python - 在 Dataframe 中的滚动窗口上应用一个函数，其中将整个数据帧传递给函数

Question

我有一个由 YearMo 索引的 5 列数据框:

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——类似于:

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

但这种方法将函数应用于每一列。指定 axis=1 也没有任何作用....

Answer 1

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。

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 ) )

Input:

               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

Output:

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 测试