to each row
            (``axis=1`` or ``'columns'``), or to the entire DataFrame at once
            with ``axis=None``.
        %(subset)s
        {text_threshold}
        vmin : float, optional
            Minimum data value that corresponds to colormap minimum value.
            If not specified the minimum value of the data (or gmap) will be used.
        vmax : float, optional
            Maximum data value that corresponds to colormap maximum value.
            If not specified the maximum value of the data (or gmap) will be used.
        gmap : array-like, optional
            Gradient map for determining the {name} colors. If not supplied
            will use the underlying data from rows, columns or frame. If given as an
            ndarray or list-like must be an identical shape to the underlying data
            considering ``axis`` and ``subset``. If given as DataFrame or Series must
            have same index and column labels considering ``axis`` and ``subset``.
            If supplied, ``vmin`` and ``vmax`` should be given relative to this
            gradient map.

            .. versionadded:: 1.3.0

        Returns
        -------
        Styler

        See Also
        --------
        Styler.{alt}_gradient: Color the {alt} in a gradient style.

        Notes
        -----
        When using ``low`` and ``high`` the range
        of the gradient, given by the data if ``gmap`` is not given or by ``gmap``,
        is extended at the low end effectively by
        `map.min - low * map.range` and at the high end by
        `map.max + high * map.range` before the colors are normalized and determined.

        If combining with ``vmin`` and ``vmax`` the `map.min`, `map.max` and
        `map.range` are replaced by values according to the values derived from
        ``vmin`` and ``vmax``.

        This method will preselect numeric columns and ignore non-numeric columns
        unless a ``gmap`` is supplied in which case no preselection occurs.

        Examples
        --------
        >>> df = pd.DataFrame(columns=["City", "Temp (c)", "Rain (mm)", "Wind (m/s)"],
        ...                   data=[["Stockholm", 21.6, 5.0, 3.2],
        ...                         ["Oslo", 22.4, 13.3, 3.1],
        ...                         ["Copenhagen", 24.5, 0.0, 6.7]])

        Shading the values column-wise, with ``axis=0``, preselecting numeric columns

        >>> df.style.{name}_gradient(axis=0)  # doctest: +SKIP

        .. figure:: ../../_static/style/{image_prefix}_ax0.png

        Shading all values collectively using ``axis=None``

        >>> df.style.{name}_gradient(axis=None)  # doctest: +SKIP

        .. figure:: ../../_static/style/{image_prefix}_axNone.png

        Compress the color map from the both ``low`` and ``high`` ends

        >>> df.style.{name}_gradient(axis=None, low=0.75, high=1.0)  # doctest: +SKIP

        .. figure:: ../../_static/style/{image_prefix}_axNone_lowhigh.png

        Manually setting ``vmin`` and ``vmax`` gradient thresholds

        >>> df.style.{name}_gradient(axis=None, vmin=6.7, vmax=21.6)  # doctest: +SKIP

        .. figure:: ../../_static/style/{image_prefix}_axNone_vminvmax.png

        Setting a ``gmap`` and applying to all columns with another ``cmap``

        >>> df.style.{name}_gradient(axis=0, gmap=df['Temp (c)'], cmap='YlOrRd')
        ...  # doctest: +SKIP

        .. figure:: ../../_static/style/{image_prefix}_gmap.png

        Setting the gradient map for a dataframe (i.e. ``axis=None``), we need to
        explicitly state ``subset`` to match the ``gmap`` shape

        >>> gmap = np.array([[1,2,3], [2,3,4], [3,4,5]])
        >>> df.style.{name}_gradient(axis=None, gmap=gmap,
        ...     cmap='YlOrRd', subset=['Temp (c)', 'Rain (mm)', 'Wind (m/s)']
        ... )  # doctest: +SKIP

        .. figure:: ../../_static/style/{image_prefix}_axNone_gmap.png
        N)	rq