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93 lines
3.8 KiB
Python
93 lines
3.8 KiB
Python
import pandas as pd
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from etl.epc.DataProcessor import construction_age_bounds_map
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from backend.onboarders.mappings.property_type import parity_map as property_map
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from backend.onboarders.mappings.age_band import party_map as age_band_map
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from backend.onboarders.mappings.built_form import parity_map as built_form_map
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def check_nulls(data, original_column, mapped_column):
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# We only allow nulls if the oroginal value was null
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null_vals = data[pd.isnull(data[mapped_column])]
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if null_vals.empty:
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return True
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# We make sure all original values were null
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assert pd.isnull(null_vals[original_column]).all(), (
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f"Some values in {mapped_column} were not mapped, but original values were not null"
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)
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# Sample input data
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data = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/2025_11_11 - Peabody "
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"- Data Extracts for Domna.xlsx",
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sheet_name="Sustainability"
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)
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# We want to map the parity fields to standard EPC references. This will allow us to
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# 1) Estimate EPCs, more accurately
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# 2) Patch incorrect EPCs with ease
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# 3) Indicate already installed measures
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# ------------ construction_age_band ------------
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# Map to EPC age bands
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# def construction_date_to_band(year):
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# if pd.isnull(year):
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# return None
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# # Get the year from the date which is numpy datetime format
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# for label, ranges in construction_age_bounds_map.items():
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# if ranges["l"] <= year <= ranges["u"]:
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# return label
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# raise NotImplementedError("year out of bounds")
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#
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#
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# data["construction_age_band"] = pd.to_datetime(data["Construction Date"]).dt.year.apply(construction_date_to_band)
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data["construction_age_band"] = data["Construction Years"].map(age_band_map)
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check_nulls(data, "Construction Years", "construction_age_band")
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# ------------ property_type ------------
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data["property_type"] = data["Type"].map(property_map)
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assert pd.isnull(data["property_type"]).sum() == 0, "Some property types were not mapped"
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# ------------ built_form ------------
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data["built_form"] = data["Attachment"].map(built_form_map)
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assert pd.isnull(data["built_form"]).sum() == 0, "Some built forms were not mapped"
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# ------------ Wall Construction ------------
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data["walls_combined"] = data["Wall Construction"] + "+" + data["Wall Insulation"].fillna("Unknown Insulation")
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data["Wall Insulation"].value_counts()
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data["Wall Construction"].value_counts()
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as_built_map = {
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"Cavity": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
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"Solid Brick": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
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"System": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
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"Timber Frame": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
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"Sandstone": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
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"Granite": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
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"Cob": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
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}
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def map_wall_construction(wall_constuction, wall_insulation, construction_age_band):
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if wall_insulation == "AsBuilt":
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# Deduce based on wall construction and age band
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bands = as_built_map.get(wall_constuction, None)
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if bands is None:
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raise NotImplementedError(f"Wall construction {wall_constuction} not in as built map")
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# We check if the age band is in insulated or partial insulated, and if neither, we assume uninsulated
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# Variables we want to map
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# 'Org Ref', 'Address 1', 'Address 2', 'Address 3', 'Postcode', 'Type',
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# 'Attachment', 'Construction Years', 'Wall Construction',
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# 'Wall Insulation', 'Roof Construction', 'Roof Insulation',
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# 'Floor Construction', 'Floor Insulation', 'Glazing', 'Heating',
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# 'Boiler Efficiency', 'Main Fuel', 'Controls Adequacy', 'UPRN',
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# 'Total Floor Area (m2)'
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