gopherdata/gophernotes

The Go kernel for Jupyter notebooks and nteract.

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Summary Information

Updated 2 minutes ago
Added to GitGenius on September 17th, 2026
Created on January 22nd, 2016
Open Issues & Pull Requests: 54 (+0)
GitHub issues: Enabled
Number of forks: 264
Total Stargazers: 3,965 (+0)
Total Subscribers: 78 (+0)

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Median issue/PR response: 2.5 days
Mean response time: 2.5 days
90th percentile: 2.5 days
Tracked items: 1

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Open issues: 2
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 367 days
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Detailed Description

Gophernotes is a Go kernel for Jupyter notebooks and nteract that enables interactive Go programming in browser-based and desktop notebook environments.

The tool solves the problem of using Go in interactive notebook settings, which traditionally support languages like Python but not compiled languages. Gophernotes works by leveraging gomacro, a Go interpreter, to evaluate Go code interactively within notebook cells. This allows developers to write and execute Go code live alongside documentation, visualizations, and explanatory text in a single shareable document.

Gophernotes suits data scientists, researchers, and Go developers who want to explore ideas interactively or create reproducible computational narratives. It works well for projects involving numerical methods, machine learning, and data analysis where Go's performance and concurrency features are valuable. The tool is particularly useful for creating shareable notebooks that can be distributed via email, version control, or the Jupyter Notebook Viewer, allowing others to view and run the code without additional setup beyond having Go and Jupyter installed.

The project maintains active engagement with its codebase through regular updates and responsiveness to issues. Development shows consistent attention to cross-platform compatibility, with documented installation paths for Linux, FreeBSD, Mac, Windows, and Docker environments. The maintainers provide example notebooks demonstrating practical applications including worker pools, matrix operations, and machine learning tasks, indicating ongoing investment in making the tool accessible to new users.