pandas how to get mean value of datetime timestamp with some conditions?-CodePudding

I have a df ,you can have it by copy and run the following code:

import pandas as pd
from io import StringIO

df = """
 b_id          duration1                  duration2                          user
 
 366           NaN                        38 days 22:05:06.807430            Test
 367           0 days 00:00:05.285239     NaN                                Test
 368           NaN                        NaN                                Test
 371           NaN                        NaN                                Test
 378           NaN                        451 days 14:59:28.830482           Test
 384           28 days 21:05:16.141263     0 days 00:00:44.999706            Test
 
 466           NaN                        38 days 22:05:06.807430            Tom
 467           0 days 00:00:05.285239     NaN                                Tom
 468           NaN                        NaN                                Tom
 471           NaN                        NaN                                Tom
 478           NaN                        451 days 14:59:28.830482           Tom
 484           28 days 21:05:16.141263     0 days 00:00:44.999706            Tom

"""
df= pd.read_csv(StringIO(df.strip()), sep='\s\s ', engine='python')
df

My question is ,how can I get the mean value of each duration of each user ?

The output should something like this(the mean value is a fake one for sample ,not the exactly mean value):

mean_duration1             mean_duration2                     user

8 days 22:05:06.807430    3 days 22:05:06.807430              Test
2 days 00:00:05.285239    4 days 22:05:06.807430              Tom

CodePudding user response：

You can use:

out = (df
   .set_index('user')
   .filter(like='duration')
   .apply(pd.to_timedelta)
   .groupby(level=0).mean()
   .reset_index()
 )

Output:

   user               duration1                duration2
0  Test 14 days 10:32:40.713251 163 days 12:21:46.879206
1   Tom 14 days 10:32:40.713251 163 days 12:21:46.879206