Update app.py

app.py

@@ -202,11 +202,9 @@ def get_correlated_portfolio_for_sim(Total_Sample_Size):
     sizesplit = round(Total_Sample_Size * sharp_split)
     
     RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines)
-    stack_num = random.randint(1, 2)
+    stack_num = random.randint(1, 3)
     stacking_dict = create_stack_options(raw_baselines, stack_num)
     
-    st.write(RandomPortfolio.head(100))
-    
     RandomPortfolio['QB'] = pd.Series(list(RandomPortfolio['QB'].map(qb_dict)), dtype="string[pyarrow]")
     RandomPortfolio['RB1'] = pd.Series(list(RandomPortfolio['RB1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
     RandomPortfolio['WR1'] = pd.Series(list(RandomPortfolio['QB'].map(stacking_dict)), dtype="string[pyarrow]")
@@ -219,8 +217,6 @@ def get_correlated_portfolio_for_sim(Total_Sample_Size):
     RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 8].drop(columns=['plyr_list','plyr_count']).\
         reset_index(drop=True)
     
-    st.write(RandomPortfolio.head(100))
-    
     del sizesplit
     del full_pos_player_dict
     del ranges_dict
@@ -259,6 +255,7 @@ def get_correlated_portfolio_for_sim(Total_Sample_Size):
     RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,26:35].astype(np.double))]
     
     RandomPortArrayOut = np.delete(RandomPortArray, np.s_[8:35], axis=1)
+    st.write(RandomPortArrayOut.head(100))
     RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['QB', 'RB1', 'WR1', 'WR2', 'FLEX1', 'FLEX2', 'DST', 'User/Field', 'Salary', 'Projection', 'Own'])
     RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
     del RandomPortArray