preparing filtered columns

backend/onboarders/base.py

@@ -1,5 +1,5 @@
 import pandas as pd
-from utils.s3 import read_from_s3, read_excel_from_s3
+from utils.s3 import read_from_s3, read_excel_from_s3, save_csv_to_s3
 
 
 class OnboarderBase:
@@ -48,8 +48,11 @@ class OnboarderBase:
         else:
             self.data = read_from_s3(bucket_name=bucket_name, s3_file_name=file_name)
 
-    def write(self):
+    def write(self, bucket_name: str, file_name: str):
-        pass
+        if self.data is None:
+            raise ValueError("No data to write. Please run transform() before writing.")
+        # Store file as csv - will store in the same route location as the input file
+        save_csv_to_s3(dataframe=self.data, bucket_name=bucket_name, file_name=file_name)
 
     @staticmethod
     def assert_nulls_only_from_source_nulls(data: pd.DataFrame, original_column: str, mapped_column: str) -> bool:

backend/onboarders/handler.py

@@ -1,10 +1,19 @@
 import json
+from pydantic import BaseModel, Field
+from typing import Optional, Literal
 from onboarders.factory import OnboarderFactory
 from utils.logger import setup_logger
 
 logger = setup_logger()
 
 
+class OnboardingEvent(BaseModel):
+    s3_uri: str = Field(..., description="S3 URI of the raw ARA input file")
+    system: Literal["parity", "generic"] = Field(..., description="Onboarding system identifier")
+    format: Literal["csv", "xlsx"]
+    sheet_name: Optional[str] = None
+
+
 def handler(event, context):
     """
     Lambda handler that triggers the model engine for each SQS message.
@@ -12,22 +21,27 @@ def handler(event, context):
     for record in event.get("Records", []):
         try:
             event_body = json.loads(record["body"])
-            # TODO: Implement logic to check which file type we have
             # Sample input data
-            event_body = {
+            # event_body = {
-                "s3_uri": "s3://retrofit-data-dev/ara_raw_inputs/peabody/2025_11_11 - Peabody - Data Extracts for "
+            #     "s3_uri": "s3://retrofit-data-dev/ara_raw_inputs/peabody/2025_11_11 - Peabody - Data Extracts for "
-                          "Domna.xlsx",
+            #               "Domna.xlsx",
-                "system": "parity",
+            #     "system": "parity",
-                "format": "xlsx",
+            #     "format": "xlsx",
-                "sheet_name": "Sustainability"
+            #     "sheet_name": "Sustainability"
-            }
+            # }
             logger.info("Processing record with body: %s", event_body)
-            Onboarder = OnboarderFactory.create_onboarder(event_body["system"])
-            onboarder = Onboarder(fileuri=event_body["s3_uri"])
 
-            logger.info("Transforming data for record with body: %s", event_body)
+            validated_event = OnboardingEvent(**event_body)
+            Onboarder = OnboarderFactory.create_onboarder(validated_event.system)
+            onboarder = Onboarder(
+                fileuri=validated_event.s3_uri,
+                format=validated_event.format,
+                sheet_name=validated_event.sheet_name
+            )
+
+            logger.info("Transforming data")
             onboarder.transform()
-            logger.info("Writing data for record with body: %s", event_body)
+            logger.info("Writing data")
             onboarder.write()
         except Exception as e:
             logger.error(f"Failed to process record: {e}")

backend/onboarders/mappings/parity/as_built_floor_classifiers.py

@@ -56,4 +56,5 @@ as_built_floor_classifiers = {
 unknown_as_built_floor_classifiers = {
     "RetroFitted": unknown_floor_retrofitted,
     "AsBuilt": unknown_floor_as_built,
+    "Unknown": unknown_floor_as_built,
 }

backend/onboarders/mappings/parity/as_built_roof_classifiers.py

@@ -1,4 +1,5 @@
-from backend.onboarders.epc_descriptions import EpcConstructionAgeBand, EpcRoofDescriptions
+from datatypes.epc.roof import EpcRoofDescriptions
+from datatypes.epc.construction_age_band import EpcConstructionAgeBand
 
 
 def map_flat_roof(age_band: EpcConstructionAgeBand) -> EpcRoofDescriptions:

backend/onboarders/mappings/parity/as_built_wall_classifiers.py

@@ -1,4 +1,5 @@
-from backend.onboarders.epc_descriptions import EpcConstructionAgeBand, EpcWallDescriptions
+from datatypes.epc.construction_age_band import EpcConstructionAgeBand
+from datatypes.epc.walls import EpcWallDescriptions
 
 
 def map_cavity_wall_insulation(age_band: EpcConstructionAgeBand):

backend/onboarders/parity.py

@@ -30,12 +30,13 @@ class ParityOnboarder(OnboarderBase):
     def __init__(
         self,
         fileuri: str,
+        **kwargs
     ):
         # Extract bucket, and filekey; Will be in the format s3://bucket/key
         bucket_name = fileuri.split("/")[2]
         file_name = "/".join(fileuri.split("/")[3:])
 
-        self.read_s3(bucket_name=bucket_name, file_name=file_name)
+        self.read_s3(bucket_name=bucket_name, file_name=file_name, **kwargs)
         pass
 
     def map_construction_age_band(self):
@@ -61,20 +62,20 @@ class ParityOnboarder(OnboarderBase):
         type and age band
         """
         # Already resolved via direct mapping
-        if row.landlord_wall_description is not None:
+        if row.landlord_wall_construction is not None:
-            return row.landlord_wall_description
+            return row.landlord_wall_construction
 
         wall_type = row["Wall Construction"]
 
         # Missing construction age → conservative fallback
-        if pd.isnull(row.construction_age_band):
+        if pd.isnull(row.landlord_construction_age_band):
             return wall_unknown_age_fallback.get(wall_type)
 
         classifier = as_built_wall_classifiers.get(wall_type)
         if classifier is None:
             return None
 
-        return classifier(row.construction_age_band)
+        return classifier(row.landlord_construction_age_band)
 
     @staticmethod
     def _resolve_wall_efficiency(
@@ -113,8 +114,8 @@ class ParityOnboarder(OnboarderBase):
 
         self.data[self.landlord_wall_efficiency] = self.data.progress_apply(
             lambda row: self._resolve_wall_efficiency(
-                row.landlord_wall_description,
+                row.landlord_wall_construction,
-                row.construction_age_band,
+                row.landlord_construction_age_band,
             ),
             axis=1,
         )
@@ -124,8 +125,8 @@ class ParityOnboarder(OnboarderBase):
     @staticmethod
     def _fill_roof_as_built(row: pd.Series) -> EpcRoofDescriptions | None:
         # Already resolved
-        if not pd.isnull(row.landlord_roof_description):
+        if not pd.isnull(row.landlord_roof_construction):
-            return row.landlord_roof_description
+            return row.landlord_roof_construction
 
         roof_type = row["Roof Construction"]
 
@@ -133,10 +134,10 @@ class ParityOnboarder(OnboarderBase):
         if classifier is None:
             raise NotImplementedError(f"No roof classifier for roof type '{roof_type}'")
 
-        if pd.isnull(row.construction_age_band):
+        if pd.isnull(row.landlord_construction_age_band):
             return roof_unknown_age_fallback.get(roof_type)
 
-        output = classifier(row.construction_age_band)
+        output = classifier(row.landlord_construction_age_band)
         if output is None:
             raise NotImplementedError(
                 f"Roof classification returned None for roof type '{roof_type}'"
@@ -180,8 +181,8 @@ class ParityOnboarder(OnboarderBase):
 
         self.data[self.landlord_roof_efficiency] = self.data.progress_apply(
             lambda row: resolve_roof_efficiency(
-                description=row.landlord_roof_description,
+                description=row.landlord_roof_construction,
-                age_band=row.construction_age_band,
+                age_band=row.landlord_construction_age_band,
                 insulation_thickness=row.roof_insulation_thickness_mm,
             ),
             axis=1,
@@ -190,17 +191,17 @@ class ParityOnboarder(OnboarderBase):
         self.assert_no_nulls(self.data, self.landlord_roof_efficiency)
 
         # Flag sloping ceiling
-        data[self.landlord_has_sloping_ceiling] = data["Roof Construction"].apply(
+        self.data[self.landlord_has_sloping_ceiling] = self.data["Roof Construction"].apply(
             lambda x: x == "PitchedWithSlopingCeiling"
         )
 
     @staticmethod
     def _fill_floor_as_built(row: pd.Series):
         # 1. Already resolved
-        if row.landlord_floor_description is not None:
+        if row.landlord_floor_construction is not None:
-            return row.landlord_floor_description
+            return row.landlord_floor_construction
 
-        age_band = row.construction_age_band
+        age_band = row.landlord_construction_age_band
         floor_type = row["Floor Construction"]
         insulation = row["Floor Insulation"]
 
@@ -281,6 +282,48 @@ class ParityOnboarder(OnboarderBase):
             columns={"Total Floor Area (m2)": self.landlord_total_floor_area_m2}
         )
 
+    def select_columns(self):
+        self.data = self.data[
+            [
+                "Org Ref",
+                "UPRN",
+                "Address 1",
+                "Address 2",
+                "Address 3",
+                "Postcode",
+                self.landlord_total_floor_area_m2,
+                self.landlord_construction_age_band,
+                self.landlord_property_type,
+                self.landlord_built_form,
+                self.landlord_wall_construction,
+                self.landlord_wall_efficiency,
+                self.landlord_roof_construction,
+                self.landlord_roof_efficiency,
+                self.landlord_has_sloping_ceiling,
+                self.landlord_floor_construction,
+                self.landlord_windows_construction,
+                self.landlord_windows_efficiency,
+                self.landlord_multi_glaze_proportion,
+                self.landlord_glazed_type,
+                self.landlord_glazed_area,
+                self.landlord_heating_construction,
+                self.landlord_heating_efficiency,
+                self.landlord_fuel_construction,
+                self.landlord_heating_controls_construction,
+                self.landlord_heating_controls_efficiency,
+                self.landlord_hot_water_system_construction,
+                self.landlord_hot_water_efficiency
+            ]
+        ].rename(
+            columns={
+                "Org Ref": "landlord_property_id",
+                "Address1": "address1",
+                "Address2": "address2",
+                "Address3": "address3",
+                "Postcode": "postcode",
+            }
+        )
+
     def transform(self):
         # ------------ construction_age_band ------------
         self.map_construction_age_band()
@@ -308,3 +351,6 @@ class ParityOnboarder(OnboarderBase):
 
         # ------------ Floor Area ------------
         self.map_floor_area()
+
+        # ------------ Formating ------------
+        self.select_columns()