Update pages/market_rent_estimation.py

pages/market_rent_estimation.py

@@ -105,8 +105,7 @@ def main():
         filtered_data = df_data[["google_ola", "market_costar", "submarket_costar", "execution_date", "rented_sf", "building_sf", "year_built", "office_rate", "min_clear_height", "max_clear_height", "docks", "drive_ins", "rent_combined"]]#pd.concat([filtered_data2])
         filtered_data.insert(loc=1, column='Similarity score', value=comps_scores)
         
-        # Formatting the DataFrame according to the rules
-        # filtered_data['Address'] = filtered_data['Address']
+        # Formatting the DataFrame 
         filtered_data['Similarity score'] = (1 - filtered_data['Similarity score']) * 100
         filtered_data['execution_date'] = pd.to_datetime(filtered_data['execution_date']).dt.strftime('%m-%d-%Y')
         filtered_data['LSF (sf)'] = filtered_data['rented_sf'].round(0).astype(int)
@@ -117,8 +116,8 @@ def main():
         filtered_data.loc[filtered_data['min_clear_height'].notna(), 'min_clear_height'] = (filtered_data.loc[filtered_data['min_clear_height'].notna(), 'min_clear_height']).round(0).astype(int)
         # filtered_data['Clear Height (feet)'] = filtered_data['min_clear_height'].round(0).astype(int)
 
-        filtered_data.loc[filtered_data['docks'].notna(), 'docks'] = (filtered_data.loc[filtered_data['docks'].notna(), 'docks']).astype(int)
-        filtered_data.loc[filtered_data['drive_ins'].notna(), 'drive_ins'] = (filtered_data.loc[filtered_data['drive_ins'].notna(), 'drive_ins']).astype(int)
+        filtered_data.loc[filtered_data['docks'].notna(), 'docks'] = (filtered_data.loc[filtered_data['docks'].notna(), 'docks']).round(0).astype(int)
+        filtered_data.loc[filtered_data['drive_ins'].notna(), 'drive_ins'] = (filtered_data.loc[filtered_data['drive_ins'].notna(), 'drive_ins']).round(0).astype(int)
 
         # filtered_data['Docks (/10ksf)'] = filtered_data['docks'].astype(int)
         # filtered_data['Doors (/10ksf)'] = filtered_data['drive_ins'].astype(int)
@@ -128,10 +127,12 @@ def main():
         filtered_data = filtered_data.drop(columns=['rented_sf', 'building_sf', 'year_built', 'max_clear_height', 'rent_combined'])
         filtered_data = filtered_data.rename(columns={
             'google_ola': 'Address',
-            'Office %': 'office_rate',
-            'Clear Height (feet)': 'min_clear_height',
-            'Docks (/10ksf)': 'docks',
-            'Doors (/10ksf)': 'drive_ins'
+            'office_rate': 'Office %',
+            'min_clear_height': 'Clear Height (feet)',
+            'docks': 'Docks (/10ksf)',
+            'drive_ins': 'Doors (/10ksf)',
+            'market_costar': 'Market',
+            'submarket_costar': 'Submarket'
         })