pandas.io.formats.style.Styler.map_index#

Styler.map_index(func, axis=0, level=None, **kwargs)[source]#

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

Updates the HTML representation with the result.

New in version 1.4.0.

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

Parameters:
funcfunction

func should take a scalar and return a string.

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.apply_index

Apply a CSS-styling function to headers level-wise.

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 an index value, if an Index, or a level value of a MultiIndex. The output of func should be CSS styles as a string, 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 "background-color: yellow;" if v == "B" else None
>>> df.style.map_index(color_b)
../../_images/appmaphead1.png

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(v):
...     return "background-color: yellow;" if "x" in v else None
>>> df.style.map_index(highlight_x, axis="columns", level=[0, 2])
...
../../_images/appmaphead2.png