James McCool
Update contest data handling in app.py and related functions to include position data
433ab29
| import streamlit as st | |
| import pandas as pd | |
| def load_contest_file(upload, sport): | |
| pos_values = ['P', 'C', '1B', '2B', '3B', 'SS', 'OF'] | |
| if upload is not None: | |
| try: | |
| try: | |
| if upload.name.endswith('.csv'): | |
| raw_df = pd.read_csv(upload) | |
| elif upload.name.endswith(('.xls', '.xlsx')): | |
| raw_df = pd.read_excel(upload) | |
| else: | |
| st.error('Please upload either a CSV or Excel file') | |
| return None | |
| except: | |
| raw_df = upload | |
| df = raw_df[['EntryId', 'EntryName', 'TimeRemaining', 'Points', 'Lineup', 'Player', 'Roster Position', '%Drafted', 'FPTS', 'Salary', 'Team']] | |
| df = df.rename(columns={'Roster Position': 'Pos', '%Drafted': 'Own'}) | |
| # Split EntryName into base name and entry count | |
| df['BaseName'] = df['EntryName'].str.replace(r'\s*\(\d+/\d+\)$', '', regex=True) | |
| df['EntryCount'] = df['EntryName'].str.extract(r'\((\d+/\d+)\)') | |
| df['EntryCount'] = df['EntryCount'].fillna('1/1') # Default to 1/1 if no entry count | |
| # Split the lineup string by replacing position indicators with commas | |
| # We need to ensure we only replace position indicators that are at the start of a player entry | |
| # and not those that might appear within player names | |
| df['Lineup'] = df['Lineup'].str.replace(r'\b(' + '|'.join(pos_values) + r')\b', r'\1,', regex=True) | |
| # Split into individual columns and remove position indicators | |
| # First, determine the maximum number of players in any lineup | |
| max_players = int(df['Lineup'].str.split(',').str.len().max()) | |
| if max_players <= 0: | |
| st.error('No valid lineups found in the uploaded file') | |
| return None | |
| # Create columns for each player | |
| for i in range(1, max_players): | |
| df[i] = df['Lineup'].str.split(',').str[i].str.strip() | |
| # Remove position indicators from the end of each entry | |
| df[i] = df[i].str.replace(r'\s+(' + '|'.join(pos_values) + r')$', '', regex=True) | |
| if sport == 'MLB': | |
| df = df.rename(columns={1: '1B', 2: '2B', 3: '3B', 4: 'C', 5: 'OF1', 6: 'OF2', 7: 'OF3', 8: 'SP1', 9: 'SP2', 10: 'SS'}) | |
| try: | |
| df['Own'] = df['Own'].str.replace('%', '').astype(float) | |
| except: | |
| df['Own'] = df['Own'].astype(float) | |
| ownership_df = df[['Player', 'Own']] | |
| fpts_df = df[['Player', 'FPTS']] | |
| salary_df = df[['Player', 'Salary']] | |
| team_df = df[['Player', 'Team']] | |
| pos_df = df[['Player', 'Pos']] | |
| cleaned_df = df.drop(columns=['EntryId', 'EntryName', 'TimeRemaining', 'Points', 'Lineup', 'Player', 'Pos', 'Own', 'FPTS', 'Salary', 'Team']) | |
| cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']] | |
| entry_list = list(set(df['BaseName'])) | |
| entry_list.sort() | |
| return cleaned_df, ownership_df, fpts_df, salary_df, team_df, pos_df, entry_list | |
| except Exception as e: | |
| st.error(f'Error loading file: {str(e)}') | |
| return None | |
| return None |