Update pages/market_rent_estimation.py

pages/market_rent_estimation.py

@@ -106,18 +106,18 @@ def main():
         filtered_data.insert(loc=1, column='Similarity score', value=comps_scores)
         
         # Formatting the DataFrame 
-        filtered_data['Similarity score'] = (1 - filtered_data['Similarity score']) * 100
+        filtered_data['Similarity score'] = ((1 - filtered_data['Similarity score']) * 100).round(2)
         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)
         filtered_data['RSF (sf)'] = filtered_data['building_sf'].round(0).astype(int)
         filtered_data['Year built'] = filtered_data['year_built'].astype(int)
         filtered_data.loc[filtered_data['office_rate'].notna(), 'office_rate'] = (filtered_data.loc[filtered_data['office_rate'].notna(), 'office_rate'] * 100).round(0)
 
-        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.loc[filtered_data['min_clear_height'].notna(), 'min_clear_height'] = (filtered_data.loc[filtered_data['min_clear_height'].notna(), 'min_clear_height']).astype(int).round(0)
         # 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']).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.loc[filtered_data['docks'].notna(), 'docks'] = (filtered_data.loc[filtered_data['docks'].notna(), 'docks']).astype(int).round(0)
+        filtered_data.loc[filtered_data['drive_ins'].notna(), 'drive_ins'] = (filtered_data.loc[filtered_data['drive_ins'].notna(), 'drive_ins']).astype(int).round(0)
 
         # filtered_data['Docks (/10ksf)'] = filtered_data['docks'].astype(int)
         # filtered_data['Doors (/10ksf)'] = filtered_data['drive_ins'].astype(int)