Update app.py

app.py

@@ -124,31 +124,38 @@ with tab3:
 
                         if prop_type_var_sp == 'Strikeouts':
                             df['Median'] = df['Strikeouts']
+                            stat_cap = 250
                         elif prop_type_var_sp == 'Wins':
                             df['Median'] = df['Wins']
+                            stat_cap = 15
                         elif prop_type_var_sp == 'Quality_starts':
                             df['Median'] = df['Quality_starts']
+                            stat_cap = 20
 
-                        flex_file = df
+                        flex_file = df.copy()
                         flex_file.rename(columns={"Name": "Player"}, inplace = True)
-                        flex_file['Floor'] = (flex_file['Median'] * .25)
-                        flex_file['Ceiling'] = flex_file['Median'] * flex_file['Ceiling_var']
+                        flex_file['Floor'] = (flex_file['Median'] * .15)
+                        flex_file['Ceiling'] = np.where((flex_file['Median'] * flex_file['Ceiling_var']) * 1.25 > stat_cap, stat_cap, (flex_file['Median'] * flex_file['Ceiling_var']) * 1.25)
                         flex_file['STD'] = (flex_file['Median']/3)
                         flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]
 
-                        hold_file = flex_file
-                        overall_file = flex_file
-                        
+                        hold_file = flex_file.copy()
+                        hold_file = hold_file.sort_values(by='Median', ascending=False)
+                        overall_file = flex_file.copy()
+                        overall_file = overall_file.sort_values(by='Median', ascending=False)
+
                         overall_players = overall_file[['Player']]
 
-                        for x in range(0,total_sims):    
-                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+                        for x in range(0,total_sims):
+                            overall_file['g'] = np.random.normal(overall_file['Median'],overall_file['STD'])
+                            overall_file[x] = np.where((overall_file['g']<=overall_file['Ceiling']),overall_file['g'],overall_file['Ceiling'])
 
-                        overall_file=overall_file.drop(['Player', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                        check_file = overall_file.copy()
+                        overall_file=overall_file.drop(['Player', 'Floor', 'Median', 'Ceiling', 'STD', 'g'], axis=1)
                         overall_file.astype('int').dtypes
 
                         players_only = hold_file[['Player']]
-                        raw_lineups_file = players_only
+                        raw_lineups_file = players_only.copy()
 
                         for x in range(0,total_sims):
                             maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
@@ -171,6 +178,7 @@ with tab3:
 
                         final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
                         final_Proj = final_Proj[['Player', '10%', 'Median', '90%', 'Top_finish', 'Top_5_finish', 'Top_10_finish']]
+                        
                     with df_hold_container.container():     
                         df_hold_container = st.empty()
                         st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
@@ -200,35 +208,44 @@ with tab4:
 
                         if prop_type_var_h == 'Hits':
                             df['Median'] = df['xHits']
+                            stat_cap = 250
                         elif prop_type_var_h == 'Doubles':
                             df['Median'] = df['Doubles']
+                            stat_cap = 50
                         elif prop_type_var_h == 'Home Runs':
                             df['Median'] = df['Homeruns']
+                            stat_cap = 65
                         elif prop_type_var_h == 'RBI':
                             df['Median'] = df['RBI']
+                            stat_cap = 150
                         elif prop_type_var_h == 'Stolen Bases':
                             df['Median'] = df['Stolen_bases']
+                            stat_cap = 80
 
-                        flex_file = df
+                        flex_file = df.copy()
                         flex_file.rename(columns={"Name": "Player"}, inplace = True)
-                        flex_file['Floor'] = (flex_file['Median'] * .25)
-                        flex_file['Ceiling'] = flex_file['Median'] * flex_file['Ceiling_var']
+                        flex_file['Floor'] = (flex_file['Median'] * .15)
+                        flex_file['Ceiling'] = np.where((flex_file['Median'] * flex_file['Ceiling_var']) * 1.25 > stat_cap, stat_cap, (flex_file['Median'] * flex_file['Ceiling_var']) * 1.25)
                         flex_file['STD'] = (flex_file['Median']/2)
                         flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]
 
-                        hold_file = flex_file
-                        overall_file = flex_file
-                        
+                        hold_file = flex_file.copy()
+                        hold_file = hold_file.sort_values(by='Median', ascending=False)
+                        overall_file = flex_file.copy()
+                        overall_file = overall_file.sort_values(by='Median', ascending=False)
+
                         overall_players = overall_file[['Player']]
 
-                        for x in range(0,total_sims):    
-                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+                        for x in range(0,total_sims):
+                            overall_file['g'] = np.random.normal(overall_file['Median'],overall_file['STD'])
+                            overall_file[x] = np.where((overall_file['g']<=overall_file['Ceiling']),overall_file['g'],overall_file['Ceiling'])
 
-                        overall_file=overall_file.drop(['Player', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                        check_file = overall_file.copy()
+                        overall_file=overall_file.drop(['Player', 'Floor', 'Median', 'Ceiling', 'STD', 'g'], axis=1)
                         overall_file.astype('int').dtypes
 
                         players_only = hold_file[['Player']]
-                        raw_lineups_file = players_only
+                        raw_lineups_file = players_only.copy()
 
                         for x in range(0,total_sims):
                             maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
@@ -252,6 +269,6 @@ with tab4:
                         final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
                         final_Proj = final_Proj[['Player', '10%', 'Median', '90%', 'Top_finish', 'Top_5_finish', 'Top_10_finish']]
 
-                        with df_hold_container.container():     
-                            df_hold_container = st.empty()
-                            st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+                    with df_hold_container.container():     
+                        df_hold_container = st.empty()
+                        st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)