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coding up logic to identify work types
This commit is contained in:
Khalim Conn-Kowlessar
parent
8bf6aa5af2
commit
c0ebffb6cb
4 changed files with 270 additions and 172 deletions
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@ -10,6 +10,7 @@ import pandas as pd
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from fuzzywuzzy import process
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from utils.logger import setup_logger
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from backend.SearchEpc import SearchEpc
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from BaseUtility import Definitions
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import asset_list.mappings.property_type as property_type_mappings
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import asset_list.mappings.walls as walls_mappings
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import asset_list.mappings.heating_systems as heating_mappings
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@ -282,7 +283,9 @@ class AssetList:
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]
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# This SAP threshold is a key search criteria for properties that may be eligible for extraction
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SAP_RATING_THRESHOLD = 75
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FILLED_CAVITY_SAP_THRESHOLD = 75
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# This SAP the
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EMPTY_CAVITY_SAP_THRESHOLD = 71
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# Any EPC deemed to have been conducted prior to this year is deemed to be unreliable
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EPC_YEAR_THRESHOLD = pd.Timestamp.now().year - 5
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@ -292,9 +295,17 @@ class AssetList:
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ATTRIBUTE_ESTIMATED_PERIMETER = "attribute_est_perimter"
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ATTRIBUTE_HEAT_LOSS_AREA = "attribute_heat_loss_area"
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ATTRIBUTE_EPC_ROOF_INSULATION_THICKNESS = "attribute_epc_roof_insulation_thickness"
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ATTRIBUTE_SAP_THRESHOLD_AND_BELOW = f"sap_rating_{SAP_RATING_THRESHOLD}_and_below"
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ATTRIBUTE_SAP_THRESHOLD_AND_BELOW = f"sap_rating_{FILLED_CAVITY_SAP_THRESHOLD}_and_below"
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ATTRIBUTE_EPC_PRE_YEAR_THRESHOLD = f"EPC is pre {EPC_YEAR_THRESHOLD}"
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# These are the descriptions that we look for in the EPC data that are indicative of no insulation
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EPC_NO_WALL_INSULATION_DESCRIPTIONS = [
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"cavity wall, as built, no insulation (assumed)",
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"cavity wall, as built, partial insulation (assumed)",
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"cavity wall, as built, partial insulation",
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"cavity wall, as built, no insulation",
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]
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def __init__(
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self,
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local_filepath,
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@ -728,12 +739,241 @@ class AssetList:
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# 1) Is the SAP rating below C75
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self.standardised_asset_list[self.ATTRIBUTE_SAP_THRESHOLD_AND_BELOW] = (
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self.standardised_asset_list[self.EPC_API_DATA_NAMES["current-energy-efficiency"]] <=
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self.SAP_RATING_THRESHOLD
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self.FILLED_CAVITY_SAP_THRESHOLD
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)
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# 2) Flag anything where the EPC is older than 5 years
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self.standardised_asset_list[self.ATTRIBUTE_EPC_PRE_YEAR_THRESHOLD] = (
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pd.to_datetime(
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self.standardised_asset_list[self.EPC_API_DATA_NAMES["lodgement-date"]]
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self.standardised_asset_list[self.EPC_API_DATA_NAMES["inspection-date"]]
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).dt.year < self.EPC_YEAR_THRESHOLD
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)
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self.process_age_band()
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def process_age_band(self):
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processed_age_band = []
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for _, x in self.standardised_asset_list.iterrows():
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if pd.isnull(x[self.EPC_API_DATA_NAMES["construction-age-band"]]) or (
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x[self.EPC_API_DATA_NAMES["construction-age-band"]] in Definitions.DATA_ANOMALY_MATCHES
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):
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processed_age_band.append(
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{
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self.DOMNA_PROPERTY_ID: x[self.DOMNA_PROPERTY_ID],
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"epc_year_lower_bound": None,
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"epc_year_upper_bound": None,
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"Does Age Match EPC Age Band?": "No EPC Age Band"
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}
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)
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continue
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# We exatract the upper and lower bounds
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if x[self.EPC_API_DATA_NAMES["construction-age-band"]] in [
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"England and Wales: 2007 onwards", "England and Wales: 2012 onwards"
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]:
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year_lower_bound = 2007 if x[self.EPC_API_DATA_NAMES[
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"construction-age-band"]] == "England and Wales: 2007 onwards" else 2012
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if pd.isnull(x[self.STANDARD_YEAR_BUILT]):
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age_band_matches = "No Year Built From Landlord"
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else:
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age_band_matches = (
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"EPC Age Band Matches Year Built" if x[self.STANDARD_YEAR_BUILT] >= year_lower_bound
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else "EPC Age Band is older than Year Built"
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)
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processed_age_band.append(
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{
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self.DOMNA_PROPERTY_ID: x[self.DOMNA_PROPERTY_ID],
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"epc_year_lower_bound": year_lower_bound,
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"epc_year_upper_bound": None,
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"Does Age Match EPC Age Band?": age_band_matches
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}
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)
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continue
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if x[self.EPC_API_DATA_NAMES["construction-age-band"]] == "England and Wales: before 1900":
