diff --git a/tests/pyspark/test_pyspark_model.py b/tests/pyspark/test_pyspark_model.py
index d371fd24f..946467147 100644
--- a/tests/pyspark/test_pyspark_model.py
+++ b/tests/pyspark/test_pyspark_model.py
@@ -224,81 +224,103 @@ class Config:
     assert PanderaSchema.get_metadata() == expected
 
 
-@pytest.fixture
-def datamodel_unique_single_column() -> pa.DataFrameModel:
-    """Fixture containing DataFrameModel with optional columns."""
+# @pytest.fixture
+# def datamodel_unique_single_column() -> pa.DataFrameModel:
+#     """Fixture containing DataFrameModel with optional columns."""
 
-    class MyDataModel(pa.DataFrameModel):
-        """Simple DataFrameModel containing a column."""
+#     class MyDataModel(pa.DataFrameModel):
+#         """Simple DataFrameModel containing a column."""
 
-        a: T.LongType = pa.Field()
-        b: T.LongType = pa.Field()
+#         a: T.LongType = pa.Field()
+#         b: T.LongType = pa.Field()
 
-        class Config:
-            """Config class."""
+#         class Config:
+#             """Config class."""
 
-            unique = "a"
+#             unique = "a"
 
-    return MyDataModel
+#     return MyDataModel
 
 
-@pytest.fixture
-def datamodel_unique_multiple_columns() -> pa.DataFrameModel:
-    """Fixture containing DataFrameModel with optional columns."""
+# @pytest.fixture
+# def datamodel_unique_multiple_columns() -> pa.DataFrameModel:
+#     """Fixture containing DataFrameModel with optional columns."""
 
-    class MyDataModel(pa.DataFrameModel):
-        """Simple DataFrameModel containing a column."""
+#     class MyDataModel(pa.DataFrameModel):
+#         """Simple DataFrameModel containing a column."""
 
-        a: T.LongType = pa.Field()
-        b: T.LongType = pa.Field()
+#         a: T.LongType = pa.Field()
+#         b: T.LongType = pa.Field()
 
-        class Config:
-            """Config class."""
+#         class Config:
+#             """Config class."""
 
-            unique = ["a", "b"]
+#             unique = ["a", "b"]
 
-    return MyDataModel
+#     return MyDataModel
 
 
 @pytest.mark.parametrize(
-    "data_model, data, expectation",
+    "data, expectation",
     [
         (
-            datamodel_unique_single_column,
-            ([1, 4], [2, 5], [3, 6]),
+            (),
             does_not_raise(),
         ),
         (
-            datamodel_unique_multiple_columns,
             ([1, 4], [2, 5], [3, 6]),
             does_not_raise(),
         ),
         (
-            datamodel_unique_single_column,
-            ([0, 0], [0, 0], [3, 6]),
-            pytest.raises(pa.PysparkSchemaError),
-        ),
-        (
-            datamodel_unique_multiple_columns,
             ([0, 0], [0, 0], [3, 6]),
             pytest.raises(pa.PysparkSchemaError),
         ),
     ],
+    ids=["no_data", "non_duplicated_data", "duplicated_data"],
 )
-def test_dataframe_schema_unique(spark, data_model, data, expectation):
+def test_dataframe_schema_unique(spark, data, expectation):
     """Test uniqueness checks on pyspark dataframes."""
-    print(f"{type(spark)=}")
-    print(f"{type(data_model)=}")
-    print(f"{type(data)=}")
-    print(f"{type(expectation)=}")
 
-    df = spark.createDataFrame(data, ["a", "b"])
+    df = spark.createDataFrame(data, "a: int, b: int")
+
+    # Test `unique` configuration with a single column
+    class UniqueSingleColumn(pa.DataFrameModel):
+        """Simple DataFrameModel containing a column."""
+
+        a: T.IntegerType = pa.Field()
+        b: T.IntegerType = pa.Field()
+
+        class Config:
+            """Config class."""
+
+            unique = "a"
+
+    assert isinstance(UniqueSingleColumn(df), DataFrame)
+
+    with expectation:
+        df_out = UniqueSingleColumn.validate(check_obj=df)
+        if df_out.pandera.errors:
+            print(f"{df_out.pandera.errors=}")
+            raise pa.PysparkSchemaError
+
+    # Test `unique` configuration with multiple columns
+    class UniqueMultipleColumns(pa.DataFrameModel):
+        """Simple DataFrameModel containing a column."""
+
+        a: T.IntegerType = pa.Field()
+        b: T.IntegerType = pa.Field()
+
+        class Config:
+            """Config class."""
+
+            unique = ["a", "b"]
 
-    # assert isinstance(data_model(df), DataFrame)
+    assert isinstance(UniqueMultipleColumns(df), DataFrame)
 
     with expectation:
-        df_out = data_model.validate(check_obj=df)
+        df_out = UniqueMultipleColumns.validate(check_obj=df)
         if df_out.pandera.errors:
+            print(f"{df_out.pandera.errors=}")
             raise pa.PysparkSchemaError