working on matching lookup

etl/customers/stonewater/Wave 3 Preparation.py

@@ -5,7 +5,8 @@ import pandas as pd
 from tqdm import tqdm
 from collections import Counter
 
-FILE_PATH = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Stonewater/Wave 3 Surveys"
+CUSTOMER_FOLDER_PATH = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Stonewater"
+FILE_PATH = os.path.join(CUSTOMER_FOLDER_PATH, "Wave 3 Surveys")
 
 
 def extract_summary_report(pdf_path):
@@ -653,6 +654,51 @@ def main():
         extracted_data["Primary Energy Use Intensity (kWh/m2/yr)"] * extracted_data["Total Floor Area (m2)"]
     )
 
+    # We now merge on the coordinator data so that against each property, we can map the measures
+    retrofit_packages_board = pd.read_excel(
+        os.path.join(CUSTOMER_FOLDER_PATH, "Stonewater_SHDF_3_0_Board_work_in_progress_- 22.10.24.xlsx"),
+        header=4
+    )
+    retrofit_packages_board = retrofit_packages_board[~pd.isnull(retrofit_packages_board["Name"])]
+    # We now match this retrofit packages board to the extracted data
+    matching_lookup = []
+    for _, home in retrofit_packages_board.iterrows():
+        filtered = extracted_data[extracted_data["Postcode"].str.lower() == home["Postcode"].lower()]
+        if filtered.empty:
+            print("Check this once we have full data")
+            continue
+
+        if filtered.shape[0] == 1:
+            matching_lookup.append(
+                {
+                    "survey_folder": filtered["survey_folder"].values[0],
+                    "Osm. ID": home["Osm. ID"],
+                    "Name": home["Name"]
+                }
+            )
+            continue
+
+        # home["Name"] should be contained in the survey_folder
+        filtered = filtered[filtered["survey_folder"].str.contains(home["Name"], case=False)]
+        # We have an edge case wher some properties have two outputs in Sharepoint
+        if home["Name"] == "197 Granby Court" and home["Postcode"] == "MK1 1NQ":
+            filtered = filtered[filtered["survey_folder"] == "113-1-197 Granby Court-MK1 1NQ"]
+
+        if filtered.empty:
+            raise Exception("somethign went wrong")
+        if filtered.shape[0] != 1:
+            raise Exception("somethign went wrong2")
+
+        matching_lookup.append(
+            {
+                "survey_folder": filtered["survey_folder"].values[0],
+                "Osm. ID": home["Osm. ID"],
+                "Name": home["Name"]
+            }
+        )
+
+    matching_lookup = pd.DataFrame(matching_lookup)
+
     # Save this as a csv
     # extracted_data.to_csv("Wave 3 Summary Data - first 200 files.csv", index=False)
 

etl/customers/stonewater/requirements/requirements-wave-3-prep.txt

@@ -1,3 +1,4 @@
 PyPDF2
 pandas
 tqdm
+openpyxl