Update app.py

app.py

@@ -64,14 +64,15 @@ def init_baselines():
     gamelog_table['FD_Fantasy'] = gamelog_table['FD_Fantasy'].astype(float)
     gamelog_table['rebound%'] = gamelog_table['REB'] / gamelog_table['reboundChancesTotal']
     gamelog_table['assists_per_pass'] = gamelog_table['assists'] / gamelog_table['passes']
+    gamelog_table['Touch_per_min'] = gamelog_table['touches'] / gamelog_table['MIN']
     gamelog_table['Fantasy_per_touch'] = gamelog_table['Fantasy'] / gamelog_table['touches']
     gamelog_table['FD_Fantasy_per_touch'] = gamelog_table['FD_Fantasy'] / gamelog_table['touches']
     data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'POS', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP'])
     gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
     gamelog_table['GAME_DATE'] = pd.to_datetime(gamelog_table['GAME_DATE']).dt.date
     
-    gamelog_table = gamelog_table.set_axis(['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
-                                            'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
+    gamelog_table = gamelog_table.set_axis(['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min', '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'], axis=1)
     
@@ -117,6 +118,8 @@ def seasonlong_build(data_sample):
                                      data_sample.groupby(['Player', 'Season'], sort=False)['REB Chance'].transform('sum').astype(int))
     season_long_table['Assists/Pass'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Assists'].transform('sum').astype(int) /
                                              data_sample.groupby(['Player', 'Season'], sort=False)['Passes'].transform('sum').astype(int))
+    season_long_table['Touch/Min'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int) /
+                                              data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('sum').astype(int))
     season_long_table['Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Fantasy'].transform('sum').astype(int) /
                                               data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int))
     season_long_table['FD Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FD_Fantasy'].transform('sum').astype(int) /
@@ -125,7 +128,7 @@ def seasonlong_build(data_sample):
 
     season_long_table = season_long_table.sort_values(by='Fantasy', ascending=False)
     
-    season_long_table = season_long_table.set_axis(['Player', 'Pos', 'Team', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
+    season_long_table = season_long_table.set_axis(['Player', 'Pos', 'Team', 'Min', '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'], axis=1)
@@ -181,7 +184,7 @@ indv_players = gamelog_table.drop_duplicates(subset='Player')
 total_players = indv_players.Player.values.tolist()
 total_dates = gamelog_table.Date.values.tolist()
 
-tab1, tab2 = st.tabs(['Gamelogs', 'Correlation Matrix'])
+tab1, tab2, tab3 = st.tabs(['Gamelogs', 'Correlation Matrix', 'Position vs. Opp'])
 
 with tab1:
     col1, col2 = st.columns([1, 9])
@@ -330,3 +333,65 @@ with tab2:
             elif corr_var == 'Minutes':
                 corr_display = run_min_corr(gamelog_table)
             display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+
+with tab3:
+    col1, col2 = st.columns([1, 9])
+    with col1:
+        if st.button("Reset Data", key='reset3'):
+                  st.cache_data.clear()
+                  gamelog_table = init_baselines()
+                  indv_teams = gamelog_table.drop_duplicates(subset='Team')
+                  total_teams = indv_teams.Team.values.tolist()
+                  indv_players = gamelog_table.drop_duplicates(subset='Player')
+                  total_players = indv_players.Player.values.tolist()
+                  total_dates = gamelog_table.Date.values.tolist()
+        
+
+        team_var3 = st.multiselect('Which opponent would you like to view?', options = total_teams, key='team_var3')
+        pos_var3 = st.selectbox('Which position would you like to view?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var3')
+        date_var3 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='split_var3')
+        
+        if date_var3 == 'Specific Dates':
+            low_date3 = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='low_date3')
+            if low_date3 is not None:
+                low_date3 = pd.to_datetime(low_date3).date()
+            high_date3 = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='high_date3')
+            if high_date3 is not None:
+                high_date3 = pd.to_datetime(high_date3).date()
+        elif split_var3 == 'All':
+            low_date3 = gamelog_table['Date'].min()
+            high_date3 = gamelog_table['Date'].max()
+        
+        min_var3 = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='min_var3')
+    
+    with col2:
+        gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date3]
+        gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date3]
+        season_long_table = seasonlong_build(gamelog_table)
+            
+        gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date3]
+        gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date3]
+        gamelog_table = gamelog_table[gamelog_table['Pos'] == pos_var3]
+        gamelog_table = gamelog_table[gamelog_table['Min'] >= min_var3[0]]
+        gamelog_table = gamelog_table[gamelog_table['Min'] <= min_var3[1]]
+        gamelog_table = gamelog_table[gamelog_table['Opp'].isin(team_var3)]
+        gamelog_display = gamelog_table.reset_index(drop=True)
+        display = st.container()
+    
+        bottom_menu = st.columns((4, 1, 1))
+        with bottom_menu[2]:
+            batch_size = st.selectbox("Page Size", options=[25, 50, 100])
+        with bottom_menu[1]:
+            total_pages = (
+                int(len(gamelog_display) / batch_size) if int(len(gamelog_display) / batch_size) > 0 else 1
+            )
+            current_page = st.number_input(
+                "Page", min_value=1, max_value=total_pages, step=1
+            )
+        with bottom_menu[0]:
+            st.markdown(f"Page **{current_page}** of **{total_pages}** ")
+        
+        
+        pages = split_frame(gamelog_display, batch_size)
+        # pages = pages.set_index('Player')
+        display.dataframe(data=pages[current_page - 1].style.format(precision=2), height=500, use_container_width=True)