Combine Date and Time columns using python pandas

I have a pandas dataframe with the following columns;

    Date              Time
    01-06-2013      23:00:00
    02-06-2013      01:00:00
    02-06-2013      21:00:00
    02-06-2013      22:00:00
    02-06-2013      23:00:00
    03-06-2013      01:00:00
    03-06-2013      21:00:00
    03-06-2013      22:00:00
    03-06-2013      23:00:00
    04-06-2013      01:00:00

How do I combine data['Date'] & data['Time'] to get the following? Is there a way of doing it using pd.to_datetime?

    Date
    01-06-2013 23:00:00
    02-06-2013 01:00:00
    02-06-2013 21:00:00
    02-06-2013 22:00:00
    02-06-2013 23:00:00
    03-06-2013 01:00:00
    03-06-2013 21:00:00
    03-06-2013 22:00:00
    03-06-2013 23:00:00
    04-06-2013 01:00:00

It's worth mentioning that you may have been able to read this in directly e.g. if you were using read_csv using parse_dates=[['Date', 'Time']].

Assuming these are just strings you could simply add them together (with a space), allowing you to apply to_datetime:

    In [11]: df['Date'] + ' ' + df['Time']
    Out[11]:
    0    01-06-2013 23:00:00
    1    02-06-2013 01:00:00
    2    02-06-2013 21:00:00
    3    02-06-2013 22:00:00
    4    02-06-2013 23:00:00
    5    03-06-2013 01:00:00
    6    03-06-2013 21:00:00
    7    03-06-2013 22:00:00
    8    03-06-2013 23:00:00
    9    04-06-2013 01:00:00
    dtype: object

    In [12]: pd.to_datetime(df['Date'] + ' ' + df['Time'])
    Out[12]:
    0   2013-01-06 23:00:00
    1   2013-02-06 01:00:00
    2   2013-02-06 21:00:00
    3   2013-02-06 22:00:00
    4   2013-02-06 23:00:00
    5   2013-03-06 01:00:00
    6   2013-03-06 21:00:00
    7   2013-03-06 22:00:00
    8   2013-03-06 23:00:00
    9   2013-04-06 01:00:00
    dtype: datetime64[ns]

Note: surprisingly (for me), this works fine with NaNs being converted to NaT, but it is worth worrying that the conversion (perhaps using theraise argument).

From: stackoverflow.com/q/17978092