Update app.py

app.py

@@ -1018,7 +1018,7 @@ with tab2:
                 for checkVar in range(len(team_list)):
                                     st.session_state.player_freq['Team'] = st.session_state.player_freq['Team'].replace(item_list, team_list)
                 
-                st.session_state.qb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[0]].values, return_counts=True)),
+                st.session_state.qb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[0, 7]].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                 st.session_state.qb_freq['Freq'] = st.session_state.qb_freq['Freq'].astype(int)
                 st.session_state.qb_freq['Position'] = st.session_state.qb_freq['Player'].map(maps_dict['Pos_map'])
@@ -1030,7 +1030,7 @@ with tab2:
                 for checkVar in range(len(team_list)):
                                     st.session_state.qb_freq['Team'] = st.session_state.qb_freq['Team'].replace(item_list, team_list)
 
-                st.session_state.rb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[1, 2]].values, return_counts=True)),
+                st.session_state.rb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[1, 2, 6, 7]].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                 st.session_state.rb_freq['Freq'] = st.session_state.rb_freq['Freq'].astype(int)
                 st.session_state.rb_freq['Position'] = st.session_state.rb_freq['Player'].map(maps_dict['Pos_map'])
@@ -1042,7 +1042,7 @@ with tab2:
                 for checkVar in range(len(team_list)):
                                     st.session_state.rb_freq['Team'] = st.session_state.rb_freq['Team'].replace(item_list, team_list)
                 
-                st.session_state.wr_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[3, 4, 5]].values, return_counts=True)),
+                st.session_state.wr_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[3, 4, 5, 6, 7]].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                 st.session_state.wr_freq['Freq'] = st.session_state.wr_freq['Freq'].astype(int)
                 st.session_state.wr_freq['Position'] = st.session_state.wr_freq['Player'].map(maps_dict['Pos_map'])
@@ -1101,7 +1101,7 @@ with tab2:
                     )
             
             with st.container():
-                tab1, tab2, tab3, tab4, tab5 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'FLEX Exposures'])
+                tab1, tab2, tab3, tab4, tab5 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures'])
                 with tab1:
                     if 'player_freq' in st.session_state:
                         st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
@@ -1138,15 +1138,6 @@ with tab2:
                             file_name='wr_freq_export.csv',
                             mime='text/csv',
                         )
-                with tab5:
-                    if 'flex_freq' in st.session_state:
-                        st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                        st.download_button(
-                            label="Export Exposures",
-                            data=st.session_state.flex_freq.to_csv().encode('utf-8'),
-                            file_name='flex_freq_export.csv',
-                            mime='text/csv',
-                        )
 
 
 gc.collect()