diff --git a/haystack/utils/squad_data.py b/haystack/utils/squad_data.py
index de847fd83..f725a7c0a 100644
--- a/haystack/utils/squad_data.py
+++ b/haystack/utils/squad_data.py
@@ -171,7 +171,8 @@ class SquadData:
                                 c += 1
         return c
 
-    def df_to_data(self, df):
+    @classmethod
+    def df_to_data(cls, df):
         """
         Convert a dataframe into SQuAD format data (list of SQuAD document dictionaries).
         """
@@ -183,7 +184,7 @@ class SquadData:
         df_aggregated_answers = (
             df[["title", "context", "question", "id", "is_impossible"]].drop_duplicates().reset_index()
         )
-        answers = df_grouped_answers.progress_apply(self._aggregate_answers).rename("answers")
+        answers = df_grouped_answers.progress_apply(cls._aggregate_answers).rename("answers")
         answers = pd.DataFrame(answers).reset_index()
         df_aggregated_answers = pd.merge(df_aggregated_answers, answers)
 
@@ -191,14 +192,14 @@ class SquadData:
         logger.info("Aggregating the questions of each paragraphs of each document")
         df_grouped_questions = df_aggregated_answers.groupby(["title", "context"])
         df_aggregated_questions = df[["title", "context"]].drop_duplicates().reset_index()
-        questions = df_grouped_questions.progress_apply(self._aggregate_questions).rename("qas")
+        questions = df_grouped_questions.progress_apply(cls._aggregate_questions).rename("qas")
         questions = pd.DataFrame(questions).reset_index()
         df_aggregated_questions = pd.merge(df_aggregated_questions, questions)
 
         logger.info("Aggregating the paragraphs of each document")
         df_grouped_paragraphs = df_aggregated_questions.groupby(["title"])
         df_aggregated_paragraphs = df[["title"]].drop_duplicates().reset_index()
-        paragraphs = df_grouped_paragraphs.progress_apply(self._aggregate_passages).rename("paragraphs")
+        paragraphs = df_grouped_paragraphs.progress_apply(cls._aggregate_passages).rename("paragraphs")
         paragraphs = pd.DataFrame(paragraphs).reset_index()
         df_aggregated_paragraphs = pd.merge(df_aggregated_paragraphs, paragraphs)
 
diff --git a/test/test_utils.py b/test/test_utils.py
index c3a5fe385..d6b10d6b7 100644
--- a/test/test_utils.py
+++ b/test/test_utils.py
@@ -1,4 +1,5 @@
 import pytest
+import pandas as pd
 from pathlib import Path
 
 from haystack.utils.preprocessing import convert_files_to_dicts, tika_convert_files_to_dicts
@@ -38,3 +39,33 @@ def test_squad_augmentation():
     original_squad = SquadData.from_file(input_)
     augmented_squad = SquadData.from_file(output)
     assert original_squad.count(unit="paragraph") == augmented_squad.count(unit="paragraph") * multiplication_factor
+
+
+def test_squad_to_df():
+    df = pd.DataFrame(
+        [["title", "context", "question", "id", "answer", 1, False]],
+        columns=["title", "context", "question", "id", "answer_text", "answer_start", "is_impossible"],
+    )
+
+    expected_result = [
+        {
+            "title": "title",
+            "paragraphs": [
+                {
+                    "context": "context",
+                    "qas": [
+                        {
+                            "question": "question",
+                            "id": "id",
+                            "answers": [{"text": "answer", "answer_start": 1}],
+                            "is_impossible": False,
+                        }
+                    ],
+                }
+            ],
+        }
+    ]
+
+    result = SquadData.df_to_data(df)
+
+    assert result == expected_result