Colormaps & Normalization

A colormap is a lookup table that turns numeric values into colors, and normalization is the step that decides which values map to the bottom and top of that color scale.

Learn Colormaps & Normalization in our free Matplotlib course — a beginner-friendly interactive lesson with worked examples, a practice exercise and a quick…

Part of the free Matplotlib course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

In this lesson you'll choose the right colormap for your data, apply it to scatter and image plots, and control the color limits with vmin , vmax , and Normalize .

Whenever you have a third number to show alongside x and y, color is the natural channel. Pass that number to the c argument of plt.scatter() and name a colormap with cmap . Matplotlib stretches the chosen scale across your value range and a colorbar serves as the legend.

What you'll see: 200 scattered dots whose color shifts smoothly from deep purple (low x+y) through teal to bright yellow (high x+y), with a colorbar on the right translating color back into a number.

Choosing the right family matters more than choosing a pretty one. Sequential maps (viridis, plasma, Blues) suit data that runs low to high. Diverging maps (coolwarm, RdBu) have a neutral middle and two contrasting ends — perfect for values above and below a center like zero. Qualitative maps (tab10, Set2) are unordered distinct colors for categories, never for continuous data.

What you'll see: three 10×10 heatmaps side by side. The viridis panel reads smoothly, the coolwarm panel splits warm and cool tones around the middle, and the tab10 panel looks blocky and discontinuous — exactly why qualitative maps are wrong for continuous data.

By default the color scale auto-stretches to your data's minimum and maximum. That is convenient, but it means two plots with different ranges use the same colors for different values . Pin the limits with vmin and vmax — or build a reusable Normalize(vmin, vmax) object — so colors stay comparable across panels, frames, or dashboards.

What you'll see: two heatmaps of the same data. The left one uses the full color range so contrast looks strong; the right one fixes the scale to 0–100, so the colors look muted but would stay consistent if you plotted another dataset beside it.

Replace each ___ to color points by temperature with a diverging map and add a labeled colorbar.

Hint: a diverging map suits values around zero — try coolwarm — and the legend comes from plt.colorbar .

You passed a color name to color= instead of values to c= . The colormap only kicks in when c receives an array of numbers.

A typo in the name. Colormap names are case-sensitive: it is viridis and coolwarm , not Viridis or CoolWarm .

Each plot auto-scaled to its own range. Set the same vmin / vmax (or share a Normalize ) on both so a value always maps to the same color.

Draw a 12×12 heatmap with the magma colormap, lock the color scale to 0–100, and give the colorbar a clear label.

Lesson complete — you can color data honestly!

You applied colormaps with cmap , learned when to pick sequential, diverging, or qualitative families, and pinned color limits with vmin , vmax , and Normalize .

🚀 Up next: Colorbars — fine-tune the legend that explains your color scale.

Practice quiz

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