python pandas - groupby.first() 返回 NaT 值

Question

我正在检索以下数据帧的 cummax() 值，

                     exit_price  trend  netgain     high      low   MFE_pr
exit_time                                                                 
2000-02-01 01:00:00     1400.25     -1     1.00  1401.50  1400.25  1400.25
2000-02-01 01:30:00     1400.75     -1     0.50  1401.00  1399.50  1399.50
2000-02-01 02:00:00     1400.00     -1     1.25  1401.00  1399.75  1399.50
2000-02-01 02:30:00     1399.25     -1     2.00  1399.75  1399.25  1399.25
2000-02-01 03:00:00     1399.50     -1     1.75  1400.00  1399.50  1399.25
2000-02-01 03:30:00     1398.25     -1     3.00  1399.25  1398.25  1398.25
2000-02-01 04:00:00     1398.75     -1     2.50  1399.00  1398.25  1398.25
2000-02-01 04:30:00     1400.00     -1     1.25  1400.25  1399.00  1398.25
2000-02-01 05:00:00     1400.25     -1     1.00  1400.50  1399.25  1398.25
2000-02-01 05:30:00     1400.50     -1     0.75  1400.75  1399.50  1398.25

用下面的公式

trade ['MFE_pr'] = np.nan
trade ['MFE_pr'] = trade ['MFE_pr'].where(trade ['trend']<0, trade.high.cummax())
trade ['MFE_pr'] = trade ['MFE_pr'].where(trade ['trend']>0, trade.low.cummin())

现在我想检索从每一行中获取 cummax() 的行的时间戳。

我一直在尝试以下方法:

trade['timestamp']= trade.index
trade ['MFE_ts'] = trade.groupby('MFE_pr')['timestamp'].first() 

但我收到的结果是:

                     exit_price  trend  netgain     high      low   MFE_pr  \
exit_time                                                                    
2000-02-01 01:00:00     1400.25     -1     1.00  1401.50  1400.25  1400.25   
2000-02-01 01:30:00     1400.75     -1     0.50  1401.00  1399.50  1399.50   
2000-02-01 02:00:00     1400.00     -1     1.25  1401.00  1399.75  1399.50   
2000-02-01 02:30:00     1399.25     -1     2.00  1399.75  1399.25  1399.25   
2000-02-01 03:00:00     1399.50     -1     1.75  1400.00  1399.50  1399.25   
2000-02-01 03:30:00     1398.25     -1     3.00  1399.25  1398.25  1398.25   
2000-02-01 04:00:00     1398.75     -1     2.50  1399.00  1398.25  1398.25   
2000-02-01 04:30:00     1400.00     -1     1.25  1400.25  1399.00  1398.25   
2000-02-01 05:00:00     1400.25     -1     1.00  1400.50  1399.25  1398.25   
2000-02-01 05:30:00     1400.50     -1     0.75  1400.75  1399.50  1398.25   

                              timestamp MFE_ts  
exit_time                                       
2000-02-01 01:00:00 2000-02-01 01:00:00    NaT  
2000-02-01 01:30:00 2000-02-01 01:30:00    NaT  
2000-02-01 02:00:00 2000-02-01 02:00:00    NaT  
2000-02-01 02:30:00 2000-02-01 02:30:00    NaT  
2000-02-01 03:00:00 2000-02-01 03:00:00    NaT  
2000-02-01 03:30:00 2000-02-01 03:30:00    NaT  
2000-02-01 04:00:00 2000-02-01 04:00:00    NaT  
2000-02-01 04:30:00 2000-02-01 04:30:00    NaT  
2000-02-01 05:00:00 2000-02-01 05:00:00    NaT  
2000-02-01 05:30:00 2000-02-01 05:30:00    NaT 

我做错了什么？

Answer 1

现在，它计算并返回每个组中第一个值的结果。

trade.groupby('MFE_pr')['timestamp'].first()
MFE_pr
1398.25   2000-02-01 03:30:00
1399.25   2000-02-01 02:30:00
1399.50   2000-02-01 01:30:00
1400.25   2000-02-01 01:00:00
Name: timestamp, dtype: datetime64[ns]

因此，当您尝试将其重新索引回原始 DF(通过将此值分配给新列)时，它会导致 NaTs 像它们一样被创建没有一个共同的索引来重建索引:

trade.groupby('MFE_pr')['timestamp'].first().reindex(trade.index)
exit_time
2000-02-01 01:00:00   NaT
2000-02-01 01:30:00   NaT
2000-02-01 02:00:00   NaT
2000-02-01 02:30:00   NaT
2000-02-01 03:00:00   NaT
2000-02-01 03:30:00   NaT
2000-02-01 04:00:00   NaT
2000-02-01 04:30:00   NaT
2000-02-01 05:00:00   NaT
2000-02-01 05:30:00   NaT
Name: timestamp, dtype: datetime64[ns]

你需要transform相反，它将这些计算值累积应用于分组系列中存在的所有行，从而保持原始 DF 的形状不变:

trade['MFE_ts'] = trade.groupby('MFE_pr')['timestamp'].transform('first') 
trade