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# pandas.Series.dt.to_pydatetime

Series.dt.to_pydatetime()

  Return the data as a Series of `datetime.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[us]

      >>> s.dt.to_pydatetime()
      0    2018-03-10 00:00:00
      1    2018-03-11 00:00:00
      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()
      0    2018-03-10 00:00:00
      1    2018-03-10 00:00:00
      dtype: object