------
        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'`.

        >>> 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
        )