TST: add extra test case for np.array(obj, copy=False) read-only behaviour (pandas-dev#60191)

Author: jorisvandenbossche

pandas/core/generic.py

@@ -2014,6 +2014,12 @@ def empty(self) -> bool:
     def __array__(
         self, dtype: npt.DTypeLike | None = None, copy: bool | None = None
     ) -> np.ndarray:
+        if copy is False and not self._mgr.is_single_block and not self.empty:
+            # check this manually, otherwise ._values will already return a copy
+            # and np.array(values, copy=False) will not raise an error
+            raise ValueError(
+                "Unable to avoid copy while creating an array as requested."
+            )
         values = self._values
         if copy is None:
             # Note: branch avoids `copy=None` for NumPy 1.x support

pandas/tests/copy_view/test_array.py

@@ -1,6 +1,8 @@
 import numpy as np
 import pytest
 
+from pandas.compat.numpy import np_version_gt2
+
 from pandas import (
     DataFrame,
     Series,
@@ -15,8 +17,12 @@
 
 @pytest.mark.parametrize(
     "method",
-    [lambda ser: ser.values, lambda ser: np.asarray(ser)],
-    ids=["values", "asarray"],
+    [
+        lambda ser: ser.values,
+        lambda ser: np.asarray(ser),
+        lambda ser: np.array(ser, copy=False),
+    ],
+    ids=["values", "asarray", "array"],
 )
 def test_series_values(method):
     ser = Series([1, 2, 3], name="name")
@@ -40,8 +46,12 @@ def test_series_values(method):
 
 @pytest.mark.parametrize(
     "method",
-    [lambda df: df.values, lambda df: np.asarray(df)],
-    ids=["values", "asarray"],
+    [
+        lambda df: df.values,
+        lambda df: np.asarray(df),
+        lambda ser: np.array(ser, copy=False),
+    ],
+    ids=["values", "asarray", "array"],
 )
 def test_dataframe_values(method):
     df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
@@ -82,7 +92,7 @@ def test_series_to_numpy():
     ser.iloc[0] = 0
     assert ser.values[0] == 0
 
-    # specify copy=False gives a writeable array
+    # specify copy=True gives a writeable array
     ser = Series([1, 2, 3], name="name")
     arr = ser.to_numpy(copy=True)
     assert not np.shares_memory(arr, get_array(ser, "name"))
@@ -130,6 +140,23 @@ def test_dataframe_multiple_numpy_dtypes():
     assert not np.shares_memory(arr, get_array(df, "a"))
     assert arr.flags.writeable is True
 
+    if np_version_gt2:
+        # copy=False semantics are only supported in NumPy>=2.
+
+        with pytest.raises(ValueError, match="Unable to avoid copy while creating"):
+            arr = np.array(df, copy=False)
+
+    arr = np.array(df, copy=True)
+    assert arr.flags.writeable is True
+
+
+def test_dataframe_single_block_copy_true():
+    # the copy=False/None cases are tested above in test_dataframe_values
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    arr = np.array(df, copy=True)
+    assert not np.shares_memory(arr, get_array(df, "a"))
+    assert arr.flags.writeable is True
+
 
 def test_values_is_ea():
     df = DataFrame({"a": date_range("2012-01-01", periods=3)})