Update app.py

app.py

@@ -46,41 +46,76 @@ def init_conn():
 
 gcservice_account = init_conn()
 
-master_hold = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=853878325'
+NBAGetGameData = 'https://docs.google.com/spreadsheets/d/1tRQrF_I5rS7Q0g9vE8NrENDZ2P3_DvtbBZzKEakwOI0/edit#gid=1373653837'
+NBABettingModel = 'https://docs.google.com/spreadsheets/d/1WBnvOHQi_zVTGF63efejK5ho02AY00HiYrMHnMJXY1E/edit#gid=1157978351'
 
 @st.cache_resource(ttl = 300)
 def init_baselines():
-    sh = gcservice_account.open_by_url(master_hold)
+    sh = gcservice_account.open_by_url(NBAGetGameData)
 
-    worksheet = sh.worksheet('Arturo Props')
+    worksheet = sh.worksheet('MinPublic')
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('', np.nan, inplace=True)
-    raw_display = raw_display[['Player', 'Pos', 'Team', 'Opponent', 'Min', 'mpgL3', 'Diff', 'Status', 'Pts', 'Rbs', 'Asst', 'TOs', '3PM',
-                              'Steals', 'Blk', 'FD', 'DK']]
-    player_stats = raw_display[raw_display['Min'] > 0]
+    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']]
+    public_minutes = raw_display[raw_display['NBAID'] != ""]
 
-    return player_stats
+    return public_minutes
+
+    sh = gcservice_account.open_by_url(NBABettingModel)
+
+    worksheet = sh.worksheet('PlayerImpactByTeam')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace('', np.nan, 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']]
+    player_impact = raw_display[raw_display['PID'] != ""]
+
+    return player_impact
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-player_stats = init_baselines()
-
-if st.button("Reset Data", key='reset1'):
-          st.cache_data.clear()
-          player_stats = 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 = player_stats['Team'].unique(), key='team_var1')
-elif split_var1 == 'All':
-    team_var1 = player_stats.Team.values.tolist()
-player_stats = player_stats[player_stats['Team'].isin(team_var1)]
-player_stats_disp = player_stats.set_index('Player')
-player_stats_disp = player_stats_disp.sort_values(by=['Team', 'Min'], ascending=False)
-st.dataframe(player_stats_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-st.download_button(
-    label="Export Prop Model",
-    data=convert_df_to_csv(player_stats),
-    file_name='AmericanNumbers_stats_export.csv',
-    mime='text/csv',
-)
+public_minutes, player_impact = init_baselines()
+
+tab1, tab2 = st.tabs(["Minutes Baselines", "Player Impacts"])
+
+with tab1:
+    if st.button("Reset Data", key='reset1'):
+              st.cache_data.clear()
+              public_minutes, player_impact = 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')
+    elif split_var1 == 'All':
+        team_var1 = public_minutes.TC.values.tolist()
+    public_minutes = public_minutes[public_minutes['Team'].isin(team_var1)]
+    player_min_disp = public_minutes.set_index('Player')
+    player_min_disp = player_min_disp.sort_values(by=['TC', 'MP (Today)'], ascending=[False, True])
+    st.dataframe(player_min_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Prop Model",
+        data=convert_df_to_csv(public_minutes),
+        file_name='AmericanNumbers_stats_export.csv',
+        mime='text/csv',
+    )
+
+with tab2:
+    if st.button("Reset Data", key='reset2'):
+              st.cache_data.clear()
+              public_minutes, player_impact = 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')
+    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=[False, True])
+    st.dataframe(player_impact_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Prop Model",
+        data=convert_df_to_csv(player_impact),
+        file_name='AmericanNumbers_stats_export.csv',
+        mime='text/csv',
+    )