{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 11.6 Resampling and Frequency Conversion(重采样和频度转换)\n",
"\n",
"重采样(Resampling)指的是把时间序列的频度变为另一个频度的过程。把高频度的数据变为低频度叫做降采样(downsampling),把低频度变为高频度叫做增采样(upsampling)。并不是所有的重采样都会落入上面这几个类型,例如,把W-WED(weekly on Wednesday)变为W-FRI,既不属于降采样,也不属于增采样。\n",
"\n",
"pandas对象自带resampe方法,用于所有的频度变化。resample有一个和groupby类似的API;我们可以用resample来对数据进行分组,然后调用聚合函数(aggregation function):"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rng = pd.date_range('2000-01-01', periods=100, freq='D')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2000-01-01 0.141136\n",
"2000-01-02 0.955511\n",
"2000-01-03 -0.334537\n",
"2000-01-04 0.927611\n",
"2000-01-05 0.522567\n",
"2000-01-06 0.843023\n",
"2000-01-07 0.108661\n",
"2000-01-08 0.805668\n",
"2000-01-09 -0.470524\n",
"2000-01-10 1.162150\n",
"2000-01-11 -0.754087\n",
"2000-01-12 -1.846421\n",
"2000-01-13 -0.322607\n",
"2000-01-14 0.769992\n",
"2000-01-15 -0.596838\n",
"2000-01-16 0.865629\n",
"2000-01-17 -0.394363\n",
"2000-01-18 1.050334\n",
"2000-01-19 0.203739\n",
"2000-01-20 0.112178\n",
"2000-01-21 -1.858528\n",
"2000-01-22 0.921361\n",
"2000-01-23 -1.034003\n",
"2000-01-24 -0.319369\n",
"2000-01-25 0.626385\n",
"2000-01-26 2.319831\n",
"2000-01-27 0.640064\n",
"2000-01-28 0.762187\n",
"2000-01-29 -0.053246\n",
"2000-01-30 0.500993\n",
" ... \n",
"2000-03-11 -1.036658\n",
"2000-03-12 0.569500\n",
"2000-03-13 -0.279623\n",
"2000-03-14 -1.593708\n",
"2000-03-15 -1.552634\n",
"2000-03-16 0.983931\n",
"2000-03-17 0.269289\n",
"2000-03-18 0.870814\n",
"2000-03-19 1.642178\n",
"2000-03-20 -0.109097\n",
"2000-03-21 -1.891613\n",
"2000-03-22 -1.867747\n",
"2000-03-23 -0.173888\n",
"2000-03-24 0.879418\n",
"2000-03-25 0.814583\n",
"2000-03-26 -1.683395\n",
"2000-03-27 -0.141228\n",
"2000-03-28 0.392206\n",
"2000-03-29 -1.288983\n",
"2000-03-30 1.052897\n",
"2000-03-31 -0.297663\n",
"2000-04-01 1.050265\n",
"2000-04-02 -0.072390\n",
"2000-04-03 1.482098\n",
"2000-04-04 -0.276297\n",
"2000-04-05 0.686525\n",
"2000-04-06 1.368484\n",
"2000-04-07 0.294756\n",
"2000-04-08 1.237246\n",
"2000-04-09 1.372567\n",
"Freq: D, dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
"ts"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2000-01-31 -0.207554\n",
"2000-02-29 0.299003\n",
"2000-03-31 -0.095402\n",
"2000-04-30 -0.146846\n",
"Freq: M, dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.resample('M').mean()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2000-01 0.210165\n",
"2000-02 -0.051811\n",
"2000-03 -0.131131\n",
"2000-04 0.793695\n",
"Freq: M, dtype: float64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.resample('M', kind='period').mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"resample是一个灵活且高效的方法,可以用于处理大量的时间序列。下面是一些相关的选项:\n",
"\n",
"![](http://oydgk2hgw.bkt.clouddn.com/pydata-book/nwc0s.png)\n",
"\n",
"# 1 Downsampling(降采样)\n",
"\n",
"把数据聚合为规律、低频度是一个很普通的时间序列任务。用于处理的数据不必是有固定频度的;我们想要设定的频度会定义箱界(bin edges),根据bin edges会把时间序列分割为多个片段,然后进行聚合。例如,转换为月度,比如'M'或'BM',我们需要把数据以月为间隔进行切割。每一个间隔都是半开放的(half-open);一个数据点只能属于一个间隔,所有间隔的合集,构成整个时间范围(time frame)。当使用resample去降采样数据的时候,有很多事情需要考虑:\n",
"\n",
"- 在每个间隔里,哪一边要闭合\n",
"- 怎样对每一个聚合的bin贴标签,可以使用间隔的开始或结束\n",
"\n",
"为了演示一下,下面用一个一分钟的数据来举例:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rng = pd.date_range('2000-01-01', periods=12, freq='T')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2000-01-01 00:00:00 0\n",
"2000-01-01 00:01:00 1\n",
"2000-01-01 00:02:00 2\n",
"2000-01-01 00:03:00 3\n",
"2000-01-01 00:04:00 4\n",
"2000-01-01 00:05:00 5\n",
"2000-01-01 00:06:00 6\n",
"2000-01-01 00:07:00 7\n",
"2000-01-01 00:08:00 8\n",
"2000-01-01 00:09:00 9\n",
"2000-01-01 00:10:00 10\n",
"2000-01-01 00:11:00 11\n",
"Freq: T, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts = pd.Series(np.arange(12), index=rng)\n",
