Update app.py

app.py

@@ -59,10 +59,15 @@ def init_baselines():
     raw_display.replace('', np.nan, inplace=True)
     raw_display.replace('#DIV/0!', np.nan, inplace=True)
     prop_frame = raw_display.dropna()
+    
+    worksheet = sh.worksheet('Pick6_ingest')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace('', np.nan, inplace=True)
+    pick_frame = raw_display.dropna(subset='Player')
 
-    return game_model, overall_stats, timestamp, prop_frame, prop_trends
+    return game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame
 
-game_model, overall_stats, timestamp, prop_frame, prop_trends = init_baselines()
+game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = 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']))
@@ -83,7 +88,7 @@ with tab1:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = 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']))
@@ -111,7 +116,7 @@ with tab2:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset2'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = 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']))
@@ -137,7 +142,7 @@ with tab3:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = 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']))
@@ -163,7 +168,7 @@ 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 = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = 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']))
@@ -190,7 +195,7 @@ 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()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = 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']))
@@ -337,7 +342,7 @@ with tab6:
     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='reset6'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = 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']))
@@ -351,6 +356,19 @@ with tab6:
         export_container = st.empty()
     
     with col1:
+        game_select_var = st.selectbox('Select prop source', options = ['Aggregate', 'Pick6'])
+        if game_select_var == 'Aggregate':
+            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+        elif game_select_var == 'Pick6':
+            prop_df = pick_frame[['Full_name', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+            prop_df.rename(columns={"Full_name": "Player"}, inplace = True)
+        st.download_button(
+            label="Download Prop Source",
+            data=convert_df_to_csv(prop_df),
+            file_name='Nba_prop_source.csv',
+            mime='text/csv',
+            key='prop_source',
+        )
         prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'pass_yards', 'rush_yards', 'rec_yards', 'receptions', 'rush_attempts'])
 
         if st.button('Simulate Prop Category'):
@@ -360,6 +378,11 @@ with tab6:
                         if prop_type_var == 'All Props':
                             for prop in all_sim_vars:
                                 
+                                if game_select_var == 'Aggregate':
+                                    prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                elif game_select_var == 'Pick6':
+                                    prop_df = pick_frame[['Full_name', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                    prop_df.rename(columns={"Full_name": "Player"}, inplace = True)
                                 prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 prop_df = prop_df.loc[prop_df['prop_type'] == prop]
                                 prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
@@ -450,9 +473,14 @@ with tab6:
                                 final_outcomes = sim_all_hold
                                 
                         elif prop_type_var != 'All Props':
+                            
+                            if game_select_var == 'Aggregate':
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                            elif game_select_var == 'Pick6':
+                                prop_df = pick_frame[['Full_name', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df.rename(columns={"Full_name": "Player"}, inplace = True)
 
                             if prop_type_var == "pass_yards":
-                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 prop_df = prop_df.loc[prop_df['prop_type'] == 'pass_yards']
                                 prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
                                 prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
@@ -462,7 +490,6 @@ with tab6:
                                 prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
                                 df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
                             elif prop_type_var == "rush_yards":
-                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 prop_df = prop_df.loc[prop_df['prop_type'] == 'rush_yards']
                                 prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
                                 prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
@@ -472,7 +499,6 @@ with tab6:
                                 prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
                                 df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
                             elif prop_type_var == "rec_yards":
-                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 prop_df = prop_df.loc[prop_df['prop_type'] == 'rec_yards']
                                 prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
                                 prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
@@ -482,7 +508,6 @@ with tab6:
                                 prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
                                 df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
                             elif prop_type_var == "receptions":
-                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 prop_df = prop_df.loc[prop_df['prop_type'] == 'receptions']
                                 prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
                                 prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
@@ -492,7 +517,6 @@ with tab6:
                                 prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
                                 df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
                             elif prop_type_var == "rush_attempts":
-                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 prop_df = prop_df.loc[prop_df['prop_type'] == 'rush_attempts']
                                 prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
                                 prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
@@ -502,7 +526,6 @@ with tab6:
                                 prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
                                 df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
                             elif prop_type_var == "pass_attempts":
-                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 prop_df = prop_df.loc[prop_df['prop_type'] == 'pass_attempts']
                                 prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
                                 prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
@@ -512,7 +535,6 @@ with tab6:
                                 prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
                                 df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
                             elif prop_type_var == "pass_completions":
-                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 prop_df = prop_df.loc[prop_df['prop_type'] == 'pass_completions']
                                 prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
                                 prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)