def to_pydatetime(self):
return self._get_values().to_pydatetime()<EOL>
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
-------
numpy.ndarray
    Object dtype array containing native Python datetime objects.

See Also
--------
datetime.datetime : Standard library value for a datetime.

Examples
--------
>>> s = pd.Series(pd.date_range('20180310', periods=2))
>>> s
0   2018-03-10
1   2018-03-11
dtype: datetime64[ns]

>>> s.dt.to_pydatetime()
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 = pd.Series(pd.date_range('20180310', periods=2, freq='ns'))
>>> s
0   2018-03-10 00:00:00.000000000
1   2018-03-10 00:00:00.000000001
dtype: datetime64[ns]

>>> s.dt.to_pydatetime()
array([datetime.datetime(2018, 3, 10, 0, 0),
       datetime.datetime(2018, 3, 10, 0, 0)], dtype=object)