add support for copy=True as well

python/pyarrow/array.pxi

@@ -1530,10 +1530,15 @@ cdef class Array(_PandasConvertible):
             # values is already a numpy array at this point, but calling np.array(..)
             # again to handle the `dtype` keyword with a no-copy guarantee
             return np.array(values, dtype=dtype, copy=False)
+
         values = self.to_numpy(zero_copy_only=False)
+        if copy is True and is_primitive(self.type.id) and self.null_count == 0:
+            # to_numpy did not yet make a copy
+            return np.array(values, dtype=dtype, copy=True)
+
         if dtype is None:
             return values
-        return values.astype(dtype)
+        return np.asarray(values, dtype=dtype)
 
     def to_numpy(self, zero_copy_only=True, writable=False):
         """

python/pyarrow/includes/libarrow.pxd

@@ -173,6 +173,7 @@ cdef extern from "arrow/api.h" namespace "arrow" nogil:
         c_string ToString()
 
     c_bool is_primitive(Type type)
+    c_bool is_numeric(Type type)
 
     cdef cppclass CArrayData" arrow::ArrayData":
         shared_ptr[CDataType] type

python/pyarrow/tests/test_array.py

@@ -3339,6 +3339,15 @@ def test_numpy_array_protocol():
         with pytest.raises(ValueError):
             np.array(arr, dtype="float64", copy=False)
 
+    # copy=True -> not yet passed by numpy, so we have to call this directly to test
+    arr = pa.array([1, 2, 3])
+    result = arr.__array__(copy=True)
+    assert result.flags.writeable
+
+    arr = pa.array([1, 2, 3])
+    result = arr.__array__(dtype=np.dtype("float64"), copy=True)
+    assert result.dtype == "float64"
+
 
 def test_array_protocol():