Python: reduce precision pandas timestamp dataframe

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You could convert the underlying datetime64[ns] values to datetime64[s] values using astype:

In [11]: df['Time'] = df['Time'].astype('datetime64[s]')

In [12]: df
Out[12]: 
   Record_ID                Time
0      94704 2014-03-10 07:19:19
1      94705 2014-03-10 07:21:44
2      94706 2014-03-10 07:21:45
3      94707 2014-03-10 07:21:54
4      94708 2014-03-10 07:21:55

Note that since Pandas Series and DataFrames store all datetime values as datetime64[ns] these datetime64[s] values are automatically converted back to datetime64[ns], so the end result is still stored as datetime64[ns] values, but the call to astype causes the fractional part of the seconds to be removed.

If you wish to have a NumPy array of datetime64[s] values, you could use df['Time'].values.astype('datetime64[s]').

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