Update app.py

app.py

@@ -117,15 +117,17 @@ def convert_df_to_csv(df):
 
 t_stamp = load_time()
 site_slates = set_slate_teams()
-col1, col2 = st.columns([1, 5])
+col1, col2, col3, col4, col5 = st.columns([2, 2, 2, 2, 2])
 
+#st.info(t_stamp)
+if st.button("Load/Reset Data", key='reset3'):
+      t_stamp = load_time()
+      st.cache_data.clear()
 with col1:
-    #st.info(t_stamp)
-    if st.button("Load/Reset Data", key='reset3'):
-          t_stamp = load_time()
-          st.cache_data.clear()
     slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'All Games'), key='slate_var1')
+with col2:
     site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1')
+with col3:
     custom_var1 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var1')
     if custom_var1 == 'No':
               if slate_var1 == 'Main Slate':
@@ -194,179 +196,181 @@ with col1:
                         slate_teams = site_slates['FD Overall'].values.tolist()
                         raw_baselines = load_fd_player_projections(all_fd_player_projections)
                         raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
-              split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
-              if split_var1 == 'Specific Games':
-                  team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var1')
-              elif split_var1 == 'Full Slate Run':
-                  team_var1 = raw_baselines.Team.values.tolist()
-              pos_split1 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1')
-              if pos_split1 == 'Specific Positions':
-                  pos_var1 = st.multiselect('What Positions would you like to view?', options = ['SP', 'P', 'C', '1B', '2B', '3B', 'SS', 'OF'])
-              elif pos_split1 == 'All Positions':
-                  pos_var1 = 'All'
-
-
-with col2:
-    if custom_var1 == 'No':
-          if slate_var1 == 'Main Slate':
-                 if site_var1 == 'Draftkings':
-                      final_Proj = load_dk_player_roo(dk_player_projections)
-                 elif site_var1 == 'Fanduel':
-                      final_Proj = load_fd_player_roo(fd_player_projections)
-          elif slate_var1 == 'Secondary Slate':
-                 if site_var1 == 'Draftkings':
-                      final_Proj = load_dk_player_roo(secondary_dk_player_projections)
-                 elif site_var1 == 'Fanduel':
-                      final_Proj = load_fd_player_roo(secondary_fd_player_projections)
-          elif slate_var1 == 'All Games':
-                 if site_var1 == 'Draftkings':
-                      final_Proj = load_dk_player_roo(all_dk_player_projections)
-                 elif site_var1 == 'Fanduel':
-                      final_Proj = load_fd_player_roo(all_fd_player_projections)
-          final_Proj = final_Proj[final_Proj['Team'].isin(team_var1)]
-          if pos_var1 != 'All':
-                 final_Proj = final_Proj[final_Proj['Position'].str.contains('|'.join(pos_var1))]
-          st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
-          st.download_button(
-                label="Export Tables",
-                data=convert_df_to_csv(final_Proj),
-                file_name='Custom_MLB_export.csv',
-                mime='text/csv',
-          )
-    elif custom_var1 == 'Yes':
-              hold_container = st.empty()
-              if st.button('Create Range of Outcomes for Slate'):
-                  with hold_container:
-           #           if slate_var1 == 'Main Slate':
-           #               if site_var1 == 'Draftkings':
-           #                   raw_baselines = load_dk_player_projections(dk_player_projections)
-           #               elif site_var1 == 'Fanduel':
-           #                   raw_baselines = load_fd_player_projections(fd_player_projections)
-           #           elif slate_var1 == 'Secondary Slate':
-           #               if site_var1 == 'Draftkings':
-           #                   raw_baselines = load_dk_player_projections(secondary_dk_player_projections)
-           #               elif site_var1 == 'Fanduel':
-           #                   raw_baselines = load_fd_player_projections(secondary_fd_player_projections)
-           #            elif slate_var1 == 'All Games':
-           #               if site_var1 == 'Draftkings':
-           #                   raw_baselines = load_dk_player_projections(all_dk_player_projections)
-           #               elif site_var1 == 'Fanduel':
-           #                   raw_baselines = load_fd_player_projections(all_fd_player_projections)
-                      working_roo = raw_baselines
-                      working_roo = working_roo[working_roo['Team'].isin(team_var1)]
-                      own_dict = dict(zip(working_roo.Player, working_roo.Own))
-                      team_dict = dict(zip(working_roo.Player, working_roo.Team))
-                      total_sims = 1000
-
-                      flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Ceiling_Var']]
-                      flex_file['Floor'] = flex_file['Median']*.25
-                      flex_file['Ceiling'] = np.where(flex_file['Position'] == 'SP', (flex_file['Median'] + (flex_file['Floor'])) + ((flex_file['Ceiling_Var'] * 10) * 3), (flex_file['Median'] + (flex_file['Floor'])) + ((flex_file['Ceiling_Var'] * 10)))
