Update app.py

app.py

@@ -720,6 +720,10 @@ with tab2:
                   raw_baselines = dk_roo_raw
               elif slate_var1 == 'Paydirt (Secondary)':
                   raw_baselines = dk_roo_raw_2
+        del dk_roo_raw
+        del dk_roo_raw_2
+        del fd_roo_raw
+        del fd_roo_raw_2
         st.info("If you are uploading a portfolio, note that there is an adjustments to projections and deviation mapping to prevent 'Projection Bias' and create a fair simulation")
         insert_port1 = st.selectbox("Are you uploading a portfolio?", ('No', 'Yes'))
         if insert_port1 == 'Yes':
@@ -746,383 +750,403 @@ with tab2:
             scaling_var = 15
             
     with col2:
-        if st.button("Simulate Contest", key='sim1'):
-            try:
-                del dst_freq
-                del flex_freq
-                del te_freq
-                del wr_freq
-                del rb_freq
-                del qb_freq
-                del player_freq
-                del Sim_Winner_Export
-                del Sim_Winner_Frame
-            except:
-                pass
-            with st.container():
-                st.write('Contest Simulation Starting')
-                Total_Runs = 1000000
-                seed_depth1 = 5
-                Total_Runs = 2500000
-                if Contest_Size <= 1000:
-                    strength_grow = .01
-                elif Contest_Size > 1000 and Contest_Size <= 2500:
-                    strength_grow = .025
-                elif Contest_Size > 2500 and Contest_Size <= 5000:
-                    strength_grow = .05
-                elif Contest_Size > 5000 and Contest_Size <= 20000:
-                    strength_grow = .075
-                elif Contest_Size > 20000:
-                    strength_grow = .1
-                    
-                field_growth = 100 * strength_grow
-        
-                Sort_function = 'Median'
-                if Sort_function == 'Median':
-                    Sim_function = 'Projection'
-                elif Sort_function == 'Own':
-                    Sim_function = 'Own'
-                
-                if slate_var1 == 'User':
-                    OwnFrame = proj_dataframe
-                    if contest_var1 == 'Large':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Medium':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Small':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
-                    Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
-                    
-                    del OwnFrame
-                    
-                elif slate_var1 != 'User':
-                    initial_proj = raw_baselines
-                    drop_frame = initial_proj.drop_duplicates(subset = 'Player',keep = 'first')
-                    OwnFrame = drop_frame[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']]
-                    if contest_var1 == 'Large':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Medium':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Small':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
-                    Overall_Proj  = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']]
-                    
-                    del initial_proj
-                    del drop_frame
-                    del OwnFrame
-                
-                if insert_port == 1:
-                    UserPortfolio = portfolio_dataframe[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']]
-                elif insert_port == 0:
-                    UserPortfolio = pd.DataFrame(columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
-        
-                Overall_Proj.replace('', np.nan, inplace=True)
-                Overall_Proj = Overall_Proj.dropna(subset=['Median'])
-                Overall_Proj = Overall_Proj.assign(Value=lambda x: (x.Median / (x.Salary / 1000)))
-                Overall_Proj['Sort_var'] = (Overall_Proj['Median'].rank(ascending=False) + Overall_Proj['Value'].rank(ascending=False)) / 2
-                Overall_Proj = Overall_Proj.sort_values(by='Sort_var', ascending=False)
-                Overall_Proj['Own'] = np.where((Overall_Proj['Median'] > 0) & (Overall_Proj['Own'] == 0), 1, Overall_Proj['Own'])
-                Overall_Proj = Overall_Proj.loc[Overall_Proj['Own'] > 0]
-        
-                Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'QB', Overall_Proj['Median'] * .5, Overall_Proj['Median'] * .25)
-                Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'WR', Overall_Proj['Median'] + Overall_Proj['Median'], Overall_Proj['Median'] + Overall_Proj['Floor'])
-                Overall_Proj['STDev'] = Overall_Proj['Median'] / 4
-        
-                Teams_used = Overall_Proj['Team'].drop_duplicates().reset_index(drop=True)
-                Teams_used = Teams_used.reset_index()
-                Teams_used['team_item'] = Teams_used['index'] + 1
-                Teams_used = Teams_used.drop(columns=['index'])
-                Teams_used_dictraw = Teams_used.drop(columns=['team_item'])
-                Teams_used_dict = Teams_used_dictraw.to_dict()
-                
-                del Teams_used_dictraw
-        
-                team_list = Teams_used['Team'].to_list()
-                item_list = Teams_used['team_item'].to_list()
-        
