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deem score worked out

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Jun-te Kim 2025-04-09 14:24:19 +00:00
parent 9b34dc58d2
commit 4452cd4347
1 changed files with 58 additions and 5 deletions
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63
etl/hubspot_to_deemed_calculator.py
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@ -23,7 +23,7 @@ for deal in deals:
"Address": deal.deal_name,
"Trickle Vent": 1 if deal.needs_trickle_ventilation else 0,
"wetrooms": deal.no_of_wet_rooms,
"hubspot_wall_insulation_info": deal.existing_wall_insulation,
"hubspot_wall_insulation": deal.existing_wall_insulation,
"POST INSTALL SAP SCORE": deal.post_sap_score,
})
@ -56,16 +56,69 @@ for survey in list_of_surveys:
data = {
"Address": survey.pre_site_note.survey_information.address,
"HubSpot Address": filtered_df["Address"].values[0],
"Pre SAP from sharepoint": number,
"Post SAP from surveyor": filtered_df["POST INSTALL SAP SCORE"].values[0],
"Surveyor's Name": survey.pre_site_note.assessor_information.name,
"floor_area_group" : floor_banding,
"wetrooms" : filtered_df["wetrooms"].values[0],
"Trickle Vent" : filtered_df["Ventilation Requirements"].values[0],
"Trickle Vent" : filtered_df["Trickle Vent"].values[0],
"survey_stated_work_type": filtered_df["hubspot_work_type"].values[0],
}
insulation = None
csr_insulation = None
merged_df = pd.DataFrame()
if survey.csr:
if survey.csr.insulation_info:
insultation = survey.csr.insulation_info.type.upper()
csr_insulation = survey.csr.insulation_info.type.upper()
hubspot_wall_insulation = None
hubspot_wall_insulation = filtered_df["hubspot_wall_insulation"].values[0]
data.update({"csr_insulation": csr_insulation})
data.update({"hubspot_wall_insulation": hubspot_wall_insulation})
if funding_type == "GBIS":
if csr_insulation is None and hubspot_wall_insulation.upper() == "EMPTY":
data.update({"funding": funding_type.upper()})
df = pd.DataFrame([data])
merged_df = pd.merge(df, price_empty, on=['funding', 'Trickle Vent', 'floor_area_group', 'wetrooms'], how='left')
elif "FOAM" in csr_insulation.upper() and "FOAM" in hubspot_wall_insulation.upper():
data.update({"funding": funding_type.upper() + " Remedial"})
df = pd.DataFrame([data])
merged_df = pd.merge(df, price_foam, on=['funding', 'Trickle Vent', 'floor_area_group', 'wetrooms'], how='left')
else:
data.update({"funding": funding_type.upper() + " Remedial"})
df = pd.DataFrame([data])
merged_df = pd.merge(df, price_general, on=['funding', 'Trickle Vent', 'floor_area_group', 'wetrooms'], how='left')
elif funding_type == "ECO4":
if csr_insulation is None and hubspot_wall_insulation.upper() == "EMPTY":
formatted_funding_type = f"{funding_type.upper()} - SAP {get_band(int(number))} to {get_band(filtered_df["POST INSTALL SAP SCORE"].values[0])}"
data.update({"funding": formatted_funding_type})
df = pd.DataFrame([data])
merged_df = pd.merge(df, price_empty, on=['funding', 'Trickle Vent', 'floor_area_group', 'wetrooms'], how='left')
elif "FOAM" in csr_insulation.upper() and "FOAM" in hubspot_wall_insulation.upper():
formatted_funding_type = f"REMEDIAL - {funding_type.upper()} - SAP {get_band(int(number))} to {get_band(filtered_df["POST INSTALL SAP SCORE"].values[0])}"
data.update({"funding": formatted_funding_type})
df = pd.DataFrame([data])
merged_df = pd.merge(df, price_foam, on=['funding', 'Trickle Vent', 'floor_area_group', 'wetrooms'], how='left')
else:
formatted_funding_type = f"REMEDIAL - {funding_type.upper()} - SAP {get_band(int(number))} to {get_band(filtered_df["POST INSTALL SAP SCORE"].values[0])}"
data.update({"funding": formatted_funding_type})
df = pd.DataFrame([data])
merged_df = pd.merge(df, price_general, on=['funding', 'Trickle Vent', 'floor_area_group', 'wetrooms'], how='left')
else:
raise RuntimeError(f"UNKNOWN FUNDING TYPE {funding_type}")
if not merged_df.empty:
total_price.append(merged_df)
final_df = pd.concat(total_price, ignore_index=True)
final_df.to_csv("survery_data.csv", index=False)
print(f"WEEK COMMENCING {WEEK_COMMENCING}")
print("Excel file 'survey_data.xlsx' created successfully!")