Cleaned up loop for top 10 owned ported from NHL version

app.py

@@ -142,9 +142,12 @@ with tab1:
             team_var1 = raw_baselines.Team.values.tolist()
         
     with col2:
-        hold_container = st.empty()
+        placeholder = st.empty()
+        displayholder = st.empty()
+
+
         if st.button('Simulate appropriate pivots'):
-            with hold_container:
+            with placeholder:
                 if site_var1 == 'Draftkings':
                           working_roo = raw_baselines
                           working_roo.replace('', 0, inplace=True)
@@ -156,7 +159,9 @@ with tab1:
                 own_dict = dict(zip(working_roo.Player, working_roo.Own))
                 team_dict = dict(zip(working_roo.Player, working_roo.Team))
                 opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
+                pos_dict = dict(zip(working_roo.Player, working_roo.Position))
                 total_sims = 1000
+
                 if check_seq == 'Single Player':
                     player_var = working_roo.loc[working_roo['Player'] == player_check]
                     player_var = player_var.reset_index()
@@ -199,7 +204,7 @@ with tab1:
                     raw_lineups_file = players_only
     
                     for x in range(0,total_sims):
-                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                        maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
                         raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
                         players_only[x] = raw_lineups_file[x].rank(ascending=False)
     
@@ -240,10 +245,12 @@ with tab1:
     
                     final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
                     final_Proj = final_Proj.set_index('Player')
-                    final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
+                    st.session_state.final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
+
                 elif check_seq == 'Top 10 Owned':
                     final_proj_list = []
                     for players in player_check:
+                        players_pos = pos_dict[players]
                         player_var = working_roo.loc[working_roo['Player'] == players]
                         player_var = player_var.reset_index()
                         
@@ -285,7 +292,7 @@ with tab1:
                         raw_lineups_file = players_only
         
                         for x in range(0,total_sims):
-                            maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                            maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
                             raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
                             players_only[x] = raw_lineups_file[x].rank(ascending=False)
         
@@ -326,26 +333,30 @@ with tab1:
         
                         final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
                         
-                        final_Proj = final_Proj.set_index('Player')
                         final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
                         final_proj_list.append(final_Proj)
+                        st.write(f'finished run for {players}')
                 
-                # Concatenate all the final_Proj dataframes
-                final_Proj_combined = pd.concat(final_proj_list)
-                final_Proj_combined = final_Proj_combined.sort_values(by='Top_finish', ascending=False)
-                final_Proj = final_Proj_combined  # Assign the combined dataframe back to final_Proj
-  
-            with hold_container:
-                hold_container = st.empty()
-                final_Proj = final_Proj
-                st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+                    # Concatenate all the final_Proj dataframes
+                    final_Proj_combined = pd.concat(final_proj_list)
+                    final_Proj_combined = final_Proj_combined.sort_values(by='LevX', ascending=False)
+                    final_Proj_combined = final_Proj_combined[final_Proj_combined['Player'] != final_Proj_combined['Pivot_source']]
+                    st.session_state.final_Proj = final_Proj_combined.reset_index(drop=True)  # Assign the combined dataframe back to final_Proj
+
+            placeholder.empty()
   
-            st.download_button(
+        with displayholder.container():
+            if 'final_Proj' in st.session_state:
+                st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+
+                st.download_button(
                     label="Export Tables",
-                    data=convert_df_to_csv(final_Proj),
+                    data=convert_df_to_csv(st.session_state.final_Proj),
                     file_name='NFL_pivot_export.csv',
                     mime='text/csv',
-            )
+                )
+            else:
+                st.write("Run some pivots my dude/dudette")
 
 with tab2:
     st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'.")