Add general exposures tab and functionality in app.py

- Introduced a new tab for displaying general exposures in the app interface, enhancing user experience by providing additional insights.
- Implemented the create_general_exposures function to calculate and display overall and percentile-based statistics for specified columns.
- Updated session state management to handle the new general exposures data, ensuring seamless integration with existing features.

app.py

@@ -13,6 +13,7 @@ from global_func.find_name_mismatches import find_name_mismatches
 from global_func.create_player_exposures import create_player_exposures
 from global_func.create_stack_exposures import create_stack_exposures
 from global_func.create_stack_size_exposures import create_stack_size_exposures
+from global_func.create_general_exposures import create_general_exposures
 
 player_exposure_format = {'Exposure Overall': '{:.2%}', 'Exposure Top 1%': '{:.2%}', 'Exposure Top 5%': '{:.2%}', 'Exposure Top 10%': '{:.2%}', 'Exposure Top 20%': '{:.2%}'}
 
@@ -237,7 +238,7 @@ with tab2:
         )
 
         with st.container():
-            tab1, tab2, tab3 = st.tabs(['Player Used Info', 'Stack Used Info', 'Stack Size Info'])
+            tab1, tab2, tab3, tab4 = st.tabs(['Player Used Info', 'Stack Used Info', 'Stack Size Info', 'General Info'])
             with tab1:
 
                 if entry_parse_var == 'All':
@@ -286,3 +287,21 @@ with tab2:
                         style.background_gradient(cmap='RdYlGn').
                         format(formatter='{:.2%}', subset=st.session_state['stack_size_frame'].iloc[:, 1:].select_dtypes(include=['number']).columns),
                         hide_index=True)
+            
+            with tab4:
+                
+                if entry_parse_var == 'All':
+                    st.session_state['general_frame'] = create_general_exposures(working_df)
+                    st.dataframe(st.session_state['general_frame'].
+                        sort_values(by='Exposure Overall', ascending=False).
+                        style.background_gradient(cmap='RdYlGn').
+                        format(formatter='{:.2%}', subset=st.session_state['general_frame'].iloc[:, 1:].select_dtypes(include=['number']).columns),
+                        hide_index=True)
+                else:
+                    st.session_state['general_frame'] = create_general_exposures(working_df, entry_names)
+                    st.dataframe(st.session_state['general_frame'].
+                        sort_values(by='Exposure Overall', ascending=False).
+                        style.background_gradient(cmap='RdYlGn').
+                        format(formatter='{:.2%}', subset=st.session_state['general_frame'].iloc[:, 1:].select_dtypes(include=['number']).columns),
+                        hide_index=True)
+

global_func/create_general_exposures.py

@@ -0,0 +1,37 @@
+import pandas as pd
+
+def create_general_exposures(df: pd.DataFrame, entrants: list = None):
+    check_cols = ['salary', 'actual_fpts', 'actual_own', 'dupes']
+    general_exposures = pd.DataFrame()
+    for each_col in check_cols:
+        
+        if entrants is not None:
+            overall_general = pd.Series(list(df[df['BaseName'].isin(entrants)][each_col])).sum()
+        else:
+            overall_general = pd.Series(list(df[each_col])).sum()
+        top_1per_general = pd.Series(list(df[df['percentile_finish'] <= 0.01][each_col])).sum()
+        top_5per_general = pd.Series(list(df[df['percentile_finish'] <= 0.05][each_col])).sum()
+        top_10per_general = pd.Series(list(df[df['percentile_finish'] <= 0.10][each_col])).sum()
+        top_20per_general = pd.Series(list(df[df['percentile_finish'] <= 0.20][each_col])).sum()
+        general_contest_len = len(df)
+        general_len_1per = len(df[df['percentile_finish'] <= 0.01])
+        general_len_5per = len(df[df['percentile_finish'] <= 0.05])
+        general_len_10per = len(df[df['percentile_finish'] <= 0.10])
+        general_len_20per = len(df[df['percentile_finish'] <= 0.20])
+        each_set_name = ['Overall', ' Top 1%', ' Top 5%', 'Top 10%', 'Top 20%']
+        each_general_set = [overall_general, top_1per_general, top_5per_general, top_10per_general, top_20per_general]
+        each_general_len_set = [general_contest_len, general_len_1per, general_len_5per, general_len_10per, general_len_20per]
+        general_count_var = 0
+        for each_general in each_general_set:
+            general_frame = pd.DataFrame()
+            general_frame['Stat'] = each_col
+            general_frame['Average'] = each_general / each_general_len_set[general_count_var]
+            general_frame = general_frame[['Stat', 'Average']]
+            general_frame = general_frame.rename(columns={'Average': f'Average {each_set_name[general_count_var]}'})
+            if len(general_exposures) == 0:
+                general_exposures = general_frame
+            else:
+                general_exposures = pd.merge(general_exposures, general_frame, on='Stat', how='outer')
+            general_count_var += 1
+
+    return general_exposures