Capstone: Build a Dashboard

Matplotlib is a Python library for creating charts and visualizations — and in this capstone you'll combine everything you've learned into a single multi-panel dashboard that tells a complete data story.

Learn Capstone: Build a Dashboard 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.

You'll lay out four coordinated charts in one figure, style them consistently, add a super-title, and export the finished result as a high-resolution image.

A dashboard is just one Figure divided into a grid of Axes . We'll use a 2×2 grid, so we get four panels to fill. plt.subplots(2, 2) returns the figure plus a 2D array of axes we can index with axs[row, col] .

What you'll see: a single window split into four empty boxes arranged 2-by-2. Each box has its own title, and a bold super-title sits across the very top. This is the skeleton we'll now fill in.

Now we draw a different chart type into each Axes using the object-oriented interface — ax.plot , ax.bar , ax.pie , and ax.scatter . Each panel keeps its own labels.

What you'll see: a polished four-panel dashboard. Top-left, a blue line with circle markers climbs across the six months. Top-right, four green bars compare regions. Bottom-left, a pie split into four labelled wedges with percentages. Bottom-right, orange dots scatter price against units. The bold title ties it all together.

Finish by applying a consistent style with plt.style.use and saving the figure to disk with fig.savefig . Use bbox_inches="tight" to trim whitespace and a higher dpi for crisp output.

What you'll see: a clean two-panel figure on a soft white-grid theme. The left panel overlays revenue and cost lines with a legend; the right shows green profit bars. A file dashboard.png is written to your folder at 150 DPI with the margins trimmed tight.

Replace each ___ to finish this two-panel dashboard.

You forgot fig.tight_layout() . Call it after all panels are drawn, or pass constrained_layout=True to plt.subplots() .

❌ TypeError: 'AxesSubplot' object is not subscriptable

With a single row or column, axs is 1D — index it as axs[0] , not axs[0, 0] . The 2D form only works for a true grid like subplots(2, 2) .

Call fig.savefig(...) before plt.show() — showing the window clears the figure.

Build a 1×3 dashboard with a line, a bar, and a pie panel, then export it.

Course complete — you can now build full data dashboards!

From your first three-line chart to a styled, exported, multi-panel dashboard, you've covered the entire Matplotlib workflow: figures and axes, every core chart type, styling, annotations, dates, pandas integration, heatmaps, 3D, and global customization.

🚀 Keep going: rebuild one of these dashboards with your own real data — a budget, a fitness log, or a project's metrics. That's how the skills truly stick.

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