class_docstr: Accessor object for datetimelike properties of the Series values.
  
  Examples
  --------
  >>> s.dt.hour
  >>> s.dt.second
  >>> s.dt.quarter
  
  Returns a Series indexed like the original Series.
  Raises TypeError if the Series does not contain datetimelike values.
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docstr: 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)
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code: def to_pydatetime(self):
      """
      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)
      """
      return self._get_values().to_pydatetime()
  
imports: import numpy
  from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_datetime_arraylike, is_integer_dtype, is_list_like, is_period_arraylike, is_timedelta64_dtype
  from pandas.core.dtypes.generic import ABCSeries
  from pandas.core.accessor import PandasDelegate, delegate_names
  from pandas.core.algorithms import take_1d
  from pandas.core.arrays import DatetimeArray, PeriodArray, TimedeltaArray
  from pandas.core.base import NoNewAttributesMixin, PandasObject
  from pandas.core.indexes.datetimes import DatetimeIndex
  from pandas.core.indexes.timedeltas import TimedeltaIndex
label_desc: Use image node_id 1 for calling the DatetimeProperties obj's underlying member method code with example usage: obj.to_pydatetime() and returns: self