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The Real Python Podcast · May 1 · 1 hr 4 min

Agentic Data Science Pair Programming With marimo pair

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 Show Links: Introducing marimo pair - marimo marimo-pair: Drop agents inside running marimo notebook sessions Marimo pair – Reactive Python notebooks as environments for agents - Hacker News Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python marimo Pair - YouTube We gave Claude Access to All Python Variables - YouTube Using the marimo editor’s AI features - marimo ty: An extremely fast Python type checker and language server, written in Rust. molab - marimo marimo: A Reactive, Reproducible Notebook – Real Python Investigating Quasar Data With Polars and Interactive marimo Notebooks – Real Python Blog - marimo Trevor Manz - LinkedIn trevor manz (@manzt.sh) — Bluesky Level up your Python skills with our expert-led courses: Getting Started With marimo Notebooks Investigating Quasar Data With Polars and Interactive marimo Notebooks Getting Started With Claude Code Support the podcast & join our community of Pythonistas

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transcript

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show notes

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

Show Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

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