python - 将新索引添加到 MultiIndex 数据框 pandas 的特定级别

Question

这是我正在尝试做的一个例子:

import io
import pandas as pd
data = io.StringIO('''Fruit,Color,Count,Price
Apple,Red,3,$1.29
Apple,Green,9,$0.99
Pear,Red,25,$2.59
Pear,Green,26,$2.79
Lime,Green,99,$0.39
''')
df_unindexed = pd.read_csv(data)
df = df_unindexed.set_index(['Fruit', 'Color'])

输出:

Out[5]: 
             Count  Price
Fruit Color              
Apple Red        3  $1.29
      Green      9  $0.99
Pear  Red       25  $2.59
      Green     26  $2.79
Lime  Green     99  $0.39

现在假设我想计算“颜色”级别中的键数:

L = []
for i in pd.unique(df.index.get_level_values(0)):
    L.append(range(df.xs(i).shape[0]))

list(np.concatenate(L))

然后我将结果列表 [0,1,0,1,0] 添加为新列:

df['Bob'] = list(np.concatenate(L))

这样:

             Count  Price  Bob
Fruit Color                   
Apple Red        3  $1.29    0
      Green      9  $0.99    1
Pear  Red       25  $2.59    0
      Green     26  $2.79    1
Lime  Green     99  $0.39    0

我的问题:

如何使 Bob 列成为与 Color 处于同一级别的索引？这就是我想要的:

                 Count  Price
Fruit Color Bob                   
Apple Red    0    3     $1.29
      Green  1    9     $0.99
Pear  Red    0   25     $2.59
      Green  1   26     $2.79
Lime  Green  0   99     $0.39

Answer 1

您是否在寻找cumcount？如果是这样，您可以放弃循环并矢量化您的解决方案。

df = df.set_index(df.groupby(level=0).cumcount(), append=True)
print(df)
               Count  Price
Fruit Color                
Apple Red   0      3  $1.29
      Green 1      9  $0.99
Pear  Red   0     25  $2.59
      Green 1     26  $2.79
Lime  Green 0     99  $0.39

或者，如果您希望一次性完成此操作，

df_unindexed = pd.read_csv(data)
df = df_unindexed.set_index(['Fruit', 'Color', df.groupby('Fruit').cumcount()])
print(df)
               Count  Price
Fruit Color                
Apple Green 0      9  $0.99
      Red   1      3  $1.29
Lime  Green 0     99  $0.39
Pear  Green 1     26  $2.79
      Red   0     25  $2.59

要重命名索引，请使用 rename_axis:

df = df.rename_axis(['Fruit', 'Color', 'Bob'])
print(df)
                 Count  Price
Fruit Color Bob              
Apple Red   0        3  $1.29
      Green 1        9  $0.99
Pear  Red   0       25  $2.59
      Green 1       26  $2.79
Lime  Green 0       99  $0.39