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Merge pull request #984 from Hestia-Homes/main

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Jun-te Kim 2026-04-14 14:58:47 +01:00 • committed by GitHub
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2 changed files with 5 additions and 35 deletions
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10
etl/bill_savings/KwhData.py
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@ -77,14 +77,8 @@ class KwhData:
'Cheapest tariff (Large legacy suppliers)', 'Cheapest tariff (All suppliers)', 'Cheapest tariff (Large legacy suppliers)', 'Cheapest tariff (All suppliers)',
'Cheapest tariff (Basket)', 'Default tariff cap level'] 'Cheapest tariff (Basket)', 'Default tariff cap level']
# Extract data rows self.retail_price_comparison = pd.DataFrame(data)
data_rows = [] self.retail_price_comparison.columns = header
for row in data[1:]:
date = row['\ufeff"']
values = row[None]
data_rows.append([date] + values)
self.retail_price_comparison = pd.DataFrame(data_rows, columns=header)
self.retail_price_comparison['Date'] = pd.to_datetime(self.retail_price_comparison['Date'], errors='coerce') self.retail_price_comparison['Date'] = pd.to_datetime(self.retail_price_comparison['Date'], errors='coerce')
@staticmethod @staticmethod

30
sfr/principal_pitch/2_export_data.py
View file

@ -230,7 +230,7 @@ for scenario_id in SCENARIOS:
# Get recs for this scenario # Get recs for this scenario
recommended_measures_df = recommendations_df[ recommended_measures_df = recommendations_df[
recommendations_df["scenario_id"] == scenario_id recommendations_df["scenario_id"] == scenario_id
][["property_id", "measure_type", "estimated_cost", "default"]] ][["property_id", "measure_type", "estimated_cost", "default"]]
recommended_measures_df = recommended_measures_df[ recommended_measures_df = recommended_measures_df[
recommended_measures_df["default"] recommended_measures_df["default"]
] ]
@ -238,7 +238,7 @@ for scenario_id in SCENARIOS:
post_install_sap = recommendations_df[ post_install_sap = recommendations_df[
recommendations_df["scenario_id"] == scenario_id recommendations_df["scenario_id"] == scenario_id
][["property_id", "default", "sap_points"]] ][["property_id", "default", "sap_points"]]
post_install_sap = post_install_sap[post_install_sap["default"]] post_install_sap = post_install_sap[post_install_sap["default"]]
# Sum up the sap points by property id # Sum up the sap points by property id
post_install_sap = ( post_install_sap = (
@ -282,6 +282,7 @@ for scenario_id in SCENARIOS:
"windows", "windows",
"current_epc_rating", "current_epc_rating",
"current_sap_points", "current_sap_points",
"original_sap_points",
"total_floor_area", "total_floor_area",
"number_of_rooms", "number_of_rooms",
"lodgement_date", "lodgement_date",
@ -303,31 +304,6 @@ for scenario_id in SCENARIOS:
) )
df["uprn"] = df["uprn"].astype(str) df["uprn"] = df["uprn"].astype(str)
relevant_plans = plans_df[plans_df["scenario_id"] == scenario_id]
df2 = df.merge(
relevant_plans[["property_id", "post_sap_points", "post_epc_rating"]],
how="left",
on="property_id",
suffixes=("", "_plan"),
)
print(df2["predicted_post_works_epc"].value_counts())
print(df2["post_epc_rating"].value_counts())
z = df2[
(df2["predicted_post_works_epc"] != "D")
& (df2["post_epc_rating"].astype(str) == "Epc.D")
]
df2["predicted_post_works_epc"].value_counts()
df2["post_epc_rating"].astype(str).value_counts()
df2[df2["total_retrofit_cost"] > 0].shape
getting_works = df[df["total_retrofit_cost"] > 0]
getting_works["predicted_post_works_epc"].value_counts()
df[df["predicted_post_works_sap"] == ""]
# Expected columns list # Expected columns list
expected_columns = [ expected_columns = [
"suspended_floor_insulation", "suspended_floor_insulation",