mirror of
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148 lines
4.9 KiB
Python
148 lines
4.9 KiB
Python
import os
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import pandas as pd
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from tqdm import tqdm
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from dotenv import load_dotenv
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from backend.SearchEpc import SearchEpc
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from etl.epc_clean.epc_attributes.RoofAttributes import RoofAttributes
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from recommendations.recommendation_utils import (
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estimate_perimeter,
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estimate_external_wall_area,
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estimate_number_of_floors
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)
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load_dotenv(dotenv_path="backend/.env")
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EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
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def app():
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"""
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This app is EPC pulling data for some properties owned by Unitas
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:return:
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"""
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# asset_list = read_excel_from_s3(
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# bucket_name="retrofit-datalake-dev",
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# file_key="customers/guiness/TGP CW Properties PV.xlsx",
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# header_row=0
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# )
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asset_list = pd.read_excel(
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"/Users/khalimconn-kowlessar/Downloads/UNITAS BUNGALOWS - EPC DATA PULL.xlsx", header=0
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)
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epc_data = []
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for _, home in tqdm(asset_list.iterrows(), total=len(asset_list)):
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searcher = SearchEpc(
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address1=str(home["Address Line 1"]),
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postcode=home["Post Code"],
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uprn=home["Property Reference"],
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auth_token=EPC_AUTH_TOKEN,
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os_api_key="",
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property_type=None,
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fast=True
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)
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# Force the skipping of estimating the EPC
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searcher.ordnance_survey_client.property_type = None
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searcher.ordnance_survey_client.built_form = None
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searcher.find_property(skip_os=True)
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if searcher.newest_epc is None:
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continue
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epc = {
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"asset_list_address": home["Address Line 1"],
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"asset_list_postcode": home["Post Code"],
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**searcher.newest_epc.copy()
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}
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epc_data.append(epc)
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epc_df = pd.DataFrame(epc_data)
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# Retrieve just the data we need
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epc_df = epc_df[
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[
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"asset_list_address",
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"uprn",
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"property-type",
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"built-form",
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"inspection-date",
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"current-energy-rating",
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"current-energy-efficiency",
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"roof-description",
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"walls-description",
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"transaction-type",
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# New fields needed
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"secondheat-description",
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"total-floor-area",
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"construction-age-band",
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"floor-height",
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"number-habitable-rooms",
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"mainheat-description"
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]
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]
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asset_list = asset_list.merge(
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epc_df,
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how="left",
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left_on=["Address Line 1"],
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right_on=["asset_list_address"]
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)
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asset_list = asset_list.drop(columns=["asset_list_address"])
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# Rename the columns
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asset_list = asset_list.rename(columns={
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"inspection-date": "Date of last EPC",
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"current-energy-efficiency": "SAP score on register",
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"current-energy-rating": "EPC rating on register",
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"property-type": "EPC Property Type",
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"built-form": "Archetype",
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"total-floor-area": "Property Floor Area",
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"construction-age-band": "Property Age Band",
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"floor-height": "Property Floor Height",
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"number-habitable-rooms": "Number of Habitable Rooms",
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"walls-description": "Wall Construction",
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"roof-description": "Roof Construction",
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"mainheat-description": "Heating Type",
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"secondheat-description": "Secondary Heating",
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"transaction-type": "Reason for last EPC"
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})
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asset_list["Estimated Number of Floors"] = asset_list.apply(
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lambda x: estimate_number_of_floors(property_type=x["EPC Property Type"]) if not pd.isnull(
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x["EPC Property Type"]) else None,
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axis=1
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)
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asset_list["Property Floor Area"] = asset_list["Property Floor Area"].astype(float)
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asset_list["Number of Habitable Rooms"] = asset_list["Number of Habitable Rooms"].astype(float)
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asset_list["Estimated Perimeter (m)"] = asset_list.apply(
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lambda x: estimate_perimeter(
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floor_area=x["Property Floor Area"] / x["Estimated Number of Floors"],
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num_rooms=x["Number of Habitable Rooms"] / x["Estimated Number of Floors"],
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) if not pd.isnull(x["uprn"]) else None, axis=1
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)
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asset_list["Estimated Heat Loss Perimeter (m)"] = asset_list.apply(
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lambda x: estimate_external_wall_area(
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num_floors=x["Estimated Number of Floors"],
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floor_height=float(x["Property Floor Height"]) if x["Property Floor Height"] else 2.5,
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perimeter=x["Estimated Perimeter (m)"],
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built_form=x["Archetype"]
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) if not pd.isnull(x["uprn"]) else None,
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axis=1
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)
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asset_list["Roof Insulation Thickness"] = asset_list.apply(
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lambda x: RoofAttributes(description=x["Roof Construction"]).process()["insulation_thickness"] if not pd.isnull(
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x["uprn"]) else None,
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axis=1
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)
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# Store as an excel
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filename = "UNITAS BUNGALOWS - EPC DATA PULL - May 30tg 2024.xlsx"
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asset_list.to_excel(filename, index=False)
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