rougier/gallery — explained in plain English
Analysis updated 2026-07-17 · repo last pushed 2016-10-05
Copy a small example script to learn how to build a specific chart type in Matplotlib.
Browse the online gallery to find a visualization style before writing your own plot.
Learn how to add custom colors and formatting to a plot from a clear, minimal example.
Submit your own clear or beautiful example as a pull request to grow the gallery.
| rougier/gallery | betta-tech/harness-sdd | emmimal/control-layer | |
|---|---|---|---|
| Stars | 46 | 46 | 46 |
| Language | Python | Python | Python |
| Last pushed | 2016-10-05 | — | — |
| Maintenance | Dormant | — | — |
| Setup difficulty | easy | easy | easy |
| Complexity | 1/5 | 2/5 | 2/5 |
| Audience | data | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Just needs Python and Matplotlib installed to run any individual script.
This repository is a collection of example code showing how to use Matplotlib, a popular Python library for creating charts, graphs, and visualizations. Think of it as a cookbook of visual examples, each one demonstrates a specific technique or concept in data visualization. The gallery works simply: it's a set of Python scripts organized by topic or difficulty level. Each script is intentionally kept small and focused on teaching one idea clearly. When you run a script, it generates a chart or visualization that you can learn from. The repository then displays these examples in an online gallery so people can browse them and see what's possible. The main audience is people learning to visualize data with Python, students, analysts, data scientists, or anyone building charts for the first time. Instead of reading dense documentation or tutorials, they can look at a concrete example that says "here's how to make a bar chart" or "here's how to add custom colors to a plot." Contributors are also welcome to submit their own examples if they create a particularly clear or beautiful visualization. What makes this useful is the emphasis on clarity and simplicity. The examples avoid coding shortcuts and jargon, use consistent formatting, and showcase both practical techniques and artful designs. It's designed to be approachable, you can read the source code and immediately understand what each line does. The project accepts pull requests, so the community can grow the gallery over time with new examples that illustrate different visualization concepts and styles.
A cookbook of small Matplotlib example scripts, each showing one clear technique for building charts and data visualizations in Python.
Mainly Python. The stack also includes Python, Matplotlib.
Dormant — no commits in 2+ years (last push 2016-10-05).
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly data.
This repo across BitVibe Labs
double-check against the repo, no cap.