pandas.Series.dt.to_pydatetime¶
-
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.
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)