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We have built JupyterLab with the abstractions needed to offer real-time collaboration on all documents (notebooks, text files, etc.).

#Jupyterlab online full

The next step beyond that is full real-time collaboration, in the style of Google Drive or Dropbox Paper. Sure, notebooks can be shared on GitHub, Dropbox or email, but that is really painful and doesn’t represent how most of us work together. As more and more groups and organizations adopt Jupyter, they need to work together with others on notebooks. Third, collaboration is a significant area of need.

#Jupyterlab online code

For example, in JupyterLab we offer a document-less code console for quick exploration that supports Jupyter’s rich output, and which is integrated with the text file editor, so you can run blocks of code in a text file interactively, outside the notebook. In JupyterLab, we are trying to address the pain points of such an evolving workflow. In the classic notebook, those transitions are really painful. A classic usage pattern we see is data scientists who begin working in an interactive Python shell, then migrate to a notebook, and eventually build and deploy a service based on that code. Second, users want to be able to combine, remix, and integrate the different building blocks to better support their workflows.

Thus, while we are making improvements to the notebook UI/UX in JupyterLab, notebooks are essentially the same (in fact, it is still 100% the same document format and server). This is important, because we could have re-thought the entire notebook abstraction itself in JupyterLab-but we didn’t. Based on all of this feedback, three key factors led us to develop JupyterLab.įirst, users love the notebook experience, and want it to improve, but without losing the core characteristics that make it the Jupyter notebook. Also, in 2015, we worked with IBM to run a user experience survey (the results of which are posted on GitHub). Some of those pain points are specific to the Jupyter Notebook and others are more general challenges they face working with code and data. Since the IPython Notebook came out in 2011, we have spent a lot of time talking to individual and organizational users to understand what delights them about the notebook, and what remains really painful. What need is JupyterLab fulfilling among Jupyter Notebook users, particularly those who work in data science or scientific/technical computing?

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Lastly, we are all grateful to Fernando Perez, creator of IPython and co-founder with us on Jupyter, for setting off into the uncharted wilderness of scientific open source software in 2001. The team building JupyterLab and PhosphorJS are the driving force behind the work I discuss here. In particular, individuals on the Jupyter Steering Council provide the foundation of the project and are all long-term contributors, without whom the project wouldn’t exist. Learn moreīefore going further, I want to acknowledge the incredible team working on Jupyter on many fronts, including software, design and organizational aspects.

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Get a free trial today and find answers on the fly, or master something new and useful. Join the O'Reilly online learning platform. From a user’s perspective, we hope that everything in JupyterLab is familiar, but even more delightful and productive to work with.

We view JupyterLab as the evolution of the classic notebook, as it allows a more flexible and powerful way for working with those same building blocks. The classic Jupyter Notebook offers a number of different building blocks for interactive computing: the notebook, file browser, text editor, terminal, outputs, etc. What is JupyterLab and how does it represent what you’ve called the “evolution of the Jupyter web interface?” We recently discussed JupyterLab, how feedback from notebook users informed its design, how its features could benefit the scientific and technical computing communities, and the role of notebooks in academia and even data journalism.

He is also an active contributor to and co-leader of the JupyterLab project, which aims to take the Jupyter Notebook interface to the next level with its flexible building blocks for interactive and collaborative computing. He started the original IPython Notebook in 2011 and is one of the co-founders of Project Jupyter. His primary area of research is interactive computing with data and, specifically, Jupyter. Brian Granger is an associate professor of physics at Cal Poly San Luis Obispo, where he also teaches in the university’s undergraduate data science program.