A weekly Python podcast hosted by Christopher Bailey with interviews, coding tips, and conversation with guests from the Python community. The show covers a wide range of topics including Python programming best practices, career tips, and related software development topics. Join us every Friday morning to hear what's new in the world of Python programming and become a more effective Pythonista.

Agentic Data Science Pair Programming With marimo pair

May 01, 2026 1:04:13 62.65 MB Downloads: 0

How do you add agent skills to your data science workflow? How can a coding agent assist with data wrangling and research? This week on the show, Trevor Manz from marimo joins us to discuss marimo pair.

Trevor is a founding engineer at marimo, where he’s been working on integrating LLM tools with marimo. We discuss the balancing act of building a skill and determining how to give an agent access to all the variables in a notebook. He shares how they built a specialized reactive REPL that eliminates hidden state and allows the agent to continue constructing a reproducible Python program.

We dig into installing and getting started with marimo pair. Trevor also covers several of the tasks an agent can tackle in a data science workflow.

Video Course Spotlight: Getting Started With marimo Notebooks

Discover how marimo notebook simplifies coding with reactive updates, UI elements, and sandboxing for safe, sharable notebooks.

Topics:

  • 00:00:00 – Introduction
  • 00:02:26 – Trevor’s role at marimo
  • 00:03:08 – Current AI tools in marimo
  • 00:06:26 – Describing marimo notebooks
  • 00:10:11 – What is marimo pair?
  • 00:18:49 – Building an agent skill
  • 00:27:34 – Setup & installation
  • 00:31:16 – Video Course Spotlight
  • 00:32:42 – Examples of EDA and data wrangling
  • 00:45:46 – Experimenting inside of a notebook
  • 00:50:40 – Managing context
  • 00:53:25 – Accessing additional libraries
  • 00:57:16 – Recent tools and updates from the marimo community
  • 00:59:31 – What are you excited about in the world of Python?
  • 01:01:10 – What do you want to learn next?
  • 01:02:26 – How can people follow your work online?
  • 01:03:13 – Thanks and goodbye

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