"{:.1f}")
        ...         .pipe(some_highlights, min_color="green")
        ...         .highlight_between(left=2, right=5))  # doctest: +SKIP

        .. figure:: ../../_static/style/df_pipe_hl2.png

        **Advanced Use**

        Sometimes it may be necessary to pre-define styling functions, but in the case
        where those functions rely on the styler, data or context. Since
        ``Styler.use`` and ``Styler.export`` are designed to be non-data dependent,
        they cannot be used for this purpose. Additionally the ``Styler.apply``
        and ``Styler.format`` type methods are not context aware, so a solution
        is to use ``pipe`` to dynamically wrap this functionality.

        Suppose we want to code a generic styling function that highlights the final
        level of a MultiIndex. The number of levels in the Index is dynamic so we
        need the ``Styler`` context to define the level.

        >>> def highlight_last_level(styler):
        ...     return styler.apply_index(
        ...         lambda v: "background-color: pink; color: yellow", axis="columns",
        ...         level=styler.columns.nlevels-1
        ...     )  # doctest: +SKIP
        >>> df.columns = pd.MultiIndex.from_product([["A", "B"], ["X", "Y"]])
        >>> df.style.pipe(highlight_last_level)  # doctest: +SKIP

        .. figure:: ../../_static/style/df_pipe_applymap.png

        Additionally suppose we want to highlight a column header if there is any
        missing data in that column.
        In this case we need the data object itself to determine the effect on the
        column headers.

        >>> def highlight_header_missing(styler, level):
        ...     def dynamic_highlight(s):
        ...         return np.where(
        ...             styler.data.isna().any(), "background-color: red;", ""
        ...         )
        ...     return styler.apply_index(dynamic_highlight, axis=1, level=level)
        >>> df.style.pipe(highlight_header_missing, level=1)  # doctest: +SKIP

        .. figure:: ../../_static/style/df_pipe_applydata.png
        )