import streamlit as st st.set_page_config(layout="wide") import numpy as np import pandas as pd import time from fuzzywuzzy import process import random ## import global functions from global_func.clean_player_name import clean_player_name from global_func.load_file import load_file from global_func.load_ss_file import load_ss_file from global_func.find_name_mismatches import find_name_mismatches from global_func.predict_dupes import predict_dupes from global_func.highlight_rows import highlight_changes, highlight_changes_winners, highlight_changes_losers from global_func.load_csv import load_csv from global_func.find_csv_mismatches import find_csv_mismatches freq_format = {'Finish_percentile': '{:.2%}', 'Lineup Edge': '{:.2%}', 'Win%': '{:.2%}'} player_wrong_names_mlb = ['Enrique Hernandez'] player_right_names_mlb = ['Kike Hernandez'] tab1, tab2, tab3 = st.tabs(["Data Load", "Late Swap", "Manage Portfolio"]) with tab1: if st.button('Clear data', key='reset1'): st.session_state.clear() # Add file uploaders to your app col1, col2, col3 = st.columns(3) with col1: st.subheader("Draftkings/Fanduel CSV") st.info("Upload the player pricing CSV from the site you are playing on. This is used in late swap exporting and/or with SaberSim portfolios, but is not necessary for the portfolio management functions.") upload_csv_col, csv_template_col = st.columns([3, 1]) with upload_csv_col: csv_file = st.file_uploader("Upload CSV File", type=['csv']) if 'csv_file' in st.session_state: del st.session_state['csv_file'] with csv_template_col: csv_template_df = pd.DataFrame(columns=['Name', 'ID', 'Roster Position', 'Salary']) st.download_button( label="CSV Template", data=csv_template_df.to_csv(index=False), file_name="csv_template.csv", mime="text/csv" ) st.session_state['csv_file'] = load_csv(csv_file) try: st.session_state['csv_file']['Salary'] = st.session_state['csv_file']['Salary'].astype(str).str.replace(',', '').astype(int) except: pass if csv_file: st.session_state['csv_file'] = st.session_state['csv_file'].drop_duplicates(subset=['Name']) st.success('Projections file loaded successfully!') st.dataframe(st.session_state['csv_file'].head(10)) with col2: st.subheader("Portfolio File") st.info("Go ahead and upload a portfolio file here. Only include player columns and an optional 'Stack' column if you are playing MLB.") saber_toggle = st.radio("Are you uploading from SaberSim?", options=['No', 'Yes']) st.info("If you are uploading from SaberSim, you will need to upload a CSV file for the slate for name matching.") if saber_toggle == 'Yes': if csv_file is not None: portfolio_file = st.file_uploader("Upload Portfolio File (CSV or Excel)", type=['csv', 'xlsx', 'xls']) if 'portfolio' in st.session_state: del st.session_state['portfolio'] if 'export_portfolio' in st.session_state: del st.session_state['export_portfolio'] else: portfolio_file = st.file_uploader("Upload Portfolio File (CSV or Excel)", type=['csv', 'xlsx', 'xls']) if 'portfolio' in st.session_state: del st.session_state['portfolio'] if 'export_portfolio' in st.session_state: del st.session_state['export_portfolio'] if portfolio_file: if saber_toggle == 'Yes': st.session_state['export_portfolio'], st.session_state['portfolio'] = load_ss_file(portfolio_file, st.session_state['csv_file']) st.session_state['export_portfolio'] = st.session_state['export_portfolio'].dropna(how='all') st.session_state['export_portfolio'] = st.session_state['export_portfolio'].reset_index(drop=True) st.session_state['portfolio'] = st.session_state['portfolio'].dropna(how='all') st.session_state['portfolio'] = st.session_state['portfolio'].reset_index(drop=True) else: st.session_state['export_portfolio'], st.session_state['portfolio'] = load_file(portfolio_file) st.session_state['export_portfolio'] = st.session_state['export_portfolio'].dropna(how='all') st.session_state['export_portfolio'] = st.session_state['export_portfolio'].reset_index(drop=True) st.session_state['portfolio'] = st.session_state['portfolio'].dropna(how='all') st.session_state['portfolio'] = st.session_state['portfolio'].reset_index(drop=True) # Check if Stack column exists in the portfolio if 'Stack' in st.session_state['portfolio'].columns: # Create dictionary mapping index to Stack values stack_dict = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Stack'])) st.write(f"Found {len(stack_dict)} stack assignments") st.session_state['portfolio'] = st.session_state['portfolio'].drop(columns=['Stack']) else: stack_dict = None st.info("No Stack column found in portfolio") if st.session_state['portfolio'] is not None: st.success('Portfolio file loaded successfully!') st.session_state['portfolio'] = st.session_state['portfolio'].apply(lambda x: x.replace(player_wrong_names_mlb, player_right_names_mlb)) st.dataframe(st.session_state['portfolio'].head(10)) with col3: st.subheader("Projections