Color Choice: Picking Palettes with Purpose
Great visualizations don’t use color just for decoration — they use it to clarify, emphasize, and guide. Effective color choices make your plots readable, accurate, and inclusive. Choosing colors thoughtfully helps avoid misleading impressions and highlights the patterns that matter most.
This no-code lesson focusses on how to choose colors with both purpose and accessibility in mind: selecting well-designed palettes, ensuring color-blind safety and sufficient contrast, and applying principles that make visualizations clearer and more reliable for everyone.
🎨 A Bit of Color Theory
Before diving deeper into palettes, it helps to understand the basic vocabulary used to describe colors . These properties determine how colors relate to one another and how we perceive them in visualizations.
- Hue refers to the type of color — red, blue, green, orange, and so on. It’s what we usually mean when we say “color.”
- Saturation (or chroma) measures how intense or pure a color is. Highly saturated colors look vivid and rich; low-saturation colors appear muted or greyish.
- Lightness (or value) describes how bright or dark a color appears. Lightness is especially important for readability and contrast.
Many modern color models — such as HSL (Hue, Saturation, Lightness) or HCL (Hue, Chroma, Luminance) — are built around these dimensions. While the well-known "Red-Blue-Green" (RGB) model is useful for digital displays, HCL or HSL are often more intuitive for designing palettes that are perceptually balanced.
📇 Coloring Data
When visualizing data, color is more than aesthetics. It’s a visual variable that decodes information.
The right palette helps reveal patterns and important details in your data, while a poor choice can obscure them. Leaving out some special cases for now, we can classify three main palette types:
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