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wrapping up ha analysis
This commit is contained in:
Khalim Conn-Kowlessar
parent
3dd30445f9
commit
724379a86d
1 changed files with 94 additions and 76 deletions
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@ -5366,6 +5366,7 @@ def forecast_remaining_sales(loader):
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results = []
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for ha_name, input_data in loader.data.items():
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# Original warmfront figures - ECO4
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original_warmfront_estimates = december_figures[december_figures["HA Name"] == ha_name]
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if original_warmfront_estimates.empty:
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@ -6032,7 +6033,7 @@ def forecast_remaining_sales(loader):
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def fml_data_pull(loader):
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has_bruh = [
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"HA7", "HA14", "HA25", "HA39", "HA16", "HA28", "HA13",
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"HA50", "HA24", "HA15", "HA32", "HA28", "HA6", "HA1", "HA107", "HA41", "HA48", "HA2", "HA63", "HA12",
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"HA50", "HA24", "HA15", "HA32", "HA6", "HA1", "HA107", "HA41", "HA48", "HA2", "HA63", "HA12",
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"HA117", "HA35", "HA34", "HA56", "HA19", "HA18", "HA9", "HA27", "HA30", "HA31", "HA54", "HA49",
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'HA8', 'HA11', 'HA21', 'HA37', 'HA42', 'HA44', 'HA45', 'HA51', 'HA52', "HA17", "HA5", "HA20",
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]
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@ -6129,7 +6130,7 @@ def fml_analysis(loader):
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assumed_ciga_pass_rate = 0.731
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has_bruh = [
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"HA7", "HA14", "HA25", "HA39", "HA16", "HA28", "HA13",
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"HA50", "HA24", "HA15", "HA32", "HA28", "HA6", "HA1", "HA107", "HA41", "HA48", "HA2", "HA63", "HA12",
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"HA50", "HA24", "HA15", "HA32", "HA6", "HA1", "HA107", "HA41", "HA48", "HA2", "HA63", "HA12",
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"HA117", "HA35", "HA34", "HA56", "HA19", "HA18", "HA9", "HA27", "HA30", "HA31", "HA54", "HA49",
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'HA8', 'HA11', 'HA21', 'HA37', 'HA42', 'HA44', 'HA45', 'HA51', 'HA52', "HA17", "HA5", "HA20",
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]
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@ -6738,89 +6739,106 @@ def app():
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loader = DataLoader(directories, december_figures_filepath, use_cache, rebuild_inputs)
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loader.load()
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loader.ha_facts_and_figures()
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forecast_remaining_sales(loader)
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conversion_rate = 0.95
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archetype_check_conversion = 0.7
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res = []
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for k, v in loader.data.items():
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asset_list = v["asset_list"].copy()
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agg = asset_list["ECO Eligibility"].value_counts()
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# We find a case where there are properties that have passed CIGA
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if not any("passed" in x for x in agg.index):
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# gbis rate
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# breakdowns = []
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# for ha, data_assets in loader.data.items():
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# asset_list = data_assets["asset_list"].copy()
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# breakdown = asset_list["ECO Eligibility"].value_counts().to_dict()
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# breakdowns.append(breakdown)
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# breakdowns = pd.DataFrame(breakdowns)
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#
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# installer = []
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# for ha, data_assets in loader.data.items():
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# survey_list = data_assets["survey_list"]
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# if survey_list.empty:
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# continue
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# if "INSTALLER" not in survey_list.columns:
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# continue
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#
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# installers = survey_list["INSTALLER"].value_counts().to_dict()
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# installers["ha_name"] = ha
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# installer.append(installers)
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# installer = pd.DataFrame(installer)
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# installer.drop(columns=["ha_name"]).sum().sum()
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# Adhoc - for HA16, get the properties that still need a CIGA check
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asset_list_ha16 = loader.data["HA16"]["asset_list"].copy()
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ha_16_need_ciga = asset_list_ha16[
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asset_list_ha16["ECO Eligibility"].str.contains("subject to ciga")
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]
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completed_cigas = loader.data["HA16"]["ciga_list"].copy()
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# Store the results
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ha_16_need_ciga.to_csv("ha16_need_ciga.csv")
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completed_cigas.to_csv("ha16_completed_cigas.csv")
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# Adhoc - look at the current pipeline and identify how many dormant, CIGA dependent properties there are for
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# live projects
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# Read excel
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orderbook_filepath = "local_data/ha_data/Warmfront HA client order book overview_20240129.xlsx"
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orderbook_workbook = openpyxl.load_workbook(orderbook_filepath)
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orderbook_sheet = orderbook_workbook["Contractual Info"]
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orderbook_colnames = [cell.value for cell in orderbook_sheet[1]]
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rows = []
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for row in orderbook_sheet.iter_rows(min_row=2, values_only=False):
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row_data = [cell.value for cell in row] # This will get you the cell values
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rows.append(row_data)
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orderbook = pd.DataFrame(rows, columns=orderbook_colnames)
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live_orderbook = orderbook[orderbook["Live, New, or Historic?"] == "LIVE"].copy()
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live_orderbook['Redacted HA'] = live_orderbook['Redacted HA'].str.replace(" ", "")
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dormant_properties = []
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missed_has = []
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for _, customer in live_orderbook.iterrows():
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if customer['Redacted HA'] not in loader.data.keys():
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missed_has.append(customer['Redacted HA'])
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continue
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asset_list = loader.data[customer['Redacted HA']]["asset_list"].copy()
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survey_list = loader.data[customer['Redacted HA']]["survey_list"].copy()
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# Remove sold
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if not survey_list.empty:
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survey_list = survey_list[~pd.isnull(survey_list["asset_list_row_id"])]
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asset_list = asset_list.merge(
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survey_list[["asset_list_row_id", "installation_status"]],
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how="left",
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on="asset_list_row_id"
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)
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# Anything that has an installation has gone to installation, and therefore is not remaining
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asset_list = asset_list[pd.isnull(asset_list["installation_status"])]
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asset_list = asset_list.drop(columns=["installation_status"])
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agg = pd.DataFrame(agg).reset_index()
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passed_ciga = agg[agg["ECO Eligibility"] == "eco4 - passed ciga"]
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passed_ciga = passed_ciga["count"].values[0] if not passed_ciga.empty else 0
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failed_ciga = agg[agg["ECO Eligibility"] == "failed ciga"]
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failed_ciga = failed_ciga["count"].values[0] if not failed_ciga.empty else 0
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ciga_pass_rate = passed_ciga / (passed_ciga + failed_ciga) if (passed_ciga + failed_ciga) > 0 else 1
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dormant_ciga = agg[
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agg["ECO Eligibility"].str.contains("subject to ciga") &
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~agg["ECO Eligibility"].str.contains("subject to archetype")
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# We pull out the properties that need a CIGA check
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need_ciga = asset_list[asset_list["ECO Eligibility"] == "eco4 (subject to ciga)"]
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need_archetype = asset_list[asset_list["ECO Eligibility"] == "eco4 (subject to archetype)"]
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need_ciga_and_archetype = asset_list[
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asset_list["ECO Eligibility"] == "eco4 (subject to ciga) (subject to archetype)"
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]
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dormant_ciga = dormant_ciga['count'].values[0] if not dormant_ciga.empty else 0
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dormant_ciga_archetype = agg[
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agg["ECO Eligibility"].str.contains("subject to ciga") &
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agg["ECO Eligibility"].str.contains("subject to archetype")
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]
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dormant_ciga_archetype = dormant_ciga_archetype['count'].values[0] if not dormant_ciga_archetype.empty else 0
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needing_check = dormant_ciga + dormant_ciga_archetype * archetype_check_conversion
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needing_check = np.round(needing_check)
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additional_jobs = (dormant_ciga * ciga_pass_rate * conversion_rate) + (
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dormant_ciga_archetype * archetype_check_conversion * ciga_pass_rate * conversion_rate
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)
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additional_jobs = np.round(additional_jobs)
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# We attempt to estimate the uplift and how much of that is attributed to surplus subject to ciga jobs
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original_estimate = loader.december_figures[
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loader.december_figures["HA Name"] == k
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]
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original_estimate = original_estimate["ECO4"].values[0] if not original_estimate.empty else 0
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base_eco_figures = agg[
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agg["ECO Eligibility"].isin(["eco4", "eco4 - passed ciga"])
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]["count"].sum()
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eco4_from_ciga = original_estimate - base_eco_figures
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eco4_from_ciga = eco4_from_ciga if eco4_from_ciga > 0 else 0
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surplus_from_dormant = additional_jobs - eco4_from_ciga
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surplus_from_dormant = 0 if surplus_from_dormant < 0 else surplus_from_dormant
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res.append(
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dormant_properties.append(
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{
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"ha_name": k,
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"additional_eco4": additional_jobs,
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"needing_check": needing_check,
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"surplus_from_dormant": surplus_from_dormant
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"HA Name": customer['Redacted HA'],
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"Need CIGA": need_ciga.shape[0],
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"Need Archetype": need_archetype.shape[0],
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"Need CIGA and Archetype": need_ciga_and_archetype.shape[0]
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}
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)
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res = pd.DataFrame(res)
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# Drop the HAs that are not in that pervious draft
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# In the v2 draft, there are 12 HAs
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dormant_properties = pd.DataFrame(dormant_properties)
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totals = dormant_properties.sum()
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totals["HA Name"] = "Total"
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v5_surplus = res[
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~res["ha_name"].isin(["HA9"])
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]["additional_eco4"].sum()
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# 7212 properties
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# This is not a perfect difference though, because of the variations in how the numbers are recorded in the November
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# all HAs sheet. E.g for HA 107, there were 1239 properties identified. In the postcode list, there are 1255,
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# however 531 are still needing a CIGA check. Therefore their original figures, in this case, included properties
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# pre-CIGA
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dormant_properties = pd.concat([dormant_properties, totals.to_frame().T])
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dormant_properties.to_csv("dormant_properties.csv")
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v5_surplus_from_dormant = res[
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~res["ha_name"].isin(["HA9"])
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]["surplus_from_dormant"].sum()
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# 5539.0
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# 9471690
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loader.december_figures["ECO4 remaining"].sum()
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december_figures = loader.december_figures.copy()
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december_figures["ECO4 remaining"] = np.where(
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december_figures["ECO4 remaining"] < 0,
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0,
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december_figures["ECO4 remaining"]
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)
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december_figures["ECO4 remaining"].sum()
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