File") st.info("upload a projections file that has 'player_names', 'salary', 'median', 'ownership', and 'captain ownership' (Needed for Showdown) columns. Note that the salary for showdown needs to be the FLEX salary, not the captain salary.") # Create two columns for the uploader and template button upload_col, template_col = st.columns([3, 1]) with upload_col: projections_file = st.file_uploader("Upload Projections File (CSV or Excel)", type=['csv', 'xlsx', 'xls']) if 'projections_df' in st.session_state: del st.session_state['projections_df'] with template_col: # Create empty DataFrame with required columns template_df = pd.DataFrame(columns=['player_names', 'position', 'team', 'salary', 'median', 'ownership', 'captain ownership']) # Add download button for template st.download_button( label="Template", data=template_df.to_csv(index=False), file_name="projections_template.csv", mime="text/csv" ) if projections_file: export_projections, projections = load_file(projections_file) if projections is not None: st.success('Projections file loaded successfully!') projections = projections.apply(lambda x: x.replace(player_wrong_names_mlb, player_right_names_mlb)) st.dataframe(projections.head(10)) if portfolio_file and projections_file: if st.session_state['portfolio'] is not None and projections is not None: st.subheader("Name Matching Analysis") # Initialize projections_df in session state if it doesn't exist if 'projections_df' not in st.session_state: st.session_state['projections_df'] = projections.copy() st.session_state['projections_df']['salary'] = (st.session_state['projections_df']['salary'].astype(str).str.replace(',', '').astype(float).astype(int)) # Update projections_df with any new matches st.session_state['projections_df'] = find_name_mismatches(st.session_state['portfolio'], st.session_state['projections_df']) if csv_file is not None and 'export_dict' not in st.session_state: # Create a dictionary of Name to Name+ID from csv_file try: name_id_map = dict(zip( st.session_state['csv_file']['Name'], st.session_state['csv_file']['Name + ID'] )) except: name_id_map = dict(zip( st.session_state['csv_file']['Nickname'], st.session_state['csv_file']['Id'] )) # Function to find best match def find_best_match(name): best_match = process.extractOne(name, name_id_map.keys()) if best_match and best_match[1] >= 85: # 85% match threshold return name_id_map[best_match[0]] return name # Return original name if no good match found # Apply the matching projections['upload_match'] = projections['player_names'].apply(find_best_match) st.session_state['export_dict'] = dict(zip(projections['player_names'], projections['upload_match'])) with tab2: if st.button('Clear data', key='reset2'): st.session_state.clear() if 'portfolio' in st.session_state and 'projections_df' in st.session_state: optimized_df = None map_dict = { 'pos_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])), 'salary_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])), 'proj_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])), 'own_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])), 'team_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])) } # Calculate new stats for optimized lineups st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply( lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row if player in map_dict['salary_map']), axis=1 ) st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply( lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row if player in map_dict['proj_map']), axis=1 ) st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply( lambda row: sum(map_dict['own_map'].get(player, 0) for player in row if player in map_dict['own_map']), axis=1 ) options_container = st.container() with options_container: col1, col2, col3, col4, col5, col6 = st.columns(6) with col1: curr_site_var = st.selectbox("Select your current site", options=['DraftKings', 'FanDuel']) with col2: curr_sport_var = st.selectbox("Select your current sport", options=['NBA', 'MLB', 'NFL', 'NHL', 'MMA']) with col3: swap_var = st.multiselect("Select late swap strategy", options=['Optimize', 'Increase volatility', 'Decrease volatility']) with col4: remove_teams_var = st.multiselect("What teams have already played?", options=st.session_state['projections_df']['team'].unique()) with col5: winners_var = st.multiselect("Are there any players doing exceptionally well?", options=st.session_state['projections_df']['player_names'].unique(), max_selections=3) with col6: losers_var = st.multiselect("Are there any players doing exceptionally poorly?", options=st.session_state['projections_df']['player_names'].unique(), max_selections=3) if st.button('Clear Late Swap'): if 'optimized_df' in st.session_state: del st.session_state['optimized_df'] map_dict = { 'pos_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])), 'salary_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])), 'proj_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])), 'own_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])), 'team_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])) } # Calculate new stats for optimized lineups st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply( lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row if player in map_dict['salary_map']), axis=1 ) st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply( lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row if player in map_dict['proj_map']), axis=1 ) st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply( lambda row: sum(map_dict['own_map'].get(player, 0) for player in row if player in map_dict['own_map']), axis=1 ) if st.button('Run Late Swap'): st.session_state['portfolio'] = st.session_state['portfolio'].drop(columns=['salary', 'median', 'Own']) if curr_sport_var == 'NBA': if curr_site_var == 'DraftKings': st.session_state['portfolio'] = st.session_state['portfolio'].set_axis(['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'], axis=1) else: st.session_state['portfolio'] = st.session_state['portfolio'].set_axis(['PG', 'PG', 'SG', 'SG', 'SF', 'SF', 'PF', 'PF', 'C'], axis=1) # Define roster position rules if curr_site_var == 'DraftKings': position_rules = { 'PG': ['PG'], 'SG': ['SG'], 'SF': ['SF'], 'PF': ['PF'], 'C': ['C'], 'G': ['PG', 'SG'], 'F': ['SF', 'PF'], 'UTIL': ['PG', 'SG', 'SF', 'PF', 'C'] } else: position_rules = { 'PG': ['PG'], 'SG': ['SG'], 'SF': ['SF'], 'PF': ['PF'], 'C': ['C'], } # Create position groups from projections data position_groups = {} for _, player in st.session_state['projections_df'].iterrows(): positions = player['position'].split('/') for pos in positions: if pos not in position_groups: position_groups[pos] = [] position_groups[pos].append({ 'player_names': player['player_names'], 'salary': player['salary'], 'median': player['median'], 'ownership': player['ownership'], 'positions': positions # Store all eligible positions }) def optimize_lineup(row): current_lineup = [] total_salary = 0 if curr_site_var == 'DraftKings': salary_cap = 50000 else: salary_cap = 60000 used_players = set() # Convert row to dictionary with roster positions roster = {} for col, player in zip(row.index, row): if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']: roster[col] = { 'name': player, 'position': map_dict['pos_map'].get(player, '').split('/'), 'team': map_dict['team_map'].get(player, ''), 'salary': map_dict['salary_map'].get(player, 0), 'median': map_dict['proj_map'].get(player, 0), 'ownership': map_dict['own_map'].get(player, 0) } total_salary += roster[col]['salary'] used_players.add(player) # Optimize each roster position in random order roster_positions = list(roster.items()) random.shuffle(roster_positions) for roster_pos, current in roster_positions: # Skip optimization for players from removed teams if current['team'] in remove_teams_var: continue valid_positions = position_rules[roster_pos] better_options = [] # Find valid replacements for this roster position for pos in valid_positions: if pos in position_groups: pos_options = [ p for p in position_groups[pos] if p['median'] > current['median'] and (total_salary - current['salary'] + p['salary']) <= salary_cap and p['player_names'] not in used_players and any(valid_pos in p['positions'] for valid_pos in valid_positions) and map_dict['team_map'].get(p['player_names']) not in remove_teams_var # Check team restriction ] better_options.extend(pos_options) if better_options: # Remove duplicates better_options = {opt['player_names']: opt for opt in better_options}.values() # Sort by median projection and take the best one best_replacement = max(better_options, key=lambda x: x['median']) # Update the lineup and tracking variables used_players.remove(current['name']) used_players.add(best_replacement['player_names']) total_salary = total_salary - current['salary'] + best_replacement['salary'] roster[roster_pos] = { 'name': best_replacement['player_names'], 'position': map_dict['pos_map'][best_replacement['player_names']].split('/'), 'team': map_dict['team_map'][best_replacement['player_names']], 'salary': best_replacement['salary'], 'median': best_replacement['median'], 'ownership': best_replacement['ownership'] } # Return optimized lineup maintaining original column order return [roster[pos]['name'] for pos in row.index if pos in roster] def optimize_lineup_winners(row): current_lineup = [] total_salary = 0 if curr_site_var == 'DraftKings': salary_cap = 50000 else: salary_cap = 60000 used_players = set() # Check if any winners are in the lineup and count them winners_in_lineup = sum(1 for player in row if player in winners_var) changes_needed = min(winners_in_lineup, 3) if winners_in_lineup > 0 else 0 changes_made = 0 # Convert row to dictionary