Update app.py

app.py

@@ -69,52 +69,62 @@ def init_baselines():
     data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'TEAM_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP'])
     gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
     
+    gamelog_table = gamelog_table.set_axis(['Player', 'Team', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
+                                            'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
+                                            'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
+                                            'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
+    
     return gamelog_table
 
 @st.cache_data(show_spinner=False)
 def seasonlong_build(data_sample):
     season_long_table = data_sample[['PLAYER_NAME', 'TEAM_NAME']]
-    season_long_table['MIN'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['MIN'].transform('mean').astype(float)
-    season_long_table['touches'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['touches'].transform('mean').astype(float)
+    season_long_table['Min'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Min'].transform('mean').astype(float)
+    season_long_table['Touches'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Touches'].transform('mean').astype(float)
     season_long_table['FGM'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGM'].transform('mean').astype(float)
     season_long_table['FGA'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGA'].transform('mean').astype(float)
-    season_long_table['FG_PCT'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGM'].transform('sum').astype(int) /
+    season_long_table['FG%'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGM'].transform('sum').astype(int) /
                                    data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGA'].transform('sum').astype(int))
     season_long_table['FG3M'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3M'].transform('mean').astype(float)
     season_long_table['FG3A'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3A'].transform('mean').astype(float)
-    season_long_table['FG3_PCT'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3M'].transform('sum').astype(int) /
+    season_long_table['FG3%'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3M'].transform('sum').astype(int) /
                                    data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3A'].transform('sum').astype(int))
     season_long_table['FTM'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTM'].transform('mean').astype(float)
     season_long_table['FTA'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTA'].transform('mean').astype(float)
-    season_long_table['FT_PCT'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTM'].transform('sum').astype(int) /
+    season_long_table['FT%'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTM'].transform('sum').astype(int) /
                                    data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTA'].transform('sum').astype(int))
-    season_long_table['reboundChancesOffensive'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesOffensive'].transform('mean').astype(float)
+    season_long_table['OREB Chance'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['OREB Chance'].transform('mean').astype(float)
     season_long_table['OREB'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['OREB'].transform('mean').astype(float)
-    season_long_table['reboundChancesDefensive'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesDefensive'].transform('mean').astype(float)
+    season_long_table['DREB Chance'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['DREB Chance'].transform('mean').astype(float)
     season_long_table['DREB'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['DREB'].transform('mean').astype(float)
-    season_long_table['reboundChancesTotal'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesTotal'].transform('mean').astype(float)
+    season_long_table['REB Chance'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['REB Chance'].transform('mean').astype(float)
     season_long_table['REB'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['REB'].transform('mean').astype(float)
-    season_long_table['passes'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['passes'].transform('mean').astype(float)
-    season_long_table['secondaryAssists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['secondaryAssists'].transform('mean').astype(float)
-    season_long_table['freeThrowAssists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['freeThrowAssists'].transform('mean').astype(float)
-    season_long_table['assists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['assists'].transform('mean').astype(float)
-    season_long_table['STL'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['STL'].transform('mean').astype(float)
-    season_long_table['BLK'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['BLK'].transform('mean').astype(float)
-    season_long_table['TOV'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['TOV'].transform('mean').astype(float)
+    season_long_table['Passes'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Passes'].transform('mean').astype(float)
+    season_long_table['Alt Assists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Alt Assists'].transform('mean').astype(float)
+    season_long_table['FT Assists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FT Assists'].transform('mean').astype(float)
+    season_long_table['Assists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Assists'].transform('mean').astype(float)
+    season_long_table['Stl'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Stl'].transform('mean').astype(float)
+    season_long_table['Blk'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Blk'].transform('mean').astype(float)
+    season_long_table['Tov'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Tov'].transform('mean').astype(float)
     season_long_table['PF'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['PF'].transform('mean').astype(float)
     season_long_table['DD'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['DD'].transform('mean').astype(float)
     season_long_table['TD'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['TD'].transform('mean').astype(float)
     season_long_table['Fantasy'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Fantasy'].transform('mean').astype(float)
     season_long_table['FD_Fantasy'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FD_Fantasy'].transform('mean').astype(float)
-    season_long_table['rebound%'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['REB'].transform('sum').astype(int) /
+    season_long_table['Rebound%'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['REB'].transform('sum').astype(int) /
                                      data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesTotal'].transform('sum').astype(int))
-    season_long_table['assists_per_pass'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['assists'].transform('sum').astype(int) /
+    season_long_table['Assists/Pass'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['assists'].transform('sum').astype(int) /
                                              data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['passes'].transform('sum').astype(int))
-    season_long_table['Fantasy_per_touch'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Fantasy'].transform('sum').astype(int) /
+    season_long_table['Fantasy/Touch'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Fantasy'].transform('sum').astype(int) /
                                               data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['touches'].transform('sum').astype(int))
-    season_long_table['FD_Fantasy_per_touch'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FD_Fantasy'].transform('sum').astype(int) /
+    season_long_table['FD Fantasy/Touch'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FD_Fantasy'].transform('sum').astype(int) /
                                                  data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['touches'].transform('sum').astype(int))
     season_long_table = season_long_table.drop_duplicates(subset='PLAYER_NAME')
+    
+    season_long_table = season_long_table.set_axis(['Player', 'Team', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
+                                                    'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
+                                                    'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
+                                                    'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
 
     return season_long_table
 
@@ -127,17 +137,26 @@ def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
 gamelog_table = init_baselines()
+total_teams = gamelog_table.Team.values.tolist()
 
 col1, col2 = st.columns([1, 9])
 with col1:
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
               gamelog_table = init_baselines()
+    
     split_var1 = st.radio("What table would you like to view?", ('Season Logs', 'Gamelogs'), key='split_var1')
+    split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
+    
+    if split_var2 == 'Specific Teams':
+        team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = total_teams, key='team_var1')
+    elif split_var2 == 'All':
+        team_var1 = total_teams
 
 with col2:
     if split_var1 == 'Season Logs':
         display = st.container()
+        gamelog_table = gamelog_table[gamelog_table['Team'].isin(team_var1)]
         season_long_table = seasonlong_build(gamelog_table)
         display.dataframe(season_long_table.style.format(precision=2), use_container_width = True)  
         
@@ -156,6 +175,6 @@ with col2:
             )
         with bottom_menu[0]:
             st.markdown(f"Page **{current_page}** of **{total_pages}** ")
-    
+        gamelog_table = gamelog_table[gamelog_table['Team'].isin(team_var1)]
         pages = split_frame(gamelog_table, batch_size)
         display.dataframe(data=pages[current_page - 1].style.format(precision=2), use_container_width=True)