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]
        )