with roster positions roster = {} for col, player in zip(row.index, row): if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']: roster[col] = { 'name': player, 'position': map_dict['pos_map'].get(player, '').split('/'), 'team': map_dict['team_map'].get(player, ''), 'salary': map_dict['salary_map'].get(player, 0), 'median': map_dict['proj_map'].get(player, 0), 'ownership': map_dict['own_map'].get(player, 0) } total_salary += roster[col]['salary'] used_players.add(player) # Only proceed with ownership-based optimization if we have winners in the lineup if changes_needed > 0: # Randomize the order of positions to optimize roster_positions = list(roster.items()) random.shuffle(roster_positions) for roster_pos, current in roster_positions: # Stop if we've made enough changes if changes_made >= changes_needed: break # Skip optimization for players from removed teams or if the current player is a winner if current['team'] in remove_teams_var or current['name'] in winners_var: continue valid_positions = list(position_rules[roster_pos]) random.shuffle(valid_positions) better_options = [] # Find valid replacements with higher ownership for pos in valid_positions: if pos in position_groups: pos_options = [ p for p in position_groups[pos] if p['ownership'] > current['ownership'] and p['median'] >= current['median'] - 3 and (total_salary - current['salary'] + p['salary']) <= salary_cap and (total_salary - current['salary'] + p['salary']) >= salary_cap - 1000 and p['player_names'] not in used_players and any(valid_pos in p['positions'] for valid_pos in valid_positions) and map_dict['team_map'].get(p['player_names']) not in remove_teams_var ] better_options.extend(pos_options) if better_options: # Remove duplicates better_options = {opt['player_names']: opt for opt in better_options}.values() # Sort by ownership and take the highest owned option best_replacement = max(better_options, key=lambda x: x['ownership']) # Update the lineup and tracking variables used_players.remove(current['name']) used_players.add(best_replacement['player_names']) total_salary = total_salary - current['salary'] + best_replacement['salary'] roster[roster_pos] = { 'name': best_replacement['player_names'], 'position': map_dict['pos_map'][best_replacement['player_names']].split('/'), 'team': map_dict['team_map'][best_replacement['player_names']], 'salary': best_replacement['salary'], 'median': best_replacement['median'], 'ownership': best_replacement['ownership'] } changes_made += 1 # Return optimized lineup maintaining original column order return [roster[pos]['name'] for pos in row.index if pos in roster] def optimize_lineup_losers(row): current_lineup = [] total_salary = 0 if curr_site_var == 'DraftKings': salary_cap = 50000 else: salary_cap = 60000 used_players = set() # Check if any winners are in the lineup and count them losers_in_lineup = sum(1 for player in row if player in losers_var) changes_needed = min(losers_in_lineup, 3) if losers_in_lineup > 0 else 0 changes_made = 0 # Convert row to dictionary with roster positions roster = {} for col, player in zip(row.index, row): if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']: roster[col] = { 'name': player, 'position': map_dict['pos_map'].get(player, '').split('/'), 'team': map_dict['team_map'].get(player, ''), 'salary': map_dict['salary_map'].get(player, 0), 'median': map_dict['proj_map'].get(player, 0), 'ownership': map_dict['own_map'].get(player, 0) } total_salary += roster[col]['salary'] used_players.add(player) # Only proceed with ownership-based optimization if we have winners in the lineup if changes_needed > 0: # Randomize the order of positions to optimize roster_positions = list(roster.items()) random.shuffle(roster_positions) for roster_pos, current in roster_positions: # Stop if we've made enough changes if changes_made >= changes_needed: break # Skip optimization for players from removed teams or if the current player is a winner if current['team'] in remove_teams_var or current['name'] in losers_var: continue valid_positions = list(position_rules[roster_pos]) random.shuffle(valid_positions) better_options = [] # Find valid replacements with higher ownership for pos in valid_positions: if pos in position_groups: pos_options = [ p for p in position_groups[pos] if p['ownership'] < current['ownership'] and p['median'] >= current['median'] - 3 and (total_salary - current['salary'] + p['salary']) <= salary_cap and (total_salary - current['salary'] + p['salary']) >= salary_cap - 1000 and p['player_names'] not in used_players and any(valid_pos in p['positions'] for valid_pos in valid_positions) and map_dict['team_map'].get(p['player_names']) not in remove_teams_var ] better_options.extend(pos_options) if better_options: # Remove duplicates better_options = {opt['player_names']: opt for opt in better_options}.values() # Sort by ownership and take the