Building a Python Workflow That Catches Bugs Before Production

Published Time: 2026-04-04T15:00:00+00:00

# Building a Python Workflow That Catches Bugs Before Production | Towards Data Science
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# Building a Python Workflow That Catches Bugs Before Production

Using modern tooling to identify defects earlier in the software lifecycle.

[Thomas Reid](https://towardsdatascience.com/author/thomas_reid/)

Apr 4, 2026

17 min read

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Python is one of those languages that can make you feel productive almost immediately.

That is a big part of why it’s so popular. Moving from idea to working code can be very quick. You don’t need a lot of scaffolding just to test an idea. Some input parsing, a few functions maybe, stitch them together, and very often you’ll have something useful in front of you within minutes.

The downside is that Python can also be very forgiving in places where sometimes you would prefer it not to be.

It will quite happily assume a dictionary key exists when it does not. It will allow you to pass around data structures with slightly different shapes until one finally breaks at runtime. It will let a typo survive longer than it should. And perhaps, sneakily, it will let the code be “correct” while still being far too slow for real-world use.

That’s why I have become more interested in code development workflows in general rather than in any single testing technique.

When people talk about code quality, the conversation usually goes straight to tests. Tests matter, and I use them constantly, but I don’t think they should carry the whole burden. It would be better if most mistakes were caught before the code is even run. Maybe some issues should be caught as soon as you save your code file. Others, when you commit your changes to GitHub. And if those pass OK, perhaps you want to run a series of tests to verify that the code behaves properly and performs well enough to withstand real-world contact.

In this article, I want to walk through a set of tools you can use to build a Python workflow to automate the tasks mentioned above. Not a giant enterprise setup or an elaborate DevOps platform. Just a practical, relatively simple toolchain that helps catch bugs in your code before deployment to production.

To make that concrete, I am going to use a small but realistic example. Imagine I am building a Python module that processes order payloads, calculates totals, and generates recent-order summaries. Here’s a deliberately rough first pass.

```python
from datetime import datetime
import json

def normalize_order(order):
created = datetime.fromisoformat(order["created_at"])
return