This talk is about a problem many Python users run into without noticing: analysis that works once, lives in a notebook, and never really becomes reusable.
I’ll walk through how a simple exploratory notebook can slowly turn into something fragile, hard to rerun, hard to share, and hard to maintain, and what to do about it without overengineering.
The focus is not on “best practices at scale” or complex project structures. It’s about small, practical steps: when a notebook is enough, when it stops being enough, and how to move from cells to scripts in a way that still feels lightweight.
Using a real example, I’ll show how to extract logic from a notebook, turn it into simple functions, organize files just enough, and keep the workflow readable and flexible. The goal is to help people make their Python work easier to reuse, rerun, and trust, without turning analysis into a software project.