在数据框的滚动窗口上应用函数，将整个数据帧传递给函数 [英] apply a function on rolling window in Dataframe where whole dataframe is passed to function

问题描述

我有一个由YearMo索引的5列数据框:

I have a dataframe of 5 columns indexed by YearMo:

yearmo = np.repeat(np.arange(2000, 2010) * 100, 12) + [x for x in range(1,13)] * 10 
rates = pd.DataFrame(data=np.random.random(120, 5)), 
                     index=pd.Series(data=yearmo, name='YearMo'), 
                     columns=['A', 'B','C', 'D', 'E'])

rates.head()                       
YearMo    A         B          C         D       E 
200411  0.237696  0.341937  0.258713  0.569689  0.470776
200412  0.601713  0.313006  0.221821  0.720162  0.889891
200501  0.024379  0.761315  0.225032  0.293682  0.302431
200502  0.996778  0.388783  0.026448  0.056188  0.744850
200503  0.942024  0.768416  0.484236  0.102904  0.287446

我想做的是能够应用滚动窗口并将所有五列传递给一个函数–类似:

What I would like to do is to be able to apply a rolling window and pass all five columns to a function – something like:

rates.rolling(window=60, min_periods=60).apply(lambda x: my_func(data=x, param=5)

，但是这种方法将函数应用于每一列.指定axis=1也不执行任何操作....

but this approach applies the function to each column. Specifying axis=1 doesn't do anything either....

推荐答案

问题:...应用滚动窗口并将所有五列传递给函数

Question: ... apply a rolling window and pass all five columns to a function

这将执行您想要的操作，min_periods=5, axis=1. .rolling(...窗口是列'A':'E'或 5 的倍数.

This will do what you want, min_periods=5, axis=1. .rolling(... window is column 'A':'E' or a multiple of 5.

def f1(data=None):
    print('f1(%s, %s) data=%s' % (str(type(data)), param, data))
    return data.sum()

subRates = rates.rolling(window=60, min_periods=5, axis=1).apply(lambda x: f1( x ) )

输入:

               A         B         C         D         E
YearMo
200001  0.666744  0.569194  0.546873  0.018696  0.240783
200002  0.035888  0.853077  0.348200  0.921997  0.283177
200003  0.652761  0.076630  0.298076  0.800504  0.041231
200004  0.537397  0.968399  0.211072  0.328157  0.929783
200005  0.759506  0.702220  0.807477  0.886935  0.022587

输出:

f1(<class 'numpy.ndarray'>, None) data=[ 0.66674393  0.56919434  0.54687296  0.01869609  0.24078329]
f1(<class 'numpy.ndarray'>, None) data=[ 0.03588751  0.85307707  0.34819965  0.92199698  0.28317727]
f1(<class 'numpy.ndarray'>, None) data=[ 0.65276067  0.07663029  0.29807589  0.80050448  0.04123137]
f1(<class 'numpy.ndarray'>, None) data=[ 0.53739687  0.96839917  0.21107155  0.32815687  0.92978308]
f1(<class 'numpy.ndarray'>, None) data=[ 0.75950632  0.70222034  0.80747698  0.88693524  0.02258685]
         A   B   C   D         E
YearMo
200001 NaN NaN NaN NaN  2.042291
200002 NaN NaN NaN NaN  2.442338
200003 NaN NaN NaN NaN  1.869203
200004 NaN NaN NaN NaN  2.974808
200005 NaN NaN NaN NaN  3.178726

使用Python:3.4.2-pandas:0.19.2

Tested with Python:3.4.2 - pandas:0.19.2

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