Pandas数据帧列具有不同的数据“费率”，用于分层列和行

Question

考虑一个带有分层列和行的pandas数据帧，如下所示：

import pandas as pd
import numpy as np

row1 = ['a', 'b']
row2 = ['c', 'd', 'e']
row3 = ['w', 'x', 'y', 'z']
row_tuple_list = []
for r1 in row1:
for r2 in row2:
    for r3 in row3:
        row_tuple_list.append((r1, r2, r3))
row_index = pd.MultiIndex.from_tuples(row_tuple_list, names=['row1', 'row2', 'row3'])

col1 = ['f']
col2 = ['g', 'h']
col_tuple_list = []
for c1 in col1:
    for c2 in col2:
        col_tuple_list.append((c1, c2))
col_index = pd.MultiIndex.from_tuples(col_tuple_list, names=['col1', 'col2'])

df = pd.DataFrame(np.random.randn(24,2), index=row_index, columns=col_index)

print(df.head(10))

产生以下输出：

col1                   f          
col2                   g         h
row1 row2 row3                    
a    c    w     0.675077 -0.409322
          x    -1.317074  0.951411
          y    -2.430066  1.457128
          z    -0.852241  1.015650
     d    w    -0.302398 -0.303503
          x     0.050624  2.195313
          y    -0.177186 -0.126222
          z     0.302745  1.186148
     e    w    -0.928050 -0.681644
          x    -1.746241  0.687433

现在，假设要添加第三列，每行2条目有一个数据点，例如：

col1                   f          
col2                   g         h
col3                                      i
row1 row2 row3                    
a    c    w     0.675077 -0.409322  0.273493
          x    -1.317074  0.951411
          y    -2.430066  1.457128
          z    -0.852241  1.015650
     d    w    -0.302398 -0.303503 -0.320943
          x     0.050624  2.195313
          y    -0.177186 -0.126222
          z     0.302745  1.186148
     e    w    -0.928050 -0.681644  1.380933
          x    -1.746241  0.687433

是否可以创建这样的“多数据速率”数据帧？如果是这样，怎么样？