ame(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)	r