Update app.py

app.py

@@ -512,7 +512,9 @@ with tab1:
                     file_name='MLB_consim_export.csv',
                     mime='text/csv',
                 )  
-        with st.container():
+        tab1, tab2 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics'])
+        
+        with tab1:
             if 'Sim_Winner_Display' in st.session_state:
                 # Create a new dataframe with summary statistics
                 summary_df = pd.DataFrame({
@@ -555,6 +557,42 @@ with tab1:
                     'Fantasy': '{:.2f}',
                     'GPP_Proj': '{:.2f}'
                 }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own', 'Fantasy', 'GPP_Proj']), use_container_width=True)
+        with tab2:
+            if 'Sim_Winner_Display' in st.session_state and 'maps_dict' in st.session_state:
+                # Apply position mapping to FLEX column
+                flex_positions = st.session_state.Sim_Winner_Display['FLEX'].map(st.session_state.maps_dict['Pos_map'])
+                
+                # Count occurrences of each position in FLEX
+                flex_counts = flex_positions.value_counts()
+                
+                # Calculate average statistics for each FLEX position
+                flex_stats = st.session_state.Sim_Winner_Display.groupby(flex_positions).agg({
+                    'Proj': 'mean',
+                    'Own': 'mean',
+                    'Fantasy': 'mean',
+                    'GPP_Proj': 'mean'
+                })
+                
+                # Combine counts and average statistics
+                flex_summary = pd.concat([flex_counts, flex_stats], axis=1)
+                flex_summary.columns = ['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
+                flex_summary = flex_summary.reset_index()
+                flex_summary.columns = ['Position', 'Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
+                
+                # Display the summary dataframe
+                st.subheader("FLEX Position Statistics")
+                st.dataframe(flex_summary.style.format({
+                    'Count': '{:.0f}',
+                    'Avg Proj': '{:.2f}',
+                    'Avg Fantasy': '{:.2f}',
+                    'Avg GPP_Proj': '{:.2f}'
+                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']), use_container_width=True)
+                
+                # Create a bar chart for FLEX position distribution
+                fig = px.bar(flex_summary, x='Position', y='Count', title='FLEX Position Distribution')
+                st.plotly_chart(fig, use_container_width=True)
+            else:
+                st.write("Simulation data or position mapping not available.")
         with st.container():
             tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB-WR-TE Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures', 'Team Exposures'])
             with tab1: