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GitHub adds support for Jupyter notebooks to help data scientists collaborate

A Jupyter notebook on GitHub.

Image Credit: GitHub

Source code-repository company GitHub today is introducing support for Jupyter notebooks, a trusted open-source technology that displays text, code, math, and data visualizations in a web browser.

The addition — which follows GitHub’s introduction last month of Large File Storage — could make GitHub into a better place for data scientists to share and collaborate on their statistical work, and not solely a virtual destination for developers working together on projects.

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Sharing an actual notebook on a GitHub page is certainly much better than sharing a screenshot of a notebook with colleagues, Arfon Smith, GitHub’s head of science, told VentureBeat in an interview.

The idea to support Jupyter came about a year or so ago, right around the time that startups like Domino Data Labs and Sense were emerging on the scene to focus on the data science collaboration market.

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But GitHub’s support for .ipynb files won’t instantly make the company a competitor to those startups, and others, like Plotly or Yhat, Smith said. GitHub isn’t performing the computations as a cloud service, but only providing a place to share results — that’s the major difference.

The move follows Google Research’s release last summer of a Chrome browser app that installs IPython, a component of Jupyter, to let people work with data in a way that’s integrated with Google Drive.

Support for Jupyter notebooks will ship in version 2.3 of the GitHub Enterprise software, which can run in companies’ on-premises data centers and on clouds like Amazon Web Services and Microsoft Azure. Version 2.3 should arrive in July, Smith said.

Data scientists at Stitch Fix, a startup that relies on stylists to deliver clothes and accessories to women, are already using Jupyter Notebooks.

And in academia, where Jupyter Notebooks are often used, GitHub could also become a better place for sharing information.

“It’s a really compelling option for people who want to combine analysis and visualization into an easily sharable format,” Smith said.

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See Smith’s blog post for more detail.

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