Grouped & Stacked Bar Charts

Grouped and stacked bar charts plot several data series in one figure — grouped bars sit side by side to compare series within each category, while stacked bars sit on top of each other to show how a total breaks into parts.

Learn Grouped & Stacked Bar Charts in our free Matplotlib course — a beginner-friendly interactive lesson with worked examples, a practice exercise and a…

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 offset bar positions with numpy, stack series with the bottom argument, add a legend, and label every bar using ax.bar_label().

To place two series side by side, you can't reuse the same x positions — the bars would land on top of each other. Instead, build a numeric position array with np.arange() , then shift one series left and the other right by half the bar width . The category labels are added afterward with ax.set_xticks() and ax.set_xticklabels() .

What you'll see: four pairs of bars, one pair per quarter. The Online bar sits just left of center and In-Store just right, both growing toward Q4. The legend in the corner maps each color to a sales channel so you can compare them quarter by quarter.

Stacked bars share the same x position but sit on top of one another. The trick is the bottom argument: it tells each layer where to start. Draw the first series normally, then draw the second with bottom=first . For a third layer, set bottom to the running total. Because numpy arrays add element-wise, first + second gives you that total in one line.

What you'll see: four tall bars, one per month, each split into three colored segments. Rent forms a steady base, Food sits on top of it, and Fun caps each bar. The full height shows total spending, while the colored bands reveal how that total splits across categories.

Modern Matplotlib makes labeling painless. Capture the bars returned by ax.bar() , then pass that object to ax.bar_label() . It writes each bar's height just above it. For stacked charts, add label_type="center" to drop the number inside each segment.

What you'll see: three stacked bars showing wins and losses per team. The win count sits centered in the lower segment, the loss count centered in the upper one, and the season total floats just above each bar — no manual text positioning required.

Replace each ___ to draw two series side by side and label one of them.

You drew both series at the same x. Offset them: x - width/2 and x + width/2 with a width below 0.5.

You forgot bottom= . Each higher layer needs bottom set to the running total below it.

Plain lists concatenate, not add. Wrap your data in np.array(...) so a + b adds element-wise.

Plot survey results for three answers across four products as grouped bars, label every bar, and add a legend.

Lesson complete — you can plot multiple series in one chart!

You offset positions with numpy to draw grouped bars, stacked series with the bottom argument, added legends, and labeled every bar automatically with ax.bar_label().

🚀 Up next: Error Bars & Bands — show uncertainty around your values.

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