names, remain unchanged.
           * - Subset
             - None
             - True
             - The specified data rows/columns and axis-Index names are hidden, but
               the axis-Index itself remains unchanged.
           * - Subset
             - Int, Str, List
             - Boolean
             - ValueError: cannot supply ``subset`` and ``level`` simultaneously.

        Note this method only hides the identifed elements so can be chained to hide
        multiple elements in sequence.

        Examples
        --------
        Simple application hiding specific rows:

        >>> df = pd.DataFrame([[1,2], [3,4], [5,6]], index=["a", "b", "c"])
        >>> df.style.hide(["a", "b"])  # doctest: +SKIP
             0    1
        c    5    6

        Hide the index and retain the data values:

        >>> midx = pd.MultiIndex.from_product([["x", "y"], ["a", "b", "c"]])
        >>> df = pd.DataFrame(np.random.randn(6,6), index=midx, columns=midx)
        >>> df.style.format("{:.1f}").hide()  # doctest: +SKIP
                         x                    y
           a      b      c      a      b      c
         0.1    0.0    0.4    1.3    0.6   -1.4
         0.7    1.0    1.3    1.5   -0.0   -0.2
         1.4   -0.8    1.6   -0.2   -0.4   -0.3
         0.4    1.0   -0.2   -0.8   -1.2    1.1
        -0.6    1.2    1.8    1.9    0.3    0.3
         0.8    0.5   -0.3    1.2    2.2   -0.8

        Hide specific rows in a MultiIndex but retain the index:

        >>> df.style.format("{:.1f}").hide(subset=(slice(None), ["a", "c"]))
        ...   # doctest: +SKIP
                                 x                    y
                   a      b      c      a      b      c
        x   b    0.7    1.0    1.3    1.5   -0.0   -0.2
        y   b   -0.6    1.2    1.8    1.9    0.3    0.3

        Hide specific rows and the index through chaining:

        >>> df.style.format("{:.1f}").hide(subset=(slice(None), ["a", "c"])).hide()
        ...   # doctest: +SKIP
                         x                    y
           a      b      c      a      b      c
         0.7    1.0    1.3    1.5   -0.0   -0.2
        -0.6    1.2    1.8    1.9    0.3    0.3

        Hide a specific level:

        >>> df.style.format("{:,.1f}").hide(level=1)  # doctest: +SKIP
                             x                    y
               a      b      c      a      b      c
        x    0.1    0.0    0.4    1.3    0.6   -1.4
             0.7    1.0    1.3    1.5   -0.0   -0.2
             1.4   -0.8    1.6   -0.2   -0.4   -0.3
        y    0.4    1.0   -0.2   -0.8   -1.2    1.1
            -0.6    1.2    1.8    1.9    0.3    0.3
             0.8    0.5   -0.3    1.2    2.2   -0.8

        Hiding just the index level names:

        >>> df.index.names = ["lev0", "lev1"]
        >>> df.style.format("{:,.1f}").hide(names=True)  # doctest: +SKIP
                                 x                    y
                   a      b      c      a      b      c
        x   a    0.1    0.0    0.4    1.3    0.6   -1.4
            b    0.7    1.0    1.3    1.5   -0.0   -0.2
            c    1.4   -0.8    1.6   -0.2   -0.4   -0.3
        y   a    0.4    1.0   -0.2   -0.8   -1.2    1.1
            b   -0.6    1.2    1.8    1.9    0.3    0.3
            c    0.8    0.5   -0.3    1.2    2.2   -0.8

        Examples all produce equivalently transposed effects with ``axis="columns"``.
        r