Removed .loc and Full_name

app.py

@@ -82,8 +82,8 @@ def init_baselines():
     return game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame
 
 game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = init_baselines()
-qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+qb_stats = overall_stats[overall_stats['Position'] == 'QB']
+non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
 team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
@@ -106,8 +106,8 @@ with tab1:
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
               game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = init_baselines()
-              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
+              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
               team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1')
@@ -137,8 +137,8 @@ with tab2:
     if st.button("Reset Data", key='reset2'):
               st.cache_data.clear()
               game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = init_baselines()
-              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
+              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
               team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
@@ -163,8 +163,8 @@ with tab3:
     if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
               game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = init_baselines()
-              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
+              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
               team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
@@ -189,8 +189,8 @@ with tab4:
     if st.button("Reset Data", key='reset4'):
               st.cache_data.clear()
               game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = init_baselines()
-              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
+              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
               team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
               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')
@@ -218,8 +218,8 @@ with tab5:
     if st.button("Reset Data", key='reset5'):
               st.cache_data.clear()
               game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = init_baselines()
-              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
+              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
               team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])
@@ -268,7 +268,7 @@ with tab5:
 
                         df.replace("", 0, inplace=True)
 
-                        player_var = df.loc[df['Player'] == player_check]
+                        player_var = df[df['Player'] == player_check]
                         player_var = player_var.reset_index()
                         
                         if prop_type_var == 'Pass Yards':
@@ -328,8 +328,8 @@ with tab5:
 
                         final_outcomes = players_only[['Player', '10%', 'Mean_Outcome', '90%', 'implied_odds', 'beat_prop']]
                         final_outcomes['Bet?'] = np.where(final_outcomes['beat_prop'] - final_outcomes['implied_odds'] >= .10, "Bet", "No Bet")
-                        final_outcomes = final_outcomes.loc[final_outcomes['Player'] == player_check]
-                        player_outcomes = player_outcomes.loc[player_outcomes['Player'] == player_check]
+                        final_outcomes = final_outcomes[final_outcomes['Player'] == player_check]
+                        player_outcomes = player_outcomes[player_outcomes['Player'] == player_check]
                         player_outcomes = player_outcomes.drop(columns=['Player']).transpose()
                         player_outcomes = player_outcomes.reset_index()
                         player_outcomes.columns = ['Instance', 'Outcome']
@@ -365,8 +365,8 @@ with tab6:
     if st.button("Reset Data/Load Data", key='reset6'):
               st.cache_data.clear()
               game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = init_baselines()
-              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
-              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
+              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
               team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])
@@ -387,8 +387,7 @@ with tab6:
         if game_select_var == 'Aggregate':
             prop_df = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
         elif game_select_var == 'Pick6':
-            prop_df = pick_frame[['Full_name', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-            prop_df.rename(columns={"Full_name": "Player"}, inplace = True)
+            prop_df = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
             book_selections = ['Pick6']
         st.download_button(
             label="Download Prop Source",
@@ -418,17 +417,16 @@ with tab6:
                                 if game_select_var == 'Aggregate':
                                     prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 elif game_select_var == 'Pick6':
-                                    prop_df_raw = pick_frame[['Full_name', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                                    prop_df_raw.rename(columns={"Full_name": "Player"}, inplace = True)
+                                    prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 
                                 for books in book_selections:
-                                    prop_df = prop_df_raw.loc[prop_df_raw['book'] == books]
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == prop]
+                                    prop_df = prop_df_raw[prop_df_raw['book'] == books]
+                                    prop_df = prop_df[prop_df['prop_type'] == prop]
                                     prop_df = prop_df[~((prop_df['over_prop'] < 15) & (prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS'))]
                                     prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
                                     prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
                                     prop_df = prop_df.drop_duplicates(subset=['Player'])
-                                    prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                                    prop_df = prop_df[prop_df['Prop'] != 0]
                                     prop_df['Over'] = 1 / prop_df['over_line']
                                     prop_df['Under'] = 1 / prop_df['under_line']
                                     df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
@@ -507,7 +505,7 @@ with tab6:
                                     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]
+                                    players_only = players_only[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'])
@@ -529,52 +527,51 @@ with tab6:
                             if game_select_var == 'Aggregate':
                                 prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                             elif game_select_var == 'Pick6':
-                                prop_df_raw = pick_frame[['Full_name', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                                prop_df_raw.rename(columns={"Full_name": "Player"}, inplace = True)
+                                prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 
                             for books in book_selections:
-                                prop_df = prop_df_raw.loc[prop_df_raw['book'] == books]
+                                prop_df = prop_df_raw[prop_df_raw['book'] == books]
                                 
                                 if prop_type_var == "NFL_GAME_PLAYER_PASSING_YARDS":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_YARDS']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_YARDS']
                                 elif prop_type_var == "Passing Yards":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Passing Yards']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'Passing Yards']
                                 elif prop_type_var == "NFL_GAME_PLAYER_RUSHING_YARDS":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS']
                                 elif prop_type_var == "Rushing Yards":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Rushing Yards']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'Rushing Yards']
                                 elif prop_type_var == "NFL_GAME_PLAYER_PASSING_ATTEMPTS":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_ATTEMPTS']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_ATTEMPTS']
                                 elif prop_type_var == "Passing Attempts":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Passing Attempts']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'Passing Attempts']
                                 elif prop_type_var == "NFL_GAME_PLAYER_PASSING_TOUCHDOWNS":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_TOUCHDOWNS']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_TOUCHDOWNS']
                                 elif prop_type_var == "Passing TDs":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Passing TDs']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'Passing TDs']
                                 elif prop_type_var == "NFL_GAME_PLAYER_RUSHING_ATTEMPTS":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS']
                                 elif prop_type_var == "Rushing Attempts":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Rushing Attempts']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'Rushing Attempts']
                                 elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_RECEPTIONS":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS']
                                 elif prop_type_var == "Receptions":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Receptions']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'Receptions']
                                 elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_YARDS":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_YARDS']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_YARDS']
                                 elif prop_type_var == "Receiving Yards":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Receiving Yards']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'Receiving Yards']
                                 elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS']
                                 elif prop_type_var == "Receiving TDs":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Receiving TDs']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'Receiving TDs']
                                 elif prop_type_var == "Rush + Rec Yards":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Rush + Rec Yards']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'Rush + Rec Yards']
                                 elif prop_type_var == "Rush + Rec TDs":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Rush + Rec TDs']
+                                    prop_df = prop_df[prop_df['prop_type'] == 'Rush + Rec TDs']
 
                                 prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
                                 prop_df = prop_df.rename(columns={"over_prop": "Prop"})
-                                prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                                prop_df = prop_df[prop_df['Prop'] != 0]
                                 prop_df = prop_df.drop_duplicates(subset=['Player'])
                                 prop_df['Over'] = 1 / prop_df['over_line']
                                 prop_df['Under'] = 1 / prop_df['under_line']
@@ -654,7 +651,7 @@ with tab6:
                                 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 = players_only[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'])