For an interactive environment for data science, the first results are jupyterlab/jupyterlab, udlbook/udlbook (This repository contains deep learning educational notebooks that run on a Jupyter environment, but it is a collection of learning content rather than the Jupyter Notebook or JupyterLab platform itself) and jupyter/jupyter (Jupyter is the core Jupyter project that provides the interactive notebook and JupyterLab interface, supporting live code in multiple languages via kernels, rich outputs, and extensibility—directly matching the flagship ecosystem described). jupyter/notebook and visualize-ml/book3_elements-of-mathematics round out the shortlist. Compare the match explanations and check the project documentation against your requirements.
We curate open-source GitHub repositories matching “jupyter”. Results are ranked by relevance to your query — pick filters below to narrow, or refine with AI.
JupyterLab is a web-based development environment designed for interactive data science, collaborative research, and computational notebook authoring. It provides a unified workspace where users can execute code, manage computational kernels, and create documents that integrate live code, rich data visualizations, and narrative text. The platform is built on a modular architecture that supports extensive customization through a plugin system. This framework allows for the dynamic loading of extensions, enabling users to define custom file viewers, interface themes, and keyboard shortcuts. By
JupyterLab is the next-generation web-based interface for Project Jupyter, providing a full interactive computing environment with live code execution, multi-language kernel support, notebook documents, rich outputs, and extensive extensibility via plugins — exactly the flagship ecosystem the visitor is looking for.
udlbook is a deep learning educational repository and a collection of interactive learning notebooks designed for studying neural network architectures. It serves as a digital repository of formatted mathematical equations and guided examples for learning deep learning concepts. The project provides a mathematical reference for supervised learning and neural network theory using LaTeX rendering. It includes interactive technical documentation and executable notebooks covering gradients, convolutions, and transformers. The system manages educational materials through a file-system based organ
This repository contains deep learning educational notebooks that run on a Jupyter environment, but it is a collection of learning content rather than the Jupyter Notebook or JupyterLab platform itself.
Jupyter is an interactive computing platform and data science workspace designed for creating documents that combine live code, equations, visualizations, and narrative text. It provides a polyglot notebook interface that connects a frontend user interface to various backend language engines through a standardized kernel protocol for real-time code evaluation. The system enables polyglot programming workflows, allowing multiple different programming languages to run within a single interface. It supports computational document authoring and data science exploration by allowing users to execut
Jupyter is the core Jupyter project that provides the interactive notebook and JupyterLab interface, supporting live code in multiple languages via kernels, rich outputs, and extensibility—directly matching the flagship ecosystem described.
This project is a browser-based interactive computing environment and data science IDE. It serves as a literate programming tool that allows users to create documents combining live code, mathematical equations, visualizations, and narrative text. As a polyglot notebook interface, it connects to various language kernels to execute code and render output within a single interface. The application distinguishes itself by separating the frontend interface from a remote compute engine through a language-agnostic kernel interface. This allows it to support multiple programming languages while main
This is the official Jupyter Notebook, the original browser-based interactive computing environment that combines live code, rich outputs, and narrative text with multi-language kernel support, exactly matching the flagship ecosystem requested.
This project is an interactive machine learning textbook and educational resource designed to teach the mathematical foundations of artificial intelligence. It functions as a structured course and digital book that covers essential topics ranging from basic arithmetic to advanced calculus, linear algebra, and statistics. The resource utilizes a math visualization library and a collection of interactive code examples to demonstrate abstract principles through algorithmic output. It transforms theoretical study into a practical experience by combining programmable examples with visual guides.
This repository is an interactive machine learning textbook that uses Jupyter Notebooks to present code and visualizations, but it is not the Jupyter Notebook or JupyterLab ecosystem itself — it is content built on top of the platform you seek.
Editor.md is an embeddable Markdown editor component for web applications that provides a real-time, dual-pane live preview alongside the raw source as the user types. It is designed as a plugin-based Markdown editor with a plugin architecture for extending functionality through custom modules, and it supports rendering LaTeX mathematical expressions using KaTeX as well as converting flowchart and sequence diagram syntax into visual diagrams within the preview. The editor distinguishes itself through its plugin-based extension system, which allows loading additional functionality through exte
Editor.md is a client-side Markdown editor with live preview and LaTeX rendering, but it does not support interactive code execution, multi-language kernels, or the .ipynb notebook format — so it is a text-editing building block rather than the Jupyter interactive computing environment you are looking for.
RoslynPad is a browser-based C# integrated development environment and interactive playground. It serves as a tool for writing and executing C# code snippets and scripts using the Roslyn compiler for immediate feedback. The environment features an integrated NuGet package manager that allows users to search for and load external .NET library assemblies into a live execution context. It functions as an in-process code executor to minimize startup latency and maintain state across script executions. The platform provides real-time semantic analysis and diagnostic feedback, offering code comple
RoslynPad is a browser-based C# code playground and IDE, but it is not a general-purpose interactive computing notebook environment like Jupyter—it lacks multi-language kernel support, notebook documents (.ipynb), and rich narrative text with equations and visualizations.
handcalcs is a mathematical documentation generator and Python LaTeX calculation renderer. It serves as an automated calculation sheet tool that converts Python code and numeric calculations into formatted LaTeX mathematical documentation, functioning as both a symbolic math formatter and a Jupyter notebook math extension. The project transforms Python variable names into Greek symbols, subscripts, and standard mathematical notation. It converts code into formatted mathematical expressions that display the original formula, the numeric substitution, and the final result, allowing for the crea
handcalcs is a Jupyter notebook extension that renders Python calculations as formatted LaTeX mathematics, not the full Jupyter Notebook or JupyterLab interactive computing environment for live code, multi-language kernels, and narrative notebooks.
KaTeX is a typesetting library and web math renderer that transforms TeX and LaTeX mathematical notation into high-quality HTML and CSS for web browsers. It functions as a math notation parser and LaTeX to HTML converter, capable of operating as both a client-side library and a server-side math renderer to generate static HTML expressions. The project supports a wide range of specialized mathematical rendering, including chemical equation rendering, Bra-ket notation for quantum mechanics, and mathematical logic typesetting. It provides comprehensive controls for structural layouts such as mat
KaTeX is a math rendering library that can be used within Jupyter notebooks to display LaTeX formulas, but it is not an interactive computing notebook environment—it lacks live code execution, multi-language kernels, and the notebook document format itself.
This project is a collection of pre-configured Docker images that provide ready-to-run environments for interactive computing and data science. It functions as a scientific computing stack and a polyglot notebook server, bundling language interpreters and libraries for Python, R, and Julia within a containerized system to ensure reproducible research environments. The collection uses a layered image hierarchy to provide versioned software dependencies and support for hardware acceleration across different CPU architectures. It allows for the creation of custom images based on a foundation of
This project provides pre-configured Docker images for running Jupyter environments, but it is not the interactive notebook ecosystem itself—it is a deployment tool for it, not the core notebook interface or kernel system.
Streamdown is a streaming markdown renderer for React that transforms incoming markdown text into sanitized HTML in real time, handling incomplete blocks as they arrive. It parses GitHub-Flavored Markdown syntax including tables, task lists, and footnotes, and renders LaTeX math expressions using KaTeX, Mermaid diagrams as interactive SVGs, and code blocks with Shiki-based syntax highlighting supporting over 200 languages with dual light and dark themes. The renderer includes an XSS-safe HTML sanitizer that strips dangerous tags and validates URLs to prevent injection attacks. What distinguis
Streamdown is a streaming Markdown renderer for React, not an interactive computing notebook environment like Jupyter — it renders rich output but lacks live code execution, kernels, or a notebook document model.
yn is a markdown text editor and knowledge management tool designed as an interactive document canvas. It functions as a networked note-taking system for organizing information via wiki-links, hashtags, and local file repositories, while integrating an AI writing assistant for automated text generation and completion. The project is distinguished by its multi-engine diagramming capabilities, which render text-based syntax into visuals using Mermaid, PlantUML, and ECharts. It employs an extensible plugin framework that allows for the addition of custom UI elements and features through JavaScri
yn is a markdown note-taking and knowledge management tool, not an interactive computing environment like Jupyter; it lacks live code execution, kernel support, and the .ipynb notebook format required for data science workflows.
| Repository | Stars | Language | License | Last push |
|---|---|---|---|---|
| jupyterlab/jupyterlab | 15.2K | TypeScript | BSD-3-Clause | |
| udlbook/udlbook | 9.1K | Jupyter Notebook | other | |
| 15.3K |
| Python |
| BSD-3-Clause |
| jupyter/notebook | 13.2K | Jupyter Notebook | BSD-3-Clause |
| visualize-ml/book3_elements-of-mathematics | 7.5K | Jupyter Notebook | — |
| pandao/editor.md | 14.3K | JavaScript | MIT |
| roslynpad/roslynpad | 2.8K | C# | mit |
| connorferster/handcalcs | 5.8K | CSS | Apache-2.0 |
| katex/katex | 20.2K | TypeScript | MIT |
| jupyter/docker-stacks | 8.4K | Python | BSD-3-Clause |