I have a df structured as so:
CUID DATE T1 T2 T3
0 1000 5/5/2016 3:58:54 4:02:00 4:15:05
1 1001 5/6/2016 17:23:39 17:24:00 17:44:32
2 1002 5/7/2016 18:20:50 18:22:00 18:37:34
3 1003 5/7/2016 21:30:29 21:35:00 21:45:51
4 1004 5/9/2016 22:33:19 22:34:00 22:41:54
...
I want to take the date (string) and concatenate to each time string (i.e., T1, T2, T3). In other words, i want it to look like this:
CUID DATE T1 T2 T3
0 1000 5/5/2016 5/5/2016 3:58:54 5/5/2016 4:02:00 5/5/2016 4:15:05
1 1001 5/6/2016 5/5/2016 17:23:39 5/5/2016 17:24:00 5/5/2016 17:44:32
2 1002 5/7/2016 5/5/2016 18:20:50 5/5/2016 18:22:00 5/5/2016 18:37:34
3 1003 5/7/2016 5/5/2016 21:30:29 5/5/2016 21:35:00 5/5/2016 21:45:51
4 1004 5/9/2016 5/5/2016 22:33:19 5/5/2016 22:34:00 5/5/2016 22:41:54
...
I know I can do this using the following code:
df['T1'] = pd.to_datetime(df.DATE.str.cat(' ' + df['T1']))
df['T2'] = pd.to_datetime(df.DATE.str.cat(' ' + df['T2']))
df['T3'] = pd.to_datetime(df.DATE.str.cat(' ' + df['T3']))
But I was wondering if there was a more elegant/efficient way?
You can call apply and in the lambda just add the DATE column with a space and the column of interest.
Here apply will iterate over the sub-selected columns:
In[8]:
df.loc[:,'T1':] = df.loc[:,'T1':].apply(lambda x: df['DATE'] + ' ' + x)
df
Out[8]:
CUID DATE T1 T2 T3
0 1000 5/5/2016 5/5/2016 3:58:54 5/5/2016 4:02:00 5/5/2016 4:15:05
1 1001 5/6/2016 5/6/2016 17:23:39 5/6/2016 17:24:00 5/6/2016 17:44:32
2 1002 5/7/2016 5/7/2016 18:20:50 5/7/2016 18:22:00 5/7/2016 18:37:34
3 1003 5/7/2016 5/7/2016 21:30:29 5/7/2016 21:35:00 5/7/2016 21:45:51
4 1004 5/9/2016 5/9/2016 22:33:19 5/9/2016 22:34:00 5/9/2016 22:41:54