Combine Date and Time columns using python pandas

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 23:00:001    02-06-2013 01:00:002    02-06-2013 21:00:003    02-06-2013 22:00:004    02-06-2013 23:00:005    03-06-2013 01:00:006    03-06-2013 21:00:007    03-06-2013 22:00:008    03-06-2013 23:00:009    04-06-2013 01:00:00dtype: objectIn [12]: pd.to_datetime(df['Date'] + ' ' + df['Time'])Out[12]:0   2013-01-06 23:00:001   2013-02-06 01:00:002   2013-02-06 21:00:003   2013-02-06 22:00:004   2013-02-06 23:00:005   2013-03-06 01:00:006   2013-03-06 21:00:007   2013-03-06 22:00:008   2013-03-06 23:00:009   2013-04-06 01:00:00dtype: 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 the raise argument).

The accepted answer works for columns that are of datatype string. For completeness: I come across this question when searching how to do this when the columns are of datatypes: date and time.

df.apply(lambda r : pd.datetime.combine(r['date_column_name'],r['time_column_name']),1)

You can cast the columns if the types are different (datetime and timestamp or str) and use to_datetime :

df.loc[:,'Date'] = pd.to_datetime(df.Date.astype(str)+' '+df.Time.astype(str))

Result :

0   2013-01-06 23:00:001   2013-02-06 01:00:002   2013-02-06 21:00:003   2013-02-06 22:00:004   2013-02-06 23:00:005   2013-03-06 01:00:006   2013-03-06 21:00:007   2013-03-06 22:00:008   2013-03-06 23:00:009   2013-04-06 01:00:00

Best,