pandas.DataFrame.tz_localize¶
- DataFrame.tz_localize(tz, axis=0, level=None, copy=True, ambiguous='raise', nonexistent='raise')[source]¶
Localize tz-naive index of a Series or DataFrame to target time zone.
This operation localizes the Index. To localize the values in a timezone-naive Series, use
Series.dt.tz_localize().- Parameters:
- tzstr or tzinfo
- axisthe axis to localize
- levelint, str, default None
If axis ia a MultiIndex, localize a specific level. Otherwise must be None.
- copybool, default True
Also make a copy of the underlying data.
- ambiguous‘infer’, 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, 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 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 an AmbiguousTimeError 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 an NonExistentTimeError if there are nonexistent times.
- Returns:
- Series/DataFrame
Same type as the input.
- 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
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’.
>>> s = pd.Series(range(2), ... index=pd.DatetimeIndex(['2015-03-29 02:30:00', ... '2015-03-29 03:30:00'])) >>> 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