Update app.py

app.py

@@ -232,42 +232,45 @@ def calculate_range_var(count, min_val, FieldStrength, field_growth):
     return min(var, len(count[0]))
 
 def create_random_portfolio(Total_Sample_Size, raw_baselines):
+
+    O_merge, full_pos_player_dict = get_overall_merged_df()
+    max_var = len(raw_baselines.filter(pl.col("Position") == "QB"))
     
-            O_merge, full_pos_player_dict = get_overall_merged_df()
-            max_var = len(raw_baselines[raw_baselines['Position'] == 'QB'])
-          
-            field_growth_rounded = round(field_growth)
-            ranges_dict = {}
-            
-            # Calculate ranges
-            for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 20, 30, 10], ['RB', 'WR', 'TE', 'FLEX']):
-                count = create_overall_dfs(pos_players, df, dict_val, key)
-                ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded)
-            if max_var <= 10:
-                ranges_dict['qb_range'] = round(max_var)
-                ranges_dict['dst_range'] = round(max_var)
-            elif max_var > 10 and max_var <= 16:
-                ranges_dict['qb_range'] = round(max_var / 1.5)
-                ranges_dict['dst_range'] = round(max_var)
-            elif max_var > 16:
-                ranges_dict['qb_range'] = round(max_var / 2)
-                ranges_dict['dst_range'] = round(max_var)
-            # Generate unique ranges
-            # for key, value in ranges_dict.items():
-            #     ranges_dict[f"{key}_Uniques"] = list(range(0, value, 1))
-            
-            # Generate random portfolios
-            rng = np.random.default_rng()
-            total_elements = [1, 2, 3, 1, 1, 1]
-            keys = ['qb', 'rb', 'wr', 'te', 'flex', 'dst']
-            
-            all_choices = [rng.choice(ranges_dict[f"{key}_range"], size=(Total_Sample_Size, elem)) for key, elem in zip(keys, total_elements)]
-            RandomPortfolio = pd.DataFrame(np.hstack(all_choices), columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])
-            RandomPortfolio['User/Field'] = 0
-            
-            del O_merge
-          
-            return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict
+    field_growth_rounded = round(field_growth)
+    ranges_dict = {}
+    
+    # Calculate ranges
+    for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 20, 30, 10], ['RB', 'WR', 'TE', 'FLEX']):
+        count = create_overall_dfs(pos_players, df, dict_val, key)
+        ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded)
+    
+    if max_var <= 10:
+        ranges_dict['qb_range'] = round(max_var)
+        ranges_dict['dst_range'] = round(max_var)
+    elif max_var > 10 and max_var <= 16:
+        ranges_dict['qb_range'] = round(max_var / 1.5)
+        ranges_dict['dst_range'] = round(max_var)
+    elif max_var > 16:
+        ranges_dict['qb_range'] = round(max_var / 2)
+        ranges_dict['dst_range'] = round(max_var)
+    
+    # Generate random portfolios
+    rng = np.random.default_rng()
+    total_elements = [1, 2, 3, 1, 1, 1]
+    keys = ['qb', 'rb', 'wr', 'te', 'flex', 'dst']
+    
+    all_choices = [rng.choice(ranges_dict[f"{key}_range"], size=(Total_Sample_Size, elem)) for key, elem in zip(keys, total_elements)]
+    
+    # Create a Polars DataFrame
+    RandomPortfolio = pl.DataFrame(
+        {name: np.hstack([choice[:, i] if choice.shape[1] > i else choice[:, 0] for choice in all_choices]) for i, name in enumerate(['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])}
+    )
+    RandomPortfolio = RandomPortfolio.with_column(pl.col("User/Field").fill_none(0))
+    
+    
+    del O_merge
+
+    return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict
 
 def get_correlated_portfolio_for_sim(Total_Sample_Size):