Added loop for top 10 owned players

app.py

@@ -78,12 +78,12 @@ def player_stat_table():
     worksheet = sh.worksheet('DK_ROO')
     load_display = pd.DataFrame(worksheet.get_all_records())
     load_display.replace('', np.nan, inplace=True)
-    dk_roo_raw = load_display.dropna(subset=['Median'])
+    dk_roo_raw = load_display.dropna(subset=['Own'])
     
     worksheet = sh.worksheet('FD_ROO')
     load_display = pd.DataFrame(worksheet.get_all_records())
     load_display.replace('', np.nan, inplace=True)
-    fd_roo_raw = load_display.dropna(subset=['Median'])
+    fd_roo_raw = load_display.dropna(subset=['Own'])
     
     worksheet = sh.worksheet('Site_Info')
     site_slates = pd.DataFrame(worksheet.get_all_records())
@@ -123,7 +123,11 @@ with tab1:
               elif data_var1 != 'User':
                   raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == 'Main Slate']
                   raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
-        player_check = st.selectbox('Select player to create comps', options = dk_roo_raw['Player'].unique(), key='dk_player')
+        check_seq = st.radio("Do you want to check a single player or the top 10 in ownership?", ('Single Player', 'Top 10 Owned'), key='check_seq')
+        if check_seq == 'Single Player':
+            player_check = st.selectbox('Select player to create comps', options = raw_baselines['Player'].unique(), key='dk_player')
+        elif check_seq == 'Top 10 Owned':
+            player_check = raw_baselines['Player'].head(10).tolist()
         Salary_var = st.number_input('Acceptable +/- Salary range', min_value = 0, max_value = 1000, value = 300, step = 100)
         Median_var = st.number_input('Acceptable +/- Median range', min_value = 0, max_value = 10, value = 3, step = 1)
         pos_var1 = st.radio("Compare to all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_var1')
@@ -153,90 +157,183 @@ with tab1:
                 team_dict = dict(zip(working_roo.Player, working_roo.Team))
                 opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
                 total_sims = 1000
+                if check_seq == 'Single Player':
+                    player_var = working_roo.loc[working_roo['Player'] == player_check]
+                    player_var = player_var.reset_index()
+                    
+                    working_roo = working_roo[working_roo['Position'].isin(pos_var_list)]
+                    working_roo = working_roo[working_roo['Team'].isin(team_var1)]
+                    working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)]
+                    working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)]
+    
+                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Median']]
+                    flex_file['Floor_raw'] = flex_file['Median'] * .25
+                    flex_file['Ceiling_raw'] = flex_file['Median'] * 1.75
+                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * .33), flex_file['Floor_raw'])
+                    flex_file['Floor'] = np.where(flex_file['Position'] == 'WR', (flex_file['Median'] * .15), flex_file['Floor_raw'])
+                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * 1.75), flex_file['Ceiling_raw'])
+                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'WR', (flex_file['Median'] * 1.85), flex_file['Ceiling_raw'])
+                    flex_file['STD'] = flex_file['Median'] / 4
+                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                    hold_file = flex_file.copy()
+                    overall_file = flex_file.copy()
+                    salary_file = flex_file.copy()
+    
+                    overall_players = overall_file[['Player']]
+    
+                    for x in range(0,total_sims):    
+                        salary_file[x] = salary_file['Salary']
+    
+                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    salary_file.astype('int').dtypes
+    
+                    salary_file = salary_file.div(1000)
+    
+                    for x in range(0,total_sims):    
+                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+    
+                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    overall_file.astype('int').dtypes
+    
+                    players_only = hold_file[['Player']]
+                    raw_lineups_file = players_only
+    
+                    for x in range(0,total_sims):
+                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+    
+                    players_only=players_only.drop(['Player'], axis=1)
+                    players_only.astype('int').dtypes
+    
+                    salary_2x_check = (overall_file - (salary_file*2))
+                    salary_3x_check = (overall_file - (salary_file*3))
+                    salary_4x_check = (overall_file - (salary_file*4))
+    
+                    players_only['Average_Rank'] = players_only.mean(axis=1)
+                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+    
+                    players_only['Player'] = hold_file[['Player']]
+    
+                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+    
+                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
+                    final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
+                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                    final_Proj['LevX'] = 0
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['CPT_Own'] = final_Proj['Own'] / 4
+    
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+                    final_Proj = final_Proj.set_index('Player')
+                    final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
+                elif check_seq == 'Top 10 Owned':
+                    final_proj_list = []
+                    for players in player_check:
+                        player_var = working_roo.loc[working_roo['Player'] == players]
+                        player_var = player_var.reset_index()
+                        
+                        working_roo_temp = working_roo[working_roo['Position'].isin(pos_var_list)]
+                        working_roo_temp = working_roo_temp[working_roo_temp['Team'].isin(team_var1)]
+                        working_roo_temp = working_roo_temp.loc[(working_roo_temp['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo_temp['Salary'] <= player_var['Salary'][0] + Salary_var)]
+                        working_roo_temp = working_roo_temp.loc[(working_roo_temp['Median'] >= player_var['Median'][0] - Median_var) & (working_roo_temp['Median'] <= player_var['Median'][0] + Median_var)]
+                        
+                        flex_file = working_roo_temp[['Player', 'Position', 'Salary', 'Median']]
+                        flex_file['Floor_raw'] = flex_file['Median'] * .25
+                        flex_file['Ceiling_raw'] = flex_file['Median'] * 1.75
+                        flex_file['Floor'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * .33), flex_file['Floor_raw'])
+                        flex_file['Floor'] = np.where(flex_file['Position'] == 'WR', (flex_file['Median'] * .15), flex_file['Floor_raw'])
+                        flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * 1.75), flex_file['Ceiling_raw'])
+                        flex_file['Ceiling'] = np.where(flex_file['Position'] == 'WR', (flex_file['Median'] * 1.85), flex_file['Ceiling_raw'])
+                        flex_file['STD'] = flex_file['Median'] / 4
+                        flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                        hold_file = flex_file.copy()
+                        overall_file = flex_file.copy()
+                        salary_file = flex_file.copy()
+                        
+                        overall_players = overall_file[['Player']]
+    
+                        for x in range(0,total_sims):    
+                            salary_file[x] = salary_file['Salary']
+        
+                        salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                        salary_file.astype('int').dtypes
+        
+                        salary_file = salary_file.div(1000)
+        
+                        for x in range(0,total_sims):    
+                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+        
+                        overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                        overall_file.astype('int').dtypes
+        
+                        players_only = hold_file[['Player']]
+                        raw_lineups_file = players_only
+        
+                        for x in range(0,total_sims):
+                            maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                            raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                            players_only[x] = raw_lineups_file[x].rank(ascending=False)
+        
+                        players_only=players_only.drop(['Player'], axis=1)
+                        players_only.astype('int').dtypes
+        
+                        salary_2x_check = (overall_file - (salary_file*2))
+                        salary_3x_check = (overall_file - (salary_file*3))
+                        salary_4x_check = (overall_file - (salary_file*4))
+        
+                        players_only['Average_Rank'] = players_only.mean(axis=1)
+                        players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                        players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                        players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                        players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                        players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                        players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                        players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+        
+                        players_only['Player'] = hold_file[['Player']]
+        
+                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+        
+                        final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                        final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+                        final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                        final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                        final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
+                        final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
+                        final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                        final_Proj['LevX'] = 0
+                        final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                        final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                        final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                        final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                        final_Proj['CPT_Own'] = final_Proj['Own'] / 4
+        
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+                        
+                        final_Proj = final_Proj.set_index('Player')
+                        final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
+                        final_proj_list.append(final_Proj)
                 
