Update app.py

app.py

@@ -58,15 +58,17 @@ def pull_baselines():
     raw_display.replace('', np.nan, inplace=True)
     prop_trends = raw_display.dropna(subset='Player')
     
+    team_dict = dict(zip(prop_table['Player'], prop_table['Team']))
+    
     worksheet = sh.worksheet('Timestamp')
     timestamp = worksheet.acell('A1').value
 
-    return prop_table, prop_trends, timestamp
+    return prop_table, prop_trends, timestamp, team_dict
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-prop_display, prop_trends, timestamp = pull_baselines()
+prop_display, prop_trends, timestamp, team_dict = pull_baselines()
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
 tab1, tab2, tab3 = st.tabs(["Player Stat Table", 'Prop Trend Table', 'Stat Specific Simulations'])
@@ -75,7 +77,7 @@ with tab1:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              prop_display, prop_trends, timestamp = pull_baselines()
+              prop_display, prop_trends, timestamp, team_dict = pull_baselines()
     prop_frame = prop_display
     st.dataframe(prop_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
     
@@ -91,7 +93,7 @@ with tab2:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
-              prop_display, prop_trends, timestamp = pull_baselines()
+              prop_display, prop_trends, timestamp, team_dict = pull_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     split_var5 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var5')
     if split_var5 == 'Specific Teams':
@@ -116,7 +118,7 @@ with tab3:
     st.info('The Over and Under percentages are a composite percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
     if st.button("Reset Data/Load Data", key='reset5'):
               st.cache_data.clear()
-              prop_display, prop_trends, timestamp = pull_baselines()
+              prop_display, prop_trends, timestamp, team_dict = pull_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])
     
@@ -215,8 +217,9 @@ with tab3:
                                 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', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
+                                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)
                                 
@@ -331,8 +334,9 @@ with tab3:
                             players_only['Edge'] = players_only['Bet_check']
     
                             players_only['Player'] = hold_file[['Player']]
+                            players_only['Team'] = players_only['Player'].map(team_dict)
     
-                            final_outcomes = players_only[['Player', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
+                            final_outcomes = players_only[['Player', 'Team', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
                         
                         final_outcomes = final_outcomes[final_outcomes['Prop'] > 0]
                         final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)