ENH: add a gradient map to background gradient

doc/source/whatsnew/v1.3.0.rst

@@ -67,6 +67,7 @@ Other enhancements
 - :meth:`.Styler.set_tooltips_class` and :meth:`.Styler.set_table_styles` amended to optionally allow certain css-string input arguments (:issue:`39564`)
 - :meth:`.Styler.apply` now more consistently accepts ndarray function returns, i.e. in all cases for ``axis`` is ``0, 1 or None`` (:issue:`39359`)
 - :meth:`.Styler.apply` and :meth:`.Styler.applymap` now raise errors if wrong format CSS is passed on render (:issue:`39660`)
+- :meth:`.Styler.background_gradient` now allows the ability to supply a specific gradient map (:issue:`22727`)
 - :meth:`Series.loc.__getitem__` and :meth:`Series.loc.__setitem__` with :class:`MultiIndex` now raising helpful error message when indexer has too many dimensions (:issue:`35349`)
 - :meth:`pandas.read_stata` and :class:`StataReader` support reading data from compressed files.
 - Add support for parsing ``ISO 8601``-like timestamps with negative signs to :meth:`pandas.Timedelta` (:issue:`37172`)

pandas/io/formats/style.py

@@ -1309,63 +1309,130 @@ def background_gradient(
         text_color_threshold: float = 0.408,
         vmin: Optional[float] = None,
         vmax: Optional[float] = None,
+        gmap: Optional[Sequence] = None,
     ) -> Styler:
         """
         Color the background in a gradient style.
 
         The background color is determined according
-        to the data in each column (optionally row). Requires matplotlib.
+        to the data in each column, row or frame, or by a given
+        gradient map. Requires matplotlib.
 
         Parameters
         ----------
         cmap : str or colormap
             Matplotlib colormap.
         low : float
-            Compress the range by the low.
+            Compress the color range at the low end. This is a multiple of the data
+            range to extend below the minimum; good values usually in [0, 1],
+            defaults to 0.
         high : float
-            Compress the range by the high.
+            Compress the color range at the high end. This is a multiple of the data
+            range to extend above the maximum; good values usually in [0, 1],
+            defaults to 0.
         axis : {0 or 'index', 1 or 'columns', None}, default 0
             Apply to each column (``axis=0`` or ``'index'``), to each row
             (``axis=1`` or ``'columns'``), or to the entire DataFrame at once
             with ``axis=None``.
         subset : IndexSlice
             A valid slice for ``data`` to limit the style application to.
         text_color_threshold : float or int
-            Luminance threshold for determining text color. Facilitates text
-            visibility across varying background colors. From 0 to 1.
-            0 = all text is dark colored, 1 = all text is light colored.
+            Luminance threshold for determining text color in [0, 1]. Facilitates text
+            visibility across varying background colors. All text is dark if 0, and
+            light if 1, defaults to 0.408.
 
             .. versionadded:: 0.24.0
 
         vmin : float, optional
             Minimum data value that corresponds to colormap minimum value.
-            When None (default): the minimum value of the data will be used.
+            If not specified the minimum value of the data (or gmap) will be used.
 
             .. versionadded:: 1.0.0
 
         vmax : float, optional
             Maximum data value that corresponds to colormap maximum value.
-            When None (default): the maximum value of the data will be used.
+            If not specified the maximum value of the data (or gmap) will be used.
 
             .. versionadded:: 1.0.0
 
+        gmap : array-like, optional
+            Gradient map for determining the background colors. If not supplied
+            will use the input data from rows, columns or frame. Must be an
+            identical shape for sampling columns, rows or DataFrame based on ``axis``.
+            If supplied ``vmin`` and ``vmax`` should be given relative to this
+            gradient map.
+
+            .. versionadded:: 1.3.0
+
         Returns
         -------
         self : Styler
 
-        Raises
-        ------
-        ValueError
-            If ``text_color_threshold`` is not a value from 0 to 1.
-
         Notes
         -----
-        Set ``text_color_threshold`` or tune ``low`` and ``high`` to keep the
-        text legible by not using the entire range of the color map. The range
-        of the data is extended by ``low * (x.max() - x.min())`` and ``high *
-        (x.max() - x.min())`` before normalizing.
+        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({
+        ...          'City': ['Stockholm', 'Oslo', 'Copenhagen'],
+        ...          'Temp (c)': [21.6, 22.4, 24.5],
+        ...          'Rain (mm)': [5.0, 13.3, 0.0],
+        ...          'Wind (m/s)': [3.2, 3.1, 6.7]
+        ... })
+
+        Shading the values column-wise, with ``axis=0``
+
+        >>> df.style.background_gradient(axis=0)
+
+        .. figure:: ../../_static/style/bg_ax0.png
+
+        Shading all values collectively using ``axis=None``
+
+        >>> df.style.background_gradient(axis=None)
+
+        .. figure:: ../../_static/style/bg_axNone.png
+
+        Compress the color map from the both ``low`` and ``high`` ends
+
+        >>> df.style.background_gradient(axis=None, low=0.75, high=1.0)
+
+        .. figure:: ../../_static/style/bg_axNone_lowhigh.png
+
+        Manually setting ``vmin`` and ``vmax`` gradient thresholds
+
+        >>> df.style.background_gradient(axis=None, vmin=6.7, vmax=21.6)
+
+        .. figure:: ../../_static/style/bg_axNone_vminvmax.png
+
+        Setting a ``gmap`` and applying to all columns with another ``cmap``
+
+        >>> df.style.background_gradient(axis=0, gmap=df['Temp (c)'], cmap='YlOrRd')
+
+        .. figure:: ../../_static/style/bg_gmap.png
+
+        Setting the gradient map for a dataframe (i.e. ``axis=None``), we need to
+        explicitly state the numeric columns here to match the ``gmap`` shape
+
+        >>> gmap = np.array([[1,2,3], [2,3,4], [3,4,5]])
+        >>> df.style.background_gradient(axis=None, gmap=gmap,
+        ...     cmap='YlOrRd', subset=['Temp (c)', 'Rain (mm)', 'Wind (m/s)']
+        ... )
+
+        .. figure:: ../../_static/style/bg_axNone_gmap.png
         """
-        if subset is None:
+        if subset is None and gmap is None:
             subset = self.data.select_dtypes(include=np.number).columns
 
         self.apply(
@@ -1378,6 +1445,7 @@ def background_gradient(
             text_color_threshold=text_color_threshold,
             vmin=vmin,
             vmax=vmax,
+            gmap=gmap,
         )
         return self
 
