应用变换函数时，如何按列保持熊猫分组

Question

这是我的熊猫数据帧，看起来像：

sampling_time            MQ2_LPG MQ2_CO MQ2_SMOKE MQ2_ALCOHOL MQ2_CH4 MQ2_H2 MQ2_PROPANE
0   2018-07-15 08:41:49.028  4.41    32.87  19.12     7.70        10.29   7.59   4.49
1   2018-07-15 08:41:49.028  2.98    19.08  12.47     4.72        6.34    5.15   3.02
2   2018-07-15 08:41:49.028  2.73    16.88  11.33     4.22        5.69    4.72   2.76
3   2018-07-15 08:41:49.028  2.69    16.47  11.11     4.13        5.57    4.64   2.71
4   2018-07-15 08:41:49.028  2.66    16.26  11.00     4.09        5.50    4.60   2.69

当我进行分组(拆分-应用-组合方法(时，我的采样时间列被删除了。

transformed = dataframe.groupby('sampling_time').transform(lambda x: (x - x.mean()) / x.std())
transformed.head()
MQ2_LPG MQ2_CO  MQ2_SMOKE   MQ2_ALCOHOL MQ2_CH4     MQ2_H2      MQ2_PROPANE
0   15.710127       15.975636   15.773724   15.876433   15.874190   15.694674
1   3.519619        3.313661    3.494836    3.408578    3.404160    3.563717
2   1.388411        1.293621    1.389884    1.316656    1.352130    1.425885
3   1.047418        0.917159    0.983665    0.940110    0.973294    1.028148
4   0.791673        0.724337    0.780556    0.772756    0.752306    0.829280

如有任何关于如何保持采样时间列的帮助或建议，我们将不胜感激。

Answer 1

您可以通过在索引中设置"sampling_time"来实现这一点，然后当您使用transform运行groupby时，您将使用索引获取transform列。

df1 = df.set_index('sampling_time')
df1.groupby('sampling_time').transform(lambda x: x-x.std())

输出：

MQ2_LPG     MQ2_CO  MQ2_SMOKE  MQ2_ALCOHOL  
sampling_time                                                          
2018-07-15 08:41:49.028  3.663522  25.760508  15.652432     6.154209   
2018-07-15 08:41:49.028  2.233522  11.970508   9.002432     3.174209   
2018-07-15 08:41:49.028  1.983522   9.770508   7.862432     2.674209   
2018-07-15 08:41:49.028  1.943522   9.360508   7.642432     2.584209   
2018-07-15 08:41:49.028  1.913522   9.150508   7.532432     2.544209   
MQ2_CH4    MQ2_H2  MQ2_PROPANE  
sampling_time                                             
2018-07-15 08:41:49.028  8.243523  6.313227       3.7205  
2018-07-15 08:41:49.028  4.293523  3.873227       2.2505  
2018-07-15 08:41:49.028  3.643523  3.443227       1.9905  
2018-07-15 08:41:49.028  3.523523  3.363227       1.9405  
2018-07-15 08:41:49.028  3.453523  3.323227       1.9205