swapped some tabs, made more mobile friendly for market props

app.py

@@ -141,7 +141,7 @@ all_sim_vars = ['NFL_GAME_PLAYER_PASSING_YARDS', 'NFL_GAME_PLAYER_RUSHING_YARDS'
 pick6_sim_vars = ['Rush + Rec Yards', 'Rush + Rec TDs', 'Passing Yards', 'Passing Attempts', 'Passing TDs', 'Completions', 'Rushing Yards', 'Receptions', 'Receiving Yards']
 sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
 
-tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", 'Market Table', "Player Prop Trends", "Player Prop Simulations", "Stat Specific Simulations"])
+tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(["Game Betting Model", 'Prop Market', "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')
@@ -180,6 +180,46 @@ with tab1:
     )
 
 with tab2:
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset4'):
+              st.cache_data.clear()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = init_baselines()
+              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
+              qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
+              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
+              non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
+              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+    market_type = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options, key = 'market_type_key')
+    disp_market = market_props.copy()
+    disp_market = disp_market[disp_market['PropType'] == market_type]
+    disp_market['No_Vig_Prop'] = disp_market.apply(calculate_no_vig, axis=1)
+    fanduel_frame = disp_market[disp_market['OddsType'] == 'FANDUEL']
+    fanduel_dict = dict(zip(fanduel_frame['Name'], fanduel_frame['No_Vig_Prop']))
+    draftkings_frame = disp_market[disp_market['OddsType'] == 'DRAFTKINGS']
+    draftkings_dict = dict(zip(draftkings_frame['Name'], draftkings_frame['No_Vig_Prop']))
+    mgm_frame = disp_market[disp_market['OddsType'] == 'MGM']
+    mgm_dict = dict(zip(mgm_frame['Name'], mgm_frame['No_Vig_Prop']))
+    bet365_frame = disp_market[disp_market['OddsType'] == 'BET_365']
+    bet365_dict = dict(zip(bet365_frame['Name'], bet365_frame['No_Vig_Prop']))
+
+    disp_market['FANDUEL'] = disp_market['Name'].map(fanduel_dict)
+    disp_market['DRAFTKINGS'] = disp_market['Name'].map(draftkings_dict)
+    disp_market['MGM'] = disp_market['Name'].map(mgm_dict)
+    disp_market['BET365'] = disp_market['Name'].map(bet365_dict)
+
+    disp_market = disp_market[['Name', 'Position','FANDUEL', 'DRAFTKINGS', 'MGM', 'BET365']]
+    disp_market = disp_market.drop_duplicates(subset=['Name', 'PropType'], keep='first', ignore_index=True)
+
+    st.dataframe(disp_market.style.background_gradient(axis=1).background_gradient(cmap='RdYlGn').format(prop_format, precision=2), height = 1000, use_container_width = True)
+    st.download_button(
+        label="Export Market Props",
+        data=convert_df_to_csv(disp_market),
+        file_name='NFL_market_props_export.csv',
+        mime='text/csv',
+    )
+
+with tab3:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset2'):
               st.cache_data.clear()
@@ -207,7 +247,7 @@ with tab2:
         key='NFL_qb_stats_export',
     )
 
-with tab3:
+with tab4:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
@@ -235,46 +275,6 @@ with tab3:
         key='NFL_nonqb_stats_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, prop_trends, pick_frame, market_props = init_baselines()
-              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
-              qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
-              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
-              non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
-              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-    market_type = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options, key = 'market_type_key')
-    disp_market = market_props.copy()
-    disp_market = disp_market[disp_market['PropType'] == market_type]
-    disp_market['No_Vig_Prop'] = disp_market.apply(calculate_no_vig, axis=1)
-    fanduel_frame = disp_market[disp_market['OddsType'] == 'FANDUEL']
-    fanduel_dict = dict(zip(fanduel_frame['Name'], fanduel_frame['No_Vig_Prop']))
-    draftkings_frame = disp_market[disp_market['OddsType'] == 'DRAFTKINGS']
-    draftkings_dict = dict(zip(draftkings_frame['Name'], draftkings_frame['No_Vig_Prop']))
-    mgm_frame = disp_market[disp_market['OddsType'] == 'MGM']
-    mgm_dict = dict(zip(mgm_frame['Name'], mgm_frame['No_Vig_Prop']))
-    bet365_frame = disp_market[disp_market['OddsType'] == 'BET_365']
-    bet365_dict = dict(zip(bet365_frame['Name'], bet365_frame['No_Vig_Prop']))
-
-    disp_market['FANDUEL NoVig'] = disp_market['Name'].map(fanduel_dict)
-    disp_market['DRAFTKINGS NoVig'] = disp_market['Name'].map(draftkings_dict)
-    disp_market['MGM NoVig'] = disp_market['Name'].map(mgm_dict)
-    disp_market['BET365 NoVig'] = disp_market['Name'].map(bet365_dict)
-
-    disp_market = disp_market[['Name', 'Position', 'PropType', 'FANDUEL NoVig', 'DRAFTKINGS NoVig', 'MGM NoVig', 'BET365 NoVig']]
-    disp_market = disp_market.drop_duplicates(subset=['Name', 'PropType'], keep='first', ignore_index=True)
-
-    st.dataframe(disp_market.style.background_gradient(axis=1).background_gradient(cmap='RdYlGn').format(prop_format, precision=2), height = 1000, use_container_width = True)
-    st.download_button(
-        label="Export Market Props",
-        data=convert_df_to_csv(disp_market),
-        file_name='NFL_market_props_export.csv',
-        mime='text/csv',
-    )
-
 with tab5:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset5'):