-                FieldStrength_raw = Strength_var + ((30 - len(Teams_used)) * .01)
-                FieldStrength = FieldStrength_raw - (FieldStrength_raw * (20000 / Contest_Size))
-                
-                del FieldStrength_raw
-                
-                if FieldStrength < 0:
-                    FieldStrength = Strength_var
-                field_split = Strength_var
-        
-                for checkVar in range(len(team_list)):
-                                    Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list)
-                
-                flex_raw = Overall_Proj
-                flex_raw.dropna(subset=['Median']).reset_index(drop=True)
-                flex_raw = flex_raw.reset_index(drop=True)
-                flex_raw = flex_raw.sort_values(by='Own', ascending=False)
-        
-                pos_players = flex_raw
-                pos_players.dropna(subset=['Median']).reset_index(drop=True)
-                pos_players = pos_players.reset_index(drop=True)
-                
-                del flex_raw
-        
-                if insert_port == 1:
-                    try:
-                        # Initialize an empty DataFrame to store raw portfolio data
-                        Raw_Portfolio = pd.DataFrame()
-                        
-                        # Split each portfolio column and concatenate to Raw_Portfolio
-                        columns_to_process = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
-                        for col in columns_to_process:
-                            temp_df = UserPortfolio[col].str.split("(", n=1, expand=True)
-                            temp_df.columns = [col, 'Drop']
-                            Raw_Portfolio = pd.concat([Raw_Portfolio, temp_df], axis=1)
-                        
-                        # Keep only required variables and remove whitespace
-                        keep_vars = columns_to_process
-                        CleanPortfolio = Raw_Portfolio[keep_vars]
-                        CleanPortfolio = CleanPortfolio.apply(lambda x: x.str.strip())
-                        
-                        # Reset index and clean up the DataFrame
-                        CleanPortfolio.reset_index(inplace=True)
-                        CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1
-                        CleanPortfolio.drop(columns=['index'], inplace=True)
-                        CleanPortfolio.replace('', np.nan, inplace=True)
-                        CleanPortfolio.dropna(subset=['CPT'], inplace=True)
-                        
-                        # Create cleaport_players DataFrame
-                        unique_vals, counts = np.unique(CleanPortfolio.iloc[:, 0:6].values, return_counts=True)
-                        cleaport_players = pd.DataFrame(np.column_stack([unique_vals, counts]), columns=['Player', 'Freq']).astype({'Freq': int}).sort_values('Freq', ascending=False).reset_index(drop=True)
-                        
-                        # Merge and update nerf_frame DataFrame
-                        nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
-                        nerf_frame[['Median', 'Floor', 'Ceiling', 'STDev']] *= 1
+        with st.container():
+            if st.button("Simulate Contest", key='sim1'):
+                try:
+                    del dst_freq
+                    del flex_freq
+                    del te_freq
+                    del wr_freq
+                    del rb_freq
+                    del qb_freq
+                    del player_freq
+                    del Sim_Winner_Export
+                    del Sim_Winner_Frame
+                except:
+                    pass
+                with st.container():
+                    st.write('Contest Simulation Starting')
+                    Total_Runs = 1000000
+                    seed_depth1 = 5
+                    Total_Runs = 2500000
+                    if Contest_Size <= 1000:
+                        strength_grow = .01
+                    elif Contest_Size > 1000 and Contest_Size <= 2500:
+                        strength_grow = .025
+                    elif Contest_Size > 2500 and Contest_Size <= 5000:
+                        strength_grow = .05
+                    elif Contest_Size > 5000 and Contest_Size <= 20000:
+                        strength_grow = .075
+                    elif Contest_Size > 20000:
+                        strength_grow = .1
                         
-                        del Raw_Portfolio
-                    except:
-                        # Reset index and perform column-wise operations
-                        CleanPortfolio = UserPortfolio.reset_index(drop=True)
-                        CleanPortfolio['User/Field'] = CleanPortfolio.index + 1
-                        CleanPortfolio.replace('', np.nan, inplace=True)
-                        CleanPortfolio.dropna(subset=['CPT'], inplace=True)
+                    field_growth = 100 * strength_grow
+            
+                    Sort_function = 'Median'
+                    if Sort_function == 'Median':
+                        Sim_function = 'Projection'
+                    elif Sort_function == 'Own':
+                        Sim_function = 'Own'
+                    
+                    if slate_var1 == 'User':
