Enhance summary statistics generation in app.py: implement conditional logic for slate type (Regular or Showdown) to accurately compute metrics for Draftkings and Fanduel, improving data representation and user experience.

app.py

@@ -516,49 +516,96 @@ with tab2:
         if 'working_seed' in st.session_state:
             # Create a new dataframe with summary statistics
             if site_var1 == 'Draftkings':
-                summary_df = pd.DataFrame({
+                if slate_type_var1 == 'Regular':
+                    summary_df = pd.DataFrame({
                     'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                    'Salary': [
-                        np.min(st.session_state.working_seed[:,8]),
-                        np.mean(st.session_state.working_seed[:,8]),
-                        np.max(st.session_state.working_seed[:,8]),
-                        np.std(st.session_state.working_seed[:,8])
-                    ],
-                    'Proj': [
-                        np.min(st.session_state.working_seed[:,9]),
-                        np.mean(st.session_state.working_seed[:,9]),
-                        np.max(st.session_state.working_seed[:,9]),
-                        np.std(st.session_state.working_seed[:,9])
-                    ],
-                    'Own': [
-                        np.min(st.session_state.working_seed[:,14]),
-                        np.mean(st.session_state.working_seed[:,14]),
-                        np.max(st.session_state.working_seed[:,14]),
-                        np.std(st.session_state.working_seed[:,14])
-                    ]
-                })
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,8]),
+                            np.mean(st.session_state.working_seed[:,8]),
+                            np.max(st.session_state.working_seed[:,8]),
+                            np.std(st.session_state.working_seed[:,8])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,9]),
+                            np.mean(st.session_state.working_seed[:,9]),
+                            np.max(st.session_state.working_seed[:,9]),
+                            np.std(st.session_state.working_seed[:,9])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,14]),
+                            np.mean(st.session_state.working_seed[:,14]),
+                            np.max(st.session_state.working_seed[:,14]),
+                            np.std(st.session_state.working_seed[:,14])
+                        ]
+                    })
+                elif slate_type_var1 == 'Showdown':
+                    summary_df = pd.DataFrame({
+                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,6]),
+                            np.mean(st.session_state.working_seed[:,6]),
+                            np.max(st.session_state.working_seed[:,6]),
+                            np.std(st.session_state.working_seed[:,6])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,7]),
+                            np.mean(st.session_state.working_seed[:,7]),
+                            np.max(st.session_state.working_seed[:,7]),
+                            np.std(st.session_state.working_seed[:,7])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,12]),
+                            np.mean(st.session_state.working_seed[:,12]),
+                            np.max(st.session_state.working_seed[:,12]),
+                            np.std(st.session_state.working_seed[:,12])
+                        ]
+                    })
+                
             elif site_var1 == 'Fanduel':
-                summary_df = pd.DataFrame({
+                if slate_type_var1 == 'Regular':
+                    summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,9]),
+                            np.mean(st.session_state.working_seed[:,9]),
+                            np.max(st.session_state.working_seed[:,9]),
+                            np.std(st.session_state.working_seed[:,9])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,10]),
+                            np.mean(st.session_state.working_seed[:,10]),
+                            np.max(st.session_state.working_seed[:,10]),
+                            np.std(st.session_state.working_seed[:,10])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,15]),
+                            np.mean(st.session_state.working_seed[:,15]),
+                            np.max(st.session_state.working_seed[:,15]),
+                            np.std(st.session_state.working_seed[:,15])
+                        ]
+                    })
+                elif slate_type_var1 == 'Showdown':
+                    summary_df = pd.DataFrame({
                     'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                    'Salary': [
-                        np.min(st.session_state.working_seed[:,9]),
-                        np.mean(st.session_state.working_seed[:,9]),
-                        np.max(st.session_state.working_seed[:,9]),
-                        np.std(st.session_state.working_seed[:,9])
-                    ],
-                    'Proj': [
-                        np.min(st.session_state.working_seed[:,10]),
-                        np.mean(st.session_state.working_seed[:,10]),
-                        np.max(st.session_state.working_seed[:,10]),
-                        np.std(st.session_state.working_seed[:,10])
-                    ],
-                    'Own': [
-                        np.min(st.session_state.working_seed[:,15]),
-                        np.mean(st.session_state.working_seed[:,15]),
-                        np.max(st.session_state.working_seed[:,15]),
-                        np.std(st.session_state.working_seed[:,15])
-                    ]
-                })
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,6]),
+                            np.mean(st.session_state.working_seed[:,6]),
+                            np.max(st.session_state.working_seed[:,6]),
+                            np.std(st.session_state.working_seed[:,6])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,7]),
+                            np.mean(st.session_state.working_seed[:,7]),
+                            np.max(st.session_state.working_seed[:,7]),
+                            np.std(st.session_state.working_seed[:,7])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,12]),
+                            np.mean(st.session_state.working_seed[:,12]),
+                            np.max(st.session_state.working_seed[:,12]),
+                            np.std(st.session_state.working_seed[:,12])
+                        ]
+                    })
 
             # Set the index of the summary dataframe as the "Metric" column
             summary_df = summary_df.set_index('Metric')
@@ -575,10 +622,16 @@ with tab2:
         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]
+                if slate_type_var1 == 'Regular':
+                    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]
+                elif slate_type_var1 == 'Showdown':
+                    if site_var1 == 'Draftkings':
+                        player_columns = st.session_state.data_export_display.iloc[:, :5]
+                    elif site_var1 == 'Fanduel':
+                        player_columns = st.session_state.data_export_display.iloc[:, :5]
                 
                 # Flatten the DataFrame and count unique values
                 value_counts = player_columns.values.flatten().tolist()
@@ -609,10 +662,16 @@ with tab2:
                 )
         with tab2:
             if 'working_seed' in st.session_state:
-                if site_var1 == 'Draftkings':
-                    player_columns = st.session_state.working_seed[:, :8]
-                elif site_var1 == 'Fanduel':
-                    player_columns = st.session_state.working_seed[:, :9]
+                if slate_type_var1 == 'Regular':
+                    if site_var1 == 'Draftkings':
+                        player_columns = st.session_state.working_seed[:, :8]
+                    elif site_var1 == 'Fanduel':
+                        player_columns = st.session_state.working_seed[:, :9]
+                elif slate_type_var1 == 'Showdown':
+                    if site_var1 == 'Draftkings':
+                        player_columns = st.session_state.working_seed[:, :5]
+                    elif site_var1 == 'Fanduel':
+                        player_columns = st.session_state.working_seed[:, :5]
                 
                 # Flatten the DataFrame and count unique values
                 value_counts = player_columns.flatten().tolist()