modin.pandas.Series.dt.tz_localize

Series.dt.tz_localize[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/.tox/docs/lib/python3.9/site-packages/modin/pandas/series_utils.py#L731-L734)

Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index.

This method takes a time zone (tz) naive Datetime Array/Index object and makes this time zone aware. It does not move the time to another time zone.

This method can also be used to do the inverse – to create a time zone unaware object from an aware object. To that end, pass tz=None.

Parameters:

  • tz (str, pytz.timezone, dateutil.tz.tzfile, datetime.tzinfo or None) – Time zone to convert timestamps to. Passing None will remove the time zone information preserving local time.

  • ambiguous (‘infer’, ‘NaT’, bool array, default ‘raise’) – When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the ambiguous parameter dictates how ambiguous times should be handled. - ‘infer’ will attempt to infer fall dst-transition hours based on order - bool-ndarray where True signifies a DST time, False signifies a non-DST time (note that this flag is only applicable for ambiguous times) - ‘NaT’ will return NaT where there are ambiguous times - ‘raise’ will raise an AmbiguousTimeError if there are ambiguous times.

  • nonexistent (‘shift_forward’, ‘shift_backward, ‘NaT’, timedelta, default ‘raise’) – A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. - ‘shift_forward’ will shift the nonexistent time forward to the closest existing time - ‘shift_backward’ will shift the nonexistent time backward to the closest existing time - ‘NaT’ will return NaT where there are nonexistent times - timedelta objects will shift nonexistent times by the timedelta - ‘raise’ will raise an NonExistentTimeError if there are nonexistent times.

Returns:

Array/Index converted to the specified time zone.

Return type:

Same type as self

Raises:

TypeError – If the Datetime Array/Index is tz-aware and tz is not None.

See also

DatetimeIndex.tz_convert

Convert tz-aware DatetimeIndex from one time zone to another.

Examples

>>> tz_naive = pd.date_range('2018-03-01 09:00', periods=3)
>>> tz_naive
DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',
               '2018-03-03 09:00:00'],
              dtype='datetime64[ns]', freq=None)
Copy

Localize DatetimeIndex in US/Eastern time zone:

>>> tz_aware = tz_naive.tz_localize(tz='US/Eastern')  
>>> 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, US/Eastern]', freq=None)
Copy

With the tz=None, we can remove the time zone information while keeping the local time (not converted to UTC):

>>> tz_aware.tz_localize(None)  
DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',
               '2018-03-03 09:00:00'],
              dtype='datetime64[ns]', freq=None)
Copy

Be careful with DST changes. When there is sequential data, pandas can infer the DST time:

>>> s = pd.to_datetime(pd.Series(['2018-10-28 01:30:00',
...                             '2018-10-28 02:00:00',
...                             '2018-10-28 02:30:00',
...                             '2018-10-28 02:00:00',
...                             '2018-10-28 02:30:00',
...                             '2018-10-28 03:00:00',
...                             '2018-10-28 03:30:00']))
>>> s.dt.tz_localize('CET', ambiguous='infer')  
0   2018-10-28 01:30:00+02:00
1   2018-10-28 02:00:00+02:00
2   2018-10-28 02:30:00+02:00
3   2018-10-28 02:00:00+01:00
4   2018-10-28 02:30:00+01:00
5   2018-10-28 03:00:00+01:00
6   2018-10-28 03:30:00+01:00
dtype: datetime64[ns, CET]
Copy

In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly

>>> s = pd.to_datetime(pd.Series(['2018-10-28 01:20:00',
...                             '2018-10-28 02:36:00',
...                             '2018-10-28 03:46:00']))
>>> s.dt.tz_localize('CET', ambiguous=np.array([True, True, False]))  
0   2018-10-28 01:20:00+02:00
1   2018-10-28 02:36:00+02:00
2   2018-10-28 03:46:00+01:00
dtype: datetime64[ns, CET]
Copy

If the DST transition causes nonexistent times, you can shift these dates forward or backwards with a timedelta object or ‘shift_forward’ or ‘shift_backwards’.

>>> s = pd.to_datetime(pd.Series(['2015-03-29 02:30:00',
...                             '2015-03-29 03:30:00']))
>>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_forward')  
0   2015-03-29 03:00:00+02:00
1   2015-03-29 03:30:00+02:00
dtype: datetime64[ns, Europe/Warsaw]
Copy
>>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_backward')  
0   2015-03-29 01:59:59.999999999+01:00
1   2015-03-29 03:30:00+02:00
dtype: datetime64[ns, Europe/Warsaw]
Copy
>>> s.dt.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h'))  
0   2015-03-29 03:30:00+02:00
1   2015-03-29 03:30:00+02:00
dtype: datetime64[ns, Europe/Warsaw]
Copy
Language: English