Is this skill suitable for academic publications?

Absolutely. It follows best practices for Matplotlib, the industry standard for high-resolution, publication-quality figures with precise control over every element.

How does the skill help with chart selection?

It includes a decision guide that maps specific data types—like trends, distributions, or correlations—to the most effective chart types, such as line charts, histograms, or heatmaps.

Can I create interactive charts with this skill?

Yes, the skill includes specific instructions for Plotly and Altair, which are ideal for creating interactive, web-ready visualizations with zoom and hover capabilities.

Does it integrate with Pandas?

Yes, the skill emphasizes using libraries like Seaborn and Plotly Express that are designed to work natively with Pandas DataFrames for efficient workflows.

Which Python libraries does this skill support?

This skill provides specialized guidance and implementation patterns for Matplotlib, Seaborn, Plotly, and Altair.

Data Visualization

Name: Data Visualization
Author: eyadsibai

byeyadsibai

0•

Data Science & ML

Generates professional data visualizations and plots using industry-standard Python libraries like Matplotlib, Seaborn, and Plotly.

The Data Visualization skill empowers Claude to create high-quality, insightful data representations by providing expert guidance on Python's most powerful plotting libraries. It bridges the gap between raw data and visual storytelling by offering best practices for chart selection, design principles, and implementation patterns across static publication-grade figures and interactive web-ready dashboards. Whether you are performing Exploratory Data Analysis (EDA) or preparing figures for a technical report, this skill ensures consistent, readable, and aesthetically pleasing results based on the grammar of graphics.

Key Features

01Support for both static publication-ready figures and interactive web plots

02Comprehensive support for Matplotlib, Seaborn, Plotly, and Altair

030 GitHub stars

04Intelligent chart type selection based on data characteristics and relationships

05Built-in design principles for high data-ink ratios and accessibility

06Detailed implementation patterns for statistical, categorical, and matrix visualizations

Use Cases

01Generating publication-quality charts for research papers or technical reports

02Creating interactive dashboards and web-embedded visualizations for data applications

03Rapid exploratory data analysis (EDA) using high-level statistical plotting commands

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add eyadsibai/ltk visualization

For use in Claude.ai and ChatGPT

Download Skill