Update app.py

app.py

@@ -396,6 +396,7 @@ with tab6:
                                 df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
                                 
                                 prop_dict = dict(zip(df.Player, df.Prop))
+                                book_dict = dict(zip(df.Player, df.book))
                                 over_dict = dict(zip(df.Player, df.Over))
                                 under_dict = dict(zip(df.Player, df.Under))
                                 
@@ -453,6 +454,7 @@ with tab6:
                                 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['Book'] = players_only['Player'].map(book_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]
@@ -467,7 +469,7 @@ with tab6:
                                 players_only['Player'] = hold_file[['Player']]
                                 players_only['Team'] = players_only['Player'].map(team_dict)
         
-                                leg_outcomes = players_only[['Player', 'Team', 'book', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
+                                leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
                                 sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
                                 
                                 final_outcomes = sim_all_hold
@@ -552,6 +554,7 @@ with tab6:
                                 df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
                             
                             prop_dict = dict(zip(df.Player, df.Prop))
+                            book_dict = dict(zip(df.Player, df.book))
                             over_dict = dict(zip(df.Player, df.Over))
                             under_dict = dict(zip(df.Player, df.Under))
                             
@@ -591,7 +594,7 @@ 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', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                            overall_file=overall_file.drop(['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
     
                             players_only = hold_file[['Player']]
     
@@ -608,6 +611,7 @@ with tab6:
                             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['Book'] = players_only['Player'].map(book_dict)
                             players_only['Prop'] = players_only['Player'].map(prop_dict)
                             players_only['Prop_avg'] = players_only['Prop'].mean() / 100
                             players_only['prop_threshold'] = .10
@@ -622,7 +626,7 @@ with tab6:
                             players_only['Player'] = hold_file[['Player']]
                             players_only['Team'] = players_only['Player'].map(team_dict)
     
-                            final_outcomes = players_only[['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)