Calculate time difference with mean in pandas dataframe?-CodePudding

Let's take any two date time columns and I wanna calculate the below formula inorder to get the mean values.

mean(24*(closed_time  - created_time ))

In excel, I tried by applying the same logic, and getting the below value,

closed time     created date         mean(24*(closed_time  - created_time ))
5/14/2022 8:35  5/11/2022 1:08         79.45
5/14/2022 8:12  5/13/2022 8:45         23.45
5/14/2022 8:34  5/13/2022 11:47        20.78333333
5/11/2022 11:21 5/9/2022 16:43         42.63333333
5/11/2022 11:30 5/8/2022 19:51         63.65
5/11/2022 11:22 5/6/2022 16:45         114.6166667
5/11/2022 11:25 5/9/2022 19:53         39.53333333
5/11/2022 11:28 5/9/2022 10:52         48.6

Any help would be appreciatable!!

CodePudding user response：

Not sure about mean, in sample data got same ouput by subtracted columns with convert seconds to hours:

cols = ['closed time','created date']
df[cols] = df[cols].apply(pd.to_datetime)

df['mean1'] = df['closed time'].sub(df['created date']).dt.total_seconds().div(3600)
print (df)
          closed time        created date        mean       mean1
0 2022-05-14 08:35:00 2022-05-11 01:08:00   79.450000   79.450000
1 2022-05-14 08:12:00 2022-05-13 08:45:00   23.450000   23.450000
2 2022-05-14 08:34:00 2022-05-13 11:47:00   20.783333   20.783333
3 2022-05-11 11:21:00 2022-05-09 16:43:00   42.633333   42.633333
4 2022-05-11 11:30:00 2022-05-08 19:51:00   63.650000   63.650000
5 2022-05-11 11:22:00 2022-05-06 16:45:00  114.616667  114.616667
6 2022-05-11 11:25:00 2022-05-09 19:53:00   39.533333   39.533333
7 2022-05-11 11:28:00 2022-05-09 10:52:00   48.600000   48.600000

Mean of both datetimes is count by:

df['mean']=pd.to_datetime(df[['closed time','created date']].astype(np.int64).mean(axis=1))
print (df)
          closed time        created date                mean
0 2022-05-14 08:35:00 2022-05-11 01:08:00 2022-05-12 16:51:30
1 2022-05-14 08:12:00 2022-05-13 08:45:00 2022-05-13 20:28:30
2 2022-05-14 08:34:00 2022-05-13 11:47:00 2022-05-13 22:10:30
3 2022-05-11 11:21:00 2022-05-09 16:43:00 2022-05-10 14:02:00
4 2022-05-11 11:30:00 2022-05-08 19:51:00 2022-05-10 03:40:30
5 2022-05-11 11:22:00 2022-05-06 16:45:00 2022-05-09 02:03:30
6 2022-05-11 11:25:00 2022-05-09 19:53:00 2022-05-10 15:39:00
7 2022-05-11 11:28:00 2022-05-09 10:52:00 2022-05-10 11:10:00