Python 多索引数据帧（Pandas）中组合的生成方法

我有一个多索引数据框，其中索引级别有多个类别，类似于：

                     |Var1|Var2|Var3|
|Level1|Level2|Level3|----|----|----|
|  A   |  A   |   A  |    |    |    |
|  A   |  A   |   B  |    |    |    |
|  A   |  B   |   A  |    |    |    |
|  A   |  B   |   B  |    |    |    |
|  B   |  A   |   A  |    |    |    |
|  B   |  A   |   B  |    |    |    |
|  B   |  B   |   A  |    |    |    |
|  B   |  B   |   B  |    |    |    |

# Resulting df to be populated
df_result = pd.DataFrame([])
# Retrieving values at Level1
lev1s = df.index.get_level_values("Level1").unique()
# Looping through each Level1 value
for lev1 in lev1s:
   # Filtering df based on Level1 value
   df_lev1 = df.query('Level1 == ' + str(lev1))
   # Repeating...
   lev2s = df_lev1.index.get_level_values("Level2").unique()
   for lev2 in lev2s:
      df_lev2 = df_lev1.query('Level2 == ' + str(lev2))
      # ... until Level3
      lev3s = df_lev2.index.get_level_values("Level3").unique()
      # Creating all combinations
      combs = itertools.combinations(lev3s, 3)
      # Looping through each combination
      for comb in combs:
         # Filtering values in combination
         df_comb = df_wl.query('Level3 in ' + str(comb))
         # Calculating means using groupby (groupby might not be necessary, 
         # but I don't believe it has much of an impact
         df_means = df_comb.reset_index().groupby(['Level1', 'Level2']).mean()
         # Extending resulting dataframe
         df_result = df_result.append(df_means)

总之，特别是在我的例子中，级别1有2个级别，级别2有24个级别，级别3有6个级别，还有级别4（674）和级别5（9）（根据具体的更高级别值有一些微小的变化-

Level1==1

实际上有24个level2，但

Level1==2

有23）

我需要在第5级生成3的所有可能组合，然后计算变量1-3的平均值

我正在尝试这样的事情：

                     |Var1|Var2|Var3|
|Level1|Level2|Level3|----|----|----|
|  A   |  A   |   A  |    |    |    |
|  A   |  A   |   B  |    |    |    |
|  A   |  B   |   A  |    |    |    |
|  A   |  B   |   B  |    |    |    |
|  B   |  A   |   A  |    |    |    |
|  B   |  A   |   B  |    |    |    |
|  B   |  B   |   A  |    |    |    |
|  B   |  B   |   B  |    |    |    |

# Resulting df to be populated
df_result = pd.DataFrame([])
# Retrieving values at Level1
lev1s = df.index.get_level_values("Level1").unique()
# Looping through each Level1 value
for lev1 in lev1s:
   # Filtering df based on Level1 value
   df_lev1 = df.query('Level1 == ' + str(lev1))
   # Repeating...
   lev2s = df_lev1.index.get_level_values("Level2").unique()
   for lev2 in lev2s:
      df_lev2 = df_lev1.query('Level2 == ' + str(lev2))
      # ... until Level3
      lev3s = df_lev2.index.get_level_values("Level3").unique()
      # Creating all combinations
      combs = itertools.combinations(lev3s, 3)
      # Looping through each combination
      for comb in combs:
         # Filtering values in combination
         df_comb = df_wl.query('Level3 in ' + str(comb))
         # Calculating means using groupby (groupby might not be necessary, 
         # but I don't believe it has much of an impact
         df_means = df_comb.reset_index().groupby(['Level1', 'Level2']).mean()
         # Extending resulting dataframe
         df_result = df_result.append(df_means)

问题是，过了一会儿，这个过程变得非常缓慢。由于我有大约2*24*6*674个级别和84个组合（9个元素，3乘3），我预计将计算超过1600万个

df_平均值

有没有更有效的方法

多谢各位