This document is a quick cheat sheet showing how the PEP 484 type annotation notation represents various common types in Python 3.
Note
Technically many of the type annotations shown below are redundant, because mypy can derive them from the type of the expression. So many of the examples have a dual purpose: show how to write the annotation, and show the inferred types.
Python 3.6 introduced a syntax for annotating variables in PEP 526 and we use it in most examples.
# This is how you declare the type of a variable type in Python 3.6
age: int = 1
# In Python 3.5 and earlier you can use a type comment instead
# (equivalent to the previous definition)
age = 1 # type: int
# You don't need to initialize a variable to annotate it
a: int # Ok (no value at runtime until assigned)
# The latter is useful in conditional branches
child: bool
if age < 18:
child = True
else:
child = Falsefrom typing import List, Set, Dict, Tuple, Optional
# For simple built-in 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, the type of the collection item is in brackets
# (Python 3.9+)
x: list[int] = [1]
x: set[int] = {6, 7}
# In Python 3.8 and earlier, the name of the collection type is
# capitalized, and the type is imported from 'typing'
x: List[int] = [1]
x: Set[int] = {6, 7}
# Same as above, but with type comment syntax (Python 3.5 and earlier)
x = [1] # type: List[int]
# For mappings, we need the types of both keys and values
x: dict[str, float] = {'field': 2.0} # Python 3.9+
x: Dict[str, float] = {'field': 2.0}
# 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+
x: Tuple[int, str, float] = (3, "yes", 7.5)
# For tuples of variable size, we use one type and ellipsis
x: tuple[int, ...] = (1, 2, 3) # Python 3.9+
x: Tuple[int, ...] = (1, 2, 3)
# Use Optional[] for values that could be None
x: Optional[str] = some_function()
# Mypy understands a value can't be None in an if-statement
if x is not None:
print(x.upper())
# If a value can never be None due to some invariants, use an assert
assert x is not None
print(x.upper())Python 3 supports an annotation syntax for function declarations.
from typing import Callable, Iterator, Union, Optional, List
# 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
# Add default value for an argument after the type annotation
def f(num1: int, my_float: float = 3.5) -> float:
return num1 + my_float
# 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='',
body: Optional[List[str]] = None
) -> bool:
...
# An argument can be declared positional-only by giving it a name
# starting with two underscores:
def quux(__x: int) -> None:
pass
quux(3) # Fine
quux(__x=3) # Errorfrom typing import Union, Any, List, Optional, 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"
# Use Union when something could be one of a few types
x: List[Union[int, str]] = [3, 5, "test", "fun"]
# 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()
# If you initialize a variable with an empty container or "None"
# you may have to help mypy a bit by providing a type annotation
x: List[str] = []
x: Optional[str] = None
# This makes each positional arg and each keyword arg a "str"
def call(self, *args: str, **kwargs: str) -> str:
request = make_request(*args, **kwargs)
return self.do_api_query(request)
# 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 comment every "ignore" with a bug link
# (in mypy, typeshed, or your own code) or an explanation of the issue.
x = confusing_function() # type: ignore # https://github.com/python/mypy/issues/1167
# "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 (no runtime check)
reveal_type(c) # -> Revealed type is "builtins.list[builtins.str]"
print(c) # -> [4]; the object is not cast
# If you want dynamic attributes on your class, have it override "__setattr__"
# or "__getattr__" in a stub or in your source code.
#
# "__setattr__" allows for dynamic assignment to names
# "__getattr__" allows for dynamic access 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 checkingIn 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, List, Set
# 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' # if we try this, mypy will throw an error...
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 :ref:`protocol-types`.
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 use the ClassVar annotation to declare a class variable
class Car:
seats: ClassVar[int] = 4
passengers: ClassVar[List[str]]
# You can also declare the type of an attribute in "__init__"
class Box:
def __init__(self) -> None:
self.items: List[str] = []See :ref:`async-and-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('T-minus {} ({})'.format(count, tag))
await asyncio.sleep(0.1)
count -= 1
return "Blastoff!"import sys
import re
from typing import Match, AnyStr, IO
# "typing.Match" describes regex matches from the re module
x: Match[str] = re.match(r'[0-9]+', "15")
# 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
else:
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
...
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 :ref:`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]:
...