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# pandas.Series.tz_localize

Series.tz_localize(tz, axis=0, level=None, copy=<no_default>, ambiguous='raise', nonexistent='raise')

  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:
    tz str or tzinfo or None

      Time zone to attach to the index; the wall time of each index label is preserved. Passing `None` detaches the time zone from a tz-aware index, returning a tz-naive index with the same wall time.

    axis{0 or ‘index’, 1 or ‘columns’}, default 0

      The axis to localize

    level int, str, default None

      If axis is a MultiIndex, localize a specific level. Otherwise must be None.

    copy bool, default False

      This keyword is now ignored; changing its value will have no impact on the method.

      Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

    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, the ambiguous parameter dictates how ambiguous times should be handled.

      - ‘infer’ will attempt to infer fall dst-transition hours based on order. Requires that the timestamps are monotonically increasing.

      - 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.

    nonexistent str, 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 on `tz`.

  Raises:
    TypeError

      If the TimeSeries is tz-aware and tz is not None.

  See also

  `Series.dt.tz_localize`

    Localize the values in a time zone naive Series.

  `Timestamp.tz_localize`

    Localize the Timestamp to a timezone.

  Examples

  Attach a time zone to tz-naive timestamps:

      >>> 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 detach the time zone from tz-aware timestamps:

      >>> 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 03:30:00.000000000+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