diff --git a/module3/exo1/influenza-like-illness-analysis.ipynb b/module3/exo1/influenza-like-illness-analysis.ipynb index 87092fc69cd90ff457ea56284b789cb0de199a41..cb7d320892ac351a97add940c6f0993a1f67a013 100644 --- a/module3/exo1/influenza-like-illness-analysis.ipynb +++ b/module3/exo1/influenza-like-illness-analysis.ipynb @@ -9,10 +9,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -25,15 +23,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The data on the incidence of influenza-like illness are available from the Web site of the [Réseau Sentinelles](http://www.sentiweb.fr/). We download them as a file in CSV format, in which each line corresponds to a week in the observation period. Only the complete dataset, starting in 1984 and ending with a recent week, is available for download." + "The data on the incidence of influenza-like illness are available from the Web site of the [Réseau Sentinelles](https://www.sentiweb.fr/france/en/?). We download them as a file in CSV format, in which each line corresponds to a week in the observation period. Only the complete dataset, starting in 1984 and ending with a recent week, is available for download." ] }, { "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\"" @@ -55,7 +51,7 @@ "| `inc100` | Estimated rate incidence per 100,000 inhabitants |\n", "| `inc100_low` | Lower bound of the estimated incidence 95% Confidence Interval |\n", "| `inc100_up` | Upper bound of the estimated rate incidence 95% Confidence Interval |\n", - "| `geo_insee` | Identifier of the geographic area, from INSEE https://www.insee.fr |\n", + "| `geo_insee` | Identifier of the geographic area, from INSEE https://www.insee.fr |\n", "| `geo_name` | Geographic label of the area, corresponding to INSEE code. This label is not an id and is only provided for human reading |\n", "\n", "The first line of the CSV file is a comment, which we ignore with `skip=1`." @@ -63,9 +59,976 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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420242132196317556.026370.03326.040.0FRFrance
520242032005715780.024334.03024.036.0FRFrance
620241931537511274.019476.02317.029.0FRFrance
720241832240917653.027165.03427.041.0FRFrance
820241732704221410.032674.04133.049.0FRFrance
920241632888223305.034459.04335.051.0FRFrance
1020241533022924648.035810.04537.053.0FRFrance
1120241433181326529.037097.04840.056.0FRFrance
1220241333509029607.040573.05345.061.0FRFrance
1320241234063934582.046696.06152.070.0FRFrance
1420241135026843331.057205.07565.085.0FRFrance
1520241036010752623.067591.09079.0101.0FRFrance
1620240937112162920.079322.010795.0119.0FRFrance
17202408310456694520.0114612.0157142.0172.0FRFrance
182024073138078127050.0149106.0207190.0224.0FRFrance
192024063190062177955.0202169.0285267.0303.0FRFrance
202024053216237203595.0228879.0324305.0343.0FRFrance
212024043213196200547.0225845.0320301.0339.0FRFrance
222024033163457152276.0174638.0245228.0262.0FRFrance
232024023129436119453.0139419.0194179.0209.0FRFrance
242024013120769109452.0132086.0181164.0198.0FRFrance
252023523115446103738.0127154.0174156.0192.0FRFrance
262023513148755136546.0160964.0224206.0242.0FRFrance
272023503147971136787.0159155.0223206.0240.0FRFrance
282023493147552136422.0158682.0222205.0239.0FRFrance
292023483124204113479.0134929.0187171.0203.0FRFrance
.................................
