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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)
lower_threshold = ((finishing_range - 1) / 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'] >= lower_threshold]
else:
working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] <= finish_threshold]
working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] >= lower_threshold]
working_portfolio = working_portfolio.sort_values(by='median', ascending = False)
working_portfolio = working_portfolio.reset_index(drop=True)
if len(working_portfolio) == 0:
continue
elif len(working_portfolio) >= 1:
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.head(10)])
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)
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