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To keep up with Project Jupyter’s motto of developing open-source software, open-standards, and services with a goal to offer interactive computing across various programming languages they released JupyterLab beta readily available for users this month.

JupyterLab is tagged as the next generation UI for Project Jupyter, and is a successor to Jupyter Notebooks, a successful and a widely adopted application launched by Project Jupyter last year.

Saying hello to JupyterLab

Jupyter Notebook is an open-source web application that allows users to create and share documentations that contains live code, visualizations, narrative text, and equations. Jupyter notebooks are used for tasks such as data cleaning, data transformation, numerical simulation, machine learning, and many more.

It is now well established that the data science community  loves using Jupyter Notebooks for interactive computing. However, there are certain barriers they face which made their interaction with Jupyter Notebook a little less than ideal.

Some of the cons include:

  • Transition from different building blocks within a workflow is difficult
  • Real-time collaboration of notebooks onto Dropbox or Google Drive is not possible with Jupyter Notebooks.
  • Too many wasted spaces on the right and left of the Jupyter notebook

These are some of the issues with Jupyter Notebooks, which are taken care of in the brand new JupyterLab.

A swift move to JupyterLab

JupyterLab has complete support for Jupyter Notebooks. So, one won’t miss working with notebooks but can do a lot more using JupyterLab.

JupyterLab is an interactive environment which allows you to work with notebooks, code, and data, all under one roof.

The most important feature of JupyterLab is real-time collaboration with several people on a single project. An add-on to this is its user-friendly interface, which makes it all the more easy-to-use. JupyterLab also shows a high level of integration between notebooks. This means, you can drag-and-drop notebooks cells and can also copy them between notebooks. You can also run code blocks from text files with .py, .R, .tex extensions.

JupyterLab can also multi-task, i.e. you can open up notebooks, text editors, terminals, and other components, view them and edit them in different tabs simultaneously. JupyterLab offers an entire range of extensions which could be used to enhance parts of JupyterLab. One can choose from a variety of themes, editors, and renderers for rich outputs on notebooks. JupyterLab extensions are npm packages (the standard package format in Javascript development). There are also many community-developed extensions being built on GitHub. To find extensions, you can search GitHub for jupyterlab-extension. You can also check out the developer documentation guide for information on developing extensions.

Some additional features of JupyterLab include:

  • JupyterLab is more about development unlike Jupyter Notebook which focuses on presentation.
  • Developers can perform syntax completion using the Tab key and object tool-tip inspection, using the Shift-Tab keys.
  • Files can be opened up in variety of formats. Also, developers can run their codes interactively inside of ‘consoles’ and not only notebooks. This promotes an imperative programming mode for them.
  • JupyterLab accommodates notebooks in multiple languages, provided the kernels for those languages are installed.
  • Browsers such as Chrome, Firefox, and Safari are compatible with JupyterLab.

The Jupyter community plans to unleash version 1.0 of JupyterLab some time later this year. The version 1.0 will replace the classic Jupyter Notebook. However, the notebook document format would be supported by both classic notebook as well as JupyterLab.

For a further detailed information on JupyterLab beta, visit Project Jupyter’s official blogpost                                                    


A Data science fanatic. Loves to be updated with the tech happenings around the globe. Loves singing and composing songs. Believes in putting the art in smart.


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