203919852132609619621.032571.04735.059.0FRFrance
204019852032789620885.034907.05138.064.0FRFrance
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2069 rows × 10 columns

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France \n", + "2067 308.0 FR France \n", + "2068 213.0 FR France \n", + "\n", + "[2069 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" @@ -80,9 +1043,73 @@ }, { "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
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2068 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202425 3 50347 42176.0 58518.0 75 63.0 \n", + "1 202424 3 41414 34928.0 47900.0 62 52.0 \n", + "2 202423 3 35875 30610.0 41140.0 54 46.0 \n", + "3 202422 3 33772 28274.0 39270.0 51 43.0 \n", + "4 202421 3 21963 17556.0 26370.0 33 26.0 \n", + "5 202420 3 20057 15780.0 24334.0 30 24.0 \n", + "6 202419 3 15375 11274.0 19476.0 23 17.0 \n", + "7 202418 3 22409 17653.0 27165.0 34 27.0 \n", + "8 202417 3 27042 21410.0 32674.0 41 33.0 \n", + "9 202416 3 28882 23305.0 34459.0 43 35.0 \n", + "10 202415 3 30229 24648.0 35810.0 45 37.0 \n", + "11 202414 3 31813 26529.0 37097.0 48 40.0 \n", + "12 202413 3 35090 29607.0 40573.0 53 45.0 \n", + "13 202412 3 40639 34582.0 46696.0 61 52.0 \n", + "14 202411 3 50268 43331.0 57205.0 75 65.0 \n", + "15 202410 3 60107 52623.0 67591.0 90 79.0 \n", + "16 202409 3 71121 62920.0 79322.0 107 95.0 \n", + "17 202408 3 104566 94520.0 114612.0 157 142.0 \n", + "18 202407 3 138078 127050.0 149106.0 207 190.0 \n", + "19 202406 3 190062 177955.0 202169.0 285 267.0 \n", + "20 202405 3 216237 203595.0 228879.0 324 305.0 \n", + "21 202404 3 213196 200547.0 225845.0 320 301.0 \n", + "22 202403 3 163457 152276.0 174638.0 245 228.0 \n", + "23 202402 3 129436 119453.0 139419.0 194 179.0 \n", + "24 202401 3 120769 109452.0 132086.0 181 164.0 \n", + "25 202352 3 115446 103738.0 127154.0 174 156.0 \n", + "26 202351 3 148755 136546.0 160964.0 224 206.0 \n", + "27 202350 3 147971 136787.0 159155.0 223 206.0 \n", + "28 202349 3 147552 136422.0 158682.0 222 205.0 \n", + "29 202348 3 124204 113479.0 134929.0 187 171.0 \n", + "... ... ... ... ... ... ... ... \n", + "2039 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2040 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2041 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2042 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2043 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2044 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2045 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2046 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2047 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2048 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2049 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2050 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2051 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2052 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2053 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2054 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2055 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2056 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2057 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2058 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2059 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2060 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2061 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2062 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2063 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2064 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2065 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2066 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2067 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2068 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 87.0 FR France \n", + "1 72.0 FR France \n", + "2 62.0 FR France \n", + "3 59.0 FR France \n", + "4 40.0 FR France \n", + "5 36.0 FR France \n", + "6 29.0 FR France \n", + "7 41.0 FR France \n", + "8 49.0 FR France \n", + "9 51.0 FR France \n", + "10 53.0 FR France \n", + "11 56.0 FR France \n", + "12 61.0 FR France \n", + "13 70.0 FR France \n", + "14 85.0 FR France \n", + "15 101.0 FR France \n", + "16 119.0 FR France \n", + "17 172.0 FR France \n", + "18 224.0 FR France \n", + "19 303.0 FR France \n", + "20 343.0 FR France \n", + "21 339.0 FR France \n", + "22 262.0 FR France \n", + "23 209.0 FR France \n", + "24 198.0 FR France \n", + "25 192.0 FR France \n", + "26 242.0 FR France \n", + "27 240.0 FR France \n", + "28 239.0 FR France \n", + "29 203.0 FR France \n", + "... ... ... ... \n", + "2039 59.0 FR France \n", + "2040 64.0 FR France \n", + "2041 97.0 FR France \n", + "2042 93.0 FR France \n", + "2043 80.0 FR France \n", + "2044 116.0 FR France \n", + "2045 149.0 FR France \n", + "2046 281.0 FR France \n", + "2047 395.0 FR France \n", + "2048 485.0 FR France \n", + "2049 544.0 FR France \n", + "2050 689.0 FR France \n", + "2051 722.0 FR France \n", + "2052 762.0 FR France \n", + "2053 926.0 FR France \n", + "2054 1113.0 FR France \n", + "2055 1236.0 FR France \n", + "2056 832.0 FR France \n", + "2057 459.0 FR France \n", + "2058 207.0 FR France \n", + "2059 190.0 FR France \n", + "2060 198.0 FR France \n", + "2061 224.0 FR France \n", + "2062 266.0 FR France \n", + "2063 219.0 FR France \n", + "2064 176.0 FR France \n", + "2065 163.0 FR France \n", + "2066 195.0 FR France \n", + "2067 308.0 FR France \n", + "2068 213.0 FR France \n", + "\n", + "[2068 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -123,10 +2117,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", @@ -154,10 +2146,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -180,9 +2170,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "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", @@ -200,9 +2198,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "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 \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 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\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()" ] @@ -365,7 +2380,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4,