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set up first basic asset list for gla demo
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Khalim Conn-Kowlessar
parent
c81b03c458
commit
dbeba4db43
2 changed files with 232 additions and 22 deletions
145
etl/customers/gla_croydon_demo/asset_list.py
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145
etl/customers/gla_croydon_demo/asset_list.py
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@ -0,0 +1,145 @@
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import pandas as pd
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from utils.s3 import save_csv_to_s3
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USER_ID = 8
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PORTFOLIO_ID = 67
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def app():
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"""
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We shall define a small portfolio of properties, based in Croydon
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:return:
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"""
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# Firstly, read in the EPC data for Croydon
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epc_data = pd.read_csv(
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"local_data/all-domestic-certificates/domestic-E09000008-Croydon/certificates.csv",
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low_memory=False
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)
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# Filter on entries where we have a UPRN
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epc_data = epc_data[~pd.isnull(epc_data["UPRN"])]
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# Get the newest EPC for each UPRN. We use LODGEMENT_DATE as a proxy for this
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epc_data["LODGEMENT_DATE"] = pd.to_datetime(epc_data["LODGEMENT_DATE"])
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epc_data = epc_data.sort_values("LODGEMENT_DATE", ascending=False).drop_duplicates("UPRN")
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# Now filter on social properties
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epc_data = epc_data[epc_data["TENURE"].isin(["rental (social)", "Rented (social)"])]
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# There are 17337 properties with a registered EPC in Croydon
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# Take below EPC C properties
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epc_data = epc_data[epc_data["CURRENT_ENERGY_EFFICIENCY"].astype(int) < 69]
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# 7994 properties are below EPC C (46%)
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# 79% D, 19% E, 1% F, 0.2% G - it probably makes the most sense to focus on E and D properties
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epc_data["CURRENT_ENERGY_RATING"].value_counts(normalize=True)
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# For the purpose of the sample, take the properties have surveys done in the last 2 years
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# This gives us 1023 remaining properties
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two_years_ago = pd.Timestamp.now() - pd.DateOffset(days=int(2.5 * 365))
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epc_data = epc_data[epc_data["LODGEMENT_DATE"] >= two_years_ago]
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# Archetype 1: defined below:
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# 1) House
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# 2) Unfilled cavity
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# 3) A roof that could be insulated (flat or pitched with no more than 50mm insulation)
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# 4) EPC E
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# Different buckets of properties
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archetype_1_sample = epc_data[
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epc_data["PROPERTY_TYPE"].isin(["House"]) &
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(epc_data["CURRENT_ENERGY_RATING"] == "E") &
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epc_data["WALLS_DESCRIPTION"].isin(["Cavity wall, as built, no insulation (assumed)"]) &
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epc_data["ROOF_DESCRIPTION"].isin(
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[
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"Pitched, 12 mm loft insulation",
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"Pitched, 0 mm loft insulation",
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"Pitched, no insulation",
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"Pitched, 50 mm loft insulation",
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"Flat, no insulation (assumed)",
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"Pitched, no insulation (assumed)"
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]
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)
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]
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archetype_1_sample_asset_list = archetype_1_sample[["UPRN", "ADDRESS1", "POSTCODE"]].copy()
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archetype_1_sample_asset_list["ARCHETYPE"] = "Archetype 1"
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# Archetype 2: defined below:
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# 1) Flat
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# 2) Unfilled cavity
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# 3) Another property above
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# 4) EPC E
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archetype_2_sample = epc_data[
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epc_data["PROPERTY_TYPE"].isin(["Flat"]) &
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(epc_data["CURRENT_ENERGY_RATING"] == "E") &
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epc_data["WALLS_DESCRIPTION"].isin(["Cavity wall, as built, no insulation (assumed)"]) &
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epc_data["ROOF_DESCRIPTION"].isin(
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[
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"(another dwelling above)"
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]
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)
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]
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archetype_2_sample_asset_list = archetype_2_sample[["UPRN", "ADDRESS1", "POSTCODE"]].copy()
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archetype_2_sample_asset_list["ARCHETYPE"] = "Archetype 2"
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# Archetype 3: defined below:
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# 1) EPC F
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# 2) Solid brick wall
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# 3) House
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# 4) Pitched roof with no insulation
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# Just 1 property (more expensive to retrofit)
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archetype_3_sample = epc_data[
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epc_data["PROPERTY_TYPE"].isin(["House"]) &
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(epc_data["CURRENT_ENERGY_RATING"] == "F") &
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epc_data["ROOF_DESCRIPTION"].isin(["Pitched, no insulation"])
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]
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archetype_3_sample_asset_list = archetype_3_sample[["UPRN", "ADDRESS1", "POSTCODE"]].copy()
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archetype_3_sample_asset_list["ARCHETYPE"] = "Archetype 3"
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# Archetype 4: defined below:
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# 1) Maisonette
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# 2) Empty cavity
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# 3) EPC E
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# 14 properties here
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archetype_4_sample = epc_data[
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epc_data["PROPERTY_TYPE"].isin(["Maisonette"]) &
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epc_data["WALLS_DESCRIPTION"].isin(["Cavity wall, as built, no insulation (assumed)"])
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]
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archetype_4_sample_asset_list = archetype_4_sample[["UPRN", "ADDRESS1", "POSTCODE"]].copy()
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archetype_4_sample_asset_list["ARCHETYPE"] = "Archetype 4"
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asset_list = pd.concat(
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[
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archetype_1_sample_asset_list,
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archetype_2_sample_asset_list,
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archetype_3_sample_asset_list,
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archetype_4_sample_asset_list
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]
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)
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asset_list = asset_list.rename(
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columns={
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"UPRN": "uprn",
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"ADDRESS1": "address",
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"POSTCODE": "postcode",
