REGR: styler.highlight_min/max did not ignore pd.NA and caused error

doc/source/whatsnew/v1.3.2.rst

@@ -22,7 +22,7 @@ Fixed regressions
 - Regression in :meth:`DataFrame.drop` does nothing if :class:`MultiIndex` has duplicates and indexer is a tuple or list of tuples (:issue:`42771`)
 - Fixed regression where :meth:`pandas.read_csv` raised a ``ValueError`` when parameters ``names`` and ``prefix`` were both set to None (:issue:`42387`)
 - Fixed regression in comparisons between :class:`Timestamp` object and ``datetime64`` objects outside the implementation bounds for nanosecond ``datetime64`` (:issue:`42794`)
--
+- Fixed regression in :meth:`.Styler.highlight_min` and :meth:`.Styler.highlight_max` where ``pandas.NA`` was not successfully ignored (:issue:`42650`)
 
 .. ---------------------------------------------------------------------------
 

pandas/io/formats/style.py

@@ -2348,7 +2348,11 @@ def highlight_max(
         """
 
         def f(data: FrameOrSeries, props: str) -> np.ndarray:
-            return np.where(data == np.nanmax(data.to_numpy()), props, "")
+            arg = {"skipna": True}
+            if isinstance(data, DataFrame):
+                return np.where(data == data.max(**arg).max(**arg), props, "")
+            else:
+                return np.where(data == data.max(**arg), props, "")
 
         if props is None:
             props = f"background-color: {color};"
@@ -2399,7 +2403,11 @@ def highlight_min(
         """
 
         def f(data: FrameOrSeries, props: str) -> np.ndarray:
-            return np.where(data == np.nanmin(data.to_numpy()), props, "")
+            arg = {"skipna": True}
+            if isinstance(data, DataFrame):
+                return np.where(data == data.min(**arg).min(**arg), props, "")
+            else:
+                return np.where(data == data.min(**arg), props, "")
 
         if props is None:
             props = f"background-color: {color};"

pandas/tests/io/formats/style/test_highlight.py

@@ -2,10 +2,10 @@
 import pytest
 
 from pandas import (
+    NA,
     DataFrame,
     IndexSlice,
 )
-import pandas._testing as tm
 
 pytest.importorskip("jinja2")
 
@@ -55,9 +55,7 @@ def test_highlight_minmax_basic(df, f):
     }
     if f == "highlight_min":
         df = -df
-    with tm.assert_produces_warning(RuntimeWarning):
-        # All-NaN slice encountered
-        result = getattr(df.style, f)(axis=1, color="red")._compute().ctx
+    result = getattr(df.style, f)(axis=1, color="red")._compute().ctx
     assert result == expected
 
 
@@ -78,6 +76,26 @@ def test_highlight_minmax_ext(df, f, kwargs):
     assert result == expected
 
 
+@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"])
+@pytest.mark.parametrize("axis", [None, 0, 1])
+def test_highlight_minmax_nulls(f, axis):
+    # GH 42750
+    expected = {
+        (1, 0): [("background-color", "yellow")],
+        (1, 1): [("background-color", "yellow")],
+    }
+    if axis == 1:
+        expected.update({(2, 1): [("background-color", "yellow")]})
+
+    if f == "highlight_max":
+        df = DataFrame({"a": [NA, 1, None], "b": [np.nan, 1, -1]})
+    else:
+        df = DataFrame({"a": [NA, -1, None], "b": [np.nan, -1, 1]})
+
+    result = getattr(df.style, f)(axis=axis)._compute().ctx
+    assert result == expected
+
+
 @pytest.mark.parametrize(
     "kwargs",
     [