My Local Python Setup Was Quietly Destroying Our Team's Productivity. Docker Fixed It.

How I moved our Python interpreter into a Docker container, wired it into VS Code, and never looked back. Let me be upfront with you, I didn't want to write this article. Not because the topic isn't worth it — it absolutely is — but because writing it means admitting that I let a fixable problem drag on for almost five months before I actually fixed it. Five months of "works on my machine." Five months of onboarding friction. Five months of my team losing hours to an issue that, once solved, took an afternoon. So consider this both a technical walkthrough and a cautionary tale from someone who waited too long. The Environment That Slowly Ate Our Standup Picture this: three developers, one Python codebase, and a Monday morning that starts with a Slack message at 9:04 AM. "Hey, the pipeline script is throwing a ModuleNotFoundError. Did something change?" Nothing changed. Nothing ever changed. That was the whole problem. We had a requirements.txt. We had virtual environments. We had a wel