熊猫groupby结果形状出乎意料

Question

我有一个“堆叠”格式的时间序列数据，并希望计算基于两列的滚动函数。但是，如下面的示例所示，groupby将水平连接而不是垂直连接我的结果。最后我可以申请stack以恢复到高格式。但是，我认为正确的行为应该是垂直连接以允许分配回原始数据帧（类似x['res'] = df.groupby(...).apply(func)）。有谁知道为什么groupby行为不如预期，或者我做错了什么？

x 
Out[52]: 
    group  month   a   b 
0 18527 2014-09-01 0.534152 0.973451 
1 18527 2014-10-01 0.079879 0.354498 
2 18527 2014-11-01 0.032298 0.203997 
3 18527 2014-12-01 0.148435 0.352703 
4 18527 2015-01-01 0.879930 0.819328 
5 18527 2015-02-01 0.475297 0.693203 
6 18527 2015-03-01 0.223759 0.731594 
7 18527 2015-04-01 0.391933 0.332801 
8 18671 2014-09-01 0.740621 0.305298 
9 18671 2014-10-01 0.230585 0.772569 
10 18671 2014-11-01 0.664834 0.755219 
11 18671 2014-12-01 0.987118 0.896310 
12 18671 2015-01-01 0.228804 0.058641 
13 18671 2015-02-01 0.415715 0.182683 
14 18671 2015-03-01 0.574570 0.144686 
15 18671 2015-04-01 0.488804 0.545102 

x.dtypes 
Out[53]: 
group    int64 
month datetime64[ns] 
a    float64 
b    float64 
dtype: object 

def func(s): 
    return pd.rolling_sum(s.a, 3)/pd.rolling_sum(s.b, 3) 


x.set_index('month').groupby('group').apply(func) 
Out[55]: 
month 2014-09-01 2014-10-01 2014-11-01 2014-12-01 2015-01-01 2015-02-01 group                   
18527   NaN   NaN 0.421900 0.286010 0.770814 0.806152 
18671   NaN   NaN 0.892505 0.776593 1.099748 1.434238 

month 2015-03-01 2015-04-01 
group       
18527 0.703609 0.620728 
18671 3.158185 1.695287 

x.set_index('month').groupby('group').apply(func).stack() 
Out[56]: 
group month  
18527 2014-11-01 0.421900 
     2014-12-01 0.286010 
     2015-01-01 0.770814 
     2015-02-01 0.806152 
     2015-03-01 0.703609 
     2015-04-01 0.620728 
18671 2014-11-01 0.892505 
     2014-12-01 0.776593 
     2015-01-01 1.099748 
     2015-02-01 1.434238 
     2015-03-01 3.158185 
     2015-04-01 1.695287 
dtype: float64 

Answer 1

您可以转换的结果func()到数据帧：

def func(s): 
    return (pd.rolling_sum(s.a, 3)/pd.rolling_sum(s.b, 3)).dropna().to_frame() 

df.groupby('group').apply(func)