Apply應用函式：

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