highest owned option best_replacement = max(better_options, key=lambda x: x['ownership']) # Update the lineup and tracking variables used_players.remove(current['name']) used_players.add(best_replacement['player_names']) total_salary = total_salary - current['salary'] + best_replacement['salary'] roster[roster_pos] = { 'name': best_replacement['player_names'], 'position': map_dict['pos_map'][best_replacement['player_names']].split('/'), 'team': map_dict['team_map'][best_replacement['player_names']], 'salary': best_replacement['salary'], 'median': best_replacement['median'], 'ownership': best_replacement['ownership'] } changes_made += 1 # Return optimized lineup maintaining original column order return [roster[pos]['name'] for pos in row.index if pos in roster] # Create a progress bar progress_bar = st.progress(0) status_text = st.empty() # Process each lineup optimized_lineups = [] total_lineups = len(st.session_state['portfolio']) for idx, row in st.session_state['portfolio'].iterrows(): # First optimization pass first_pass = optimize_lineup(row) first_pass_series = pd.Series(first_pass, index=row.index) second_pass = optimize_lineup(first_pass_series) second_pass_series = pd.Series(second_pass, index=row.index) third_pass = optimize_lineup(second_pass_series) third_pass_series = pd.Series(third_pass, index=row.index) fourth_pass = optimize_lineup(third_pass_series) fourth_pass_series = pd.Series(fourth_pass, index=row.index) fifth_pass = optimize_lineup(fourth_pass_series) fifth_pass_series = pd.Series(fifth_pass, index=row.index) # Second optimization pass final_lineup = optimize_lineup(fifth_pass_series) optimized_lineups.append(final_lineup) if 'Optimize' in swap_var: progress = (idx + 1) / total_lineups progress_bar.progress(progress) status_text.text(f'Optimizing Lineups {idx + 1} of {total_lineups}') else: pass # Create new dataframe with optimized lineups if 'Optimize' in swap_var: st.session_state['optimized_df_medians'] = pd.DataFrame(optimized_lineups, columns=st.session_state['portfolio'].columns) else: st.session_state['optimized_df_medians'] = st.session_state['portfolio'] # Create a progress bar progress_bar_winners = st.progress(0) status_text_winners = st.empty() # Process each lineup optimized_lineups_winners = [] total_lineups = len(st.session_state['optimized_df_medians']) for idx, row in st.session_state['optimized_df_medians'].iterrows(): final_lineup = optimize_lineup_winners(row) optimized_lineups_winners.append(final_lineup) if 'Decrease volatility' in swap_var: progress_winners = (idx + 1) / total_lineups progress_bar_winners.progress(progress_winners) status_text_winners.text(f'Lowering Volatility around Winners {idx + 1} of {total_lineups}') else: pass # Create new dataframe with optimized lineups if 'Decrease volatility' in swap_var: st.session_state['optimized_df_winners'] = pd.DataFrame(optimized_lineups_winners, columns=st.session_state['optimized_df_medians'].columns) else: st.session_state['optimized_df_winners'] = st.session_state['optimized_df_medians'] # Create a progress bar progress_bar_losers = st.progress(0) status_text_losers = st.empty() # Process each lineup optimized_lineups_losers = [] total_lineups = len(st.session_state['optimized_df_winners']) for idx, row in st.session_state['optimized_df_winners'].iterrows(): final_lineup = optimize_lineup_losers(row) optimized_lineups_losers.append(final_lineup) if 'Increase volatility' in swap_var: progress_losers = (idx + 1) / total_lineups progress_bar_losers.progress(progress_losers) status_text_losers.text(f'Increasing Volatility around Losers {idx + 1} of {total_lineups}') else: pass # Create new dataframe with optimized lineups if 'Increase volatility' in swap_var: st.session_state['optimized_df'] = pd.DataFrame(optimized_lineups_losers, columns=st.session_state['optimized_df_winners'].columns) else: st.session_state['optimized_df'] = st.session_state['optimized_df_winners'] # Calculate new stats for optimized lineups st.session_state['optimized_df']['salary'] = st.session_state['optimized_df'].apply( lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row if player in map_dict['salary_map']), axis=1 ) st.session_state['optimized_df']['median'] = st.session_state['optimized_df'].apply( lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row if player in map_dict['proj_map']), axis=1 ) st.session_state['optimized_df']['Own'] = st.session_state['optimized_df'].apply( lambda row: sum(map_dict['own_map'].get(player, 0) for player in row if player in map_dict['own_map']), axis=1 ) # Display results st.success('Optimization complete!') if 'optimized_df' in st.session_state: st.write("Increase in median highlighted in yellow, descrease in volatility highlighted in blue, increase in volatility highlighted in red:") st.dataframe( st.session_state['optimized_df'].style .apply(highlight_changes, axis=1) .apply(highlight_changes_winners, axis=1) .apply(highlight_changes_losers, axis=1) .background_gradient(axis=0) .background_gradient(cmap='RdYlGn') .format(precision=2), height=1000, use_container_width=True ) # Option to download optimized lineups if st.button('Prepare Late Swap Export'): export_df = st.session_state['optimized_df'].copy() # Map player names to their export IDs for all player columns for col in export_df.columns: if col not in ['salary', 'median', 'Own']: export_df[col] = export_df[col].map(st.session_state['export_dict']) csv = export_df.to_csv(index=False) st.download_button( label="Download CSV", data=csv, file_name="optimized_lineups.csv", mime="text/csv" ) else: st.write("Current Portfolio") st.dataframe( st.session_state['portfolio'].style .background_gradient(axis=0) .background_gradient(cmap='RdYlGn') .format(precision=2), height=1000, use_container_width=True ) with tab3: if st.button('Clear data', key='reset3'): st.session_state.clear() if 'portfolio' in st.session_state and 'projections_df' in st.session_state: col1, col2, col3 = st.columns([1, 8, 1]) excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Win%', 'Lineup Edge'] with col1: site_var = st.selectbox("Select Site", ['Draftkings', 'Fanduel']) sport_var = st.selectbox("Select Sport", ['NFL', 'MLB', 'NBA', 'NHL', 'MMA']) st.info("It currently does not matter what sport you select, it may matter in the future.") type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown']) Contest_Size = st.number_input("Enter Contest Size", value=25000, min_value=1, step=1) strength_var = st.selectbox("Select field strength", ['Average', 'Sharp', 'Weak']) if site_var == 'Draftkings': if type_var == 'Classic': map_dict = { 'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])), 'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])), 'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])), 'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])), 'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])), 'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))), 'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])), 'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)), 'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership'])) } elif type_var == 'Showdown': if sport_var == 'NFL': map_dict = { 'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])), 'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])), 'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])), 'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])), 'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])), 'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))), 'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'] * 1.5)), 'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)), 'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership'])) } elif sport_var != 'NFL': map_dict = { 'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])), 'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])), 'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'] / 1.5)), 'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])), 'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])), 'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))), 'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])), 'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)), 'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership'])) } elif site_var == 'Fanduel': map_dict = { 'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])), 'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])), 'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])), 'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])), 'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])), 'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))), 'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])), 'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)), 'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership'])) } if type_var == 'Classic': st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1) st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1) st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1) if stack_dict is not None: st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].index.map(stack_dict) elif type_var == 'Showdown': # Calculate salary (CPT uses cpt_salary_map, others use salary_map) st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply( lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]), axis=1 ) # Calculate median (CPT uses cpt_proj_map, others use proj_map) st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply( lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) + sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]), axis=1 ) # Calculate ownership (CPT uses cpt_own_map, others use