From e6a25ab7beea79e33cbaa5478c7ba675a8179feb Mon Sep 17 00:00:00 2001
From: Khalim Conn-Kowlessar <kconnkowlessar@gmail.com>
Date: Tue, 26 Dec 2023 21:32:14 +0000
Subject: [PATCH] fixed missed cases and completed merge between assets and
 surveys

---
 etl/eligibility/ha_15_32/ha16_app.py | 24 ++++++++++++++++++++----
 1 file changed, 20 insertions(+), 4 deletions(-)

diff --git a/etl/eligibility/ha_15_32/ha16_app.py b/etl/eligibility/ha_15_32/ha16_app.py
index bc8fbfbd..402527df 100644
--- a/etl/eligibility/ha_15_32/ha16_app.py
+++ b/etl/eligibility/ha_15_32/ha16_app.py
@@ -193,6 +193,10 @@ def load_data():
                                                                                         "grasmere avenue, wardley")
     survey_list["Street / Block Name"] = survey_list["Street / Block Name"].str.replace("mardale avenue wardle",
                                                                                         "mardale avenue, wardle")
+    survey_list["Street / Block Name"] = survey_list["Street / Block Name"].str.replace("carleach grove",
+                                                                                        "cartleach Grove")
+    survey_list["Street / Block Name"] = survey_list["Street / Block Name"].str.replace("arbour grove",
+                                                                                        "arbor Grove")
 
     # Replacement for clively avenue 66-68
     survey_list["NO."] = np.where(
@@ -208,9 +212,6 @@ def load_data():
     matched = []
     for _, row in tqdm(survey_list.iterrows(), total=len(survey_list)):
 
-        if row["Street / Block Name"] in ["carleach grove", "arbour grove"]:
-            continue
-
         house_number = row["NO."]
         if isinstance(house_number, str):
             house_number = house_number.lower()
@@ -229,6 +230,21 @@ def load_data():
         matched.append(
             {
                 "survey_key": row["survey_key"],
-                "matched_address": df["Address"].values[0]
+                "matched_address": df["Address"].values[0],
+                "survey_house_no": row["NO."],
+                "survey_street_name": row["Street / Block Name"],
+                "survey_postcode": row["Post Code"],
+                "survey_status": row["INSTALLED OR CANCELLED"]
             }
         )
+
+    matched = pd.DataFrame(matched)
+    matched["warmfront_identified"] = True
+
+    # Combine asset list and surveys
+    data = asset_list.merge(
+        matched, how="left", left_on="Address", right_on="matched_address",
+    )
+    data["warmfront_identified"] = data["warmfront_identified"].fillna(False)
+
+    return data