Spliting nested date lists in DF columns and calculate AVG hour

Question

Lets say i have this DF

ID	date_time
1	2020-03-13 21:10:56, 2020-06-02 22:18:06, 2020-04-14 22:10:56, 2021-06-02 22:18:06
2	2010-09-13 21:43:09, 2011-05-04 23:08:15,2012-06-04 23:08:16
3	2013-06-14 23:29:17, 2014-08-13 23:20:22,2014-08-13 23:20:22

I want to remove YYYYMMDD string at the first position after every single comma and calculate AVG hour from list

Final output would be:

ID	date_time	AVG_hour
1	21:10:56,22:18:06,22:10:56	22
2	21:43:09,23:08:15,23:08:16	22
3	23:29:17,23:20:22,23:20:22	22

I tried following; but it did not work:

df['date_time'] = [para.split(None, 1)[1] for para in df['date_time']]
df.head()

Asked By: d9d9

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Source

Answer 1

here is one way to do it

# Split on comma, convert each value to date time and then to time delta
# take the total seconds and convert to hours
# np.mean to take average and then round the result

df['Avg_hour']=df['date_time'].str.split(',').apply(lambda x: round(np.mean([ pd.to_timedelta((pd.to_datetime(i)).strftime('%H:%M:%S')).total_seconds()/3600 for i in x])))
df

    ID  date_time                                           Avg_hour
0   1   2020-03-13 21:10:56, 2020-06-02 22:18:06, 2020...   22
1   2   2010-09-13 21:43:09, 2011-05-04 23:08:15,2012-...   23
2   3   2013-06-14 23:29:17, 2014-08-13 23:20:22,2014-...   23

#same as above, round to 2 decimal places

df['Avg_hour']=df['date_time'].str.split(',').apply(lambda x: round(np.mean([ pd.to_timedelta((pd.to_datetime(i)).strftime('%H:%M:%S')).total_seconds()/3600 for i in x]), 2))
df

    ID  date_time                                           Avg_hour
0   1   2020-03-13 21:10:56, 2020-06-02 22:18:06, 2020...   21.99
1   2   2010-09-13 21:43:09, 2011-05-04 23:08:15,2012-...   22.66
2   3   2013-06-14 23:29:17, 2014-08-13 23:20:22,2014-...   23.39

Answered By: Naveed

Spliting nested date lists in DF columns and calculate AVG hour

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