Update app.py

app.py

@@ -63,17 +63,24 @@ def pull_baselines():
     prop_trends['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'],
                                   ['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True)
     
+    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')
+    pick_frame['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'],
+                                  ['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True)
+    
     team_dict = dict(zip(prop_table['Player'], prop_table['Team']))
     
     worksheet = sh.worksheet('Timestamp')
     timestamp = worksheet.acell('A1').value
 
-    return prop_table, prop_trends, timestamp, team_dict
+    return prop_table, prop_trends, pick_frame, timestamp, team_dict
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-prop_display, prop_trends, timestamp, team_dict = pull_baselines()
+prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines()
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
 tab1, tab2, tab3 = st.tabs(["Player Stat Table", 'Prop Trend Table', 'Stat Specific Simulations'])
@@ -82,7 +89,7 @@ with tab1:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              prop_display, prop_trends, timestamp, team_dict = pull_baselines()
+              prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines()
     prop_frame = prop_display
     st.dataframe(prop_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
     
@@ -98,7 +105,7 @@ with tab2:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
-              prop_display, prop_trends, timestamp, team_dict = pull_baselines()
+              prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_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':
@@ -123,7 +130,7 @@ with tab3:
     st.info('The Over and Under percentages are a composite 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'):
               st.cache_data.clear()
-              prop_display, prop_trends, timestamp, team_dict = pull_baselines()
+              prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])
     
@@ -134,6 +141,19 @@ with tab3:
         export_container = st.empty()
     
     with col1:
+        game_select_var = st.selectbox('Select prop source', options = ['Draftkings', 'Pick6'])
+        if game_select_var == 'Draftkings':
+            prop_df = prop_trends[['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', 'SOG', 'points', 'blocked_shots', 'assists'])
         if prop_type_var == 'All Props':
             st.info('please note that the All Props run can take some time, you will see progress as tables show up in the sim area to the right')