-                      flex_file['STD'] = (flex_file['Median']/4)
-                      flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
-                      if pos_split1 == 'All Positions':
-                          flex_file = flex_file
-                      elif pos_split1 != 'All Positions':
-                          if pos_var1 == 'Pitchers':
-                              flex_file = flex_file[flex_file['Position'] == 'SP']
-                          elif pos_var1 == 'Hitters':
-                              flex_file = flex_file[flex_file['Position'] != 'SP']
-                          elif pos_var1 not in ['Pitchers', 'Hitters']: 
-                              flex_file = flex_file[flex_file['Position'].str.contains('|'.join(pos_var1))]
-                      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))
-                      gpp_check = (overall_file - ((salary_file*2)+10))
-
-                      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['GPP%'] = gpp_check[gpp_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%', 'GPP%']]
-
-                      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%', 'GPP%']]
-                      final_Proj['Own'] = final_Proj['Player'].map(own_dict)
-                      final_Proj['Team'] = final_Proj['Player'].map(team_dict)
-                      final_Proj['Own'] = final_Proj['Own'].astype('float')
-                      if contest_var1 == 'Small Field GPP':
-                            if site_var1 == 'Draftkings':
-                                final_Proj['Own%'] = np.where((final_Proj['Position'] == 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean(), final_Proj['Own'])
-                                final_Proj['Own%'] = np.where((final_Proj['Position'] != 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (10 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean(), final_Proj['Own%'])
-                                final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
-                            elif site_var1 == 'Fanduel':
-                                final_Proj['Own%'] = np.where((final_Proj['Position'] == 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean(), final_Proj['Own'])
-                                final_Proj['Own%'] = np.where((final_Proj['Position'] != 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (10 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean())/150) + final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean(), final_Proj['Own%'])
-                                final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
-                      elif contest_var1 == 'Large Field GPP':
-                            if site_var1 == 'Draftkings':
-                                final_Proj['Own%'] = np.where((final_Proj['Position'] == 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (2.5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean(), final_Proj['Own'])
-                                final_Proj['Own%'] = np.where((final_Proj['Position'] != 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean(), final_Proj['Own%'])
-                                final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
-                            elif site_var1 == 'Fanduel':
-                                final_Proj['Own%'] = np.where((final_Proj['Position'] == 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (2.5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean(), final_Proj['Own'])
-                                final_Proj['Own%'] = np.where((final_Proj['Position'] != 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean())/150) + final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean(), final_Proj['Own%'])
-                                final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
-                      elif contest_var1 == 'Cash':
-                            if site_var1 == 'Draftkings':
-                                final_Proj['Own%'] = np.where((final_Proj['Position'] == 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (6 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean(), final_Proj['Own'])
-                                final_Proj['Own%'] = np.where((final_Proj['Position'] != 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (11 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean(), final_Proj['Own%'])
-                                final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])      
-                            elif site_var1 == 'Fanduel':
-                                final_Proj['Own%'] = np.where((final_Proj['Position'] == 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (6 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean(), final_Proj['Own'])
-                                final_Proj['Own%'] = np.where((final_Proj['Position'] != 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (11 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean())/150) + final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean(), final_Proj['Own%'])
