python - how to apply ceiling to pandas DateTime -

suppose have pandas dataframe column values datetime64[ns].

out[204]:  0   2015-03-20 00:00:28 1   2015-03-20 00:01:44 2   2015-03-20 00:02:55 3   2015-03-20 00:03:39 4   2015-03-20 00:04:32 5   2015-03-20 00:05:52 6   2015-03-20 00:06:36 7   2015-03-20 00:07:44 8   2015-03-20 00:08:56 9   2015-03-20 00:09:47 name: datetime, dtype: datetime64[ns] 

is there easy way convert them nearest minute after time? i.e. want following:

out[204]:  0   2015-03-20 00:01:00 1   2015-03-20 00:02:00 2   2015-03-20 00:03:00 3   2015-03-20 00:04:00 4   2015-03-20 00:05:00 5   2015-03-20 00:06:00 6   2015-03-20 00:07:00 7   2015-03-20 00:08:00 8   2015-03-20 00:09:00 9   2015-03-20 00:10:00 name: datetime, dtype: datetime64[ns] 

i wrote complicate code first converts them string , extracts 3 portions of 00:09:47, convert them integers, unless last portion (seconds) 00, make last portion (seconds) 00, adds 1 middle portion (minutes) except if middle portion (minutes) 59 in case adds first portion (hours). recombine new integers string , reconstruct datetime.

but thinking may there might existing simpler solution. have suggestions?

* edit *

@jeff, @unutbu, answers. can select 1 answer in so, both work.

given dataframe column of dtype datetime64[ns], use

df['date'] += np.array(-df['date'].dt.second % 60, dtype='<m8[s]') 

to add appropriate number of seconds obtain ceiling.

---

for example,

import io import sys import numpy np import pandas pd stringio = io.bytesio if sys.version < '3' else io.stringio  df = '''\ 2015-03-20 00:00:00 2015-03-20 00:00:28 2015-03-20 00:01:44 2015-03-20 00:02:55 2015-03-20 00:03:39 2015-03-20 00:04:32 2015-03-20 00:05:52 2015-03-20 00:06:36 2015-03-20 00:07:44 2015-03-20 00:08:56 2015-03-20 00:09:47'''  df = pd.read_table(stringio(df), sep='\s{2,}',                     header=none, parse_dates=[0], names=['date'])  df['date'] += np.array(-df['date'].dt.second % 60, dtype='<m8[s]') print(df) 

yields

                  date 0  2015-03-20 00:00:00 1  2015-03-20 00:01:00 2  2015-03-20 00:02:00 3  2015-03-20 00:03:00 4  2015-03-20 00:04:00 5  2015-03-20 00:05:00 6  2015-03-20 00:06:00 7  2015-03-20 00:07:00 8  2015-03-20 00:08:00 9  2015-03-20 00:09:00 10 2015-03-20 00:10:00