Splitting timestamp column into separate date and time columns

Question

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

Answer 1

Had same problem and this worked for me.

Suppose the date column in your dataset is called "date"

import pandas as pd df = pd.read_csv(file_path)  df['Dates'] = pd.to_datetime(df['date']).dt.date df['Time'] = pd.to_datetime(df['date']).dt.time 

This will give you two columns "Dates" and "Time" with splited dates.

Answer 2

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)