Refactor load_contest_file function to include contest type parameter for improved data handling

- Updated the load_contest_file function to accept a new 'type' parameter, allowing for differentiated processing of 'Classic' and 'Showdown' contest types.
- Adjusted the logic for cleaning and structuring lineups based on the contest type, enhancing the flexibility and clarity of data handling.
- Maintained existing functionality while improving the organization and readability of contest data processing in both app.py and load_contest_file.py.

app.py

@@ -124,7 +124,7 @@ with tab1:
                 pass
 
     if 'Contest_file' in st.session_state:
-        st.session_state['Contest'], st.session_state['ownership_df'], st.session_state['actual_df'], st.session_state['entry_list'], check_lineups = load_contest_file(st.session_state['Contest_file'], st.session_state['player_info'], sport_select)
+        st.session_state['Contest'], st.session_state['ownership_df'], st.session_state['actual_df'], st.session_state['entry_list'], check_lineups = load_contest_file(st.session_state['Contest_file'], type_var, st.session_state['player_info'], sport_select)
         st.session_state['Contest'] = st.session_state['Contest'].dropna(how='all')
         st.session_state['Contest'] = st.session_state['Contest'].reset_index(drop=True)
         if st.session_state['Contest'] is not None:

global_func/load_contest_file.py

@@ -2,7 +2,7 @@ import streamlit as st
 import pandas as pd
 from rapidfuzz import process, fuzz
 
-def load_contest_file(upload, helper = None, sport = None):
+def load_contest_file(upload, type, helper = None, sport = None):
     if upload is not None:
         try:
             try:
@@ -101,30 +101,41 @@ def load_contest_file(upload, helper = None, sport = None):
             
             # Create the cleaned dataframe with just the essential columns
             cleaned_df = df[['BaseName', 'Lineup']]
-            if sport == 'MLB':
-                cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF '], value=',', regex=True)
-            elif sport == 'MMA':
-                cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF ', ' F ', 'F '], value=',', regex=True)
-            elif sport == 'GOLF':
-                cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF ', ' G ', 'G '], value=',', regex=True)
-            print(sport)
-            check_lineups = cleaned_df.copy()
-            if sport == 'MLB':
-                cleaned_df[['Remove', '1B', '2B', '3B', 'C', 'OF1', 'OF2', 'OF3', 'P1', 'P2', 'SS']] = cleaned_df['Lineup'].str.split(',', expand=True)
-            elif sport == 'MMA':
-                cleaned_df[['Remove', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']] = cleaned_df['Lineup'].str.split(',', expand=True)
-            elif sport == 'GOLF':
-                cleaned_df[['Remove', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']] = cleaned_df['Lineup'].str.split(',', expand=True)
-            cleaned_df = cleaned_df.drop(columns=['Lineup', 'Remove'])
-            entry_counts = cleaned_df['BaseName'].value_counts()
-            cleaned_df['EntryCount'] = cleaned_df['BaseName'].map(entry_counts)
-            if sport == 'MLB':
-                cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'P1', 'P2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']]
-            elif sport == 'MMA':
-                cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']]
-            elif sport == 'GOLF':
-                cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']]
-            st.table(cleaned_df.head(10))
+            if type == 'Classic':
+                if sport == 'MLB':
+                    cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF '], value=',', regex=True)
+                elif sport == 'MMA':
+                    cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF ', ' F ', 'F '], value=',', regex=True)
+                elif sport == 'GOLF':
+                    cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF ', ' G ', 'G '], value=',', regex=True)
+                print(sport)
+                check_lineups = cleaned_df.copy()
+                if sport == 'MLB':
+                    cleaned_df[['Remove', '1B', '2B', '3B', 'C', 'OF1', 'OF2', 'OF3', 'P1', 'P2', 'SS']] = cleaned_df['Lineup'].str.split(',', expand=True)
+                elif sport == 'MMA':
+                    cleaned_df[['Remove', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']] = cleaned_df['Lineup'].str.split(',', expand=True)
+                elif sport == 'GOLF':
+                    cleaned_df[['Remove', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']] = cleaned_df['Lineup'].str.split(',', expand=True)
+                cleaned_df = cleaned_df.drop(columns=['Lineup', 'Remove'])
+                entry_counts = cleaned_df['BaseName'].value_counts()
+                cleaned_df['EntryCount'] = cleaned_df['BaseName'].map(entry_counts)
+                if sport == 'MLB':
+                    cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'P1', 'P2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']]
+                elif sport == 'MMA':
+                    cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']]
+                elif sport == 'GOLF':
+                    cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']]
+                st.table(cleaned_df.head(10))
+            elif type == 'Showdown':
+                cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' UTIL ', 'CPT '], value=',', regex=True)
+                print(type)
+                check_lineups = cleaned_df.copy()
+                cleaned_df[['Remove', 'CPT', 'UTIL', 'UTIL', 'UTIL', 'UTIL', 'UTIL']] = cleaned_df['Lineup'].str.split(',', expand=True)
+                cleaned_df = cleaned_df.drop(columns=['Lineup', 'Remove'])
+                entry_counts = cleaned_df['BaseName'].value_counts()
+                cleaned_df['EntryCount'] = cleaned_df['BaseName'].map(entry_counts)
+                cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'CPT', 'UTIL', 'UTIL', 'UTIL', 'UTIL', 'UTIL']]
+                st.table(cleaned_df.head(10))
             
             print('Made it through check_lineups')