Update app.py

app.py

@@ -117,6 +117,7 @@ with tab3:
                     with df_hold_container.container():
 
                         df = pitcher_proj
+                        df.rename(columns={"Name": "Player"}, inplace = True)
 
                         total_sims = 5000
 
@@ -138,29 +139,31 @@ with tab3:
                         hold_file = flex_file
                         overall_file = flex_file
                         salary_file = flex_file
-
-                        overall_players = overall_file[['Player']]
-
-                        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', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                        
+                        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
 
-                        player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
+                        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['Mean_Outcome'] = overall_file.mean(axis=1)
-                        players_only['10%'] = overall_file.quantile(0.1, axis=1)
-                        players_only['90%'] = overall_file.quantile(0.9, axis=1)
+                        players_only=players_only.drop(['Player'], axis=1)
+                        players_only.astype('int').dtypes
+
+                        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['10%'] = overall_file.quantile(0.1, axis=1)
+                        players_only['90%'] = overall_file.quantile(0.9, axis=1)
+
                         players_only['Player'] = hold_file[['Player']]
 
-                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', 'Median', '90%']]
+                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', '90%']]
 
                         final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
                         final_Proj = final_Proj[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', 'Median', '90%']]
@@ -185,6 +188,7 @@ with tab4:
                     with df_hold_container.container():
 
                         df = pitcher_proj
+                        df.rename(columns={"Name": "Player"}, inplace = True)
 
                         total_sims = 5000
 
@@ -206,29 +210,31 @@ with tab4:
                         hold_file = flex_file
                         overall_file = flex_file
                         salary_file = flex_file
-
-                        overall_players = overall_file[['Player']]
-
-                        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', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                        
+                        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
 
-                        player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
+                        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['Mean_Outcome'] = overall_file.mean(axis=1)
-                        players_only['10%'] = overall_file.quantile(0.1, axis=1)
-                        players_only['90%'] = overall_file.quantile(0.9, axis=1)
+                        players_only=players_only.drop(['Player'], axis=1)
+                        players_only.astype('int').dtypes
+
+                        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['10%'] = overall_file.quantile(0.1, axis=1)
+                        players_only['90%'] = overall_file.quantile(0.9, axis=1)
+
                         players_only['Player'] = hold_file[['Player']]
 
-                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', 'Median', '90%']]
+                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', '90%']]
 
                         final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
                         final_Proj = final_Proj[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', 'Median', '90%']]