Update app.py

app.py

@@ -40,6 +40,8 @@ gcservice_account = init_conn()
 master_hold = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=853878325'
 
 game_format = {'Win%': '{:.2%}'}
+prop_format = {'L5 Success': '{:.2%}', 'L10 Success': '{:.2%}', 'L20 Success': '{:.2%}', 'Matchup Boost': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}'}
+prop_table_options = ['points', 'threes', 'rebounds', 'assists', 'blocks', 'steals']
 
 @st.cache_resource(ttl = 300)
 def init_baselines():
@@ -73,7 +75,7 @@ def convert_df_to_csv(df):
 game_model, player_stats, prop_frame, timestamp = init_baselines()
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
-tab1, tab2, tab3, tab4 = st.tabs(["Game Betting Model", "Player Projections", "Player Prop Simulations", "Stat Specific Simulations"])
+tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "Player Projections", "Prop Trend Table", "Player Prop Simulations", "Stat Specific Simulations"])
 
 with tab1:
     st.info(t_stamp)
@@ -123,6 +125,30 @@ with tab2:
     )
     
 with tab3:
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset5'):
+              st.cache_data.clear()
+              game_model, player_stats, prop_frame, timestamp = init_baselines()
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+    split_var5 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var5')
+    if split_var5 == 'Specific Teams':
+        team_var5 = st.multiselect('Which teams would you like to include in the tables?', options = player_stats['Team'].unique(), key='team_var5')
+    elif split_var5 == 'All':
+        team_var5 = player_stats.Team.values.tolist()
+    prop_type_var2 = st.selectbox('Select type of prop to simulate', options = prop_table_options, default = prop_table_options)
+    prop_frame = prop_frame[prop_frame['Team'].isin(team_var5)]
+    prop_frame = prop_frame[prop_frame['prop_type'].isin(prop_type_var2)]
+    prop_frame_disp = prop_frame.set_index('Player')
+    prop_frame_disp = prop_frame_disp.sort_values(by='Trending Over', ascending=False)
+    st.dataframe(prop_frame_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(prop_format, precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Prop Trends Model",
+        data=convert_df_to_csv(prop_frame),
+        file_name='NBA_prop_trends_export.csv',
+        mime='text/csv',
+    )
+    
+with tab4:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
@@ -266,7 +292,7 @@ with tab3:
                             plot_hold_container = st.empty()
                             st.plotly_chart(fig, use_container_width=True)
 
-with tab4:
+with tab5:
     st.info(t_stamp)
     st.info('The Over and Under percentages are a compositve percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
     if st.button("Reset Data/Load Data", key='reset5'):