{ "cells": [ { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from datetime import datetime, date" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n", " '2013-01-05', '2013-01-06'],\n", " dtype='datetime64[ns]', freq='D')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dates = pd.date_range('20130101', periods=6)\n", "dates" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCD
2013-01-01-0.0508230.142731-2.082915-0.649777
2013-01-020.2521040.225202-0.126795-0.039806
2013-01-030.0822791.0022030.296082-0.142417
2013-01-040.804723-0.067008-1.283606-0.013937
2013-01-05-0.3098380.966221-0.2554481.114112
2013-01-06-0.944737-0.1663560.838028-1.472370
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" ], "text/plain": [ " A B C D\n", "2013-01-01 -0.050823 0.142731 -2.082915 -0.649777\n", "2013-01-02 0.252104 0.225202 -0.126795 -0.039806\n", "2013-01-03 0.082279 1.002203 0.296082 -0.142417\n", "2013-01-04 0.804723 -0.067008 -1.283606 -0.013937\n", "2013-01-05 -0.309838 0.966221 -0.255448 1.114112\n", "2013-01-06 -0.944737 -0.166356 0.838028 -1.472370" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1 = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\n", "df1" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCD
2013-01-01-0.2324960.795638-0.664480-0.048818
2013-01-02-0.680090-2.043307-0.5744300.195735
2013-01-03-0.269628-0.0102880.4746500.924644
2013-01-040.756782-0.520419-0.059370-0.803062
2013-01-050.0149470.0661320.8262950.217646
2013-01-06-1.1304431.718015-1.5264151.480180
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" ], "text/plain": [ " A B C D\n", "2013-01-01 -0.232496 0.795638 -0.664480 -0.048818\n", "2013-01-02 -0.680090 -2.043307 -0.574430 0.195735\n", "2013-01-03 -0.269628 -0.010288 0.474650 0.924644\n", "2013-01-04 0.756782 -0.520419 -0.059370 -0.803062\n", "2013-01-05 0.014947 0.066132 0.826295 0.217646\n", "2013-01-06 -1.130443 1.718015 -1.526415 1.480180" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\n", "df2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCD
2013-01-010.391974-0.7474050.637964-0.237240
2013-01-02-0.8546691.017091-0.671113-0.087779
2013-01-03-0.892535-1.2881510.684799-2.273715
2013-01-04-0.4546331.780200-0.4044912.412129
2013-01-050.599057-0.7132490.442731-0.109480
2013-01-061.669014-3.5666030.449308-0.829849
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" ], "text/plain": [ " A B C D\n", "2013-01-01 0.391974 -0.747405 0.637964 -0.237240\n", "2013-01-02 -0.854669 1.017091 -0.671113 -0.087779\n", "2013-01-03 -0.892535 -1.288151 0.684799 -2.273715\n", "2013-01-04 -0.454633 1.780200 -0.404491 2.412129\n", "2013-01-05 0.599057 -0.713249 0.442731 -0.109480\n", "2013-01-06 1.669014 -3.566603 0.449308 -0.829849" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1-df2" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n", " '2013-01-05', '2013-01-06'],\n", " dtype='datetime64[ns]', freq='D')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.index" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['A', 'B', 'C', 'D'], dtype='object')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.columns" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.15947796, 0.04823215, -0.02651611, -0.28605851],\n", " [-1.53475903, -1.02621568, -1.24554343, 0.10795592],\n", " [-1.16216299, -1.29843858, 1.15944901, -1.3490703 ],\n", " [ 0.30214898, 1.25978075, -0.46386119, 1.609067 ],\n", " [ 0.61400412, -0.64711735, 1.26902556, 0.10816601],\n", " [ 0.53857047, -1.84858888, -1.07710728, 0.65033087]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.values" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCD
count6.0000006.0000006.0000006.000000
mean-0.180453-0.585391-0.0640920.140065
std0.9267701.1052941.0823590.981783
min-1.534759-1.848589-1.245543-1.349070
25%-0.831753-1.230383-0.923796-0.187555
50%0.230813-0.836667-0.2451890.108061
75%0.479465-0.1256050.8629580.514790
