diff --git a/pandas/tests/series/methods/test_drop_duplicates.py b/pandas/tests/series/methods/test_drop_duplicates.py
index 31ef8ff896bccd..493b3e900c2f7f 100644
--- a/pandas/tests/series/methods/test_drop_duplicates.py
+++ b/pandas/tests/series/methods/test_drop_duplicates.py
@@ -75,10 +75,22 @@ class TestSeriesDropDuplicates:
         params=["int_", "uint", "float64", "str_", "timedelta64[h]", "datetime64[D]"]
     )
     def dtype(self, request):
+        """
+        Fixture that provides different data types for testing.
+        The parameterized fixture returns various numpy data types including
+        integer, unsigned integer, float, string, timedelta, and datetime.
+        """
         return request.param
 
     @pytest.fixture
     def cat_series_unused_category(self, dtype, ordered):
+        """
+        Fixture that creates a Categorical Series with some unused categories.
+        This fixture creates a Categorical Series based on the given dtype and 
+        ordered parameters. The input series contains some categories that are 
+        not used in the actual data, allowing the testing of categorical 
+        behavior with unused categories.
+        """
         # Test case 1
         cat_array = np.array([1, 2, 3, 4, 5], dtype=np.dtype(dtype))
 
@@ -141,7 +153,13 @@ def test_drop_duplicates_categorical_non_bool_keepfalse(
 
     @pytest.fixture
     def cat_series(self, dtype, ordered):
-        # no unused categories, unlike cat_series_unused_category
+        """
+        Fixture that creates a Categorical Series with no unused categories.
+        This fixture creates a Categorical Series based on the given dtype and 
+        ordered parameters. The input series contains categories that are all 
+        used in the actual data, allowing the testing of categorical behavior 
+        without unused categories.
+        """
         cat_array = np.array([1, 2, 3, 4, 5], dtype=np.dtype(dtype))
 
         input2 = np.array([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype(dtype))