pandas.Series.dt.tz_localize   Series.dt.tz_localize(*args, **kwargs)[source]
 
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 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 
 Same type as self

Array/Index converted to the specified time zone.    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='D')
  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)
  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)
  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]
  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]
  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]
  
>>> 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]
  
>>> 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]