richie richie - 6 months ago 229
Python Question

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

Answer

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 the raise argument).