Update app.py

app.py

@@ -51,78 +51,71 @@ NBABettingModel = 'https://docs.google.com/spreadsheets/d/1WBnvOHQi_zVTGF63efejK
 
 @st.cache_resource(ttl = 300)
 def init_baselines():
-    sh = gcservice_account.open_by_url(NBAGetGameData)
+    sh = gcservice_account.open_by_url(NBABettingModel)
 
-    worksheet = sh.worksheet('MinPublic')
+    worksheet = sh.worksheet('ExportTable')
     raw_display = pd.DataFrame(worksheet.get_values())
     raw_display.columns = raw_display.iloc[0]
     raw_display = raw_display[1:]
     raw_display = raw_display.reset_index(drop=True)
     raw_display.replace('', np.nan, inplace=True)
-    raw_display = raw_display[['NBAID', 'PID', 'Player', 'TC', 'MP (Today)', 'Next Game', 'H/R', 'Injury Notes', 'Player Impact per 48', 'Player Impact',
-                               'Team PM', 'Last Updated']]
     raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
-    public_minutes = raw_display[raw_display['NBAID'] != ""]
-
-    sh = gcservice_account.open_by_url(NBABettingModel)
+    raw_display['Team Date'] = raw_display['Team'] + " " + raw_display['Date'].astype(str)
+    game_model = raw_display[raw_display['Date'] != ""]
 
-    worksheet = sh.worksheet('PlayerImpactByTeam')
+    worksheet = sh.worksheet('SeasonExport')
     raw_display = pd.DataFrame(worksheet.get_values())
     raw_display.columns = raw_display.iloc[0]
     raw_display = raw_display[1:]
     raw_display = raw_display.reset_index(drop=True)
     raw_display.replace('', 0, inplace=True)
-    raw_display = raw_display[['PID', 'Player', 'Team', 'Avg Minutes last 30 days for team', 'Minutes Projection', 'Rotation Impact (versus last 30 days)',
-                               'Injury Notes', 'Minute Change', 'Baseline Team PM', 'Net Rotation PM +/- for Team', 'Projected PM for Game', 'Offset', 'Rank']]
-    raw_display['Minute Change'].replace('+', '', inplace=True)
     raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
-    player_impact = raw_display[raw_display['PID'] != ""]
+    season_model = raw_display[raw_display['Team'] != ""]
 
-    return public_minutes, player_impact
+    return game_model, season_model
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-public_minutes, player_impact = init_baselines()
+game_model, season_model = init_baselines()
 
-tab1, tab2 = st.tabs(["Minutes Baselines", "Player Impacts"])
+tab1, tab2 = st.tabs(["Game Betting Model", "Season and Futures"])
 
 with tab1:
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              public_minutes, player_impact = init_baselines()
+              game_model, season_model = init_baselines()
     split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
     if split_var1 == 'Specific Teams':
-        team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = public_minutes['TC'].unique(), key='team_var1')
+        team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = game_model['Team'].unique(), key='team_var1')
     elif split_var1 == 'All':
-        team_var1 = public_minutes.TC.values.tolist()
-    public_minutes = public_minutes[public_minutes['TC'].isin(team_var1)]
-    player_min_disp = public_minutes.set_index('Player')
-    player_min_disp = player_min_disp.sort_values(by=['TC', 'MP (Today)'], ascending=[True, False])
-    st.dataframe(player_min_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        team_var1 = game_model.Team.values.tolist()
+    game_model = game_model[game_model['Team'].isin(team_var1)]
+    game_display = game_model.set_index('Team')
+    st.dataframe(game_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
     st.download_button(
-        label="Export Minutes Baselines",
-        data=convert_df_to_csv(public_minutes),
-        file_name='AmericanNumbers_Min_Baseline_export.csv',
+        label="Export Game Model",
+        data=convert_df_to_csv(game_model),
+        file_name='AmericanNumbers_Game_Model_export.csv',
         mime='text/csv',
     )
 
 with tab2:
     if st.button("Reset Data", key='reset2'):
               st.cache_data.clear()
-              public_minutes, player_impact = init_baselines()
+              game_model, season_model = init_baselines()
     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_var2 = st.multiselect('Which teams would you like to include in the tables?', options = player_impact['Team'].unique(), key='team_var2')
+        team_var2 = st.multiselect('Which teams would you like to include in the tables?', options = season_model['Team'].unique(), key='team_var2')
     elif split_var2 == 'All':
-        team_var2 = player_impact.Team.values.tolist()
-    player_impact = player_impact[player_impact['Team'].isin(team_var2)]
-    player_impact_disp = player_impact.set_index('Player')
-    player_impact_disp = player_impact_disp.sort_values(by=['Team', 'Rotation Impact (versus last 30 days)'], ascending=[True, False])
-    st.dataframe(player_impact_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        team_var2 = season_model.Team.values.tolist()
+    season_model = season_model[season_model['Team'].isin(team_var2)]
+    season_display = season_model.set_index('Team')
+    season_display = season_display.sort_values(by=['Win Projection Now'], ascending=False)
+    st.dataframe(season_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
     st.download_button(
-        label="Export Player Impacts",
-        data=convert_df_to_csv(player_impact),
-        file_name='AmericanNumbers_Impact_export.csv',
+        label="Export Futures Model",
+        data=convert_df_to_csv(season_model),
+        file_name='AmericanNumbers_Season_Futures.csv',
         mime='text/csv',
     )