Update app.py

app.py

@@ -435,7 +435,7 @@ with tab6:
                                 for x in range(0,total_sims):
                                     overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
         
-                                overall_file=overall_file.drop(['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                                overall_file=overall_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
         
                                 players_only = hold_file[['Player']]
         
@@ -591,7 +591,7 @@ with tab6:
                             for x in range(0,total_sims):
                                 overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
     
-                            overall_file=overall_file.drop(['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                            overall_file=overall_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
     
                             players_only = hold_file[['Player']]
     
@@ -599,32 +599,30 @@ with tab6:
     
                             prop_check = (overall_file - prop_file)
     
-                            player_outcomes['Mean_Outcome'] = overall_file.mean(axis=1)
-                            player_outcomes['10%'] = overall_file.quantile(0.1, axis=1)
-                            player_outcomes['90%'] = overall_file.quantile(0.9, axis=1)
-                            player_outcomes['Over'] = prop_check[prop_check > 0].count(axis=1)/float(total_sims)
-                            player_outcomes['Imp Over'] = player_outcomes['Player'].map(over_dict)
-                            player_outcomes['Over%'] = player_outcomes[["Over", "Imp Over"]].mean(axis=1)
-                            player_outcomes['Under'] = prop_check[prop_check < 0].count(axis=1)/float(total_sims)
-                            player_outcomes['Imp Under'] = player_outcomes['Player'].map(under_dict)
-                            player_outcomes['Under%'] = player_outcomes[["Under", "Imp Under"]].mean(axis=1)
-                            player_outcomes['Prop'] = player_outcomes['Player'].map(prop_dict)
-                            player_outcomes['Prop_avg'] = player_outcomes['Prop'].mean() / 100
-                            player_outcomes['prop_threshold'] = .10
-                            player_outcomes = player_outcomes.loc[player_outcomes['Mean_Outcome'] > 0]
-                            player_outcomes['Over_diff'] = player_outcomes['Over%'] - player_outcomes['Imp Over']
-                            player_outcomes['Under_diff'] = player_outcomes['Under%'] - player_outcomes['Imp Under']
-                            player_outcomes['Bet_check'] = np.where(player_outcomes['Over_diff'] > player_outcomes['Under_diff'], player_outcomes['Over_diff'] , player_outcomes['Under_diff'])
-                            player_outcomes['Bet_suggest'] = np.where(player_outcomes['Over_diff'] > player_outcomes['Under_diff'], "Over" , "Under")
-                            player_outcomes['Bet?'] = np.where(player_outcomes['Bet_check'] >= player_outcomes['prop_threshold'], player_outcomes['Bet_suggest'], "No Bet")
-                            player_outcomes['Edge'] = player_outcomes['Bet_check']
+                            players_only['Mean_Outcome'] = overall_file.mean(axis=1)
+                            players_only['10%'] = overall_file.quantile(0.1, axis=1)
+                            players_only['90%'] = overall_file.quantile(0.9, axis=1)
+                            players_only['Over'] = prop_check[prop_check > 0].count(axis=1)/float(total_sims)
+                            players_only['Imp Over'] = players_only['Player'].map(over_dict)
+                            players_only['Over%'] = players_only[["Over", "Imp Over"]].mean(axis=1)
+                            players_only['Under'] = prop_check[prop_check < 0].count(axis=1)/float(total_sims)
+                            players_only['Imp Under'] = players_only['Player'].map(under_dict)
+                            players_only['Under%'] = players_only[["Under", "Imp Under"]].mean(axis=1)
+                            players_only['Prop'] = players_only['Player'].map(prop_dict)
+                            players_only['Prop_avg'] = players_only['Prop'].mean() / 100
+                            players_only['prop_threshold'] = .10
+                            players_only = players_only.loc[players_only['Mean_Outcome'] > 0]
+                            players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
+                            players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
+                            players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
+                            players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under")
+                            players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet")
+                            players_only['Edge'] = players_only['Bet_check']
     
-                            player_outcomes['Player'] = hold_file[['Player']]
-                            player_outcomes['Team'] = players_only['Player'].map(team_dict)
-                            
-                            st.table(player_outcomes.head(10))
+                            players_only['Player'] = hold_file[['Player']]
+                            players_only['Team'] = players_only['Player'].map(team_dict)
     
-                            final_outcomes = player_outcomes[['Player', 'Team', 'book', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
+                            final_outcomes = players_only[['Player', 'Team', 'book', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
                         
                         final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)