Refactor payout calculation application in app.py

- Modified the application of the get_payout_for_position function to use the DataFrame's apply method with axis=1, allowing for more accurate handling of individual row data, including the count of tied entries.
- Updated the function signature to remove the default value for dupes_count, ensuring clarity in its usage.

app.py

@@ -106,7 +106,7 @@ def export_contest_file(db, sport, type, contest_date, contest_id, contest_data)
 
     return return_message
 
-def get_payout_for_position(finish_pos, payout_df, dupes_count=1):
+def get_payout_for_position(finish_pos, payout_df, dupes_count):
     """
     Calculate payout for a position, handling ties by splitting the combined payout.
     
@@ -445,9 +445,10 @@ if selected_tab == 'Data Load':
             working_df['finish'] = working_df['index']
             working_df = working_df.drop(['sorted', 'index'], axis=1)
             try:
-                working_df['payout'] = working_df['finish'].apply(
-                        lambda x: get_payout_for_position(x, st.session_state['payout_info'], working_df['dupes'])
-                    )
+                working_df['payout'] = working_df.apply(
+                    lambda row: get_payout_for_position(row['finish'], st.session_state['payout_info'], row['dupes']), 
+                    axis=1
+                )
             except:
                 pass
         
@@ -526,9 +527,10 @@ if selected_tab == 'Data Load':
             working_df['finish'] = working_df['index']
             working_df = working_df.drop(['sorted', 'index'], axis=1)
             try:
-                working_df['payout'] = working_df['finish'].apply(
-                        lambda x: get_payout_for_position(x, st.session_state['payout_info'], working_df['dupes'])
-                    )
+                working_df['payout'] = working_df.apply(
+                    lambda row: get_payout_for_position(row['finish'], st.session_state['payout_info'], row['dupes']), 
+                    axis=1
+                )
             except:
                 pass