@@ -1390,26 +1458,29 @@ def _background_gradient(
         text_color_threshold: float = 0.408,
         vmin: Optional[float] = None,
         vmax: Optional[float] = None,
+        gmap: Optional[Sequence] = None,
     ):
         """
-        Color background in a range according to the data.
+        Color background in a range according to the data or a gradient map
         """
-        if (
-            not isinstance(text_color_threshold, (float, int))
-            or not 0 <= text_color_threshold <= 1
-        ):
-            msg = "`text_color_threshold` must be a value from 0 to 1."
-            raise ValueError(msg)
-
+        if gmap is None:
+            gmap = s.to_numpy(dtype=float)
+        else:
+            try:
+                gmap = np.asarray(gmap, dtype=float).reshape(s.shape)
+            except ValueError:
+                raise ValueError(
+                    "supplied 'gmap' is not right shape for data over "
+                    f"selected 'axis': got {np.asarray(gmap).shape}, "
+                    f"expected {s.shape}"
+                )
         with _mpl(Styler.background_gradient) as (plt, colors):
-            smin = np.nanmin(s.to_numpy()) if vmin is None else vmin
-            smax = np.nanmax(s.to_numpy()) if vmax is None else vmax
+            smin = np.nanmin(gmap) if vmin is None else vmin
+            smax = np.nanmax(gmap) if vmax is None else vmax
             rng = smax - smin
             # extend lower / upper bounds, compresses color range
             norm = colors.Normalize(smin - (rng * low), smax + (rng * high))
-            # matplotlib colors.Normalize modifies inplace?
-            # https://github.com/matplotlib/matplotlib/issues/5427
-            rgbas = plt.cm.get_cmap(cmap)(norm(s.to_numpy(dtype=float)))
+            rgbas = plt.cm.get_cmap(cmap)(norm(gmap))
 
             def relative_luminance(rgba) -> float:
                 """

pandas/tests/io/formats/test_style.py

@@ -1974,15 +1974,6 @@ def test_text_color_threshold(self, cmap, expected):
         for k in expected.keys():
             assert result[k] == expected[k]
 
-    @pytest.mark.parametrize("text_color_threshold", [1.1, "1", -1, [2, 2]])
-    def test_text_color_threshold_raises(self, text_color_threshold):
-        df = DataFrame([[1, 2], [2, 4]], columns=["A", "B"])
-        msg = "`text_color_threshold` must be a value from 0 to 1."
-        with pytest.raises(ValueError, match=msg):
-            df.style.background_gradient(
-                text_color_threshold=text_color_threshold
-            )._compute()
-
     @td.skip_if_no_mpl
     def test_background_gradient_axis(self):
         df = DataFrame([[1, 2], [2, 4]], columns=["A", "B"])
@@ -2025,6 +2016,55 @@ def test_background_gradient_int64(self):
         assert ctx2[(1, 0)] == ctx1[(1, 0)]
         assert ctx2[(2, 0)] == ctx1[(2, 0)]
 
+    @pytest.mark.parametrize(
+        "axis, gmap, expected",
+        [
+            (
+                0,
+                [1, 2],
+                {
+                    (0, 0): [("background-color", "#fff7fb"), ("color", "#000000")],
+                    (1, 0): [("background-color", "#023858"), ("color", "#f1f1f1")],
+                    (0, 1): [("background-color", "#fff7fb"), ("color", "#000000")],
+                    (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")],
+                },
+            ),
+            (
+                1,
+                [1, 2],
+                {
+                    (0, 0): [("background-color", "#fff7fb"), ("color", "#000000")],
+                    (1, 0): [("background-color", "#fff7fb"), ("color", "#000000")],
+                    (0, 1): [("background-color", "#023858"), ("color", "#f1f1f1")],
+                    (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")],
+                },
+            ),
+            (
+                None,
+                np.array([[2, 1], [1, 2]]),
+                {
+                    (0, 0): [("background-color", "#023858"), ("color", "#f1f1f1")],
+                    (1, 0): [("background-color", "#fff7fb"), ("color", "#000000")],
+                    (0, 1): [("background-color", "#fff7fb"), ("color", "#000000")],
+                    (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")],
+                },
+            ),
+        ],
+    )
+    def test_background_gradient_gmap(self, axis, gmap, expected):
+        df = DataFrame([[1, 2], [2, 1]])
+        result = df.style.background_gradient(axis=axis, gmap=gmap)._compute().ctx
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "gmap, axis", [([1, 2, 3], 0), ([1, 2], 1), (np.array([[1, 2], [1, 2]]), None)]
+    )
+    def test_background_gradient_gmap_raises(self, gmap, axis):
+        df = DataFrame([[1, 2, 3], [1, 2, 3]])
+        msg = "supplied 'gmap' is not right shape"
+        with pytest.raises(ValueError, match=msg):
+            df.style.background_gradient(gmap=gmap, axis=axis)._compute()
+
 
 def test_block_names():
     # catch accidental removal of a block