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data-visualization

Generate Python data visualizations with Matplotlib, Seaborn, and Plotly.

37|3|Updated Mar 1, 2026
Search Tags:#data visualization#python#data analysis#plotly#matplotlib#charting#seaborn
One-click install
npx skills add https://github.com/eren726290/opencode-cowork-plugins --skill data-visualization-eren726290
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/eren726290/opencode-cowork-plugins/tree/main/data/agents/data/skills/data-visualization
Command: npx skills add https://github.com/eren726290/opencode-cowork-plugins --skill data-visualization-eren726290

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users create clear, informative, and accessible data visualizations using Python, guiding them in choosing the right chart types and applying best practices.

Core Features & Use Cases

  • Chart Selection: Provides guidance on selecting the most appropriate chart type based on the data relationship being shown.
  • Python Code Examples: Offers ready-to-use Python code snippets for various chart types (line, bar, histogram, heatmap, small multiples) using libraries like Matplotlib, Seaborn, and Plotly.
  • Design Principles: Explains key principles for effective visualization, including color usage, typography, layout, and accuracy.
  • Accessibility: Details considerations for making visualizations accessible to all users, including those with color blindness and screen reader users.
  • Use Case: You have a dataset of sales figures over time and want to present it as a clear line chart. This Skill can provide the Python code and styling to generate a publication-quality visualization.

Quick Start

Use the data-visualization skill to generate a bar chart showing sales by category from the provided data.

Frequently Asked Questions about data-visualization

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

FAQPage Schema
How do I choose the right Python chart type for my data visualization?▼

Choosing the right data visualization chart type depends on the data relationship you are showing. This Skill provides chart selection guidance to help you pick the most appropriate Python visualization for your specific dataset.

Can I generate Plotly interactive charts with Python code examples?▼

Yes, you can generate Plotly interactive visualizations using Python. This Skill provides ready-to-use Python code snippets for various chart types using libraries like Plotly, Matplotlib, and Seaborn.

What's the best way to make Python data visualizations accessible?▼

To make Python data visualizations accessible, you should apply specific design principles. This Skill details accessibility considerations for color blindness and screen reader users, alongside color usage and typography best practices.

How do I create a publication-quality line chart in Matplotlib?▼

To create a publication-quality line chart in Matplotlib, use the Python code snippets and styling patterns provided by this Skill. It facilitates clear visual data representation for analytical and reporting contexts.

Do I need Python libraries like Seaborn for effective data analysis charting?▼

You need Python libraries like Seaborn for effective data analysis charting. This Skill offers code patterns for various chart types, including heatmaps and small multiples, facilitating impactful visual data representation.

Does this Skill provide Python code snippets for small multiples and heatmaps?▼

Yes, this Skill provides ready-to-use Python code snippets for various chart types, including heatmaps and small multiples. It uses libraries like Matplotlib, Seaborn, and Plotly to generate the visualizations.

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