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if pd.isnull(x[self.STANDARD_YEAR_BUILT]):
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age_band_matches = "No Year Built From Landlord"
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else:
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age_band_matches = (
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"EPC Age Band Matches Year Built" if x[self.STANDARD_YEAR_BUILT] < 1900
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else "EPC Age Band is newer than Year Built"
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)
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processed_age_band.append(
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{
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self.DOMNA_PROPERTY_ID: x[self.DOMNA_PROPERTY_ID],
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"epc_year_lower_bound": None,
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"epc_year_upper_bound": 1899,
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"Does Age Match EPC Age Band?": age_band_matches
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}
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)
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continue
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if x[self.EPC_API_DATA_NAMES["construction-age-band"]].isdigit():
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if pd.isnull(x[self.STANDARD_YEAR_BUILT]):
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age_band_matches = "No Year Built From Landlord"
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else:
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age_band_matches = (
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"EPC Age Band Matches Year Built" if x[self.STANDARD_YEAR_BUILT] == int(
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x[self.EPC_API_DATA_NAMES["construction-age-band"]]
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)
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else "EPC Age Band is different from Year Built"
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)
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processed_age_band.append(
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{
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self.DOMNA_PROPERTY_ID: x[self.DOMNA_PROPERTY_ID],
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"epc_year_lower_bound": int(x[self.EPC_API_DATA_NAMES["construction-age-band"]]),
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"epc_year_upper_bound": int(x[self.EPC_API_DATA_NAMES["construction-age-band"]]),
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"Does Age Match EPC Age Band?": age_band_matches
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}
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)
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continue
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# Oherwise, we extract the upper and lower bounds
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age_band = x[self.EPC_API_DATA_NAMES["construction-age-band"]].split(": ")[1]
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lower_date, upper_date = age_band.split("-")
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age_band_matches = (
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"EPC Age Band Matches Year Built" if (x[self.STANDARD_YEAR_BUILT] >= float(lower_date)) and (
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x[self.STANDARD_YEAR_BUILT] <= float(upper_date)
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)
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else "EPC Age Band is older than Year Built" if x[self.STANDARD_YEAR_BUILT] > float(upper_date)
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else "EPC Age Band is newer than Year Built"
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)
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processed_age_band.append(
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{
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self.DOMNA_PROPERTY_ID: x[self.DOMNA_PROPERTY_ID],
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"epc_year_lower_bound": int(lower_date),
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"epc_year_upper_bound": int(upper_date),
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"Does Age Match EPC Age Band?": age_band_matches
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}
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)
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processed_age_band = pd.DataFrame(processed_age_band)
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self.standardised_asset_list = self.standardised_asset_list.merge(
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processed_age_band, how="left"
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)
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def identify_worktypes(self):
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# If we have non-intrusives completed, we can use this to identify work types
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if self.non_intrusives_present:
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######################################################
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# Empty cavity:
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######################################################
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# 1) Has been flagged on the non-intrusives as being a cavity wall, empty or partially filled
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# 2) The age is before 1995
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# TODO: 3) Remove anything that likley has access issues
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self.standardised_asset_list["non_intrusive_indicates_empty_cavity"] = (
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(~self.standardised_asset_list[self.STANDARD_PROPERTY_TYPE].isin(["bedsit"])) &
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(self.standardised_asset_list['non-intrusives: Construction'] == "CAVITY") &
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self.standardised_asset_list['non-intrusives: Insulated'].isin(["EMPTY", "PARTIAL"]) &
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(self.standardised_asset_list[self.STANDARD_YEAR_BUILT] <= 2000)
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)
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self.standardised_asset_list["epc_indicates_empty_cavity"] = (
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self.standardised_asset_list[self.EPC_API_DATA_NAMES["walls-description"]].str.lower().isin(
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self.EPC_NO_WALL_INSULATION_DESCRIPTIONS
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) & (
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self.standardised_asset_list["epc_year_upper_bound"] <= 1995
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) & (
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~self.standardised_asset_list[self.ATTRIBUTE_EPC_PRE_YEAR_THRESHOLD]
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) & (
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self.standardised_asset_list[
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self.EPC_API_DATA_NAMES["current-energy-efficiency"]] <= self.EMPTY_CAVITY_SAP_THRESHOLD
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)
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)
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######################################################
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# Extraction
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######################################################
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# TODO When filterting like this, 627 properties are flagged as not needing a CIGA check and 582 are flagged
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# as needing a CIGA check. What is the logic we should be applying here?
