xorbits.pandas.Series.dt.to_pydatetime

Series.dt.to_pydatetime() → numpy.ndarray

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

Deprecated since version 2.1.0(pandas): 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

Object dtype array containing native Python datetime objects.

Return type

numpy.ndarray

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)

This docstring was copied from pandas.core.indexes.accessors.CombinedDatetimelikeProperties.