| import pandas as pd | |
| def distribute_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list): | |
| for slack_var in range(1, 20): | |
| concat_portfolio = pd.DataFrame(columns=portfolio.columns) | |
| for finishing_range in range(1, 20): | |
| finish_threshold = (finishing_range / 100) | |
| rows_to_drop = [] | |
| working_portfolio = portfolio.copy() | |
| if finishing_range == 1: | |
| working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] <= finish_threshold] | |
| elif finishing_range == 20: | |
| working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] >= (finish_threshold - 1)] | |
| else: | |
| working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] <= finish_threshold] | |
| working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] >= (finish_threshold - 1)] | |
| working_portfolio = working_portfolio.sort_values(by='median', ascending = False) | |
| working_portfolio = working_portfolio.reset_index(drop=True) | |
| curr_own_type_max = working_portfolio.loc[0, 'Weighted Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Weighted Own']) | |
| for i in range(1, len(working_portfolio)): | |
| if working_portfolio.loc[i, 'Weighted Own'] > curr_own_type_max: | |
| rows_to_drop.append(i) | |
| else: | |
| curr_own_type_max = working_portfolio.loc[i, 'Weighted Own'] + (slack_var / 20 * working_portfolio.loc[i, 'Weighted Own']) | |
| working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True) | |
| concat_portfolio = pd.concat([concat_portfolio, working_portfolio]) | |
| if len(concat_portfolio) >= lineup_target: | |
| return concat_portfolio.sort_values(by='Finish_percentile', ascending=True).head(lineup_target) | |
| return concat_portfolio.sort_values(by='Finish_percentile', ascending=True) | |