diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..678dfc3e7c40efad1280e5fe9551fb5cca6a9514 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2424 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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120244038489576454.093336.0127114.0140.0FRFrance
220243939166082937.0100383.0137124.0150.0FRFrance
320243839178682903.0100669.0138125.0151.0FRFrance
420243735646049319.063601.08574.096.0FRFrance
520243633365727906.039408.05041.059.0FRFrance
620243532740422036.032772.04133.049.0FRFrance
720243432671721003.032431.04031.049.0FRFrance
820243332062315349.025897.03123.039.0FRFrance
920243232318717532.028842.03527.043.0FRFrance
1020243132603520267.031803.03930.048.0FRFrance
1120243033639328593.044193.05543.067.0FRFrance
1220242933956032592.046528.05949.069.0FRFrance
1320242835434245781.062903.08168.094.0FRFrance
1420242734736440234.054494.07160.082.0FRFrance
1520242634421936956.051482.06655.077.0FRFrance
1620242534720440300.054108.07161.081.0FRFrance
1720242434111034671.047549.06252.072.0FRFrance
1820242333587530610.041140.05446.062.0FRFrance
1920242233377228274.039270.05143.059.0FRFrance
2020242132196317556.026370.03326.040.0FRFrance
2120242032005715780.024334.03024.036.0FRFrance
2220241931537511274.019476.02317.029.0FRFrance
2320241832240917653.027165.03427.041.0FRFrance
2420241732704221410.032674.04133.049.0FRFrance
2520241632888223305.034459.04335.051.0FRFrance
2620241533022924648.035810.04537.053.0FRFrance
2720241433181326529.037097.04840.056.0FRFrance
2820241333509029607.040573.05345.061.0FRFrance
2920241234063934582.046696.06152.070.0FRFrance
.................................
205519852132609619621.032571.04735.059.0FRFrance
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2085 rows × 10 columns

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3 35875 30610.0 41140.0 54 46.0 \n", + "19 202422 3 33772 28274.0 39270.0 51 43.0 \n", + "20 202421 3 21963 17556.0 26370.0 33 26.0 \n", + "21 202420 3 20057 15780.0 24334.0 30 24.0 \n", + "22 202419 3 15375 11274.0 19476.0 23 17.0 \n", + "23 202418 3 22409 17653.0 27165.0 34 27.0 \n", + "24 202417 3 27042 21410.0 32674.0 41 33.0 \n", + "25 202416 3 28882 23305.0 34459.0 43 35.0 \n", + "26 202415 3 30229 24648.0 35810.0 45 37.0 \n", + "27 202414 3 31813 26529.0 37097.0 48 40.0 \n", + "28 202413 3 35090 29607.0 40573.0 53 45.0 \n", + "29 202412 3 40639 34582.0 46696.0 61 52.0 \n", + "... ... ... ... ... ... ... ... \n", + "2055 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2056 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2057 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2058 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2059 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2060 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2061 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2062 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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18481989193-NaNNaN-NaNNaNFRFrance
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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420243735646049319.063601.08574.096.0FRFrance
