BUG: Fixup __array__ copy paths based on review

A few of these were just wrong, a few others are enhancements to
allow the cases that clearly should work without copy to still pass.

Co-authored-by: Joris Van den Bossche <[email protected]>

Author: seberg

pandas/core/arrays/arrow/array.py

@@ -673,7 +673,8 @@ def __array__(
                 "Unable to avoid copy while creating an array as requested."
             )
         elif copy is None:
-            copy = False  # The NumPy copy=False meaning is different here.
+            # `to_numpy(copy=False)` has the meaning of NumPy `copy=None`.
+            copy = False
 
         return self.to_numpy(dtype=dtype, copy=copy)
 

pandas/core/arrays/categorical.py

@@ -1697,10 +1697,8 @@ def __array__(
         # we need to ensure __array__ gets all the way to an
         # ndarray.
 
-        if copy is None:
-            # Branch required since copy=None is not defined on 1.x
-            return np.asarray(ret, dtype=dtype)
-        return np.array(ret, dtype=dtype)
+        # `take_nd` should already make a copy, so don't force again.
+        return np.asarray(ret, dtype=dtype)
 
     def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
         # for binary ops, use our custom dunder methods

pandas/core/arrays/period.py

@@ -389,14 +389,20 @@ def freqstr(self) -> str:
     def __array__(
         self, dtype: NpDtype | None = None, copy: bool | None = None
     ) -> np.ndarray:
+        if dtype == "i8":
+            # For NumPy 1.x compatibility we cannot use copy=None.  And
+            # `copy=False` has the meaning of `copy=None` here:
+            if not copy:
+                return np.asarray(self, dtype=dtype)
+            else:
+                return np.array(self, dtype=dtype)
+
         if copy is False:
             raise ValueError(
                 "Unable to avoid copy while creating an array as requested."
             )
 
-        if dtype == "i8":
-            return self.asi8
-        elif dtype == bool:
+        if dtype == bool:
             return ~self._isnan
 
         # This will raise TypeError for non-object dtypes

pandas/core/arrays/sparse/array.py

@@ -547,16 +547,20 @@ def from_spmatrix(cls, data: spmatrix) -> Self:
     def __array__(
         self, dtype: NpDtype | None = None, copy: bool | None = None
     ) -> np.ndarray:
+        if self.sp_index.ngaps == 0:
+            # Compat for na dtype and int values.
+            if copy is True:
+                return np.array(self.sp_values)
+            else:
+                return self.sp_values
+
         if copy is False:
             raise ValueError(
                 "Unable to avoid copy while creating an array as requested."
             )
 
         fill_value = self.fill_value
 
-        if self.sp_index.ngaps == 0:
-            # Compat for na dtype and int values.
-            return self.sp_values
         if dtype is None:
             # Can NumPy represent this type?
             # If not, `np.result_type` will raise. We catch that

pandas/core/generic.py

@@ -2022,7 +2022,7 @@ def __array__(
             arr = np.array(values, dtype=dtype, copy=copy)
 
         if (
-            copy is not False
+            copy is not True
             and astype_is_view(values.dtype, arr.dtype)
             and self._mgr.is_single_block
         ):

pandas/core/indexes/multi.py

@@ -1391,9 +1391,11 @@ def copy(  # type: ignore[override]
 
     def __array__(self, dtype=None, copy=None) -> np.ndarray:
         """the array interface, return my values"""
-        if copy is True:
-            # Note: branch avoids `copy=None` for NumPy 1.x support
-            return np.array(self.values, dtype=dtype, copy=copy)
+        if copy is False:
+            # self.values is always a newly construct array, so raise.
+            raise ValueError(
+                "Unable to avoid copy while creating an array as requested."
+            )
         return self.values
 
     def view(self, cls=None) -> Self: