Getting started#

This chapter introduces some core concepts of mypy, including function annotations, the typing module, stub files, and more.

If you’re looking for a quick intro, see the mypy cheatsheet.

If you’re unfamiliar with the concepts of static and dynamic type checking, be sure to read this chapter carefully, as the rest of the documentation may not make much sense otherwise.

Installing and running mypy#

Mypy requires Python 3.7 or later to run. You can install mypy using pip:

$ python3 -m pip install mypy

Once mypy is installed, run it by using the mypy tool:

$ mypy program.py

This command makes mypy type check your program.py file and print out any errors it finds. Mypy will type check your code statically: this means that it will check for errors without ever running your code, just like a linter.

This also means that you are always free to ignore the errors mypy reports, if you so wish. You can always use the Python interpreter to run your code, even if mypy reports errors.

However, if you try directly running mypy on your existing Python code, it will most likely report little to no errors. This is a feature! It makes it easy to adopt mypy incrementally.

In order to get useful diagnostics from mypy, you must add type annotations to your code. See the section below for details.

Function signatures and dynamic vs static typing#

A function without type annotations is considered to be dynamically typed by mypy:

def greeting(name):
    return 'Hello ' + name

By default, mypy will not type check dynamically typed functions. This means that with a few exceptions, mypy will not report any errors with regular unannotated Python.

This is the case even if you misuse the function: for example, mypy would currently not report any errors if you tried running greeting(3) or greeting(b"Alice") even though those function calls would result in errors at runtime.

You can teach mypy to detect these kinds of bugs by adding type annotations (also known as type hints). For example, you can teach mypy that greeting both accepts and returns a string like so:

def greeting(name: str) -> str:
    return 'Hello ' + name

This function is now statically typed: mypy can use the provided type hints to detect incorrect usages of the greeting function. For example, it will reject the following calls since the arguments have invalid types:

def greeting(name: str) -> str:
    return 'Hello ' + name

greeting(3)         # Argument 1 to "greeting" has incompatible type "int"; expected "str"
greeting(b'Alice')  # Argument 1 to "greeting" has incompatible type "bytes"; expected "str"

Being able to pick whether you want a function to be dynamically or statically typed can be very helpful. For example, if you are migrating an existing Python codebase to use static types, it’s usually easier to migrate by incrementally adding type hints to your code rather than adding them all at once. Similarly, when you are prototyping a new feature, it may be convenient to initially implement the code using dynamic typing and only add type hints later once the code is more stable.

Once you are finished migrating or prototyping your code, you can make mypy warn you if you add a dynamic function by mistake by using the --disallow-untyped-defs flag. See The mypy command line for more information on configuring mypy.

More function signatures#

Here are a few more examples of adding type hints to function signatures.

If a function does not explicitly return a value, give it a return type of None. Using a None result in a statically typed context results in a type check error:

def p() -> None:
    print('hello')

a = p()  # Error: "p" does not return a value

Make sure to remember to include None: if you don’t, the function will be dynamically typed. For example:

def f():
    1 + 'x'  # No static type error (dynamically typed)

def g() -> None:
    1 + 'x'  # Type check error (statically typed)

Arguments with default values can be annotated like so:

def greeting(name: str, excited: bool = False) -> str:
    message = f'Hello, {name}'
    if excited:
        message += '!!!'
    return message

*args and **kwargs arguments can be annotated like so:

def stars(*args: int, **kwargs: float) -> None:
    # 'args' has type 'tuple[int, ...]' (a tuple of ints)
    # 'kwargs' has type 'dict[str, float]' (a dict of strs to floats)
    for arg in args:
        print(arg)
    for key, value in kwargs.items():
        print(key, value)

Additional types, and the typing module#

So far, we’ve added type hints that use only basic concrete types like str and float. What if we want to express more complex types, such as “a list of strings” or “an iterable of ints”?

For example, to indicate that some function can accept a list of strings, use the list[str] type (Python 3.9 and later):

def greet_all(names: list[str]) -> None:
    for name in names:
        print('Hello ' + name)

names = ["Alice", "Bob", "Charlie"]
ages = [10, 20, 30]

greet_all(names)   # Ok!
greet_all(ages)    # Error due to incompatible types

The list type is an example of something called a generic type: it can accept one or more type parameters. In this case, we parameterized list by writing list[str]. This lets mypy know that greet_all accepts specifically lists containing strings, and not lists containing ints or any other type.

