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

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

Deepnote is a hosted Jupyter notebook with a host of features to make notebooks better for collaboration and easier to use for less technical users.

By having realtime collaboration, versioning, and comments, it’s a great fit for teams who need to collaborate on the same notebook. Furthermore, teams can organize their notebooks in a wiki-like structure so everything is easy to find.

It adds a number of features to make working in notebooks easier. For example, it lets you connect directly to external data sources (e.g. Snowflake and Postgres) and has a visual chart builder.

Deepnote has a free tier and offers even more free perks to educational users.

Deepnote capabilities

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)
SQL

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 visualization can you do?

Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)
UI for building charts

Reactivity

No reactivity, you decide the execution order

Notebook scheduling

Notebook scheduling is built in

Management

Reproducibility

Environments are reproducible by default
Run notebooks in containers

Version history

Version history is built in

Collaborative editing

Multiple editors at the same time

Comments

Comment on items within a notebook

Notebook organization

View notebooks in a tree, like a wiki

Licensing

License

Proprietary

Price

Free tier
Pay for compute
Pay-per-user

Deepnote examples

A/B testing
Comparing conversion rates for different versions of a website.
Open →
Rise and fall of Wordle
Analyzing the popularity of Wordle using sentiment analysis of tweets.
Open →
SQL cells
Parameterizing SQL with calculations from Python.
Open →

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Data Science Notebooks

Popular notebook tools

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