Lint

pipeline/transform/join_epc_pp.py

@@ -128,9 +128,7 @@ def main():
 
     # Social tenure fork: flag properties that were ever social housing
     social_tenure = (
-        epc_base.filter(
-            pl.col("TENURE").str.to_lowercase().str.contains("social")
-        )
+        epc_base.filter(pl.col("TENURE").str.to_lowercase().str.contains("social"))
         .select("epc_address", "POSTCODE")
         .unique()
         .with_columns(pl.lit("Yes").alias("was_council_house"))
@@ -139,16 +137,20 @@ def main():
     print(f"Former council houses (EPC social tenure): {social_tenure.height}")
 
     # Left-join events and social tenure back onto dedup EPC
-    epc = epc.join(
-        events.lazy(),
-        on=["epc_address", "POSTCODE"],
-        how="left",
-    ).join(
-        social_tenure.lazy(),
-        on=["epc_address", "POSTCODE"],
-        how="left",
-    ).with_columns(
-        pl.col("was_council_house").fill_null("No"),
+    epc = (
+        epc.join(
+            events.lazy(),
+            on=["epc_address", "POSTCODE"],
+            how="left",
+        )
+        .join(
+            social_tenure.lazy(),
+            on=["epc_address", "POSTCODE"],
+            how="left",
+        )
+        .with_columns(
+            pl.col("was_council_house").fill_null("No"),
+        )
     )
 
     print("EPC dataset")

pipeline/transform/transform_poi.py

@@ -1092,6 +1092,7 @@ GROCERY_FASCIA_ICON_NAMES: dict[str, str] = {
     "Asda Living": "Asda Living",
     "Asda PFS": "Asda PFS",
     "Cooltrader": "Heron Foods",
+    "Co-op Food": "Co-op",
     "Cook": "COOK",
     "Eurospar": "Spar",
     "Eurospar PFS": "Spar",
@@ -1144,9 +1145,7 @@ def transform_grocery_retail_points(
     required = {"id", "retailer", "fascia", "store_name", "long_wgs", "lat_wgs"}
     missing = required - set(grocery_df.columns)
     if missing:
-        raise ValueError(
-            f"GEOLYTIX retail points missing columns: {sorted(missing)}"
-        )
+        raise ValueError(f"GEOLYTIX retail points missing columns: {sorted(missing)}")
 
     df = (
         grocery_df.select(