diff --git a/python/pyspark/pandas/categorical.py b/python/pyspark/pandas/categorical.py
index 77a3cee76c..fa11228543 100644
--- a/python/pyspark/pandas/categorical.py
+++ b/python/pyspark/pandas/categorical.py
@@ -22,6 +22,7 @@ from pandas.api.types import CategoricalDtype, is_dict_like, is_list_like
 
 from pyspark.pandas.internal import InternalField
 from pyspark.pandas.spark import functions as SF
+from pyspark.pandas.data_type_ops.categorical_ops import _to_cat
 from pyspark.sql import functions as F
 from pyspark.sql.types import StructField
 
@@ -735,7 +736,7 @@ class CategoricalAccessor(object):
                 return self._data.copy()
         else:
             dtype = CategoricalDtype(categories=new_categories, ordered=ordered)
-            psser = self._data.astype(dtype)
+            psser = _to_cat(self._data).astype(dtype)
 
             if inplace:
                 internal = self._data._psdf._internal.with_new_spark_column(
diff --git a/python/pyspark/pandas/data_type_ops/categorical_ops.py b/python/pyspark/pandas/data_type_ops/categorical_ops.py
index b524cddb6b..c1be683dda 100644
--- a/python/pyspark/pandas/data_type_ops/categorical_ops.py
+++ b/python/pyspark/pandas/data_type_ops/categorical_ops.py
@@ -57,7 +57,9 @@ class CategoricalOps(DataTypeOps):
     def astype(self, index_ops: IndexOpsLike, dtype: Union[str, type, Dtype]) -> IndexOpsLike:
         dtype, _ = pandas_on_spark_type(dtype)
 
-        if isinstance(dtype, CategoricalDtype) and cast(CategoricalDtype, dtype).categories is None:
+        if isinstance(dtype, CategoricalDtype) and (
+            (dtype.categories is None) or (index_ops.dtype == dtype)
+        ):
             return index_ops.copy()
 
         return _to_cat(index_ops).astype(dtype)
diff --git a/python/pyspark/pandas/tests/data_type_ops/test_categorical_ops.py b/python/pyspark/pandas/tests/data_type_ops/test_categorical_ops.py
index 11871ea2ba..5e79eb3682 100644
--- a/python/pyspark/pandas/tests/data_type_ops/test_categorical_ops.py
+++ b/python/pyspark/pandas/tests/data_type_ops/test_categorical_ops.py
@@ -192,13 +192,11 @@ class CategoricalOpsTest(PandasOnSparkTestCase, TestCasesUtils):
         self.assert_eq(pser.astype("category"), psser.astype("category"))
 
         cat_type = CategoricalDtype(categories=[3, 1, 2])
+        # CategoricalDtype is not updated if the dtype is same from pandas 1.3.
         if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
-            # TODO(SPARK-36367): Fix the behavior to follow pandas >= 1.3
-            pass
-        elif LooseVersion(pd.__version__) >= LooseVersion("1.2"):
             self.assert_eq(pser.astype(cat_type), psser.astype(cat_type))
         else:
-            self.assert_eq(pd.Series(data).astype(cat_type), psser.astype(cat_type))
+            self.assert_eq(psser.astype(cat_type), pser)
 
     def test_neg(self):
         self.assertRaises(TypeError, lambda: -self.psser)
diff --git a/python/pyspark/pandas/tests/indexes/test_category.py b/python/pyspark/pandas/tests/indexes/test_category.py
index 652036330d..69d4667c83 100644
--- a/python/pyspark/pandas/tests/indexes/test_category.py
+++ b/python/pyspark/pandas/tests/indexes/test_category.py
@@ -172,25 +172,23 @@ class CategoricalIndexTest(PandasOnSparkTestCase, TestUtils):
         )
 
         pcidx = pidx.astype(CategoricalDtype(["c", "a", "b"]))
-        kcidx = psidx.astype(CategoricalDtype(["c", "a", "b"]))
+        pscidx = psidx.astype(CategoricalDtype(["c", "a", "b"]))
 
-        self.assert_eq(kcidx.astype("category"), pcidx.astype("category"))
+        self.assert_eq(pscidx.astype("category"), pcidx.astype("category"))
 
+        # CategoricalDtype is not updated if the dtype is same from pandas 1.3.
         if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
-            # TODO(SPARK-36367): Fix the behavior to follow pandas >= 1.3
-            pass
-        elif LooseVersion(pd.__version__) >= LooseVersion("1.2"):
             self.assert_eq(
-                kcidx.astype(CategoricalDtype(["b", "c", "a"])),
+                pscidx.astype(CategoricalDtype(["b", "c", "a"])),
                 pcidx.astype(CategoricalDtype(["b", "c", "a"])),
             )
         else:
             self.assert_eq(
-                kcidx.astype(CategoricalDtype(["b", "c", "a"])),
-                pidx.astype(CategoricalDtype(["b", "c", "a"])),
+                pscidx.astype(CategoricalDtype(["b", "c", "a"])),
+                pcidx,
             )
 
-        self.assert_eq(kcidx.astype(str), pcidx.astype(str))
+        self.assert_eq(pscidx.astype(str), pcidx.astype(str))
 
     def test_factorize(self):
         pidx = pd.CategoricalIndex([1, 2, 3, None])
diff --git a/python/pyspark/pandas/tests/test_categorical.py b/python/pyspark/pandas/tests/test_categorical.py
index 1335d59d77..1fb0d5862a 100644
--- a/python/pyspark/pandas/tests/test_categorical.py
+++ b/python/pyspark/pandas/tests/test_categorical.py
@@ -239,25 +239,23 @@ class CategoricalTest(PandasOnSparkTestCase, TestUtils):
         )
 
         pcser = pser.astype(CategoricalDtype(["c", "a", "b"]))
-        kcser = psser.astype(CategoricalDtype(["c", "a", "b"]))
+        pscser = psser.astype(CategoricalDtype(["c", "a", "b"]))
 
-        self.assert_eq(kcser.astype("category"), pcser.astype("category"))
+        self.assert_eq(pscser.astype("category"), pcser.astype("category"))
 
+        # CategoricalDtype is not updated if the dtype is same from pandas 1.3.
         if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
-            # TODO(SPARK-36367): Fix the behavior to follow pandas >= 1.3
-            pass
-        elif LooseVersion(pd.__version__) >= LooseVersion("1.2"):
             self.assert_eq(
-                kcser.astype(CategoricalDtype(["b", "c", "a"])),
+                pscser.astype(CategoricalDtype(["b", "c", "a"])),
                 pcser.astype(CategoricalDtype(["b", "c", "a"])),
             )
         else:
             self.assert_eq(
-                kcser.astype(CategoricalDtype(["b", "c", "a"])),
-                pser.astype(CategoricalDtype(["b", "c", "a"])),
+                pscser.astype(CategoricalDtype(["b", "c", "a"])),
+                pcser,
             )
 
-        self.assert_eq(kcser.astype(str), pcser.astype(str))
+        self.assert_eq(pscser.astype(str), pcser.astype(str))
 
     def test_factorize(self):
         pser = pd.Series(["a", "b", "c", None], dtype=CategoricalDtype(["c", "a", "d", "b"]))