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self.standardised_asset_list["non_intrusive_indicates_cavity_extraction"] = (
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(self.standardised_asset_list["non-intrusives: Construction"] == "CAVITY") &
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(self.standardised_asset_list["non-intrusives: Insulated"].isin(["RETRO DRILLED", "FILLED AT BUILD"])) &
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(~self.standardised_asset_list['non-intrusives: Material'].isin(["GREY LOOSE BEAD", "FORMALDEHYDE"])
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) & (
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self.standardised_asset_list[self.ATTRIBUTE_SAP_THRESHOLD_AND_BELOW]
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)
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)
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######################################################
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# Solar
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######################################################
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# Criteria:
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# TODO: Standardise these columns with our cleaned_data object
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# Check 1: Does the property have a valid heating system?
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self.standardised_asset_list["solar_landlord_data_indicates_correct_heating_system"] = (
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self.standardised_asset_list[self.STANDARD_HEATING_SYSTEM].isin(
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["air source heat pump", "ground source heat pump", "high heat retention storage heaters"]
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)
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)
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self.standardised_asset_list["solar_epc_data_indicates_correct_heating_system"] = (
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(
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self.standardised_asset_list[self.EPC_API_DATA_NAMES["mainheat-description"]]
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.str.lower().str.contains("air source heat pump|ground source heat pump")
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) | (
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self.standardised_asset_list[
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self.EPC_API_DATA_NAMES["mainheat-description"]].str.lower().str.contains(
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"electric storage heaters"
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) & (
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self.standardised_asset_list[self.EPC_API_DATA_NAMES[
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"mainheatcont-description"]] == "Controls for high heat retention storage heaters"
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)
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)
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)
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# Check 2: Does the property have solar already
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self.standardised_asset_list["property_has_solar"] = (
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(self.standardised_asset_list[self.STANDARD_EXISTING_PV] == "already has PV") |
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(self.standardised_asset_list["non-intrusives: PV, ACCESS ISSUE, SEE NOTES"] == "SOLAR PV ON ROOF") |
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(self.standardised_asset_list[self.ATTRIBUTE_HAS_SOLAR])
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)
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# Check 3: Does the property meet the fabric condition
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# Solar PV installs are subject to the minimum insulation requirements which means:
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# 1) one of the following insulation measures must be installed as part of the same
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# ECO4 project:
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# • roof insulation (flat roof, pitched roof, room-in-roof)
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# • exterior facing wall insulation (cavity wall, solid wall)
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# • party cavity wall insulation
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# • floor insulation (solid and underfloor)
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#
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# OR
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#
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# all measures (except any exempted measure referred to in paragraph 4.28)
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# listed in paragraph a) must already be installed
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#
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# With this in mind, we look for 2 clases
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# 1) The property is fully insulated apart from the loft (<200mm insulation)
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# 2) THe property is fully insulated
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self.standardised_asset_list["solar_landlord_walls_insulated"] = (
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self.standardised_asset_list[self.STANDARD_WALL_CONSTRUCTION].isin(
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["filled cavity", "insulated solid brick"]
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)
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)
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EPC_INSULATED_WALLS_SUBSTRINGS = [
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", insulated", "with external insulation", "with internal insulation", "filled cavity"
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]
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self.standardised_asset_list["landlord_wall_construction"].value_counts()
