Fix linting errors

Author: nicolasazrak

python/pyspark/sql/tests/test_arrow.py

@@ -414,13 +414,15 @@ def test_createDataFrame_with_map_type(self):
     def test_createDataFrame_with_string_dtype(self):
         # SPARK-34521: spark.createDataFrame does not support Pandas StringDtype extension type
         with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": True}):
-            pandas_df = pd.DataFrame([["abc"], ["def"], [None], ["ghi"], [None]], columns=["col"], dtype="string")
+            data = [["abc"], ["def"], [None], ["ghi"], [None]]
+            pandas_df = pd.DataFrame(data, columns=["col"], dtype="string")
             schema = StructType([StructField("col", StringType(), True)])
             df = self.spark.createDataFrame(pandas_df, schema=schema)
 
-            # dtypes won't match. Pandas has two different ways to store string columns: using ndarray (when dtype isn't
-            # specified) or using a StringArray when dtype="string". When calling dataframe#toPandas() it will use the
-            # ndarray version. Changing that to use a StringArray would be backwards incompatible.
+            # dtypes won't match. Pandas has two different ways to store string columns:
+            # using ndarray (when dtype isn't specified) or using a StringArray when dtype="string".
+            # When calling dataframe#toPandas() it will use the ndarray version.
+            # Changing that to use a StringArray would be backwards incompatible.
             assert_frame_equal(pandas_df, df.toPandas(), check_dtype=False)
 
     def test_toPandas_with_map_type(self):