own_map) st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply( lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) + sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]), axis=1 ) with col3: with st.form(key='filter_form'): max_dupes = st.number_input("Max acceptable dupes?", value=1000, min_value=1, step=1) min_salary = st.number_input("Min acceptable salary?", value=1000, min_value=1000, step=100) max_salary = st.number_input("Max acceptable salary?", value=60000, min_value=1000, step=100) max_finish_percentile = st.number_input("Max acceptable finish percentile?", value=.50, min_value=0.005, step=.001) player_names = set() for col in st.session_state['portfolio'].columns: if col not in excluded_cols: player_names.update(st.session_state['portfolio'][col].unique()) player_lock = st.multiselect("Lock players?", options=sorted(list(player_names)), default=[]) player_remove = st.multiselect("Remove players?", options=sorted(list(player_names)), default=[]) if stack_dict is not None: stack_toggle = st.selectbox("Include specific stacks?", options=['All Stacks', 'Specific Stacks'], index=0) stack_selections = st.multiselect("If Specific Stacks, Which to include?", options=sorted(list(set(stack_dict.values()))), default=[]) stack_remove = st.multiselect("If Specific Stacks, Which to remove?", options=sorted(list(set(stack_dict.values()))), default=[]) submitted = st.form_submit_button("Submit") with col2: st.session_state['portfolio'] = predict_dupes(st.session_state['portfolio'], map_dict, site_var, type_var, Contest_Size, strength_var) st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Dupes'] <= max_dupes] st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] >= min_salary] st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] <= max_salary] st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Finish_percentile'] <= max_finish_percentile] if stack_dict is not None: if stack_toggle == 'All Stacks': st.session_state['portfolio'] = st.session_state['portfolio'] st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)] else: st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Stack'].isin(stack_selections)] st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)] if player_remove: # Create mask for lineups that contain any of the removed players player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols] remove_mask = st.session_state['portfolio'][player_columns].apply( lambda row: not any(player in list(row) for player in player_remove), axis=1 ) st.session_state['portfolio'] = st.session_state['portfolio'][remove_mask] if player_lock: # Create mask for lineups that contain all locked players player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols] lock_mask = st.session_state['portfolio'][player_columns].apply( lambda row: all(player in list(row) for player in player_lock), axis=1 ) st.session_state['portfolio'] = st.session_state['portfolio'][lock_mask] export_file = st.session_state['portfolio'].copy() st.session_state['portfolio'] = st.session_state['portfolio'].sort_values(by='median', ascending=False) if csv_file is not None: player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols] for col in player_columns: export_file[col] = export_file[col].map(st.session_state['export_dict']) with st.expander("Download options"): if stack_dict is not None: with st.form(key='stack_form'): st.subheader("Stack Count Adjustments") st.info("This allows you to fine tune the stacks that you wish to export. If you want to make sure you don't export any of a specific stack you can 0 it out.") # Create a container for stack value inputs sort_container = st.container() with sort_container: sort_var = st.selectbox("Sort export portfolio by:", options=['median', 'Lineup Edge', 'Own']) # Get unique stack values unique_stacks = sorted(list(set(stack_dict.values()))) # Create a dictionary to store stack multipliers if 'stack_multipliers' not in st.session_state: st.session_state.stack_multipliers = {stack: 0.0 for stack in unique_stacks} # Create columns for the stack inputs num_cols = 6 # Number of columns to display for i in range(0, len(unique_stacks), num_cols): cols = st.columns(num_cols) for j, stack in enumerate(unique_stacks[i:i+num_cols]): with cols[j]: # Create a unique key for each number input key = f"stack_count_{stack}" # Get the current count of this stack in the portfolio current_stack_count = len(st.session_state['portfolio'][st.session_state['portfolio']['Stack'] == stack]) # Create number input with current value and max value based on actual count st.session_state.stack_multipliers[stack] = st.number_input( f"{stack} count", min_value=0.0, max_value=float(current_stack_count), value=0.0, step=1.0, key=key ) # Create a copy of the portfolio portfolio_copy = st.session_state['portfolio'].copy() # Create a list to store selected rows selected_rows = [] # For