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EPC_INSULATED_ROOF_SUBSTRINGS = [
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"(another dwelling above)", "limited insulation", "(other premises above)",
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", no insulation",
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]
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@ -13,6 +13,7 @@ STANDARD_HEATING_SYSTEMS = {
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"electric boiler",
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"unknown",
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"communal gas boiler",
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"high heat retention storage heaters",
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}
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HEATING_MAPPINGS = {
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@ -1,8 +1,10 @@
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from asset_list.AssetList import DataRemapper
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STANDARD_WALL_CONSTRUCTIONS = {
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"uninsulated cavity", "filled cavity", "partial insulated cavity", "timber frame", "solid brick",
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"system built", "granite or whinstone", "other", "unknown", "sandstone or limestone", "cob",
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"uninsulated cavity", "filled cavity", "partial insulated cavity", "cavity unknown insulation",
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"timber frame", "uninsulated solid brick",
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"insulated solid brick", "system built", "granite or whinstone", "other", "unknown", "sandstone or limestone",
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"cob",
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"new build - average thermal transmittance",
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}
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@ -26,7 +28,8 @@ WALL_CONSTRUCTION_MAPPINGS = {
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'Average thermal transmittance 0.26 W/m-¦K': 'unknown', 'Average thermal transmittance 0.62 W/m?K': 'unknown',
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'Average thermal transmittance 0.64 W/m?K': 'unknown', 'Average thermal transmittance 0.61 W/m?K': 'unknown',
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'Sandstone or limestone, as built, no insulation (assumed)': 'sandstone or limestone',
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'Average thermal transmittance 0.33 W/m?K': 'unknown', 'Cavity wall,': 'unknown',
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'Average thermal transmittance 0.33 W/m?K': 'unknown',
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'Cavity wall,': "cavity unknown insulation",
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'Cavity wall, as built, partial insulation (assumed)': 'partial insulated cavity',
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'Average thermal transmittance 0.29 W/m-¦K': 'unknown', 'Average thermal transmittance 0.32 W/m-¦K': 'unknown',
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'Average thermal transmittance 0.19 W/m-¦K': 'unknown', 'Average thermal transmittance 0.27 W/m?K': 'unknown',
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@ -55,7 +58,7 @@ WALL_CONSTRUCTION_MAPPINGS = {
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'average thermal transmittance 0.26 w/m-¦k': 'unknown', 'average thermal transmittance 0.62 w/m?k': 'unknown',
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'average thermal transmittance 0.64 w/m?k': 'unknown', 'average thermal transmittance 0.61 w/m?k': 'unknown',
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'sandstone or limestone, as built, no insulation (assumed)': 'sandstone or limestone',
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'average thermal transmittance 0.33 w/m?k': 'unknown', 'cavity wall,': 'unknown',
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'average thermal transmittance 0.33 w/m?k': 'unknown', 'cavity wall,': "cavity unknown insulation",
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'cavity wall, as built, partial insulation (assumed)': 'partial insulated cavity',
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'average thermal transmittance 0.29 w/m-¦k': 'unknown', 'average thermal transmittance 0.32 w/m-¦k': 'unknown',
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'average thermal transmittance 0.19 w/m-¦k': 'unknown', 'average thermal transmittance 0.27 w/m?k': 'unknown',
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@ -67,4 +70,20 @@ WALL_CONSTRUCTION_MAPPINGS = {
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'average thermal transmittance 0.32 w/m?k': 'unknown', 'average thermal transmittance 0.24 w/m-¦k': 'unknown',
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'cavity wall, with internal insulation': 'filled cavity', 'average thermal transmittance 0.17 w/m-¦k': 'unknown',
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'average thermal transmittance 0.28 w/m?k': 'unknown',
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'Cavity wall, filled cavity': 'filled cavity',
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'Cavity wall, filled cavity and external insulation': 'filled cavity',
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'Granite or whinstone, as built, no insulation (assumed)': 'granite or '
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'whinstone',
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'Solid brick, as built, insulated (assumed)': 'insulated solid brick',
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'Solid brick, as built, no insulation (assumed)': 'uninsulated solid brick',
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'Solid brick, with external insulation': 'insulated solid brick',
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'Solid brick, with internal insulation': 'insulated solid brick',
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'System built, as built, insulated (assumed)': 'system built',
