paretoset
Compute the Pareto (non-dominated) set, i.e., skyline operator/query.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a pure Python wheel.
- Maintenance status is aging—last release was 446 days ago—but the repository remains active and the package is marked Production/Stable.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal attribution requirements.
last release 2025-05-25 (446 days) · last repo commit 2025-05-25 · 69 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 108,201 downloads/mo, #12,570 on PyPI
Alternatives
Verify before relying
pip install paretoset
from paretoset import paretoset
import pandas as pd
hotels = pd.DataFrame({"price": [50, 53, 62], "distance": [13, 21, 19]})
mask = paretoset(hotels, sense=["min", "min"])
pareto_hotels = hotels[mask]- Whether performance scales acceptably for datasets with thousands of objectives or millions of rows
- Whether the aging maintenance status (446 days since last release) signals active development or dormancy
What it is and 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—that 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.
Typical 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.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
paretoset on PyPI
Before you install
Low install friction with a pure Python wheel. Maintenance status is aging—last release was 446 days ago—but the repository remains active and the package is marked Production/Stable.
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal attribution requirements.
Quickstart
pip install paretoset
from paretoset import paretoset
import pandas as pd
hotels = pd.DataFrame({"price": [50, 53, 62], "distance": [13, 21, 19]})
mask = paretoset(hotels, sense=["min", "min"])
pareto_hotels = hotels[mask]Verify before relying
- Whether performance scales acceptably for datasets with thousands of objectives or millions of rows
- Whether the aging maintenance status (446 days since last release) signals active development or dormancy
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesnumbanumpypandas |
| Maintenance | Aging 446 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 108,201 / month, #12,570 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9 |
Evidence: paretoset-1.2.5-py3-none-any.whl
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