本文介绍了将第二个副本中的值移动到第一个副本的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
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.
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
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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