add handling of empty responses

src/delta_barth/analysis/forecast.py

@@ -11,6 +11,7 @@ from xgboost import XGBRegressor
 from delta_barth.analysis import parse
 from delta_barth.api.requests import (
     SalesPrognosisResponse,
+    SalesPrognosisResponseEntry,
     SalesPrognosisResults,
     SalesPrognosisResultsExport,
     get_sales_prognosis_data,
@@ -50,6 +51,10 @@ def _parse_api_resp_to_df(
     """
     data = resp.model_dump()["daten"]
 
+    if not data:
+        target_features = SalesPrognosisResponseEntry.__annotations__.keys()
+        data = {feat: [] for feat in target_features}
+
     return pd.DataFrame(data)
 
 

tests/analysis/test_forecast.py

@@ -8,6 +8,7 @@ from pydantic import ValidationError
 
 import delta_barth.analysis.forecast
 from delta_barth.analysis import forecast as fc
+from delta_barth.api.requests import SalesPrognosisResponse, SalesPrognosisResponseEntry
 from delta_barth.errors import STATUS_HANDLER
 from delta_barth.types import DualDict, PipeResult
 
@@ -102,16 +103,14 @@ def sales_data_real_preproc(sales_data_real, feature_map) -> pd.DataFrame:
 def test_parse_api_resp_to_df(exmpl_api_sales_prognosis_resp):
     resp = exmpl_api_sales_prognosis_resp
     df = fc._parse_api_resp_to_df(resp)
-    features = set(
-        (
-            "artikelId",
-            "warengruppeId",
-            "firmaId",
-            "betrag",
-            "menge",
-            "buchungsDatum",
-        )
-    )
+    features = set(SalesPrognosisResponseEntry.__annotations__.keys())
+    assert all(col in features for col in df.columns)
+
+
+def test_parse_api_resp_to_df_empty():
+    resp = SalesPrognosisResponse(daten=tuple())
+    df = fc._parse_api_resp_to_df(resp)
+    features = set(SalesPrognosisResponseEntry.__annotations__.keys())
     assert all(col in features for col in df.columns)