ENH: Styler.background_gradient to accept vmin vmax and dtype Int64 (pandas-dev#29245)

* ENH: Styler.background_gradient to accept vmin vmax and dtype Int64

Resolve pandas-dev#12145 and pandas-dev#28869
For `vmin` and `vmax` use the same implementation in `Styler.bar`
For dtype `Int64` issue, deprecated `.values` and use `.to_numpy` instead
Here explicitly assign the dtype to float since we are doing normalize

Author: immaxchen

doc/source/whatsnew/v1.0.0.rst

@@ -112,6 +112,7 @@ Other enhancements
 - :meth:`read_stata` can read Stata 119 dta files. (:issue:`28250`)
 - Added ``encoding`` argument to :meth:`DataFrame.to_string` for non-ascii text (:issue:`28766`)
 - Added ``encoding`` argument to :func:`DataFrame.to_html` for non-ascii text (:issue:`28663`)
+- :meth:`Styler.background_gradient` now accepts ``vmin`` and ``vmax`` arguments (:issue:`12145`)
 
 Build Changes
 ^^^^^^^^^^^^^
@@ -391,6 +392,7 @@ I/O
 - Bug in :meth:`DataFrame.read_excel` with ``engine='ods'`` when ``sheet_name`` argument references a non-existent sheet (:issue:`27676`)
 - Bug in :meth:`pandas.io.formats.style.Styler` formatting for floating values not displaying decimals correctly (:issue:`13257`)
 - Bug in :meth:`DataFrame.to_html` when using ``formatters=<list>`` and ``max_cols`` together. (:issue:`25955`)
+- Bug in :meth:`Styler.background_gradient` not able to work with dtype ``Int64`` (:issue:`28869`)
 
 Plotting
 ^^^^^^^^

pandas/io/formats/style.py

@@ -8,6 +8,7 @@
 import copy
 from functools import partial
 from itertools import product
+from typing import Optional
 from uuid import uuid1
 
 import numpy as np
@@ -18,7 +19,6 @@
 from pandas.util._decorators import Appender
 
 from pandas.core.dtypes.common import is_float, is_string_like
-from pandas.core.dtypes.generic import ABCSeries
 
 import pandas as pd
 from pandas.api.types import is_dict_like, is_list_like
@@ -963,6 +963,8 @@ def background_gradient(
         axis=0,
         subset=None,
         text_color_threshold=0.408,
+        vmin: Optional[float] = None,
+        vmax: Optional[float] = None,
     ):
         """
         Color the background in a gradient style.
@@ -991,6 +993,18 @@ def background_gradient(
 
             .. 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.
+
+            .. 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.
+
+            .. versionadded:: 1.0.0
+
         Returns
         -------
         self : Styler
@@ -1017,11 +1031,21 @@ def background_gradient(
             low=low,
             high=high,
             text_color_threshold=text_color_threshold,
+            vmin=vmin,
+            vmax=vmax,
         )
         return self
 
     @staticmethod
-    def _background_gradient(s, cmap="PuBu", low=0, high=0, text_color_threshold=0.408):
+    def _background_gradient(
+        s,
+        cmap="PuBu",
+        low=0,
+        high=0,
+        text_color_threshold=0.408,
+        vmin: Optional[float] = None,
+        vmax: Optional[float] = None,
+    ):
         """
         Color background in a range according to the data.
         """
@@ -1033,14 +1057,14 @@ def _background_gradient(s, cmap="PuBu", low=0, high=0, text_color_threshold=0.4
             raise ValueError(msg)
 
         with _mpl(Styler.background_gradient) as (plt, colors):
-            smin = s.values.min()
-            smax = s.values.max()
+            smin = np.nanmin(s.to_numpy()) if vmin is None else vmin
+            smax = np.nanmax(s.to_numpy()) 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.values))
+            rgbas = plt.cm.get_cmap(cmap)(norm(s.to_numpy(dtype=float)))
 
             def relative_luminance(rgba):
                 """
@@ -1111,12 +1135,8 @@ def _bar(s, align, colors, width=100, vmin=None, vmax=None):
         Draw bar chart in dataframe cells.
         """
         # Get input value range.
-        smin = s.min() if vmin is None else vmin
-        if isinstance(smin, ABCSeries):
-            smin = smin.min()
-        smax = s.max() if vmax is None else vmax
-        if isinstance(smax, ABCSeries):
-            smax = smax.max()
+        smin = np.nanmin(s.to_numpy()) if vmin is None else vmin
+        smax = np.nanmax(s.to_numpy()) if vmax is None else vmax
         if align == "mid":
             smin = min(0, smin)
             smax = max(0, smax)
@@ -1125,7 +1145,7 @@ def _bar(s, align, colors, width=100, vmin=None, vmax=None):
             smax = max(abs(smin), abs(smax))
             smin = -smax
         # Transform to percent-range of linear-gradient
-        normed = width * (s.values - smin) / (smax - smin + 1e-12)
+        normed = width * (s.to_numpy(dtype=float) - smin) / (smax - smin + 1e-12)
         zero = -width * smin / (smax - smin + 1e-12)
 
         def css_bar(start, end, color):
@@ -1304,17 +1324,15 @@ def _highlight_extrema(data, color="yellow", max_=True):
         Highlight the min or max in a Series or DataFrame.
         """
         attr = "background-color: {0}".format(color)
+
+        if max_:
+            extrema = data == np.nanmax(data.to_numpy())
+        else:
+            extrema = data == np.nanmin(data.to_numpy())
+
         if data.ndim == 1:  # Series from .apply
-            if max_:
-                extrema = data == data.max()
-            else:
-                extrema = data == data.min()
             return [attr if v else "" for v in extrema]
         else:  # DataFrame from .tee
-            if max_:
-                extrema = data == data.max().max()
-            else:
-                extrema = data == data.min().min()
             return pd.DataFrame(
                 np.where(extrema, attr, ""), index=data.index, columns=data.columns
             )

pandas/tests/io/formats/test_style.py

@@ -1648,6 +1648,23 @@ def test_background_gradient_axis(self):
         assert result[(1, 0)] == mid
         assert result[(1, 1)] == high
 
+    def test_background_gradient_vmin_vmax(self):
+        # GH 12145
+        df = pd.DataFrame(range(5))
+        ctx = df.style.background_gradient(vmin=1, vmax=3)._compute().ctx
+        assert ctx[(0, 0)] == ctx[(1, 0)]
+        assert ctx[(4, 0)] == ctx[(3, 0)]
+
+    def test_background_gradient_int64(self):
+        # GH 28869
+        df1 = pd.Series(range(3)).to_frame()
+        df2 = pd.Series(range(3), dtype="Int64").to_frame()
+        ctx1 = df1.style.background_gradient()._compute().ctx
+        ctx2 = df2.style.background_gradient()._compute().ctx
+        assert ctx2[(0, 0)] == ctx1[(0, 0)]
+        assert ctx2[(1, 0)] == ctx1[(1, 0)]
+        assert ctx2[(2, 0)] == ctx1[(2, 0)]
+
 
 def test_block_names():
     # catch accidental removal of a block