+                        OwnFrame = proj_dataframe
+                        if contest_var1 == 'Large':
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
+                            OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
+                            OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
+                        if contest_var1 == 'Medium':
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
+                            OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
+                            OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
+                        if contest_var1 == 'Small':
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
+                            OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
+                            OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
+                        Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
                         
-                        # Create cleaport_players DataFrame
-                        unique_vals, counts = np.unique(CleanPortfolio.iloc[:, 0:6].values, return_counts=True)
-                        cleaport_players = pd.DataFrame({'Player': unique_vals, 'Freq': counts}).sort_values('Freq', ascending=False).reset_index(drop=True).astype({'Freq': int})
+                        del OwnFrame
                         
-                        # Merge and update nerf_frame DataFrame
-                        nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
-                        nerf_frame[['Median', 'Floor', 'Ceiling', 'STDev']] *= 1
+                    elif slate_var1 != 'User':
+                        initial_proj = raw_baselines
+                        drop_frame = initial_proj.drop_duplicates(subset = 'Player',keep = 'first')
+                        OwnFrame = drop_frame[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']]
+                        if contest_var1 == 'Large':
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
+                            OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
+                            OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
+                        if contest_var1 == 'Medium':
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
+                            OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
+                            OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
+                        if contest_var1 == 'Small':
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
+                            OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
+                            OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
+                            OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
+                        Overall_Proj  = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']]
                         
-                        st.table(nerf_frame)
-
-                elif insert_port == 0:
-                    CleanPortfolio = UserPortfolio
-                    cleaport_players = pd.DataFrame(np.column_stack(np.unique(CleanPortfolio.iloc[:,0:6].values, return_counts=True)),
-                                               columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                    cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
-                    nerf_frame = Overall_Proj
+                        del initial_proj
+                        del drop_frame
+                        del OwnFrame
                     
-                ref_dict = {
-                    'pos':['FLEX'],
-                    'pos_dfs':['FLEX_Table'],
-                    'pos_dicts':['flex_dict']
-                    }
-        
-                maps_dict = {
-                    'Floor_map':dict(zip(Overall_Proj.Player,Overall_Proj.Floor)),
-                    'Projection_map':dict(zip(Overall_Proj.Player,Overall_Proj.Median)),
-                    'Ceiling_map':dict(zip(Overall_Proj.Player,Overall_Proj.Ceiling)),
-                    'Salary_map':dict(zip(Overall_Proj.Player,Overall_Proj.Salary)),
-                    'Pos_map':dict(zip(Overall_Proj.Player,Overall_Proj.Position)),
-                    'Own_map':dict(zip(Overall_Proj.Player,Overall_Proj.Own)),
-                    'Team_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)),
-                    'STDev_map':dict(zip(Overall_Proj.Player,Overall_Proj.STDev)),
-                    'team_check_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team))
-                    }
-                
-                up_dict = {
-                    'Floor_map':dict(zip(cleaport_players.Player,nerf_frame.Floor)),
-                    'Projection_map':dict(zip(cleaport_players.Player,nerf_frame.Median)),
-                    'Ceiling_map':dict(zip(cleaport_players.Player,nerf_frame.Ceiling)),
-                    'Salary_map':dict(zip(cleaport_players.Player,nerf_frame.Salary)),
-                    'Pos_map':dict(zip(cleaport_players.Player,nerf_frame.Position)),
-                    'Own_map':dict(zip(cleaport_players.Player,nerf_frame.Own)),
-                    'Team_map':dict(zip(cleaport_players.Player,nerf_frame.Team)),
-                    'STDev_map':dict(zip(cleaport_players.Player,nerf_frame.STDev)),
