mars.dataframe.Series.dt.to_pydatetime#
- Series.dt.to_pydatetime() ndarray #
Return the data as an array of native Python datetime objects.
Timezone information is retained if present.
Warning
Python’s datetime uses microsecond resolution, which is lower than pandas (nanosecond). The values are truncated.
- Returns
Object dtype array containing native Python datetime objects.
- Return type
See also
datetime.datetime
Standard library value for a datetime.
Examples
>>> import mars.dataframe as md >>> s = md.Series(md.date_range('20180310', periods=2)) >>> s.execute() 0 2018-03-10 1 2018-03-11 dtype: datetime64[ns]
>>> s.dt.to_pydatetime().execute() array([datetime.datetime(2018, 3, 10, 0, 0), datetime.datetime(2018, 3, 11, 0, 0)], dtype=object)
pandas’ nanosecond precision is truncated to microseconds.
>>> s = md.Series(md.date_range('20180310', periods=2, freq='ns')) >>> s.execute() 0 2018-03-10 00:00:00.000000000 1 2018-03-10 00:00:00.000000001 dtype: datetime64[ns]
>>> s.dt.to_pydatetime().execute() array([datetime.datetime(2018, 3, 10, 0, 0), datetime.datetime(2018, 3, 10, 0, 0)], dtype=object)