Add general comparison feature in app.py and create_general_comparison.py

- Introduced a new function, create_general_comparison, to facilitate the comparison of general exposures based on user selections.
- Updated app.py to include a form for selecting generals to compare, enhancing user interaction and data analysis capabilities.
- Improved data display for general comparisons, ensuring accurate representation of exposure data in the DataFrame.

app.py

@@ -66,6 +66,7 @@ from global_func.grab_contest_data import grab_contest_data
 from global_func.create_player_comparison import create_player_comparison
 from global_func.create_stack_comparison import create_stack_comparison
 from global_func.create_size_comparison import create_size_comparison
+from global_func.create_general_comparison import create_general_comparison
 
 def is_valid_input(file):
     if isinstance(file, pd.DataFrame):
@@ -551,14 +552,29 @@ with tab2:
                             hide_index=True)
             
             with tab4:
-                
-                if st.session_state['entry_parse_var'] == 'All':
-                    st.session_state['general_frame'] = create_general_exposures(st.session_state['display_contest_info'])
-                    st.dataframe(st.session_state['general_frame'].style.background_gradient(cmap='RdYlGn', axis=1).format(precision=2), hide_index=True)
-                    
+                with st.form(key='general_comp_form'):
+                    col1, col2 = st.columns(2)
+                    with col1:
+                        comp_general_var = st.selectbox("Would you like to compare with anyone?",  ['No', 'Yes'], key='comp_general_var')
+                    with col2:
+                        comp_general_select = st.multiselect("Select generals to compare with:", st.session_state['display_contest_info']['BaseName'].sort_values().unique(), key='comp_general_select')
+                    submitted = st.form_submit_button("Submit")
+                    if submitted:
+                        if comp_general_var == 'No':
+                            comp_general_select = None
+                        else:
+                            comp_general_select = comp_general_select
+                if comp_general_var == 'Yes':
+                    general_comp = create_general_comparison(st.session_state['display_contest_info'], comp_general_select)
+                    st.dataframe(general_comp.style.background_gradient(cmap='RdYlGn', axis=0).format(formatter='{:.2%}', subset=general_comp.select_dtypes(include=['number']).columns), hide_index=True)
                 else:
-                    st.session_state['general_frame'] = create_general_exposures(st.session_state['display_contest_info'], st.session_state['entry_names'])
-                    st.dataframe(st.session_state['general_frame'].style.background_gradient(cmap='RdYlGn', axis=1).format(precision=2), hide_index=True)
+                    if st.session_state['entry_parse_var'] == 'All':
+                        st.session_state['general_frame'] = create_general_exposures(st.session_state['display_contest_info'])
+                        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(st.session_state['display_contest_info'], st.session_state['entry_names'])
+                        st.dataframe(st.session_state['general_frame'].style.background_gradient(cmap='RdYlGn', axis=1).format(precision=2), hide_index=True)
 
             with tab5:
                 with st.form(key='dupe_form'):
@@ -580,7 +596,7 @@ with tab2:
                     dupe_frame['under_5%'] = dupe_frame['under_5'] / dupe_frame['EntryCount']
                     dupe_frame['under_10%'] = dupe_frame['under_10'] / dupe_frame['EntryCount']
                     dupe_frame = dupe_frame[['BaseName', 'EntryCount', 'average_dupes', 'uniques', 'uniques%', 'under_5', 'under_5%', 'under_10', 'under_10%']].drop_duplicates(subset='BaseName', keep='first')
-                    st.session_state['duplication_frame'] = dupe_frame.sort_values(by='EntryCount', ascending=False)
+                    st.session_state['duplication_frame'] = dupe_frame.sort_values(by='uniques%', ascending=False)
                 if user_dupe_var == 'Specific':
                     st.session_state['duplication_frame'] = st.session_state['duplication_frame'][st.session_state['duplication_frame']['BaseName'].isin(user_dupe_select)]
                 

global_func/create_general_comparison.py

@@ -0,0 +1,33 @@
+import pandas as pd
+
+def create_general_comparison(df: pd.DataFrame, entrants: list = None):
+    loop_list = ['Overall'] + entrants
+    for entrant in loop_list:
+        check_cols = ['salary', 'actual_fpts', 'actual_own', 'dupes', 'uniques', 'under_5', 'under_10']
+        general_exposures = pd.DataFrame()
+        for each_col in check_cols:
+            general_frame = pd.DataFrame()
+            
+            if entrant == 'Overall':
+                overall_general = pd.Series(list(df[each_col])).sum()
+            else:
+                overall_general = pd.Series(list(df[df['BaseName'] == entrant][each_col])).sum()
+            general_contest_len = len(df)
+            each_set_name = ['Overall']
+            each_general_set = [overall_general]
+            each_general_len_set = [general_contest_len]
+            general_count_var = 0
+            for each_general in each_general_set:
+                general_frame['Stat'] = [each_col]
+                try:
+                    general_frame['Average'] = [each_general / each_general_len_set[general_count_var]]
+                except:
+                    general_frame['Average'] = [0]
+                general_frame = general_frame.rename(columns={'Average': f'Average {entrant}'})
+            general_exposures = pd.concat([general_exposures, general_frame], ignore_index = True, axis = 0)
+        general_exposures['Stat'] = general_exposures['Stat'].replace(['salary', 'actual_fpts', 'actual_own', 'dupes', 'uniques', 'under_5', 'under_10'], ['Salary Used', 'Finishing Points', 'Total Ownership', 'Duplications', 'Uniques', 'Under 5', 'Under 10'])
+        if entrant == 'Overall':
+            final_exposures = general_exposures
+        else:
+            final_exposures = pd.merge(final_exposures, general_exposures, on='Stat', how='outer')
+    return final_exposures