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

Google Colab

A screenshot of Google Colab

Colab notebooks allow you to combine executable code and rich text in a single document, along with images, HTML, LaTeX and more.

WebsiteAlternativesExamples

Google Colab (which is short for Colaboratory) is a hosted Jupyter notebook. It is a great option for data scientists and python library authors because:

  • It is free to use.
  • It is easy to open notebooks directly from GitHub.

If you’re browsing Github for notebooks, you’ll find a lot of links to open them on Google Colab for this reason.

Because it’s based on Jupyter, you can use the vast array of scientific computing libraries available on Python. However, just like Jupyter, it’s got a bit of a steep learning curve. It works best for technical people who are comfortable with Git.

It has rich integration with Google Drive. You can load files directly from Google Drive which is especially convenient if you have datasets in Google Sheets. If you’re not opening notebooks from GitHub, you can conveniently open notebooks from and save to Google Drive, meaning notebooks live alongside your other Google Drive files.

Despite “colab” being in its name, it’s not the best option for teams. It lacks realtime collaboration and versioning, which means you’ll have a much harder time working with other people.

Google Colab 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)

What kind of data sources can you connect to?

Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)
Google Drive

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

There is no support for reproducibility

Version history

No version history

Collaborative editing

No support for collaborative editors

Comments

No support for comments

Notebook organization

View notebooks in a list

Licensing

License

Proprietary

Price

Free tier
Pay-per-user
Pay for compute

Google Colab examples

Overview of Colaboratory features
The different types of cells in Google Colab
Open →
Getting started with BigQuery
Open →
Loading external data
Read data from Google Drive and more
Open →

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Data Science Notebooks

Popular notebook tools

DeepnoteGoogle ColabHexJupyterJetbrains DataloreSagemaker