more fixes to the individual run

app.py

@@ -150,7 +150,7 @@ with tab1:
                     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', 'Minutes']]
                     flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
                     flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
@@ -161,27 +161,27 @@ with tab1:
                     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 = 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)
-
+    
                     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,overall_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)
 
                     salary_4x_check = (overall_file - (salary_file*4))
@@ -210,6 +210,7 @@ with tab1:
                     final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
                     final_Proj['Team'] = final_Proj['Player'].map(team_dict)
                     final_Proj['Own'] = final_Proj['Own'].astype('float')
+                    final_Proj = final_Proj[['Player', 'Minutes Proj',, 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own']]
                     final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True)
                     final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
                     final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100
@@ -219,7 +220,6 @@ with tab1:
 
                     final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'LevX', 'ValX']]
                     final_Proj = final_Proj.set_index('Player')
-                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
                     
                     st.session_state.final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
         
@@ -326,7 +326,7 @@ with tab1:
                 st.download_button(
                     label="Export Tables",
                     data=convert_df_to_csv(st.session_state.final_Proj),
-                    file_name='NFL_pivot_export.csv',
+                    file_name='NBA_pivot_export.csv',
                     mime='text/csv',
                 )
             else: