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

Deepnote vs nteract

Comparing two data science notebooks.

Deepnote

nteract

A screenshot of Deepnote

Deepnote is a new kind of data notebook that’s built for collaboration — Jupyter compatible, works magically in the cloud, and sharing is as easy as sending a link.

WebsiteAlternativesExamples
A screenshot of nteract

nteract is an open-source organization committed to creating fantastic interactive computing experiences that allow people to collaborate with ease.

WebsiteAlternatives

Setup

Is it managed?

Fully managed (setup in minutes)

Is it managed?

No, you must host it yourself

Can you self-host?

No, you must use a managed offering

Can you self-host?

You can self-host (setup in minutes)

Features

Is it Jupyter compatible?

Jupyter-compatible

Is it Jupyter compatible?

Jupyter-compatible

Programming languages

Jupyter languages (e.g. Python, R)
SQL

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)
Connect to data warehouses (AWS, GCP, etc.)
Connect to databases (Postgres, MongoDB, etc.)
Provided file storage

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)
UI for building charts

What kind of data visualization can you do?

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

Reactivity

No reactivity, you decide the execution order

Reactivity

No reactivity, you decide the execution order

Notebook scheduling

Notebook scheduling is built in

Notebook scheduling

Notebook scheduling with additional tools

Management

Reproducibility

Environments are reproducible by default
Run notebooks in containers

Reproducibility

With effort, you can make reproducible environments

Version history

Version history is built in

Version history

File-based (use Git)

Collaborative editing

Multiple editors at the same time

Collaborative editing

File-based (use Git)

Comments

Comment on items within a notebook

Comments

File-based (use GitHub)

Notebook organization

View notebooks in a tree, like a wiki

Notebook organization

File-based

Licensing

License

Proprietary

License

Open-source (BSD 3-Clause)

Price

Free tier
Pay for compute
Pay-per-user

Price

Free

Alternatives to Deepnote and nteract

Compare Deepnote and nteract with other data science notebook tools.

ToolSetupJupyter compatibilityProgramming languagesData visualizationCollaborative editingPricingLicense
Deepnote		Fully managedJupyter-compatibleJupyter, SQLVisualize with code or UIRealtime collaborationFree and paid optionsProprietary
nteract		Self-hostedJupyter-compatibleJupyterVisualize with codeFile-based or asynchronous collaborationFreeOpen source
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Data Science Notebooks

Popular notebook tools

DeepnoteGoogle ColabHexJupyterJetbrains DataloreSagemaker

Popular notebook comparisons

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Notebook tools by feature

Jupyter notebooks onlineScheduling Jupyter notebooksRealtime collaboration in Jupyter notebooksVersion control in Jupyter notebooksComments in Jupyter notebooksOpen source notebooks