将第二个副本中的值移动到第一个副本 [英] Move the value in the second duplicate to the first duplicate

问题描述

这篇文章是将最后一行的值赋值给第一行:Move last value to第一个值.

我想将第二个副本中的值移动到第一个副本，并将其他副本设置为 NaT.

<前>ID OutBedTime DateOutBed1 16/05/2018 0:17 16/05/20181 16/05/2018 4:05 16/05/20181 16/05/2018 6:05 16/05/20181 17/05/2018 1:27 17/05/20181 17/05/2018 4:41 17/05/20181 17/05/2018 5:32 17/05/2018

预期输出

<前>ID OutBedTime DateOutBed TimeOutBedFinal1 16/05/2018 0:17 16/05/2018 16/05/2018 4:051 16/05/2018 4:05 16/05/2018 NaT1 16/05/2018 6:05 16/05/2018 NaT1 17/05/2018 1:27 17/05/2018 17/05/2018 4:411 17/05/2018 4:41 17/05/2018 NaT1 17/05/2018 5:32 17/05/2018 NaT

谢谢.

解决方案

让我们用 apply 做 reindex 并选择第二行，然后和我们一样来自上一个问题

df['New']=df.groupby('DateOutBed')['OutBedTime'].apply(lambda x : x.iloc[[1]]).reset_index(level=1,drop=真).reindex(df.DateOutBed).valuesdf['New']=df.New.mask(df.DateOutBed.duplicated())dfID OutBedTime DateOutBed 新0 1 16/05/20180:17 16/05/2018 16/05/20184:051 1 16/05/20184:05 16/05/2018 NaN2 1 16/05/20186:05 16/05/2018 NaN3 1 17/05/20181:27 17/05/2018 17/05/20184:414 1 17/05/20184:41 17/05/2018 NaN5 1 17/05/20185:32 17/05/2018 NaN

检查更新

df['New']=df.groupby('DateOutBed')['OutBedTime'].transform(lambda x : x.iloc[1] if len(x)>1 else x.iloc[0])df['New']=df.New.mask(df.DateOutBed.duplicated())

This post is to assign value of last row to first row: Move last value to first value.

I would like to move the value in the second duplicate to the first duplicate and set others to NaT.

ID  OutBedTime        DateOutBed
1   16/05/2018 0:17   16/05/2018
1   16/05/2018 4:05   16/05/2018
1   16/05/2018 6:05   16/05/2018
1   17/05/2018 1:27   17/05/2018
1   17/05/2018 4:41   17/05/2018
1   17/05/2018 5:32   17/05/2018

Expected output

ID  OutBedTime        DateOutBed    TimeOutBedFinal
1   16/05/2018 0:17   16/05/2018    16/05/2018 4:05
1   16/05/2018 4:05   16/05/2018    NaT
1   16/05/2018 6:05   16/05/2018    NaT
1   17/05/2018 1:27   17/05/2018    17/05/2018 4:41
1   17/05/2018 4:41   17/05/2018    NaT
1   17/05/2018 5:32   17/05/2018    NaT

Thank you.

解决方案

Let us do reindex with apply and select the second of row , then do the same as we did from pervious question

df['New']=df.groupby('DateOutBed')['OutBedTime'].apply(lambda x : x.iloc[[1]]).reset_index(level=1,drop=True).reindex(df.DateOutBed).values
df['New']=df.New.mask(df.DateOutBed.duplicated())
df
   ID      OutBedTime  DateOutBed             New
0   1  16/05/20180:17  16/05/2018  16/05/20184:05
1   1  16/05/20184:05  16/05/2018             NaN
2   1  16/05/20186:05  16/05/2018             NaN
3   1  17/05/20181:27  17/05/2018  17/05/20184:41
4   1  17/05/20184:41  17/05/2018             NaN
5   1  17/05/20185:32  17/05/2018             NaN

Check the update

df['New']=df.groupby('DateOutBed')['OutBedTime'].transform(lambda x : x.iloc[1] if len(x)>1 else x.iloc[0])
df['New']=df.New.mask(df.DateOutBed.duplicated())

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