... ... ... ... ... \n", + "1750 199126 7 17608 11304 23912 31 20 \n", + "1751 199125 7 16169 10700 21638 28 18 \n", + "1752 199124 7 16171 10071 22271 28 17 \n", + "1753 199123 7 11947 7671 16223 21 13 \n", + "1754 199122 7 15452 9953 20951 27 17 \n", + "1755 199121 7 14903 8975 20831 26 16 \n", + "1756 199120 7 19053 12742 25364 34 23 \n", + "1757 199119 7 16739 11246 22232 29 19 \n", + "1758 199118 7 21385 13882 28888 38 25 \n", + "1759 199117 7 13462 8877 18047 24 16 \n", + "1760 199116 7 14857 10068 19646 26 18 \n", + "1761 199115 7 13975 9781 18169 25 18 \n", + "1762 199114 7 12265 7684 16846 22 14 \n", + "1763 199113 7 9567 6041 13093 17 11 \n", + "1764 199112 7 10864 7331 14397 19 13 \n", + "1765 199111 7 15574 11184 19964 27 19 \n", + "1766 199110 7 16643 11372 21914 29 20 \n", + "1767 199109 7 13741 8780 18702 24 15 \n", + "1768 199108 7 13289 8813 17765 23 15 \n", + "1769 199107 7 12337 8077 16597 22 15 \n", + "1770 199106 7 10877 7013 14741 19 12 \n", + "1771 199105 7 10442 6544 14340 18 11 \n", + "1772 199104 7 7913 4563 11263 14 8 \n", + "1773 199103 7 15387 10484 20290 27 18 \n", + "1774 199102 7 16277 11046 21508 29 20 \n", + "1775 199101 7 15565 10271 20859 27 18 \n", + "1776 199052 7 19375 13295 25455 34 23 \n", + "1777 199051 7 19080 13807 24353 34 25 \n", + "1778 199050 7 11079 6660 15498 20 12 \n", + "1779 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 18 FR France \n", + "1 14 FR France \n", + "2 11 FR France \n", + "3 11 FR France \n", + "4 15 FR France \n", + "5 13 FR France \n", + "6 10 FR France \n", + "7 5 FR France \n", + "8 5 FR France \n", + "9 6 FR France \n", + "10 5 FR France \n", + "11 5 FR France \n", + "12 6 FR France \n", + "13 5 FR France \n", + "14 5 FR France \n", + "15 6 FR France \n", + "16 2 FR France \n", + "17 2 FR France \n", + "18 5 FR France \n", + "19 4 FR France \n", + "20 7 FR France \n", + "21 5 FR France \n", + "22 11 FR France \n", + "23 10 FR France \n", + "24 15 FR France \n", + "25 18 FR France \n", + "26 18 FR France \n", + "27 20 FR France \n", + "28 28 FR France \n", + "29 22 FR France \n", + "... ... ... ... \n", + "1750 42 FR France \n", + "1751 38 FR France \n", + "1752 39 FR France \n", + "1753 29 FR France \n", + "1754 37 FR France \n", + "1755 36 FR France \n", + "1756 45 FR France \n", + "1757 39 FR France \n", + "1758 51 FR France \n", + "1759 32 FR France \n", + "1760 34 FR France \n", + "1761 32 FR France \n", + "1762 30 FR France \n", + "1763 23 FR France \n", + "1764 25 FR France \n", + "1765 35 FR France \n", + "1766 38 FR France \n", + "1767 33 FR France \n", + "1768 31 FR France \n", + "1769 29 FR France \n", + "1770 