added seed frame frequencies

app.py

@@ -513,35 +513,70 @@ with tab2:
                 }).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 = player_columns.values.flatten().tolist()
-                value_counts = pd.Series(value_counts).value_counts()
-                
-                percentages = (value_counts / lineup_num_var * 100).round(2)
-                
-                # Create a DataFrame with the results
-                summary_df = pd.DataFrame({
-                    'Player': value_counts.index,
-                    'Frequency': value_counts.values,
-                    'Percentage': percentages.values
-                })
+            tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
+            with tab1:
+                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 = player_columns.values.flatten().tolist()
+                    value_counts = pd.Series(value_counts).value_counts()
+                    
+                    percentages = (value_counts / lineup_num_var * 100).round(2)
+                    
+                    # Create a DataFrame with the results
+                    summary_df = pd.DataFrame({
+                        'Player': value_counts.index,
+                        'Frequency': value_counts.values,
+                        'Percentage': percentages.values
+                    })
+                    
+                    # Sort by frequency in descending order
+                    summary_df = summary_df.sort_values('Frequency', ascending=False)
+                    
+                    # Display the table
+                    st.write("Player Frequency Table:")
+                    st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
                 
-                # Sort by frequency in descending order
-                summary_df = summary_df.sort_values('Frequency', ascending=False)
+                    st.download_button(
+                        label="Export player frequency",
+                        data=convert_df_to_csv(summary_df),
+                        file_name='NBA_player_frequency.csv',
+                        mime='text/csv',
+                    )
+            with tab2:
+                if 'working_seed' in st.session_state:
+                    if site_var1 == 'Draftkings':
+                        player_columns = st.session_state.working_seed.iloc[:, :8]
+                    elif site_var1 == 'Fanduel':
+                        player_columns = st.session_state.working_seed.iloc[:, :9]
+                    
+                    # Flatten the DataFrame and count unique values
+                    value_counts = player_columns.values.flatten().tolist()
+                    value_counts = pd.Series(value_counts).value_counts()
+                    
+                    percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
+                    
+                    # Create a DataFrame with the results
+                    summary_df = pd.DataFrame({
+                        'Player': value_counts.index,
+                        'Frequency': value_counts.values,
+                        'Percentage': percentages.values
+                    })
+                    
+                    # Sort by frequency in descending order
+                    summary_df = summary_df.sort_values('Frequency', ascending=False)
+                    
+                    # Display the table
+                    st.write("Seed Frame Frequency Table:")
+                    st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
                 
-                # Display the table
-                st.write("Player Frequency Table:")
-                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
-            
-                st.download_button(
-                    label="Export player frequency",
-                    data=convert_df_to_csv(summary_df),
-                    file_name='NBA_player_frequency.csv',
-                    mime='text/csv',
-                )
+                    st.download_button(
+                        label="Export seed frame frequency",
+                        data=convert_df_to_csv(summary_df),
+                        file_name='NBA_seed_frame_frequency.csv',
+                        mime='text/csv',
+                    )