-                                final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
-
-                      final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%', 'Own%']]
-                      final_Proj = final_Proj.set_index('Player')
-                      final_Proj = final_Proj.sort_values(by='Median', ascending=False)
-
-                  with hold_container:
-                      hold_container = st.empty()
-                      final_Proj = final_Proj
-                      st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
-
-                  st.download_button(
-                          label="Export Tables",
-                          data=convert_df_to_csv(final_Proj),
-                          file_name='Custom_MLB_export.csv',
-                          mime='text/csv',
-                  )
+with col4:
+    split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
+    if split_var1 == 'Specific Games':
+        team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var1')
+    elif split_var1 == 'Full Slate Run':
+        team_var1 = raw_baselines.Team.values.tolist()
+with col5:
+    pos_split1 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1')
+    if pos_split1 == 'Specific Positions':
+        pos_var1 = st.multiselect('What Positions would you like to view?', options = ['SP', 'P', 'C', '1B', '2B', '3B', 'SS', 'OF'])
+    elif pos_split1 == 'All Positions':
+        pos_var1 = 'All'
+
+
+
+if custom_var1 == 'No':
+      if slate_var1 == 'Main Slate':
+             if site_var1 == 'Draftkings':
+                  final_Proj = load_dk_player_roo(dk_player_projections)
+             elif site_var1 == 'Fanduel':
+                  final_Proj = load_fd_player_roo(fd_player_projections)
+      elif slate_var1 == 'Secondary Slate':
+             if site_var1 == 'Draftkings':
+                  final_Proj = load_dk_player_roo(secondary_dk_player_projections)
+             elif site_var1 == 'Fanduel':
+                  final_Proj = load_fd_player_roo(secondary_fd_player_projections)
+      elif slate_var1 == 'All Games':
+             if site_var1 == 'Draftkings':
+                  final_Proj = load_dk_player_roo(all_dk_player_projections)
+             elif site_var1 == 'Fanduel':
+                  final_Proj = load_fd_player_roo(all_fd_player_projections)
+      final_Proj = final_Proj[final_Proj['Team'].isin(team_var1)]
+      if pos_var1 != 'All':
+             final_Proj = final_Proj[final_Proj['Position'].str.contains('|'.join(pos_var1))]
+      st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+      st.download_button(
+            label="Export Tables",
+            data=convert_df_to_csv(final_Proj),
+            file_name='Custom_MLB_export.csv',
+            mime='text/csv',
+      )
+elif custom_var1 == 'Yes':
+          hold_container = st.empty()
+          if st.button('Create Range of Outcomes for Slate'):
+              with hold_container:
+       #           if slate_var1 == 'Main Slate':
+       #               if site_var1 == 'Draftkings':
+       #                   raw_baselines = load_dk_player_projections(dk_player_projections)
+       #               elif site_var1 == 'Fanduel':
+       #                   raw_baselines = load_fd_player_projections(fd_player_projections)
+       #           elif slate_var1 == 'Secondary Slate':
+       #               if site_var1 == 'Draftkings':
+       #                   raw_baselines = load_dk_player_projections(secondary_dk_player_projections)
+       #               elif site_var1 == 'Fanduel':
+       #                   raw_baselines = load_fd_player_projections(secondary_fd_player_projections)
+       #            elif slate_var1 == 'All Games':
+       #               if site_var1 == 'Draftkings':
+       #                   raw_baselines = load_dk_player_projections(all_dk_player_projections)
+       #               elif site_var1 == 'Fanduel':
+       #                   raw_baselines = load_fd_player_projections(all_fd_player_projections)
+                  working_roo = raw_baselines
+                  working_roo = working_roo[working_roo['Team'].isin(team_var1)]
+                  own_dict = dict(zip(working_roo.Player, working_roo.Own))
+                  team_dict = dict(zip(working_roo.Player, working_roo.Team))
+                  total_sims = 1000
+
+                  flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Ceiling_Var']]
+                  flex_file['Floor'] = flex_file['Median']*.25
+                  flex_file['Ceiling'] = np.where(flex_file['Position'] == 'SP', (flex_file['Median'] + (flex_file['Floor'])) + ((flex_file['Ceiling_Var'] * 10) * 3), (flex_file['Median'] + (flex_file['Floor'])) + ((flex_file['Ceiling_Var'] * 10)))
+                  flex_file['STD'] = (flex_file['Median']/4)
+                  flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                  if pos_split1 == 'All Positions':
+                      flex_file = flex_file
+                  elif pos_split1 != 'All Positions':
+                      if pos_var1 == 'Pitchers':
+                          flex_file = flex_file[flex_file['Position'] == 'SP']
+                      elif pos_var1 == 'Hitters':
