使用Pandas TimeGrouper创建重叠的组 [英] Create overlapping groups with pandas timegrouper

问题描述

我正在使用Pandas Timegrouper在python中的pandas数据框中对数据点进行分组:

I am using Pandas Timegrouper to group datapoints in a pandas dataframe in python:

grouped = data.groupby(pd.TimeGrouper('30S'))

我想知道是否有一种方法可以实现窗口重叠，如该问题所示:窗口在熊猫中重叠，同时将熊猫数据框保持为数据结构.

I would like to know if there's a way to achieve window overlap, like suggested in this question: Window overlap in Pandas while keeping the pandas dataframe as data structure.

更新:测试了下面提出的三种解决方案的时机，滚动平均值似乎更快:

Update: tested timing of the three solutions proposed below and the rolling mean seems faster:

%timeit df.groupby(pd.TimeGrouper('30s',closed='right')).mean()
%timeit df.resample('30s',how='mean',closed='right')
%timeit pd.rolling_mean(df,window=30).iloc[29::30]

产量:

1000 loops, best of 3: 336 µs per loop
1000 loops, best of 3: 349 µs per loop
1000 loops, best of 3: 199 µs per loop

推荐答案

创建恰好3 x 30s长的数据

Create some data exactly 3 x 30s long

In [51]: df = DataFrame(randn(90,2),columns=list('AB'),index=date_range('20130101 9:01:01',freq='s',periods=90))

以这种方式使用TimeGrouper相当于重新采样(这就是重新采样的实际作用) 请注意，我使用closed来确保准确包含30个观察值

Using a TimeGrouper in this way is equivalent of resample (and that's what resample actually does) Note that I used closed to make sure that exactly 30 observations are included

In [57]: df.groupby(pd.TimeGrouper('30s',closed='right')).mean()
Out[57]: 
                            A         B
2013-01-01 09:01:00 -0.214968 -0.162200
2013-01-01 09:01:30 -0.090708 -0.021484
2013-01-01 09:02:00 -0.160335 -0.135074

In [52]: df.resample('30s',how='mean',closed='right')
Out[52]: 
                            A         B
2013-01-01 09:01:00 -0.214968 -0.162200
2013-01-01 09:01:30 -0.090708 -0.021484
2013-01-01 09:02:00 -0.160335 -0.135074

如果您随后选择30秒间隔，则也是如此

This is also equivalent if you then pick out the 30s intervals

In [55]: pd.rolling_mean(df,window=30).iloc[28:40]
Out[55]: 
                            A         B
2013-01-01 09:01:29       NaN       NaN
2013-01-01 09:01:30 -0.214968 -0.162200
2013-01-01 09:01:31 -0.150401 -0.180492
2013-01-01 09:01:32 -0.160755 -0.142534
2013-01-01 09:01:33 -0.114918 -0.181424
2013-01-01 09:01:34 -0.098945 -0.221110
2013-01-01 09:01:35 -0.052450 -0.169884
2013-01-01 09:01:36 -0.011172 -0.185132
2013-01-01 09:01:37  0.100843 -0.178179
2013-01-01 09:01:38  0.062554 -0.097637
2013-01-01 09:01:39  0.048834 -0.065808
2013-01-01 09:01:40  0.003585 -0.059181

因此，根据您要实现的目标，可以使用rolling_mean轻松实现重叠 然后选择您想要的任何频率".例如，这是一个30秒间隔的5秒重采样.

So depending on what you want to achieve, its easy to do an overlap, by using rolling_mean and then pick out whatever 'frequency' you want. Eg here is a 5s resample with a 30s interval.

In [61]: pd.rolling_mean(df,window=30)[9::5]
Out[61]: 
                            A         B
2013-01-01 09:01:10       NaN       NaN
2013-01-01 09:01:15       NaN       NaN
2013-01-01 09:01:20       NaN       NaN
2013-01-01 09:01:25       NaN       NaN
2013-01-01 09:01:30 -0.214968 -0.162200
2013-01-01 09:01:35 -0.052450 -0.169884
2013-01-01 09:01:40  0.003585 -0.059181
2013-01-01 09:01:45 -0.055886 -0.111228
2013-01-01 09:01:50 -0.110191 -0.045032
2013-01-01 09:01:55  0.093662 -0.036177
2013-01-01 09:02:00 -0.090708 -0.021484
2013-01-01 09:02:05 -0.286759  0.020365
2013-01-01 09:02:10 -0.273221 -0.073886
2013-01-01 09:02:15 -0.222720 -0.038865
2013-01-01 09:02:20 -0.175630  0.001389
2013-01-01 09:02:25 -0.301671 -0.025603
2013-01-01 09:02:30 -0.160335 -0.135074

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