CoW: __array__ not recognizing ea dtypes

pandas/core/generic.py

@@ -107,6 +107,7 @@
     ensure_object,
     ensure_platform_int,
     ensure_str,
+    is_1d_only_ea_dtype,
     is_bool,
     is_bool_dtype,
     is_datetime64_any_dtype,
@@ -1995,10 +1996,13 @@ def empty(self) -> bool_t:
     def __array__(self, dtype: npt.DTypeLike | None = None) -> np.ndarray:
         values = self._values
         arr = np.asarray(values, dtype=dtype)
-        if arr is values and using_copy_on_write():
-            # TODO(CoW) also properly handle extension dtypes
-            arr = arr.view()
-            arr.flags.writeable = False
+        if arr is values and using_copy_on_write() and self._mgr.is_single_block:
+            # Check if self._values coerced data
+            if not is_1d_only_ea_dtype(self.dtypes.iloc[0]) or not is_numeric_dtype(
+                self.dtypes.iloc[0]
+            ):
+                arr = arr.view()
+                arr.flags.writeable = False
         return arr
 
     @final

pandas/core/series.py

@@ -58,6 +58,7 @@
     validate_percentile,
 )
 
+from pandas.core.dtypes.astype import astype_is_view
 from pandas.core.dtypes.cast import (
     LossySetitemError,
     convert_dtypes,
@@ -900,8 +901,7 @@ def __array__(self, dtype: npt.DTypeLike | None = None) -> np.ndarray:
         """
         values = self._values
         arr = np.asarray(values, dtype=dtype)
-        if arr is values and using_copy_on_write():
-            # TODO(CoW) also properly handle extension dtypes
+        if using_copy_on_write() and astype_is_view(values.dtype, arr.dtype):
             arr = arr.view()
             arr.flags.writeable = False
         return arr

pandas/tests/copy_view/test_array.py

@@ -110,3 +110,55 @@ def test_series_to_numpy(using_copy_on_write):
     arr = ser.to_numpy(dtype="float64")
     assert not np.shares_memory(arr, get_array(ser, "name"))
     assert arr.flags.writeable is True
+
+
+def test_series_array_ea_dtypes(using_copy_on_write):
+    ser = Series([1, 2, 3], dtype="Int64")
+    arr = np.asarray(ser, dtype="int64")
+    assert np.shares_memory(arr, get_array(ser))
+    if using_copy_on_write:
+        assert arr.flags.writeable is False
+    else:
+        assert arr.flags.writeable is True
+
+    arr = np.asarray(ser)
+    assert not np.shares_memory(arr, get_array(ser))
+    if using_copy_on_write:
+        # TODO(CoW): This should be True
+        assert arr.flags.writeable is False
+    else:
+        assert arr.flags.writeable is True
+
+
+def test_dataframe_array_ea_dtypes(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3]}, dtype="Int64")
+    arr = np.asarray(df, dtype="int64")
+    # TODO: This should be able to share memory, but we are roundtripping
+    # through object
+    assert not np.shares_memory(arr, get_array(df, "a"))
+    assert arr.flags.writeable is True
+
+    arr = np.asarray(df)
+    if using_copy_on_write:
+        # TODO(CoW): This should be True
+        assert arr.flags.writeable is False
+    else:
+        assert arr.flags.writeable is True
+
+
+def test_dataframe_array_string_dtype(using_copy_on_write, using_array_manager):
+    df = DataFrame({"a": ["a", "b"]}, dtype="string")
+    arr = np.asarray(df)
+    if not using_array_manager:
+        assert np.shares_memory(arr, get_array(df, "a"))
+    if using_copy_on_write:
+        assert arr.flags.writeable is False
+    else:
+        assert arr.flags.writeable is True
+
+
+def test_dataframe_multiple_numpy_dtypes():
+    df = DataFrame({"a": [1, 2, 3], "b": 1.5})
+    arr = np.asarray(df)
+    assert not np.shares_memory(arr, get_array(df, "a"))
+    assert arr.flags.writeable is True