Refactor small_field_preset function to ensure consistent DataFrame structure by resetting the index after filtering and sorting the working portfolio by Median before returning results, improving accuracy in lineup selection.

app.py

@@ -1115,7 +1115,7 @@ with tab2:
                         #     parsed_frame = volatile_preset(st.session_state['working_frame'], lineup_target)
                         # elif preset_choice == 'Distributed':
                         #     parsed_frame = distributed_preset(st.session_state['working_frame'], lineup_target)
-                        st.session_state['working_frame'] = parsed_frame.sort_values(by='Own', ascending=False)
+                        st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
                         st.session_state['export_merge'] = st.session_state['working_frame'].copy()
 
         with col2:

global_func/small_field_preset.py

@@ -5,7 +5,8 @@ def small_field_preset(portfolio: pd.DataFrame, lineup_target: int):
     for slack_var in range(1, 10):
         rows_to_drop = []
         working_portfolio = portfolio.sort_values(by='Own', ascending = False).reset_index(drop=True)
-        working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] <= .10].reset_index(drop=True)
+        working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] <= .10]
+        working_portfolio = working_portfolio.reset_index(drop=True)
         curr_own_type_max = working_portfolio.loc[0, 'Weighted Own'] + (slack_var / 10 * working_portfolio.loc[0, 'Weighted Own'])
 
         for i in range(1, len(working_portfolio)):
@@ -16,6 +17,6 @@ def small_field_preset(portfolio: pd.DataFrame, lineup_target: int):
 
         working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
         if len(working_portfolio) >= lineup_target:
-            return working_portfolio.head(lineup_target)
+            return working_portfolio.sort_values(by='Median', ascending=False).head(lineup_target)
         
-    return working_portfolio
+    return working_portfolio.sort_values(by='Median', ascending=False)