Update app.py

app.py

@@ -43,6 +43,8 @@ team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}',
 player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
                    '4x%': '{:.2%}','GPP%': '{:.2%}'}
 
+expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'}
+
 all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348'
 
 @st.cache_resource(ttl=600)
@@ -97,10 +99,28 @@ def load_fd_player_projections_2():
 
     return raw_display
 
+@st.cache_resource(ttl = 3600)
+def set_export_ids():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('SD_Projections')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    raw_display = load_display.dropna(subset=['Median'])
+    dk_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
+    
+    worksheet = sh.worksheet('FD_SD_Projections')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    raw_display = load_display.dropna(subset=['Median'])
+    fd_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
+
+    return dk_ids, fd_ids
+
 dk_roo_raw = load_dk_player_projections()
 dk_roo_raw_2 = load_dk_player_projections_2()
 fd_roo_raw = load_fd_player_projections()
 fd_roo_raw_2 = load_fd_player_projections_2()
+dkid_dict, fdid_dict = set_export_ids()
 
 @st.cache_data
 def convert_df_to_csv(df):
@@ -143,6 +163,7 @@ with tab2:
               dk_roo_raw_2 = load_dk_player_projections_2()
               fd_roo_raw = load_fd_player_projections()
               fd_roo_raw_2 = load_fd_player_projections_2()
+              dkid_dict, fdid_dict = set_export_ids()
         slate_var2 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'User'), key='slate_var2')
         site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
         if slate_var2 == 'User':
@@ -589,6 +610,53 @@ with tab3:
                         portfolio = portfolio.drop_duplicates()
     
                         final_outcomes = portfolio
+                        
+                        player_freq = pd.DataFrame(np.column_stack(np.unique(portfolio.iloc[:,0:8].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                        player_freq['Freq'] = player_freq['Freq'].astype(int)
+                        player_freq['Position'] = player_freq['Player'].map(player_pos)
+                        player_freq['Salary'] = player_freq['Player'].map(player_sal)
+                        player_freq['Proj Own'] = player_freq['Player'].map(player_own) / 100
+                        player_freq['Exposure'] = player_freq['Freq']/(linenum_var1)
+                        player_freq['Team'] = player_freq['Player'].map(player_team)
+                        
+                        final_outcomes_export = pd.DataFrame()
+                        
+                        if site_var1 == 'Draftkings':
+                            final_outcomes_export['CPT'] = final_outcomes['CPT']
+                            final_outcomes_export['FLEX1'] = final_outcomes['FLEX1']
+                            final_outcomes_export['FLEX2'] = final_outcomes['FLEX2']
+                            final_outcomes_export['FLEX3'] = final_outcomes['FLEX3']
+                            final_outcomes_export['FLEX4'] = final_outcomes['FLEX4']
+                            final_outcomes_export['FLEX5'] = final_outcomes['FLEX5']
+                            final_outcomes_export['Salary'] = final_outcomes['Cost']
+                            final_outcomes_export['Own'] = final_outcomes['Own']
+                            final_outcomes_export['Proj'] = final_outcomes['Proj']
+                            
+                            final_outcomes_export['CPT'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['FLEX1'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['FLEX2'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['FLEX3'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['FLEX4'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['FLEX5'].replace(dkid_dict, inplace=True)
+                        elif site_var1 == 'Fanduel':
+                            final_outcomes_export['CPT'] = final_outcomes['CPT']
+                            final_outcomes_export['FLEX1'] = final_outcomes['FLEX1']
+                            final_outcomes_export['FLEX2'] = final_outcomes['FLEX2']
+                            final_outcomes_export['FLEX3'] = final_outcomes['FLEX3']
+                            final_outcomes_export['FLEX4'] = final_outcomes['FLEX4']
+                            final_outcomes_export['Salary'] = final_outcomes['Cost']
+                            final_outcomes_export['Own'] = final_outcomes['Own']
+                            final_outcomes_export['Proj'] = final_outcomes['Proj']
+                            
+                            final_outcomes_export['MVP'].replace(fdid_dict, inplace=True)
+                            final_outcomes_export['FLEX1'].replace(fdid_dict, inplace=True)
+                            final_outcomes_export['FLEX2'].replace(fdid_dict, inplace=True)
+                            final_outcomes_export['FLEX3'].replace(fdid_dict, inplace=True)
+                            final_outcomes_export['FLEX4'].replace(fdid_dict, inplace=True)
+          
+                        player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']]
+                        player_freq = player_freq.set_index('Player')
     
                 with optimize_container:
                     optimize_container = st.empty()
@@ -600,3 +668,7 @@ with tab3:
                     file_name='MLB_optimals_export.csv',
                     mime='text/csv',
                 )
+                
+                with st.container():
+                    freq_container = st.empty()
+                    st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True)