Update pages/market_rent_estimation.py

pages/market_rent_estimation.py

@@ -56,15 +56,15 @@ def main():
 
     #comps page
     with tab1:
-        filtered_data = reordered_df_properties_filtered[["Similarity score", "google_ola", "market_costar", "submarket_costar", 'distance_from_first (km)', "execution_date", "rented_sf", "building_sf", "year_built", "office_rate", "min_clear_height", "max_clear_height", "docks", "drive_ins", "rent_combined", "lat", "long"]]#pd.concat([filtered_data2])
+        filtered_data = reordered_df_properties_filtered[["Similarity score", "google_ola", "market_costar", "submarket_costar", 'distance_from_first (km)', "months_since", "rented_sf", "building_sf", "year_built", "office_rate", "min_clear_height", "max_clear_height", "docks", "drive_ins", "rent_combined", "lat", "long"]]#pd.concat([filtered_data2])
         # comps_scores = sorted_distances
 
         # filtered_data.insert(loc=1, column='Similarity score', value=comps_scores)
 
         # Formatting the DataFrame
         # filtered_data['Similarity score'] = ((1 - filtered_data['Similarity score']) * 100).apply(lambda x: f"{x:.2f}")
-        filtered_data['Similarity score'] = (filtered_data['Similarity score'] * 100).apply(lambda x: f"{x:.2f}")
-        filtered_data['execution_date'] = pd.to_datetime(filtered_data['execution_date']).dt.strftime('%m-%d-%Y')
+        # filtered_data['Similarity score'] = (filtered_data['Similarity score'] * 100).apply(lambda x: f"{x:.2f}")
+        # 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'].fillna(0).astype(int)