lmcinnes/umap
Uniform Manifold Approximation and Projection
When you have complex datasets with many features, UMAP helps you understand their underlying patterns by reducing the number of dimensions. It takes high-dimensional data, like survey responses or gene expression profiles, and transforms it into a 2D or 3D visualization, making it easier to spot clusters, trends, and relationships. This is ideal for data analysts, researchers, or anyone needing to explore and interpret intricate data visually.
8,114 stars. Used by 79 other packages. Actively maintained with 25 commits in the last 30 days. Available on PyPI.
Use this if you need to visualize the complex structure of high-dimensional data or reduce its dimensions while preserving important relationships for further analysis.
Not ideal if your data is already low-dimensional or if you require an interpretable linear transformation for your specific use case.
Stars
8,114
Forks
860
Language
Python
License
BSD-3-Clause
Category
Last pushed
Mar 10, 2026
Commits (30d)
25
Dependencies
6
Reverse dependents
79
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