pandas.Series.dt.to_pydatetime¶

Series.dt.to_pydatetime()[source]¶

Return the data as an array 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[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)

pandas.Series.dt.to_period

pandas.Series.dt.tz_localize