Pandas添加了两个Multiindex Dataframes

Question

我正在尝试添加两个具有Multiindex列和不同索引大小的数据帧.什么是最优雅的解决方案.例如:

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

现在我尝试将df1添加到df2,其逻辑是刚刚添加了缺失列,并且df1的索引0被添加到df2中的所有索引.

所以我期望以上数字:

  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

什么是最快速和内存效率的解决方案？任何帮助赞赏.

Answer 1

建立

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

这将对齐帧并将nan填充为0但不广播

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

这是播放第一个的方式

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