Backport PR #42861 on branch 1.3.x (#42879)

Author: attack68

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

@@ -2312,15 +2312,15 @@ def highlight_max(
         Styler.highlight_quantile: Highlight values defined by a quantile with a style.
         """
 
-        def f(data: FrameOrSeries, props: str) -> np.ndarray:
-            return np.where(data == np.nanmax(data.to_numpy()), props, "")
-
         if props is None:
             props = f"background-color: {color};"
         # error: Argument 1 to "apply" of "Styler" has incompatible type
         # "Callable[[FrameOrSeries, str], ndarray]"; expected "Callable[..., Styler]"
         return self.apply(
-            f, axis=axis, subset=subset, props=props  # type: ignore[arg-type]
+            partial(_highlight_value, op="max"),  # type: ignore[arg-type]
+            axis=axis,
+            subset=subset,
+            props=props,
         )
 
     def highlight_min(
@@ -2363,15 +2363,15 @@ def highlight_min(
         Styler.highlight_quantile: Highlight values defined by a quantile with a style.
         """
 
-        def f(data: FrameOrSeries, props: str) -> np.ndarray:
-            return np.where(data == np.nanmin(data.to_numpy()), props, "")
-
         if props is None:
             props = f"background-color: {color};"
         # error: Argument 1 to "apply" of "Styler" has incompatible type
         # "Callable[[FrameOrSeries, str], ndarray]"; expected "Callable[..., Styler]"
         return self.apply(
-            f, axis=axis, subset=subset, props=props  # type: ignore[arg-type]
+            partial(_highlight_value, op="min"),  # type: ignore[arg-type]
+            axis=axis,
+            subset=subset,
+            props=props,
         )
 
     def highlight_between(
@@ -2870,3 +2870,13 @@ def _highlight_between(
         else np.full(data.shape, True, dtype=bool)
     )
     return np.where(g_left & l_right, props, "")
+
+
+def _highlight_value(data: FrameOrSeries, op: str, props: str) -> np.ndarray:
+    """
+    Return an array of css strings based on the condition of values matching an op.
+    """
+    value = getattr(data, op)(skipna=True)
+    if isinstance(data, DataFrame):  # min/max must be done twice to return scalar
+        value = getattr(value, op)(skipna=True)
+    return np.where(data == value, props, "")

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",
     [