How does it improve visualization workflow?

It uses a declarative approach, allowing you to focus on what the data represents rather than the low-level plotting code.

What libraries does this skill require?

It requires holoviews (>= 1.18.0), pandas, numpy, and bokeh, with networkx added for graph visualizations.

Can this skill handle large datasets?

Yes, it utilizes aggregation and integration with Datashader to efficiently render millions of data points without performance lag.

Does it support interactive elements?

Absolutely. It supports interactive streams, linked brushing, and dynamic maps for responsive, user-driven data exploration.

HoloViz Data Visualization

Name: HoloViz Data Visualization
Author: robertpelloni

byrobertpelloni

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Data Science & ML

Simplifies the creation of complex, interactive, and multi-dimensional data visualizations using the HoloViz ecosystem.

About

This skill empowers Claude to master the HoloViz ecosystem, specifically HoloViews, for creating sophisticated declarative visualizations. It facilitates the construction of complex multi-dimensional plots, interactive data streams, and hierarchical network diagrams while ensuring optimal performance through aggregation and rasterization techniques. It is particularly useful for data scientists and analysts needing to explore large datasets or build responsive, publication-quality dashboards with minimal boilerplate code.

Key Features

High-performance rendering for large datasets via Datashader integration
Advanced network and hierarchical visualization support
Interactive streams for responsive selections and linked brushing
Declarative element composition including overlays, layouts, and faceting
3 GitHub stars
Automated styling, theming, and accessible color palettes

Use Cases

Exploratory data analysis of multi-dimensional scientific or financial datasets
Generating publication-quality statistical and network visualizations
Building interactive dashboards and real-time monitoring tools

About

Key Features

High-performance rendering for large datasets via Datashader integration
Advanced network and hierarchical visualization support
Interactive streams for responsive selections and linked brushing
Declarative element composition including overlays, layouts, and faceting
3 GitHub stars
Automated styling, theming, and accessible color palettes

Use Cases

Exploratory data analysis of multi-dimensional scientific or financial datasets
Generating publication-quality statistical and network visualizations
Building interactive dashboards and real-time monitoring tools