Fix: EA attribute specifying whether copy=False is ignored

pandas/tests/extension/base/missing.py

@@ -6,6 +6,8 @@
 
 
 class BaseMissingTests:
+    _supports_fillna_copy_false = True
+
     def test_isna(self, data_missing):
         expected = np.array([True, False])
 
@@ -123,18 +125,28 @@ def test_fillna_no_op_returns_copy(self, data):
         tm.assert_extension_array_equal(result, data)
 
     def test_fillna_readonly(self, data_missing):
+        fill_value = data_missing[1]
         data = data_missing.copy()
         data._readonly = True
 
+        expected = data_missing._from_sequence(
+            [fill_value, fill_value], dtype=data_missing.dtype
+        )
+
         # by default fillna(copy=True), then this works fine
-        result = data.fillna(data_missing[1])
-        assert result[0] == data_missing[1]
+        res_copy = data.fillna(fill_value, copy=True)
+        tm.assert_extension_array_equal(res_copy, expected)
         tm.assert_extension_array_equal(data, data_missing)
 
-        # but with copy=False, this raises for EAs that respect the copy keyword
-        with pytest.raises(ValueError, match="Cannot modify read-only array"):
-            data.fillna(data_missing[1], copy=False)
-        tm.assert_extension_array_equal(data, data_missing)
+        if self._supports_fillna_copy_false:
+            # but with copy=False, this raises for EAs that respect the copy keyword
+            with pytest.raises(ValueError, match="Cannot modify read-only array"):
+                data.fillna(fill_value, copy=False)
+            tm.assert_extension_array_equal(data, data_missing)
+        else:
+            res_no_copy = data.fillna(fill_value, copy=False)
+            tm.assert_extension_array_equal(res_no_copy, res_copy)
+            tm.assert_extension_array_equal(data, data_missing)
 
     def test_fillna_series(self, data_missing):
         fill_value = data_missing[1]

pandas/tests/extension/test_numpy.py

@@ -342,11 +342,6 @@ def test_fillna_frame(self, data_missing):
         # Non-scalar "scalar" values.
         super().test_fillna_frame(data_missing)
 
-    @skip_nested
-    def test_fillna_readonly(self, data_missing):
-        # Non-scalar "scalar" values.
-        super().test_fillna_readonly(data_missing)
-
     @skip_nested
     def test_setitem_invalid(self, data, invalid_scalar):
         # object dtype can hold anything, so doesn't raise

pandas/tests/extension/test_sparse.py

@@ -97,6 +97,8 @@ def data_for_compare(request):
 
 
 class TestSparseArray(base.ExtensionTests):
+    _supports_fillna_copy_false = False
+
     def _supports_reduction(self, obj, op_name: str) -> bool:
         return True
 
@@ -237,21 +239,6 @@ def test_isna(self, data_missing):
     def test_fillna_no_op_returns_copy(self, data, request):
         super().test_fillna_no_op_returns_copy(data)
 
-    def test_fillna_readonly(self, data_missing):
-        # copy keyword is ignored by SparseArray.fillna
-        # -> copy=True vs False doesn't make a difference
-        data = data_missing.copy()
-        data._readonly = True
-
-        result = data.fillna(data_missing[1])
-        assert result[0] == data_missing[1]
-        tm.assert_extension_array_equal(data, data_missing)
-
-        # fillna(copy=False) is ignored -> so same result as above
-        result = data.fillna(data_missing[1], copy=False)
-        assert result[0] == data_missing[1]
-        tm.assert_extension_array_equal(data, data_missing)
-
     @pytest.mark.xfail(reason="Unsupported")
     def test_fillna_series(self, data_missing):
         # this one looks doable.