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adding work reasons
This commit is contained in:
Khalim Conn-Kowlessar
parent
ea1a7b559d
commit
7b4218299f
2 changed files with 91 additions and 24 deletions
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@ -296,7 +296,7 @@ class AssetList:
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ATTRIBUTE_HEAT_LOSS_AREA = "attribute_heat_loss_area"
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ATTRIBUTE_EPC_ROOF_INSULATION_THICKNESS = "attribute_epc_roof_insulation_thickness"
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ATTRIBUTE_SAP_THRESHOLD_AND_BELOW = f"sap_rating_{FILLED_CAVITY_SAP_THRESHOLD}_and_below"
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ATTRIBUTE_EPC_PRE_YEAR_THRESHOLD = f"EPC is pre {EPC_YEAR_THRESHOLD}"
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ATTRIBUTE_EPC_PRE_YEAR_THRESHOLD = f"epc_is_pre_{EPC_YEAR_THRESHOLD}"
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# These are the descriptions that we look for in the EPC data that are indicative of no insulation
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EPC_NO_WALL_INSULATION_DESCRIPTIONS = [
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@ -775,7 +775,7 @@ class AssetList:
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self.DOMNA_PROPERTY_ID: x[self.DOMNA_PROPERTY_ID],
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"epc_year_lower_bound": None,
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"epc_year_upper_bound": None,
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"Does Age Match EPC Age Band?": "No EPC Age Band"
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"does_age_band_match_epc_age_band": "No EPC Age Band"
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}
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)
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continue
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@ -800,7 +800,7 @@ class AssetList:
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self.DOMNA_PROPERTY_ID: x[self.DOMNA_PROPERTY_ID],
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"epc_year_lower_bound": year_lower_bound,
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"epc_year_upper_bound": None,
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"Does Age Match EPC Age Band?": age_band_matches
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"does_age_band_match_epc_age_band": age_band_matches
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}
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)
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continue
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@ -820,7 +820,7 @@ class AssetList:
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self.DOMNA_PROPERTY_ID: x[self.DOMNA_PROPERTY_ID],
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"epc_year_lower_bound": None,
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"epc_year_upper_bound": 1899,
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"Does Age Match EPC Age Band?": age_band_matches
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"does_age_band_match_epc_age_band": age_band_matches
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}
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)
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continue
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@ -842,7 +842,7 @@ class AssetList:
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self.DOMNA_PROPERTY_ID: x[self.DOMNA_PROPERTY_ID],
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"epc_year_lower_bound": int(x[self.EPC_API_DATA_NAMES["construction-age-band"]]),
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"epc_year_upper_bound": int(x[self.EPC_API_DATA_NAMES["construction-age-band"]]),
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"Does Age Match EPC Age Band?": age_band_matches
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"does_age_band_match_epc_age_band": age_band_matches
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}
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)
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continue
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@ -864,7 +864,7 @@ class AssetList:
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self.DOMNA_PROPERTY_ID: x[self.DOMNA_PROPERTY_ID],
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"epc_year_lower_bound": int(lower_date),
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"epc_year_upper_bound": int(upper_date),
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"Does Age Match EPC Age Band?": age_band_matches
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"does_age_band_match_epc_age_band": age_band_matches
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}
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)
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@ -892,7 +892,12 @@ class AssetList:
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(~self.standardised_asset_list[self.STANDARD_PROPERTY_TYPE].isin(["bedsit"])) &
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(self.standardised_asset_list['non-intrusives: Construction'] == "CAVITY") &
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self.standardised_asset_list['non-intrusives: Insulated'].isin(["EMPTY", "PARTIAL"]) &
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(self.standardised_asset_list[self.STANDARD_YEAR_BUILT] <= 2000)
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(self.standardised_asset_list[self.STANDARD_YEAR_BUILT] <= 2000) &
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(
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self.standardised_asset_list[
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self.EPC_API_DATA_NAMES["current-energy-efficiency"]
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] <= self.EMPTY_CAVITY_SAP_THRESHOLD
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)
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)
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self.standardised_asset_list["epc_indicates_empty_cavity"] = (
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@ -1206,6 +1211,11 @@ class AssetList:
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self.standardised_asset_list["solar_epc_floor_is_other_insulated"]
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)
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# Drop anything we don't need
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self.standardised_asset_list = self.standardised_asset_list.drop(
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columns=["walls_u_value", "roof_u_value", "floor_u_value"]
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)
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# Produce some aggregate figures
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self.work_type_figures = {
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# Empty cavity from non-intrusives
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@ -1219,7 +1229,11 @@ class AssetList:
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).sum()
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),
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"Cavity Extraction": (
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self.standardised_asset_list["non_intrusive_indicates_cavity_extraction"].sum()
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(
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~self.standardised_asset_list["non_intrusive_indicates_empty_cavity"] &
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~self.standardised_asset_list["epc_indicates_empty_cavity"] &
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self.standardised_asset_list["non_intrusive_indicates_cavity_extraction"]
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).sum()
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),
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"Solar PV (Solid Floor)": (
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self.standardised_asset_list["solar_eligible_solid_floor"].sum()
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@ -1234,3 +1248,51 @@ class AssetList:
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self.standardised_asset_list["solar_eligible_other_floor_needs_loft"].sum()
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)
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}
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# Finally, we note why each property has been flagged
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self.standardised_asset_list["cavity_reason"] = None
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self.standardised_asset_list["cavity_reason"] = np.where(
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self.standardised_asset_list["non_intrusive_indicates_empty_cavity"],
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"Non-Intrusive Data Showed Empty Cavity",
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self.standardised_asset_list["cavity_reason"]
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)
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self.standardised_asset_list["cavity_reason"] = np.where(
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(
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self.standardised_asset_list["epc_indicates_empty_cavity"] &
