diff --git a/etl/bill_savings/KwhData.py b/etl/bill_savings/KwhData.py
index 30e11698..e815a12b 100644
--- a/etl/bill_savings/KwhData.py
+++ b/etl/bill_savings/KwhData.py
@@ -77,14 +77,8 @@ class KwhData:
                   'Cheapest tariff (Large legacy suppliers)', 'Cheapest tariff (All suppliers)',
                   'Cheapest tariff (Basket)', 'Default tariff cap level']
 
-        # Extract data rows
-        data_rows = []
-        for row in data[1:]:
-            date = row['\ufeff"']
-            values = row[None]
-            data_rows.append([date] + values)
-
-        self.retail_price_comparison = pd.DataFrame(data_rows, columns=header)
+        self.retail_price_comparison = pd.DataFrame(data)
+        self.retail_price_comparison.columns = header
         self.retail_price_comparison['Date'] = pd.to_datetime(self.retail_price_comparison['Date'], errors='coerce')
 
     @staticmethod
diff --git a/sfr/principal_pitch/2_export_data.py b/sfr/principal_pitch/2_export_data.py
index 7c80f4dc..06727f86 100644
--- a/sfr/principal_pitch/2_export_data.py
+++ b/sfr/principal_pitch/2_export_data.py
@@ -230,7 +230,7 @@ for scenario_id in SCENARIOS:
     # Get recs for this scenario
     recommended_measures_df = recommendations_df[
         recommendations_df["scenario_id"] == scenario_id
-    ][["property_id", "measure_type", "estimated_cost", "default"]]
+        ][["property_id", "measure_type", "estimated_cost", "default"]]
     recommended_measures_df = recommended_measures_df[
         recommended_measures_df["default"]
     ]
@@ -238,7 +238,7 @@ for scenario_id in SCENARIOS:
 
     post_install_sap = recommendations_df[
         recommendations_df["scenario_id"] == scenario_id
-    ][["property_id", "default", "sap_points"]]
+        ][["property_id", "default", "sap_points"]]
     post_install_sap = post_install_sap[post_install_sap["default"]]
     # Sum up the sap points by property id
     post_install_sap = (
@@ -282,6 +282,7 @@ for scenario_id in SCENARIOS:
                 "windows",
                 "current_epc_rating",
                 "current_sap_points",
+                "original_sap_points",
                 "total_floor_area",
                 "number_of_rooms",
                 "lodgement_date",
@@ -303,31 +304,6 @@ for scenario_id in SCENARIOS:
     )
     df["uprn"] = df["uprn"].astype(str)
 
-    relevant_plans = plans_df[plans_df["scenario_id"] == scenario_id]
-    df2 = df.merge(
-        relevant_plans[["property_id", "post_sap_points", "post_epc_rating"]],
-        how="left",
-        on="property_id",
-        suffixes=("", "_plan"),
-    )
-    print(df2["predicted_post_works_epc"].value_counts())
-    print(df2["post_epc_rating"].value_counts())
-
-    z = df2[
-        (df2["predicted_post_works_epc"] != "D")
-        & (df2["post_epc_rating"].astype(str) == "Epc.D")
-    ]
-
-    df2["predicted_post_works_epc"].value_counts()
-    df2["post_epc_rating"].astype(str).value_counts()
-
-    df2[df2["total_retrofit_cost"] > 0].shape
-
-    getting_works = df[df["total_retrofit_cost"] > 0]
-    getting_works["predicted_post_works_epc"].value_counts()
-
-    df[df["predicted_post_works_sap"] == ""]
-
     # Expected columns list
     expected_columns = [
         "suspended_floor_insulation",