James McCool
Remove table display of salary and cleaned contest data in app.py and load_contest_file.py for a cleaner user interface. This change streamlines the data presentation by eliminating unnecessary table outputs while maintaining existing functionality.
5abfd03
| import streamlit as st | |
| import pandas as pd | |
| from rapidfuzz import process, fuzz | |
| from numpy import where as np_where | |
| def load_contest_file(upload, type, helper = None, sport = None): | |
| 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 | |
| if helper is not None: | |
| helper_df = helper | |
| print('Made it through initial upload') | |
| # Select and rename essential columns for the actual upload | |
| if helper is None: | |
| df = raw_df[['EntryId', 'EntryName', 'TimeRemaining', 'Points', 'Lineup', 'Player', 'Roster Position', '%Drafted', 'FPTS', 'Salary', 'Team']] | |
| else: | |
| df = raw_df[['EntryId', 'EntryName', 'TimeRemaining', 'Points', 'Lineup', 'Player', 'Roster Position', '%Drafted', 'FPTS']] | |
| df = df.rename(columns={'Roster Position': 'Pos', '%Drafted': 'Own'}) | |
| print('Made it through rename') | |
| # 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 | |
| if type == 'Showdown': | |
| df['FPTS'] = np_where(df['Pos'] == 'CPT', df['FPTS'] / 1.5, df['FPTS']) | |
| # Convert ownership percentage to float | |
| try: | |
| df['Own'] = df['Own'].str.replace('%', '').astype(float) | |
| except: | |
| df['Own'] = df['Own'].astype(float) | |
| print('Made it through ownership conversion') | |
| # Select and rename essential columns for the actual upload | |
| if helper is not None: | |
| df_helper = helper_df[['Player', 'Salary', 'Team']] | |
| print('Made it through helper') | |
| contest_names = df.Player.unique() | |
| if helper is not None: | |
| helper_names = helper_df.Player.unique() | |
| contest_match_dict = {} | |
| helper_match_dict = {} | |
| for names in contest_names: | |
| match = process.extractOne( | |
| names, | |
| helper_names, | |
| score_cutoff = 85 | |
| ) | |
| if match: | |
| contest_match_dict[names] = match[0] | |
| else: | |
| contest_match_dict[names] = names | |
| for names in helper_names: | |
| match = process.extractOne( | |
| names, | |
| contest_names, | |
| score_cutoff = 85 | |
| ) | |
| if match: | |
| helper_match_dict[names] = match[0] | |
| else: | |
| helper_match_dict[names] = names | |
| for key, value in helper_match_dict.items(): | |
| if key not in contest_match_dict: | |
| contest_match_dict[key] = value | |
| df_helper['Player'] = df_helper['Player'].map(contest_match_dict) | |
| df_helper = df_helper.drop_duplicates(subset='Player', keep='first') | |
| # Create separate dataframes for different player attributes | |
| if helper is not None: | |
| ownership_df = df[['Player', 'Own']] | |
| fpts_df = df[['Player', 'FPTS']] | |
| salary_df = df_helper[['Player', 'Salary']] | |
| team_df = df_helper[['Player', 'Team']] | |
| pos_df = df[['Player', 'Pos']] | |
| else: | |
| 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']] | |
| print('Made it through dictionaries') | |
| # Create the cleaned dataframe with just the essential columns | |
| cleaned_df = df[['BaseName', 'Lineup']] | |
| 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']] | |
| 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', 'UTIL1', 'UTIL2', 'UTIL3', 'UTIL4', 'UTIL5']] = 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', 'UTIL1', 'UTIL2', 'UTIL3', 'UTIL4', 'UTIL5']] | |
| print('Made it through check_lineups') | |
| # Get unique entry names | |
| entry_list = list(set(df['BaseName'].dropna())) | |
| entry_list.sort() | |
| return cleaned_df, ownership_df, fpts_df, entry_list, check_lineups | |
| except Exception as e: | |
| st.error(f'Error loading file: {str(e)}') | |
| return None | |
| return None |