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# pandas.io.formats.style.Styler.apply_index

Styler.apply_index(func, axis=0, level=None, **kwargs)

  Apply a CSS-styling function to the index or column headers, level-wise.

  Updates the HTML representation with the result.

  Added in version 1.4.0.

  Added in version 2.1.0: Styler.applymap_index was deprecated and renamed to Styler.map_index.

  Parameters:
    funcfunction

      `func` should take a Series and return a string array of the same length.

    axis{0, 1, “index”, “columns”}

      The headers over which to apply the function.

    levelint, str, list, optional

      If index is MultiIndex the level(s) over which to apply the function.

    **kwargsdict

      Pass along to `func`.

  Returns:
    Styler

  See also

  `Styler.map_index`

    Apply a CSS-styling function to headers elementwise.

  `Styler.apply`

    Apply a CSS-styling function column-wise, row-wise, or table-wise.

  `Styler.map`

    Apply a CSS-styling function elementwise.

  Notes

  Each input to `func` will be the index as a Series, if an Index, or a level of a MultiIndex. The output of `func` should be an identically sized array of CSS styles as strings, in the format ‘attribute: value; attribute2: value2; …’ or, if nothing is to be applied to that element, an empty string or `None`.

  Examples

  Basic usage to conditionally highlight values in the index.

      >>> df = pd.DataFrame([[1,2], [3,4]], index=["A", "B"])
      >>> def color_b(s):
      ...     return np.where(s == "B", "background-color: yellow;", "")
      >>> df.style.apply_index(color_b)

  Selectively applying to specific levels of MultiIndex columns.

      >>> midx = pd.MultiIndex.from_product([['ix', 'jy'], [0, 1], ['x3', 'z4']])
      >>> df = pd.DataFrame([np.arange(8)], columns=midx)
      >>> def highlight_x(s):
      ...     return ["background-color: yellow;" if "x" in v else "" for v in s]
      >>> df.style.apply_index(highlight_x, axis="columns", level=[0, 2])
      ...