How to Keep Your Python Code Standard

If you've ever joined a new team and opened a Python file only to be greeted with wildly inconsistent spacing, cryptic variable names, or half-written comments, you already know why “standard Python” matters. But what exactly is standard Python? Who decides what counts as “normal” in a language used by everyone from AI researchers to high school students building games?

Let’s walk through it, and talk about how sticking to standards can help you write better software, work more easily with others, and avoid some seriously annoying bugs.

By writing standard Python, you’ll amaze your teammates… especially if you are a standard python.

What Is “Standard Python,” Anyway?

When developers talk about “standard Python,” they usually mean the set of practices and guidelines that the Python community agrees are best. The most important of these is PEP 8, a document created in 2001 by Python core developers that lays out style conventions for writing Python code.

PEP stands for Python Enhancement Proposal. These documents are a formal way to suggest changes or best practices to the language, and they’re reviewed and accepted (or rejected) by the people who maintain Python itself. PEP 8 doesn’t change how Python runs, but it defines how Python should look if you want to write clean, readable code.

Some highlights:

• Use four spaces per indentation level.

• Keep line length to 79 characters or less.

• Use snake_case for function and variable names.

• Use docstrings to document modules, functions, classes, and methods.

You can read the full PEP 8 here, and there's a solid guide from Real Python that makes it more digestible.

Who’s in Charge of Python Standards?

Python was created by Guido van Rossum, who served as its “Benevolent Dictator for Life” (yes, that was his actual title) until he stepped down in 2018. Today, the language is maintained by the Python Steering Council, a small group of experienced developers elected by Python contributors.

The Python Software Foundation (PSF), a nonprofit organization, supports the community and infrastructure behind Python. While the PSF doesn’t dictate language changes directly, they play a huge role in promoting community-driven development and encouraging best practices.

When someone submits a new PEP, the Steering Council reviews it and decides whether it fits with Python’s design philosophy: readability counts, simplicity is better than complexity, and there should be one obvious way to do it.

Why Should Devs Care About “Standard” Code?

Here’s the short version: because you’ll write better code, faster, and with fewer bugs.

Let’s break that down:

1. Code Becomes Easier to Read and Maintain

Most codebases are read far more than they’re written. When you use standard conventions, the next developer (which could be you six months from now) won’t have to guess what your function is doing or why it’s named x123_opt_final2. Following conventions helps reduce the time it takes to onboard new team members and makes your code feel predictable and professional.

2. It Prevents Silly Mistakes

Python’s syntax is famously clean—but that also means it's easy to break things with inconsistent indentation or misleading variable names. Tools like flake8, black, and pylint exist to automatically check or fix your code according to PEP 8 and other standards. These tools save you from wasting time on problems that shouldn’t have been problems in the first place.

3. It Plays Nicely With DevOps and CI/CD

If you’re working in a DevOps environment, your code is probably going through automated checks before it’s merged or deployed. Following standard Python practices helps these tools work better. Static analysis, test coverage, dependency scanning—these all assume some level of consistency in how your code is structured. It’s not just about aesthetics, it’s about stability and automation.

4. It Builds Better Team Culture

Writing clean, standard code isn’t just good hygiene; it’s a form of respect for your teammates. You’re signaling that you care about the codebase, the project, and the people who will work with it. High-functioning software teams (and especially DevOps teams) depend on shared norms and clear communication. Code is part of that.

5. Onboard New Team Members Faster

If your team establishes engineering standards for writing clean, standard code, it may initially take a bit of effort, but eventually will become a habit that you may not think about often. But when a new team member joins your team, that effort will pay off in a big way. Instead of spending weeks (or months!) learning your team’s idiosyncratic ways, your new team member will be able to get up-to-speed writing standard Python.

How to Keep Your Python Code “Standard”

The good news is that you don’t need to memorize all of PEP 8 to write clean code. There are excellent tools that can enforce standards automatically, and they’re easy to plug into any DevOps workflow.

Here are the key ones to know:

black: the Uncompromising Code Formatter

black takes your Python code and reformats it to follow a consistent style. It’s fast, opinionated, and removes the need for bikeshedding in code reviews. That’s when a team spends too much time discussing small issues, while ignoring more important, complex problems. Want to stop arguing about whether to use single or double quotes? Just use black.

To format your entire project:

black .

isort: Automatic Import Sorting

isort keeps your import statements organized and consistent. It can be used alongside black without conflicts.

ruff: Fast All-in-One Linter

ruff is a newer tool that combines the speed of black with the flexibility of flake8, pylint, and more. It runs incredibly fast, supports hundreds of rules, and can often replace multiple tools in your pipeline.

pre-commit: Glue It All Together

The pre-commit framework lets you run all these tools automatically before a commit is made. This guarantees that no one forgets to format or lint their code. Add a .pre-commit-config.yaml file to your repo and install hooks—your CI/CD pipeline (and your teammates) will thank you.

Bonus: Type Checkers Like mypy

Typing is not part of PEP 8, but type annotations are increasingly common in modern Python. Tools like mypy can verify that your code uses type hints correctly, catching bugs that would otherwise only appear at runtime.

The Bottom Line

“Standard Python” isn’t some bureaucratic mandate. It’s the accumulated wisdom of thousands of developers who’ve learned how to make Python code easier to read, easier to test, and easier to share.

If you want to work on a high-functioning team, write software that scales, and contribute to open-source projects, making these standards part of your team’s engineering standards is one of the best investments you can make.

If that sounds like a tall order, Caparra can help. We’re building AI-powered tools that help your team establish and enforce engineering standards (especially for repetitive DevOps basics like formatting, testing, and deployment), so you can focus on the high-leverage work. Open a free account and start learning with us today: