TST: fix Nullable xfails (#44685)

Author: jbrockmendel

doc/source/whatsnew/v1.4.0.rst

@@ -629,6 +629,7 @@ Numeric
 - Bug in :class:`DataFrame` arithmetic ops with a subclass whose :meth:`_constructor` attribute is a callable other than the subclass itself (:issue:`43201`)
 - Bug in arithmetic operations involving :class:`RangeIndex` where the result would have the incorrect ``name`` (:issue:`43962`)
 - Bug in arithmetic operations involving :class:`Series` where the result could have the incorrect ``name`` when the operands having matching NA or matching tuple names (:issue:`44459`)
+- Bug in division with ``IntegerDtype`` or ``BooleanDtype`` array and NA scalar incorrectly raising (:issue:`44685`)
 -
 
 Conversion

pandas/core/arrays/boolean.py

@@ -508,6 +508,8 @@ def _arith_method(self, other, op):
             # actual op, so we need to choose the resulting dtype manually
             if op_name in {"floordiv", "rfloordiv", "mod", "rmod", "pow", "rpow"}:
                 dtype = "int8"
+            elif op_name in {"truediv", "rtruediv"}:
+                dtype = "float64"
             else:
                 dtype = "bool"
             result = np.zeros(len(self._data), dtype=dtype)

pandas/core/arrays/numeric.py

@@ -136,6 +136,11 @@ def _arith_method(self, other, op):
 
         if other is libmissing.NA:
             result = np.ones_like(self._data)
+            if "truediv" in op_name and self.dtype.kind != "f":
+                # The actual data here doesn't matter since the mask
+                #  will be all-True, but since this is division, we want
+                #  to end up with floating dtype.
+                result = result.astype(np.float64)
         else:
             with np.errstate(all="ignore"):
                 result = op(self._data, other)

pandas/tests/arrays/masked/test_arithmetic.py

@@ -49,10 +49,6 @@ def test_array_scalar_like_equivalence(data, all_arithmetic_operators):
 
 
 def test_array_NA(data, all_arithmetic_operators, request):
-    if "truediv" in all_arithmetic_operators and data[0].dtype.kind != "f":
-        mark = pytest.mark.xfail(reason="division with pd.NA fails")
-        request.node.add_marker(mark)
-
     data, _ = data
     op = tm.get_op_from_name(all_arithmetic_operators)
     check_skip(data, all_arithmetic_operators)
@@ -169,14 +165,22 @@ def test_error_len_mismatch(data, all_arithmetic_operators):
 def test_unary_op_does_not_propagate_mask(data, op, request):
     # https://github.com/pandas-dev/pandas/issues/39943
     data, _ = data
-    if data.dtype in ["Float32", "Float64"] and op == "__invert__":
-        request.node.add_marker(
-            pytest.mark.xfail(
-                raises=TypeError, reason="invert is not implemented for float ea dtypes"
-            )
-        )
-    s = pd.Series(data)
-    result = getattr(s, op)()
+    ser = pd.Series(data)
+
+    if op == "__invert__" and data.dtype.kind == "f":
+        # we follow numpy in raising
+        msg = "ufunc 'invert' not supported for the input types"
+        with pytest.raises(TypeError, match=msg):
+            getattr(ser, op)()
+        with pytest.raises(TypeError, match=msg):
+            getattr(data, op)()
+        with pytest.raises(TypeError, match=msg):
+            # Check that this is still the numpy behavior
+            getattr(data._data, op)()
+
+        return
+
+    result = getattr(ser, op)()
     expected = result.copy(deep=True)
-    s[0] = None
+    ser[0] = None
     tm.assert_series_equal(result, expected)

pandas/tests/extension/test_boolean.py

@@ -150,14 +150,6 @@ def check_opname(self, s, op_name, other, exc=None):
         # overwriting to indicate ops don't raise an error
         super().check_opname(s, op_name, other, exc=None)
 
-    @pytest.mark.skip(reason="Tested in tests/arrays/test_boolean.py")
-    def test_compare_scalar(self, data, comparison_op):
-        pass
-
-    @pytest.mark.skip(reason="Tested in tests/arrays/test_boolean.py")
-    def test_compare_array(self, data, comparison_op):
-        pass
-
 
 class TestReshaping(base.BaseReshapingTests):
     pass
@@ -378,7 +370,6 @@ def check_reduce(self, s, op_name, skipna):
         tm.assert_almost_equal(result, expected)
 
 
-@pytest.mark.skip(reason="Tested in tests/reductions/test_reductions.py")
 class TestBooleanReduce(base.BaseBooleanReduceTests):
     pass
 

pandas/tests/extension/test_floating.py

@@ -89,10 +89,7 @@ def data_for_grouping(dtype):
 
 
 class TestDtype(base.BaseDtypeTests):
-    @pytest.mark.skip(reason="using multiple dtypes")
-    def test_is_dtype_unboxes_dtype(self):
-        # we have multiple dtypes, so skip
-        pass
+    pass
 
 
 class TestArithmeticOps(base.BaseArithmeticOpsTests):

pandas/tests/extension/test_integer.py

@@ -101,10 +101,7 @@ def data_for_grouping(dtype):
 
 
 class TestDtype(base.BaseDtypeTests):
-    @pytest.mark.skip(reason="using multiple dtypes")
-    def test_is_dtype_unboxes_dtype(self):
-        # we have multiple dtypes, so skip
-        pass
+    pass
 
 
 class TestArithmeticOps(base.BaseArithmeticOpsTests):
@@ -196,6 +193,7 @@ class TestMissing(base.BaseMissingTests):
 
 class TestMethods(base.BaseMethodsTests):
     @pytest.mark.skip(reason="uses nullable integer")
+    @pytest.mark.parametrize("dropna", [True, False])
     def test_value_counts(self, all_data, dropna):
         all_data = all_data[:10]
         if dropna: