Type hints cheat sheet#

This document is a quick cheat sheet showing how to use type annotations for various common types in Python.


Technically many of the type annotations shown below are redundant, since mypy can usually infer the type of a variable from its value. See Type inference and type annotations for more details.

# This is how you declare the type of a variable
age: int = 1

# You don't need to initialize a variable to annotate it
a: int  # Ok (no value at runtime until assigned)

# Doing so is useful in conditional branches
child: bool
if age < 18:
    child = True
    child = False

Useful built-in types#

# For most types, just use the name of the type
x: int = 1
x: float = 1.0
x: bool = True
x: str = "test"
x: bytes = b"test"

# For collections on Python 3.9+, the type of the collection item is in brackets
x: list[int] = [1]
x: set[int] = {6, 7}

# For mappings, we need the types of both keys and values
x: dict[str, float] = {"field": 2.0}  # Python 3.9+

# For tuples of fixed size, we specify the types of all the elements
x: tuple[int, str, float] = (3, "yes", 7.5)  # Python 3.9+

# For tuples of variable size, we use one type and ellipsis
x: tuple[int, ...] = (1, 2, 3)  # Python 3.9+

# On Python 3.8 and earlier, the name of the collection type is
# capitalized, and the type is imported from the 'typing' module
from typing import List, Set, Dict, Tuple
x: List[int] = [1]
x: Set[int] = {6, 7}
x: Dict[str, float] = {"field": 2.0}
x: Tuple[int, str, float] = (3, "yes", 7.5)
x: Tuple[int, ...] = (1, 2, 3)

from typing import Union, Optional

# On Python 3.10+, use the | operator when something could be one of a few types
x: list[int | str] = [3, 5, "test", "fun"]  # Python 3.10+
# On earlier versions, use Union
x: list[Union[int, str]] = [3, 5, "test", "fun"]

# Use Optional[X] for a value that could be None
# Optional[X] is the same as X | None or Union[X, None]
x: Optional[str] = "something" if some_condition() else None
# Mypy understands a value can't be None in an if-statement
if x is not None:
# If a value can never be None due to some invariants, use an assert
assert x is not None


from typing import Callable, Iterator, Union, Optional

# This is how you annotate a function definition
def stringify(num: int) -> str:
    return str(num)

# And here's how you specify multiple arguments
def plus(num1: int, num2: int) -> int:
    return num1 + num2

# If a function does not return a value, use None as the return type
# Default value for an argument goes after the type annotation
def show(value: str, excitement: int = 10) -> None:
    print(value + "!" * excitement)

# This is how you annotate a callable (function) value
x: Callable[[int, float], float] = f

# A generator function that yields ints is secretly just a function that
# returns an iterator of ints, so that's how we annotate it
def g(n: int) -> Iterator[int]:
    i = 0
    while i < n:
        yield i
        i += 1

# You can of course split a function annotation over multiple lines
def send_email(address: Union[str, list[str]],
               sender: str,
               cc: Optional[list[str]],
               bcc: Optional[list[str]],
               subject: str = '',
               body: Optional[list[str]] = None
               ) -> bool:

# Mypy understands positional-only and keyword-only arguments
# Positional-only arguments can also be marked by using a name starting with
# two underscores
def quux(x: int, / *, y: int) -> None:

quux(3, y=5)  # Ok
quux(3, 5)  # error: Too many positional arguments for "quux"
quux(x=3, y=5)  # error: Unexpected keyword argument "x" for "quux"

# This says each positional arg and each keyword arg is a "str"
def call(self, *args: str, **kwargs: str) -> str:
    reveal_type(args)  # Revealed type is "tuple[str, ...]"
    reveal_type(kwargs)  # Revealed type is "dict[str, str]"
    request = make_request(*args, **kwargs)
    return self.do_api_query(request)


class MyClass:
    # You can optionally declare instance variables in the class body
    attr: int
    # This is an instance variable with a default value
    charge_percent: int = 100

    # The "__init__" method doesn't return anything, so it gets return
    # type "None" just like any other method that doesn't return anything
    def __init__(self) -> None:

    # For instance methods, omit type for "self"
    def my_method(self, num: int, str1: str) -> str:
        return num * str1

# User-defined classes are valid as types in annotations
x: MyClass = MyClass()

# You can also declare the type of an attribute in "__init__"
class Box:
    def __init__(self) -> None:
        self.items: list[str] = []

# You can use the ClassVar annotation to declare a class variable
class Car:
    seats: ClassVar[int] = 4
    passengers: ClassVar[list[str]]

# If you want dynamic attributes on your class, have it
# override "__setattr__" or "__getattr__":
# - "__getattr__" allows for dynamic access to names
# - "__setattr__" allows for dynamic assignment to names
class A:
    # This will allow assignment to any A.x, if x is the same type as "value"
    # (use "value: Any" to allow arbitrary types)
    def __setattr__(self, name: str, value: int) -> None: ...

