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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.

Kaggle

A screenshot of Kaggle

Explore and run machine learning code with Kaggle Notebooks, a cloud computational environment that enables reproducible and collaborative analysis.

WebsiteAlternatives

Setup

Is it managed?

Fully managed (setup in minutes)

Can you self-host?

No, you must use a managed offering

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)
Kaggle datasets

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

Limited scheduling capabilities

Management

Reproducability

Environments are reproducible by default

Version history

Version history is built in

Collaborative editing

No support for collaborative editors

Comments

Comment on a notebook as a whole

Notebook organization

View notebooks in a list

Licensing

License

Proprietary

Price

Free

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Data Science Notebooks

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