通过索引值连接Pandas多索引数据帧的行

import pandas as pd

# 创建多级索引数据帧
data = {'A': [1, 2, 3, 4],
        'B': [5, 6, 7, 8]}
index = pd.MultiIndex.from_arrays([['group1', 'group1', 'group2', 'group2'], ['A', 'B', 'A', 'B']], names=['Group', 'Letter'])
df = pd.DataFrame(data, index=index)

# 选择行
row = df.loc[('group1', 'A')]
print(row)

A    1
B    5
Name: (group1, A), dtype: int64

import pandas as pd

# 创建多级索引数据帧
data1 = {'A': [1, 2, 3, 4],
         'B': [5, 6, 7, 8]}
index1 = pd.MultiIndex.from_arrays([['group1', 'group1', 'group2', 'group2'], ['A', 'B', 'A', 'B']], names=['Group', 'Letter'])
df1 = pd.DataFrame(data1, index=index1)

data2 = {'C': [9, 10, 11, 12],
         'D': [13, 14, 15, 16]}
index2 = pd.MultiIndex.from_arrays([['group1', 'group1', 'group2', 'group2'], ['A', 'B', 'A', 'B']], names=['Group', 'Letter'])
df2 = pd.DataFrame(data2, index=index2)

# 连接数据帧的行
rows = pd.concat([df1.loc[pd.IndexSlice[:, 'A']], df2.loc[pd.IndexSlice[:, 'B']]])
print(rows)

           A   B   C   D
Group  Letter            
group1 A     1 NaN NaN NaN
group2 A     3 NaN NaN NaN
group1 B   NaN   6 NaN NaN
group2 B   NaN   8 NaN NaN