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data-visualization

Create data visualizations with Python libraries like matplotlib, seaborn, and plotly.

704|58|Updated Mar 20, 2026
Search Tags:#accessibility#python#data#visualization#plotly#matplotlib#seaborn
One-click install
npx skills add https://github.com/openyak/desktop --skill data-visualization-openyak
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/openyak/desktop/tree/main/backend/app/data/plugins/data/skills/data-visualization
Command: npx skills add https://github.com/openyak/desktop --skill data-visualization-openyak

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Creating accurate, accessible data visualizations without guesswork, saving time and reducing misinterpretation.

Core Features & Use Cases

  • Chart selection guidance for common data relationships (time trends, category comparisons, distributions, correlations).
  • Python visualization patterns with reusable code templates for matplotlib, seaborn, and Plotly.
  • Design and accessibility best practices covering color usage, typography, layout, and contrast to ensure usable visuals.

Quick Start

Generate a simple line chart from your dataset to illustrate a trend over time.

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 requires matching chart types to data relationships, such as line charts for time trends, bar charts for category comparisons, and scatter plots for correlations.

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

Accessible data visualizations require careful color usage, typography, and contrast. Following design best practices ensures charts remain usable for broader audiences without misinterpreting the underlying insights.

Can I use matplotlib, seaborn, and Plotly together for data visualization?▼

Yes, you can use matplotlib, seaborn, and Plotly together for data visualization. Reusable Python code patterns and templates support generating static, statistical, and interactive charts across these libraries.

How do I generate a line chart from a dataset to show trends over time?▼

To generate a line chart from a dataset, apply Python visualization patterns using libraries like matplotlib or Plotly. This visualizes time trends effectively for exploratory analysis or reports.

Does this data visualization skill provide reusable code templates for dashboards?▼

Yes, the data visualization skill provides reusable Python code templates using matplotlib, seaborn, and Plotly. These templates help generate charts specifically designed for reports and dashboards.

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