4 keys to writing modern Python in 2022 – InfoWorld

By Serdar Yegulalp
Senior Writer, InfoWorld |
Although Python turned 30 years old last year (2021), only in the last few years has it enjoyed the great explosion of adoption, growth, and forward-thinking development that we’ve come to associate with the language. Many features of Python have remained unchanged since its inception, but with every passing year, and every new edition of Python, along come new ways of doing things and new libraries that take advantage of those advances.
So Python has its old ways and its new ways. Naturally, it makes sense to learn how to work with Python using its most modern and convenient features. Here we’ll run down the key concepts you need to understand to write modern Python in 2022—software that uses Python’s latest and greatest idioms, concepts, and capabilities.
Python’s recently introduced type hinting syntax allows linters and third-party code quality tools to analyze your code before runtime, and to detect possible errors before they buzz out. The more you create Python code to be shared with others, the more likely you (and everyone else!) will benefit from using type hints.
Each successive revision of Python rolls out more sophisticated and powerful type annotations. If you get into the habit of learning how to use type annotations in the short run, you will be better equipped to make use of each new type hinting innovation as they’re introduced.
It’s important to remember that type hints are optional, not mandatory. Not every project needs them. Use type hints to make your bigger projects comprehensible, but feel free to omit them from a 50-line throwaway script. And note that, while type hints are not enforced at runtime, you can use Pydantic to make that possible. Many widely used Python projects, like FastAPI, use Pydantic extensively.
For simple projects and undemanding development jobs, you can often just use Python’s built-in venv tool to keep projects and their requirements separate. But recent advances in Python’s tooling give you more options:
Python’s evolution has meant many new additions to the language itself. The last few versions of Python have added useful syntactical constructions that allow for more powerful and succinct progamming. Some recent additions include:
The biggest recent addition, structural pattern matching, which arrived in Python 3.10, is more than just “switch/case for Python” as it has sometimes been described. It lets you make control flow decisions based on the contents or structure of objects.
So named for its appearance (:=), the “walrus operator”, added in Python 3.8, introduces assignment expressions, a way to assign a value to a variable and then apply a test to the variable in a single step. It makes for less verbose code in many common situations, such as checking a function’s return value while also preserving the results.
A minor but useful recent addition to Python’s syntax, positional-only parameters, lets you indicate which function parameters must be specified as positional ones, not as keyword arguments. The rationales for doing this generally involve improving the clarity and easing the future development of a codebase, goals that many of Python’s other new features also focus on.
Writing tests for a codebase is like flossing daily: Everyone agrees it’s a good thing, few of us actually do it, and even fewer do it properly. Modern Python codebases deserve to have test suites, and the current tooling for testing makes creating test suites easier than ever.
Python has its own built-in testing framework, Unittest, and while Unittest isn’t bad as a default, its design and behaviors are dated. The Pytest framework has risen to prominence as a common substitute. It’s more flexible (you can declare tests in any part of your code, not just a subset) and requires writing far less boilerplate. Plus, Pytest has plenty of add-ons to expand its functionality (e.g., for testing async code).
Another important adjunct to testing is code coverage, determining how much of one’s codebase the tests actually cover. The module Coverage has you, er, covered for this, and Pytest even comes with a plug-in to work with it.
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Serdar Yegulalp is a senior writer at InfoWorld, focused on machine learning, containerization, devops, the Python ecosystem, and periodic reviews.
Copyright © 2022 IDG Communications, Inc.
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