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mmahalwy
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data-visualization

Create accessible static and interactive visualizations from tabular datasets with matplotlib, seaborn, and plotly.

Updated Aug 27, 2026
Search Tags:#accessibility#plotly#matplotlib#seaborn#data-visualization#chart-design#data-storytelling
One-click install
npx skills add https://github.com/mmahalwy/cooper --skill data-visualization-mmahalwy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/mmahalwy/cooper/tree/main/.agents/skills/data-visualization
Command: npx skills add https://github.com/mmahalwy/cooper --skill data-visualization-mmahalwy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Turning raw tables and metrics into clear, accurate, and accessible visual stories is time-consuming and error-prone. This Skill helps you choose the right chart type, apply best-practice styling and formatting, and produce export-ready static or interactive figures that communicate insights reliably.

Core Features & Use Cases

  • Chart selection guidance for trends, comparisons, distributions, correlations, geospatial patterns, and part-to-whole relationships.
  • Reusable Python patterns for matplotlib, seaborn, and plotly including line charts, bar charts, histograms, heatmaps, small multiples, and interactive exports.
  • Design and accessibility principles covering color palettes (colorblind-friendly), typography, annotation, axis formatting, and an accessibility checklist for screen readers and print.
  • Use cases: create publication-quality figures for reports, build exploratory dashboards, generate interactive HTML charts for stakeholders, and ensure visualizations meet accessibility standards.

Quick Start

Create a line chart of monthly revenue by category from your CSV and export it as a publication-quality PNG.

Frequently Asked Questions about data-visualization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create publication-quality data visualizations from a CSV in Python?▼

Create publication-quality data visualizations by loading your CSV into Python and using matplotlib, seaborn, or plotly to generate charts with formatted axes, annotations, and colorblind-friendly palettes. Export results as static PNG files or interactive HTML.

What is the best chart type for visualizing time series, distributions, and correlations?▼

For time series use line charts, for distributions use histograms, and for correlations use heatmaps. Effective data visualization matches chart type to data shape, applying best-practice styling to accurately communicate trends, comparisons, and part-to-whole relationships.

Can I build interactive HTML dashboards with plotly for stakeholders?▼

Yes, you can build interactive HTML dashboards with plotly. This data visualization workflow supports generating interactive charts and exporting them as HTML files, allowing stakeholders to explore time series, categorical comparisons, and geospatial patterns directly.

Does this data visualization approach include colorblind-friendly palettes and accessibility support?▼

Yes, this approach includes colorblind-friendly palettes and accessibility support. It provides design principles covering typography, annotation, and an accessibility checklist to ensure visualizations meet standards for both screen readers and print formats.

How do I generate small multiples for categorical comparisons in matplotlib or seaborn?▼

Generate small multiples for categorical comparisons in matplotlib or seaborn by applying reusable Python patterns. This data visualization technique partitions data into multiple small charts, enabling clear distribution and trend analysis across different categories.

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