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CeeKay1901
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data-visualization

Selects charts and writes Python visualizations using matplotlib, seaborn, and plotly.

Updated Jul 8, 2026
Search Tags:#data visualization#accessibility#python#plotly#matplotlib#seaborn#chart selection
One-click install
npx skills add https://github.com/CeeKay1901/pilot-skillmarkt --skill data-visualization-ceekay1901
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/CeeKay1901/pilot-skillmarkt/tree/main/skills/dataviz
Command: npx skills add https://github.com/CeeKay1901/pilot-skillmarkt --skill data-visualization-ceekay1901

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you choose the right chart, write effective visualization code, and present data clearly so people can understand trends, comparisons, distributions, and relationships without confusion.

Core Features & Use Cases

  • Chart selection guidance: Pick the best visualization type for time series, categories, distributions, correlations, geography, and workflows.
  • Python plotting patterns: Create polished charts with matplotlib, seaborn, and plotly using reusable code patterns for line charts, bars, histograms, heatmaps, and small multiples.
  • Design and accessibility: Apply color, typography, layout, accuracy, and accessibility principles so charts are readable, honest, and usable for more people.
  • Use case: You have sales data and need a presentation-ready chart that highlights the trend, avoids misleading design, and works for colorblind viewers.

Quick Start

Ask for the best chart type and a Python example for your dataset, including accessibility improvements and publication-quality styling.

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 best chart type for my data visualization task?▼

Choosing the best chart type for data visualization depends on your data relationship: line charts suit time series, bars fit category comparisons, histograms show distributions, and heatmaps reveal correlations to communicate trends clearly without confusion.

How do I create accessible charts in Python using matplotlib and seaborn?▼

To create accessible charts in Python using matplotlib and seaborn, apply colorblind-friendly palettes, clear typography, and proper labeling patterns to ensure your data visualizations are readable, honest, and usable for wider audiences.

What's the best way to visualize correlations and distributions in Python?▼

The best way to visualize correlations and distributions in Python is using seaborn heatmaps for correlation analysis and histograms for distribution analysis, applying accessibility and color design guidance for accurate figures.

Does this Skill provide code patterns for interactive plots with plotly?▼

Yes, it provides reusable plotly patterns for interactive plotting alongside matplotlib and seaborn templates, covering line charts, bars, histograms, heatmaps, and small multiples for presentation-ready data visualization.

How do I build small multiple reports for geographic patterns in Python?▼

To build small multiple reports for geographic patterns in Python, apply matplotlib and seaborn small multiple patterns to display data subsets across consistent axes, ensuring clear visual comparison of regional distributions and trends.

Why does my matplotlib chart mislead viewers, and how do I fix it?▼

Matplotlib charts mislead viewers when they use truncated axes or poor color choices; fix this by applying accurate labeling, honest color design, and accessibility principles so your data visualization presents trends without distortion.

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