once,
        rather than column-wise or row-wise.

        Examples
        --------
        >>> def highlight_max(x, color):
        ...     return np.where(x == np.nanmax(x.to_numpy()), f"color: {color};", None)
        >>> df = pd.DataFrame(np.random.randn(5, 2), columns=["A", "B"])
        >>> df.style.apply(highlight_max, color='red')  # doctest: +SKIP
        >>> df.style.apply(highlight_max, color='blue', axis=1)  # doctest: +SKIP
        >>> df.style.apply(highlight_max, color='green', axis=None)  # doctest: +SKIP

        Using ``subset`` to restrict application to a single column or multiple columns

        >>> df.style.apply(highlight_max, color='red', subset="A")
        ... # doctest: +SKIP
        >>> df.style.apply(highlight_max, color='red', subset=["A", "B"])
        ... # doctest: +SKIP

        Using a 2d input to ``subset`` to select rows in addition to columns

        >>> df.style.apply(highlight_max, color='red', subset=([0, 1, 2], slice(None)))
        ... # doctest: +SKIP
        >>> df.style.apply(highlight_max, color='red', subset=(slice(0, 5, 2), "A"))
        ... # doctest: +SKIP

        Using a function which returns a Series / DataFrame of unequal length but
        containing valid index labels

        >>> df = pd.DataFrame([[1, 2], [3, 4], [4, 6]], index=["A1", "A2", "Total"])
        >>> total_style = pd.Series("font-weight: bold;", index=["Total"])
        >>> df.style.apply(lambda s: total_style)  # doctest: +SKIP

        See `Table Visualization <../../user_guide/style.ipynb>`_ user guide for
        more details.
        c