mars.dataframe.Series.dt.to_pydatetime¶

Series.dt.to_pydatetime() → numpy.ndarray¶

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

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

>>> import mars.dataframe as md
>>> s = md.Series(md.date_range('20180310', periods=2))
>>> s.execute()
0   2018-03-10
1   2018-03-11
dtype: datetime64[ns]

>>> s.dt.to_pydatetime().execute()
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 = md.Series(md.date_range('20180310', periods=2, freq='ns'))
>>> s.execute()
0   2018-03-10 00:00:00.000000000
1   2018-03-10 00:00:00.000000001
dtype: datetime64[ns]

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

mars.dataframe.Series.dt.to_period

mars.dataframe.Series.dt.tz_localize