code: def rename(self, index=None, **kwargs):
          
          kwargs['inplace'] = validate_bool_kwarg(kwargs.get('inplace', False),
                                                  'inplace')
  
          non_mapping = is_scalar(index) or (is_list_like(index) and
                                             not is_dict_like(index))
          if non_mapping:
              return self._set_name(index, inplace=kwargs.get('inplace'))
          return super().rename(index=index, **kwargs)
docs: Alter Series index labels or name.
  
          Function / dict values must be unique (1-to-1). Labels not contained in
          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
              dict-like or functions are transformations to apply to
              the index.
              Scalar or hashable sequence-like will alter the ``Series.name``
              attribute.
          copy : bool, default True
              Whether to copy underlying data.
          inplace : bool, default False
              Whether to return a new Series. If True then value of copy is
              ignored.
          level : int or level name, default None
              In case of a MultiIndex, only rename labels in the specified
              level.
  
          Returns
          -------
          Series
              Series with index labels or name altered.
  
          See Also
          --------
          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