-                    'team_check_map':dict(zip(cleaport_players.Player,nerf_frame.Team))
-                    }
-                
-                del Overall_Proj
-                del nerf_frame
-                
-                RunsVar = 1
-                st.write('Seed frame creation')
-                FinalPortfolio, maps_dict = run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs)
-                
-                Sim_size = linenum_var1
-                SimVar = 1
-                Sim_Winners = []
-                fp_array = FinalPortfolio.values
-                
-                if insert_port == 1:
-                    up_array = CleanPortfolio.values
-                
-                # Pre-vectorize functions
-                vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
-                vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
-                
-                if insert_port == 1:
-                    vec_up_projection_map = np.vectorize(up_dict['Projection_map'].__getitem__)
-                    vec_up_stdev_map = np.vectorize(up_dict['STDev_map'].__getitem__)
-                st.write('Simulating contest on frames')
-                while SimVar <= Sim_size:
+                    if insert_port == 1:
+                        UserPortfolio = portfolio_dataframe[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']]
+                    elif insert_port == 0:
+                        UserPortfolio = pd.DataFrame(columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
+            
+                    Overall_Proj.replace('', np.nan, inplace=True)
+                    Overall_Proj = Overall_Proj.dropna(subset=['Median'])
+                    Overall_Proj = Overall_Proj.assign(Value=lambda x: (x.Median / (x.Salary / 1000)))
+                    Overall_Proj['Sort_var'] = (Overall_Proj['Median'].rank(ascending=False) + Overall_Proj['Value'].rank(ascending=False)) / 2
+                    Overall_Proj = Overall_Proj.sort_values(by='Sort_var', ascending=False)
+                    Overall_Proj['Own'] = np.where((Overall_Proj['Median'] > 0) & (Overall_Proj['Own'] == 0), 1, Overall_Proj['Own'])
+                    Overall_Proj = Overall_Proj.loc[Overall_Proj['Own'] > 0]
+            
+                    Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'QB', Overall_Proj['Median'] * .5, Overall_Proj['Median'] * .25)
+                    Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'WR', Overall_Proj['Median'] + Overall_Proj['Median'], Overall_Proj['Median'] + Overall_Proj['Floor'])
+                    Overall_Proj['STDev'] = Overall_Proj['Median'] / 4
+            
+                    Teams_used = Overall_Proj['Team'].drop_duplicates().reset_index(drop=True)
+                    Teams_used = Teams_used.reset_index()
+                    Teams_used['team_item'] = Teams_used['index'] + 1
+                    Teams_used = Teams_used.drop(columns=['index'])
+                    Teams_used_dictraw = Teams_used.drop(columns=['team_item'])
+                    Teams_used_dict = Teams_used_dictraw.to_dict()
                     
+                    del Teams_used_dictraw
+            
+                    team_list = Teams_used['Team'].to_list()
+                    item_list = Teams_used['team_item'].to_list()
+            
+                    FieldStrength_raw = Strength_var + ((30 - len(Teams_used)) * .01)
+                    FieldStrength = FieldStrength_raw - (FieldStrength_raw * (20000 / Contest_Size))
+                    
+                    del FieldStrength_raw
+                    
+                    if FieldStrength < 0:
+                        FieldStrength = Strength_var
+                    field_split = Strength_var
+            
+                    for checkVar in range(len(team_list)):
+                                        Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list)
+                    
+                    flex_raw = Overall_Proj
+                    flex_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    flex_raw = flex_raw.reset_index(drop=True)
+                    flex_raw = flex_raw.sort_values(by='Own', ascending=False)
+            
+                    pos_players = flex_raw
+                    pos_players.dropna(subset=['Median']).reset_index(drop=True)
+                    pos_players = pos_players.reset_index(drop=True)
+                    
+                    del flex_raw
+            
                     if insert_port == 1:
-                        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))]
+                        try:
+                            # Initialize an empty DataFrame to store raw portfolio data
+                            Raw_Portfolio = pd.DataFrame()
+                            
+                            # Split each portfolio column and concatenate to Raw_Portfolio
+                            columns_to_process = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
+                            for col in columns_to_process:
+                                temp_df = UserPortfolio[col].str.split("(", n=1, expand=True)
+                                temp_df.columns = [col, 'Drop']
+                                Raw_Portfolio = pd.concat([Raw_Portfolio, temp_df], axis=1)
+                            
