Python pandas Excel

[J Conrado]

HI,


I have an excel file with several columns, the first day/month,/year and 
hour:


Data
01/11/2017 00:00
01/11/2017 03:00
01/11/2017 06:00
01/11/2017 09:00
01/11/2017 12:00
01/11/2017 15:00
01/11/2017 18:00
01/11/2017 21:00
02/11/2017 00:00
02/11/2017 03:00
02/11/2017 06:00
02/11/2017 09:00
02/11/2017 12:00
02/11/2017 15:00
02/11/2017 18:00
02/11/2017 21:00
03/11/2017 00:00
03/11/2017 03:00
03/11/2017 06:00
03/11/2017 09:00
03/11/2017 12:00
03/11/2017 15:00
03/11/2017 18:00
03/11/2017 21:00
04/11/2017 00:00
04/11/2017 03:00
04/11/2017 06:00
04/11/2017 09:00
04/11/2017 12:00
04/11/2017 15:00
04/11/2017 18:00
04/11/2017 21:00
05/11/2017 00:00
05/11/2017 03:00
05/11/2017 06:00
05/11/2017 09:00
05/11/2017 12:00
05/11/2017 15:00
05/11/2017 18:00
05/11/2017 21:00
06/11/2017 00:00
06/11/2017 03:00
06/11/2017 06:00
06/11/2017 09:00
06/11/2017 12:00
06/11/2017 15:00
06/11/2017 18:00
06/11/2017 21:00
07/11/2017 00:00
07/11/2017 03:00
07/11/2017 06:00
07/11/2017 09:00
07/11/2017 12:00
07/11/2017 15:00
07/11/2017 18:00
07/11/2017 21:00
08/11/2017 00:00
08/11/2017 03:00
08/11/2017 06:00
08/11/2017 09:00
08/11/2017 12:00
08/11/2017 15:00
08/11/2017 21:00
09/11/2017 00:00
09/11/2017 03:00
09/11/2017 06:00
09/11/2017 09:00
09/11/2017 12:00
09/11/2017 15:00
09/11/2017 18:00
09/11/2017 21:00
10/11/2017 00:00
10/11/2017 03:00
10/11/2017 06:00
10/11/2017 09:00
10/11/2017 12:00
10/11/2017 15:00
10/11/2017 18:00
10/11/2017 21:00
11/11/2017 00:00
11/11/2017 03:00
11/11/2017 06:00
11/11/2017 09:00
11/11/2017 12:00
11/11/2017 15:00
11/11/2017 18:00
11/11/2017 21:00
12/11/2017 00:00
12/11/2017 03:00
12/11/2017 06:00
12/11/2017 09:00
12/11/2017 12:00
12/11/2017 15:00
12/11/2017 18:00
12/11/2017 21:00
13/11/2017 00:00
13/11/2017 03:00
13/11/2017 06:00
13/11/2017 09:00
13/11/2017 12:00
13/11/2017 15:00
13/11/2017 18:00
13/11/2017 21:00
14/11/2017 00:00
14/11/2017 03:00
14/11/2017 06:00
14/11/2017 09:00
14/11/2017 12:00
14/11/2017 15:00
14/11/2017 18:00
14/11/2017 21:00
15/11/2017 00:00
15/11/2017 03:00
15/11/2017 06:00
15/11/2017 09:00
15/11/2017 12:00
15/11/2017 15:00
15/11/2017 18:00
15/11/2017 21:00
16/11/2017 00:00
16/11/2017 03:00
16/11/2017 06:00
16/11/2017 09:00
16/11/2017 12:00
16/11/2017 15:00
16/11/2017 18:00
16/11/2017 21:00
17/11/2017 00:00
17/11/2017 03:00
17/11/2017 06:00
17/11/2017 09:00
17/11/2017 12:00
17/11/2017 15:00
17/11/2017 18:00
18/11/2017 00:00
18/11/2017 03:00
18/11/2017 06:00
18/11/2017 09:00
18/11/2017 12:00
18/11/2017 15:00
18/11/2017 18:00
18/11/2017 21:00
19/11/2017 00:00
19/11/2017 03:00
19/11/2017 06:00
19/11/2017 09:00
19/11/2017 12:00
19/11/2017 15:00
19/11/2017 18:00
19/11/2017 21:00
20/11/2017 00:00
20/11/2017 03:00
20/11/2017 06:00
20/11/2017 09:00
20/11/2017 12:00
20/11/2017 15:00
20/11/2017 18:00
20/11/2017 21:00
21/11/2017 00:00
21/11/2017 03:00
21/11/2017 06:00
21/11/2017 09:00
21/11/2017 12:00
21/11/2017 15:00
21/11/2017 18:00
22/11/2017 03:00
22/11/2017 06:00
22/11/2017 09:00
22/11/2017 12:00
22/11/2017 15:00
22/11/2017 18:00
22/11/2017 21:00
23/11/2017 00:00
23/11/2017 03:00
23/11/2017 06:00
23/11/2017 09:00
23/11/2017 12:00
23/11/2017 15:00
23/11/2017 18:00
23/11/2017 21:00
24/11/2017 00:00
24/11/2017 03:00
24/11/2017 06:00
24/11/2017 09:00
24/11/2017 12:00
24/11/2017 15:00
24/11/2017 18:00
24/11/2017 21:00
25/11/2017 00:00
25/11/2017 03:00
25/11/2017 06:00
25/11/2017 09:00
25/11/2017 12:00
25/11/2017 15:00
25/11/2017 18:00
25/11/2017 21:00
26/11/2017 00:00
26/11/2017 03:00
26/11/2017 06:00
26/11/2017 09:00
26/11/2017 12:00
26/11/2017 15:00
26/11/2017 18:00
26/11/2017 21:00
27/11/2017 03:00
27/11/2017 06:00
27/11/2017 09:00
27/11/2017 12:00
27/11/2017 15:00
27/11/2017 18:00
27/11/2017 21:00
28/11/2017 06:00
28/11/2017 09:00
28/11/2017 12:00
28/11/2017 15:00
28/11/2017 18:00
28/11/2017 21:00
29/11/2017 00:00
29/11/2017 03:00
29/11/2017 06:00
29/11/2017 09:00
29/11/2017 12:00
29/11/2017 15:00
29/11/2017 18:00
29/11/2017 21:00
30/11/2017 00:00
30/11/2017 03:00
30/11/2017 06:00
30/11/2017 09:00
30/11/2017 12:00
30/11/2017 15:00
30/11/2017 18:00
30/11/2017 21:00


This is the value tha a have using pandas:


print(data)


0     2017-01-11 00:00:00
1     2017-01-11 03:00:00
2     2017-01-11 06:00:00
3     2017-01-11 09:00:00
4     2017-01-11 12:00:00
               ...
228   2017-11-30 09:00:00
229   2017-11-30 12:00:00
230   2017-11-30 15:00:00
231   2017-11-30 18:00:00
232   2017-11-30 21:00:00

Please, how can I get four arrays for day, month, year and hour this 
column of my excel.


:


Conrado