Update app.py

app.py

@@ -919,87 +919,86 @@ with tab2:
         dst_freq = pd.DataFrame()
             
     with col2:
-        if sim_done == 1:
-            player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'))
-            if player_split_var2 == 'Specific Players':
-                      find_var2 = st.multiselect('Which players must be included in the lineups?', options = player_freq['Player'].unique())
-            elif player_split_var2 == 'Full Players':
-                      find_var2 = static_exposure.Player.values.tolist()
-            if player_split_var2 == 'Specific Players':
-                      Sim_Winner_Frame = Sim_Winner_Frame[np.equal.outer(Sim_Winner_Frame.to_numpy(copy=False),  find_var2).any(axis=1).all(axis=1)]
-            elif player_split_var2 == 'Full Players':
-                      Sim_Winner_Frame = Sim_Winner_Frame
-                      
-            with st.container():
-                display_winner_dataframe = Sim_Winner_Frame.copy()
-                st.dataframe(display_winner_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
-            
+        player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'))
+        if player_split_var2 == 'Specific Players':
+                  find_var2 = st.multiselect('Which players must be included in the lineups?', options = player_freq['Player'].unique())
+        elif player_split_var2 == 'Full Players':
+                  find_var2 = static_exposure.Player.values.tolist()
+        if player_split_var2 == 'Specific Players':
+                  Sim_Winner_Frame = Sim_Winner_Frame[np.equal.outer(Sim_Winner_Frame.to_numpy(copy=False),  find_var2).any(axis=1).all(axis=1)]
+        elif player_split_var2 == 'Full Players':
+                  Sim_Winner_Frame = Sim_Winner_Frame
+                  
+        with st.container():
+            display_winner_dataframe = Sim_Winner_Frame.copy()
+            st.dataframe(display_winner_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
+        
+            st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(Sim_Winner_Export),
+                file_name='NFL_consim_export.csv',
+                mime='text/csv',
+            )
+        
+        with st.container():
+            freq_container = st.empty()
+            tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures'])
+            with tab1:
+                st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                 st.download_button(
-                    label="Export Tables",
-                    data=convert_df_to_csv(Sim_Winner_Export),
-                    file_name='NFL_consim_export.csv',
+                    label="Export Exposures",
+                    data=convert_df_to_csv(player_freq),
+                    file_name='player_freq_export.csv',
+                    mime='text/csv',
+                )
+            with tab2:
+                st.dataframe(qb_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=convert_df_to_csv(qb_freq),
+                    file_name='qb_freq_export.csv',
+                    mime='text/csv',
+                )
+            with tab3:
+                st.dataframe(rb_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=convert_df_to_csv(rb_freq),
+                    file_name='rb_freq_export.csv',
+                    mime='text/csv',
+                )
+            with tab4:
+                st.dataframe(wr_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=convert_df_to_csv(wr_freq),
+                    file_name='wr_freq_export.csv',
+                    mime='text/csv',
+                )
+            with tab5:
+                st.dataframe(te_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=convert_df_to_csv(te_freq),
+                    file_name='te_freq_export.csv',
+                    mime='text/csv',
+                )
+            with tab6:
+                st.dataframe(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=convert_df_to_csv(flex_freq),
+                    file_name='flex_freq_export.csv',
+                    mime='text/csv',
+                )
+            with tab7:
+                st.dataframe(dst_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=convert_df_to_csv(dst_freq),
+                    file_name='dst_freq_export.csv',
                     mime='text/csv',
                 )
-            
-            with st.container():
-                freq_container = st.empty()
-                tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures'])
-                with tab1:
-                    st.dataframe(player_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=convert_df_to_csv(player_freq),
-                        file_name='player_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab2:
-                    st.dataframe(qb_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=convert_df_to_csv(qb_freq),
-                        file_name='qb_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab3:
-                    st.dataframe(rb_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=convert_df_to_csv(rb_freq),
-                        file_name='rb_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab4:
-                    st.dataframe(wr_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=convert_df_to_csv(wr_freq),
-                        file_name='wr_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab5:
-                    st.dataframe(te_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=convert_df_to_csv(te_freq),
-                        file_name='te_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab6:
-                    st.dataframe(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=convert_df_to_csv(flex_freq),
-                        file_name='flex_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab7:
-                    st.dataframe(dst_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=convert_df_to_csv(dst_freq),
-                        file_name='dst_freq_export.csv',
-                        mime='text/csv',
-                    )
         
         if st.button("Simulate Contest"):
             with st.container():