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James McCool
Implement vectorized calculations for salary, median, and ownership in app.py to enhance performance and memory efficiency. Refactor reassess_edge and stratification_function to minimize DataFrame copies and improve memory management. Update filtering logic to use boolean masks for better efficiency.
7eef51a
| 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) | |