Wsrtjtyk

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

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)

I think the most easiest way is to use dt attribute of pandas Series. For your case you need to 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

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.

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

If your timestamp is a string, you can convert it to a datetime object:

from datetime import datetime



timestamp = '2016-02-22 14:59:44.561776'

dt = datetime.strptime(timestamp, '%Y-%m-%d %H:%M:%S.%f')

From then on you can bring it to whatever format you like.

Try

s = '2016-02-22 14:59:44.561776'



date,time = s.split()

then convert time as needed.

If you want to further split the time,

hour, minute, second = time.split(':')