symb dates
4 BLK 01/03/2014 09:00:00
0 BBR 02/06/2014 09:00:00
21 HZ 02/06/2014 09:00:00
24 OMNI 02/07/2014 09:00:00
31 NOTE
I am not sure how you are getting the data, but if you are importing it from some source such as a CSV you could use pandas.read_csv and set parse_dates=True
. The question is what is the type of the dates column? You an easily change them to datelike objects using `dateutil.parse.parse. For example,
import pandas
import dateutil
data = {'symb': ['BLK', 'BBR', 'HZ', 'OMNI', 'NOTE', 'AMP', 'RBY'],
'dates': ['01/03/2014 09:00:00', '02/06/2014 09:00:00', '02/06/2014 09:00:00',
'02/07/2014 09:00:00', '03/04/2014 09:00:00', '03/04/2016 09:00:00',
'04/07/2014 09:00:00']}
df = pandas.DataFrame.from_dict(data)
df.dates = df.dates.apply(dateutil.parser.parse)
print df.to_string()
# OUTPUT
# 0 2014-01-03 09:00:00 BLK
# 1 2014-02-06 09:00:00 BBR
# 2 2014-02-06 09:00:00 HZ
# 3 2014-02-07 09:00:00 OMNI
# 4 2014-03-04 09:00:00 NOTE
# 5 2016-03-04 09:00:00 AMP
# 6 2014-04-07 09:00:00 RBY
This gets you the [ISO8601 format] which may be preferable to the dd/mm/yyyy
format, but if you must have that format you can use the code recommended by @umutto