Adjusted salary thresholds, added player source in loop

app.py

@@ -182,9 +182,9 @@ with tab1:
 
                     players_only=players_only.drop(['Player'], axis=1)
 
-                    salary_2x_check = (overall_file - (salary_file*4))
-                    salary_3x_check = (overall_file - (salary_file*5))
-                    salary_4x_check = (overall_file - (salary_file*6))
+                    salary_4x_check = (overall_file - (salary_file*4))
+                    salary_5x_check = (overall_file - (salary_file*5))
+                    salary_6x_check = (overall_file - (salary_file*6))
                     gpp_check = (overall_file - ((salary_file*5)+10))
 
                     players_only['Average_Rank'] = players_only.mean(axis=1)
@@ -192,17 +192,17 @@ with tab1:
                     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['3x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
-                    players_only['4x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
-                    players_only['5x%'] = salary_4x_check[salary_4x_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['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims)
                     players_only['GPP%'] = salary_4x_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+%', '3x%', '4x%', '5x%', 'GPP%']]
+                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', '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+%', '3x%', '4x%', '5x%', 'GPP%']]
+                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
 
                     final_Proj['Own'] = final_Proj['Player'].map(own_dict)
                     final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
@@ -215,7 +215,7 @@ with tab1:
                     final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
                     final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
 
-                    final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%', 'Own', 'LevX', 'ValX']]
+                    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)
                     
@@ -268,9 +268,9 @@ with tab1:
         
                         players_only=players_only.drop(['Player'], axis=1)
         
-                        salary_2x_check = (overall_file - (salary_file*4))
-                        salary_3x_check = (overall_file - (salary_file*5))
-                        salary_4x_check = (overall_file - (salary_file*6))
+                        salary_4x_check = (overall_file - (salary_file*4))
+                        salary_5x_check = (overall_file - (salary_file*5))
+                        salary_6x_check = (overall_file - (salary_file*6))
                         gpp_check = (overall_file - ((salary_file*5)+10))
 
                         players_only['Average_Rank'] = players_only.mean(axis=1)
@@ -278,17 +278,17 @@ with tab1:
                         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['3x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
-                        players_only['4x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
-                        players_only['5x%'] = salary_4x_check[salary_4x_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['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims)
+                        players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims)
                         players_only['GPP%'] = salary_4x_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+%', '3x%', '4x%', '5x%', 'GPP%']]
+                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', '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+%', '3x%', '4x%', '5x%', 'GPP%']]
+                        final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
 
                         final_Proj['Own'] = final_Proj['Player'].map(own_dict)
                         final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
@@ -300,8 +300,9 @@ with tab1:
                         final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
                         final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
                         final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
+                        final_Proj['Pivot_source'] = players
         
-                        final_Proj = final_Proj[['Player', 'Pivot_source', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+                        final_Proj = final_Proj[['Player', 'Pivot_source', '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.sort_values(by='Top_finish', ascending=False)
                         final_proj_list.append(final_Proj)