xorbits.pandas.Series.dt.to_pydatetime

Series.dt.to_pydatetime() → numpy.ndarray

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

2.1.0(pandas) 版后已移除: 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.

警告

Python’s datetime uses microsecond resolution, which is lower than pandas (nanosecond). The values are truncated.

返回

Object dtype array containing native Python datetime objects.

返回类型

numpy.ndarray

参见

datetime.datetime

Standard library value for a datetime.

实际案例

>>> 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.