+                            # Keep only required variables and remove whitespace
+                            keep_vars = columns_to_process
+                            CleanPortfolio = Raw_Portfolio[keep_vars]
+                            CleanPortfolio = CleanPortfolio.apply(lambda x: x.str.strip())
+                            
+                            # Reset index and clean up the DataFrame
+                            CleanPortfolio.reset_index(inplace=True)
+                            CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1
+                            CleanPortfolio.drop(columns=['index'], inplace=True)
+                            CleanPortfolio.replace('', np.nan, inplace=True)
+                            CleanPortfolio.dropna(subset=['CPT'], inplace=True)
+                            
+                            # Create cleaport_players DataFrame
+                            unique_vals, counts = np.unique(CleanPortfolio.iloc[:, 0:6].values, return_counts=True)
+                            cleaport_players = pd.DataFrame(np.column_stack([unique_vals, counts]), columns=['Player', 'Freq']).astype({'Freq': int}).sort_values('Freq', ascending=False).reset_index(drop=True)
+                            
+                            # Merge and update nerf_frame DataFrame
+                            nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
+                            nerf_frame[['Median', 'Floor', 'Ceiling', 'STDev']] *= 1
+                            
+                            del Raw_Portfolio
+                        except:
+                            # Reset index and perform column-wise operations
+                            CleanPortfolio = UserPortfolio.reset_index(drop=True)
+                            CleanPortfolio['User/Field'] = CleanPortfolio.index + 1
+                            CleanPortfolio.replace('', np.nan, inplace=True)
+                            CleanPortfolio.dropna(subset=['CPT'], inplace=True)
+                            
+                            # Create cleaport_players DataFrame
+                            unique_vals, counts = np.unique(CleanPortfolio.iloc[:, 0:6].values, return_counts=True)
+                            cleaport_players = pd.DataFrame({'Player': unique_vals, 'Freq': counts}).sort_values('Freq', ascending=False).reset_index(drop=True).astype({'Freq': int})
+                            
+                            # Merge and update nerf_frame DataFrame
+                            nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
+                            nerf_frame[['Median', 'Floor', 'Ceiling', 'STDev']] *= 1
+                            
+                            st.table(nerf_frame)
+    
                     elif insert_port == 0:
-                        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
+                        CleanPortfolio = UserPortfolio
+                        cleaport_players = pd.DataFrame(np.column_stack(np.unique(CleanPortfolio.iloc[:,0:6].values, return_counts=True)),
+                                                   columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                        cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
+                        nerf_frame = Overall_Proj
                         
-                    sample_arrays1 = np.c_[
-                        fp_random, 
-                        np.sum(np.random.normal(
-                            loc=vec_projection_map(fp_random[:, :-5]),
-                            scale=vec_stdev_map(fp_random[:, :-5])),
-                        axis=1)
-                    ]
+                    ref_dict = {
+                        'pos':['FLEX'],
+                        'pos_dfs':['FLEX_Table'],
+                        'pos_dicts':['flex_dict']
+                        }
             
+                    maps_dict = {
+                        'Floor_map':dict(zip(Overall_Proj.Player,Overall_Proj.Floor)),
+                        'Projection_map':dict(zip(Overall_Proj.Player,Overall_Proj.Median)),
+                        'Ceiling_map':dict(zip(Overall_Proj.Player,Overall_Proj.Ceiling)),
+                        'Salary_map':dict(zip(Overall_Proj.Player,Overall_Proj.Salary)),
+                        'Pos_map':dict(zip(Overall_Proj.Player,Overall_Proj.Position)),
+                        'Own_map':dict(zip(Overall_Proj.Player,Overall_Proj.Own)),
+                        'Team_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)),
+                        'STDev_map':dict(zip(Overall_Proj.Player,Overall_Proj.STDev)),
+                        'team_check_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team))
+                        }
+                    
+                    up_dict = {
+                        'Floor_map':dict(zip(cleaport_players.Player,nerf_frame.Floor)),
+                        'Projection_map':dict(zip(cleaport_players.Player,nerf_frame.Median)),
+                        'Ceiling_map':dict(zip(cleaport_players.Player,nerf_frame.Ceiling)),
+                        'Salary_map':dict(zip(cleaport_players.Player,nerf_frame.Salary)),
+                        'Pos_map':dict(zip(cleaport_players.Player,nerf_frame.Position)),
+                        'Own_map':dict(zip(cleaport_players.Player,nerf_frame.Own)),
