Comments in Jupyter notebooks

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Data science is a team sport, and the conversation needs to include a number of stakeholders.

There are technical people, other data scientists, who can often offer their point of view on the methods or the technical implementation.

Then there are business stakeholders or domain experts, who can offer unique insights on the bigger picture and overall goal, review the conclusion and take action.

Currently, Jupyter notebooks offer a solo experience. Code reviews with technical peers in GitHub are problematic, and collaboration with business stakholders requires introducing several degrees of separation from the original work (such as screnshots in Slides).

A new generation of tools aims to solve these problems. They help prepare the narratives using notebooks, present them in views appropriate for non-technical people, and tighten the feedback loop.

Deepnote

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Deepnote is a new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud. Oh, and it's free.

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Hex

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The Data Workspace for Teams. Work with data in collaborative SQL and Python notebooks. Share as interactive data apps that anyone can use.

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Jetbrains Datalore

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A powerful online environment for Jupyter notebooks. Use smart coding assistance for Python in online Jupyter notebooks, run code on powerful CPUs and GPUs, collaborate in real-time, and easily share the results.

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Zepl

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Notebook-powered analytics for enterprise teams.

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Databricks Notebooks

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A notebook is a web-based interface to a document that contains runnable code, visualizations, and narrative text.

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Nextjournal

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Runs anything you can put into a Docker container. Improve your workflow with polyglot notebooks, automatic versioning and real-time collaboration. Save time and money with on-demand provisioning, including GPU support.

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Observable

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Make sense of the world with data, together. Explore, visualize, and analyze data. Collaborate with the community. Learn and be inspired. Share insights with the world.

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