Python groupby-根据其他列中的值创建一个新列

Question

我有一个非常大的数据框。
我想首先按“ id”列进行分组。
然后根据其他现有列创建一个新列“ reply_time”。

import pandas as pd
import numpy as np

id = ['793601486525702000','793601486525702000','793601710614802000','793601355214561000','793601355214561000','793601355214561000','793601355214561000','788130215436230000','788130215436230000','788130215436230000','788130215436230000','788130215436230000']
time = ['11/1/2016 16:53','11/1/2016 16:53','11/1/2016 16:52','11/1/2016 16:55','11/1/2016 16:53','11/1/2016 16:53','11/1/2016 16:51','11/1/2016 3:09','11/1/2016 3:04','11/1/2016 2:36','11/1/2016 2:08','11/1/2016 0:28']
reply = ['3','3','0','3','3','2','1','3','2','3','3','1']

df = pd.DataFrame({"id": id, "time": time, "reply": reply})

        id                 time       reply 
793601486525702000  11/1/2016 16:53     3       
793601486525702000  11/1/2016 16:53     3       
793601710614802000  11/1/2016 16:52     0       
793601355214561000  11/1/2016 16:55     3       
793601355214561000  11/1/2016 16:53     3       
793601355214561000  11/1/2016 16:53     2       
793601355214561000  11/1/2016 16:51     1   
788130215436230000  11/1/2016 3:09      3       
788130215436230000  11/1/2016 3:04      2       
788130215436230000  11/1/2016 2:36      3       
788130215436230000  11/1/2016 2:08      3       
788130215436230000  11/1/2016 0:28      1   

此新列“ reply_time”中有两种类型的值。

1. 'time'：首先按'id'列进行分组，如果reply ='1'，则返回reply ='2'的'time'值。
2. 'na'：如果不满足上述条件，则应将其余行分配给'na'。

在这种情况下，我的输出数据帧将是：

        id                 time       reply   reply_time
793601486525702000  11/1/2016 16:53     3        na
793601486525702000  11/1/2016 16:53     3        na
793601710614802000  11/1/2016 16:52     0        na
793601355214561000  11/1/2016 16:55     3        na
793601355214561000  11/1/2016 16:53     3        na
793601355214561000  11/1/2016 16:53     2        na
793601355214561000  11/1/2016 16:51     1    11/1/2016 16:53
788130215436230000  11/1/2016 3:09      3        na
788130215436230000  11/1/2016 3:04      2        na
788130215436230000  11/1/2016 2:36      3        na
788130215436230000  11/1/2016 2:08      3        na
788130215436230000  11/1/2016 0:28      1    11/1/2016 3:04 

我不知道实现此目标的最佳方法。 有人可以帮忙吗？

提前致谢！

Answer 1

切片后尝试merge并replace

yourdf=df.merge(df.query("reply=='2'").replace({'reply':{'2':'1'}}).rename(columns={'time':'reply_time'}),how='left')