Data Science Notebooks

Data science gets done in notebooks. This website exists to compare the features in different data science notebook tools.

Robert Lacok
About the author

My name is Robert Lacok, and I’m a data notebook enthusiast. Because I keep on top of the latest developments in the space, I wanted to share it with the world.

I’m also a product manager at Deepnote. I try to be unbiased — if you believe any tools are missing or misrepresented, please email me or open a pull request on GitHub.

Need help?

If you need help picking a data notebook for your next project, feel free to reach out to me at my personal email address. I’d be happy to chat about the pros and cons of each solution.

View all notebooks

CoCalc

A screenshot of CoCalc

Your best choice for teaching remote scientific courses.

WebsiteAlternatives

CoCalc capabilities

Setup

Is it managed?

Fully managed (setup in minutes)

Can you self-host?

You can self-host (setup in hours)

Features

Is it Jupyter compatible?

Jupyter-compatible

Programming languages

Jupyter languages (e.g. Python, R)

What kind of data sources can you connect to?

Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)
Provided file storage

What kind of data visualization can you do?

Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)

Reactivity

No reactivity, you decide the execution order

Notebook scheduling

No notebook scheduling

Management

Reproducibility

Environments are reproducible by default

Version history

Version history is built in

Collaborative editing

Multiple editors at the same time

Comments

No support for comments

Notebook organization

View notebooks in a list

Licensing

License

Open-source (GNU AGPLv3)

Price

Free tier
Pay for compute

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Data Science Notebooks

Popular notebook tools

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Popular notebook comparisons

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