What it does
UMAP is a method for dimension reduction: it takes data with many measurements per item and squeezes it into two or three numbers, so similar items end up near each other on a chart.
It is often used to visualise data and the inner workings of machine learning models, much like the older t-SNE method. It works with the popular scikit-learn library.
The package also includes densMAP, a variant that keeps more information about how tightly packed the data is.
Who it's for
Data scientists and researchers exploring or visualising large datasets.
Can I use it without coding?
Developers only. It is a Python library used in code. New to this? Start here.
Can I run it?
- Cost
- FreeNothing to pay or sign up for.
- Runs on
- Mac, Windows, Linux
- Computer
- An ordinary computer is fine
- Privacy
- Runs on your own device: your data stays with you
- API key
- Not needed
Try it
Using Claude Code, Cursor or another AI coding assistant? Paste the prompt and it will walk you through the setup.New to this?
Before you install
- Only download from the official GitHub page. Copies with similar names exist for many popular projects.
- License (BSD-3-Clause): Permissive: free to use, change and share, including for business.
- More on staying safe
Where people are talking about it
- UMAP: Uniform Manifold Approximation and Projection for Dimension ReductionResearch paper · Feb 2018