Added summary frame statistics

app.py

@@ -247,12 +247,6 @@ with tab2:
             elif player_var1 == 'Full Slate':
                     player_var2 = fd_raw.Player.values.tolist()
             
-            own_var_low, own_var_high = st.slider("Select ownership range", 
-                                  min_value=float(min_own), 
-                                  max_value=float(max_own), 
-                                  value=(float(min_own), float(max_own)), 
-                                  step=float(10.00))
-            
     with col2:
         
         if site_var1 == 'Draftkings':
@@ -302,11 +296,49 @@ with tab2:
         
         with st.container():
             if 'data_export_display' in st.session_state:
-                # Get the first 9 columns
-                first_9_columns = st.session_state.data_export_display.iloc[:, :9]
+                # Create a new dataframe with summary statistics
+                summary_df = pd.DataFrame({
+                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                    'Salary': [
+                        st.session_state.Sim_Winner_Display['salary'].min(),
+                        st.session_state.Sim_Winner_Display['salary'].mean(),
+                        st.session_state.Sim_Winner_Display['salary'].max(),
+                        st.session_state.Sim_Winner_Display['salary'].std()
+                    ],
+                    'Proj': [
+                        st.session_state.Sim_Winner_Display['proj'].min(),
+                        st.session_state.Sim_Winner_Display['proj'].mean(),
+                        st.session_state.Sim_Winner_Display['proj'].max(),
+                        st.session_state.Sim_Winner_Display['proj'].std()
+                    ],
+                    'Own': [
+                        st.session_state.Sim_Winner_Display['Own'].min(),
+                        st.session_state.Sim_Winner_Display['Own'].mean(),
+                        st.session_state.Sim_Winner_Display['Own'].max(),
+                        st.session_state.Sim_Winner_Display['Own'].std()
+                    ]
+                })
+
+                # Set the index of the summary dataframe as the "Metric" column
+                summary_df = summary_df.set_index('Metric')
+
+                # Display the summary dataframe
+                st.subheader("Optimal Statistics")
+                st.dataframe(summary_df.style.format({
+                    'Salary': '{:.2f}',
+                    'Proj': '{:.2f}',
+                    'Own': '{:.2f}'
+                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)
+
+        with st.container():
+            if 'data_export_display' in st.session_state:
+                if site_var1 == 'Draftkings':
+                    player_columns = st.session_state.data_export_display.iloc[:, :8]
+                elif site_var1 == 'Fanduel':
+                    player_columns = st.session_state.data_export_display.iloc[:, :9]
                 
                 # Flatten the DataFrame and count unique values
-                value_counts = first_9_columns.values.flatten().tolist()
+                value_counts = player_columns.values.flatten().tolist()
                 value_counts = pd.Series(value_counts).value_counts()
                 
                 percentages = (value_counts / lineup_num_var * 100).round(2)