-                player_var = working_roo.loc[working_roo['Player'] == player_check]
-                player_var = player_var.reset_index()
-                
-                working_roo = working_roo[working_roo['Position'].isin(pos_var_list)]
-                working_roo = working_roo[working_roo['Team'].isin(team_var1)]
-                working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)]
-                working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)]
-  
-                flex_file = working_roo[['Player', 'Position', 'Salary', 'Median']]
-                flex_file['Floor_raw'] = flex_file['Median'] * .20
-                flex_file['Ceiling_raw'] = flex_file['Median'] * 1.9
-                flex_file['Floor'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * .33), flex_file['Floor_raw'])
-                flex_file['Floor'] = np.where(flex_file['Position'] == 'RB', (flex_file['Median'] * .15), flex_file['Floor_raw'])
-                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * 1.75), flex_file['Ceiling_raw'])
-                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'RB', (flex_file['Median'] * 1.85), flex_file['Ceiling_raw'])
-                flex_file['STD'] = flex_file['Median'] / 4
-                flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
-                hold_file = flex_file
-                overall_file = flex_file
-                salary_file = flex_file
-  
-                overall_players = overall_file[['Player']]
-  
-                for x in range(0,total_sims):    
-                    salary_file[x] = salary_file['Salary']
-  
-                salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                salary_file.astype('int').dtypes
-  
-                salary_file = salary_file.div(1000)
-  
-                for x in range(0,total_sims):    
-                    overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
-  
-                overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                overall_file.astype('int').dtypes
-  
-                players_only = hold_file[['Player']]
-                raw_lineups_file = players_only
-  
-                for x in range(0,total_sims):
-                    maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
-                    raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
-                    players_only[x] = raw_lineups_file[x].rank(ascending=False)
-  
-                players_only=players_only.drop(['Player'], axis=1)
-                players_only.astype('int').dtypes
-  
-                salary_2x_check = (overall_file - (salary_file*2))
-                salary_3x_check = (overall_file - (salary_file*3))
-                salary_4x_check = (overall_file - (salary_file*4))
-  
-                players_only['Average_Rank'] = players_only.mean(axis=1)
-                players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
-                players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
-                players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
-                players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
-                players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
-                players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
-                players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
-  
-                players_only['Player'] = hold_file[['Player']]
-  
-                final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
-  
-                final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
-                final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
-                final_Proj['Own'] = final_Proj['Player'].map(own_dict)
-                final_Proj['Team'] = final_Proj['Player'].map(team_dict)
-                final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
-                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
-                final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
-                final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
-                final_Proj['LevX'] = 0
-                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
-                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
-                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
-                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
-                final_Proj['CPT_Own'] = final_Proj['Own'] / 4
-  
-                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
-                final_Proj = final_Proj.set_index('Player')
-                final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
+                # Concatenate all the final_Proj dataframes
+                final_Proj_combined = pd.concat(final_proj_list)
+                final_Proj_combined = final_Proj_combined.sort_values(by='Top_finish', ascending=False)
+                final_Proj = final_Proj_combined  # Assign the combined dataframe back to final_Proj
   
             with hold_container:
                 hold_container = st.empty()
@@ -249,7 +346,7 @@ with tab1:
                     file_name='NFL_pivot_export.csv',
                     mime='text/csv',
             )
-            
+
 with tab2:
     st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'.")
     col1, col2 = st.columns([1, 5])