matplotlib/cheatsheets

Official Matplotlib cheat sheets

View on GitHub ↗Jump to charts ↓

Summary Information

Updated 26 minutes ago
Added to GitGenius on September 8th, 2026
Created on May 5th, 2020
Open Issues & Pull Requests: 16 (+0)
GitHub issues: Enabled
Number of forks: 917
Total Stargazers: 7,729 (+0)
Total Subscribers: 133 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 3.0 hours
Mean response time: 4.8 days
90th percentile: 15.9 days
Tracked items: 4

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 0
New in 7 days: 0
Closed in 7 days: 0
Avg open age: N/A days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

No label distribution available yet.

Most active issues this week

No issue events were indexed in the last 7 days.

Detailed Description

Matplotlib cheatsheets is a collection of reference materials for Matplotlib users.

The project provides downloadable PDF cheat sheets and handouts designed to help users quickly find and understand Matplotlib's plotting capabilities. The materials are organized by skill level, with a beginner handout covering foundational concepts, an intermediate handout for more advanced usage, a tips handout for practical techniques, and a general cheatsheets reference that summarizes key functionality. The handouts use visual examples and concise explanations to make Matplotlib's API accessible without requiring users to search through full documentation.

These materials suit anyone learning Matplotlib or needing quick reference during development. The beginner handout is appropriate for those new to the library, while the intermediate handout and tips materials serve developers already familiar with basic plotting who want to deepen their skills or discover less obvious features. The project is maintained as an official Matplotlib resource, so the content stays aligned with the library's current API and best practices.

The project maintains its reference materials through a build process that generates figures from Python code and compiles them into PDF documents. Contributors can modify the source materials and regenerate the outputs, though this requires setting up a fonts directory with specific typefaces and running compilation steps. The development activity shows ongoing maintenance to keep the materials current with Matplotlib's evolution, ensuring the cheat sheets remain accurate references rather than becoming outdated guides.