Refactor summary statistics calculations in app.py: streamline the computation of minimum, average, maximum, and standard deviation for salary, project, and own metrics by correcting column indices, enhancing data accuracy and clarity in the displayed results.

app.py

@@ -740,22 +740,22 @@ with tab2:
                         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])
                             ],
-                            'Proj': [
-                                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])
-                            ],
                             'Own': [
-                                np.min(st.session_state.working_seed[:,13]),
-                                np.mean(st.session_state.working_seed[:,13]),
-                                np.max(st.session_state.working_seed[:,13]),
-                                np.std(st.session_state.working_seed[:,13])
+                                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 slate_type_var1 == 'Showdown':
@@ -832,22 +832,22 @@ with tab2:
                         summary_df = pd.DataFrame({
                             'Metric': ['Min', 'Average', 'Max', 'STDdev'],
                             'Salary': [
+                                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])
+                            ],
+                            'Proj': [
                                 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])
+                                np.min(st.session_state.working_seed[:,13]),
+                                np.mean(st.session_state.working_seed[:,13]),
+                                np.max(st.session_state.working_seed[:,13]),
+                                np.std(st.session_state.working_seed[:,13])
                             ]
                         })
                     elif slate_type_var1 == 'Showdown':