Fix column indexing in app.py for FLEX and goalie calculations based on site selection. Updated DataFrame slicing to ensure accurate frequency counts for Draftkings and Fanduel, enhancing data processing consistency in simulations.

app.py

@@ -430,7 +430,7 @@ with tab1:
             st.session_state.dmen_freq = dmen_working.copy()
             
             if sim_site_var1 == 'Draftkings':
-                flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
+                flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
             elif sim_site_var1 == 'Fanduel':
                 flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:8].values, return_counts=True)),
@@ -445,7 +445,7 @@ with tab1:
             st.session_state.flex_freq = flex_working.copy()
             
             if sim_site_var1 == 'Draftkings':
-                goalie_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
+                goalie_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
             elif sim_site_var1 == 'Fanduel':
                 goalie_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),