Enhance find_name_mismatches function and app.py session state management

- Updated the find_name_mismatches function to include a new parameter for calc_toggle, allowing for better tracking of calculation states.
- Modified app.py to ensure calc_toggle is properly managed within session state, improving the overall functionality and user experience during name matching processes.
- Streamlined the handling of data frames by removing unnecessary columns, focusing on relevant metrics for clearer data presentation.

app.py

@@ -18,11 +18,13 @@ from global_func.load_csv import load_csv
 from global_func.find_csv_mismatches import find_csv_mismatches
 
 player_exposure_format = {'Exposure Overall': '{:.2%}', 'Exposure Top 1%': '{:.2%}', 'Exposure Top 5%': '{:.2%}', 'Exposure Top 10%': '{:.2%}', 'Exposure Top 20%': '{:.2%}'}
+st.session_state['calc_toggle'] = False
 
 tab1, tab2 = st.tabs(["Data Load", "Contest Analysis"])
 with tab1:
     if st.button('Clear data', key='reset1'):
         st.session_state.clear()
+    st.session_state['calc_toggle'] = False
     sport_select = st.selectbox("Select Sport", ['MLB', 'NBA', 'NFL'])
     # Add file uploaders to your app
     col1, col2 = st.columns(2)
@@ -71,15 +73,15 @@ with tab1:
                 st.success('Projections file loaded successfully!')
                 st.dataframe(st.session_state['projections_df'].head(10))
 
+
+with tab2:
     if Contest_file and projections_file:
         st.subheader("Name Matching functions")
-        st.session_state['Contest'], st.session_state['projections_df'], st.session_state['ownership_dict'], st.session_state['actual_dict'] = find_name_mismatches(st.session_state['Contest'], st.session_state['projections_df'], st.session_state['ownership_dict'], st.session_state['actual_dict'])
+        st.session_state['Contest'], st.session_state['projections_df'], st.session_state['ownership_dict'], st.session_state['actual_dict'], st.session_state['calc_toggle'] = find_name_mismatches(st.session_state['Contest'], st.session_state['projections_df'], st.session_state['ownership_dict'], st.session_state['actual_dict'], st.session_state['calc_toggle'])
         st.session_state['projections_df']['salary'] = (st.session_state['projections_df']['salary'].astype(str).str.replace(',', '').astype(float).astype(int))
         st.session_state['ownership_dict'] = dict(zip(st.session_state['ownership_dict']['Player'], st.session_state['ownership_dict']['Own']))
         st.session_state['actual_dict'] = dict(zip(st.session_state['actual_dict']['Player'], st.session_state['actual_dict']['FPTS']))
 
-
-with tab2:
     if 'Contest' in st.session_state and 'projections_df' in st.session_state:
         col1, col2 = st.columns([1, 8])
         excluded_cols = ['BaseName', 'EntryCount']
@@ -251,8 +253,7 @@ with tab2:
                     for each_set in each_frame_set:
                         set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Player', 'count': 'Count'})
                         set_frame['Percent'] = set_frame['Count'] / each_len_set[player_count_var]
-                        set_frame = set_frame[['Player', 'Count', 'Percent']]
-                        set_frame = set_frame.rename(columns={'Count': f'Count {each_set_name[player_count_var]}'})
+                        set_frame = set_frame[['Player', 'Percent']]
                         set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_set_name[player_count_var]}'})
                         if 'player_frame' not in st.session_state:
                             st.session_state['player_frame'] = set_frame
@@ -313,9 +314,7 @@ with tab2:
                     for each_stack in each_stacks_set:
                         stack_frame = each_stack.to_frame().reset_index().rename(columns={'index': 'Stack', 'count': 'Count'})
                         stack_frame['Percent'] = stack_frame['Count'] / each_stacks_len_set[stack_count_var]
-                        stack_frame = stack_frame[['Stack', 'Count', 'Percent']]
-                        st.write(stack_frame['Count'])
-                        stack_frame = stack_frame.rename(columns={'Count': f'Count {each_set_name[stack_count_var]}'})
+                        stack_frame = stack_frame[['Stack', 'Percent']]
                         stack_frame = stack_frame.rename(columns={'Percent': f'Exposure {each_set_name[stack_count_var]}'})
                         if 'stack_frame' not in st.session_state:
                             st.session_state['stack_frame'] = stack_frame

global_func/find_name_mismatches.py

@@ -4,7 +4,7 @@ import pandas as pd
 import time
 from fuzzywuzzy import process
 
-def find_name_mismatches(contest_df, projections_df, ownership_df, fpts_df):
+def find_name_mismatches(contest_df, projections_df, ownership_df, fpts_df, calc_toggle):
     
     name_columns = [col for col in contest_df.columns if not col in ['BaseName', 'EntryCount']]
     
@@ -90,11 +90,10 @@ def find_name_mismatches(contest_df, projections_df, ownership_df, fpts_df):
                         st.write(contest_name + ' ' + projection_name)
                         st.success(f"Replaced '{selected_name}' with '{projection_name}'")
                 
+                calc_toggle = True
+                
                 st.success("All changes applied successfully!")
-                return contest_df, projections_df, ownership_df, fpts_df
-            else:
-                st.success("No changes applied")
-                return contest_df, projections_df, ownership_df, fpts_df
+                return contest_df, projections_df, ownership_df, fpts_df, calc_toggle
     else:
         st.success("All players have been automatically matched!")
         # Apply automatic matches
@@ -104,4 +103,5 @@ def find_name_mismatches(contest_df, projections_df, ownership_df, fpts_df):
             ownership_df['Player'] = ownership_df['Player'].replace(contest_name, projection_name)
             fpts_df['Player'] = fpts_df['Player'].replace(contest_name, projection_name)
             st.write(contest_name + ' ' + projection_name)
-        return contest_df, projections_df, ownership_df, fpts_df
+            calc_toggle = True
+        return contest_df, projections_df, ownership_df, fpts_df, calc_toggle