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formatting output

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This commit is contained in:
Khalim Conn-Kowlessar 2025-02-04 14:35:24 +00:00
parent 139db23592
commit 7885467fa4
1 changed files with 39 additions and 3 deletions
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42
etl/customers/stonewater/Wave 3 Preparation.py
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@ -3777,6 +3777,21 @@ def revised_model():
no_match = []
matches = []
for _, home in tqdm(new_priority_postcodes.iterrows(), total=len(new_priority_postcodes)):
# We check if the property was surveyed
survey_result = coordinated_packages[
coordinated_packages["Organisation Reference"] == home["Organisation Reference"]
]
if not survey_result.empty:
to_extend = [
{
"Organisation Reference": home["Organisation Reference"],
"Best Match Organisation Reference": m,
"Was Surveyed": True
} for m in survey_result["Organisation Reference"].values
]
matches.extend(to_extend)
closest_match = find_nearest_matching_property(coordinated_packages, home)
if closest_match is None:
no_match.append(home["Organisation Reference"])
@ -3785,7 +3800,8 @@ def revised_model():
to_extend = [
{
"Organisation Reference": home["Organisation Reference"],
"Best Match Organisation Reference": m
"Best Match Organisation Reference": m,
"Was Surveyed": False
} for m in closest_match["Organisation Reference"].values
]
matches.extend(to_extend)
@ -4010,7 +4026,8 @@ def revised_model():
[
'Organisation Reference', 'Address', 'Postcode', 'Address ID', 'uprn', 'Archetype ID',
'SAP', 'SAP Band', "Property Type", "Walls", "Roofs", 'Glazing',
'Heating', 'Main Fuel', 'Hot Water', 'Estimated SAP Rating', 'Estimated EPC Rating'
'Heating', 'Main Fuel', 'Hot Water', 'Number of matches', 'Proportion',
'Estimated SAP Rating', 'Estimated EPC Rating'
]
].rename(
columns={
@ -4023,6 +4040,7 @@ def revised_model():
'Heating': 'Parity - Heating',
'Main Fuel': 'Parity - Main Fuel',
'Hot Water': 'Parity - Hot Water',
'Proportion': 'Proportion of matched properties with same EPC rating',
}
).merge(
epc_df[
@ -4061,7 +4079,25 @@ def revised_model():
on='Organisation Reference'
)
worksheet["Years Since Last EPC"]
worksheet["Years Since Last EPC"] = pd.Timestamp.now().year - pd.to_datetime(
worksheet["Last EPC - Date Lodged"]).dt.year
worksheet["Last EPC - uprn"] = worksheet["Last EPC - uprn"].astype("Int64").astype(str)
worksheet["uprn"] = np.where(
pd.isnull(worksheet["uprn"]) & pd.notnull(worksheet["Last EPC - uprn"]),
worksheet["Last EPC - uprn"],
worksheet["uprn"]
)
worksheet["uprn"] = worksheet["uprn"].replace("<NA>", "")
# Save to Excel with multiple sheets
excel_path = os.path.join(CUSTOMER_FOLDER_PATH, "Jan 2025 Project", "04022025 Stonewater Priority List.xlsx")
with pd.ExcelWriter(excel_path, engine="xlsxwriter") as writer:
worksheet.to_excel(writer, sheet_name="Worksheet", index=False, header=True)
mapped_lookup.to_excel(writer, sheet_name="Lookup Table", index=False, header=True)
output_coordination_sheet.to_excel(writer, sheet_name="Coordination", index=False, header=True)
# if __name__ == "__main__":
# main()