This chapter introduces some core concepts of mypy, including function annotations, the typing module and library stubs. Read it carefully, as the rest of documentation may not make much sense otherwise.

Function signatures

A function without a type annotation is considered dynamically typed:

def greeting(name):
    return 'Hello, {}'.format(name)

You can declare the signature of a function using the Python 3 annotation syntax (Python 2 is discussed later in Type checking Python 2 code). This makes the function statically typed, and that causes type checker report type errors within the function.

Here’s a version of the above function that is statically typed and will be type checked:

def greeting(name: str) -> str:
    return 'Hello, {}'.format(name)

If a function does not explicitly return a value we give the return type as None. Using a None result in a statically typed context results in a type check error:

def p() -> None:

a = p()   # Type check error: p has None return value

Arguments with default values can be annotated as follows:

def greeting(name: str, prefix: str = 'Mr.') -> str:
   return 'Hello, {} {}'.format(name, prefix)

Running mypy

You can type check a program by using the mypy tool, which is basically a linter – it checks your program for errors without actually running it:

$ mypy

All errors reported by mypy are essentially warnings that you are free to ignore, if you so wish.

The next chapter explains how to download and install mypy: Getting started.

More command line options are documented in The mypy command line.


Depending on how mypy is configured, you may have to run mypy like this:

$ python3 -m mypy

The typing module

The typing module contains many definitions that are useful in statically typed code. You typically use from ... import to import them (we’ll explain Iterable later in this document):

from typing import Iterable

def greet_all(names: Iterable[str]) -> None:
    for name in names:
        print('Hello, {}'.format(name))

For brevity, we often omit the typing import in code examples, but mypy will give an error if you use definitions such as Iterable without first importing them.

Mixing dynamic and static typing

Mixing dynamic and static typing within a single file is often useful. For example, if you are migrating existing Python code to static typing, it may be easiest to do this incrementally, such as by migrating a few functions at a time. Also, when prototyping a new feature, you may decide to first implement the relevant code using dynamic typing and only add type signatures later, when the code is more stable.

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

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


The earlier stages of mypy, known as the semantic analysis, may report errors even for dynamically typed functions. However, you should not rely on this, as this may change in the future.

Library stubs and the Typeshed repo

In order to type check code that uses library modules such as those included in the Python standard library, you need to have library stubs. A library stub defines a skeleton of the public interface of the library, including classes, variables and functions and their types, but dummy function bodies.

For example, consider this code:

x = chr(4)

Without a library stub, the type checker would have no way of inferring the type of x and checking that the argument to chr has a valid type. Mypy incorporates the typeshed project, which contains library stubs for the Python builtins and the standard library. The stub for the builtins contains a definition like this for chr:

def chr(code: int) -> str: ...

In stub files we don’t care about the function bodies, so we use an ellipsis instead. That ... is three literal dots!

Mypy complains if it can’t find a stub (or a real module) for a library module that you import. You can create a stub easily; here is an overview:

  • Write a stub file for the library and store it as a .pyi file in the same directory as the library module.

  • Alternatively, put your stubs (.pyi files) in a directory reserved for stubs (e.g., myproject/stubs). In this case you have to set the environment variable MYPYPATH to refer to the directory. For example:

    $ export MYPYPATH=~/work/myproject/stubs

Use the normal Python file name conventions for modules, e.g. csv.pyi for module csv. Use a subdirectory with __init__.pyi for packages.

If a directory contains both a .py and a .pyi file for the same module, the .pyi file takes precedence. This way you can easily add annotations for a module even if you don’t want to modify the source code. This can be useful, for example, if you use 3rd party open source libraries in your program (and there are no stubs in typeshed yet).

That’s it! Now you can access the module in mypy programs and type check code that uses the library. If you write a stub for a library module, consider making it available for other programmers that use mypy by contributing it back to the typeshed repo.

There is more information about creating stubs in the mypy wiki. The following sections explain the kinds of type annotations you can use in your programs and stub files.


You may be tempted to point MYPYPATH to the standard library or to the site-packages directory where your 3rd party packages are installed. This is almost always a bad idea – you will likely get tons of error messages about code you didn’t write and that mypy can’t analyze all that well yet, and in the worst case scenario mypy may crash due to some construct in a 3rd party package that it didn’t expect.