parameters: (
          self,
          mapper: Optional[Renamer] = None,
          *,
          index: Optional[Renamer] = None,
          columns: Optional[Renamer] = None,
          axis: Optional[Axis] = None,
          copy: bool = True,
          inplace: bool = False,
          level: Optional[Level] = None,
          errors: str = "ignore",
      )
return_statement: return super().rename(
              mapper=mapper,
              index=index,
              columns=columns,
              axis=axis,
              copy=copy,
              inplace=inplace,
              level=level,
              errors=errors,
          )
docstring: Alter axes labels.
  
          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.
  
          See the :ref:`user guide <basics.rename>` for more.
  
          Parameters
          ----------
          mapper : dict-like or function
              Dict-like or functions transformations to apply to
              that axis' values. Use either ``mapper`` and ``axis`` to
              specify the axis to target with ``mapper``, or ``index`` and
              ``columns``.
          index : dict-like or function
              Alternative to specifying axis (``mapper, axis=0``
              is equivalent to ``index=mapper``).
          columns : dict-like or function
              Alternative to specifying axis (``mapper, axis=1``
              is equivalent to ``columns=mapper``).
          axis : int or str
              Axis to target with ``mapper``. Can be either the axis name
              ('index', 'columns') or number (0, 1). The default is 'index'.
          copy : bool, default True
              Also copy underlying data.
          inplace : bool, default False
              Whether to return a new DataFrame. 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.
          errors : {'ignore', 'raise'}, default 'ignore'
              If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
              or `columns` 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
          -------
          DataFrame
              DataFrame with the renamed axis labels.
  
          Raises
          ------
          KeyError
              If any of the labels is not found in the selected axis and
              "errors='raise'".
  
          See Also
          --------
          DataFrame.rename_axis : Set the name of the axis.
  
          Examples
          --------
  
          ``DataFrame.rename`` supports two calling conventions
  
          * ``(index=index_mapper, columns=columns_mapper, ...)``
          * ``(mapper, axis={'index', 'columns'}, ...)``
  
          We *highly* recommend using keyword arguments to clarify your
          intent.
  
          Rename columns using a mapping:
  
          >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
          >>> df.rename(columns={"A": "a", "B": "c"})
             a  c
          0  1  4
          1  2  5
          2  3  6
  
          Rename index using a mapping:
  
          >>> df.rename(index={0: "x", 1: "y", 2: "z"})
             A  B
          x  1  4
          y  2  5
          z  3  6
  
          Cast index labels to a different type:
  
          >>> df.index
          RangeIndex(start=0, stop=3, step=1)
          >>> df.rename(index=str).index
          Index(['0', '1', '2'], dtype='object')
  
          >>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
          Traceback (most recent call last):
          KeyError: ['C'] not found in axis
  
          Using axis-style parameters
  
          >>> df.rename(str.lower, axis='columns')
             a  b
          0  1  4
          1  2  5
          2  3  6
  
          >>> df.rename({1: 2, 2: 4}, axis='index')
             A  B
          0  1  4
          2  2  5
          4  3  6
docstring_summary: Alter axes labels.
docstring_tokens: Alter axes labels .
function: def rename(
          self,
          mapper: Optional[Renamer] = None,
          *,
          index: Optional[Renamer] = None,
          columns: Optional[Renamer] = None,
          axis: Optional[Axis] = None,
          copy: bool = True,
          inplace: bool = False,
          level: Optional[Level] = None,
          errors: str = "ignore",
      ) -> Optional["DataFrame"]:
  
          """
          Alter axes labels.
  
          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.
  
          See the :ref:`user guide <basics.rename>` for more.
  
          Parameters
          ----------
          mapper : dict-like or function
              Dict-like or functions transformations to apply to
              that axis' values. Use either ``mapper`` and ``axis`` to
              specify the axis to target with ``mapper``, or ``index`` and
              ``columns``.
          index : dict-like or function
              Alternative to specifying axis (``mapper, axis=0``
              is equivalent to ``index=mapper``).
          columns : dict-like or function
              Alternative to specifying axis (``mapper, axis=1``
              is equivalent to ``columns=mapper``).
          axis : int or str
              Axis to target with ``mapper``. Can be either the axis name
              ('index', 'columns') or number (0, 1). The default is 'index'.
          copy : bool, default True
              Also copy underlying data.
          inplace : bool, default False
              Whether to return a new DataFrame. 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.
          errors : {'ignore', 'raise'}, default 'ignore'
              If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
              or `columns` 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
          -------
          DataFrame
              DataFrame with the renamed axis labels.
  
          Raises
          ------
          KeyError
              If any of the labels is not found in the selected axis and
              "errors='raise'".
  
          See Also
          --------
          DataFrame.rename_axis : Set the name of the axis.
  
          Examples
          --------
  
          ``DataFrame.rename`` supports two calling conventions
  
          * ``(index=index_mapper, columns=columns_mapper, ...)``
          * ``(mapper, axis={'index', 'columns'}, ...)``
  
          We *highly* recommend using keyword arguments to clarify your
          intent.
  
          Rename columns using a mapping:
  
          >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
          >>> df.rename(columns={"A": "a", "B": "c"})
             a  c
          0  1  4
          1  2  5
          2  3  6
  
          Rename index using a mapping:
  
          >>> df.rename(index={0: "x", 1: "y", 2: "z"})
             A  B
          x  1  4
          y  2  5
          z  3  6
  
          Cast index labels to a different type:
  
          >>> df.index
          RangeIndex(start=0, stop=3, step=1)
          >>> df.rename(index=str).index
          Index(['0', '1', '2'], dtype='object')
  
          >>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
          Traceback (most recent call last):
          KeyError: ['C'] not found in axis
  
          Using axis-style parameters
  
          >>> df.rename(str.lower, axis='columns')
             a  b
          0  1  4
          1  2  5
          2  3  6
  
          >>> df.rename({1: 2, 2: 4}, axis='index')
             A  B
          0  1  4
          2  2  5
          4  3  6
          """
          return super().rename(
              mapper=mapper,
              index=index,
              columns=columns,
              axis=axis,
              copy=copy,
              inplace=inplace,
              level=level,
              errors=errors,
          )
function_tokens: def rename ( self , mapper : Optional [ Renamer ] = None , * , index : Optional [ Renamer ] = None , columns : Optional [ Renamer ] = None , axis : Optional [ Axis ] = None , copy : bool = True , inplace : bool = False , level : Optional [ Level ] = None , errors : str = "ignore" , ) -> Optional [ "DataFrame" ] : return super ( ) . rename ( mapper = mapper , index = index , columns = columns , axis = axis , copy = copy , inplace = inplace , level = level , errors = errors , )