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refactoring creation of epc dataset
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Khalim Conn-Kowlessar
parent
4a6802a5a2
commit
37cc43adb1
2 changed files with 59 additions and 60 deletions
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@ -202,6 +202,33 @@ class AssetList:
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This class is used to standardise asset lists so that we can process the core information in a consistent manner.
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"""
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EPC_API_DATA_NAMES = {
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"uprn": "epc_os_uprn",
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"address1": "epc_address1",
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"address": "epc_address",
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"postcode": "epc_postcode",
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"inspection-date": "epc_inspection_date",
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"current-energy-efficiency": "epc_sap_score_on_register",
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"current-energy-rating": "epc_rating_on_register",
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"property-type": "epc_property_type",
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"built-form": "epc_archetype",
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"total-floor-area": "epc_total_floor_area",
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"construction-age-band": "epc_age_band",
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"floor-height": "epc_floor_height",
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"number-habitable-rooms": "epc_number_habitable_rooms",
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"walls-description": "epc_wall_construction",
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"roof-description": "epc_roof_construction",
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"floor-description": "epc_floor_construction",
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"mainheat-description": "epc_heating_type",
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'mainheatcont-description': "epc_heating_controls",
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"secondheat-description": "epc_secondary_heating",
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"transaction-type": "epc_reason",
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"energy-consumption-current": "epc_heat_demand",
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}
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FIND_EPC_DATA_NAMES = {
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}
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DATETIME_REMAP = {
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"Pre 1900": datetime(year=1899, month=12, day=31),
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}
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@ -590,3 +617,18 @@ class AssetList:
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def create_lookup_mappings(self):
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pass
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def merge_data(self, df: pd.DataFrame):
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"""
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Used to insert data into the standardised asset list, based on the domna property id
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:return:
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"""
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if self.DOMNA_PROPERTY_ID not in df.columns:
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raise ValueError(f"Dataframe must contain the column {self.DOMNA_PROPERTY_ID}")
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if df[self.DOMNA_PROPERTY_ID].duplicated().sum():
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raise ValueError(f"{self.DOMNA_PROPERTY_ID} contains duplicated IDs")
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self.standardised_asset_list = self.standardised_asset_list.merge(
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df, how="left", on=self.DOMNA_PROPERTY_ID
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)
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@ -474,20 +474,22 @@ 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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# TODO: TEMP!!!
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epc_df = epc_df.rename(columns={"row_id": asset_list.DOMNA_PROPERTY_ID})
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# We expand out the recommendations
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recommendations_df = epc_df[["row_id", "recommendations"]]
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recommendations_df = epc_df[[asset_list.DOMNA_PROPERTY_ID, "recommendations"]]
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unique_recommendations = set()
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for _, row in recommendations_df.iterrows():
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unique_recommendations.update([rec["improvement-summary-text"] for rec in row["recommendations"]])
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columns = ["row_id"] + list(unique_recommendations)
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columns = [asset_list.DOMNA_PROPERTY_ID] + list(unique_recommendations)
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transformed_data = []
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for _, row in recommendations_df.iterrows():
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# Initialize a dictionary for this row with False for all recommendations
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row_data = {col: False for col in columns}
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row_data["row_id"] = row["row_id"]
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row_data[asset_list.DOMNA_PROPERTY_ID] = row[asset_list.DOMNA_PROPERTY_ID]
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# Set True for each recommendation present in this row
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for rec in row["recommendations"]:
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@ -500,10 +502,11 @@ def app():
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transformed_df = pd.DataFrame(transformed_data)
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# At the moment, we're only using a limited set of columns - let's jut keep cavity wall insulation
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# recommendations
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transformed_df = transformed_df[["row_id", "Cavity wall insulation"]]
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transformed_df = transformed_df[[asset_list.DOMNA_PROPERTY_ID, "Cavity wall insulation"]]
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# Get the find my epc data
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find_my_epc_data = epc_df[["row_id", "find_my_epc_data"]].drop(columns=["find_my_epc_data"]).join(
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find_my_epc_data = epc_df[[asset_list.DOMNA_PROPERTY_ID, "find_my_epc_data"]].drop(
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columns=["find_my_epc_data"]).join(
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pd.json_normalize(epc_df["find_my_epc_data"])
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)
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# We check if we get the solar pv column:
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@ -513,46 +516,15 @@ def app():
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# Retrieve just the data we need
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epc_df = epc_df[
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[
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"row_id",
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"uprn",
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"address1",
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"address",
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"postcode",
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"property-type",
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"built-form",
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"inspection-date",
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"current-energy-rating",
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"current-energy-efficiency",
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"roof-description",
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"walls-description",
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"floor-description",
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"transaction-type",
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"secondheat-description",
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"total-floor-area",
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"construction-age-band",
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"floor-height",
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"number-habitable-rooms",
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"mainheat-description",
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'mainheatcont-description',
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"energy-consumption-current",
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"photo-supply",
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]
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].rename(
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columns={"address1": "Address1 on EPC", "address": "Address on EPC", "postcode": "Postcode on EPC"}
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[asset_list.DOMNA_PROPERTY_ID] + list(asset_list.EPC_API_DATA_NAMES.keys())
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].rename(
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columns=asset_list.EPC_API_DATA_NAMES
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)
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asset_list.merge_data(epc_df)
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asset_list.insert_
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asset_list = asset_list.merge(
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epc_df,
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how="left",
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on="row_id"
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).merge(
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epc_df = epc_df.merge(
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find_my_epc_data[
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[
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"row_id", "heating_text", "hot_water_text", 'Assessor’s name',
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asset_list.DOMNA_PROPERTY_ID, "heating_text", "hot_water_text", 'Assessor’s name',
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"Assessor's Telephone", "Assessor's Email", "Accreditation scheme",
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"Assessor’s ID", "Solar photovoltaics"
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]
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@ -564,31 +536,16 @@ def app():
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}
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),
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how="left",
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on="row_id"
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on=asset_list.DOMNA_PROPERTY_ID
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)
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asset_list.merge_data(epc_df)
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asset_list["Has Solar PV"] = asset_list["Has Solar PV"] | ~asset_list["photo-supply"].isin(["0.0", 0, None, ""])
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asset_list = asset_list.drop(columns=["photo-supply"])
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# Rename the columns
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asset_list = asset_list.rename(columns={
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"inspection-date": "Date of last EPC",
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"current-energy-efficiency": "SAP score on register",
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"current-energy-rating": "EPC rating on register",
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"property-type": "Property Type",
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"built-form": "Archetype - EPC",
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"total-floor-area": "Property Floor Area",
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"construction-age-band": "Property Age Band",
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"floor-height": "Property Floor Height",
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"number-habitable-rooms": "Number of Habitable Rooms",
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"walls-description": "Wall Construction",
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"roof-description": "Roof Construction",
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"floor-description": "Floor Construction",
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"mainheat-description": "Heating Type",
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"secondheat-description": "Secondary Heating",
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"transaction-type": "Reason for last EPC",
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"energy-consumption-current": "Heat Demand (kWh/m2)",
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})
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asset_list = asset_list
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asset_list["Estimated Number of Floors"] = asset_list.apply(
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lambda x: estimate_number_of_floors(property_type=x["Property Type"]) if not pd.isnull(
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