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"ARCHETYPE": "archetype"
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}
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)
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filename = f"{USER_ID}/{PORTFOLIO_ID}/inputs.csv"
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save_csv_to_s3(
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dataframe=asset_list,
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bucket_name="retrofit-plan-inputs-dev",
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file_name=filename
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)
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body = {
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"portfolio_id": str(PORTFOLIO_ID),
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"housing_type": "Social",
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"goal": "Increase EPC",
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"goal_value": "C",
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"trigger_file_path": filename,
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"budget": None,
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"exclusions": ["floor_insulation"]
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}
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print(body)
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@ -6692,6 +6692,92 @@ def create_final_report():
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revenue.to_csv("HA Analysis Final - revenue.csv")
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def identify_eco_works(loader):
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# ha_names = [
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# "HA16", # For Housing
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# "HA39", # Rooftop
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# "HA41", # Settle
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# "HA23", # Lambeth
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# "HA14", # EMH
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# "HA7", # Believe
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# "HA102", # Thrive
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# ]
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# Unitas, fairhive, acis, LHP
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ha_names = [
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"HA50", # Unitas
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"HA15", # Fairhive
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"HA107", # ACIS
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"HA24", # LHP
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]
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names = {
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"HA50": "Unitas",
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"HA15": "Fairhive",
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"HA107": "ACIS",
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"HA24": "LHP"
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}
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# gbis rate
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breakdowns = []
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# lists = {}
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for ha, data_assets in loader.data.items():
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if ha not in ha_names:
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continue
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asset_list = data_assets["asset_list"].copy()
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survey_list = data_assets["survey_list"].copy()
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# Remove things that have sold
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if not survey_list.empty:
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asset_list = asset_list.merge(
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survey_list[["asset_list_row_id", "installation_status"]],
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how="left",
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on="asset_list_row_id"
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)
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# Anything that has an installation has gone to installation, and therefore is not remaining
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asset_list = asset_list[pd.isnull(asset_list["installation_status"])]
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asset_list = asset_list.drop(columns=["installation_status"])
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# Needing a CIGA check
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needs_cga = asset_list[
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asset_list["ECO Eligibility"] == "eco4 (subject to ciga)"
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].copy()
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eco4 = asset_list[
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asset_list["ECO Eligibility"] == "eco4"
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].copy()
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eco4_passed_ciga = asset_list[
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asset_list["ECO Eligibility"] == "eco4 - passed ciga"
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].copy()
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# lists[ha] = {
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# "needs_cga": needs_cga,
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# "eco4": eco4,
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# "eco4_passed_ciga": eco4_passed_ciga
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# }
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# Store the data
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if not needs_cga.empty:
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needs_cga.to_csv(f"local_data/{names[ha]} - needs ciga.csv")
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if not eco4.empty:
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eco4.to_csv(f"local_data/{names[ha]} - eco4.csv")
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if not eco4_passed_ciga.empty:
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eco4_passed_ciga.to_csv(f"local_data/{names[ha]} - eco4 passed ciga.csv")
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summary = {
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"HA Name": ha,
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"n_needing_ciga": needs_cga.shape[0],
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"eco4": eco4.shape[0],
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"eco4_passed_ciga": eco4_passed_ciga.shape[0]
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}
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breakdowns.append(summary)
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breakdowns = pd.DataFrame(breakdowns)
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breakdowns = breakdowns.fillna(0)
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def app():
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"""
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This app contains the housin association analysis for HAs 1, 6, 14, 39 and 107.
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@ -6739,29 +6825,8 @@ def app():
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loader = DataLoader(directories, december_figures_filepath, use_cache, rebuild_inputs)
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loader.load()
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loader.ha_facts_and_figures()
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forecast_remaining_sales(loader)
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# gbis rate
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# breakdowns = []
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# for ha, data_assets in loader.data.items():
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# asset_list = data_assets["asset_list"].copy()
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# breakdown = asset_list["ECO Eligibility"].value_counts().to_dict()
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# breakdowns.append(breakdown)
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# breakdowns = pd.DataFrame(breakdowns)
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#
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# installer = []
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# for ha, data_assets in loader.data.items():
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# survey_list = data_assets["survey_list"]
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# if survey_list.empty:
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# continue
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# if "INSTALLER" not in survey_list.columns:
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# continue
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#
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# installers = survey_list["INSTALLER"].value_counts().to_dict()
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# installers["ha_name"] = ha
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# installer.append(installers)
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# installer = pd.DataFrame(installer)
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# installer.drop(columns=["ha_name"]).sum().sum()
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forecast_remaining_sales(loader)
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# Adhoc - for HA16, get the properties that still need a CIGA check
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asset_list_ha16 = loader.data["HA16"]["asset_list"].copy()
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