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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.

JupyterLab

A screenshot of JupyterLab

JupyterLab is the next-generation web-based user interface for Project Jupyter.

WebsiteAlternatives

Setup

Is it managed?

No, you must host it yourself

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)

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

Notebook scheduling with additional tools

Management

Reproducability

With effort, you can make reproducible environments

Version history

File-based (use Git)

Collaborative editing

File-based (use Git)
Experimental JupyterLab support

Comments

File-based (use GitHub)

Notebook organization

File-based

Licensing

License

Open-source (BSD)

Price

Free

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Data Science Notebooks

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