{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Computes the Pareto (non-dominated) set from multi-dimensional data, identifying points that are not worse than any other point across all objectives.","skillfed_tags":["optimization","multi-objective","data-filtering"],"use_cases":["Filter hotel listings to show only those that are cheap or close to the beach, eliminating dominated options","Identify top-performing salespeople with high sales relative to salary within each department","Extract non-dominated solutions from a multi-objective optimization run to show the efficient frontier","Analyze product features to find models that are not strictly worse than competitors on all dimensions","Rank candidates in hiring by eliminating those who are strictly dominated on all evaluation criteria"],"what_it_does":"paretoset identifies the subset of observations that represent the best trade-offs across multiple competing objectives. Given a dataset with multiple dimensions, it returns a boolean mask marking which rows are not dominated by any other row\u2014that is, rows where you cannot improve one objective without worsening another. The package wraps numba, numpy, and pandas to provide efficient computation on tabular data.\n\nTypical use cases include finding hotels that balance price and distance, identifying employees with strong performance-to-salary ratios, or filtering optimization solutions to show only Pareto-efficient candidates. You specify the optimization sense for each dimension (minimize, maximize, or ignore) and receive a mask you can use to filter your original data.","worth_installing":"Yes, if you need to filter multi-dimensional data for non-dominated points. The package is stable, has no known vulnerabilities, and installs without friction. The aging maintenance status (446 days since last release) is a minor concern but not a blocker for a mature, focused tool. Install it if your workflow involves skyline queries or Pareto front extraction."},"id":"paretoset","links":{"html":"https://skillfed.io/packages/paretoset","md":"https://skillfed.io/packages/paretoset.md","pypi":"https://pypi.org/project/paretoset/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-05-25","license_spdx":null,"license_treatment":"permissive","name":"paretoset","python_support":"supports_current","summary":"Compute the Pareto (non-dominated) set, i.e., skyline operator/query."},"popularity":{"monthly_downloads":108201,"position":12570,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.2.5"}