---
id: colorcet
version: "3.2.1"
license: CC-BY-4.0
license_treatment: unclear
maintenance: active
---

# colorcet — Collection of perceptually uniform colormaps

License: unclear · Maintenance: active · Downloads: 2.6M/mo

## What it is and what it does

Colorcet is a curated library of perceptually uniform colormaps built on research by Peter Kovesi at the Center for Exploration Targeting. It provides over 100 named colormaps optimized for scientific visualization, where color perception remains consistent across the full range of values—avoiding the perceptual distortions that can mislead viewers in standard colormaps.

The package integrates directly with popular Python plotting libraries (matplotlib, bokeh, holoviews, datashader) and requires no external dependencies beyond Python itself. It's actively maintained, supports modern Python versions, and is designed as a drop-in replacement for standard colormap collections when perceptual uniformity matters for your visualization.

Use it for:

- Create scientific plots where color gradients must represent data magnitude accurately without perceptual bias.
- Build interactive dashboards with bokeh or holoviews using consistent, research-backed color schemes.
- Visualize large datasets with datashader using colormaps optimized for high-density point rendering.
- Generate publication-quality figures in matplotlib where colormap choice affects reader interpretation.
- Ensure accessibility in visualizations by selecting colormaps designed for perceptual uniformity across viewers.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Colorcet provides a collection of perceptually uniform colormaps designed for scientific visualization with Python plotting libraries like matplotlib, bokeh, holoviews, and datashader.

Yes. Colorcet is actively maintained, has zero known vulnerabilities, installs with no dependencies, and supports current Python versions. The only caveat is verifying CC-BY-4.0 licensing terms for your specific use case—if you're using it for visualization in research, education, or open-source work, it's a straightforward choice.

## Install

pip install colorcet
uv add colorcet
poetry add colorcet

## Installing colorcet

Before you install: Low install friction with no runtime dependencies. Active maintenance with a recent release 108 days ago and ongoing commits; supports current Python versions 3.10 through 3.14.

License in practice: Licensed under CC-BY-4.0 (Creative Commons Attribution 4.0). The license treatment is marked unclear in the metadata, so you should verify the specific terms for your use case, particularly if redistributing or modifying the colormaps.

Quickstart:

pip install colorcet

import colorcet as cc
# Access a colormap by name
colormap = cc.cm.viridis

Requires Python 3.10 or greater.

Verify before relying:

- Whether CC-BY-4.0 licensing applies to the colormap data itself or only to documentation/code, and what attribution is required for use in applications

## Package facts

- License: CC-BY-4.0 (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 2.6M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

perceptually uniform colormaps, scientific visualization colors, matplotlib colormap collection, bokeh color schemes, datashader colormaps, uniform color palettes, visualization color maps, visualization, scientific-computing, color-science

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