matplotlib

Create custom Matplotlib visualizations for data analysis and publication.

13|3|Updated Jun 10, 2026

Search Tags:#data visualization #python #plotting #matplotlib #publication quality

Ready to run

npx skills add https://github.com/tassiovale/claude-code-kit --skill matplotlib-tassiovale

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: matplotlib
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/matplotlib
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill matplotlib-tassiovale

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, numpy, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps users create static, animated, and interactive plots with Matplotlib, providing fine-grained control and customization for publication-quality visualizations.

Core Features & Use Cases

Plot Creation: Offers guidance on creating various plot types like line, scatter, bar, histogram, heatmap, contour, and more.
Customization: Allows for detailed customization of plot elements such as colors, styles, labels, legends, and aspect ratios.
Exporting: Supports exporting visualizations to PNG, PDF, SVG, and other formats.
Use Case: If you need to create a detailed line plot of a dataset and export it to a high-resolution PDF for a report, this Skill provides the necessary guidance and commands.

Quick Start

To create a basic line plot, run the following command: uv run python scripts/plot_template.py --plot-type line --output line_plot.png

Frequently Asked Questions about matplotlib

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create publication-quality data visualizations with Python?▼

To create publication-quality data visualizations with Python, use Matplotlib to generate custom static, animated, and interactive plots with fine-grained control over plot elements like colors, styles, and labels.

Can I export Matplotlib plots to high-resolution PDF or SVG formats?▼

Yes, you can export Matplotlib plots to high-resolution PDF, SVG, and PNG formats. The library supports exporting visualizations to multiple file types for use in reports and publications.

What plot types does Matplotlib support for data analysis?▼

Matplotlib supports various plot types for data analysis including line, scatter, bar, histogram, heatmap, and contour plots, providing comprehensive documentation on creating and customizing each.

Do I need Jupyter notebooks to run Matplotlib scripts?▼

No, you do not need Jupyter notebooks to run Matplotlib scripts, though it can utilize Jupyter notebooks and interactive backends. The core requirement is Python 3.10+ and Matplotlib 3.10.x.

How do I customize colors, styles, and legends in Matplotlib?▼

You customize colors, styles, labels, legends, and aspect ratios in Matplotlib by adjusting plot elements directly, allowing detailed fine-grained control for your specific data visualization requirements.

What's the best way to generate a line plot from a dataset using Matplotlib?▼

The best way to generate a line plot from a dataset using Matplotlib is running a script like `uv run python scripts/plot_template.py --plot-type line --output line_plot.png` to create and export the visualization.