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pandas time-series plot frequency adjustment
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import numpy as np\n", | |
"import pandas as pd\n", | |
"import datetime" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Regular + Regular" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Annually + Annually" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x1035c7550>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# both are annually freq, thus it should be converted to PeriodIndex \n", | |
"\n", | |
"s1 = pd.Series([1, 2, 3], index=pd.DatetimeIndex(['1995-12-31', '2000-12-31', '2005-12-31']), name='dt1')\n", | |
"s2 = pd.Series([1, 2, 3], index=pd.DatetimeIndex(['1997-12-31', '2002-12-31', '2007-12-31']), name='dt2')\n", | |
"\n", | |
"ax = s1.plot(marker='o', legend=True)\n", | |
"ax = s2.plot(marker='^', legend=True, ax=ax)\n", | |
"s1.plot(marker='o', ax=ax);" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x106623150>" | |
] | |
}, | |
"execution_count": 6, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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fOx6z5Oe/mUyGE/7m6yxd9TLPXHgHg2ZPYe2UB6i79rSouyYJ02LxdvcPgA+C6S1mthro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\nCN0nAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x106611d90>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# plotting order should not affect to the result\n", | |
"\n", | |
"ax = s2.plot(marker='^', legend=True)\n", | |
"s1.plot(marker='o', ax=ax, legend=True);\n", | |
"s2.plot(marker='^', ax=ax, legend=True)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Annually + Monthly" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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SrZ9S5rE2QLHUH/0QhH9fZfsVsMO/T9+r1dtXEXMuqLhb9kCzs4Fs51xf3+f3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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x1068942d0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# if freq is contained to others, it should be resampled to higher one.\n", | |
"\n", | |
"s1 = pd.Series(np.arange(12, 48), index=pd.date_range('2000-01-01', '2002-12-31', freq='M'), name='dt1')\n", | |
"s2 = pd.Series([0, 12, 24, 36], index=pd.date_range('1999-12-31', '2002-12-31', freq='A'), name='dt2')\n", | |
"\n", | |
"ax = s1.plot(marker='o', legend=True)\n", | |
"ax = s2.plot(marker='^', legend=True, ax=ax)\n", | |
"s1.plot(marker='o', ax=ax);" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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uDvtlIMU595KZDQVqOudOWnhUDttj2XtzqDykH3urn0G9z96mUcemge6SiASx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\nRURChAK2iEiIUMAWEQkR/w+MXnY8s+qjLAAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x106653590>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"ax = s2.plot(marker='^', legend=True)\n", | |
"ax = s1.plot(marker='o', legend=True, ax=ax)\n", | |
"s2.plot(marker='^', ax=ax);" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Weekly + Daily" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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JIL1BOgAZDTP4+9t/P2XHJ0B2Zjbze75MhdaXcuR3MdROSaKFirjIGVEhlzM2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\nkFMhFxEJcv8POCMmwrti/WsAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x10664a850>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# if freq is contained to others, it should be resampled to higher one.\n", | |
"# below results are buggy, it should be resampled to daily freq\n", | |
"\n", | |
"s1 = pd.Series(np.arange(0, 63, 7), index=pd.date_range('2000-01-01', '2000-02-28', freq='W'), name='dt1')\n", | |
"s2 = pd.Series(np.arange(20, 42), index=pd.date_range('2000-01-20', '2000-02-10', freq='D'), name='dt2')\n", | |
"\n", | |
"ax = s1.plot(marker='o', legend=True)\n", | |
"ax = s2.plot(marker='^', legend=True, ax=ax)\n", | |
"s1.plot(marker='o', ax=ax);" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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8mhLrvAI87Yr3q0ac3sBewA/wAXYC1wAXO60TTvFVqlbGeQvQE0h1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\nuCD7BIn42nAAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x106f31f90>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"ax = s2.plot(marker='^', legend=True)\n", | |
"ax = s1.plot(marker='o', legend=True, ax=ax)\n", | |
"s2.plot(marker='^', ax=ax);" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Regular + Irregular" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"# if either index isn't on freq, plot on absolute time.\n", | |
"\n", | |
"pidx = pd.PeriodIndex(start='2010-01-01', freq='m', periods=5)\n", | |
"didx = pd.DatetimeIndex(['2010-01-01', '2010-02-05', '2010-03-10', '2010-04-15', '2010-05-25'])\n", | |
"x1 = pd.Series(np.arange(5), index=pidx)\n", | |
"x2 = pd.Series(np.arange(5), index=didx)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x1060a9790>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"ax = x1.plot(marker='o')\n", | |
"x2.plot(marker='^', ax=ax)\n", | |
"ax.set_title(\"Period then DatetimeIndex\");" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 13, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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Hl4TxhhtcDq1HHvG3IjkVvrJMgolZJobhHw4kHODx7x9n2dZljL5/NJWLVY60SGmyfz/8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\nKCgYMcpkUlBQUFAwYvwf3xjKxvnJdLYAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x107c38310>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"ax = x2.plot(marker='^')\n", | |
"x1.plot(marker='o', ax=ax)\n", | |
"ax.set_title(\"Datetime then PeriodIndex\");" | |
] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 2", | |
"language": "python", | |
"name": "python2" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 2 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython2", | |
"version": "2.7.10" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 0 | |
} |
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