Add player salary mapping to optimize data display and analysis

- Create player_salaries dictionary mapping players to their salaries
- Update summary DataFrame generation to include player salaries
- Ensure salary information is available when resetting data and generating player frequency reports

app.py

@@ -110,6 +110,7 @@ def convert_df(array):
     return array.to_csv().encode('utf-8')
 
 roo_data, sd_roo_data, timestamp = init_baselines()
+player_salaries = map(dict, roo_data[['Player', 'Salary']].values)
 hold_display = roo_data
 lineup_display = []
 check_list = []
@@ -178,6 +179,7 @@ with tab2:
         if st.button("Load/Reset Data", key='reset2'):
             st.cache_data.clear()
             roo_data, sd_roo_data, timestamp = init_baselines()
+            player_salaries = map(dict, roo_data[['Player', 'Salary']].values)
             hold_display = roo_data
             dk_lineups = init_DK_lineups('Regular')
             fd_lineups = init_FD_lineups('Regular')
@@ -379,6 +381,7 @@ with tab2:
                     # Create a DataFrame with the results
                     summary_df = pd.DataFrame({
                         'Player': value_counts.index,
+                        'Salary': value_counts.index.map(player_salaries),
                         'Frequency': value_counts.values,
                         'Percentage': percentages.values
                     })
@@ -411,6 +414,7 @@ with tab2:
                     # Create a DataFrame with the results
                     summary_df = pd.DataFrame({
                         'Player': value_counts.index,
+                        'Salary': value_counts.index.map(player_salaries),
                         'Frequency': value_counts.values,
                         'Percentage': percentages.values
                     })