update test to be explicit about copy vs nocopy + allow copy=False for masked arrays in case of no NAs

Author: jorisvandenbossche

pandas/core/arrays/masked.py

@@ -507,7 +507,7 @@ def to_numpy(
         else:
             with warnings.catch_warnings():
                 warnings.filterwarnings("ignore", category=RuntimeWarning)
-                data = self._data.astype(dtype, copy=copy)
+                data = np.array(self._data, dtype=dtype, copy=copy)
         return data
 
     @doc(ExtensionArray.tolist)
@@ -581,7 +581,7 @@ def __array__(
         the array interface, return my values
         We return an object array here to preserve our scalar values
         """
-        if copy is False:
+        if copy is False and self._hasna:
             raise ValueError(
                 "Unable to avoid copy while creating an array as requested."
             )

pandas/tests/base/test_conversion.py

@@ -298,24 +298,27 @@ def test_array_multiindex_raises():
 
 
 @pytest.mark.parametrize(
-    "arr, expected",
+    "arr, expected, zero_copy",
     [
-        (np.array([1, 2], dtype=np.int64), np.array([1, 2], dtype=np.int64)),
-        (pd.Categorical(["a", "b"]), np.array(["a", "b"], dtype=object)),
+        (np.array([1, 2], dtype=np.int64), np.array([1, 2], dtype=np.int64), True),
+        (pd.Categorical(["a", "b"]), np.array(["a", "b"], dtype=object), False),
         (
             pd.core.arrays.period_array(["2000", "2001"], freq="D"),
             np.array([pd.Period("2000", freq="D"), pd.Period("2001", freq="D")]),
+            False,
         ),
-        (pd.array([0, np.nan], dtype="Int64"), np.array([0, np.nan])),
+        (pd.array([0, np.nan], dtype="Int64"), np.array([0, np.nan]), False),
         (
             IntervalArray.from_breaks([0, 1, 2]),
             np.array([pd.Interval(0, 1), pd.Interval(1, 2)], dtype=object),
+            False,
         ),
-        (SparseArray([0, 1]), np.array([0, 1], dtype=np.int64)),
+        (SparseArray([0, 1]), np.array([0, 1], dtype=np.int64), False),
         # tz-naive datetime
         (
             DatetimeArray._from_sequence(np.array(["2000", "2001"], dtype="M8[ns]")),
             np.array(["2000", "2001"], dtype="M8[ns]"),
+            True,
         ),
         # tz-aware stays tz`-aware
         (
@@ -330,6 +333,7 @@ def test_array_multiindex_raises():
                     Timestamp("2000-01-02", tz="US/Central"),
                 ]
             ),
+            False,
         ),
         # Timedelta
         (
@@ -338,6 +342,7 @@ def test_array_multiindex_raises():
                 dtype=np.dtype("m8[ns]"),
             ),
             np.array([0, 3600000000000], dtype="m8[ns]"),
+            True,
         ),
         # GH#26406 tz is preserved in Categorical[dt64tz]
         (
@@ -348,10 +353,11 @@ def test_array_multiindex_raises():
                     Timestamp("2016-01-02", tz="US/Pacific"),
                 ]
             ),
+            False,
         ),
     ],
 )
-def test_to_numpy(arr, expected, index_or_series_or_array, request):
+def test_to_numpy(arr, expected, zero_copy, index_or_series_or_array):
     box = index_or_series_or_array
 
     with tm.assert_produces_warning(None):
@@ -374,15 +380,16 @@ def test_to_numpy(arr, expected, index_or_series_or_array, request):
         # copy=False semantics are only supported in NumPy>=2.
         return
 
-    try:
-        result_nocopy1 = np.array(thing, copy=False)
-    except ValueError:
-        # An error is always acceptable for `copy=False`
-        return
+    if not zero_copy:
+        with pytest.raises(ValueError, match="Unable to avoid copy while creating"):
+            # An error is always acceptable for `copy=False`
+            np.array(thing, copy=False)
 
-    result_nocopy2 = np.array(thing, copy=False)
-    # If copy=False was given, these must share the same data
-    assert np.may_share_memory(result_nocopy1, result_nocopy2)
+    else:
+        result_nocopy1 = np.array(thing, copy=False)
+        result_nocopy2 = np.array(thing, copy=False)
+        # If copy=False was given, these must share the same data
+        assert np.may_share_memory(result_nocopy1, result_nocopy2)
 
 
 @pytest.mark.xfail(