umap-learn
Uniform Manifold Approximation and Projection
Decision gist · record as of 2026-08-14
Yes. UMAP is actively maintained, has no known vulnerabilities, low install friction, and a permissive BSD license. It is widely used in scientific and ML workflows. Install it if you need dimension reduction with better global structure preservation, or as a general-purpose manifold learning tool for exploratory analysis and preprocessing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; numba JIT compilation may take time on first run but subsequent calls are fast.
- Low install friction with a pure-Python wheel distribution.
- Requires numba for JIT compilation and dependencies including numpy and scipy, all of which are well-maintained.
License · maintenance · safety
BSD (permissive) — BSD permissive license allows use in commercial and proprietary projects with minimal restrictions; you may use, modify, and distribute with proper attribution.
last release 2026-04-08 (128 days) · last repo commit 2026-08-10 · 8,252 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 7,491,195 downloads/mo, #1,736 on PyPI
Alternatives
Verify before relying
pip install umap-learn
import umap
embedding = umap.UMAP().fit_transform(data)- Whether the package supports GPU acceleration or if computation is CPU-only via numba
- Performance characteristics on datasets with over a million dimensions (mentioned in description but not quantified)
- Whether densMAP mode requires additional configuration or dependencies beyond the base install
What it is and what it does
UMAP is a dimension reduction algorithm that embeds high-dimensional data into lower-dimensional space while preserving manifold structure. It models data as lying on a Riemannian manifold and uses fuzzy topological structure to find embeddings that balance local neighborhood relationships with global structure. The package follows a familiar transformer API, integrating naturally into existing ML pipelines.
The algorithm depends on numpy, scipy, scikit-learn, numba (for performance), pynndescent (for nearest-neighbor search), and tqdm (for progress reporting). It supports sparse matrices, custom distance metrics, and includes densMAP for density-preserving embeddings. You can use it for exploratory visualization, as a preprocessing step before clustering, or as a general-purpose non-linear dimensionality reduction technique.
Use it for
- Visualize high-dimensional datasets (e.g., image pixels, gene expression) in 2D or 3D for exploratory data analysis
- Preprocess data before clustering to improve cluster quality
- Reduce dimensionality as a feature engineering step before training downstream models
- Preserve local density information in embeddings using densMAP for single-cell transcriptomics or similar applications
- Handle sparse, very high-dimensional data where alternatives become computationally prohibitive
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
UMAP is actively maintained, has no known vulnerabilities, low install friction, and a permissive BSD license. It is widely used in scientific and ML workflows. Install it if you need dimension reduction with better global structure preservation, or as a general-purpose manifold learning tool for exploratory analysis and preprocessing.
Install
umap-learn on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Requires numba for JIT compilation and dependencies including numpy and scipy, all of which are well-maintained. Active maintenance with a recent release 128 days ago.
Requires Python 3.9 or later; numba JIT compilation may take time on first run but subsequent calls are fast.
License in practice
BSD permissive license allows use in commercial and proprietary projects with minimal restrictions; you may use, modify, and distribute with proper attribution.
Quickstart
pip install umap-learn
import umap
embedding = umap.UMAP().fit_transform(data)Verify before relying
- Whether the package supports GPU acceleration or if computation is CPU-only via numba
- Performance characteristics on datasets with over a million dimensions (mentioned in description but not quantified)
- Whether densMAP mode requires additional configuration or dependencies beyond the base install
Package facts
| License | BSD permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesnumpyscipyscikit-learnnumbapynndescenttqdm |
| Maintenance | Actively maintained 128 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 7,491,195 / month, #1,736 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI ApprovedOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: CProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: umap_learn-0.5.12-py3-none-any.whl
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