max0.6140041.2597811.2690261.609067
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" ], "text/plain": [ " A B C D\n", "count 6.000000 6.000000 6.000000 6.000000\n", "mean -0.180453 -0.585391 -0.064092 0.140065\n", "std 0.926770 1.105294 1.082359 0.981783\n", "min -1.534759 -1.848589 -1.245543 -1.349070\n", "25% -0.831753 -1.230383 -0.923796 -0.187555\n", "50% 0.230813 -0.836667 -0.245189 0.108061\n", "75% 0.479465 -0.125605 0.862958 0.514790\n", "max 0.614004 1.259781 1.269026 1.609067" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.describe()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2013-01-01 0.159478\n", "2013-01-02 -1.534759\n", "2013-01-03 -1.162163\n", "2013-01-04 0.302149\n", "2013-01-05 0.614004\n", "2013-01-06 0.538570\n", "Freq: D, Name: A, dtype: float64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1['A']" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCD
2013-01-040.3021491.259781-0.4638611.609067
2013-01-050.614004-0.6471171.2690260.108166
2013-01-060.538570-1.848589-1.0771070.650331
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" ], "text/plain": [ " A B C D\n", "2013-01-04 0.302149 1.259781 -0.463861 1.609067\n", "2013-01-05 0.614004 -0.647117 1.269026 0.108166\n", "2013-01-06 0.538570 -1.848589 -1.077107 0.650331" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1[df1['A']>0.3]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "A 0.302149\n", "B 1.259781\n", "C -0.463861\n", "D 1.609067\n", "Name: 2013-01-04 00:00:00, dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.iloc[3]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2013-01-01 -0.286059\n", "2013-01-02 0.107956\n", "2013-01-03 -1.349070\n", "2013-01-04 1.609067\n", "2013-01-05 0.108166\n", "2013-01-06 0.650331\n", "Freq: D, Name: D, dtype: float64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.iloc[:,3]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCD
0-0.613985-1.059690-0.173678-1.827621
1-1.4734790.1486011.851009-1.775998
21.546853-0.328106-2.6821980.818066
3-0.097835-1.5330430.036097-1.083091
4-1.399138-0.269051-0.396322-1.083094
5-1.8195260.4313790.6494140.774973
\n", "
" ], "text/plain": [ " A B C D\n", "0 -0.613985 -1.059690 -0.173678 -1.827621\n", "1 -1.473479 0.148601 1.851009 -1.775998\n", "2 1.546853 -0.328106 -2.682198 0.818066\n", "3 -0.097835 -1.533043 0.036097 -1.083091\n", "4 -1.399138 -0.269051 -0.396322 -1.083094\n", "5 -1.819526 0.431379 0.649414 0.774973" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3 = pd.DataFrame(np.random.randn(6,4), columns=list('ABCD'))\n", "df3" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig=plt.figure(facecolor='white')\n", "ax=fig.gca()\n", "x=[np.datetime64('2013-%02d-01' % d) for d in range(1,7)]\n", "y=(np.random.rand(6))\n", "ymin = min(y)\n", "ymax = max(y)\n", "ax.fill((x[2],x[3],x[3],x[2]) , (ymin, ymin, ymax, ymax),color='lightsalmon')\n", "ax.fill((x[1],x[2],x[2],x[1]) , (ymin, ymin, ymax, ymax),color='lightblue')\n", "locs, labels = plt.xticks()\n", "plt.setp(labels, rotation=45)\n", "ax.plot(x,y)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TABLECODE7471_Data_dac9fa37-e563-4ae4-9132-c9ea97b94abc\n" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PERIODYearLGRRegionSEXSexAGEGPAge GroupETHGPEthnic GroupMEASUREMeasureValueFlags
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" ], "text/plain": [ " PERIOD Year LGR Region SEX Sex AGEGP \\\n", "0 199806 1998 98 Total Regions 98 Total Both Sexes 98 \n", "1 199906 1999 98 Total Regions 98 Total Both Sexes 98 \n", "\n", " Age Group ETHGP Ethnic Group MEASURE \\\n", "0 Total Age Groups 98 Total Ethnic Groups AV_WEEK_INC \n", "1 Total Age Groups 98 Total Ethnic Groups AV_WEEK_INC \n", "\n", " Measure Value Flags \n", "0 Average Weekly Income 413.0 NaN \n", "1 Average Weekly Income 429.0 NaN " ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df00=pd.read_csv('Stats/Earnings/TABLECODE7471_Data_dac9fa37-e563-4ae4-9132-c9ea97b94abc.csv')\n", "df00.head(2)" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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200220020698Total Regions98Total Both Sexes98Total Age Groups98Total Ethnic GroupsAV_WEEK_INCAverage Weekly Income483.0NaN
