diff --git a/python/pyspark/sql/pandas/serializers.py b/python/pyspark/sql/pandas/serializers.py
index 15b9f2fdeb0f0..42562e1fb9c46 100644
--- a/python/pyspark/sql/pandas/serializers.py
+++ b/python/pyspark/sql/pandas/serializers.py
@@ -143,10 +143,7 @@ def _create_batch(self, series):
         import pandas as pd
         import pyarrow as pa
         from pyspark.sql.pandas.types import _check_series_convert_timestamps_internal
-        try:
-            from pandas import CategoricalDtype
-        except ImportError:
-            from pandas.api.types import CategoricalDtype
+        from pandas.api.types import is_categorical
         # Make input conform to [(series1, type1), (series2, type2), ...]
         if not isinstance(series, (list, tuple)) or \
                 (len(series) == 2 and isinstance(series[1], pa.DataType)):
@@ -158,7 +155,7 @@ def create_array(s, t):
             # Ensure timestamp series are in expected form for Spark internal representation
             if t is not None and pa.types.is_timestamp(t):
                 s = _check_series_convert_timestamps_internal(s, self._timezone)
-            elif type(s.dtype) == CategoricalDtype:
+            elif is_categorical(s.dtype):
                 # Note: This can be removed once minimum pyarrow version is >= 0.16.1
                 s = s.astype(s.dtypes.categories.dtype)
             try: