Update app.py to correct column indices for player metrics in DraftKings and FanDuel sections, ensuring accurate calculations for 'Proj' and 'Own' metrics, thereby improving data integrity and application functionality.

app.py

@@ -478,44 +478,44 @@ with tab3:
                 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])
                     ],
+                    'Proj': [
+                        np.min(st.session_state.working_seed[:,11]),
+                        np.mean(st.session_state.working_seed[:,11]),
+                        np.max(st.session_state.working_seed[:,11]),
+                        np.std(st.session_state.working_seed[:,11])
+                    ],
                     '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])
+                        np.min(st.session_state.working_seed[:,16]),
+                        np.mean(st.session_state.working_seed[:,16]),
+                        np.max(st.session_state.working_seed[:,16]),
+                        np.std(st.session_state.working_seed[:,16])
                     ]
                 })
             elif site_var == 'Fanduel':
                 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])
                     ],
+                    '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[:,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])
+                        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])
                     ]
                 })