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" ], "text/plain": [ " PERIOD LGR Region SEX Sex AGEGP \\\n", "Year \n", "1998 199806 98 Total Regions 98 Total Both Sexes 98 \n", "1999 199906 98 Total Regions 98 Total Both Sexes 98 \n", "2000 200006 98 Total Regions 98 Total Both Sexes 98 \n", "2001 200106 98 Total Regions 98 Total Both Sexes 98 \n", "2002 200206 98 Total Regions 98 Total Both Sexes 98 \n", "\n", " Age Group ETHGP Ethnic Group MEASURE \\\n", "Year \n", "1998 Total Age Groups 98 Total Ethnic Groups AV_WEEK_INC \n", "1999 Total Age Groups 98 Total Ethnic Groups AV_WEEK_INC \n", "2000 Total Age Groups 98 Total Ethnic Groups AV_WEEK_INC \n", "2001 Total Age Groups 98 Total Ethnic Groups AV_WEEK_INC \n", "2002 Total Age Groups 98 Total Ethnic Groups AV_WEEK_INC \n", "\n", " Measure Value Flags \n", "Year \n", "1998 Average Weekly Income 413.0 NaN \n", "1999 Average Weekly Income 429.0 NaN \n", "2000 Average Weekly Income 436.0 NaN \n", "2001 Average Weekly Income 462.0 NaN \n", "2002 Average Weekly Income 483.0 NaN " ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df01=df00.set_index('Year')\n", "df01.head()" ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Year\n", "1998 AV_WEEK_INC\n", "1998 MED_WEEK_INC\n", "1998 NO_PEOPLE\n", "Name: MEASURE, dtype: object" ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df01['MEASURE'].drop_duplicates()" ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [], "source": [ "dfav = pd.DataFrame(index=df01.index)" ] }, { "cell_type": "code", "execution_count": 158, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "63" ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dfav.index)" ] }, { "cell_type": "code", "execution_count": 159, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "21" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(df00[df00['MEASURE']=='AV_WEEK_INC']['Value'].values)" ] }, { "cell_type": "code", "execution_count": 160, "metadata": {}, "outputs": [], "source": [ "dfav['Average'] = df01[df01['MEASURE']=='AV_WEEK_INC']['Value']\n", "dfav['Median'] = df01[df01['MEASURE']=='MED_WEEK_INC']['Value']\n" ] }, { "cell_type": "code", "execution_count": 161, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64Index([1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,\n", " 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 1998,\n", " 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n", " 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 1998, 1999,\n", " 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,\n", " 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018],\n", " dtype='int64', name='Year')" ] }, "execution_count": 161, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df01.index" ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AverageMedian
Year
1998413.0289.0
1999429.0300.0
2000436.0313.0
2001462.0340.0
2002483.0350.0
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" ], "text/plain": [ " Average Median\n", "Year \n", "1998 413.0 289.0\n", "1999 429.0 300.0\n", "2000 436.0 313.0\n", "2001 462.0 340.0\n", "2002 483.0 350.0" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfav.head()" ] }, { "cell_type": "code", "execution_count": 164, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AverageMedian
Year
2014735.0560.0
2015755.0579.0
2016787.0600.0
2017811.0614.0
2018859.0671.0
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" ], "text/plain": [ " Average Median\n", "Year \n", "2014 735.0 560.0\n", "2015 755.0 579.0\n", "2016 787.0 600.0\n", "2017 811.0 614.0\n", "2018 859.0 671.0" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfav.tail()" ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [], "source": [ "dfav.drop_duplicates(inplace=True)" ] }, { "cell_type": "code", "execution_count": 215, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Resident Government and Earnings')" ] }, "execution_count": 215, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig=plt.figure(facecolor='white')\n", " \n", "ax=fig.gca()\n", "\n", "terms = ((1998, 1999, 'N'), (1999, 2008, 'L'), (2008, 2017,'N'),(2017,2018,'L'))\n", "\n", "ymin = min(dfav['Average'].min(), dfav['Median'].min())\n", "ymax = max(dfav['Average'].max(), dfav['Median'].max())\n", "ax.set_ylim(ymin, ymax)\n", "\n", "colours = {'N':'lightblue','L':'lightsalmon'}\n", "\n", "for d1,d2,govt in terms:\n", " ax.fill((d1,d2,d2,d1) , (ymin, ymin, ymax, ymax),color=colours[govt])\n", "ax.plot(dfav.index, dfav)\n", "ax.set_ylabel('Weekly Earnings $')\n", "ax.set_xticks(dfav.index[::2])\n", "ax.legend(['Average','Median'])\n", "ax.set_title('Resident Government and Earnings')\n" ] }, { "cell_type": "code", "execution_count": 163, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AverageMedian
Year
1998413.0289.0
1999429.0300.0
2000436.0313.0
2001462.0340.0
2002483.0350.0
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" ], "text/plain": [ " Average Median\n", "Year \n", "1998 413.0 289.0\n", "1999 429.0 300.0\n", "2000 436.0 313.0\n", "2001 462.0 340.0\n", "2002 483.0 350.0" ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfav.head()" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [], "source": [ "dfav['Average'] = df01['Value'].values" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Average
Year
1998413.0
1999429.0
2000436.0
2001462.0
2002483.0
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" ], "text/plain": [ " Average\n", "Year \n", "1998 413.0\n", "1999 429.0\n", "2000 436.0\n", "2001 462.0\n", "2002 483.0" ] }, "execution_count": 132, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfav.head()" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "No group keys passed!", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\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[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfav\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Value'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/venvs/scipy/lib/python3.6/site-packages/pandas/core/reshape/pivot.py\u001b[0m in \u001b[0;36mpivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m \u001b[0mgrouped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 83\u001b[0m \u001b[0magged\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/venvs/scipy/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, **kwargs)\u001b[0m\n\u001b[1;32m 6657\u001b[0m return groupby(self, by=by, axis=axis, level=level, as_index=as_index,\n\u001b[1;32m 6658\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgroup_keys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6659\u001b[0;31m observed=observed, **kwargs)\n\u001b[0m\u001b[1;32m 6660\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6661\u001b[0m def asfreq(self, freq, method=None, how=None, normalize=False,\n", "\u001b[0;32m~/venvs/scipy/lib/python3.6/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(obj, by, **kwds)\u001b[0m\n\u001b[1;32m 2150\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'invalid type: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2152\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mby\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[0m\u001b[1;32m 2153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/venvs/scipy/lib/python3.6/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, **kwargs)\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 598\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 599\u001b[0;31m mutated=self.mutated)\n\u001b[0m\u001b[1;32m 600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 601\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/venvs/scipy/lib/python3.6/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_get_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, mutated, validate)\u001b[0m\n\u001b[1;32m 3318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3320\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No group keys passed!