Update app.py

app.py

@@ -46,6 +46,11 @@ def init_baselines():
     raw_display.replace('', np.nan, inplace=True)
     overall_stats = raw_display.dropna()
     
+    worksheet = sh.worksheet('prop_frame')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace('', np.nan, inplace=True)
+    prop_trends = raw_display.dropna()
+    
     worksheet = sh.worksheet('DK_ROO')
     timestamp = worksheet.acell('U2').value
     
@@ -55,18 +60,21 @@ def init_baselines():
     raw_display.replace('#DIV/0!', np.nan, inplace=True)
     prop_frame = raw_display.dropna()
 
-    return game_model, overall_stats, timestamp, prop_frame
+    return game_model, overall_stats, timestamp, prop_frame, prop_trends
 
-game_model, overall_stats, timestamp, prop_frame = init_baselines()
+game_model, overall_stats, timestamp, prop_frame, prop_trends = init_baselines()
 qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
 non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
 team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
+prop_table_options = ['pass_yards', 'rush_yards', 'rec_yards', 'receptions', 'rush_attempts', 'pass_attempts']
+prop_format = {'L3 Success': '{:.2%}', 'L6_Success': '{:.2%}', 'L10_success': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}',
+               'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'}
 all_sim_vars = ['pass_yards', 'rush_yards', 'rec_yards', 'receptions', 'rush_attempts']
 sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
 
-tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", "Player Prop Simulations", "Stat Specific Simulations"])
+tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", "Player Prop Trends", "Player Prop Simulations", "Stat Specific Simulations"])
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
@@ -75,7 +83,7 @@ with tab1:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends = init_baselines()
               qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
               non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
               team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
@@ -103,7 +111,7 @@ with tab2:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset2'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends = init_baselines()
               qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
               non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
               team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
@@ -129,7 +137,7 @@ with tab3:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends = init_baselines()
               qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
               non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
               team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
@@ -150,12 +158,39 @@ with tab3:
         mime='text/csv',
         key='hitter_prop_export',
     )
-
+    
 with tab4:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset4'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends = init_baselines()
+              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
+              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
+              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 = prop_trends['Team'].unique(), key='team_var5')
+    elif split_var5 == 'All':
+        team_var5 = prop_trends.Team.values.tolist()
+    prop_type_var2 = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options)
+    prop_frame_disp = prop_trends[prop_trends['Team'].isin(team_var5)]
+    prop_frame_disp = prop_frame_disp[prop_frame_disp['prop_type'] == prop_type_var2]
+    prop_frame_disp = prop_frame_disp.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_disp),
+        file_name='NFL_prop_trends_export.csv',
+        mime='text/csv',
+    )
+
+with tab5:
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset5'):
+              st.cache_data.clear()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends = init_baselines()
               qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
               non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
               team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
@@ -297,12 +332,12 @@ with tab4:
                             plot_hold_container = st.empty()
                             st.plotly_chart(fig, use_container_width=True)
 
-with tab5:
+with tab6:
     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'):
+    if st.button("Reset Data/Load Data", key='reset6'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends = init_baselines()
               qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
               non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
               team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))