Datamode is a free, open source tool to quickly build data science pipelines. Our Python data visualization and transformation tool runs directly in Jupyter Notebook. We help you:

  • Explore your data
  • Reshape your data
  • Deploy your data and models to production.

Here’s a screenshot of Datamode running in a Jupyter notebook:


How will this help me?

Getting to know messy data is… well, messy.

Exploratory data analysis is a critical part of any data project but is often a painful and frustrating process. Data engineers and data scientists waste valuable time writing code to load data, extract basic summary statistics, create visualizations, and reshape data. This process is often done in an ad hoc way thus making deployment to a scalable infrastructure or any production like system a time and resource intensive project.

Who is this for?

Anyone and everyone that needs to handle messy data. Our data visualization and inspection tools are built into Jupyter notebook so you can get started in just a few lines of code. We’ve also built a set of Transforms that reduce the amount of code you need to write to get to the same transforms while still being flexible and transparent.

How does Datamode help?

Datamode is built for anyone that needs to quickly undestand data, reshape variables, and push these transforms into a production pipeline. Developers can write and execute code in their Jupyter notebooks and deploy that same code to be used in a scalable environment.

How can I get started?

Check out Quickstart/Installation to try Datamode on your own data or you can try a live demo.

If you have any questions, feedback, or issues please e-mail


Datamode currently requires Python 3.6+ (64-bit). If you’re having problems installing, see these pages:

Feedback / Contributing

If you’re trying out Datamode, we’d love to hear from you! We’re especially interested in what’s working/not working for you, and what’s missing that you really need. Email us:

If you want to contribute transforms or other code, see Contributing.