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3321\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3322\u001b[0m \u001b[0;31m# create the internals grouper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: No group keys passed!" ] } ], "source": [ "pd.pivot_table(dfav,values='Value')" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.9447374056292628\n", "(-0.05500000000000001, 0.05500000000000001) (-0.05500000000000001, 0.05500000000000001) [numpy.datetime64('2013-01-01'), numpy.datetime64('2013-02-01'), numpy.datetime64('2013-03-01'), numpy.datetime64('2013-04-01'), numpy.datetime64('2013-05-01')]\n" ] }, { "data": { "image/png": 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nSzJ/Lut5O0uS1MwQkSQ1G3KI7Jh1BwboQp2TC3XcZ+KcfI1z8XzndD4G+2BdkjR8Q74SkSQNXG8hkuTKJPcmOZjkQJIf77ZflmRvksPd+6Xd9m9Pcl+SP0/y7rHzvCzJ/07y6e48P3OGmlu78x5OsnVs+88meSzJ032Nr8VQ5iTJNyX5WJLPdcffdiGMu9v+38aO//dJlp3NsZ+hf4OZk7H9u5J89myM90yGNBdJPpHkkSQPd69Xnc2xn6ZvQ5qPFUl2JHm0+37xg0sOoKp6eQFrgDd0yy8HHgU2AO8HtnfbtwPv65ZfBXwn8LPAu8fOE+Dibvki4AHgjRPqXQYc7d4v7ZYv7fa9sevP032N73yeE+CbgL/VtVkB/B7w9m/0cXf7Lhk7128AN1zIXwtj+38A+FXgsxfyXACfAEaz+JoY6Hz8DPDebvklwMql+t/blUhVHa+qT3XLXwIOAVcAW4CdXbOdwPVdmxNVtR/4i0Xnqao6dQVxUfea9ODm+4C9VfUnVfUFYC+wuTvH/VV1vK+xtRrKnFTVM1V1b3euZ4FPAWv7G+nzDWXc3Tn+rGuznIUAnclDwCHNSZKLgZ8E3tvfCF+4Ic3FEAxsPn4E+Nfd+b5aVUt+iPOsPBNJsg54PQtJuHrsG/oTwOoXcPyyJA8DJ1gY7AMTml0BPDa2fqzbNkhDmZMkrwT+LrDvRQ6hyRDGnWRPd/yXgLte/Cj6NYA5eQ/wQeCZlv73aQBzAfCfu1tZ/ypJXvwo+jPL+ei+NwC8J8mnknw0yZI1ew+R7qec3wB+YuynQGAhKXkBPwlW1Veq6moWflremOS1fffzXBrKnCRZDvwa8ItVdfTFHt9QbxDjrqrvY+GWwUuB73mxx/dp1nOS5Grgr1TVb724nvdv1nPR+eGq+g7gLd3rH7/I43szgPlY3h33v6rqDcB9wL9Z6qBeQyTJRSxMwn+tqt/sNj+ZZE23fw0LCfmCVNUXgXuBzUm+a+zh13XA48CVY83XdtsGZWBzsgM4XFU/3z6iF2Zg46aqvgzczcItgpkYyJy8CRgl+UPgfwCvTvKJ6Ub24g1kLqiqU+9fYuEZ0cbpRtZmIPPxFAtXp6fqfxR4w1K1+vztrAAfBg5V1c+N7doFnHr6v5WFf8hnOs+qU5dVSf4ScC3wuap6oKqu7l67gD3A9ya5tPuthe/ttg3GkOYkyXuBVwA/0d8IT9vfQYw7ycVj/wiXA38b+Fx/I33hhjInVfXLVfWXq2od8N3Ao1X1tv5GurShzEWS5UlWdsdfBPwdYBa/rTaI+eiudn4HeFt3ymuAg0sOoPr7DYPvZuFy6zPAw93r+4FvYeH++2HgHuCyrv3lLNyL+zPgi93yJcDrgIe683wWuOUMNX8EONK9/unY9vd35/tq9/7TfY3zfJwTFn7SKBYe2J3qx7sugHGvBvaPHf9LwPIL+Wth0f51zOa3swYxF8A3Aw92xx8AfgFYdqHOR7f9W4FPdufYB8wt1X8/sS5JauYn1iVJzQwRSVIzQ0SS1MwQkSQ1M0QkSc0MEUlSM0NEktTMEJEkNft/DUfuUCmJK98AAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib.dates import date2num\n", "x = [np.datetime64('2013-%02d-01' % d) for d in range(1,len(df1['A']+1))]\n", "print(df1['A'].min())\n", "fig=plt.figure()\n", "ax=fig.gca()\n", "ax.plot( x=x,y=df1['A'], fmt='ro')\n", "ax.plot( x=date2num(datetime(2013,3,1)),y=0, fmt='ro')\n", "print (ax.get_ylim(), ax.get_xlim(), x)\n", "xl = ax.get_xlim()\n", "yl = ax.get_ylim()\n", "\n", "#ax.fill((2,3,3,2) , (yl[0], yl[0], yl[1], yl[1]),'r')\n", "ax.set_xlim(np.datetime64('2013-01-01'), np.datetime64('2013-06-01'))\n", "ax.set_ylim(df1['A'].min(),df1['A'].max())\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'B')" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.rcParams[\"figure.figsize\"] = [12,9]\n", "fig, axes = plt.subplots(nrows=2, ncols=2)\n", "plt.subplots_adjust(hspace=0.4)\n", "df1['A'].plot(ax=axes[0,0]); axes[0,0].set_title('A')\n", "df1['B'].plot(ax=axes[1,0]); axes[1,0].set_title('B')" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 3\n", "2 4\n" ] } ], "source": [ "for x,y in ((0,3),(2,4)):\n", " print (x,y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }