pandas.Series.dt.to_pydatetime — pandas 0.24.2 documentation (original) (raw)

Series.dt. to_pydatetime()[source]¶

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.

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)