pandas.Series.dt.to_pydatetime#

Series.dt.to_pydatetime()[source]#

Return the data as an array ofdatetime.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 pythondatetime objects instead of a ndarray.

Timezone information is retained if present.

Warning

Python’s datetime uses microsecond resolution, which is lower thanpandas (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))>>>s0   2018-03-101   2018-03-11dtype: 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'))>>>s0   2018-03-10 00:00:00.0000000001   2018-03-10 00:00:00.000000001dtype: datetime64[ns]

>>>s.dt.to_pydatetime()array([datetime.datetime(2018, 3, 10, 0, 0),       datetime.datetime(2018, 3, 10, 0, 0)], dtype=object)