Update app.py

app.py

@@ -180,6 +180,16 @@ def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
 gamelog_table = init_baselines()
+basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
+basic_season_cols = ['Player', 'Pos', 'Team', 'Min']
+data_cols = ['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', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch']
+season_data_cols = ['Touches', 'Touch/Min', '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']
 indv_teams = gamelog_table.drop_duplicates(subset='Team')
 total_teams = indv_teams.Team.values.tolist()
 indv_players = gamelog_table.drop_duplicates(subset='Player')
@@ -194,6 +204,16 @@ with tab1:
         if st.button("Reset Data", key='reset1'):
                   st.cache_data.clear()
                   gamelog_table = init_baselines()
+                  basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min']
+                  basic_season_cols = ['Player', 'Pos', 'Team', 'Min']
+                  data_cols = ['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', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch']
+                  season_data_cols = ['Touches', 'Touch/Min', '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']
                   indv_teams = gamelog_table.drop_duplicates(subset='Team')
                   total_teams = indv_teams.Team.values.tolist()
                   indv_players = gamelog_table.drop_duplicates(subset='Player')
@@ -233,6 +253,10 @@ with tab1:
     with col2:
         working_data = gamelog_table
         if split_var1 == 'Season Logs':
+            choose_cols = st.container()
+            with choose_cols:
+                choose_disp = st.multiselect('Which stats would you like to view?', options = season_data_cols, default = season_data_cols, key='col_display')
+            disp_stats = basic_season_cols + choose_disp
             display = st.container()
             working_data = working_data[working_data['Date'] >= low_date]
             working_data = working_data[working_data['Date'] <= high_date]
@@ -242,7 +266,8 @@ with tab1:
             working_data = working_data[working_data['Player'].isin(player_var1)]
             season_long_table = seasonlong_build(working_data)
             season_long_table = season_long_table.set_index('Player')
-            display.dataframe(season_long_table.style.format(precision=2), height=750, use_container_width = True)  
+            season_long_table_disp = season_long_table.reindex(disp_stats,axis="columns")
+            display.dataframe(season_long_table_disp.style.format(precision=2), height=750, use_container_width = True)  
             st.download_button(
                     label="Export seasonlogs Model",
                     data=convert_df_to_csv(season_long_table),
@@ -251,6 +276,10 @@ with tab1:
             )
             
         elif split_var1 == 'Gamelogs':
+            choose_cols = st.container()
+            with choose_cols:
+                choose_disp = st.multiselect('Which stats would you like to view?', options = data_cols, default = data_cols, key='col_display')
+            disp_stats = basic_cols + choose_disp
             working_data = working_data[working_data['Date'] >= low_date]
             working_data = working_data[working_data['Date'] <= high_date]
             working_data = working_data[working_data['Min'] >= min_var1[0]]
@@ -258,6 +287,7 @@ with tab1:
             working_data = working_data[working_data['Team'].isin(team_var1)]
             working_data = working_data[working_data['Player'].isin(player_var1)]
             working_data = working_data.reset_index(drop=True)
+            gamelog_data = working_data.reindex(disp_stats,axis="columns")
             display = st.container()
         
             bottom_menu = st.columns((4, 1, 1))
@@ -265,7 +295,7 @@ with tab1:
                 batch_size = st.selectbox("Page Size", options=[25, 50, 100])
             with bottom_menu[1]:
                 total_pages = (
-                    int(len(working_data) / batch_size) if int(len(working_data) / batch_size) > 0 else 1
+                    int(len(gamelog_data) / batch_size) if int(len(gamelog_data) / batch_size) > 0 else 1
                 )
                 current_page = st.number_input(
                     "Page", min_value=1, max_value=total_pages, step=1
@@ -274,12 +304,12 @@ with tab1:
                 st.markdown(f"Page **{current_page}** of **{total_pages}** ")
             
             
-            pages = split_frame(working_data, batch_size)
+            pages = split_frame(gamelog_data, batch_size)
             # pages = pages.set_index('Player')
             display.dataframe(data=pages[current_page - 1].style.format(precision=2), height=500, use_container_width=True)
             st.download_button(
                     label="Export gamelogs Model",
-                    data=convert_df_to_csv(working_data),
+                    data=convert_df_to_csv(gamelog_data),
                     file_name='Gamelogs_NBA_View.csv',
                     mime='text/csv',
             )