Update app.py

app.py

@@ -153,80 +153,81 @@ with tab3:
                                 prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
                                 prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
                                 df = pd.merge(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                                if len(df) > 0:
                                 
-                                prop_dict = dict(zip(df.Player, df.Prop))
-                                over_dict = dict(zip(df.Player, df.Over))
-                                under_dict = dict(zip(df.Player, df.Under))
-                                
-                                total_sims = 5000
-        
-                                df.replace("", 0, inplace=True)
-        
-                                if prop == 'points':
-                                    df['Median'] = df['Player TP']
-                                elif prop == 'SOG':
-                                    df['Median'] = df['Player SOG']
-                                elif prop == 'assists':
-                                    df['Median'] = df['Player Assists']
-                                elif prop == 'blocked_shots':
-                                    df['Median'] = df['Player Blocks']
-        
-                                flex_file = df
-                                flex_file['Floor'] = (flex_file['Median'] * .15)
-                                flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .85)
-                                flex_file['STD'] = (flex_file['Median']/3)
-                                flex_file['Prop'] = flex_file['Player'].map(prop_dict)
-                                flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
-        
-                                hold_file = flex_file
-                                overall_file = flex_file
-                                prop_file = flex_file
-                                      
-                                overall_players = overall_file[['Player']]
-        
-                                for x in range(0,total_sims):    
-                                    prop_file[x] = prop_file['Prop']
-        
-                                prop_file = prop_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-        
-                                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)
-        
-                                players_only = hold_file[['Player']]
-        
-                                player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
-        
-                                prop_check = (overall_file - prop_file)
-        
-                                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']
-                                players_only['Prop type'] = prop
-        
-                                players_only['Player'] = hold_file[['Player']]
-                                players_only['Team'] = players_only['Player'].map(team_dict)
-        
-                                leg_outcomes = players_only[['Player', 'Team', '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)
+                                    prop_dict = dict(zip(df.Player, df.Prop))
+                                    over_dict = dict(zip(df.Player, df.Over))
+                                    under_dict = dict(zip(df.Player, df.Under))
+                                    
+                                    total_sims = 5000
+            
+                                    df.replace("", 0, inplace=True)
+            
+                                    if prop == 'points':
+                                        df['Median'] = df['Player TP']
+                                    elif prop == 'SOG':
+                                        df['Median'] = df['Player SOG']
+                                    elif prop == 'assists':
+                                        df['Median'] = df['Player Assists']
+                                    elif prop == 'blocked_shots':
+                                        df['Median'] = df['Player Blocks']
+            
+                                    flex_file = df
+                                    flex_file['Floor'] = (flex_file['Median'] * .15)
+                                    flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .85)
+                                    flex_file['STD'] = (flex_file['Median']/3)
+                                    flex_file['Prop'] = flex_file['Player'].map(prop_dict)
+                                    flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
+            
+                                    hold_file = flex_file
+                                    overall_file = flex_file
+                                    prop_file = flex_file
+                                          
+                                    overall_players = overall_file[['Player']]
+            
+                                    for x in range(0,total_sims):    
+                                        prop_file[x] = prop_file['Prop']
+            
+                                    prop_file = prop_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+            
+                                    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)
+            
+                                    players_only = hold_file[['Player']]
+            
+                                    player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
+            
+                                    prop_check = (overall_file - prop_file)
+            
+                                    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']
+                                    players_only['Prop type'] = prop
+            
+                                    players_only['Player'] = hold_file[['Player']]
+                                    players_only['Team'] = players_only['Player'].map(team_dict)
+            
+                                    leg_outcomes = players_only[['Player', 'Team', '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