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Python 3.11 only made one change to unittest, but it’s a good one: context manager methods. These methods can simplify setup and teardown logic in many cases, such as dynamic use of unittest.mock.
Take this code, which prints a shruggy man:
Sometimes code uses boolean checks on variables that can only be true. This is normally a sign of a mistake, either in the type hints or the implementation. Mypy has an optional check that can find such problematic boolean usage with its truthy-bool error code.
The original type hint proposal, PEP 484, initially allowed implicit optional types in function signatures. That is, a parameter with a default value of None would have its type automatically interpreted as optional. For example, this signature:
As type hints have evolved, Python has added simpler, more succinct syntaxes. But you still need to know the older forms, since Mypy uses them when reporting types.
Exhaustiveness checking is a very handy type checker feature. It ensures that all possible types of a variable are handled. If your code changes to add another possible type, you can guarantee that exhaustiveness-checked code paths handle the new case.
Python has no syntax to add type hints to lambdas, but that doesn’t mean you can’t use them in type-checked code. In this post we’ll look at how Mypy can infer the types for lambdas, based on where they’re used.
Hynek Schlawack recently described graduality as Python’s super power: the ability to prototype in the REPL, and gradually add linting, type checking, and other practices to refine your code into maintainable, production-ready software. You can also apply graduality within tools, activating checks one at a time and fixing the resulting errors as you go.
partial() with transaction.on_commit() to avoid late-binding bugsDjango’s transaction.on_commit() allows you to run a function after the current database transaction is committed. This is useful to ensure that actions with external services, like sending emails, don’t run until the relevant data is definitely saved.
Python’s requests package is very popular. Even if you don’t use it directly, it’s highly likely one of your dependencies does.
I joined host Tobias Macey on Podcast.__init__, in Episode 349, published this monday. The episode is titled “Improve Your Productivity By Investing In Developer Experience Design For Your Projects”. We covered various topics related to “developer experience” and general ways to improve it on any Python project.
Here’s a small problem I’ve seen where several modules share versions of the same “constant” variable. It came up in the context of a Django project with multiple settings files, but it could happen in different contexts.
The typing module continues to evolve, with new features in every Python version. This can make it tricky if you’re trying to type code that supports multiple Python versions. To help write such code, Mypy identifies version checks using sys.version_info and reads the appropriate branch.
Here’s an innocent enough looking Python file:
SimpleNamespaceWhen testing Python code you may need a mock object. That’s okay! But what’s the best way to build a simple mock?
On Monday I released my new book “Boost Your Django DX”. It covers many tools and practices that are useful for developing Django projects.
Python 3.6 reached its end of life on the 23rd December. As its release manager put on the Python forum, it has gracefully “ridden off into the sunset”.
Python’s built-in collection types come in mutable and immutable flavours, but one is conspicuously missing:
This post is not about importing typing.Optional, but instead imports that are themselves optional. Libraries often have optional dependencies, and the code should work whether or not the import is there. A common pattern to solve this to catch ImportError and replace the module with None:
If you use Gunicorn, it’s likely you have a configuration file. This is a Python module that contains settings as module-level variables. Here’s an example with some essential settings:
Developer Experience (DX) is a catch-all term for anything that can improve your development workflow. Such improvements can help you write better code, faster, with fewer bugs.
The descriptor protocol allow us to completely customize attribute access. Python’s documentation describes the protocol with types involved described with words. Let’s look at how we can write those as type hints.
Earlier this week I shared my Python script template. Here’s an extended version with sub-command support, and an example script.
When dealing with evolvng APIs, it may be useful to rename an attribute in a class, but keep the old name around for backwards compatibility. This would mean making one attribute an alias for another. In this post we’ll look at two ways to achieve this.
functools.partial() in DjangoI remain convinced that Python’s functools.partial() is underappreciated. Following my previous post, here are three more ways to use partial() with Django.