In Python 3.8 and earlier, you can instead import the List type from the typing module:

from typing import List  # Python 3.8 and earlier

def greet_all(names: List[str]) -> None:
    for name in names:
        print('Hello ' + name)

...

You can find many of these more complex static types in the typing module.

In the above examples, the type signature is perhaps a little too rigid. After all, there’s no reason why this function must accept specifically a list – it would run just fine if you were to pass in a tuple, a set, or any other custom iterable.

You can express this idea using the collections.abc.Iterable (or typing.Iterable in Python 3.8 and earlier) type instead of list :

from collections.abc import Iterable  # or "from typing import Iterable"

def greet_all(names: Iterable[str]) -> None:
    for name in names:
        print('Hello ' + name)

As another example, suppose you want to write a function that can accept either ints or strings, but no other types. You can express this using the Union type:

from typing import Union

def normalize_id(user_id: Union[int, str]) -> str:
    if isinstance(user_id, int):
        return f'user-{100_000 + user_id}'
    else:
        return user_id

Similarly, suppose that you want the function to accept only strings or None. You can again use Union and use Union[str, None] – or alternatively, use the type Optional[str]. These two types are identical and interchangeable: Optional[str] is just a shorthand or alias for Union[str, None]. It exists mostly as a convenience to help function signatures look a little cleaner:

from typing import Optional

def greeting(name: Optional[str] = None) -> str:
    # Optional[str] means the same thing as Union[str, None]
    if name is None:
        name = 'stranger'
    return 'Hello, ' + name

The typing module contains many other useful types. You can find a quick overview by looking through the mypy cheatsheet and a more detailed overview (including information on how to make your own generic types or your own type aliases) by looking through the type system reference.

Note

When adding types, the convention is to import types using the form from typing import Union (as opposed to doing just import typing or import typing as t or from typing import *).

For brevity, we often omit imports from typing or collections.abc in code examples, but mypy will give an error if you use types such as Iterable without first importing them.

Note

In some examples we use capitalized variants of types, such as List, and sometimes we use plain list. They are equivalent, but the prior variant is needed if you are using Python 3.8 or earlier.

Local type inference#

Once you have added type hints to a function (i.e. made it statically typed), mypy will automatically type check that function’s body. While doing so, mypy will try and infer as many details as possible.

We saw an example of this in the normalize_id function above – mypy understands basic isinstance checks and so can infer that the user_id variable was of type int in the if-branch and of type str in the else-branch. Similarly, mypy was able to understand that name could not possibly be None in the greeting function above, based both on the name is None check and the variable assignment in that if statement.

As another example, consider the following function. Mypy can type check this function without a problem: it will use the available context and deduce that output must be of type list[float] and that num must be of type float:

def nums_below(numbers: Iterable[float], limit: float) -> list[float]:
    output = []
    for num in numbers:
        if num < limit:
            output.append(num)
    return output

Mypy will warn you if it is unable to determine the type of some variable – for example, when assigning an empty dictionary to some global value:

my_global_dict = {}  # Error: Need type annotation for "my_global_dict"

You can teach mypy what type my_global_dict is meant to have by giving it a type hint. For example, if you knew this variable is supposed to be a dict of ints to floats, you could annotate it using either variable annotations (introduced in Python 3.6 by PEP 526) or using a comment-based syntax like so:

# If you're using Python 3.9+
my_global_dict: dict[int, float] = {}

# If you're using Python 3.6+
my_global_dict: Dict[int, float] = {}

Types and classes#

So far, we’ve only seen examples of pre-existing types like the int or float builtins, or generic types from collections.abc and typing, such as Iterable. However, these aren’t the only types you can use: in fact, you can use any Python class as a type!

