BUG: Logical and comparison ops with ArrowDtype & masked

doc/source/whatsnew/v2.0.1.rst

@@ -34,6 +34,7 @@ Bug fixes
 - Bug in :meth:`DataFrame.max` and related casting different :class:`Timestamp` resolutions always to nanoseconds (:issue:`52524`)
 - Bug in :meth:`Series.describe` not returning :class:`ArrowDtype` with ``pyarrow.float64`` type with numeric data (:issue:`52427`)
 - Bug in :meth:`Series.dt.tz_localize` incorrectly localizing timestamps with :class:`ArrowDtype` (:issue:`52677`)
+- Bug in logical and comparison operations between :class:`ArrowDtype` and numpy masked types (e.g. ``"boolean"``) (:issue:`52625`)
 - Fixed bug in :func:`merge` when merging with ``ArrowDtype`` one one and a NumPy dtype on the other side (:issue:`52406`)
 - Fixed segfault in :meth:`Series.to_numpy` with ``null[pyarrow]`` dtype (:issue:`52443`)
 

pandas/core/arrays/arrow/array.py

@@ -55,6 +55,7 @@
     ExtensionArray,
     ExtensionArraySupportsAnyAll,
 )
+from pandas.core.arrays.masked import BaseMaskedArray
 from pandas.core.arrays.string_ import StringDtype
 import pandas.core.common as com
 from pandas.core.indexers import (
@@ -450,6 +451,9 @@ def _cmp_method(self, other, op):
             result = pc_func(self._pa_array, other._pa_array)
         elif isinstance(other, (np.ndarray, list)):
             result = pc_func(self._pa_array, other)
+        elif isinstance(other, BaseMaskedArray):
+            # GH 52625
+            result = pc_func(self._pa_array, other.__arrow_array__())
         elif is_scalar(other):
             try:
                 result = pc_func(self._pa_array, pa.scalar(other))
@@ -497,6 +501,9 @@ def _evaluate_op_method(self, other, op, arrow_funcs):
             result = pc_func(self._pa_array, other._pa_array)
         elif isinstance(other, (np.ndarray, list)):
             result = pc_func(self._pa_array, pa.array(other, from_pandas=True))
+        elif isinstance(other, BaseMaskedArray):
+            # GH 52625
+            result = pc_func(self._pa_array, other.__arrow_array__())
         elif is_scalar(other):
             if isna(other) and op.__name__ in ARROW_LOGICAL_FUNCS:
                 # pyarrow kleene ops require null to be typed

pandas/tests/extension/test_arrow.py

@@ -21,6 +21,7 @@
     BytesIO,
     StringIO,
 )
+import operator
 import pickle
 import re
 
@@ -1216,7 +1217,7 @@ def test_add_series_with_extension_array(self, data, request):
 
 
 class TestBaseComparisonOps(base.BaseComparisonOpsTests):
-    def test_compare_array(self, data, comparison_op, na_value, request):
+    def test_compare_array(self, data, comparison_op, na_value):
         ser = pd.Series(data)
         # pd.Series([ser.iloc[0]] * len(ser)) may not return ArrowExtensionArray
         # since ser.iloc[0] is a python scalar
@@ -1255,6 +1256,20 @@ def test_invalid_other_comp(self, data, comparison_op):
         ):
             comparison_op(data, object())
 
+    @pytest.mark.parametrize("masked_dtype", ["boolean", "Int64", "Float64"])
+    def test_comp_masked_numpy(self, masked_dtype, comparison_op):
+        # GH 52625
+        data = [1, 0, None]
+        ser_masked = pd.Series(data, dtype=masked_dtype)
+        ser_pa = pd.Series(data, dtype=f"{masked_dtype.lower()}[pyarrow]")
+        result = comparison_op(ser_pa, ser_masked)
+        if comparison_op in [operator.lt, operator.gt, operator.ne]:
+            exp = [False, False, None]
+        else:
+            exp = [True, True, None]
+        expected = pd.Series(exp, dtype=ArrowDtype(pa.bool_()))
+        tm.assert_series_equal(result, expected)
+
 
 class TestLogicalOps:
     """Various Series and DataFrame logical ops methods."""
@@ -1399,6 +1414,23 @@ def test_kleene_xor_scalar(self, other, expected):
             a, pd.Series([True, False, None], dtype="boolean[pyarrow]")
         )
 
+    @pytest.mark.parametrize(
+        "op, exp",
+        [
+            ["__and__", True],
+            ["__or__", True],
+            ["__xor__", False],
+        ],
+    )
+    def test_logical_masked_numpy(self, op, exp):
+        # GH 52625
+        data = [True, False, None]
+        ser_masked = pd.Series(data, dtype="boolean")
+        ser_pa = pd.Series(data, dtype="boolean[pyarrow]")
+        result = getattr(ser_pa, op)(ser_masked)
+        expected = pd.Series([exp, False, None], dtype=ArrowDtype(pa.bool_()))
+        tm.assert_series_equal(result, expected)
+
 
 def test_arrowdtype_construct_from_string_type_with_unsupported_parameters():
     with pytest.raises(NotImplementedError, match="Passing pyarrow type"):