"ts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"假设我们想要按5分钟一个数据块来进行聚合,然后对每一个组计算总和:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1999-12-31 23:55:00 0\n",
"2000-01-01 00:00:00 15\n",
"2000-01-01 00:05:00 40\n",
"2000-01-01 00:10:00 11\n",
"Freq: 5T, dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.resample('5min', closed='right').sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们传入的频度定义了每个bin的边界按5分钟递增。默认,bin的左边界是闭合的,所以`00:00`值是属于`00:00`到`00:05`间隔的。设定closed='right',会让间隔的右边闭合:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1999-12-31 23:55:00 0\n",
"2000-01-01 00:00:00 15\n",
"2000-01-01 00:05:00 40\n",
"2000-01-01 00:10:00 11\n",
"Freq: 5T, dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.resample('5min', closed='right').sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"默认,每一个bin的左边的时间戳,会被用来作为结果里时间序列的标签。通过设置label='right',我们可以使用bin右边的时间戳来作为标签:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2000-01-01 00:00:00 0\n",
"2000-01-01 00:05:00 15\n",
"2000-01-01 00:10:00 40\n",
"2000-01-01 00:15:00 11\n",
"Freq: 5T, dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.resample('5min', closed='right', label='right').sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"可以看下图方便理解:\n",
"\n",
"![](http://oydgk2hgw.bkt.clouddn.com/pydata-book/d770h.png)\n",
"\n",
"最后,我们可能想要对结果的索引进行位移,比如在右边界减少一秒。想要实现的话,传递一个字符串或日期偏移给loffset:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1999-12-31 23:59:59 0\n",
"2000-01-01 00:04:59 15\n",
"2000-01-01 00:09:59 40\n",
"2000-01-01 00:14:59 11\n",
"Freq: 5T, dtype: int64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.resample('5min', closed='right', \n",
" label='right', loffset='-1s').sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们也可以使用shift方法来实现上面loffset的效果。\n",
"\n",
"### Open-High-Low-Close (OHLC) resampling(股价图重取样)\n",
"\n",
"> Open-High-Low-Close: 开盘-盘高-盘低-收盘图;股票图;股价图\n",
"\n",
"在经济界,一个比较流行的用法,是对时间序列进行聚合,计算每一个桶(bucket)里的四个值:first(open),last(close),maximum(high),minimal(low),即开盘-收盘-盘高-盘低,四个值。使用ohlc聚合函数可以得到这四个聚合结果:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" open | \n",
" high | \n",
" low | \n",
" close | \n",
"
\n",
" \n",
" \n",
" \n",
" 2000-01-01 00:00:00 | \n",
" 0 | \n",
" 4 | \n",
" 0 | \n",
" 4 | \n",
"
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" \n",
" 2000-01-01 00:05:00 | \n",
" 5 | \n",
" 9 | \n",
" 5 | \n",
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" 2000-01-01 00:10:00 | \n",
" 10 | \n",
" 11 | \n",
" 10 | \n",
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"
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"
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],
"text/plain": [
" open high low close\n",
"2000-01-01 00:00:00 0 4 0 4\n",
"2000-01-01 00:05:00 5 9 5 9\n",
"2000-01-01 00:10:00 10 11 10 11"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts.resample('5min').ohlc()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2 Upsampling and Interpolation(增采样和插值)\n",
"\n",
"把一个低频度转换为高频度,是不需要进行聚合的。下面是一个有周数据的DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Colorado | \n",
" Texas | \n",
" New York | \n",
" Ohio | \n",
"
\n",
" \n",
" \n",
" \n",
" 2000-01-05 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
\n",
" \n",
" 2000-01-12 | \n",
" -1.125212 | \n",
" -0.824337 | \n",
" 0.803721 | \n",
" -0.672660 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Colorado Texas New York Ohio\n",
"2000-01-05 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-12 -1.125212 -0.824337 0.803721 -0.672660"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame = pd.DataFrame(np.random.randn(2, 4),\n",
" index=pd.date_range('1/1/2000', periods=2,\n",
" freq='W-WED'),\n",
" columns=['Colorado', 'Texas', 'New York', 'Ohio'])\n",
"frame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"当我们对这个数据进行聚合的的时候,每个组只有一个值,以及gap(间隔)之间的缺失值。在不使用任何聚合函数的情况下,我们使用asfreq方法将其转换为高频度:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Colorado | \n",
