bio-data-visualization-matplotlib-fundamentals
Build publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.
npx skills add BioTender-max/awesome-bio-agent-skills --skill matplotlib-fundamentals --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: matplotlib 3.8+, seaborn 0.13+, numpy 1.26+, pandas 2.2+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # matplotlib Fundamentals **"Make a publication figure in Python"** -> Build via the **object-oriented Figure/Axes API** (not pyplot state-machine), with `constrained_layout` for axes alignment, `pdf.fonttype=42` for journal-compliant TrueType fonts, CVD-safe palettes, and rasterized point layers for large scatter. The pyplot interface is for notebook scratch; the Figure/Axes API is for reproducible figures. - Python: `fig, ax = plt.subplots()` → `ax.scatter` / `ax.plot` / `ax.bar`; `seaborn.objects` (new grammar API) for ggplot-like ## The Three Modern Defaults 1. **Object-oriented API** — `fig, ax = plt.subplots(figsize=(4, 3))` then `ax.scatter(x, y)`, `ax.set_xlabel(...)`. The pyplot state-machine (`plt.scatter`, `plt.xlabel`) hides which axes are bei
- Version Compatibility
- The Three Modern Defaults
- Standard Setup for Publication
- Figure / Axes API
- Common Chart Types
- seaborn Integration
- Axis Formatting
- Color and Palette
- Saving
- Common Failure Modes
- Default Type-3 fonts rejected by journals
- tightlayout fails on complex grids
- pyplot state-machine in multi-subplot
- Scatter of 100000 points crashes PDF viewer
What does the bio-data-visualization-matplotlib-fundamentals skill do?
Build publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill matplotlib-fundamentals --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.