Remove ownership calculation and related columns from Sim_Winner_Frame in Streamlit app for streamlined data processing.

src/streamlit_app.py

@@ -615,55 +615,11 @@ if selected_tab == "Showdown Contest Sims":
             Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
             Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
             Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
-            # Add percent rank columns for ownership at each roster position
-            # Calculate Dupes column for Fanduel
-            dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank']
-            own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own']
-            calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc']
-            Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True)
-            Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True)
-            Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True)
-            Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True)
-            Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True)
-            Sim_Winner_Frame['FLEX5_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']).rank(pct=True)
-            Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100
-            Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100
-            Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100
-            Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100
-            Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100
-            Sim_Winner_Frame['FLEX5_Own'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']) / 100
-
-            # Calculate ownership product and convert to probability
-            Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1))
-            
-            # Calculate average of ownership percent rank columns
-            Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
-            
-            # Calculate dupes formula
-            Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 49800) / 100)
-            
-            # Round and handle negative values
-            Sim_Winner_Frame['Dupes'] = np.where(
-                np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
-                0, 
-                np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1
-            )
-            Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2
-                
-            Sim_Winner_Frame['Dupes'] = np.round(Sim_Winner_Frame['Dupes'], 0)
-            Sim_Winner_Frame['Dupes'] = np.where(
-                    np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
-                    0, 
-                    np.round(Sim_Winner_Frame['dupes_calc'], 0)
-                )
-            Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=dup_count_columns)
-            Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=own_columns)
-            Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=calc_columns)
 
             Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
             
             # Type Casting
-            type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32, 'Dupes': int}
+            type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
             Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
             
             # Sorting