For example, suppose you’ve defined a custom class representing a bank account:

class BankAccount:
    # Note: It is ok to omit type hints for the "self" parameter.
    # Mypy will infer the correct type.

    def __init__(self, account_name: str, initial_balance: int = 0) -> None:
        # Note: Mypy will infer the correct types of your fields
        # based on the types of the parameters.
        self.account_name = account_name
        self.balance = initial_balance

    def deposit(self, amount: int) -> None:
        self.balance += amount

    def withdraw(self, amount: int) -> None:
        self.balance -= amount

    def overdrawn(self) -> bool:
        return self.balance < 0

You can declare that a function will accept any instance of your class by simply annotating the parameters with BankAccount:

def transfer(src: BankAccount, dst: BankAccount, amount: int) -> None:
    src.withdraw(amount)
    dst.deposit(amount)

account_1 = BankAccount('Alice', 400)
account_2 = BankAccount('Bob', 200)
transfer(account_1, account_2, 50)

In fact, the transfer function we wrote above can accept more then just instances of BankAccount: it can also accept any instance of a subclass of BankAccount. For example, suppose you write a new class that looks like this:

class AuditedBankAccount(BankAccount):
    def __init__(self, account_name: str, initial_balance: int = 0) -> None:
        super().__init__(account_name, initial_balance)
        self.audit_log: list[str] = []

    def deposit(self, amount: int) -> None:
        self.audit_log.append(f"Deposited {amount}")
        self.balance += amount

    def withdraw(self, amount: int) -> None:
        self.audit_log.append(f"Withdrew {amount}")
        self.balance -= amount

Since AuditedBankAccount is a subclass of BankAccount, we can directly pass in instances of it into our transfer function:

audited = AuditedBankAccount('Charlie', 300)
transfer(account_1, audited, 100)   # Type checks!

This behavior is actually a fundamental aspect of the PEP 484 type system: when we annotate some variable with a type T, we are actually telling mypy that variable can be assigned an instance of T, or an instance of a subclass of T. The same rule applies to type hints on parameters or fields.

See Class basics to learn more about how to work with code involving classes.

Stubs files and typeshed#

Mypy also understands how to work with classes found in the standard library. For example, here is a function which uses the Path object from the pathlib standard library module:

from pathlib import Path

def load_template(template_path: Path, name: str) -> str:
    # Mypy understands that 'file_path.read_text()' returns a str...
    template = template_path.read_text()

    # ...so understands this line type checks.
    return template.replace('USERNAME', name)

This behavior may surprise you if you’re familiar with how Python internally works. The standard library does not use type hints anywhere, so how did mypy know that Path.read_text() returns a str, or that str.replace(...) accepts exactly two str arguments?

The answer is that mypy comes bundled with stub files from the the typeshed project, which contains stub files for the Python builtins, the standard library, and selected third-party packages.

A stub file is a file containing a skeleton of the public interface of that Python module, including classes, variables, functions – and most importantly, their types.

Mypy complains if it can’t find a stub (or a real module) for a library module that you import. Some modules ship with stubs or inline annotations that mypy can automatically find, or you can install additional stubs using pip (see Missing imports and Using installed packages for the details). For example, you can install the stubs for the requests package like this:

$ python3 -m pip install types-requests

The stubs are usually packaged in a distribution named types-<distribution>. Note that the distribution name may be different from the name of the package that you import. For example, types-PyYAML contains stubs for the yaml package. Mypy can often suggest the name of the stub distribution:

prog.py:1: error: Library stubs not installed for "yaml"
prog.py:1: note: Hint: "python3 -m pip install types-PyYAML"
...

You can also create stubs easily. We discuss strategies for handling errors about missing stubs in Missing imports.

Configuring mypy#

Mypy supports many command line options that you can use to tweak how mypy behaves: see The mypy command line for more details.

For example, suppose you want to make sure all functions within your codebase are using static typing and make mypy report an error if you add a dynamically-typed function by mistake. You can make mypy do this by running mypy with the --disallow-untyped-defs flag.

Another potentially useful flag is --strict, which enables many (though not all) of the available strictness options – including --disallow-untyped-defs.

This flag is mostly useful if you’re starting a new project from scratch and want to maintain a high degree of type safety from day one. However, this flag will probably be too aggressive if you either plan on using many untyped third party libraries or are trying to add static types to a large, existing codebase. See Using mypy with an existing codebase for more suggestions on how to handle the latter case.

Next steps#

If you are in a hurry and don’t want to read lots of documentation before getting started, here are some pointers to quick learning resources:

You can also continue reading this document and skip sections that aren’t relevant for you. You don’t need to read sections in order.