Splitting timestamp column into separate date and time columns

If your timestamps are already in pandas format (not string), then:

df["date"] = df["timestamp"].date
dt["time"] = dt["timestamp"].time

If your timestamp is a string, you can parse it using the datetime module:

from datetime import datetime
data1["timestamp"] = df["timestamp"].apply(lambda x: \
    datetime.strptime(x,"%Y-%m-%d %H:%M:%S.%f"))

Source: http://pandas.pydata.org/pandas-docs/stable/timeseries.html

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.

The easiest way is to use the pandas.Series dt accessor, which works on columns with a datetime dtype (see pd.to_datetime). For this case, pd.date_range creates an example column with a datetime dtype, therefore use .dt.date and .dt.time:

df = pd.DataFrame({'full_date': pd.date_range('2016-1-1 10:00:00.123', periods=10, freq='5H')})
df['date'] = df['full_date'].dt.date
df['time'] = df['full_date'].dt.time

In [166]: df
Out[166]:
                full_date        date             time
0 2016-01-01 10:00:00.123  2016-01-01  10:00:00.123000
1 2016-01-01 15:00:00.123  2016-01-01  15:00:00.123000
2 2016-01-01 20:00:00.123  2016-01-01  20:00:00.123000
3 2016-01-02 01:00:00.123  2016-01-02  01:00:00.123000
4 2016-01-02 06:00:00.123  2016-01-02  06:00:00.123000
5 2016-01-02 11:00:00.123  2016-01-02  11:00:00.123000
6 2016-01-02 16:00:00.123  2016-01-02  16:00:00.123000
7 2016-01-02 21:00:00.123  2016-01-02  21:00:00.123000
8 2016-01-03 02:00:00.123  2016-01-03  02:00:00.123000
9 2016-01-03 07:00:00.123  2016-01-03  07:00:00.123000

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)