I was playing with Python3.5’s type hints for a few hours, and thought I’d summarize what I found here.

A quick disclaimer and blogging note: I have around a dozen unfinished blog post drafts about various subjects, so I thought I’d focus on getting this published more than about being super thorough. Proceed with caution.

The Project

I started a small project in Python2.7. My style nowadays involves calling a bunch of functions on namedtuple models.

After a little refactor, and a long time battling confusing bugs and saying for the nth time “this could have been avoided if Python checked types”, I remembered hearing something about Python having type-checking.

Not quite

First, things are more subtler than “Python has type-checking now.” It’s not type-checking like Haskell or Rust. Namely:

  • The interpreter more-or-less ignores types. It will run the code whether or not you provide type annotations.

  • Python 3 has had type annotation syntax for a while. Python3.5 introduced the typing module, which allows for more complex types, e.g. List[int].

  • mypy-lang is a Python package that gives you mypy, a program that does static type-checking. It’s up to you to run mypy. It runs over all of the code you point at, not just the code that is executed. Like Guido says, it’s more like a really powerful linter.

  • Types feel like an afterthought. It’s not a huge deal, just some extra work at the moment. Don’t expect package documentation to mention types, like Haskell or Rust documentation does.

But aside from that, you can get that type-checking of your dreams.

What worked for me

Type-checking with mypy is kind of all-or-nothing. Type annotations are optional. But mypy will silently fail to notice issues if it can’t infer types. So it requires enough types hints to be useful.

I used these guidelines:

In the package, I annotate all function definitions and models, and optionally annotate everything else. I call mypy --disallow-untyped-defs to enforce type annotations on all function definitions.

In tests, I switched things up a bit: I don’t annotate test functions and use mypy --check-untyped-defs option to make sure type-checking happens inside of the test. (Note: I still haven’t gotten a handle of --check-untyped-defs. I feel like sometimes it’s skipping chunks of code. Since these are already tests, I feel okay about it.)

I run mypy on my package and tests every time I run my full test suite.

I require the output of mypy to be empty if nothing is wrong. To avoid error: No library stub file for module 'feedparser', I added minimal stub files (.pyi) in my package for dependent packages, and call with MYPYPATH=package_name mypy ... . (See also python/typeshed)

The complete commands look like

check_types: venv
	MYPYPATH=podcast $(PREFIX)/mypy podcast --disallow-untyped-defs
	MYPYPATH=podcast $(PREFIX)/mypy tests --check-untyped-defs

If following these guidelines, I’d recommend adding types to a project as early as possible. I wouldn’t use this approach to add types to an existing large project. It was a lot of work to add all annotations, and I needed to do that before I started getting value from them.

Example 1: Why use --disallow-untyped-defs

Okay, a couple of concrete examples. Let’s say I have code like

def say_hi(name):
    return "Hello " + name
def do_stuff(name):
    return say_hi(name)

I’d love for mypy to notice that I was doing something silly with "Hello " + 123. It doesn’t at the moment.

So I add type annotations to the say_hi function.

def say_hi(name: str) -> str:
    return "Hello " + name
def do_stuff(name):
    return say_hi(name)


mypy won’t notice this either.

I think this is for two reasons. For one, do_stuff isn’t annotated, so it won’t check the body without mypy --check-untyped-defs.

Another reason is that it doesn’t know do_stuff’s type. I can show this using mypy’s method for debug types by adding reveal_type(...) to my code.

def say_hi(name: str) -> str:
    return "Hello " + name
def do_stuff(name):
    return say_hi(name)


mypy will output

Revealed type is 'def (name: Any) -> Any'

I haven’t read up on the type system, but the rule-of-thumb I use is once an Any type shows up, mypy says any code interacting with it is fine, and won’t detect errors.

With --disallow-untyped-defs, I’d remember to annotate all functions, and could end up with something like this:

def say_hi(name: str) -> str:
    return "Hello " + name

def do_stuff(name: int) -> str:
    return say_hi(name)

Finally! mypy will complain

Argument 1 to "say_hi" has incompatible type "int"; expected "str"

Example 2: Other places to use types

Aside from function defs, it can be useful to add type-checking to weirder structures. For example, I’d annotate a mapping of command names to functions.

def print_status(radio: Radio) -> Radio:

def update_radio(channel: Channel) -> Radio:  # uh oh

def download_radio(radio: Radio) -> Radio:

radio_action = {
    'status': print_status,
    'update': update_radio,  # nope, not allowed
    'download': download_radio,
}  # type: typing.Dict[str, typing.Callable[[Radio], Radio]]

radio = radio_action[args.command](radio)

mypy will give us the error List item 1 has incompatible type "Tuple[str, Callable[[Channel], Radio]]"

Ignoring the mentions of List and Tuple, it’s saying something’s wrong with the second item in this dictionary definition.

Catching bugs

After finding an mypy setup that caught issues, and doing the busy-work of annotating my functions, I found a few errors!

In my actual project, I caught:

  • accidentally iterating through a namedtuple instead of one of its fields
  • typo in a namedtuple field name
  • swapping method arguments

It was super gratifying.

I might have caught some of these with 100% test coverage, but there’s always a possibility of having a bug return a reasonable but completely incorrect result. I’d also argue the type-checking error messages are easier to debug than failing tests.

See Also