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 3.8 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

This command makes mypy type check your 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.

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!

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

# These calls will fail when the program runs, but mypy does not report an error
# because "greeting" does not have type annotations.

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

# The "name: str" annotation says that the "name" argument should be a string
# The "-> str" annotation says that "greeting" will return a string
def greeting(name: str) -> str:
    return 'Hello ' + name

This function is now statically typed: mypy will use the provided type hints to detect incorrect use of the greeting function and incorrect use of variables within the greeting function. For example:

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"
greeting("World!")  # No error

def bad_greeting(name: str) -> str:
    return 'Hello ' * name  # Unsupported operand types for * ("str" and "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. You can also get mypy to provide some limited checking of dynamically typed functions by using the --check-untyped-defs flag. See The mypy command line for more information on configuring mypy.

Strict mode and configuration

Mypy has a strict mode that enables a number of additional checks, like --disallow-untyped-defs.

If you run mypy with the --strict flag, you will basically never get a type related error at runtime without a corresponding mypy error, unless you explicitly circumvent mypy somehow.

However, this flag will probably be too aggressive if you are trying to add static types to a large, existing codebase. See Using mypy with an existing codebase for suggestions on how to handle that case.

Mypy is very configurable, so you can start with using --strict and toggle off individual checks. For instance, if you use many third party libraries that do not have types, --ignore-missing-imports may be useful. See Introduce stricter options for how to build up to --strict.

See The mypy command line and The mypy configuration file for a complete reference on configuration options.

More complex types

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 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

from import Iterable  # or "from typing import Iterable"

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

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 subtype of T. That is, list[str] is a subtype of Iterable[str].

This also applies to inheritance, so if you have a class Child that inherits from Parent, then a value of type Child can be assigned to a variable of type Parent. For example, a RuntimeError instance can be passed to a function that is annotated as taking an Exception.

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. For example, int is a subtype of Union[int, str]:

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}'
        return user_id

The typing module contains many other useful types.

For a quick overview, look through the mypy cheatsheet.

For a detailed overview (including information on how to make your own generic types or your own type aliases), look through the type system reference.


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 in code examples, but mypy will give an error if you use types such as Iterable without first importing them.


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.

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:
    return output

For more details, see Type inference and type annotations.

Types from libraries

Mypy can also understand how to work with types from libraries that you use.

For instance, mypy comes out of the box with an intimate knowledge of the Python 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 knows that `template_path` has a `read_text` method that returns a str
    template = template_path.read_text()
    # it understands this line type checks
    return template.replace('USERNAME', name)

If a third party library you use declares support for type checking, mypy will type check your use of that library based on the type hints it contains.

However, if the third party library does not have type hints, mypy will complain about missing type information. error: Library stubs not installed for "yaml" note: Hint: "python3 -m pip install types-PyYAML" error: Library stubs not installed for "requests" note: Hint: "python3 -m pip install types-requests"

In this case, you can provide mypy a different source of type information, by installing a stub package. A stub package is a package that contains type hints for another library, but no actual code.

$ python3 -m pip install types-PyYAML types-requests

Stubs packages for a distribution are often named types-<distribution>. Note that a distribution name may be different from the name of the package that you import. For example, types-PyYAML contains stubs for the yaml package.

For more discussion on strategies for handling errors about libraries without type information, refer to Missing imports.

For more information about stubs, see Stub files.

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.