What are common issues with using notebooks for Python development? How do you know the current state, share reproducible results, or create interactive applications? This week on the show, we speak with Akshay Agrawal about the open-source reactive marimo notebook for Python.

Before writing any code, Akshay wrote a 2,500-word design document. He wanted to create a maintainable and reproducible tool that avoided the hidden state of traditional notebooks. We discuss solving the hidden state problem by building the notebook as a directed acyclic graph (DAG).

Akshay shares how marimo notebooks are stored as pure Python files, which makes them easy to read, importable, and git-friendly. We discuss serializing package requirements using PEP 723 inline metadata to create standalone reproducible notebooks. We also cover how marimo notebooks can be deployed as a web app or dashboard using Pyodide.

Course Spotlight: Navigating Namespaces and Scope in Python

In this course, you’ll learn about Python namespaces, the structures used to store and organize the symbolic names created during execution of a Python program. You’ll learn when namespaces are created, how they are implemented, and how they define variable scope.

Topics:

00:00:00 – Introduction
00:02:06 – Akshay’s background and studies
00:04:14 – Work at Google and PhD program
00:06:29 – Sharing notebooks
00:08:18 – Starting work on marimo 2 years ago
00:12:48 – Avoiding notebook issues and building a DAG
00:18:39 – The difference of reactivity
00:20:39 – What is a marimo notebook?
00:23:39 – Video Course Spotlight
00:24:50 – Reproducibility and managing package requirements
00:27:49 – Using decorators for cells
00:30:23 – Writing a design document before any coding
00:34:08 – Interactivity and UI widgets
00:38:20 – Design decisions and built-in widgets
00:42:05 – Creating a deployable web application
00:44:34 – Exploring examples and tutorials
00:46:13 – Supporting DataFrame libraries with narwhals
00:48:00 – Migrating from a Jupyter notebook
00:52:02 – Working with cells and not running code
00:54:30 – A couple favorite tutorials
00:56:17 – What are you excited about in the world of Python?
00:57:39 – What do you want to learn next?
00:59:34 – How can people follow the project and yourself?
01:00:12 – Thanks and goodbye

Show Links:

marimo - a next-generation Python notebook
marimo: an open-source reactive notebook for Python - Akshay Agrawal (Nbpy2024) - YouTube
TensorFlow
Made with marimo - marimo
FAQ - marimo
Pluto.jl — interactive Julia programming environment
Observable: Build expressive charts and dashboards with code
We Downloaded 10,000,000 Jupyter Notebooks From Github – This Is What We Learned - The Datalore Blog
A Large-scale Study about Quality and Reproducibility of Jupyter Notebooks
Lessons learned reinventing the Python notebook - marimo
Episode #226: PySheets: Spreadsheets in the Browser Using PyScript
PEP 723 – Inline script metadata
Inline script metadata - Python Packaging User Guide
Serializing package requirements in marimo notebooks - marimo
uv: Unified Python packaging
marimo Newsletter 7 - Jupyter to marimo
Custom UI elements - marimo
anywidget - anywidget
Interactive elements - marimo
Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries
Calmcode - marimo: Introduction
Join the marimo Discord
marimo newsletter
marimo on Twitter
marimo on LinkedIn
Akshay Agrawal’s website
Aksahy on Twitter

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