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~self.standardised_asset_list["non_intrusive_indicates_empty_cavity"]
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),
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"EPC Data Showed Empty Cavity",
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self.standardised_asset_list["cavity_reason"]
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)
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# Flag extraction
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self.standardised_asset_list["cavity_reason"] = np.where(
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(
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self.standardised_asset_list["non_intrusive_indicates_cavity_extraction"] &
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pd.isnull(self.standardised_asset_list["cavity_reason"])
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),
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"Non-Intrusive Data Showed Cavity Extraction",
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self.standardised_asset_list["cavity_reason"]
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)
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# Flag solar
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self.standardised_asset_list["solar_reason"] = None
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self.standardised_asset_list["solar_reason"] = np.where(
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self.standardised_asset_list["solar_eligible_solid_floor"],
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"Solid Floor, Insulated, No Solar",
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self.standardised_asset_list["solar_reason"]
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)
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self.standardised_asset_list["solar_reason"] = np.where(
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self.standardised_asset_list["solar_eligible_solid_floor_needs_loft"],
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"Solid Floor, Insulated, Needs Loft",
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self.standardised_asset_list["solar_reason"]
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)
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self.standardised_asset_list["solar_reason"] = np.where(
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self.standardised_asset_list["solar_eligible_other_floor"],
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"Other Floor, Insulated, No Solar",
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self.standardised_asset_list["solar_reason"]
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)
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self.standardised_asset_list["solar_reason"] = np.where(
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self.standardised_asset_list["solar_eligible_other_floor_needs_loft"],
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"Other Floor, Insulated, Needs Loft",
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self.standardised_asset_list["solar_reason"]
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)
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@ -376,6 +376,7 @@ def app():
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epc_data.append(csv_data)
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epc_df = pd.concat(epc_data)
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epc_df["estimated"] = epc_df["estimated"].fillna(False)
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# We expand out the recommendations
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recommendations_df = epc_df[[asset_list.DOMNA_PROPERTY_ID, "recommendations"]]
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@ -454,36 +455,40 @@ def app():
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asset_list.identify_worktypes(cleaned)
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from pprint import pprint
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pprint(asset_list.work_type_figures)
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# TODO: We should do this breakdown for flats
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def flat_analysis(asset_list):
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# We need to deduce the building name - we strip out the house number
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def extract_building_name(x):
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# TODO: This doesn't really work
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if pd.isnull(x):
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return None
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house_no = SearchEpc.get_house_number(address=x, postcode=None)
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if house_no:
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return x.replace(house_no, "").strip()
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return x.split(",")[0].strip()
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# We want to deduce if flats have 50% of the properties below C75
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# We group by postcode and property type
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grouped = asset_list.groupby([POSTCODE_COLUMN, "Property Type"])
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grouped = asset_list.standardised_asset_list.groupby(
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[asset_list.STANDARD_POSTCODE, asset_list.STANDARD_PROPERTY_TYPE]
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)
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flat_data = []
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for _, group in grouped:
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if "flat" in group["Property Type"].str.lower().values:
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num_flats = group["Property Type"].str.lower().value_counts().get("flat", 0)
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num_below_c75 = group["SAP score on register"].lt(75).sum()
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if "flat" in group[asset_list.STANDARD_PROPERTY_TYPE].values:
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num_flats = group[asset_list.STANDARD_PROPERTY_TYPE].shape[0]
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num_below_c75 = group[
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asset_list.EPC_API_DATA_NAMES["current-energy-efficiency"]
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].lt(asset_list.FILLED_CAVITY_SAP_THRESHOLD).sum()
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# Check if any flats are below C69
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num_flats_below_c69 = group[
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asset_list.EPC_API_DATA_NAMES["current-energy-efficiency"]
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].lt(69).sum()
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flat_data.append(
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{
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"Postcode": group[POSTCODE_COLUMN].iloc[0],
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"Postcode": group[asset_list.STANDARD_POSTCODE].iloc[0],
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"Property Type": "Flat",
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"Number of Flats with EPC": num_flats,
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"Number of Flats below C75": num_below_c75,
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"Proportion of Flat EPCs below C75": round(100 * num_below_c75 / num_flats)
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"Proportion of Flat EPCs below C75": round(100 * num_below_c75 / num_flats),
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"num_flats_below_c69": num_flats_below_c69,
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}
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)
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@ -494,11 +499,11 @@ def app():
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flat_data = flat_analysis(asset_list)
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# Store as an excel
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filename = os.path.join(DATA_FOLDER, ".".join(DATA_FILENAME.split(".")[:-1])) + " EPC Data Pull.xlsx"
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filename = os.path.join(DATA_FOLDER, ".".join(DATA_FILENAME.split(".")[:-1])) + " - Standardised.xlsx"
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# Store the data in two tabs. One for the asset list with the EPC data and the second with the flat data
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with pd.ExcelWriter(filename) as writer:
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asset_list.to_excel(writer, sheet_name="EPC Data", index=False)
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asset_list.standardised_asset_list.to_excel(writer, sheet_name="Standardised Asset List", index=False)
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flat_data.to_excel(writer, sheet_name="Flat Data", index=False)
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matches_review = asset_list[
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