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data-visualization

Guide chart selection and generate Python plotting patterns for data visualizations.

2|Updated Apr 8, 2026

Search Tags:#data visualization #accessibility #python #plotly #matplotlib #seaborn #chart design

One-click install

npx skills add https://github.com/miptah21/skills --skill data-visualization-miptah21

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/miptah21/skills/tree/main/.agents/skills/data-visualization
Command: npx skills add https://github.com/miptah21/skills --skill data-visualization-miptah21

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you choose the right chart type and generate publication-quality Python visualizations that communicate the underlying story clearly and accessibly.

Core Features & Use Cases

Chart selection guidance: Pick appropriate visual encodings for trends, comparisons, ranking, composition, distributions, correlations, geography, flow/process, networks, KPI tracking, and multi-KPI summaries.
Python visualization code patterns: Use practical matplotlib/seaborn templates for line charts, bar charts, histograms, heatmaps, small multiples, formatting helpers, and optional interactive Plotly charts.
Design, accuracy, and accessibility principles: Apply color theory (including colorblind-safe palettes), readable typography, layout hygiene, correct baselines/scales, uncertainty labeling, and screen-reader-friendly alternatives (alt text and data tables).

Quick Start

Use the data-visualization skill to help you design and implement a colorblind-friendly matplotlib or seaborn chart for your dataset, then add the right labels, title insight, and a data-table alternative.

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 in Python?▼

To create colorblind-safe matplotlib charts, apply colorblind-safe palettes, ensure readable typography, maintain layout hygiene, and provide screen-reader-friendly alternatives like alt text and data tables.

What is the best way to visualize a correlation matrix in Python?▼

Yes, you can create interactive Plotly charts alongside static matplotlib and seaborn templates, allowing you to generate both publication-quality visuals and interactive dashboard components.

How do I create accessible data visualizations for screen readers?▼

For dashboard chart creation, use small-multiple summaries and categorical comparison patterns to display multi-KPI tracking, ensuring accurate visual encoding and readable typography across all widgets.

Why does my matplotlib bar chart misrepresent the underlying data?▼

For exploratory analysis, use matplotlib and seaborn templates for distributions and categorical comparisons, applying design principles for accuracy and readability to communicate the underlying story.