Colorbars

A colorbar is the labeled strip beside a plot that acts as the legend for a colormap, translating each color back into the numeric value it represents.

Learn Colorbars in our free Matplotlib course — a beginner-friendly interactive lesson with worked examples, a practice exercise and a quick reference.

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 attach a colorbar with fig.colorbar() , give it a label and custom ticks, change its orientation and size, and share a single bar across several subplots.

Color-carrying plots like imshow and scatter return a mappable — an object that knows its colormap and value range. Capture it in a variable and hand it to fig.colorbar(mappable, ax=ax) . Without that mappable, Matplotlib has nothing to build the legend from.

What you'll see: a 10×10 viridis heatmap with a vertical colorbar on its right edge, labeled "Value", running from dark purple at the bottom to bright yellow at the top.

A colorbar is highly tunable. Use orientation="horizontal" to drop it below the plot, ticks=[...] to mark exactly the values you care about, shrink to scale its length, and pad to set the gap between plot and bar. Combine them to fit any layout.

What you'll see: a plasma heatmap with a shorter, horizontal colorbar sitting below it, marked only at 0, 25, 50, 75, and 100, and labeled "Percent".

When several panels share a scale, a single colorbar is cleaner than one per panel. The key is to give every panel the same vmin and vmax so a color means the same thing everywhere, then pass a list of Axes to the ax argument.

What you'll see: two cividis heatmaps side by side titled "Sensor A" and "Sensor B", with a single colorbar on the far right serving both — because both panels use the identical 0–1 scale.

Replace each ___ to add a labeled colorbar to a colored scatter plot.

Hint: the figure method is colorbar , and the label method on the returned object is set_label .

You called fig.colorbar() without passing the plot object. Capture it first: im = ax.imshow(...) , then fig.colorbar(im, ax=ax) .

Use constrained_layout=True and tune shrink / pad , instead of letting Matplotlib carve a large default slice out of the Axes.

The panels auto-scaled differently. Give every panel the same vmin and vmax before sharing one bar.

Draw a temperature heatmap and give it a shrunk, padded vertical colorbar with custom ticks, a label, and smaller tick text.

Lesson complete — your color scales now explain themselves!

You attached colorbars with fig.colorbar() , labeled and re-ticked them, changed orientation and size with shrink and pad , and shared a single bar across multiple panels.

🚀 Up next: Contour & Filled Contour Plots — visualize a surface as nested level curves.

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