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# pandas.DataFrame.rename

DataFrame.rename(mapper=None, *, index=None, columns=None, axis=None, copy=None, inplace=False, level=None, errors='ignore')

  Rename columns or index 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 user guide for more.

  Parameters:
    mapper dict-like or function

      Dict-like or function 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{0 or ‘index’, 1 or ‘columns’}, default 0

      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.

      Note

      The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

      You can already get the future behavior and improvements through enabling copy on write `pd.options.mode.copy_on_write = True`

    inplace bool, default False

      Whether to modify the DataFrame rather than creating a new one. 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 or None

      DataFrame with the renamed axis labels or None if `inplace=True`.

  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