Splitting timestamp column into separate date and time columns

Issue

I have a pandas dataframe with over 1000 timestamps (below) that I would like to loop through:

2016-02-22 14:59:44.561776

I’m having a hard time splitting this time stamp into 2 columns- ‘date’ and ‘time’. The date format can stay the same, but the time needs to be converted to CST (including milliseconds).

Thanks for the help

Solution

I’m not sure why you would want to do this in the first place, but if you really must…

df = pd.DataFrame({'my_timestamp': pd.date_range('2016-1-1 15:00', periods=5)})

>>> df
         my_timestamp
0 2016-01-01 15:00:00
1 2016-01-02 15:00:00
2 2016-01-03 15:00:00
3 2016-01-04 15:00:00
4 2016-01-05 15:00:00

df['new_date'] = [d.date() for d in df['my_timestamp']]
df['new_time'] = [d.time() for d in df['my_timestamp']]

>>> df
         my_timestamp    new_date  new_time
0 2016-01-01 15:00:00  2016-01-01  15:00:00
1 2016-01-02 15:00:00  2016-01-02  15:00:00
2 2016-01-03 15:00:00  2016-01-03  15:00:00
3 2016-01-04 15:00:00  2016-01-04  15:00:00
4 2016-01-05 15:00:00  2016-01-05  15:00:00

The conversion to CST is more tricky. I assume that the current timestamps are ‘unaware’, i.e. they do not have a timezone attached? If not, how would you expect to convert them?

For more details:

https://docs.python.org/2/library/datetime.html

How to make an unaware datetime timezone aware in python

EDIT

An alternative method that only loops once across the timestamps instead of twice:

new_dates, new_times = zip(*[(d.date(), d.time()) for d in df['my_timestamp']])
df = df.assign(new_date=new_dates, new_time=new_times)

Answered By – Alexander

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