Python 指示组内的rejected_time是否在creation_timestamp后5分钟内

如果在0和5 Minutes之间，则可以将每personal_id和application_id的移动值与rejected_time进行比较:

df['creation_timestamp'] = pd.to_datetime(df['creation_timestamp'])
df['rejected_time'] = pd.to_datetime(df['rejected_time'])

#for correct output are columns sorted 
df.sort_values(by=['personal_id', 'creation_timestamp'], inplace=True)


td = pd.Timedelta('5 Min')

#create shifted timestamps per groups
s = (df[['personal_id','application_id','creation_timestamp']]
         .drop_duplicates()
         .set_index(['personal_id','application_id'])['creation_timestamp'].shift(-1))

#subtract shifted values and compare
df['new'] = (df['rejected_time'].sub(df.join(s.rename('new'),
                                             on=['personal_id','application_id'])['new'])
                                .between(pd.Timedelta(0), td).astype(int))

print (df)
   personal_id application_id         creation_timestamp approved_amount  \
0          5a           694f  2023-01-24 13:01:07.939534         8000.0    
1          5a           694f  2023-01-24 13:01:07.939534         8000.0    
2          5a           694f  2023-01-24 13:01:07.939534         8000.0    
3          5a           694f  2023-01-24 13:01:07.939534            NaN    
4          5a           43fa  2023-01-24 14:36:08.287521            NaN    
5          5a           43fa  2023-01-24 14:36:08.287521        13000.0    
6          5a           43fa  2023-01-24 14:36:08.287521        13000.0    
7          5a           43fa  2023-01-24 14:36:08.287521            NaN    
8          5a           a4b6  2023-01-24 14:38:42.625969         5000.0    
9          5a           a4b7  2023-01-24 14:38:42.625969            NaN    
10         5a           a4b8  2023-01-24 14:38:42.625969         8000.0    

                rejected_time  new_application_causes_rejection  new  
0  2023-01-24 13:13:15.499000                                 0    0  
1  2023-01-24 14:38:02.359000                                 1    1  
2  2023-01-24 14:37:18.616000                                 1    1  
3  2023-01-24 13:03:59.626000                                 0    0  
4  2023-01-24 14:37:22.096000                                 0    0  
5  2023-01-24 14:39:31.750000                                 1    1  
6  2023-02-02 08:42:26.980106                                 1    0  
7  2023-01-24 14:37:22.948214                                 0    0  
8  2023-02-02 08:42:26.980106                                 0    0  
9  2023-01-24 14:38:46.922000                                 0    1  
10 2023-02-02 08:42:26.980106                                 0    0