🚀 Raise ValueError if response is empty in CouldForCustomers source

CHANGELOG.md

@@ -10,7 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Fixed
 
 ### Changed
-
+- Changed `CloudForCustomers` methods: `_to_records_report` and `_to_records_other` to rasie ValueError if response from data source is empty 
 ### Removed
 
 

tests/integration/test_cloud_for_customers.py

@@ -1,4 +1,6 @@
+import pytest
 from viadot.sources import CloudForCustomers
+from unittest import mock
 
 
 def test_credentials():
@@ -8,3 +10,29 @@ def test_credentials():
         fields=["ProductRecipientPartyName", "CreationDateTime", "CreatedBy"]
     )
     assert df.empty == False
+
+
+class MockResponse:
+    def __init__(
+        self,
+    ):
+        self.json_data = {"d": {"results": []}}
+        self.status_code = 200
+
+    def json(self):
+        return self.json_data
+
+
+@mock.patch(
+    "viadot.sources.CloudForCustomers.get_response", return_value=MockResponse()
+)
+def test_c4c_empty_response(mock_get):
+    with pytest.raises(
+        ValueError,
+        match="Response from the cloud for customers for specified parameters is empty!",
+    ):
+        endpoint = "ServiceRequestCollection"
+        c4c = CloudForCustomers(endpoint=endpoint, params={"$top": "2"})
+        df = c4c.to_df(
+            fields=["ProductRecipientPartyName", "CreationDateTime", "CreatedBy"]
+        )

viadot/sources/cloud_for_customers.py

@@ -90,7 +90,6 @@ def _to_records_report(self, url: str) -> List[Dict[str, Any]]:
             records.extend(new_records)
 
             url = response_json["d"].get("__next")
-
         return records
 
     def _to_records_other(self, url: str) -> List[Dict[str, Any]]:
@@ -222,6 +221,11 @@ def to_df(
         """
         records = self.to_records()
         df = pd.DataFrame(data=records, **kwargs)
+
+        if df.empty:
+            raise ValueError(
+                "Response from cloud for customers for specified parameters were empty!"
+            )
         if dtype:
             df = df.astype(dtype)
         if fields: