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Apply应用函数：
In [135]: df = pd.DataFrame(data={'A' : [[2,4,8,16],[100,200],[10,20,30]], 'B' : [['a','b','c'],['jj','kk'],['ccc']]},index=['I','II','III']); df
A B
I [2, 4, 8, 16] [a, b, c]
II [100, 200] [jj, kk]
III [10, 20, 30] [ccc]
In [136]: def SeriesFromSubList(aList):
.....: return pd.Series(aList)
.....:
In [137]: df_orgz = pd.concat(dict([ (ind,row.apply(SeriesFromSubList)) for ind,row in df.iterrows() ])); df_orgz
0 1 2 3
I A 2 4 8 16.0
B a b c NaN
II A 100 200 NaN NaN
B jj kk NaN NaN
III A 10 20 30 NaN
B ccc NaN NaN NaN
Rolling Apply to multiple columns where function calculates a Series before a Scalar from the Series is returned
In [138]: df = pd.DataFrame(data=np.random.randn(2000,2)/10000,
.....: index=pd.date_range('2001-01-01',periods=2000),
.....: columns=['A','B']); df
.....:
Out[138]:
A B
2001-01-01 0.000032 -0.000004
2001-01-02 -0.000001 0.000207
2001-01-03 0.000120 -0.000220
2001-01-04 -0.000083 -0.000165
2001-01-05 -0.000047 0.000156
2001-01-06 0.000027 0.000104
2001-01-07 0.000041 -0.000101
... ... ...
2006-06-17 -0.000034 0.000034
2006-06-18 0.000002 0.000166
2006-06-19 0.000023 -0.000081
2006-06-20 -0.000061 0.000012
2006-06-21 -0.000111 0.000027
2006-06-22 -0.000061 -0.000009
2006-06-23 0.000074 -0.000138
[2000 rows x 2 columns]
In [139]: def gm(aDF,Const):
.....: v = ((((aDF.A+aDF.B)+1).cumprod())-1)*Const
.....: return (aDF.index[0],v.iloc[-1])
.....:
In [140]: S = pd.Series(dict([ gm(df.iloc[i:min(i+51,len(df)-1)],5) for i in range(len(df)-50) ])); S
Out[140]:
2001-01-01 -0.001373
2001-01-02 -0.001705
2001-01-03 -0.002885
2001-01-04 -0.002987
2001-01-05 -0.002384
2001-01-06 -0.004700
2001-01-07 -0.005500
...
2006-04-28 -0.002682
2006-04-29 -0.002436
2006-04-30 -0.002602
2006-05-01 -0.001785
2006-05-02 -0.001799
2006-05-03 -0.000605
2006-05-04 -0.000541
Length: 1950, dtype: float64