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matplotlib

Create publication-quality figures with Matplotlib's object-oriented interface.

14|2|Updated Feb 24, 2026
Search Tags:#python#styling#visualization#plotting#matplotlib#export#data-visualization
One-click install
npx skills add https://github.com/andikarachman/data-science-plugin --skill matplotlib-andikarachman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: matplotlib
Source: https://github.com/andikarachman/data-science-plugin/tree/main/skills/matplotlib
Command: npx skills add https://github.com/andikarachman/data-science-plugin --skill matplotlib-andikarachman

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Matplotlib provides a flexible, battle-tested foundation for creating high-quality visualizations across analysis, reporting, and dashboards, solving the tedious, error-prone process of stitching plots together manually.

Core Features & Use Cases

  • Publication-quality figures using both the pyplot and object-oriented interfaces for explicit control.
  • Wide range of plot types including line, scatter, bar, histogram, heatmap, contour, and 3D plots, plus layout, styling, and export options.
  • Reusable style sheets and presets, robust layout management, and support for saving figures to multiple formats suitable for notebooks, reports, and publications.

Quick Start

Create a basic publication-ready plot by importing matplotlib, creating a figure with plt.subplots, plotting your data, and saving the figure to a file.

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 plots with Python for my research report?▼

Publication-quality plots are produced using Matplotlib's pyplot and object-oriented interfaces, giving explicit control over figure layouts, styling, and export to multiple file formats suitable for notebooks and reports.

What types of data visualizations can I generate using Matplotlib in Python?▼

Matplotlib visualizations cover a wide range of plot types including line, scatter, bar, histogram, heatmap, contour, and 3D plots, supporting diverse data-visualization needs across analysis and reporting workflows.

How do I save and export styled figures from a Python notebook?▼

Saving figures is supported through Matplotlib export workflows that output to multiple formats, utilizing reusable style sheets, presets, and robust layout management to ensure visuals are production-ready for notebooks and publications.

Does this Python plotting approach support both pyplot and object-oriented interfaces?▼

Both the pyplot and object-oriented interfaces are supported for creating figures, allowing users to choose between quick plotting convenience and explicit control over layout and styling for publication-quality results.

What dependencies do I need to set up for advanced data visualization in Python?▼

Advanced data visualization requires numpy and scipy alongside Matplotlib to support comprehensive plotting, styling, and scientific computing workflows within your Python environment.

What's the best way to manage figure layouts and styling for multiple plots in Python?▼

Figure layouts and styling are managed using Matplotlib's robust layout management and reusable style sheets, providing consistent visualization presets across multiple plot types for publication-ready outputs.

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