" Texas | \n",
" New York | \n",
" Ohio | \n",
"
\n",
" \n",
" \n",
" \n",
" 2000-01-05 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
\n",
" \n",
" 2000-01-06 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
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" \n",
" 2000-01-07 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
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"
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" \n",
" 2000-01-08 | \n",
" NaN | \n",
" NaN | \n",
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" NaN | \n",
"
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" \n",
" 2000-01-09 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
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" \n",
" 2000-01-10 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
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" \n",
" 2000-01-11 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" 2000-01-12 | \n",
" -1.125212 | \n",
" -0.824337 | \n",
" 0.803721 | \n",
" -0.672660 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Colorado Texas New York Ohio\n",
"2000-01-05 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-06 NaN NaN NaN NaN\n",
"2000-01-07 NaN NaN NaN NaN\n",
"2000-01-08 NaN NaN NaN NaN\n",
"2000-01-09 NaN NaN NaN NaN\n",
"2000-01-10 NaN NaN NaN NaN\n",
"2000-01-11 NaN NaN NaN NaN\n",
"2000-01-12 -1.125212 -0.824337 0.803721 -0.672660"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_daily = frame.resample('D').asfreq()\n",
"df_daily"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"假设我们想要用每周的值来填写非周三的部分。这种方法叫做填充(filling)或插值(interpolation),可以使用fillna或reindex方法来实现重采样:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Colorado | \n",
" Texas | \n",
" New York | \n",
" Ohio | \n",
"
\n",
" \n",
" \n",
" \n",
" 2000-01-05 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
\n",
" \n",
" 2000-01-06 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
\n",
" \n",
" 2000-01-07 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
\n",
" \n",
" 2000-01-08 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
\n",
" \n",
" 2000-01-09 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
\n",
" \n",
" 2000-01-10 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
\n",
" \n",
" 2000-01-11 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
\n",
" \n",
" 2000-01-12 | \n",
" -1.125212 | \n",
" -0.824337 | \n",
" 0.803721 | \n",
" -0.672660 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Colorado Texas New York Ohio\n",
"2000-01-05 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-06 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-07 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-08 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-09 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-10 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-11 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-12 -1.125212 -0.824337 0.803721 -0.672660"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame.resample('D').ffill()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们可以选择只对一部分的周期进行填写:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
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" \n",
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" | \n",
" Colorado | \n",
" Texas | \n",
" New York | \n",
" Ohio | \n",
"
\n",
" \n",
" \n",
" \n",
" 2000-01-05 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
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" \n",
" 2000-01-06 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
\n",
" \n",
" 2000-01-07 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
\n",
" \n",
" 2000-01-08 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" 2000-01-09 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" 2000-01-10 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" 2000-01-11 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" 2000-01-12 | \n",
" -1.125212 | \n",
" -0.824337 | \n",
" 0.803721 | \n",
" -0.672660 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Colorado Texas New York Ohio\n",
"2000-01-05 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-06 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-07 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-08 NaN NaN NaN NaN\n",
"2000-01-09 NaN NaN NaN NaN\n",
"2000-01-10 NaN NaN NaN NaN\n",
"2000-01-11 NaN NaN NaN NaN\n",
"2000-01-12 -1.125212 -0.824337 0.803721 -0.672660"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame.resample('D').ffill(limit=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"注意,新的日期索引不能与旧的有重叠:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Colorado | \n",
" Texas | \n",
" New York | \n",
" Ohio | \n",
"
\n",
" \n",
" \n",
" \n",
" 2000-01-06 | \n",
" 0.138355 | \n",
" 1.881517 | \n",
" 0.655367 | \n",
" 1.496932 | \n",
"
\n",
" \n",
" 2000-01-13 | \n",
" -1.125212 | \n",
" -0.824337 | \n",
" 0.803721 | \n",
" -0.672660 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Colorado Texas New York Ohio\n",
"2000-01-06 0.138355 1.881517 0.655367 1.496932\n",
"2000-01-13 -1.125212 -0.824337 0.803721 -0.672660"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame.resample('W-THU').ffill()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3 Resampling with Periods(对周期进行重采样)\n",
"\n",
"对周期的索引进行重采样的过程,与之前时间戳的方法相似:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Colorado | \n",
" Texas | \n",
" New York | \n",
" Ohio | \n",
"
\n",
" \n",
" \n",
" \n",
" 2000-01 | \n",
" 1.451095 | \n",
" 0.236027 | \n",
" -1.114785 | \n",
" 1.245450 | \n",
"
\n",
" \n",
" 2000-02 | \n",
" 1.720449 | \n",
" -0.724853 | \n",
" -1.870676 | \n",
" 1.089338 | \n",
"
\n",
" \n",
" 2000-03 | \n",
" 0.411774 | \n",
" -0.785979 | \n",
" 1.749024 | \n",
" 0.164739 | \n",
"
\n",
" \n",
" 2000-04 | \n",
" -1.549051 | \n",
" -0.050722 | \n",
" 0.002775 | \n",
" -1.606657 | \n",
"
\n",
" \n",
" 2000-05 | \n",
" 1.011998 | \n",
" 0.149377 | \n",
" -1.608262 | \n",
" 0.992927 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Colorado Texas New York Ohio\n",
"2000-01 1.451095 0.236027 -1.114785 1.245450\n",
"2000-02 1.720449 -0.724853 -1.870676 1.089338\n",
"2000-03 0.411774 -0.785979 1.749024 0.164739\n",
"2000-04 -1.549051 -0.050722 0.002775 -1.606657\n",
"2000-05 1.011998 0.149377 -1.608262 0.992927"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame = pd.DataFrame(np.random.randn(24, 4),\n",
" index=pd.period_range('1-2000', '12-2001',\n",
" freq='M'),\n",
" columns=['Colorado', 'Texas', 'New York', 'Ohio'])\n",
"frame[:5]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Colorado | \n",
" Texas | \n",
" New York | \n",
" Ohio | \n",
"
\n",
" \n",
" \n",
" \n",
" 2000 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2001 | \n",
" -0.401946 | \n",
" 0.368050 | \n",
" -0.209196 | \n",
" -0.155851 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Colorado Texas New York Ohio\n",
"2000 0.208662 -0.109971 -0.233464 0.138465\n",
"2001 -0.401946 0.368050 -0.209196 -0.155851"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"annual_frame = frame.resample('A-DEC').mean()\n",
"annual_frame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"增采样需要考虑的要多一些,比如在重采样前,选择哪一个时间跨度作为结束,就像asfreq方法那样。convertion参数默认是'start',但也能用'end':"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Colorado | \n",
" Texas | \n",
" New York | \n",
" Ohio | \n",
"
\n",
" \n",
" \n",
" \n",
" 2000Q1 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2000Q2 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2000Q3 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2000Q4 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2001Q1 | \n",
