Closes #5335: Bug: pd.array fails on Arkouda-backed Categorical with dtype specified

arkouda/pandas/extension/_arkouda_array.py

@@ -111,11 +111,13 @@ def __init__(
 
     @classmethod
     def _from_sequence(cls, scalars, dtype=None, copy=False):
+        from arkouda.numpy.numeric import cast as ak_cast
+        from arkouda.numpy.pdarrayclass import pdarray
         from arkouda.numpy.pdarraycreation import array as ak_array
+        from arkouda.pandas.categorical import Categorical
 
         from ._dtypes import ArkoudaBigintDtype
 
-        # normalize dtype input
         if (
             dtype is not None
             and (
@@ -130,7 +132,21 @@ def _from_sequence(cls, scalars, dtype=None, copy=False):
         if dtype is not None and hasattr(dtype, "numpy_dtype"):
             dtype = dtype.numpy_dtype
 
-        # If scalars is already a numpy array, we can preserve its dtype
+        if isinstance(scalars, Categorical):
+            codes = scalars.codes
+
+            # Some implementations might return an ArkoudaArray here
+            if isinstance(codes, ArkoudaArray):
+                codes = codes._data
+
+            if not isinstance(codes, pdarray):
+                raise TypeError(f"Categorical.codes expected pdarray, got {type(codes).__name__}")
+
+            if dtype is not None:
+                codes = ak_cast(codes, dtype)
+
+            return cls(codes)
+
         return cls(ak_array(scalars, dtype=dtype, copy=copy))
 
     def __getitem__(self, key: Any) -> Any:

tests/pandas/extension/arkouda_categorical_extension.py

@@ -185,6 +185,30 @@ def test_categorical_isna_and_isnull(self):
             assert np.array_equal(out_isna, expected)
             assert np.array_equal(out_isnull, expected)
 
+    def test_pd_array_with_dtype_on_ak_categorical_should_not_iterate(self):
+        """
+        Reproducer for: #5335 pd.array on an Arkouda-backed Categorical fails when dtype is provided.
+
+        Expected: should succeed (without iterating the Categorical) and produce an ak_int64
+        array representing the categorical codes.
+        """
+        cat = Categorical(ak.array(["a", "a", "b"]))
+
+        expected = cat.codes.to_ndarray()
+
+        result = pd.array(cat, dtype="ak_int64")
+
+        assert len(result) == len(expected)
+
+        if hasattr(result, "to_ndarray"):
+            got = result.to_ndarray()
+        elif hasattr(result, "to_numpy"):
+            got = result.to_numpy()
+        else:
+            got = np.asarray(result)
+
+        assert np.array_equal(got, expected)
+
 
 class TestArkoudaCategoricalAsType:
     def test_categorical_array_astype_category_stays_extension(