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Hello I have the following dataframe
df = Record_ID Time 94704 2014-03-10 07:19:19.647342 94705 2014-03-10 07:21:44.479363 94706 2014-03-10 07:21:45.479581 94707 2014-03-10 07:21:54.481588 94708 2014-03-10 07:21:55.481804 Is it possible to the have following?
df1 = Record_ID Time 94704 2014-03-10 07:19:19 94705 2014-03-10 07:21:44 94706 2014-03-10 07:21:45 94707 2014-03-10 07:21:54 94708 2014-03-10 07:21:55 
999
replace() function is used to replace the member values of the given Timestamp. The function implements datetime. replace, and it also handles nanoseconds. Example #1: Use Timestamp.
Timestamp is the pandas equivalent of python's Datetime and is interchangeable with it in most cases. It's the type used for the entries that make up a DatetimeIndex, and other timeseries oriented data structures in pandas.
class pandas. DatetimeIndex [source] Immutable ndarray of datetime64 data, represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata such as frequency information.
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]').
87
If you really must remove the microsecond part of the datetime, you can use the Timestamp.replace method along with Series.apply method to apply it across the series , to replace the microsecond part with 0. Example -
df['Time'] = df['Time'].apply(lambda x: x.replace(microsecond=0)) Demo -
In [25]: df Out[25]: Record_ID Time 0 94704 2014-03-10 07:19:19.647342 1 94705 2014-03-10 07:21:44.479363 2 94706 2014-03-10 07:21:45.479581 3 94707 2014-03-10 07:21:54.481588 4 94708 2014-03-10 07:21:55.481804 In [26]: type(df['Time'][0]) Out[26]: pandas.tslib.Timestamp In [27]: df['Time'] = df['Time'].apply(lambda x: x.replace(microsecond=0)) In [28]: df Out[28]: 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 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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