" -0.401946 | \n",
" 0.368050 | \n",
" -0.209196 | \n",
" -0.155851 | \n",
"
\n",
" \n",
" 2001Q2 | \n",
" -0.401946 | \n",
" 0.368050 | \n",
" -0.209196 | \n",
" -0.155851 | \n",
"
\n",
" \n",
" 2001Q3 | \n",
" -0.401946 | \n",
" 0.368050 | \n",
" -0.209196 | \n",
" -0.155851 | \n",
"
\n",
" \n",
" 2001Q4 | \n",
" -0.401946 | \n",
" 0.368050 | \n",
" -0.209196 | \n",
" -0.155851 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Colorado Texas New York Ohio\n",
"2000Q1 0.208662 -0.109971 -0.233464 0.138465\n",
"2000Q2 0.208662 -0.109971 -0.233464 0.138465\n",
"2000Q3 0.208662 -0.109971 -0.233464 0.138465\n",
"2000Q4 0.208662 -0.109971 -0.233464 0.138465\n",
"2001Q1 -0.401946 0.368050 -0.209196 -0.155851\n",
"2001Q2 -0.401946 0.368050 -0.209196 -0.155851\n",
"2001Q3 -0.401946 0.368050 -0.209196 -0.155851\n",
"2001Q4 -0.401946 0.368050 -0.209196 -0.155851"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Q-DEC: Quarterly, year ending in December\n",
"annual_frame.resample('Q-DEC').ffill()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Colorado | \n",
" Texas | \n",
" New York | \n",
" Ohio | \n",
"
\n",
" \n",
" \n",
" \n",
" 2000Q4 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2001Q1 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2001Q2 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2001Q3 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2001Q4 | \n",
" -0.401946 | \n",
" 0.368050 | \n",
" -0.209196 | \n",
" -0.155851 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Colorado Texas New York Ohio\n",
"2000Q4 0.208662 -0.109971 -0.233464 0.138465\n",
"2001Q1 0.208662 -0.109971 -0.233464 0.138465\n",
"2001Q2 0.208662 -0.109971 -0.233464 0.138465\n",
"2001Q3 0.208662 -0.109971 -0.233464 0.138465\n",
"2001Q4 -0.401946 0.368050 -0.209196 -0.155851"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"annual_frame.resample('Q-DEC', convention='end').ffill()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"增采样和降采样的规则更严格一些:\n",
"\n",
"- 降采样中,目标频度必须是原频度的子周期(subperiod)\n",
"- 增采样中,目标频度必须是原频度的母周期(superperiod)\n",
"\n",
"如果不满足上面的规则,会报错。主要会影响到季度,年度,周度频度;例如,用Q-MAR定义的时间跨度只与A-MAR, A-JUN, A-SEP, A-DEC进行对齐(line up with):"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Colorado | \n",
" Texas | \n",
" New York | \n",
" Ohio | \n",
"
\n",
" \n",
" \n",
" \n",
" 2000Q4 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2001Q1 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2001Q2 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2001Q3 | \n",
" 0.208662 | \n",
" -0.109971 | \n",
" -0.233464 | \n",
" 0.138465 | \n",
"
\n",
" \n",
" 2001Q4 | \n",
" -0.401946 | \n",
" 0.368050 | \n",
" -0.209196 | \n",
" -0.155851 | \n",
"
\n",
" \n",
" 2002Q1 | \n",
" -0.401946 | \n",
" 0.368050 | \n",
" -0.209196 | \n",
" -0.155851 | \n",
"
\n",
" \n",
" 2002Q2 | \n",
" -0.401946 | \n",
" 0.368050 | \n",
" -0.209196 | \n",
" -0.155851 | \n",
"
\n",
" \n",
" 2002Q3 | \n",
" -0.401946 | \n",
" 0.368050 | \n",
" -0.209196 | \n",
" -0.155851 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Colorado Texas New York Ohio\n",
"2000Q4 0.208662 -0.109971 -0.233464 0.138465\n",
"2001Q1 0.208662 -0.109971 -0.233464 0.138465\n",
"2001Q2 0.208662 -0.109971 -0.233464 0.138465\n",
"2001Q3 0.208662 -0.109971 -0.233464 0.138465\n",
"2001Q4 -0.401946 0.368050 -0.209196 -0.155851\n",
"2002Q1 -0.401946 0.368050 -0.209196 -0.155851\n",
"2002Q2 -0.401946 0.368050 -0.209196 -0.155851\n",
"2002Q3 -0.401946 0.368050 -0.209196 -0.155851"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"annual_frame.resample('Q-MAR').ffill()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [py35]",
"language": "python",
"name": "Python [py35]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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