Backport PR #45808: bug: fix pd.NA in highlighters (#45828)

Co-authored-by: JHM Darbyshire <[email protected]>

Author: meeseeksmachine

doc/source/whatsnew/v1.4.1.rst

@@ -33,6 +33,7 @@ Bug fixes
 - Stopped emitting unnecessary ``FutureWarning`` in :meth:`DataFrame.sort_values` with sparse columns (:issue:`45618`)
 - Fixed window aggregations in :meth:`DataFrame.rolling` and :meth:`Series.rolling` to skip over unused elements (:issue:`45647`)
 - Bug in :func:`api.types.is_bool_dtype` was raising an ``AttributeError`` when evaluating a categorical :class:`Series` (:issue:`45615`)
+- Fixed builtin highlighters in :class:`.Styler` to be responsive to ``NA`` with nullable dtypes (:issue:`45804`)
 
 .. ---------------------------------------------------------------------------
 

pandas/io/formats/style.py

@@ -3640,11 +3640,15 @@ def _highlight_between(
         if left is not None
         else np.full(data.shape, True, dtype=bool)
     )
+    if isinstance(g_left, (DataFrame, Series)):
+        g_left = g_left.where(pd.notna(g_left), False)
     l_right = (
         ops[1](data, right)
         if right is not None
         else np.full(data.shape, True, dtype=bool)
     )
+    if isinstance(l_right, (DataFrame, Series)):
+        l_right = l_right.where(pd.notna(l_right), False)
     return np.where(g_left & l_right, props, "")
 
 
@@ -3655,7 +3659,9 @@ def _highlight_value(data: DataFrame | Series, op: str, props: str) -> np.ndarra
     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, "")
+    cond = data == value
+    cond = cond.where(pd.notna(cond), False)
+    return np.where(cond, props, "")
 
 
 def _bar(

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

@@ -12,9 +12,12 @@
 from pandas.io.formats.style import Styler
 
 
-@pytest.fixture
-def df():
-    return DataFrame({"A": [0, np.nan, 10], "B": [1, None, 2]})
+@pytest.fixture(params=[(None, "float64"), (NA, "Int64")])
+def df(request):
+    # GH 45804
+    return DataFrame(
+        {"A": [0, np.nan, 10], "B": [1, request.param[0], 2]}, dtype=request.param[1]
+    )
 
 
 @pytest.fixture