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Matplotlib Best Practices

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mindrally·vlatest·data·Apache-2.0

Generates matplotlib visualizations adhering to style guidelines, ensuring clarity, aesthetics, and accessibility.

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Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add mindrally/skills --skill matplotlib-best-practices -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

mindrally

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Live repository

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Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

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  • Pinned to a commit: passed

    Scanned mindrally/skills at 7682ca7771.

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Commit
7682ca7771
Manifest
matplotlib-best-practices/SKILL.md
Scanned
1 file, 123 lines
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About this skill

What it does

This skill provides expert guidance on creating high-quality Matplotlib visualizations in Python. It focuses on data visualization best practices, including code style, plot selection, labeling, color choices, and figure layout, ensuring plots are clear, aesthetically pleasing, accessible (especially for those with colorblindness), and suitable for publication or presentation. The skill emphasizes using the object-oriented API for complex figures while also covering the pyplot interface for quick exploration.

When to use it

  • When creating visualizations for scientific papers or presentations requiring a professional look.
  • For generating plots in Jupyter notebooks where rapid prototyping and interactive exploration are needed.
  • To ensure your Matplotlib visualizations adhere to accessibility guidelines, particularly regarding colorblindness.
  • When needing guidance on selecting the appropriate plot type (line, scatter, bar, histogram, boxplot, heatmap) for a given dataset.
  • For creating complex figures with multiple subplots and customized layouts.

Key capabilities

  • Code Style Guidance: Follows PEP 8 style guidelines for Python code.
  • Plot Type Selection: Provides recommendations for various plot types based on data characteristics.
  • Labeling & Annotation: Emphasizes clear axis labels, descriptive titles, legends, and annotations.
  • Color Accessibility: Recommends colorblind-friendly palettes and avoids problematic color combinations.
  • Object-Oriented API Usage: Guides users in using fig, ax = plt.subplots() for explicit control over figure elements.
  • Style Sheet Customization: Allows the use of built-in styles (e.g., seaborn, ggplot) or creation of custom style files.

Example prompts

  • "Generate a line plot showing sales trends over time with clear axis labels and a descriptive title."
  • "Create a scatter plot to visualize the relationship between two variables, ensuring colorblind accessibility."
  • "Show me how to create a figure with subplots using the object-oriented API in Matplotlib."

Tips & gotchas

  • For complex figures or production code, prioritize the object-oriented API (fig, ax = plt.subplots()) for greater control and maintainability.
  • Always consider accessibility when choosing colors; use colorblind-friendly palettes like 'viridis' or 'plasma'.
  • When using Jupyter notebooks, remember to include %matplotlib inline to display plots directly in the notebook output.