+                        'Team_map':dict(zip(cleaport_players.Player,nerf_frame.Team)),
+                        'STDev_map':dict(zip(cleaport_players.Player,nerf_frame.STDev)),
+                        'team_check_map':dict(zip(cleaport_players.Player,nerf_frame.Team))
+                        }
+                    
+                    del Overall_Proj
+                    del nerf_frame
+                    
+                    RunsVar = 1
+                    st.write('Seed frame creation')
+                    FinalPortfolio, maps_dict = run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs)
+                    
+                    Sim_size = linenum_var1
+                    SimVar = 1
+                    Sim_Winners = []
+                    fp_array = FinalPortfolio.values
+                    
+                    if insert_port == 1:
+                        up_array = CleanPortfolio.values
+                    
+                    # Pre-vectorize functions
+                    vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
+                    vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
+                    
                     if insert_port == 1:
-                        sample_arrays2 = np.c_[
-                            up_array, 
+                        vec_up_projection_map = np.vectorize(up_dict['Projection_map'].__getitem__)
+                        vec_up_stdev_map = np.vectorize(up_dict['STDev_map'].__getitem__)
+                    st.write('Simulating contest on frames')
+                    while SimVar <= Sim_size:
+                        
+                        if insert_port == 1:
+                            fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))]
+                        elif insert_port == 0:
+                            fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
+                            
+                        sample_arrays1 = np.c_[
+                            fp_random, 
                             np.sum(np.random.normal(
-                                loc=vec_up_projection_map(up_array[:, :-5]),
-                                scale=vec_up_stdev_map(up_array[:, :-5])),
+                                loc=vec_projection_map(fp_random[:, :-5]),
+                                scale=vec_stdev_map(fp_random[:, :-5])),
                             axis=1)
                         ]
-                        sample_arrays = np.vstack((sample_arrays1, sample_arrays2))
-                    else:
-                        sample_arrays = sample_arrays1
-            
-                    final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
-                    best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
-                    Sim_Winners.append(best_lineup)
-                    SimVar += 1
-                st.write('Contest simulation complete')
-
-                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy'])
-                Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2
-                Sim_Winner_Frame['Salary'] = Sim_Winner_Frame['Salary'].astype(int)
-                Sim_Winner_Frame['Projection'] = Sim_Winner_Frame['Projection'].astype(np.float16)
-                Sim_Winner_Frame['Fantasy'] = Sim_Winner_Frame['Fantasy'].astype(np.float16)
-                Sim_Winner_Frame['GPP_Proj'] = Sim_Winner_Frame['GPP_Proj'].astype(np.float16)
-                Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by='GPP_Proj', ascending=False)
                 
-                player_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:6].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(maps_dict['Pos_map'])
-                player_freq['Salary'] = player_freq['Player'].map(maps_dict['Salary_map'])
-                player_freq['Proj Own'] = (player_freq['Player'].map(maps_dict['Own_map']) / 100)
-                player_freq['Exposure'] = player_freq['Freq']/(Sim_size)
-                player_freq['Edge'] = player_freq['Exposure'] - player_freq['Proj Own']
-                player_freq['Team'] = player_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    player_freq['Team'] = player_freq['Team'].replace(item_list, team_list)
-
-                player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                        if insert_port == 1:
+                            sample_arrays2 = np.c_[
+                                up_array, 
+                                np.sum(np.random.normal(
+                                    loc=vec_up_projection_map(up_array[:, :-5]),
+                                    scale=vec_up_stdev_map(up_array[:, :-5])),
+                                axis=1)
+                            ]
+                            sample_arrays = np.vstack((sample_arrays1, sample_arrays2))
+                        else:
+                            sample_arrays = sample_arrays1
                 
-                cpt_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:1].values, return_counts=True)),
-                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                cpt_freq['Freq'] = cpt_freq['Freq'].astype(int)
-                cpt_freq['Position'] = cpt_freq['Player'].map(maps_dict['Pos_map'])
-                cpt_freq['Salary'] = cpt_freq['Player'].map(maps_dict['Salary_map'])
-                cpt_freq['Proj Own'] = (cpt_freq['Player'].map(maps_dict['Own_map']) / 4) / 100
-                cpt_freq['Exposure'] = cpt_freq['Freq']/(Sim_size)
-                cpt_freq['Edge'] = cpt_freq['Exposure'] - cpt_freq['Proj Own']
-                cpt_freq['Team'] = cpt_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    cpt_freq['Team'] = cpt_freq['Team'].replace(item_list, team_list)
-
-                cpt_freq = cpt_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-                
-                flex_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[1, 2, 3, 4, 5]].values, return_counts=True)),
-                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                flex_freq['Freq'] = flex_freq['Freq'].astype(int)
-                flex_freq['Position'] = flex_freq['Player'].map(maps_dict['Pos_map'])
-                flex_freq['Salary'] = flex_freq['Player'].map(maps_dict['Salary_map'])
-                flex_freq['Proj Own'] = (flex_freq['Player'].map(maps_dict['Own_map']) / 100) - ((flex_freq['Player'].map(maps_dict['Own_map']) / 4) / 100)
-                flex_freq['Exposure'] = flex_freq['Freq']/(Sim_size)
-                flex_freq['Edge'] = flex_freq['Exposure'] - flex_freq['Proj Own']
-                flex_freq['Team'] = flex_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    flex_freq['Team'] = flex_freq['Team'].replace(item_list, team_list)
-
-                flex_freq = flex_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-                
-                del fp_random
-                del sample_arrays
-                del final_array
-                del fp_array
-                try:
-                    del up_array
-                except:
-                    pass
-                del best_lineup
-                del CleanPortfolio
-                del FinalPortfolio
-                del maps_dict
-                del team_list
-                del item_list
-                del Sim_size
+                        final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
+                        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
+                        Sim_Winners.append(best_lineup)
+                        SimVar += 1
+                    st.write('Contest simulation complete')
+    
+                    Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy'])
+                    Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2
+                    Sim_Winner_Frame['Salary'] = Sim_Winner_Frame['Salary'].astype(int)
+                    Sim_Winner_Frame['Projection'] = Sim_Winner_Frame['Projection'].astype(np.float16)
+                    Sim_Winner_Frame['Fantasy'] = Sim_Winner_Frame['Fantasy'].astype(np.float16)
+                    Sim_Winner_Frame['GPP_Proj'] = Sim_Winner_Frame['GPP_Proj'].astype(np.float16)
+                    st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by='GPP_Proj', ascending=False)
+                    st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                    
+                    del Sim_Winner_Frame
+                    
+                    player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,0:6].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(maps_dict['Pos_map'])
+                    player_freq['Salary'] = player_freq['Player'].map(maps_dict['Salary_map'])
+                    player_freq['Proj Own'] = (player_freq['Player'].map(maps_dict['Own_map']) / 100)
+                    player_freq['Exposure'] = player_freq['Freq']/(Sim_size)
+                    player_freq['Edge'] = player_freq['Exposure'] - player_freq['Proj Own']
+                    player_freq['Team'] = player_freq['Player'].map(maps_dict['Team_map'])
+                    for checkVar in range(len(team_list)):
+                                        player_freq['Team'] = player_freq['Team'].replace(item_list, team_list)
+    
+                    st.session_state.player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    
+                    cpt_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,0:1].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                    cpt_freq['Freq'] = cpt_freq['Freq'].astype(int)
+                    cpt_freq['Position'] = cpt_freq['Player'].map(maps_dict['Pos_map'])
+                    cpt_freq['Salary'] = cpt_freq['Player'].map(maps_dict['Salary_map'])
+                    cpt_freq['Proj Own'] = (cpt_freq['Player'].map(maps_dict['Own_map']) / 4) / 100
+                    cpt_freq['Exposure'] = cpt_freq['Freq']/(Sim_size)
+                    cpt_freq['Edge'] = cpt_freq['Exposure'] - cpt_freq['Proj Own']
+                    cpt_freq['Team'] = cpt_freq['Player'].map(maps_dict['Team_map'])
+                    for checkVar in range(len(team_list)):
+                                        cpt_freq['Team'] = cpt_freq['Team'].replace(item_list, team_list)
+    
+                    st.session_state.cpt_freq = cpt_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    
+                    flex_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,[1, 2, 3, 4, 5]].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                    flex_freq['Freq'] = flex_freq['Freq'].astype(int)
