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data-visualization

Generate Python visualizations with matplotlib, seaborn, and plotly code templates.

1|Updated Feb 26, 2026
Search Tags:#accessibility#python#plotly#matplotlib#seaborn#data-visualization#chart-design
One-click install
npx skills add https://github.com/tmorrowdev/data-plugin --skill data-visualization-tmorrowdev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/tmorrowdev/data-plugin/tree/main/skills/data-visualization
Command: npx skills add https://github.com/tmorrowdev/data-plugin --skill data-visualization-tmorrowdev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data professionals often struggle to transform raw datasets into clear, actionable visuals that communicate insights accurately and quickly.

Core Features & Use Cases

  • Chart selection guidance for choosing effective chart types.
  • Python visualization code patterns for matplotlib, seaborn, and plotly.
  • Accessibility-focused design guidelines for color, typography, and layout.

Quick Start

Provide a dataset and chart target, then apply the included Python visualization patterns to generate a figure.

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 dataset?▼

Python visualization patterns provide chart selection guidance by matching your dataset characteristics to the most effective chart types. This ensures your data visualization accurately and quickly communicates actionable insights to your audience.

How do I make accessible data visuals with matplotlib and seaborn?▼

Accessible data visualization with matplotlib and seaborn applies specific color, typography, and layout guidelines. These design patterns ensure your publication-quality charts remain readable for users with visual impairments.

Can I use plotly to generate publication-ready charts in Python?▼

Plotly code templates allow you to generate publication-ready charts in Python by applying structured visualization patterns. These templates support creating accessible, interactive figures that meet publication-quality design standards.

What's the best way to turn raw data into clear visuals using Python?▼

The best way to turn raw data into clear visuals is applying Python visualization patterns from matplotlib, seaborn, and plotly. This approach combines chart selection guidance with accessibility considerations to produce actionable figures.

Do I need to prepare my dataset before applying Python visualization patterns?▼

You need to provide a structured dataset and chart target before applying Python visualization patterns. Once your raw data is prepared, the templates generate ready-to-run code for creating accessible, publication-quality visuals.

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