Refactor player column selection in app.py to dynamically adjust based on slate type for DraftKings and FanDuel, ensuring accurate data representation and enhancing user experience.

app.py

@@ -422,7 +422,7 @@ with tab3:
                 column_names = dk_sd_columns
             # Get the minimum and maximum ownership values from dk_lineups
             min_own = np.min(dk_lineups[:,12])
-            max_own = np.max(dk_lineups[:,11])
+            max_own = np.max(dk_lineups[:,12])
             
             
             player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
@@ -441,7 +441,7 @@ with tab3:
                 player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
                 column_names = fd_sd_columns
             # Get the minimum and maximum ownership values from dk_lineups
-            min_own = np.min(fd_lineups[:,12])
+            min_own = np.min(fd_lineups[:,11])
             max_own = np.max(fd_lineups[:,11])
             
             
@@ -620,9 +620,15 @@ with tab3:
         with tab1:
             if 'data_export_display' in st.session_state:
                 if site_var == 'Draftkings':
-                    player_columns = st.session_state.data_export_display.iloc[:, :10]
+                    if slate_type_var3 == 'Regular':
+                        player_columns = st.session_state.data_export_display.iloc[:, :10]
+                    elif slate_type_var3 == 'Showdown':
+                        player_columns = st.session_state.data_export_display.iloc[:, :7]
                 elif site_var == 'Fanduel':
-                    player_columns = st.session_state.data_export_display.iloc[:, :9]
+                    if slate_type_var3 == 'Regular':
+                        player_columns = st.session_state.data_export_display.iloc[:, :9]
+                    elif slate_type_var3 == 'Showdown':
+                        player_columns = st.session_state.data_export_display.iloc[:, :6]
                 
                 # Flatten the DataFrame and count unique values
                 value_counts = player_columns.values.flatten().tolist()
@@ -656,9 +662,15 @@ with tab3:
         with tab2:
             if 'working_seed' in st.session_state:
                 if site_var == 'Draftkings':
-                    player_columns = st.session_state.working_seed[:, :10]
+                    if slate_type_var3 == 'Regular':
+                        player_columns = st.session_state.working_seed[:, :10]
+                    elif slate_type_var3 == 'Showdown':
+                        player_columns = st.session_state.working_seed[:, :7]
                 elif site_var == 'Fanduel':
-                    player_columns = st.session_state.working_seed[:, :9]
+                    if slate_type_var3 == 'Regular':
+                        player_columns = st.session_state.working_seed[:, :9]
+                    elif slate_type_var3 == 'Showdown':
+                        player_columns = st.session_state.working_seed[:, :6]
                 
                 # Flatten the DataFrame and count unique values
                 value_counts = player_columns.flatten().tolist()