如何重新索引 pandas 数据透视表

Question

我创建了一个数据透视表，该表根据其中一列和时间索引的月份进行分组。

这是数据透视表

                    AWRT       AWFT       AWDT
Time     type                                 
April    All   38.190119  65.789103  27.598984
         DHW   19.676627  60.889196  41.212569
         SH    47.342757  61.335566  13.992809
February All   43.896487  57.982944  14.086457
         SH    40.864670  50.567133   9.702463
March    All   42.083836  69.139818  27.055982
         DHW   18.908873  62.936898  44.028024
         SH    52.249342  70.013904  17.764563

现在我希望二月、三月和四月按时间顺序出现，而不是按字母顺序排列

尝试以这种方式重新索引：

new_index=[['February', 'March', 'April'], ['All', 'DHW', 'SH']]
df1=df1.reindex(new_index)

我得到以下内容，它不再是数据透视表：

        AWRT       AWFT       AWDT
Time     type                                 
February All   43.896487  57.982944  14.086457
March    DHW   18.908873  62.936898  44.028024
April    SH    47.342757  61.335566  13.992809

我还尝试直接访问数据透视表索引的标签，但我被告知这些标签是不可变的。

在此先感谢您的帮助

Answer 1

仅重新索引相关级别 ( level=0)：

import pandas as pd

idx = pd.MultiIndex(levels=[['April', 'February', 'March'], ['All', 'DHW', 'SH']],
                    labels=[[0, 0, 0, 1, 1, 2, 2, 2], [0, 1, 2, 0, 2, 0, 1, 2]],
                    names=['Time', 'type'])
df = pd.DataFrame([[38.190119, 65.789103, 27.598984],
                   [19.676627, 60.889196, 41.212569],
                   [47.342757, 61.335566, 13.992809],
                   [43.896487, 57.982944, 14.086457],
                   [40.864670, 50.567133, 9.702463],
                   [42.083836, 69.139818, 27.055982],
                   [18.908873, 62.936898, 44.028024],
                   [52.249342, 70.013904, 17.764563]],
                  columns=['AWRT', 'AWFT', 'AWDT'],
                  index=idx)

print(df)

#                     AWRT       AWFT       AWDT
# Time     type                                 
# April    All   38.190119  65.789103  27.598984
#          DHW   19.676627  60.889196  41.212569
#          SH    47.342757  61.335566  13.992809
# February All   43.896487  57.982944  14.086457
#          SH    40.864670  50.567133   9.702463
# March    All   42.083836  69.139818  27.055982
#          DHW   18.908873  62.936898  44.028024
#          SH    52.249342  70.013904  17.764563

print(df.reindex(['February', 'March', 'April'], level=0))

#                     AWRT       AWFT       AWDT
# Time     type                                 
# February All   43.896487  57.982944  14.086457
#          SH    40.864670  50.567133   9.702463
# March    All   42.083836  69.139818  27.055982
#          DHW   18.908873  62.936898  44.028024
#          SH    52.249342  70.013904  17.764563
# April    All   38.190119  65.789103  27.598984
#          DHW   19.676627  60.889196  41.212569
#          SH    47.342757  61.335566  13.992809