Key Principles for Choosing Categorical Colors

Is there enough contrast? Can we actually see the data?
- Sometimes the contrast of the color against the background is so weak that it is difficult to see
- This problem can be exacerbated when printed to paper or displayed in a different media format
Are your colors easily distinguishable?
- Color blindness: To use colors that are distinguishable for all viewers, we must make sure our graphs/figures are clear for those who are color-blind
  - Color blind individuals can have a tough time distinguishing reds and greens, so we should be careful when utilizing these "Christmas colors"
- Media: will our visualization be viewed online (mobile or desktop)? In print (color or greyscale)?
Do you have too many categories/colors?
- General rule, the fewer the categories/colors, the easier your visualization becomes to understand
- More than 5-7 colors? —> you should ask —> How can I reduce the number of categories?

Practical Tips for Categorical Colors

Print your figures
- Regular print: Controls for many issues with contrast when and a figure is translated to a professional setting
- Printing in greyscale: this helps control for even more situations like color blind viewers and if someone transfers your visualization to a new format (like greyscale)
Use services that pick colors for you
- colorbrewer2.org
- scicolor: a Python package for selecting a handful of color maps that make it easy to return color maps for specific types of data. This package was created by Kaicheng Yang.