26 FR France \n", + "1771 25 FR France \n", + "1772 20 FR France \n", + "1773 36 FR France \n", + "1774 38 FR France \n", + "1775 36 FR France \n", + "1776 45 FR France \n", + "1777 43 FR France \n", + "1778 28 FR France \n", + "1779 5 FR France \n", + "\n", + "[1780 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(local_copy, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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
\n", + "
" + ], + "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": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pas pour l'instant, nous pouvons utiliser les données sans pré-traitement" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data = raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nos données utilisent une convention inhabituelle: le numéro de semaine est collé à l'année, donnant l'impression qu'il s'agit 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 semaine. Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vontdans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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": "markdown", + "metadata": {}, + "source": [ + "Il restent deux petites modifications à faire.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données. Ceci en fait\n", + "une suite chronologique, ce qui sera pratique par la suite.\n", + "\n", + "Deuxièmement, nous trions les points par période, dans le sens chronologique. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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 le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n", + "\n", + "Tout va bien." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un zoom sur les dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Etude de l'incidence annuelle\n", + "Par consigne de l'exercice, nous définissons la période de référence, 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.\n", + "\n", + "Encore un petit détail: les données commencent an decembre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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": 12, + "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": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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i50fEQYD0PCPFZwPvFJU9kGKz0/HI+LAyETEIfACcW6KuYSStltQlqauvr6/Ml2RmZmNV7hLfL0bEu5JmAJ2SflLiXJ0kFiXiWcucCERsADZAYTirRNvMzKyKyuqJRMS76bkXeJbCfMd7aYiK9NybTj8AXFBUfA7wborPOUl8WBlJTcA5QH+JuszMrA6MmkQknS1pytAxsBR4HdgCDK2W6gCeS8dbgPa04upiYB6wKw15HZG0OM133DiizFBd1wIvp3mTF4Clkqal1V9LU8zMzOpAOcNZ5wPPptW4TcCTEfE/Je0GNklaBewHrgOIiL2SNgFvAIPArRFxLNV1M/AocCawNT0AHgYel9RDoQfSnurql3QvsDudd09E9I/h9ZqZNaTew0f5s6de4YHrr5jQ+6y8FbyZWQO469nX+P6u/dyw8ELWXfO5istnXeLrvbPMzHKs1vvPedsTM7Mcq/X+c04iZmY5Vuv95zycZWaWc7X8HHlPrJuZmT9j3czMJp6TiJmZZeYkYmZmmTmJmJlZZk4iZmaWmZOImZll5iRiZmaZOYmYmVlmTiJmZpaZk4iZmWXmJGJmZpk5iZiZWWZOImZmlpmTiJmZZeYkYmZmmTmJmJlZZk4iZmaWWdlJRNIZkl6R9A/p6+mSOiV1p+dpReeuldQjaZ+kZUXxBZJeS99bL0kp3izpmRTfKWluUZmO9DO6JXVU40WbmVl1VNITuQ14s+jrNcC2iJgHbEtfI2k+0A5cCiwHvivpjFTmQWA1MC89lqf4KuBQRHwa+A5wf6prOnA3sAhYCNxdnKzMzKy2ykoikuYAXwG+VxReAWxMxxuBlUXxpyNiICLeAnqAhZJmAlMjYkcUPtj9sRFlhuraDCxJvZRlQGdE9EfEIaCTE4nHzMxqrNyeyF8DtwPHi2LnR8RBgPQ8I8VnA+8UnXcgxWan45HxYWUiYhD4ADi3RF1mZlYHRk0ikv4I6I2IPWXWqZPEokQ8a5niNq6W1CWpq6+vr8xmmpnZWJXTE/kicLWkt4GngS9LegJ4Lw1RkZ570/kHgAuKys8B3k3xOSeJDysjqQk4B+gvUdcwEbEhItoioq21tbWMl2RmZtUwahKJiLURMSci5lKYMH85Iv4E2AIMrZbqAJ5Lx1uA9rTi6mIKE+i70pDXEUmL03zHjSPKDNV1bfoZAbwALJU0LU2oL00xMzOrA01jKHsfsEnSKmA/cB1AROyVtAl4AxgEbo2IY6nMzcCjwJnA1vQAeBh4XFIPhR5Ie6qrX9K9wO503j0R0T+GNpuZWRWp8Ia/cbS1tUVXV1etm2FmliuS9kREW6XlfMe6mVmV9R4+ylcf2kHvkaO1bsq4cxIxM6uy9du62f12P+tf6q51U8bdWOZEzMysyCV3bWVg8MTtdE/s3M8TO/fT3DSJfeuuqmHLxo97ImZmVbL99iu5+vJZtEwu/GltmTyJFZfPYvsdV46p3noeHnMSMTOrkhlTW5jS3MTA4HGamyYxMHicKc1NzJjSMqZ663l4zMNZZmZV9P6HA9yw6CKuX3ghT+7aT98Yeg95GB7zEl8zszrVe/go655/kxf3/pKjHx2nZfIkll36Ke78ymfH3LsZyUt8zcwazHgNj1WTh7PMzOpYNYfHxoOHs8zMzMNZZmY28ZxEzMwsMycRMzPLzEnEzMwycxIxM7PMnETMzCwzJxEzM8vMScTMzDJzEjEzs8ycRMzMLDMnETMzy8xJxMzMMnMSMTOzzEZNIpJaJO2S9GNJeyX9pxSfLqlTUnd6nlZUZq2kHkn7JC0rii+Q9Fr63npJSvFmSc+k+E5Jc4vKdKSf0S2po5ov3szMxqacnsgA8OWI+B3gcmC5pMXAGmBbRMwDtqWvkTQfaAcuBZYD35V0RqrrQWA1MC89lqf4KuBQRHwa+A5wf6prOnA3sAhYCNxdnKzMzKy2Rk0iUfBh+nJyegSwAtiY4huBlel4BfB0RAxExFtAD7BQ0kxgakTsiMKHmDw2osxQXZuBJamXsgzojIj+iDgEdHIi8ZiZWY2VNSci6QxJrwK9FP6o7wTOj4iDAOl5Rjp9NvBOUfEDKTY7HY+MDysTEYPAB8C5JeoyM7M6UFYSiYhjEXE5MIdCr+KyEqfrZFWUiGctc+IHSqsldUnq6uvrK9E0MzOrpopWZ0XEr4AfUBhSei8NUZGee9NpB4ALiorNAd5N8TkniQ8rI6kJOAfoL1HXyHZtiIi2iGhrbW2t5CWZmdkYlLM6q1XSJ9PxmcAfAD8BtgBDq6U6gOfS8RagPa24upjCBPquNOR1RNLiNN9x44gyQ3VdC7yc5k1eAJZKmpYm1JemmJmZ1YGmMs6ZCWxMK6wmAZsi4h8k7QA2SVoF7AeuA4iIvZI2AW8Ag8CtEXEs1XUz8ChwJrA1PQAeBh6X1EOhB9Ke6uqXdC+wO513T0T0j+UFm5lZ9ajwhr9xtLW1RVdXV62bYWaWK5L2RERbpeV8x7qZmWXmJGJmZpk5iZiZWWZOImZmlpmTiJmZZeYkYmZmmTmJmJlZZk4iZmaWmZOImZll5iRiZmaZOYmYmVlmTiJmZpaZk4iZmWXmJGJmZpk5iZiZWWZOImZmlpmTiJmZZeYkYmZWgd7DR/nqQzvoPXK01k2pC04iZmYVWL+tm91v97P+pe5aN6UuNNW6AWZmeXDJXVsZGDz+8ddP7NzPEzv309w0iX3rrqphy2rLPREzszJsv/1Krr58Fi2TC382WyZPYsXls9h+x5U1blltOYmYmZVhxtQWpjQ3MTB4nOamSQwMHmdKcxMzprTUumk15eEsM7Myvf/hADcsuojrF17Ik7v20+fJdRQRpU+QLgAeAz4FHAc2RMTfSJoOPAPMBd4GvhoRh1KZtcAq4Bjw5xHxQoovAB4FzgSeB26LiJDUnH7GAuD/AV+LiLdTmQ7grtScdRGxsVR729raoqurq/wrYGZmSNoTEW2VlitnOGsQ+A8R8VlgMXCrpPnAGmBbRMwDtqWvSd9rBy4FlgPflXRGqutBYDUwLz2Wp/gq4FBEfBr4DnB/qms6cDewCFgI3C1pWqUv0szMxseoSSQiDkbEj9LxEeBNYDawAhjqFWwEVqbjFcDTETEQEW8BPcBCSTOBqRGxIwrdn8dGlBmqazOwRJKAZUBnRPSnXk4nJxKPmZnVWEUT65LmAlcAO4HzI+IgFBINMCOdNht4p6jYgRSbnY5HxoeViYhB4APg3BJ1mZlZHSg7iUj6LeC/Ad+KiMOlTj1JLErEs5YpbttqSV2Suvr6+ko0zczMqqmsJCJpMoUE8v2I+PsUfi8NUZGee1P8AHBBUfE5wLspPuck8WFlJDUB5wD9JeoaJiI2RERbRLS1traW85LMzKwKRk0iaW7iYeDNiPgvRd/aAnSk4w7guaJ4u6RmSRdTmEDflYa8jkhanOq8cUSZobquBV5O8yYvAEslTUsT6ktTzMzM6kA5S3y/BGwHXqOwxBfg2xTmRTYBFwL7gesioj+VuRO4icLKrm9FxNYUb+PEEt+twDfTEt8W4HEK8y39QHtE/CyVuSn9PIC/jIhHRmlvH/DzMl9/PTgPeL/WjRiDPLc/z22HfLc/z22Hxmz/RRFR8VDOqEnExpekrixrs+tFntuf57ZDvtuf57aD21/M256YmVlmTiJmZpaZk0jtbah1A8Yoz+3Pc9sh3+3Pc9vB7f+Y50TMzCwz90TMzCwzJ5FxIOnvJPVKer0o9juSdkh6TdJ/lzQ1xT8h6ZEU/7Gk3y8q8wNJ+yS9mh4zTvLjqt32CyT9L0lvStor6bYUny6pU1J3ep5WVGatpJ7U1mVF8QXpdfVIWp/uD8pL2+v+2ks6N53/oaQHRtRV19d+lLbn4dr/oaQ96RrvkfTlorom9NqPQ/sru/4R4UeVH8DvAV8AXi+K7Qb+TTq+Cbg3Hd8KPJKOZwB7gEnp6x8AbRPc9pnAF9LxFOCfgfnAXwFrUnwNcH86ng/8GGgGLgZ+CpyRvrcL+F0K29dsBa7KUdvzcO3PBr4EfAN4YERd9X7tS7U9D9f+CmBWOr4M+EWtrv04tL+i6++eyDiIiH+kcNNksUuAf0zHncAfp+P5FLbSJyJ6gV8BNVt/HhOza3Ndt30821hKpe2PiF9HxP8Ghn0yUh6u/anaXisZ2v9KRAxtwbQXaFFhl44Jv/bVbH+Wn+0kMnFeB65Ox9dxYk+wHwMrJDWpsE3MAobvF/ZI6lL+x4noFhfT+O3aPO7G2PYh9X7tTyUP1340ebr2fwy8EhED1Pjaw5jbP6Ts6+8kMnFuovCBXnsodDf/JcX/jsIvWhfw18D/pbBdDMANEfE54F+nx9cnqrEa312bx1UV2g75uPanrOIksXq79qXk5tpLupTCh+j96VDoJKdN2BLYKrQfKrz+TiITJCJ+EhFLI2IB8BSF8XciYjAi/iIiLo+IFcAnge70vV+k5yPAk0zQUIvGf9fmcVOltufl2p9KHq79KeXl2kuaAzwL3BgRP03hmlz71J5qtL/i6+8kMkGGVjhImkThM+P/Nn19lqSz0/EfAoMR8UYa3jovxScDf0RhSGy82zkRuzbXddtzdO1PKifX/lT15OLaS/ok8D+AtRHxf4ZOrsW1r2b7M13/SlcB+FHWSomngIPARxTemawCbqOwYuKfgfs4caPnXGAfhYmwlyjspAmF1St7gH+iMPH1N6SVQ+Pc9i9R6H7/E/BqevxbCp80uY1CL2kbML2ozJ0Uelb7KFqJQmGBwOvpew8MveZ6b3vOrv3bFBZxfJh+1+bn6Nr/Rtvzcu0pvBH8ddG5rwIzanHtq9n+LNffd6ybmVlmHs4yM7PMnETMzCwzJxEzM8vMScTMzDJzEjEzs8ycRMzMLDMnETMzy8xJxMzMMvv/r4lGV8I8mfkAAAAASUVORK5CYII=\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": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\n", + "2023 366227\n", + "2021 376290\n", + "2024 479258\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": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +1425,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - diff --git a/module3/exo2/varicelle_local_copy.csv b/module3/exo2/varicelle_local_copy.csv new file mode 100644 index 0000000000000000000000000000000000000000..8a863ff5729433940f697a28ec55557f17e9551a --- /dev/null +++ b/module3/exo2/varicelle_local_copy.csv @@ -0,0 +1,1782 @@ +# {"source":"réseau Sentinelles, INSERM, Sorbonne Université, 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