Checkpoint: Chart Types

A checkpoint is a review lesson that pulls every chart type from this section together, so you can practice picking the right visualization for a given dataset and build a multi-panel figure end to end.

Learn Checkpoint: Chart Types 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.

You'll recap box and violin plots, grouped and stacked bars, error bars, fill_between, stem and step plots, polar plots, hexbin, and stackplots — then prove it by building a small dashboard and acing a quiz.

Each chart in this section answers a specific question. The fastest way to choose is to ask what you're trying to show , then pick the chart built for it. Keep this table handy — it's the heart of the whole section.

Time to put it together. Below is a starter that sets up a 2×2 grid of axes and hands you four datasets. Your job: pick the right chart for each panel and draw it. Think before you code — which chart answers each dataset's question? Try it yourself first, then open the solution to compare.

The logic: distributions call for a box plot, a revenue split over time is a stackplot, a 20,000-point cloud needs hexbin, and a noisy trend wants a line with a fill_between band. Same toolbox, four different jobs.

One subtlety: a polar panel needs polar axes, which you request per-subplot. Use the subplot_kw trick or build a mixed figure where one axis is polar. Here's a compact example pairing a grouped bar chart with a radar-style polar bar — proof you can mix projections in one figure.

What you'll see: a normal grouped bar chart on the left and a green radar-style polar bar chart on the right, living happily in one figure — exactly the building block of a real dashboard.

Replace each ___ to pick the right call for each panel: a density map and a stacked area chart.

You called set_theta_zero_location on normal axes. Create the axis with projection="polar" first.

Call plt.tight_layout() (or fig.tight_layout() ) after drawing all panels to space them out.

If a scatter is a blob, switch to density; if you need parts of a whole, stack; if comparing distributions, use box or violin.

Build a one-row report: error bars for measured points, a stacked bar of a budget, and a hexbin density map — each chosen for its data.

⏱ Timed Quiz

Answer each in your head, then click to reveal. No peeking first!

A density plot — ax.hexbin(x, y) or ax.hist2d(x, y) . Both bin the points and color each cell by count so the structure shows through even when points overlap.

Grouped bars sit side by side (offset with x ± width/2 ) to compare series within a category. Stacked bars sit on top of each other (using bottom= ) to show the category total and its breakdown.

A violin plot. It draws a smoothed density curve, so a bimodal distribution shows two bulges, while a box plot would flatten it into a single box.

Use ax.fill_between(x, y - err, y + err, alpha=0.3) to wrap the line in a translucent confidence band — cleaner than dozens of individual error bars for dense data.

projection="polar" — passed via subplot_kw={"projection": "polar"} to plt.subplots() or to fig.add_subplot() . Then you plot with angle (theta) and radius (r).

A 100% stacked area chart — normalize each series by the column total ( series / total * 100 ) and draw with ax.stackplot() so the bands always fill the full height and reveal share.

Checkpoint complete — you can choose and combine chart types!

You recapped all eight chart types, matched each to the question it answers, built a multi-panel dashboard, mixed Cartesian and polar projections, and tested yourself with the quiz.

🚀 Up next: Complex Layouts with GridSpec — arrange panels of any size precisely.

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