each stack, select the top N rows based on the count value for stack in unique_stacks: if stack in st.session_state.stack_multipliers: count = int(st.session_state.stack_multipliers[stack]) # Get rows for this stack stack_rows = portfolio_copy[portfolio_copy['Stack'] == stack] # Sort by median and take top N rows top_rows = stack_rows.nlargest(count, sort_var) selected_rows.append(top_rows) # Combine all selected rows portfolio_copy = pd.concat(selected_rows) # Update export_file with filtered data export_file = portfolio_copy.copy() for col in export_file.columns: if col not in excluded_cols: export_file[col] = export_file[col].map(st.session_state['export_dict']) submitted = st.form_submit_button("Submit") if submitted: st.write('Export portfolio updated!') st.download_button(label="Download Portfolio", data=export_file.to_csv(index=False), file_name="portfolio.csv", mime="text/csv") # Display the paginated dataframe first st.dataframe( st.session_state['portfolio'].style .background_gradient(axis=0) .background_gradient(cmap='RdYlGn') .background_gradient(cmap='RdYlGn_r', subset=['Finish_percentile', 'Own', 'Dupes']) .format(freq_format, precision=2), height=1000, use_container_width=True ) # Add pagination controls below the dataframe total_rows = len(st.session_state['portfolio']) rows_per_page = 500 total_pages = (total_rows + rows_per_page - 1) // rows_per_page # Ceiling division # Initialize page number in session state if not exists if 'current_page' not in st.session_state: st.session_state.current_page = 1 # Display current page range info and pagination control in a single line st.write( f"Showing rows {(st.session_state.current_page - 1) * rows_per_page + 1} " f"to {min(st.session_state.current_page * rows_per_page, total_rows)} of {total_rows}" ) # Add page number input st.session_state.current_page = st.number_input( f"Page (1-{total_pages})", min_value=1, max_value=total_pages, value=st.session_state.current_page ) # Calculate start and end indices for current page start_idx = (st.session_state.current_page - 1) * rows_per_page end_idx = min(start_idx + rows_per_page, total_rows) # Get the subset of data for the current page current_page_data = st.session_state['portfolio'].iloc[start_idx:end_idx] # Create player summary dataframe player_stats = [] player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols] if type_var == 'Showdown': # Handle Captain positions for player in player_names: # Create mask for lineups where this player is Captain (first column) cpt_mask = st.session_state['portfolio'][player_columns[0]] == player if cpt_mask.any(): player_stats.append({ 'Player': f"{player} (CPT)", 'Lineup Count': cpt_mask.sum(), 'Avg Median': st.session_state['portfolio'][cpt_mask]['median'].mean(), 'Avg Own': st.session_state['portfolio'][cpt_mask]['Own'].mean(), 'Avg Dupes': st.session_state['portfolio'][cpt_mask]['Dupes'].mean(), 'Avg Finish %': st.session_state['portfolio'][cpt_mask]['Finish_percentile'].mean(), 'Avg Lineup Edge': st.session_state['portfolio'][cpt_mask]['Lineup Edge'].mean(), }) # Create mask for lineups where this player is FLEX (other columns) flex_mask = st.session_state['portfolio'][player_columns[1:]].apply( lambda row: player in list(row), axis=1 ) if flex_mask.any(): player_stats.append({ 'Player': f"{player} (FLEX)", 'Lineup Count': flex_mask.sum(), 'Avg Median': st.session_state['portfolio'][flex_mask]['median'].mean(), 'Avg Own': st.session_state['portfolio'][flex_mask]['Own'].mean(), 'Avg Dupes': st.session_state['portfolio'][flex_mask]['Dupes'].mean(), 'Avg Finish %': st.session_state['portfolio'][flex_mask]['Finish_percentile'].mean(), 'Avg Lineup Edge': st.session_state['portfolio'][flex_mask]['Lineup Edge'].mean(), }) else: # Original Classic format processing for player in player_names: player_mask = st.session_state['portfolio'][player_columns].apply( lambda row: player in list(row), axis=1 ) if player_mask.any(): player_stats.append({ 'Player': player, 'Lineup Count': player_mask.sum(), 'Avg Median': st.session_state['portfolio'][player_mask]['median'].mean(), 'Avg Own': st.session_state['portfolio'][player_mask]['Own'].mean(), 'Avg Dupes': st.session_state['portfolio'][player_mask]['Dupes'].mean(), 'Avg Finish %': st.session_state['portfolio'][player_mask]['Finish_percentile'].mean(), 'Avg Lineup Edge': st.session_state['portfolio'][player_mask]['Lineup Edge'].mean(), }) player_summary = pd.DataFrame(player_stats) player_summary = player_summary.sort_values('Lineup Count', ascending=False) st.subheader("Player Summary") st.dataframe( player_summary.style .background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Avg Finish %', 'Avg Own', 'Avg Dupes']) .format({ 'Avg Median': '{:.2f}', 'Avg Own': '{:.2f}', 'Avg Dupes': '{:.2f}', 'Avg Finish %': '{:.2%}', 'Avg Lineup Edge': '{:.2%}' }), height=400, use_container_width=True )