markusschanta/awesome-jupyter, "Hosted Notebook Solutions"
A curated list of awesome Jupyter projects, libraries, and resources for data science and interactive computing.
What is markusschanta/awesome-jupyter, "Hosted Notebook Solutions"?
Awesome Jupyter is a curated GitHub repository that aggregates and categorizes a wide array of projects, libraries, and resources related to the Jupyter ecosystem. It helps data scientists, researchers, and developers discover tools for extending Jupyter notebooks, JupyterLab, and JupyterHub for tasks like visualization, collaboration, publishing, and education.
Data scientists, researchers, educators, and developers who use Jupyter for interactive computing and want to enhance their workflow with extensions, libraries, and best practices.
It saves time by providing a single, well-organized source for discovering high-quality Jupyter tools, avoiding the need to search scattered resources, and is maintained by the community to ensure relevance and coverage.
Overview
A curated list of awesome Jupyter projects, libraries and resources
Use Cases
Best For
- Finding JupyterLab extensions to customize your development environment
- Discovering visualization libraries for interactive data exploration in notebooks
- Locating tools for converting or publishing Jupyter notebooks to other formats
- Exploring educational resources and collaboration platforms for teaching with Jupyter
- Identifying domain-specific Jupyter projects for fields like geospatial analysis or finance
- Browsing hosted notebook solutions and deployment options for cloud-based work
Not Ideal For
- Teams seeking an all-in-one, pre-configured Jupyter environment with tools pre-installed and integrated
- Projects requiring real-time, automated alerts or rankings for new Jupyter tools and updates
- Beginners who need guided, interactive tutorials rather than a passive list of resources
Pros & Cons
Pros
Organizes hundreds of Jupyter projects into clear sections like 'Runtimes/Frontends', 'Visualization', and 'JupyterLab Extensions', making it easy to browse specific tool types without scattered searches.
Accepts contributions via GitHub pull requests, as noted in the 'Contributing' section, helping keep the list updated with emerging tools and preventing staleness.
Links to tutorials, talks, and documentation under 'Articles/Guides/Tutorials' and 'Community Resources', offering learning aids beyond mere tool listings.
Spans from core runtimes like JupyterLab to domain-specific projects (e.g., geospatial analysis) and hosted solutions, serving as a one-stop hub for Jupyter enhancements.
Cons
The list catalogs tools without ratings, reviews, or usage metrics, forcing users to independently vet each project's stability and community support.
It merely provides links without installation help, compatibility checks, or integration guidance; users must navigate to external repos for practical details.
With dozens of entries per category, the sheer volume can be intimidating without curated recommendations or beginner-friendly filters.
Frequently Asked Questions
Related Projects
ml-tooling/best-of-jupyter, "Notebook Environments"🏆 A ranked list of awesome Jupyter Notebook, Hub and Lab projects (extensions, kernels, tools). Updated weekly.
Google Colabprovides "free" GPUs
Deepnoteallows for real-time collaboration
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.