Rank historic EPC records by address similarity 🟩

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

datatypes/epc/domain/historic_epc_matching.py

@@ -66,7 +66,31 @@ def rank_historic_epc(
     """Score ``records`` against ``user_address`` (best first), keeping every
     record — including hard-zero non-matches. The pure scoring half of the
     historic-EPC lookup: no I/O, so it is unit-testable without S3."""
-    raise NotImplementedError
+    if not user_address:
+        raise ValueError("user_address must be non-empty")
+    if not records:
+        return []
+
+    df = pd.DataFrame(
+        {
+            address_column: [r.address for r in records],
+            uprn_column: [r.uprn for r in records],
+        }
+    )
+    scored = rank_address_similarity(
+        df,
+        user_address=user_address,
+        address_column=address_column,
+        uprn_column=uprn_column,
+    )
+    return [
+        ScoredHistoricEpc(
+            record=records[i],
+            lexiscore=float(row["lexiscore"]),
+            lexirank=int(row["lexirank"]),
+        )
+        for i, row in scored.iterrows()
+    ]
 
 
 def _sanitise_postcode(postcode: str) -> str: