More types

This section introduces a few additional kinds of types, including NoReturn, NewType, TypedDict, and types for async code. All of these are only situationally useful, so feel free to skip this section and come back when you have a need for some of them.

Here’s a quick summary of what’s covered here:

  • NoReturn lets you tell mypy that a function never returns normally.
  • NewType lets you define a variant of a type that is treated as a separate type by mypy but is identical to the original type at runtime. For example, you can have UserId as a variant of int that is just an int at runtime.
  • TypedDict lets you give precise types for dictionaries that represent objects with a fixed schema, such as {'id': 1, 'items': ['x']}.
  • Async types let you type check programs using async and await.

The NoReturn type

Mypy provides support for functions that never return. For example, a function that unconditionally raises an exception:

from typing import NoReturn

def stop() -> NoReturn:
    raise Exception('no way')

Mypy will ensure that functions annotated as returning NoReturn truly never return, either implicitly or explicitly. Mypy will also recognize that the code after calls to such functions is unreachable and will behave accordingly:

def f(x: int) -> int:
    if x == 0:
        return x
    return 'whatever works'  # No error in an unreachable block

In earlier Python versions you need to install typing_extensions using pip to use NoReturn in your code. Python 3 command line:

python3 -m pip install --upgrade typing-extensions

This works for Python 2:

pip install --upgrade typing-extensions


There are situations where you may want to avoid programming errors by creating simple derived classes that are only used to distinguish certain values from base class instances. Example:

class UserId(int):

get_by_user_id(user_id: UserId):

However, this approach introduces some runtime overhead. To avoid this, the typing module provides a helper function NewType that creates simple unique types with almost zero runtime overhead. Mypy will treat the statement Derived = NewType('Derived', Base) as being roughly equivalent to the following definition:

class Derived(Base):
    def __init__(self, _x: Base) -> None:

However, at runtime, NewType('Derived', Base) will return a dummy function that simply returns its argument:

def Derived(_x):
    return _x

Mypy will require explicit casts from int where UserId is expected, while implicitly casting from UserId where int is expected. Examples:

from typing import NewType

UserId = NewType('UserId', int)

def name_by_id(user_id: UserId) -> str:

UserId('user')          # Fails type check

name_by_id(42)          # Fails type check
name_by_id(UserId(42))  # OK

num = UserId(5) + 1     # type: int

NewType accepts exactly two arguments. The first argument must be a string literal containing the name of the new type and must equal the name of the variable to which the new type is assigned. The second argument must be a properly subclassable class, i.e., not a type construct like Union, etc.

The function returned by NewType accepts only one argument; this is equivalent to supporting only one constructor accepting an instance of the base class (see above). Example:

from typing import NewType

class PacketId:
    def __init__(self, major: int, minor: int) -> None:
        self._major = major
        self._minor = minor

TcpPacketId = NewType('TcpPacketId', PacketId)

packet = PacketId(100, 100)
tcp_packet = TcpPacketId(packet)  # OK

tcp_packet = TcpPacketId(127, 0)  # Fails in type checker and at runtime

You cannot use isinstance() or issubclass() on the object returned by NewType(), because function objects don’t support these operations. You cannot create subclasses of these objects either.


Unlike type aliases, NewType will create an entirely new and unique type when used. The intended purpose of NewType is to help you detect cases where you accidentally mixed together the old base type and the new derived type.

For example, the following will successfully typecheck when using type aliases:

UserId = int

def name_by_id(user_id: UserId) -> str:

name_by_id(3)  # ints and UserId are synonymous

But a similar example using NewType will not typecheck:

from typing import NewType

UserId = NewType('UserId', int)

def name_by_id(user_id: UserId) -> str:

name_by_id(3)  # int is not the same as UserId

Typing async/await

Mypy supports the ability to type coroutines that use the async/await syntax introduced in Python 3.5. For more information regarding coroutines and this new syntax, see PEP 492.

Functions defined using async def are typed just like normal functions. The return type annotation should be the same as the type of the value you expect to get back when await-ing the coroutine.

import asyncio

async def format_string(tag: str, count: int) -> str:
    return 'T-minus {} ({})'.format(count, tag)

async def countdown_1(tag: str, count: int) -> str:
    while count > 0:
        my_str = await format_string(tag, count)  # has type 'str'
        await asyncio.sleep(0.1)
        count -= 1
    return "Blastoff!"

loop = asyncio.get_event_loop()
loop.run_until_complete(countdown_1("Millennium Falcon", 5))

The result of calling an async def function without awaiting will be a value of type typing.Coroutine[Any, Any, T], which is a subtype of Awaitable[T]:

my_coroutine = countdown_1("Millennium Falcon", 5)
reveal_type(my_coroutine)  # has type 'Coroutine[Any, Any, str]'


reveal_type() displays the inferred static type of an expression.

If you want to use coroutines in Python 3.4, which does not support the async def syntax, you can instead use the @asyncio.coroutine decorator to convert a generator into a coroutine.

Note that we set the YieldType of the generator to be Any in the following example. This is because the exact yield type is an implementation detail of the coroutine runner (e.g. the asyncio event loop) and your coroutine shouldn’t have to know or care about what precisely that type is.

from typing import Any, Generator
import asyncio

def countdown_2(tag: str, count: int) -> Generator[Any, None, str]:
    while count > 0:
        print('T-minus {} ({})'.format(count, tag))
        yield from asyncio.sleep(0.1)
        count -= 1
    return "Blastoff!"

loop = asyncio.get_event_loop()
loop.run_until_complete(countdown_2("USS Enterprise", 5))

As before, the result of calling a generator decorated with @asyncio.coroutine will be a value of type Awaitable[T].


At runtime, you are allowed to add the @asyncio.coroutine decorator to both functions and generators. This is useful when you want to mark a work-in-progress function as a coroutine, but have not yet added yield or yield from statements:

import asyncio

def serialize(obj: object) -> str:
    # todo: add yield/yield from to turn this into a generator
    return "placeholder"

However, mypy currently does not support converting functions into coroutines. Support for this feature will be added in a future version, but for now, you can manually force the function to be a generator by doing something like this:

from typing import Generator
import asyncio

def serialize(obj: object) -> Generator[None, None, str]:
    # todo: add yield/yield from to turn this into a generator
    if False:
    return "placeholder"

You may also choose to create a subclass of Awaitable instead:

from typing import Any, Awaitable, Generator
import asyncio

class MyAwaitable(Awaitable[str]):
    def __init__(self, tag: str, count: int) -> None:
        self.tag = tag
        self.count = count

    def __await__(self) -> Generator[Any, None, str]:
        for i in range(n, 0, -1):
            print('T-minus {} ({})'.format(i, tag))
            yield from asyncio.sleep(0.1)
        return "Blastoff!"

def countdown_3(tag: str, count: int) -> Awaitable[str]:
    return MyAwaitable(tag, count)

loop = asyncio.get_event_loop()
loop.run_until_complete(countdown_3("Heart of Gold", 5))

To create an iterable coroutine, subclass AsyncIterator:

from typing import Optional, AsyncIterator
import asyncio

class arange(AsyncIterator[int]):
    def __init__(self, start: int, stop: int, step: int) -> None:
        self.start = start
        self.stop = stop
        self.step = step
        self.count = start - step

    def __aiter__(self) -> AsyncIterator[int]:
        return self

    async def __anext__(self) -> int:
        self.count += self.step
        if self.count == self.stop:
            raise StopAsyncIteration
            return self.count

async def countdown_4(tag: str, n: int) -> str:
    async for i in arange(n, 0, -1):
        print('T-minus {} ({})'.format(i, tag))
        await asyncio.sleep(0.1)
    return "Blastoff!"

loop = asyncio.get_event_loop()
loop.run_until_complete(countdown_4("Serenity", 5))

For a more concrete example, the mypy repo has a toy webcrawler that demonstrates how to work with coroutines. One version uses async/await and one uses yield from.



TypedDict is an officially supported feature, but it is still experimental.

Python programs often use dictionaries with string keys to represent objects. Here is a typical example:

movie = {'name': 'Blade Runner', 'year': 1982}

Only a fixed set of string keys is expected ('name' and 'year' above), and each key has an independent value type (str for 'name' and int for 'year' above). We’ve previously seen the Dict[K, V] type, which lets you declare uniform dictionary types, where every value has the same type, and arbitrary keys are supported. This is clearly not a good fit for movie above. Instead, you can use a TypedDict to give a precise type for objects like movie, where the type of each dictionary value depends on the key:

from mypy_extensions import TypedDict

Movie = TypedDict('Movie', {'name': str, 'year': int})

movie = {'name': 'Blade Runner', 'year': 1982}  # type: Movie

Movie is a TypedDict type with two items: 'name' (with type str) and 'year' (with type int). Note that we used an explicit type annotation for the movie variable. This type annotation is important – without it, mypy will try to infer a regular, uniform Dict type for movie, which is not what we want here.


