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Showing content from https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.to_pydatetime.html below:

pandas.Series.dt.to_pydatetime — pandas 2.3.1 documentation

Series.dt.to_pydatetime()[source]#

Return the data as an array of datetime.datetime objects.

Deprecated since version 2.1.0: The current behavior of dt.to_pydatetime is deprecated. In a future version this will return a Series containing python datetime objects instead of a ndarray.

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.

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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