+                          flex_file = flex_file[flex_file['Position'] != 'SP']
+                      elif pos_var1 not in ['Pitchers', 'Hitters']: 
+                          flex_file = flex_file[flex_file['Position'].str.contains('|'.join(pos_var1))]
+                  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))
+                  gpp_check = (overall_file - ((salary_file*2)+10))
+
+                  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['GPP%'] = gpp_check[gpp_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%', 'GPP%']]
+
+                  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%', 'GPP%']]
+                  final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                  final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                  final_Proj['Own'] = final_Proj['Own'].astype('float')
+                  if contest_var1 == 'Small Field GPP':
+                        if site_var1 == 'Draftkings':
+                            final_Proj['Own%'] = np.where((final_Proj['Position'] == 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean(), final_Proj['Own'])
+                            final_Proj['Own%'] = np.where((final_Proj['Position'] != 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (10 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean(), final_Proj['Own%'])
+                            final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
+                        elif site_var1 == 'Fanduel':
+                            final_Proj['Own%'] = np.where((final_Proj['Position'] == 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean(), final_Proj['Own'])
+                            final_Proj['Own%'] = np.where((final_Proj['Position'] != 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (10 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean())/150) + final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean(), final_Proj['Own%'])
+                            final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
+                  elif contest_var1 == 'Large Field GPP':
+                        if site_var1 == 'Draftkings':
+                            final_Proj['Own%'] = np.where((final_Proj['Position'] == 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (2.5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean(), final_Proj['Own'])
+                            final_Proj['Own%'] = np.where((final_Proj['Position'] != 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean(), final_Proj['Own%'])
+                            final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
+                        elif site_var1 == 'Fanduel':
+                            final_Proj['Own%'] = np.where((final_Proj['Position'] == 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (2.5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean(), final_Proj['Own'])
+                            final_Proj['Own%'] = np.where((final_Proj['Position'] != 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean())/150) + final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean(), final_Proj['Own%'])
+                            final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
+                  elif contest_var1 == 'Cash':
+                        if site_var1 == 'Draftkings':
+                            final_Proj['Own%'] = np.where((final_Proj['Position'] == 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (6 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean(), final_Proj['Own'])
+                            final_Proj['Own%'] = np.where((final_Proj['Position'] != 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (11 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean(), final_Proj['Own%'])
+                            final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])      
+                        elif site_var1 == 'Fanduel':
+                            final_Proj['Own%'] = np.where((final_Proj['Position'] == 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (6 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean(), final_Proj['Own'])
+                            final_Proj['Own%'] = np.where((final_Proj['Position'] != 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (11 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean())/150) + final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean(), final_Proj['Own%'])
+                            final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
+
+                  final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%', 'Own%']]
+                  final_Proj = final_Proj.set_index('Player')
+                  final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+
+              with hold_container:
+                  hold_container = st.empty()
+                  final_Proj = final_Proj
+                  st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+
+              st.download_button(
+                      label="Export Tables",
+                      data=convert_df_to_csv(final_Proj),
+                      file_name='Custom_MLB_export.csv',
+                      mime='text/csv',
+              )