diff --git a/dataprofiler/profilers/categorical_column_profile.py b/dataprofiler/profilers/categorical_column_profile.py
index 03263aa1..5393d5d2 100644
--- a/dataprofiler/profilers/categorical_column_profile.py
+++ b/dataprofiler/profilers/categorical_column_profile.py
@@ -476,7 +476,7 @@ def _check_stop_condition_is_met(self, sample_size: int, unqiue_ratio: float):
             return True
         return False
 
-    def _update_stop_condition(self, data: DataFrame):
+    def _update_stop_condition(self, data: DataFrame | pl.DataFrame):
         """Return value stop_condition_is_met given stop conditions.
 
         :param data: Dataframe currently being processed by categorical profiler
@@ -499,8 +499,8 @@ def _get_categories_cms(self, df_series, len_df):
         """Return count min sketch and heavy hitters for both the batch and stream case.
 
         :param df_series: Series currently being processed by categorical profiler
-        :type df_series: Series
-        :param len_df: the total number of samples iin df_series
+        :type df_series: polars.Series
+        :param len_df: the total number of samples in df_series
         :type len_df: int
         :return: cms, heavy_hitter_dict, missing_heavy_hitter_dict
         """
@@ -603,13 +603,13 @@ def _get_categories_full(self, df_series) -> dict:
         :return: dict of counts for each unique value
         :rtype: dict
         """
-        category_count: dict = dict(df_series.value_counts(sort=True).iter_rows())
+        category_count: dict = Series(df_series).value_counts(dropna=False).to_dict()
         return category_count
 
     @BaseColumnProfiler._timeit(name="categories")
     def _update_categories(
         self,
-        df_series: DataFrame,
+        df_series: DataFrame | pl.DataFrame,
         prev_dependent_properties: dict = None,
         subset_properties: dict = None,
     ) -> None:
@@ -659,7 +659,9 @@ def _update_categories(
             if self._stop_condition_is_met:
                 self._categories = {}
 
-    def _update_helper(self, df_series_clean: Series, profile: dict) -> None:
+    def _update_helper(
+        self, df_series_clean: Series | pl.Series, profile: dict
+    ) -> None:
         """
         Update col profile properties with clean dataset and its known profile.
 
@@ -671,7 +673,7 @@ def _update_helper(self, df_series_clean: Series, profile: dict) -> None:
         """
         self._update_column_base_properties(profile)
 
-    def update(self, df_series: Series) -> CategoricalColumn:
+    def update(self, df_series: pl.Series | Series) -> CategoricalColumn:
         """
         Update the column profile.
 
@@ -687,12 +689,17 @@ def update(self, df_series: Series) -> CategoricalColumn:
         if len(df_series) == 0 or self._stop_condition_is_met:
             return self
 
+        if isinstance(df_series, pl.Series):
+            pandas_df = df_series.to_pandas()
+        else:
+            pandas_df = df_series
+
         profile = dict(sample_size=len(df_series))
         CategoricalColumn._update_categories(self, df_series)
         BaseColumnProfiler._perform_property_calcs(
             self,
             self.__calculations,
-            df_series=df_series,
+            df_series=pandas_df,
             prev_dependent_properties={},
             subset_properties=profile,
         )
diff --git a/dataprofiler/tests/profilers/test_categorical_column_profile.py b/dataprofiler/tests/profilers/test_categorical_column_profile.py
index aac6486e..27ffc913 100644
--- a/dataprofiler/tests/profilers/test_categorical_column_profile.py
+++ b/dataprofiler/tests/profilers/test_categorical_column_profile.py
@@ -1181,7 +1181,7 @@ def test_fewer_than_MAXIMUM_UNIQUE_VALUES_TO_CLASSIFY_AS_CATEGORICAL(self):
         ]
 
         len_unique = len(set(cat_sentence_list))
-        cat_sentence_df = pd.Series(cat_sentence_list)
+        cat_sentence_df = pl.Series(cat_sentence_list)
         column_profile = StructuredColProfiler(cat_sentence_df)
         cat_profiler = column_profile.profiles["data_stats_profile"]._profiles[
             "category"
@@ -1206,7 +1206,7 @@ def test_greater_than_CATEGORICAL_THRESHOLD_DEFAULT_identify_as_text(self):
         )
         cat_sentence_list = list_unique_values * num_sentences
 
-        cat_sentence_df = pd.Series(cat_sentence_list)
+        cat_sentence_df = pl.Series(cat_sentence_list)
         column_profile = StructuredColProfiler(cat_sentence_df)
         cat_profiler = column_profile.profiles["data_stats_profile"]._profiles[
             "category"
@@ -1233,7 +1233,7 @@ def test_less_than_CATEGORICAL_THRESHOLD_DEFAULT(self):
         cat_sentence_list = list_unique_values * num_sentences
 
         len_unique = len(set(cat_sentence_list))
-        cat_sentence_df = pd.Series(cat_sentence_list)
+        cat_sentence_df = pl.Series(cat_sentence_list)
         column_profile = StructuredColProfiler(cat_sentence_df)
         cat_profiler = column_profile.profiles["data_stats_profile"]._profiles[
             "category"
@@ -1263,7 +1263,7 @@ def test_uppercase_less_than_CATEGORICAL_THRESHOLD_DEFAULT(self):
         cat_sentence_list[-3] = self.test_sentence_upper3 + str(num_sentences - 2)
 
         len_unique = len(set(cat_sentence_list))
-        cat_sentence_df = pd.Series(cat_sentence_list)
+        cat_sentence_df = pl.Series(cat_sentence_list)
         column_profile = StructuredColProfiler(cat_sentence_df)
         cat_profiler = column_profile.profiles["data_stats_profile"]._profiles[
             "category"
@@ -1288,7 +1288,7 @@ def test_long_sentences_fewer_than_MAXIMUM_UNIQUE_VALUES_TO_CLASSIFY_AS_CATEGORI
         ]
 
         len_unique = len(set(cat_sentence_list))
-        cat_sentence_df = pd.Series(cat_sentence_list)
+        cat_sentence_df = pl.Series(cat_sentence_list)
         column_profile = StructuredColProfiler(cat_sentence_df)
         cat_profiler = column_profile.profiles["data_stats_profile"]._profiles[
             "category"