520243633365727906.039408.05041.059.0FRFrance
620243532740422036.032772.04133.049.0FRFrance
720243432671721003.032431.04031.049.0FRFrance
820243332062315349.025897.03123.039.0FRFrance
920243232318717532.028842.03527.043.0FRFrance
1020243132603520267.031803.03930.048.0FRFrance
1120243033639328593.044193.05543.067.0FRFrance
1220242933956032592.046528.05949.069.0FRFrance
1320242835434245781.062903.08168.094.0FRFrance
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1720242434111034671.047549.06252.072.0FRFrance
1820242333587530610.041140.05446.062.0FRFrance
1920242233377228274.039270.05143.059.0FRFrance
2020242132196317556.026370.03326.040.0FRFrance
2120242032005715780.024334.03024.036.0FRFrance
2220241931537511274.019476.02317.029.0FRFrance
2320241832240917653.027165.03427.041.0FRFrance
2420241732704221410.032674.04133.049.0FRFrance
2520241632888223305.034459.04335.051.0FRFrance
2620241533022924648.035810.04537.053.0FRFrance
2720241433181326529.037097.04840.056.0FRFrance
2820241333509029607.040573.05345.061.0FRFrance
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2084 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202441 3 84683 74972.0 94394.0 127 112.0 \n", + "1 202440 3 84895 76454.0 93336.0 127 114.0 \n", + "2 202439 3 91660 82937.0 100383.0 137 124.0 \n", + "3 202438 3 91786 82903.0 100669.0 138 125.0 \n", + "4 202437 3 56460 49319.0 63601.0 85 74.0 \n", + "5 202436 3 33657 27906.0 39408.0 50 41.0 \n", + "6 202435 3 27404 22036.0 32772.0 41 33.0 \n", + "7 202434 3 26717 21003.0 32431.0 40 31.0 \n", + "8 202433 3 20623 15349.0 25897.0 31 23.0 \n", + "9 202432 3 23187 17532.0 28842.0 35 27.0 \n", + "10 202431 3 26035 20267.0 31803.0 39 30.0 \n", + "11 202430 3 36393 28593.0 44193.0 55 43.0 \n", + "12 202429 3 39560 32592.0 46528.0 59 49.0 \n", + "13 202428 3 54342 45781.0 62903.0 81 68.0 \n", + "14 202427 3 47364 40234.0 54494.0 71 60.0 \n", + "15 202426 3 44219 36956.0 51482.0 66 55.0 \n", + "16 202425 3 47204 40300.0 54108.0 71 61.0 \n", + "17 202424 3 41110 34671.0 47549.0 62 52.0 \n", + "18 202423 3 35875 30610.0 41140.0 54 46.0 \n", + "19 202422 3 33772 28274.0 39270.0 51 43.0 \n", + "20 202421 3 21963 17556.0 26370.0 33 26.0 \n", + "21 202420 3 20057 15780.0 24334.0 30 24.0 \n", + "22 202419 3 15375 11274.0 19476.0 23 17.0 \n", + "23 202418 3 22409 17653.0 27165.0 34 27.0 \n", + "24 202417 3 27042 21410.0 32674.0 41 33.0 \n", + "25 202416 3 28882 23305.0 34459.0 43 35.0 \n", + "26 202415 3 30229 24648.0 35810.0 45 37.0 \n", + "27 202414 3 31813 26529.0 37097.0 48 40.0 \n", + "28 202413 3 35090 29607.0 40573.0 53 45.0 \n", + "29 202412 3 40639 34582.0 46696.0 61 52.0 \n", + "... ... ... ... ... ... ... ... \n", + "2055 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2056 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2057 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2058 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2059 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2060 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2061 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2062 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2063 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2064 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2065 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2066 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2067 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2068 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2069 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2070 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2071 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2072 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2073 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2074 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2075 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2076 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2077 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2078 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2079 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2080 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2081 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2082 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2083 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2084 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 142.0 FR France \n", + "1 140.0 FR France \n", + "2 150.0 FR France \n", + "3 151.0 FR France \n", + "4 96.0 FR France \n", + "5 59.0 FR France \n", + "6 49.0 FR France \n", + "7 49.0 FR France \n", + "8 39.0 FR France \n", + "9 43.0 FR France \n", + "10 48.0 FR France \n", + "11 67.0 FR France \n", + "12 69.0 FR France \n", + "13 94.0 FR France \n", + "14 82.0 FR France \n", + "15 77.0 FR France \n", + "16 81.0 FR France \n", + "17 72.0 FR France \n", + "18 62.0 FR France \n", + "19 59.0 FR France \n", + "20 40.0 FR France \n", + "21 36.0 FR France \n", + "22 29.0 FR France \n", + "23 41.0 FR France \n", + "24 49.0 FR France \n", + "25 51.0 FR France \n", + "26 53.0 FR France \n", + "27 56.0 FR France \n", + "28 61.0 FR France \n", + "29 70.0 FR France \n", + "... ... ... ... \n", + "2055 59.0 FR France \n", + "2056 64.0 FR France \n", + "2057 97.0 FR France \n", + "2058 93.0 FR France \n", + "2059 80.0 FR France \n", + "2060 116.0 FR France \n", + "2061 149.0 FR France \n", + "2062 281.0 FR France \n", + "2063 395.0 FR France \n", + "2064 485.0 FR France \n", + "2065 544.0 FR France \n", + "2066 689.0 FR France \n", + "2067 722.0 FR France \n", + "2068 762.0 FR France \n", + "2069 926.0 FR France \n", + "2070 1113.0 FR France \n", + "2071 1236.0 FR France \n", + "2072 832.0 FR France \n", + "2073 459.0 FR France \n", + "2074 207.0 FR France \n", + "2075 190.0 FR France \n", + "2076 198.0 FR France \n", + "2077 224.0 FR France \n", + "2078 266.0 FR France \n", + "2079 219.0 FR France \n", + "2080 176.0 FR France \n", + "2081 163.0 FR France \n", + "2082 195.0 FR France \n", + "2083 308.0 FR France \n", + "2084 213.0 FR France \n", + "\n", + "[2084 rows x 10 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "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": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + " sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "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", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].replace('-', np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "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 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\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()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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", + "Freq: W-SUN, Name: inc, dtype: object\n" + ] + } + ], + "source": [ + "print(sorted_data['inc'].head()) # Print the first few values of the 'inc' column" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "print(sorted_data['inc'].isna().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data['inc'] = pd.to_numeric(sorted_data['inc'], errors='coerce') # Coerce any non-numeric values to NaN" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = sorted_data.dropna(subset=['inc']) # Drop rows where 'inc' is NaN\n", + "# Alternatively, fill NaN values with 0 or the mean\n", + "# sorted_data['inc'] = sorted_data['inc'].fillna(0) # Fill NaN with 0" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + " sorted_data['inc'][-200:].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + " first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\n", + " sorted_data.index[-1].year)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_august_week[:-1],\n", + " first_august_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": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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rp8Ss1vyes2rwGsogan0ur2abp+05doKvPfES911/SUO2z4bXbO+5ZtcqnxmvoTSAZpunrfc0iY1ds73nmp0/MwP5FxurrBlOF+LTcYwvzfCea3b+zAzOU17maRKzEWq1z4ynvKxiniYxGxl/ZgbngNIihjsuoRW2UJrl5M/Mr/OUV4vw6WHMbLQqnfLyovw458XDkWmVbaBm1eApr3HOP507Mt4GajZ6HqGMc148rIxHcmZj5xFKC/Di4fA8kjMbO49QWoB/Ond4HsmZjZ0DilniI8zNxsbbhs3MrCwfKW9mZjXlgGJmZlk4oJg1If+sdG24n0fGAcWsCfkAzNpwP4+MF+XNmoh/4rc23M8DeVHebBzyAZi1kbOfW2nazAHFLLNqfoH4AMzayNnPrTRt5gMbzTIr/gKpxk8F+ADM2hhrP7fi+eG8hmKWiefdR2a8/1TAePqZYK+hmNWY1zdGZrxPBbXi9KSnvMwyacUvkNFopamgVpuedEAxy6jVvkBG48U7Fg45FTTetNqZvh1QzDJqtS+Q0fBIbvzyGoqZ1Zx/9C2fRjrOxbu8zMya2OqnX+WxnQe4YcH5VdmmDhl3eUk6T9JWSW9I2iPptpQ+VdJmSd3pekpRmVWS9knaK2lxUfqlkl5Nj62TpJTeLunJlL5DUkdRmeXpObolLS9Kn5vydqeyp1faOWZmjWK0I4z5qzfSsfJZHt1xgIjC5oaOlc8yf/XGKrV0eJVMefUBfxIRnwIuB26VdCGwEtgSEfOALek+6bFlwEXAEuDbkk5Ldd0PrADmpcuSlH4TcDQiPgncC6xNdU0F7gIuAxYAdxUFrrXAven5j6Y6zMyaymi3TzfiNvVhF+Uj4hBwKN0+LukNYDawFLgyZXsE2AbcmdLXR8RJ4OeS9gELJO0HzoyI7QCSvgdcC2xMZb6R6voBcF8avSwGNkfEkVRmM7BE0nrgKuD6ouf/BoWAZWbW8Ma6fboRNzeMaFE+TUVdAuwAzk3Bpj/ozEjZZgNvFRU7mNJmp9ul6QPKREQf8B4wrUxd04B3U97SukrbvEJSl6Su3t7ekbxcM7OqyTHCaLTNDRVvG5b0ceCvgT+OiGNp+WPQrIOkRZn00ZQpV9fAxIgHgQehsCg/WB4zs1rLMcJotG3qFY1QJE2kEEwei4gfpuR3JM1Mj88EelL6QeC8ouJzgLdT+pxB0geUkdQGnAUcKVPXYeDslLe0LjOzptBoI4yxGnaEktYyHgLeiIhvFT20AVgO3JOunylKf1zSt4BZFBbfd0bEB5KOS7qcwpTZjcD/KKlrO3Ad8EJEhKRNwH8pWohfBKxKj21NedeXPL+ZWVNotBHGWFUyQrkC+ApwlaSX0+V3KQSSqyV1A1en+0TEHuAp4HXgOeDWiPgg1XUL8B1gH/AmhQV5KASsaWkB/3bSjrG0GH83sCtdvtm/QE9hA8Dtqcy0VIfVUSMdYGVmtecDGy2bWhxgZWa1V+mBjT6Xl41ZK5091syG5nN52Zg14gFWZlZ7Dig2Zo14gJWZ1Z6nvCwL/w6ImXlR3szMyvJvypuZWU05oIwTPgbEzOrNAWWcGO0psM3McvGifJPzMSDWqnqOneBrT7zEfddf4h2FDcIjlCbnY0CsVXlU3ng8QmlyPgbEWo1H5Y3LI5RxYLydAtusHI/KG5dHKOPAeDsFtlk5HpU3LgcUM2s6PjNDY/KR8mZmVpaPlDczs5pyQDEzsywcUMzMLAsHFDMzy8IBxczMsnBAMTOzLBxQzMwsCwcUMzPLwgHFzMyycEAxM7MsHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUMzMLAsHFDMzy8IBxczMsnBAMTOzLIYNKJIeltQj6bWitKmSNkvqTtdTih5bJWmfpL2SFhelXyrp1fTYOklK6e2SnkzpOyR1FJVZnp6jW9LyovS5KW93Knv62LvCzMzGopIRyl8CS0rSVgJbImIesCXdR9KFwDLgolTm25JOS2XuB1YA89Klv86bgKMR8UngXmBtqmsqcBdwGbAAuKsocK0F7k3PfzTVYWZmdTRsQImInwBHSpKXAo+k248A1xalr4+IkxHxc2AfsEDSTODMiNgeEQF8r6RMf10/AD6XRi+Lgc0RcSQijgKbgSXpsatS3tLnNzOzOhntGsq5EXEIIF3PSOmzgbeK8h1MabPT7dL0AWUiog94D5hWpq5pwLspb2ldv0bSCkldkrp6e3tH+DLNzKxSuRflNUhalEkfTZlydf36AxEPRkRnRHROnz59qGxmZjZGow0o76RpLNJ1T0o/CJxXlG8O8HZKnzNI+oAyktqAsyhMsQ1V12Hg7JS3tC4zM6uT0QaUDUD/rqvlwDNF6cvSzq25FBbfd6ZpseOSLk9rIDeWlOmv6zrghbTOsglYJGlKWoxfBGxKj21NeUuf38zM6qRtuAySngCuBM6RdJDCzqt7gKck3QQcAL4EEBF7JD0FvA70AbdGxAepqlso7Bj7GLAxXQAeAr4vaR+FkcmyVNcRSXcDu1K+b0ZE/+aAO4H1ktYAL6U6zMysjlT4h781dHZ2RldXV72bYWbWVCTtjojO4fL5SHkzM8vCAcXMzLJwQDEza2A9x07w5Qe203P8RL2bMiwHFDOzBrZuSze79h9h3fPd9W7KsIbd5WVmZrU3f/VGTvad+vD+ozsO8OiOA7S3TWDvms/XsWVD8wjFzKwBvXjHQq65eBaTJha+pidNnMDSi2fx4p0L69yyoTmgmJk1oBlnTmJyexsn+07R3jaBk32nmNzexozJk+rdtCF5ysvMrEEdfv8kN1x2AdcvOJ/Hdx6gt8EX5n1go5mZleUDG83MrKYcUMzMLAsHFDMzy8IBxczMsnBAMTOzLBxQzMwsi5baNiypF/iHIR4+h8LPCzcytzEPtzGPZmgjNEc7G72NF0TE9OEytVRAKUdSVyX7rOvJbczDbcyjGdoIzdHOZmhjJTzlZWZmWTigmJlZFg4oH3mw3g2ogNuYh9uYRzO0EZqjnc3QxmF5DcXMzLLwCMXMzLIYtwFF0sOSeiS9VpT2LyRtl/SqpP8p6cyUPlHSIyn9DUmrispsk7RX0svpMqNObTxd0ndT+iuSriwqc2lK3ydpnSQ1YBur2Y/nSdqa/nZ7JN2W0qdK2iypO11PKSqzKvXXXkmLi9Kr0peZ21iVvhxpGyVNS/nfl3RfSV3VfE/mbGej9OXVknanPtst6aqiuqrWl9lFxLi8AP8K+G3gtaK0XcC/Trf/ELg73b4eWJ9u/wawH+hI97cBnQ3QxluB76bbM4DdwIR0fyfwWUDARuDzDdjGavbjTOC30+3JwN8DFwL/FViZ0lcCa9PtC4FXgHZgLvAmcFo1+zJzG6vSl6No4xnA7wB/BNxXUlc135M529kofXkJMCvd/jTwj7Xoy9yXcTtCiYifAEdKkucDP0m3NwO/358dOENSG/Ax4J+AYw3WxguBLalcD/Au0ClpJnBmRGyPwrvve8C1jdTGXG0p08ZDEfF/0u3jwBvAbGAp8EjK9ggf9ctSCv9AnIyInwP7gAXV7MtcbczRllxtjIhfRsTfAgN+9akG78ks7aymUbTxpYh4O6XvASZJaq92X+Y2bgPKEF4Drkm3vwScl27/APglcAg4APy3iCj+Ev1uGg7/pxoMN4dq4yvAUkltkuYCl6bHZgMHi8ofTGmN1MZ+Ve9HSR0U/tvbAZwbEYeg8AGnMGqCQv+8VVSsv89q0pdjbGO/qvZlhW0cSs3ek2NsZ79G68vfB16KiJPU5/M9aq0WUP4QuFXSbgrD0H9K6QuAD4BZFKYX/kTSP0uP3RARnwH+Zbp8pU5tfJjCm6kL+HPgfwF9FIbBpaq9dW+kbYQa9KOkjwN/DfxxRJQbYQ7VZ1XvywxthCr35QjaOGQVg6Rlf09maCc0WF9KughYC9zcnzRItobdmttSASUifhYRiyLiUuAJCvPSUFhDeS4ifpWmav6ONFUTEf+Yro8Dj1P9aYdB2xgRfRHxHyLi4ohYCpwNdFP4Ap9TVMUc4O3Seuvcxqr3o6SJFD64j0XED1PyO2nKoH8apielH2TgyKm/z6ral5naWNW+HGEbh1L192SmdjZUX0qaAzwN3BgR/d9NNf98j0VLBZT+HRySJgCrgb9IDx0ArlLBGcDlwM/S1M05qcxE4AsUpntq3kZJv5HahqSrgb6IeD0Nm49LujwN128EnmmkNla7H9Prfgh4IyK+VfTQBmB5ur2cj/plA7AszVHPBeYBO6vZl7naWM2+HEUbB1Xt92SudjZSX0o6G3gWWBURf9efuR6f7zHJvcrfKBcK/zkfAn5FIcrfBNxGYbfF3wP38NGBnR8H/orCYtjrwH+Mj3aH7AZ+mh7776SdNnVoYwewl8Li3vMUzv7ZX08nhQ/Cm8B9/WUapY016MffoTAN8FPg5XT5XWAahU0C3el6alGZr6f+2kvRrplq9WWuNlazL0fZxv0UNm28n94fF9bgPZmlnY3UlxT+MftlUd6XgRnV7svcFx8pb2ZmWbTUlJeZmVWPA4qZmWXhgGJmZlk4oJiZWRYOKGZmloUDipmZZeGAYmZmWTigmJlZFv8fpJr4KXuy0UkAAAAASUVORK5CYII=\n", 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5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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