matplotlib-scientific-plotting
Low-level Python plotting for scientific figures: publication-quality line, scatter, bar, heatmap, contour, 3D; multi-panel layouts; fine control of every element. PNG/PDF/SVG export. Use seaborn for quick stats, plotly for interactive.
npx skills add BioTender-max/awesome-bio-agent-skills --skill matplotlib-scientific-plotting --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.
# matplotlib ## Overview Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. It provides both a MATLAB-style pyplot interface and an object-oriented API for full control over figures, axes, and artists. Essential for generating publication-quality scientific figures. ## When to Use - Creating publication-quality plots with precise control over every element (fonts, ticks, colors, spacing) - Building multi-panel figures with complex subplot layouts for papers - Generating standard scientific plot types: line, scatter, bar, histogram, heatmap, box, violin, contour - Exporting figures to vector formats (PDF, SVG) for journal submission - Creating 3D surface, scatter, or wireframe plots - Customizing colormaps and color schemes for accessibility (colorblind-friendly) - Integrating plots with NumPy arrays and pandas DataFrames - For quick statistical visualizations (distributions, regressions), use `seaborn` instead - For interactive/web-based plots with hover and zoom, use `plotly` instead ## Prerequisites - **Python packages**: `matplotlib`, `numpy` - **Optional**: `pandas` (for DataFrame plotting), `seaborn` (for style prese
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: Figure and Axes Creation
- Module 2: Plot Types
- Module 3: Styling and Customization
- Module 4: Advanced Layouts
- Module 5: 3D Visualization
- Module 6: Export and Saving
- Common Workflows
- Workflow 1: Multi-Panel Figure for Publication
- Workflow 2: Statistical Comparison Plot
pip install matplotlib numpy
What does the matplotlib-scientific-plotting skill do?
Low-level Python plotting for scientific figures: publication-quality line, scatter, bar, heatmap, contour, 3D; multi-panel layouts; fine control of every element. PNG/PDF/SVG export. Use seaborn for quick stats, plotly for interactive.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill matplotlib-scientific-plotting --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.