Update app.py

app.py

@@ -110,49 +110,63 @@ def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
 def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs):
-    RunsVar = 1
-    seed_depth_def = seed_depth1
-    Strength_var_def = Strength_var
-    strength_grow_def = strength_grow
-    Teams_used_def = Teams_used
-    Total_Runs_def = Total_Runs
-    while RunsVar <= seed_depth_def:
-        if RunsVar <= 3:
-            FieldStrength = Strength_var_def
-            RandomPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio = RandomPortfolio
-            FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0)
-            maps_dict.update(maps_dict2)
-            del FinalPortfolio2
-            del maps_dict2
-        elif RunsVar > 3 and RunsVar <= 4:
-            FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001))
-            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio3], axis=0)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio4], axis=0)
-            FinalPortfolio = FinalPortfolio.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
-            maps_dict.update(maps_dict3)
-            maps_dict.update(maps_dict4)
-            del FinalPortfolio3
-            del maps_dict3
-            del FinalPortfolio4
-            del maps_dict4
-        elif RunsVar > 4:
-            FieldStrength = 1
-            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio3], axis=0)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio4], axis=0)
-            FinalPortfolio = FinalPortfolio.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
-            maps_dict.update(maps_dict3)
-            maps_dict.update(maps_dict4)
-            del FinalPortfolio3
-            del maps_dict3
-            del FinalPortfolio4
-            del maps_dict4
-        RunsVar += 1
+ RunsVar = 1
+seed_depth_def = seed_depth1
+Strength_var_def = Strength_var
+strength_grow_def = strength_grow
+Teams_used_def = Teams_used
+Total_Runs_def = Total_Runs
+
+while RunsVar <= seed_depth_def:
+    if RunsVar <= 3:
+        FieldStrength = Strength_var_def
+        RandomPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * 0.1)
+        FinalPortfolio = RandomPortfolio
+        FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * 0.1)
+        
+        # Replace pd.concat with pl.vstack
+        FinalPortfolio = pl.vstack([FinalPortfolio, FinalPortfolio2])
+        
+        maps_dict.update(maps_dict2)
+        del FinalPortfolio2
+        del maps_dict2
+    elif 3 < RunsVar <= 4:
+        FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * 0.001))
+        FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * 0.1)
+        FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * 0.1)
+        
+        # Replace pd.concat with pl.vstack
+        FinalPortfolio = pl.vstack([FinalPortfolio, FinalPortfolio3, FinalPortfolio4])
+        
+        # Replace drop_duplicates and reset_index
+        FinalPortfolio = FinalPortfolio.drop_duplicates(subset=["Projection", "Own"]).sort("index")
+
+        maps_dict.update(maps_dict3)
+        maps_dict.update(maps_dict4)
+        del FinalPortfolio3
+        del maps_dict3
+        del FinalPortfolio4
+        del maps_dict4
+    elif RunsVar > 4:
+        FieldStrength = 1
+        FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * 0.1)
+        FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * 0.1)
+        
+        # Replace pd.concat with pl.vstack
+        FinalPortfolio = pl.vstack([FinalPortfolio, FinalPortfolio3, FinalPortfolio4])
+        
+        # Replace drop_duplicates and reset_index
+        FinalPortfolio = FinalPortfolio.drop_duplicates(subset=["Projection", "Own"]).sort("index")
+        
+        maps_dict.update(maps_dict3)
+        maps_dict.update(maps_dict4)
+        del FinalPortfolio3
+        del maps_dict3
+        del FinalPortfolio4
+        del maps_dict4
+        
+    RunsVar += 1
+
         
     return FinalPortfolio, maps_dict