Data Science

JupyterLab – Data Science Notebook

JupyterLab is an extensible open source environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture.

JupyterLab is the next-generation user interface for Project Jupyter. It provides the same building blocks of the classic Jupyter Notebook (notebook, terminal, text editor, file browser, rich outputs, etc.) in a flexible and powerful user interface.

It enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner.

The software is currently in beta state but it’s ready for general usage. JupyterLab 1.0 will eventually replace the classic Jupyter Notebook.

Features include:

  • Interface consists of a main work area containing tabs of documents and activities, a collapsible left sidebar, and a menu bar.
  • Text editor includes syntax highlighting, configurable indentation (tabs or spaces), key maps and basic theming.
  • Jupyter notebooks (.ipynb files) are fully supported. Jupyter notebooks are documents that combine live runnable code with narrative text (Markdown), equations (LaTeX), images, interactive visualizations and other rich output.
  • Uses the same notebook document format as the classic Jupyter Notebook.
  • Code Consoles provide transient scratchpads for running code interactively, with full support for rich output. A code console can be linked to a notebook kernel as a computation log from the notebook. Code consoles also display rich output, just like notebook cells.
  • Kernel-backed documents enable code in any text file (Markdown, Python, R, LaTeX, etc.) to be run interactively in any Jupyter kernel.
  • Terminals provide full support for system shells (bash, tsch, etc.) on Mac/Linux and PowerShell on Windows.
  • Notebook cell outputs can be mirrored into their own tab, side by side with the notebook, enabling simple dashboards with interactive controls backed by a kernel.
  • Multiple views of documents with different editors or viewers enable live editing of documents reflected in other viewers. For example, it is easy to have live preview of Markdown, Delimiter-separated Values, or Vega/Vega-Lite documents.
  • Offers a unified model for viewing and handling data formats. JupyterLab understands many file formats (images, CSV, JSON, Markdown, PDF, Vega, Vega-Lite, etc.) and can also display rich kernel output in these formats.
  • Customizable keyboard shortcuts and the ability to use key maps from vim, Emacs, and Sublime Text in the text editor.
  • Extensions – customize or enhance any part of JupyterLab, including new themes, file editors, and custom components. JupyterLab extensions are npm packages (the standard package format in JavaScript development).
  • Connect any open text file to a code console and kernel. This means you can easily run code from the text file in the kernel interactively.
  • Unified architecture for viewing and editing data in a wide variety of formats.

Support: GitHub
Developer: Project Jupyter Contributors
License: BSD 3-Clause License


JupyterLab is written in JavaScript and TypeScript. Learn JavaScript with our recommended free books and free tutorials. Learn TypeScript with our recommended free books and free tutorials.

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