How to remove seconds from datetime?

Question

I have the following date and I tried the following code,

df[\'start_date_time\'] = [\"2016-05-19 08:25:00\",\"2016-05-19 16:00:00\",\"2016-05-20 07:45:00\",\"20         


        

Answer 1

Set seconds to 0

pd.to_datetime will return datetime objects, which have second as attribute : there's not much you can do about it. You can set second to 0, but the attribute will still be here and the standard representation will still include a trailing ':00'.

You need to apply replace on each element of df:

import pandas as pd

df = pd.DataFrame({'start_date_time': ["2016-05-19 08:25:23","2016-05-19 16:00:45","2016-05-20 07:45:00","2016-05-24 12:50:00","2016-05-25 23:00:00","2016-05-26 19:45:00"]})
df['start_date_time'] = pd.to_datetime(df['start_date_time'])
df['start_date_time'] = df['start_date_time'].apply(lambda t: t.replace(second=0))

print(df)
#       start_date_time
# 0 2016-05-19 08:25:00
# 1 2016-05-19 16:00:00
# 2 2016-05-20 07:45:00
# 3 2016-05-24 12:50:00
# 4 2016-05-25 23:00:00
# 5 2016-05-26 19:45:00

:23 and :45 from the first times have been replaced by :00, but they are still printed.

Remove ':00' from the strings

If you just want a string representation of those times and only parse the strings to datetime objects in order to remove ':00' at the end of the string, you could just remove the last 3 characters :

>>> "2016-05-19 08:25:00"[:-3]
'2016-05-19 08:25'

You could apply this to every element in your list, before initializing df['start_date_time']:

>>> start_date_time = ["2016-05-19 08:25:00","2016-05-19 16:00:00","2016-05-20 07:45:00","2016-05-24 12:50:00","2016-05-25 23:00:00","2016-05-26 19:45:00"]
>>> map(lambda s: s[:-3], start_date_time)
['2016-05-19 08:25', '2016-05-19 16:00', '2016-05-20 07:45', '2016-05-24 12:50', '2016-05-25 23:00', '2016-05-26 19:45']

Display datetimes without seconds

If you want to work with datetime objects but don't want to show seconds :

print(df['start_date_time'].apply(lambda t: t.strftime('%Y-%m-%d %H:%M')))
# 0    2016-05-19 08:25
# 1    2016-05-19 16:00
# 2    2016-05-20 07:45
# 3    2016-05-24 12:50
# 4    2016-05-25 23:00
# 5    2016-05-26 19:45
# Name: start_date_time, dtype: object