cudf.DatetimeIndex.tz_localize#
- DatetimeIndex.tz_localize(tz: str | None, ambiguous: Literal['NaT'] = 'NaT', nonexistent: Literal['NaT'] = 'NaT')[source]#
Localize timezone-naive data to timezone-aware data.
- Parameters:
- tzstr
Timezone to convert timestamps to.
- Returns:
- DatetimeIndex containing timezone aware timestamps.
Notes
‘NaT’ is currently the only supported option for the
ambiguous
andnonexistent
arguments. Any ambiguous or nonexistent timestamps are converted to ‘NaT’.Examples
>>> import cudf >>> import pandas as pd >>> tz_naive = cudf.date_range('2018-03-01 09:00', periods=3, freq='D') >>> tz_aware = tz_naive.tz_localize("America/New_York") >>> tz_aware DatetimeIndex(['2018-03-01 09:00:00-05:00', '2018-03-02 09:00:00-05:00', '2018-03-03 09:00:00-05:00'], dtype='datetime64[ns, America/New_York]', freq='D')
Ambiguous or nonexistent datetimes are converted to NaT.
>>> s = cudf.to_datetime(cudf.Series(['2018-10-28 01:20:00', ... '2018-10-28 02:36:00', ... '2018-10-28 03:46:00'])) >>> s.dt.tz_localize("CET") 0 2018-10-28 01:20:00.000000000 1 NaT 2 2018-10-28 03:46:00.000000000 dtype: datetime64[ns, CET]