pandas.DataFrame.tz_localize — pandas 3.0.0.dev0+2103.g41968a550a documentation (original) (raw)
DataFrame.tz_localize(tz, axis=0, level=None, copy=<no_default>, ambiguous='raise', nonexistent='raise')[source]#
Localize time zone naive index of a Series or DataFrame to target time zone.
This operation localizes the Index. To localize the values in a time zone naive Series, use Series.dt.tz_localize().
Parameters:
tzstr or tzinfo or None
Time zone to localize. Passing None will remove the time zone information and preserve local time.
axis{0 or ‘index’, 1 or ‘columns’}, default 0
The axis to localize
levelint, str, default None
If axis ia a MultiIndex, localize a specific level. Otherwise must be None.
copybool, default False
Also make a copy of the underlying data.
Note
The copy keyword will change behavior in pandas 3.0.Copy-on-Writewill be enabled by default, which means that all methods with acopy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True
Deprecated since version 3.0.0.
ambiguous‘infer’, bool, bool-ndarray, ‘NaT’, 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, theambiguous parameter dictates how ambiguous times should be handled.
- ‘infer’ will attempt to infer fall dst-transition hours based on order
- bool (or bool-ndarray) where True signifies a DST time, False designates 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 a ValueError if there are ambiguous times.
nonexistentstr, default ‘raise’
A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. Valid values are:
- ‘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 a ValueError if there are nonexistent times.
Returns:
Series/DataFrame
Same type as the input, with time zone naive or aware index, depending ontz.
Raises:
TypeError
If the TimeSeries is tz-aware and tz is not None.
Examples
Localize local times:
s = pd.Series( ... [1], ... index=pd.DatetimeIndex(["2018-09-15 01:30:00"]), ... ) s.tz_localize("CET") 2018-09-15 01:30:00+02:00 1 dtype: int64
Pass None to convert to tz-naive index and preserve local time:
s = pd.Series([1], index=pd.DatetimeIndex(["2018-09-15 01:30:00+02:00"])) s.tz_localize(None) 2018-09-15 01:30:00 1 dtype: int64
Be careful with DST changes. When there is sequential data, pandas can infer the DST time:
s = pd.Series( ... range(7), ... index=pd.DatetimeIndex( ... [ ... "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.tz_localize("CET", ambiguous="infer") 2018-10-28 01:30:00+02:00 0 2018-10-28 02:00:00+02:00 1 2018-10-28 02:30:00+02:00 2 2018-10-28 02:00:00+01:00 3 2018-10-28 02:30:00+01:00 4 2018-10-28 03:00:00+01:00 5 2018-10-28 03:30:00+01:00 6 dtype: int64
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.Series( ... range(3), ... index=pd.DatetimeIndex( ... [ ... "2018-10-28 01:20:00", ... "2018-10-28 02:36:00", ... "2018-10-28 03:46:00", ... ] ... ), ... ) s.tz_localize("CET", ambiguous=np.array([True, True, False])) 2018-10-28 01:20:00+02:00 0 2018-10-28 02:36:00+02:00 1 2018-10-28 03:46:00+01:00 2 dtype: int64
If the DST transition causes nonexistent times, you can shift these dates forward or backward with a timedelta object or ‘shift_forward’or ‘shift_backward’.
dti = pd.DatetimeIndex( ... ["2015-03-29 02:30:00", "2015-03-29 03:30:00"], dtype="M8[ns]" ... ) s = pd.Series(range(2), index=dti) s.tz_localize("Europe/Warsaw", nonexistent="shift_forward") 2015-03-29 03:00:00+02:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 s.tz_localize("Europe/Warsaw", nonexistent="shift_backward") 2015-03-29 01:59:59.999999999+01:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 s.tz_localize("Europe/Warsaw", nonexistent=pd.Timedelta("1h")) 2015-03-29 03:30:00+02:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64