Resources for data visualisation in Python
- Included in
matplotlib, but the best documentation is for R: https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html - Perceptually uniform, striking appearance
- Available as Python package for use in
matplotlib:https://matplotlib.org/cmocean/ - Beautiful perceptually uniform palettes, aimed particularly at water-themed applications
- Available as Python package for use in
matplotlib: https://colorcet.holoviz.org/user_guide/ - Extensive collection of perceptually uniform, categorical and cyclical palettes
- Available as Python package for use in
matplotlib: https://www.fabiocrameri.ch/colourmaps/ - Perceptually uniform, diverging and cyclical palettes
- Included in
matplotlib; browsable here: https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3 - Designed for cartographic applications
- Good categorical options
- Upload an image to test accessibility for various forms of colourblindness: https://www.color-blindness.com/coblis-color-blindness-simulator/
Updating this wiki: If you notice anything incorrect or out of date in this wiki, please feel free to make an edit!
License: All code in this repository is licensed under the Apache License, Version 2.0. Digital Earth Australia data is licensed under the Creative Commons by Attribution 4.0 license.
Contact: If you need assistance with any of the Jupyter Notebooks or Python code in this repository, please post a question on the Open Data Cube Discord chat or on the GIS Stack Exchange using the open-data-cube tag (you can view previously asked questions here). If you would like to report an issue with any notebook, you can file one on Github.