BUG: fix treatment of NaNs when .apply() function is used on categorical columns.

doc/source/whatsnew/v3.0.0.rst

@@ -544,7 +544,7 @@ Bug fixes
 
 Categorical
 ^^^^^^^^^^^
--
+- Removed support for ``action="ignore"`` for :class:`SeriesApply` in :meth:`SeriesApply.apply_standard`, categorical NA value now returns ``False`` (:issue:`59938`)
 -
 
 Datetimelike

pandas/core/apply.py

@@ -38,10 +38,7 @@
     is_numeric_dtype,
     is_sequence,
 )
-from pandas.core.dtypes.dtypes import (
-    CategoricalDtype,
-    ExtensionDtype,
-)
+from pandas.core.dtypes.dtypes import ExtensionDtype
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
     ABCNDFrame,
@@ -1465,14 +1462,7 @@ def curried(x):
 
         else:
             curried = func
-
-        # row-wise access
-        # apply doesn't have a `na_action` keyword and for backward compat reasons
-        # we need to give `na_action="ignore"` for categorical data.
-        # TODO: remove the `na_action="ignore"` when that default has been changed in
-        #  Categorical (GH51645).
-        action = "ignore" if isinstance(obj.dtype, CategoricalDtype) else None
-        mapped = obj._map_values(mapper=curried, na_action=action)
+        mapped = obj._map_values(mapper=curried)
 
         if len(mapped) and isinstance(mapped[0], ABCSeries):
             # GH#43986 Need to do list(mapped) in order to get treated as nested

pandas/tests/apply/test_frame_apply.py

@@ -741,8 +741,9 @@ def test_apply_category_equalness(val):
 
     result = df.a.apply(lambda x: x == val)
     expected = Series(
-        [np.nan if pd.isnull(x) else x == val for x in df_values], name="a"
+        [False if pd.isnull(x) else x == val for x in df_values], name="a"
     )
+    # False since behavior of NaN for categorical dtype has been changed (GH 59966)
     tm.assert_series_equal(result, expected)
 
 

pandas/tests/apply/test_series_apply.py

@@ -236,10 +236,10 @@ def test_apply_categorical_with_nan_values(series, by_row):
         with pytest.raises(AttributeError, match=msg):
             s.apply(lambda x: x.split("-")[0], by_row=by_row)
         return
-
-    result = s.apply(lambda x: x.split("-")[0], by_row=by_row)
+    # NaN for cat dtype fixed in (GH 59966)
+    result = s.apply(lambda x: x.split("-")[0] if pd.notna(x) else False, by_row=by_row)
     result = result.astype(object)
-    expected = Series(["1", "1", np.nan], dtype="category")
+    expected = Series(["1", "1", False], dtype="category")
     expected = expected.astype(object)
     tm.assert_series_equal(result, expected)