    # This will allow access to any A.x, if x is compatible with the return type
    def __getattr__(self, name: str) -> int: ...

a.foo = 42  # Works
a.bar = 'Ex-parrot'  # Fails type checking

When you’re puzzled or when things are complicated#

from typing import Union, Any, Optional, TYPE_CHECKING, cast

# To find out what type mypy infers for an expression anywhere in
# your program, wrap it in reveal_type().  Mypy will print an error
# message with the type; remove it again before running the code.
reveal_type(1)  # Revealed type is "builtins.int"

# If you initialize a variable with an empty container or "None"
# you may have to help mypy a bit by providing an explicit type annotation
x: list[str] = []
x: Optional[str] = None

# Use Any if you don't know the type of something or it's too
# dynamic to write a type for
x: Any = mystery_function()
# Mypy will let you do anything with x!
x.whatever() * x["you"] + x("want") - any(x) and all(x) is super  # no errors

# Use a "type: ignore" comment to suppress errors on a given line,
# when your code confuses mypy or runs into an outright bug in mypy.
# Good practice is to add a comment explaining the issue.
x = confusing_function()  # type: ignore  # confusing_function won't return None here because ...

# "cast" is a helper function that lets you override the inferred
# type of an expression. It's only for mypy -- there's no runtime check.
a = [4]
b = cast(list[int], a)  # Passes fine
c = cast(list[str], a)  # Passes fine despite being a lie (no runtime check)
reveal_type(c)  # Revealed type is "builtins.list[builtins.str]"
print(c)  # Still prints [4] ... the object is not changed or casted at runtime

# Use "TYPE_CHECKING" if you want to have code that mypy can see but will not
# be executed at runtime (or to have code that mypy can't see)
    import json
    import orjson as json  # mypy is unaware of this

In some cases type annotations can cause issues at runtime, see Annotation issues at runtime for dealing with this.

Standard “duck types”#

In typical Python code, many functions that can take a list or a dict as an argument only need their argument to be somehow “list-like” or “dict-like”. A specific meaning of “list-like” or “dict-like” (or something-else-like) is called a “duck type”, and several duck types that are common in idiomatic Python are standardized.

from typing import Mapping, MutableMapping, Sequence, Iterable

# Use Iterable for generic iterables (anything usable in "for"),
# and Sequence where a sequence (supporting "len" and "__getitem__") is
# required
def f(ints: Iterable[int]) -> list[str]:
    return [str(x) for x in ints]

f(range(1, 3))

# Mapping describes a dict-like object (with "__getitem__") that we won't
# mutate, and MutableMapping one (with "__setitem__") that we might
def f(my_mapping: Mapping[int, str]) -> list[int]:
    my_mapping[5] = 'maybe'  # mypy will complain about this line...
    return list(my_mapping.keys())

f({3: 'yes', 4: 'no'})

def f(my_mapping: MutableMapping[int, str]) -> set[str]:
    my_mapping[5] = 'maybe'  # ...but mypy is OK with this.
    return set(my_mapping.values())

f({3: 'yes', 4: 'no'})

You can even make your own duck types using Protocols and structural subtyping.

Coroutines and asyncio#

See Typing async/await for the full detail on typing coroutines and asynchronous code.

import asyncio

# A coroutine is typed like a normal function
async def countdown35(tag: str, count: int) -> str:
    while count > 0:
        print(f'T-minus {count} ({tag})')
        await asyncio.sleep(0.1)
        count -= 1
    return "Blastoff!"


import sys
from typing import IO

# Use IO[] for functions that should accept or return any
# object that comes from an open() call (IO[] does not
# distinguish between reading, writing or other modes)
def get_sys_IO(mode: str = 'w') -> IO[str]:
    if mode == 'w':
        return sys.stdout
    elif mode == 'r':
        return sys.stdin
        return sys.stdout

# Forward references are useful if you want to reference a class before
# it is defined
def f(foo: A) -> int:  # This will fail at runtime with 'A' is not defined

class A:

# If you use the string literal 'A', it will pass as long as there is a
# class of that name later on in the file
def f(foo: 'A') -> int:  # Ok


Decorator functions can be expressed via generics. See Declaring decorators for more details.

from typing import Any, Callable, TypeVar

F = TypeVar('F', bound=Callable[..., Any])

def bare_decorator(func: F) -> F:

def decorator_args(url: str) -> Callable[[F], F]: