Add percentile finish calculation in app.py

- Introduced a new column 'percentile_finish' to calculate the percentile rank of 'actual_fpts' for players, enhancing the metrics available for analysis.
- This change improves the granularity of player performance insights and supports future enhancements in data processing and visualization.

app.py

@@ -147,6 +147,7 @@ with tab2:
                 axis=1
             )
             working_df['dupes'] = working_df.groupby('sorted').transform('size')
+            working_df['percentile_finish'] = working_df['actual_fpts'].rank(pct=True)
             working_df = working_df.drop('sorted', axis=1)
         elif type_var == 'Showdown':
             working_df['stack'] = working_df.apply(
@@ -183,6 +184,7 @@ with tab2:
                 axis=1
             )
             working_df['dupes'] = working_df.groupby('sorted').transform('size')
+            working_df['percentile_finish'] = working_df['actual_fpts'].rank(pct=True)
             working_df = working_df.drop('sorted', axis=1)
         
         # Initialize pagination in session state if not exists
@@ -236,6 +238,7 @@ with tab2:
         len_5per = len(st.session_state['Contest']) * 0.05
         len_10per = len(st.session_state['Contest']) * 0.10
         len_20per = len(st.session_state['Contest']) * 0.20
+        ######## Going to try a groupby based on finishing percentiles here next
         player_counts = pd.Series(list(contest_players[player_columns].values.flatten())).value_counts()
         player_1per_counts = pd.Series(list(players_1per[player_columns].values.flatten())).value_counts()
         player_5per_counts = pd.Series(list(players_5per[player_columns].values.flatten())).value_counts()