a dict / Series will be left as-is. Extra labels listed don't throw an
        error.

        Alternatively, change ``Series.name`` with a scalar value.

        See the :ref:`user guide <basics.rename>` for more.

        Parameters
        ----------
        index : scalar, hashable sequence, dict-like or function optional
            Functions or dict-like are transformations to apply to
            the index.
            Scalar or hashable sequence-like will alter the ``Series.name``
            attribute.
        axis : {0 or 'index'}
            Unused. Parameter needed for compatibility with DataFrame.
        copy : bool, default True
            Also copy underlying data.
        inplace : bool, default False
            Whether to return a new Series. If True the value of copy is ignored.
        level : int or level name, default None
            In case of MultiIndex, only rename labels in the specified level.
        errors : {'ignore', 'raise'}, default 'ignore'
            If 'raise', raise `KeyError` when a `dict-like mapper` or
            `index` contains labels that are not present in the index being transformed.
            If 'ignore', existing keys will be renamed and extra keys will be ignored.

        Returns
        -------
        Series or None
            Series with index labels or name altered or None if ``inplace=True``.

        See Also
        --------
        DataFrame.rename : Corresponding DataFrame method.
        Series.rename_axis : Set the name of the axis.

        Examples
        --------
        >>> s = pd.Series([1, 2, 3])
        >>> s
        0    1
        1    2
        2    3
        dtype: int64
        >>> s.rename("my_name")  # scalar, changes Series.name
        0    1
        1    2
        2    3
        Name: my_name, dtype: int64
        >>> s.rename(lambda x: x ** 2)  # function, changes labels
        0    1
        1    2
        4    3
        dtype: int64
        >>> s.rename({1: 3, 2: 5})  # mapping, changes labels
        0    1
        3    2
        5    3
        dtype: int64
        N)