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

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

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(label):
...     return np.where(label == "B", "background-color: yellow;", "")
>>> df.style.apply_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(label):
...     return ["background-color: yellow;" if "x" in v else "" for v in label]
>>> df.style.apply_index(
...     highlight_x, axis="columns", level=[0, 2])
../../_images/appmaphead2.png