When working on data science projects we are often tempted to leave our code to rot in scattered notebooks or Python modules deep in repositories.

However, even when you can’t openly release your code, breaking some important components into standalone Python packages can help with managing technical debt and code maintenance, facilitate in-house code reuse and repurposing, and make production-ising and deployment of code easier.

In this talk I'll demonstrate how packaging your code can benefit both your colleagues and (present and future) you, and review the tools Python provides for building and managing these packages, both in-company and openly, sharing from my experience.