pandas.Series.dt.to_pydatetime#
- 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.
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)