If you pass a TypedDict object as an argument to a function, no type annotation is usually necessary since mypy can infer the desired type based on the declared argument type. Also, if an assignment target has been previously defined, and it has a TypedDict type, mypy will treat the assigned value as a TypedDict, not Dict.

Now mypy will recognize these as valid:

name = movie['name']  # Okay; type of name is str
year = movie['year']  # Okay; type of year is int

Mypy will detect an invalid key as an error:

director = movie['director']  # Error: 'director' is not a valid key

Mypy will also reject a runtime-computed expression as a key, as it can’t verify that it’s a valid key. You can only use string literals as TypedDict keys.

The TypedDict type object can also act as a constructor. It returns a normal dict object at runtime – a TypedDict does not define a new runtime type:

toy_story = Movie(name='Toy Story', year=1995)

This is equivalent to just constructing a dictionary directly using { ... } or dict(key=value, ...). The constructor form is sometimes convenient, since it can be used without a type annotation, and it also makes the type of the object explicit.

Like all types, TypedDicts can be used as components to build arbitrarily complex types. For example, you can define nested TypedDicts and containers with TypedDict items. Unlike most other types, mypy uses structural compatibility checking (or structural subtyping) with TypedDicts. A TypedDict object with extra items is compatible with a narrower TypedDict, assuming item types are compatible (totality also affects subtyping, as discussed below).


You need to install mypy_extensions using pip to use TypedDict:

python3 -m pip install --upgrade mypy-extensions

Or, if you are using Python 2:

pip install --upgrade mypy-extensions


By default mypy ensures that a TypedDict object has all the specified keys. This will be flagged as an error:

# Error: 'year' missing
toy_story = {'name': 'Toy Story'}  # type: Movie

Sometimes you want to allow keys to be left out when creating a TypedDict object. You can provide the total=False argument to TypedDict(...) to achieve this:

GuiOptions = TypedDict(
    'GuiOptions', {'language': str, 'color': str}, total=False)
options = {}  # type: GuiOptions  # Okay
options['language'] = 'en'

You may need to use get() to access items of a partial (non-total) TypedDict, since indexing using [] could fail at runtime. However, mypy still lets use [] with a partial TypedDict – you just need to be careful with it, as it could result in a KeyError. Requiring get() everywhere would be too cumbersome. (Note that you are free to use get() with total TypedDicts as well.)

Keys that aren’t required are shown with a ? in error messages:

# Revealed type is 'TypedDict('GuiOptions', {'language'?: builtins.str,
#                                            'color'?: builtins.str})'

Totality also affects structural compatibility. You can’t use a partial TypedDict when a total one is expected. Also, a total TypedDict is not valid when a partial one is expected.

Class-based syntax

An alternative, class-based syntax to define a TypedDict is supported in Python 3.6 and later:

from mypy_extensions import TypedDict

class Movie(TypedDict):
    name: str
    year: int

The above definition is equivalent to the original Movie definition. It doesn’t actually define a real class. This syntax also supports a form of inheritance – subclasses can define additional items. However, this is primarily a notational shortcut. Since mypy uses structural compatibility with TypedDicts, inheritance is not required for compatibility. Here is an example of inheritance:

class Movie(TypedDict):
    name: str
    year: int

class BookBasedMovie(Movie):
    based_on: str

Now BookBasedMovie has keys name, year and based_on.

Mixing required and non-required items

In addition to allowing reuse across TypedDict types, inheritance also allows you to mix required and non-required (using total=False) items in a single TypedDict. Example:

class MovieBase(TypedDict):
    name: str
    year: int

class Movie(MovieBase, total=False):
    based_on: str

Now Movie has required keys name and year, while based_on can be left out when constructing an object. A TypedDict with a mix of required and non-required keys, such as Movie above, will only be compatible with another TypedDict if all required keys in the other TypedDict are required keys in the first TypedDict, and all non-required keys of the other TypedDict are also non-required keys in the first TypedDict.