UV kernel for Jupyter.

reproducible, dependency-aware, and Git-friendly Jupyter Notebooks.

Automate code & data workflows with interactive notebooks. Get rid of scripts, manual steps, and outdated docs. Use Elixir and Livebook to share knowledge, deploy apps, visualize data, run machine learning models, debug systems, and more!

• Livebook @ GitHub.

Restrict the scope of functions for reproducible code execution and peace of mind.

Have you ever hunted bugs caused by accidentally using a global variable in a function in a Jupyter notebook? Have you ever scratched your head because your code broke after restarting the Python kernel? localscope can help by restricting the variables a function can access.

• localscope @ GitHub.

Build beautiful, publication-quality books and documents from computational content.

Jupyter Book is an open-source tool for building publication-quality books and documents from computational material.

• Jupyter Book @ GitHub.

A Supercharged IDE for Data Science.

Zasper is an IDE designed from the ground up to support massive concurrency. It provides a minimal memory footprint, exceptional speed, and the ability to handle numerous concurrent connections.

It's perfectly suited for running REPL-style data applications, e.g. Jupyter notebooks.

• Zasper @ GitHub.

Microsoft Threat Intelligence Python Security Tools. msticpy is a library for InfoSec investigation and hunting in Jupyter Notebooks.

With JupyterHub you can create a multi-user Hub that spawns, manages, and proxies multiple instances of the single-user Jupyter notebook server. Project Jupyter created JupyterHub to support many users. The Hub can offer notebook servers to a class of students, a corporate data science workgroup, a scientific research project, or a high-performance computing group. JupyterHub brings the power of notebooks to groups of users. It gives users access to computational environments and resources without burdening the users with installation and maintenance tasks. Users - including students, researchers, and data scientists - can get their work done in their own workspaces on shared resources which can be managed efficiently by system administrators. JupyterHub @ GitHub

Tutorials notebooks for data science

Your Machine Learning and Data Science Community.

Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time.