data-visualization

Name: data-visualization
Availability: InStock
Author: Bauhaus-InfAU

Generate data visualizations with Matplotlib, Seaborn, and Plotly.

Updated Feb 12, 2026

Search Tags:#design #charts #data analysis #visualization #plotly #matplotlib #seaborn

Ready to run

npx skills add https://github.com/Bauhaus-InfAU/infau-skill-base --skill data-visualization-bauhaus-infau

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/Bauhaus-InfAU/infau-skill-base/tree/main/data/skills/data-visualization
Command: npx skills add https://github.com/Bauhaus-InfAU/infau-skill-base --skill data-visualization-bauhaus-infau

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps users create effective, accessible, and publication-quality data visualizations by providing guidance on chart selection, Python code patterns, and design principles.

Core Features & Use Cases

Chart Selection: Recommends appropriate chart types based on the data relationship being shown.
Python Code Examples: Provides ready-to-use code snippets for common chart types using libraries like Matplotlib, Seaborn, and Plotly.
Design & Accessibility: Offers best practices for color, typography, layout, and accessibility to ensure visualizations are clear and usable by all audiences.
Use Case: You have a dataset and need to visualize trends over time. This Skill will guide you to select a line chart, provide Python code to generate it, and ensure it's colorblind-friendly.

Quick Start

Use the data-visualization skill to create a bar chart showing sales by region.

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 chart type for my data visualization in Python?▼

Choosing the right data visualization chart requires matching chart types to your data relationships. This Skill guides you in selecting appropriate visualizations and provides Python code examples using Matplotlib, Seaborn, and Plotly.

How do I create publication-ready data visualizations using Matplotlib and Seaborn?▼

To create publication-ready data visualizations, apply design principles for color, typography, and layout. This Skill provides Python code snippets and accessibility best practices to ensure your charts are clear and informative.

Can I use Plotly to generate interactive data visualizations with this Skill?▼

Yes, you can generate interactive data visualizations using Plotly. This Skill provides ready-to-use Python code snippets for common chart types across Matplotlib, Seaborn, and Plotly to address various data relationships.

What is the best way to ensure my data visualizations are colorblind-friendly?▼

The best way to ensure accessible data visualizations is to follow design and accessibility best practices. This Skill offers guidance on color selection, typography, and layout to make visual data representation usable by all audiences.

Do I need pandas and numpy installed to use these data visualization code patterns?▼

Yes, pandas and numpy are required dependencies for running the data visualization code patterns. This Skill relies on these libraries alongside Matplotlib, Seaborn, and Plotly to process data and generate visual representations.

Why does my data visualization lack clarity for different data distributions?▼

Data visualization lacks clarity when chart types do not match data distributions. This Skill guides you in selecting appropriate chart types, implementing design principles, and ensuring accessibility for various data relationships.