Pandas 行的多级索引

Question

这应该是一件简单的事情，但是经过几个小时的搜索，我仍然不知道自己做错了什么。

我尝试了使用 MultiIndexing.from_ 和其他多种方法的不同方法，但我就是做不对。

我需要这样的东西：

但是我得到：
我做错了什么？

import pandas as pd

list_of_customers = ['Client1', 'Client2', 'Client3']
stat_index = ['max', 'current', 'min']
list_of_historic_timeframes = ['16:10', '16:20', '16:30']

timeblock = pd.DataFrame(index=([list_of_customers, stat_index]), columns=list_of_historic_timeframes)
timeblock.fillna(0, inplace=True)

print(timeblock)

Answer 1

list_of_customers = ['Client1', 'Client2', 'Client3']
stat_index = ['max', 'current', 'min']
list_of_historic_timeframes = ['16:10', '16:20', '16:30']


timeblock = pd.DataFrame(
    0,
    pd.MultiIndex.from_product(
        [list_of_customers, stat_index],
        names=['Customer', 'Stat']
    ),
    list_of_historic_timeframes
)

print(timeblock)

                  16:10  16:20  16:30
Customer Stat                        
Client1  max          0      0      0
         current      0      0      0
         min          0      0      0
Client2  max          0      0      0
         current      0      0      0
         min          0      0      0
Client3  max          0      0      0
         current      0      0      0
         min          0      0      0

Pandas 行的多级索引

Pandas Multilevel index for rows

multi-index

pandas