Refine dataframe display and column name replacement in app.py and create_general_exposures function

- Streamlined the dataframe display code in app.py by consolidating the background gradient and formatting methods for improved readability.
- Updated the column name replacement logic in create_general_exposures to target the 'Stat' column, enhancing clarity in the displayed data.

app.py

@@ -292,14 +292,9 @@ with tab2:
                 
                 if entry_parse_var == 'All':
                     st.session_state['general_frame'] = create_general_exposures(working_df)
-                    st.dataframe(st.session_state['general_frame'].
-                        style.background_gradient(cmap='RdYlGn', axis=1).
-                        format(precision=2),
-                        hide_index=True)
+                    st.dataframe(st.session_state['general_frame'].style.background_gradient(cmap='RdYlGn', axis=1).format(precision=2), hide_index=True)
+                    
                 else:
                     st.session_state['general_frame'] = create_general_exposures(working_df, entry_names)
-                    st.dataframe(st.session_state['general_frame'].
-                        style.background_gradient(cmap='RdYlGn', axis=1).
-                        format(precision=2),
-                        hide_index=True)
+                    st.dataframe(st.session_state['general_frame'].style.background_gradient(cmap='RdYlGn', axis=1).format(precision=2), hide_index=True)
 

global_func/create_general_exposures.py

@@ -36,5 +36,5 @@ def create_general_exposures(df: pd.DataFrame, entrants: list = None):
             general_exposures = general_row
         else:
             general_exposures = pd.concat([general_exposures, general_frame], ignore_index = True, axis = 0)
-    general_exposures = general_exposures.replace(['salary', 'actual_fpts', 'actual_own', 'dupes'], ['Salary Used', 'Finishing Points', 'Total Ownership', 'Duplications'])
+    general_exposures['Stat'] = general_exposures['Stat'].replace(['salary', 'actual_fpts', 'actual_own', 'dupes'], ['Salary Used', 'Finishing Points', 'Total Ownership', 'Duplications'])
     return general_exposures