+                    flex_freq['Position'] = flex_freq['Player'].map(maps_dict['Pos_map'])
+                    flex_freq['Salary'] = flex_freq['Player'].map(maps_dict['Salary_map'])
+                    flex_freq['Proj Own'] = (flex_freq['Player'].map(maps_dict['Own_map']) / 100) - ((flex_freq['Player'].map(maps_dict['Own_map']) / 4) / 100)
+                    flex_freq['Exposure'] = flex_freq['Freq']/(Sim_size)
+                    flex_freq['Edge'] = flex_freq['Exposure'] - flex_freq['Proj Own']
+                    flex_freq['Team'] = flex_freq['Player'].map(maps_dict['Team_map'])
+                    for checkVar in range(len(team_list)):
+                                        flex_freq['Team'] = flex_freq['Team'].replace(item_list, team_list)
+    
+                    st.session_state.flex_freq = flex_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    
+                    del fp_random
+                    del sample_arrays
+                    del final_array
+                    del fp_array
+                    try:
+                        del up_array
+                    except:
+                        pass
+                    del best_lineup
+                    del CleanPortfolio
+                    del FinalPortfolio
+                    del maps_dict
+                    del team_list
+                    del item_list
+                    del Sim_size
                 
-            with st.container():
-                    st.dataframe(Sim_Winner_Frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
-            
-            with st.container():
-                    tab1, tab2, tab3 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX Exposures'])
-                    with tab1:
-                        st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+        with st.container():
+            simulate_container = st.empty()
+            if 'player_freq' in st.session_state: 
+                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
+                if player_split_var2 == 'Specific Players':
+                          find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
+                elif player_split_var2 == 'Full Players':
+                          find_var2 = st.session_state.player_freq.Player.values.tolist()
+    
+                if player_split_var2 == 'Specific Players':
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(copy=False),  find_var2).any(axis=1).all(axis=1)]
+                if player_split_var2 == 'Full Players':
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
+            if 'Sim_Winner_Display' in st.session_state:
+                st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
+            if 'Sim_Winner_Export' in st.session_state:
+                st.download_button(
+                    label="Export Tables",
+                    data=convert_df_to_csv(st.session_state.Sim_Winner_Export),
+                    file_name='NFL_consim_export.csv',
+                    mime='text/csv',
+                )
+        
+        with st.container():
+                tab1, tab2, tab3 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX Exposures'])
+                with tab1:
+                    if 'player_freq' in st.session_state:
+                        st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                         st.download_button(
                             label="Export Exposures",
-                            data=convert_df_to_csv(player_freq),
+                            data=convert_df_to_csv(st.session_state.player_freq),
                             file_name='player_freq_export.csv',
                             mime='text/csv',
                         )
-                    with tab2:
-                        st.dataframe(cpt_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                with tab2:
+                    if 'player_freq' in st.session_state:
+                        st.dataframe(st.session_state.cpt_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                         st.download_button(
                             label="Export Exposures",
-                            data=convert_df_to_csv(cpt_freq),
+                            data=convert_df_to_csv(st.session_state.cpt_freq),
                             file_name='cpt_freq_export.csv',
                             mime='text/csv',
                         )
-                    with tab3:
-                        st.dataframe(flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                with tab3:
+                    if 'player_freq' in st.session_state:
+                        st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                         st.download_button(
                             label="Export Exposures",
-                            data=convert_df_to_csv(flex_freq),
+                            data=convert_df_to_csv(st.session_state.flex_freq),
                             file_name='flex_freq_export.csv',
                             mime='text/csv',
-                        )
-
-            st.download_button(
-                label="Export Tables",
-                data=convert_df_to_csv(Sim_Winner_Frame),
-                file_name='NFL_consim_export.csv',
-                mime='text/csv',
-            )
+                        )