diff --git a/quinn/dataframe_validator.py b/quinn/dataframe_validator.py
index 0d7df9c8..3c235834 100644
--- a/quinn/dataframe_validator.py
+++ b/quinn/dataframe_validator.py
@@ -53,7 +53,7 @@ def validate_presence_of_columns(df: DataFrame, required_col_names: list[str]) -
 def validate_schema(
     required_schema: StructType,
     ignore_nullable: bool = False,
-    _df: DataFrame = None,
+    df_to_be_validated: DataFrame = None,
 ) -> Callable[[Any, Any], Any]:
     """Function that validate if a given DataFrame has a given StructType as its schema.
     Implemented as a decorator factory so can be used both as a standalone function or as
@@ -64,9 +64,9 @@ def validate_schema(
     :param ignore_nullable: (Optional) A flag for if nullable fields should be
     ignored during validation
     :type ignore_nullable: bool, optional
-    :param _df: DataFrame to validate, mandatory when called as a function. Not required
+    :param df_to_be_validated: DataFrame to validate, mandatory when called as a function. Not required
     when called as a decorator
-    :type _df: DataFrame
+    :type df_to_be_validated: DataFrame
 
     :raises DataFrameMissingStructFieldError: if any StructFields from the required
     schema are not included in the DataFrame schema
@@ -96,12 +96,12 @@ def wrapper(*args: object, **kwargs: object) -> DataFrame:
             return dataframe
         return wrapper
 
-    if _df is None:
+    if df_to_be_validated is None:
         # This means the function is being used as a decorator
         return decorator
 
     # This means the function is being called directly with a DataFrame
-    return decorator(lambda: _df)()
+    return decorator(lambda: df_to_be_validated)()
 
 
 def validate_absence_of_columns(df: DataFrame, prohibited_col_names: list[str]) -> None:
diff --git a/tests/test_dataframe_validator.py b/tests/test_dataframe_validator.py
index 9d299392..a71a55f3 100644
--- a/tests/test_dataframe_validator.py
+++ b/tests/test_dataframe_validator.py
@@ -34,7 +34,7 @@ def it_raises_when_struct_field_is_missing1():
             ]
         )
         with pytest.raises(quinn.DataFrameMissingStructFieldError) as excinfo:
-            quinn.validate_schema(required_schema, _df=source_df)
+            quinn.validate_schema(required_schema, df_to_be_validated=source_df)
 
         current_spark_version = semver.Version.parse(spark.version)
         spark_330 = semver.Version.parse("3.3.0")
@@ -53,7 +53,7 @@ def it_does_nothing_when_the_schema_matches():
                 StructField("age", LongType(), True),
             ]
         )
-        quinn.validate_schema(required_schema, _df=source_df)
+        quinn.validate_schema(required_schema, df_to_be_validated=source_df)
 
     def nullable_column_mismatches_are_ignored():
         data = [("jose", 1), ("li", 2), ("luisa", 3)]
@@ -64,7 +64,7 @@ def nullable_column_mismatches_are_ignored():
                 StructField("age", LongType(), False),
             ]
         )
-        quinn.validate_schema(required_schema, ignore_nullable=True, _df=source_df)
+        quinn.validate_schema(required_schema, ignore_nullable=True, df_to_be_validated=source_df)
 
 
 def describe_validate_absence_of_columns():