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import pandas as pd
import numpy as np
def stratification_function(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list, sport: str, sorting_choice: str, low_threshold: float, high_threshold: float):
excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity']
player_columns = [col for col in portfolio.columns if col not in excluded_cols]
# Work with indices instead of copying entire DataFrame
if sorting_choice == 'Finish_percentile':
sorted_indices = portfolio[sorting_choice].sort_values(ascending=True).index
else:
sorted_indices = portfolio[sorting_choice].sort_values(ascending=False).index
# Calculate quantiles without copying
similarity_floor = portfolio[sorting_choice].quantile(low_threshold / 100)
similarity_ceiling = portfolio[sorting_choice].quantile(high_threshold / 100)
# Create evenly spaced target similarity scores
target_similarities = np.linspace(similarity_floor, similarity_ceiling, lineup_target)
# Find the closest lineup to each target similarity score
selected_indices = []
for target_sim in target_similarities:
# Find the index of the closest similarity score
closest_idx = (portfolio[sorting_choice] - target_sim).abs().idxmin()
if closest_idx not in selected_indices: # Avoid duplicates
selected_indices.append(closest_idx)
# Return view instead of copy
return portfolio.loc[selected_indices].sort_values(by=sorting_choice, ascending=False)
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