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'System built, as built, no insulation (assumed)': 'system built',
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'System built, with external insulation': 'system built',
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'System built, with internal insulation': 'system built',
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'Timber frame, as built, insulated (assumed)': 'timber frame',
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'Timber frame, as built, no insulation (assumed)': 'timber frame',
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'Timber frame, as built, partial insulation (assumed)': 'timber frame',
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'Timber frame, with additional insulation': 'timber frame',
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}
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@ -4,7 +4,6 @@ import json
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import pandas as pd
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import numpy as np
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from tqdm import tqdm
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from BaseUtility import Definitions
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from asset_list.AssetList import AssetList
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from asset_list.mappings.property_type import PROPERTY_MAPPING
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from asset_list.mappings.walls import WALL_CONSTRUCTION_MAPPINGS
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@ -14,13 +13,6 @@ from asset_list.mappings.exising_pv import EXISTING_PV_MAPPINGS
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from dotenv import load_dotenv
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from backend.SearchEpc import SearchEpc
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from etl.find_my_epc.RetrieveFindMyEpc import RetrieveFindMyEpc
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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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from etl.epc_clean.epc_attributes.attribute_utils import (
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extract_thermal_transmittance
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@ -177,109 +169,6 @@ def extract_address1(asset_list, full_address_col, postcode_col, method="first_t
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raise ValueError(f"Method {method} not recognized")
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def process_age_band(asset_list, year_built_column):
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processed_age_band = []
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for _, x in asset_list.iterrows():
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|
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if pd.isnull(x["Property Age Band"]) or (
|
||||
x["Property Age Band"] in Definitions.DATA_ANOMALY_MATCHES
|
||||
):
|
||||
processed_age_band.append({
|
||||
"row_id": x["row_id"],
|
||||
"epc_year_lower_bound": None,
|
||||
"epc_year_upper_bound": None,
|
||||
"Does Age Match EPC Age Band?": "No EPC Age Band"
|
||||
})
|
||||
continue
|
||||
|
||||
# We exatract the upper and lower bounds
|
||||
if x["Property Age Band"] in ["England and Wales: 2007 onwards", "England and Wales: 2012 onwards"]:
|
||||
year_lower_bound = 2007 if x["Property Age Band"] == "England and Wales: 2007 onwards" else 2012
|
||||
|
||||
if pd.isnull(x[year_built_column]):
|
||||
age_band_matches = "No Year Built From Landlord"
|
||||
else:
|
||||
age_band_matches = (
|
||||
"EPC Age Band Matches Year Built" if x[year_built_column] >= year_lower_bound
|
||||
else "EPC Age Band is older than Year Built"
|
||||
)
|
||||
|
||||
processed_age_band.append(
|
||||
{
|
||||
"row_id": x["row_id"],
|
||||
"epc_year_lower_bound": year_lower_bound,
|
||||
"epc_year_upper_bound": None,
|
||||
"Does Age Match EPC Age Band?": age_band_matches
|
||||
}
|
||||
)
|
||||
continue
|
||||
|
||||
if x["Property Age Band"] == "England and Wales: before 1900":
|
||||
|
||||
if pd.isnull(x[year_built_column]):
|
||||
age_band_matches = "No Year Built From Landlord"
|
||||
else:
|
||||
age_band_matches = (
|
||||
"EPC Age Band Matches Year Built" if x[year_built_column] < 1900
|
||||
else "EPC Age Band is newer than Year Built"
|
||||
)
|
||||
|
||||
processed_age_band.append(
|
||||
{
|
||||
"row_id": x["row_id"],
|
||||
"epc_year_lower_bound": None,
|
||||
"epc_year_upper_bound": 1899,
|
||||
"Does Age Match EPC Age Band?": age_band_matches
|
||||
}
|
||||
)
|
||||
continue
|
||||
|
||||
if x["Property Age Band"].isdigit():
|
||||
|
||||
if pd.isnull(x[year_built_column]):
|
||||
age_band_matches = "No Year Built From Landlord"
|
||||
else:
|
||||
age_band_matches = (
|
||||
"EPC Age Band Matches Year Built" if x[year_built_column] == int(x["Property Age Band"])
|
||||
else "EPC Age Band is different from Year Built"
|
||||
)
|
||||
|
||||
processed_age_band.append(
|
||||
{
|
||||
"row_id": x["row_id"],
|
||||
"epc_year_lower_bound": int(x["Property Age Band"]),
|
||||
"epc_year_upper_bound": int(x["Property Age Band"]),
|
||||
"Does Age Match EPC Age Band?": age_band_matches
|
||||
}
|
||||
)
|
||||
continue
|
||||
|
||||
# Oherwise, we extract the upper and lower bounds
|
||||
age_band = x["Property Age Band"].split(": ")[1]
|
||||
lower_date, upper_date = age_band.split("-")
|
||||
|
||||
age_band_matches = (
|
||||
"EPC Age Band Matches Year Built" if (x[year_built_column] >= float(lower_date)) and (
|
||||
x[year_built_column] <= float(upper_date)
|
||||
)
|
||||
else "EPC Age Band is older than Year Built" if x[year_built_column] > float(upper_date)
|
||||
else "EPC Age Band is newer than Year Built"
|
||||
)
|
||||
|
||||
processed_age_band.append(
|
||||
{
|
||||
"row_id": x["row_id"],
|
||||
"epc_year_lower_bound": int(lower_date),
|
||||
"epc_year_upper_bound": int(upper_date),
|
||||
"Does Age Match EPC Age Band?": age_band_matches
|
||||
}
|
||||
)
|
||||
|
||||
processed_age_band = pd.DataFrame(processed_age_band)
|
||||
|
||||
return processed_age_band
|
||||
|
||||
|
||||
def app():
|
||||
"""
|
||||
This app is EPC pulling data for some properties owned by Livewest
|
||||
|
|
@ -531,62 +420,11 @@ def app():
