大熊猫添加两个Multiindex数据框

Question

我想添加两个数据框与Multiindex列和不同的索引大小在一起。什么是最优雅的解决方案。并且例子是：

names = ['Level 0', 'Level 1'] 
cols1 = pd.MultiIndex.from_arrays([['A', 'A', 'B'],['A1', 'A2', 'B1']], names = names) 
cols2 = pd.MultiIndex.from_arrays([['A', 'A', 'B'],['A1', 'A3', 'B1']], names = names) 
df1 = pd.DataFrame(np.random.randn(1, 3), index=range(1), columns=cols1) 
df2 = pd.DataFrame(np.random.randn(5, 3), index=range(5), columns=cols2) 
print(df1) 
print(df2) 

Level 0   A     B 
Level 1  A1  A2  B1 
0  -0.116975 -0.391591 0.446029 

Level 0   A     B 
Level 1  A1  A3  B1 
0  1.179689 0.693096 -0.102621 
1  -0.913441 0.187332 1.465217 
2  -0.089724 -1.907706 -0.963699 
3  0.203217 -1.233399 0.006726 
4  0.218911 -0.027446 0.982764 

现在我尝试添加DF1与刚刚添加缺少的列的逻辑DF2和DF1的索引0添加到DF2所有索引。

，所以我期望与上面的数字：

Level 0   A         B 
    Level 1   A1   A2   A3   B1 
    0   1.062714 -0.391591 0.693096 0.343408 
    1  -1.030416 -0.391591 0.187332 1.911246 
    2  -0.206699 -0.391591 -1.907706 -0.51767 
    3   0.086242 -0.391591 -1.233399 0.452755 
    4   0.101936 -0.391591 -0.027446 1.428793 

什么是最速度和内存高效的解决方案？任何帮助赞赏。

Answer 1

设置

In [76]: df1 
Out[76]: 
Level 0  A     B 
Level 1  A1  A2  B1 
0  -0.28667 1.852091 -0.134793 

In [77]: df2 
Out[77]: 
Level 0   A     B 
Level 1  A1  A3  B1 
0  -0.023582 -0.713594 0.487355 
1  0.628819 0.764721 -1.118777 
2  -0.572421 1.326448 -0.788531 
3  -0.160608 1.985142 0.344845 
4  -0.184555 -1.075794 0.630975 

这将调整帧，并填补了南与0 但没有播出

In [63]: df1a,df2a = df1.align(df2,fill_value=0) 

In [64]: df1a+df2a 
Out[64]: 
Level 0   A        B 
Level 1  A1  A2  A3  B1 
0  -0.310253 1.852091 -0.713594 0.352561 
1  0.628819 0.000000 0.764721 -1.118777 
2  -0.572421 0.000000 1.326448 -0.788531 
3  -0.160608 0.000000 1.985142 0.344845 
4  -0.184555 0.000000 -1.075794 0.630975 

这是广播的第一个

In [65]: df1a,df2a = df1.align(df2) 

In [66]: df1a.ffill().fillna(0) + df2a.fillna(0) 
Out[66]: 
Level 0   A        B 
Level 1  A1  A2  A3  B1 
0  -0.310253 1.852091 -0.713594 0.352561 
1  0.342149 1.852091 0.764721 -1.253570 
2  -0.859091 1.852091 1.326448 -0.923324 
3  -0.447278 1.852091 1.985142 0.210052 
4  -0.471226 1.852091 -1.075794 0.496181