Guidelines
Suggest edit

Keep categorical palettes to seven colors or fewer

For category comparison, avoid large color sets on charts that rely on categorical color encoding to improve readability and mitigate repeated legend checking for readers.

  • purpose:refine
  • basis:heuristic
  • data:categorical
  • quality:readability
  • lever:encoding
  • group-cardinality:many
  • polish:palette
  • aesthetic:color:avoid

advice

Categorical palette size

Reduce the number of distinct category colors to seven or fewer when color is the main category code. For example, group categories together or move to a chart form that depends less on many category hues when your chart needs more than seven colors.

reason

Why fewer category colors read faster

Too many category colors make readers spend more effort matching hues to labels instead of reading the data pattern.

Mechanism: Fewer distinct hues lower the memory load of category matching and reduce back-and-forth checking of the color key.

Evidence: The post says that once more than seven colors represent data in a chart, quick reading becomes harder and readers need to consult the color key more often, so grouping categories or using another chart type is recommended (Muth, 2018).

context

When to use this limit

  • User Goal: Compare categories quickly.
  • Data: Many categories are distinguished primarily by color.
  • Chart Setting: The chart relies on a legend or key to decode category hues.
  • Success Criterion: Readers can identify categories without repeated legend lookup.

exceptions

When this limit does not apply

Break it when: Color is not the main way categories are distinguished. Why: The seven-color limit is specifically about charts where readers must tell categories apart by hue.

costs

Tradeoffs of fewer category colors

Sacrifice: You may need to combine smaller categories or simplify the grouping. Risk: Over-grouping can hide distinctions that matter. Mitigation: If grouping loses too much detail, switch to a chart form that does not depend on many category colors.

mistakes

Common palette-size mistake

Mistake: Adding a new hue for every additional category. Why it fails: The chart becomes slower to read and more dependent on the color key.

check

How to check palette size

Failure Sign: The chart uses more than seven data-carrying category colors. Quick Check: Count the distinct category hues in the plotted data, not just the legend. Stronger Test: Ask whether a reader would need to keep returning to the key to identify categories.

fix

How to fix palette size problems

  • Group lower-priority categories together.
  • Remove category colors that do not support the main comparison.
  • Change to a chart form that relies less on many category hues.

References

Muth, L. C. (2018). What to consider when choosing colors for data visualization. https://www.datawrapper.de/blog/colors