diff --git a/arkouda/numpy/pdarrayclass.py b/arkouda/numpy/pdarrayclass.py
index c4eb8a2c972..9b224a838da 100644
--- a/arkouda/numpy/pdarrayclass.py
+++ b/arkouda/numpy/pdarrayclass.py
@@ -6,7 +6,7 @@
 from functools import reduce
 from math import ceil
 from sys import modules
-from typing import TYPE_CHECKING, List, Optional, Tuple, Union, cast, overload
+from typing import TYPE_CHECKING, List, Optional, Sequence, Tuple, Union, cast, overload
 
 import numpy as np
 
@@ -2644,7 +2644,17 @@ def bigint_to_uint_arrays(self) -> List[pdarray]:
         ret_list = json.loads(generic_msg(cmd=cmd, args={"array": self}))
         return list(reversed([create_pdarray(a) for a in ret_list]))
 
-    def reshape(self, *shape):
+    @overload
+    def reshape(self, shape: Sequence[int_scalars]) -> pdarray: ...
+
+    @overload
+    def reshape(self, *shape: int_scalars) -> pdarray: ...
+
+    @overload
+    def reshape(self, shape: pdarray) -> pdarray: ...
+
+    @typechecked
+    def reshape(self, *shape: Union[int_scalars, Sequence[int_scalars], pdarray]) -> pdarray:
         """
         Gives a new shape to an array without changing its data.
 
@@ -2678,21 +2688,40 @@ def reshape(self, *shape):
         # For example, a.reshape(10, 11) is equivalent to a.reshape((10, 11))
         # the lenshape variable addresses an error that occurred when a single integer was
         # passed
+        from typing import get_args
+
         from arkouda.client import generic_msg
 
+        shape_seq: Sequence[int_scalars]
+
         if len(shape) == 1:
-            shape = shape[0]
-            lenshape = 1
-        if (not isinstance(shape, int)) and (not isinstance(shape, pdarray)):
-            shape = [i for i in shape]
-            lenshape = len(shape)
+            arg = shape[0]
+
+            if isinstance(arg, get_args(int_scalars)):
+                shape_seq = (arg,)
+
+            elif isinstance(arg, Sequence):
+                shape_seq = tuple(arg)
+
+            elif isinstance(arg, pdarray):
+                shape_seq = cast(Sequence[int_scalars], arg.tolist())
+
+            else:
+                raise TypeError(f"Invalid shape argument {shape}")
+
+        else:
+            shape_seq = cast(Sequence[int_scalars], shape)
+
+        shape_arg: list[int_scalars] = list(shape_seq)
+
+        lenshape = len(shape_arg)
 
         return create_pdarray(
             generic_msg(
                 cmd=f"reshape<{self.dtype},{self.ndim},{lenshape}>",
                 args={
                     "name": self.name,
-                    "shape": shape,
+                    "shape": shape_arg,
                 },
             ),
             max_bits=self.max_bits,
diff --git a/tests/numpy/pdarrayclass_test.py b/tests/numpy/pdarrayclass_test.py
index 830bf641128..316764c327f 100644
--- a/tests/numpy/pdarrayclass_test.py
+++ b/tests/numpy/pdarrayclass_test.py
@@ -143,7 +143,7 @@ def test_flatten(self, size, dtype):
     def test_flatten_multidim(self, size, dtype):
         size = size - (size % 4)
         a = ak.arange(size, dtype=dtype)
-        b = a.reshape((2, 2, size / 4))
+        b = a.reshape((2, 2, size // 4))
         ak_assert_equal(b.flatten(), a)
 
     @pytest.mark.parametrize("size", pytest.prob_size)