|
|||
# TODO: TEMP!!!
|
||||
epc_df["epc_os_uprn"] = epc_df["epc_os_uprn"].astype("Int64").astype(str)
|
||||
asset_list.standardised_asset_list = asset_list.standardised_asset_list.merge(
|
||||
epc_df, how="left", left_on="ordnance_survey_uprn", right_on="epc_os_uprn"
|
||||
epc_df.drop(columns=["domna_property_id"]), how="left", left_on="ordnance_survey_uprn", right_on="epc_os_uprn"
|
||||
)
|
||||
|
||||
asset_list.extract_attributes()
|
||||
|
||||
asset_list["Estimated Number of Floors"] = asset_list.apply(
|
||||
lambda x: estimate_number_of_floors(property_type=x["Property Type"]) if not pd.isnull(
|
||||
x["Property Type"]) else None, axis=1
|
||||
)
|
||||
|
||||
asset_list["Property Floor Area"] = asset_list["Property Floor Area"].astype(float)
|
||||
# Replace "" value with None
|
||||
asset_list["Number of Habitable Rooms"] = asset_list["Number of Habitable Rooms"].replace("", None)
|
||||
asset_list["Number of Habitable Rooms"] = asset_list["Number of Habitable Rooms"].astype(float)
|
||||
|
||||
asset_list["Estimated Perimeter (m)"] = asset_list.apply(
|
||||
lambda x: estimate_perimeter(
|
||||
floor_area=x["Property Floor Area"] / x["Estimated Number of Floors"],
|
||||
num_rooms=x["Number of Habitable Rooms"] / x["Estimated Number of Floors"],
|
||||
), axis=1
|
||||
)
|
||||
|
||||
asset_list["Estimated Heat Loss Perimeter (m2)"] = asset_list.apply(
|
||||
lambda x: estimate_external_wall_area(
|
||||
num_floors=x["Estimated Number of Floors"],
|
||||
floor_height=float(x["Property Floor Height"]) if x["Property Floor Height"] else 2.5,
|
||||
perimeter=x["Estimated Perimeter (m)"],
|
||||
built_form=x["Archetype - EPC"]
|
||||
),
|
||||
axis=1
|
||||
)
|
||||
|
||||
asset_list["Roof Insulation Thickness"] = asset_list.apply(
|
||||
lambda x: RoofAttributes(description=x["Roof Construction"]).process()["insulation_thickness"] if not pd.isnull(
|
||||
x["Roof Construction"]) else None,
|
||||
axis=1
|
||||
)
|
||||
|
||||
# We produce some additional fields
|
||||
# 1) Is the SAP rating below C75
|
||||
asset_list["SAP Rating is 75 and below"] = asset_list["SAP score on register"] <= 75
|
||||
# 2) Flag anything where the EPC is older than 5 years
|
||||
cutoff_year = pd.Timestamp.now().year - 5
|
||||
asset_list[f"EPC is pre {cutoff_year}"] = (
|
||||
pd.to_datetime(asset_list["Date of last EPC"]).dt.year < cutoff_year
|
||||
)
|
||||
|
||||
# 3) If we have year in the asset list, we flag entries where the built year is different from the
|
||||
# EPC Age band
|
||||
if PROPERTY_YEAR_BUILT is not None:
|
||||
# We process the age band and merge it on
|
||||
processed_age_band = process_age_band(asset_list, PROPERTY_YEAR_BUILT)
|
||||
asset_list = asset_list.merge(
|
||||
processed_age_band, how="left", on="row_id"
|
||||
)
|
||||
|
||||
if HAS_NON_INTRUSIVES:
|
||||
# Empty cavity:
|
||||
# 1) Has been flagged on the non-intrusives as being empty or partially filled
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue