Mypy syntax cheat sheet (Python 2)

This document is a quick cheat sheet showing how the PEP 484 type language represents various common types in Python 2.


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.

Built-in types

from typing import List, Set, Dict, Tuple, Text, Optional

# For simple built-in types, just use the name of the type.
x = 1 # type: int
x = 1.0 # type: float
x = True # type: bool
x = "test" # type: str
x = u"test" # type: unicode

# For collections, the name of the type is capitalized, and the
# name of the type inside the collection is in brackets.
x = [1] # type: List[int]
x = set([6, 7]) # type: Set[int]

# Empty Tuple types are a bit special
x = ()  # type: Tuple[()]

# For mappings, we need the types of both keys and values.
x = dict(field=2.0) # type: Dict[str, float]

# For tuples, we specify the types of all the elements.
x = (3, "yes", 7.5) # type: Tuple[int, str, float]

# For textual data, use Text.
# This is `unicode` in Python 2 and `str` in Python 3.
x = ["string", u"unicode"] # type: List[Text]

# Use Optional for values that could be None.
input_str = f() # type: Optional[str]
if input_str is not None:
   print input_str


from typing import Callable, Iterable

# This is how you annotate a function definition.
def stringify(num):
    # type: (int) -> str
    """Your function docstring goes here after the type definition."""
    return str(num)

# This function has no parameters and also returns nothing. Annotations
# can also be placed on the same line as their function headers.
def greet_world(): # type: () -> None
    print "Hello, world!"

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

# Add type annotations for kwargs as though they were positional args.
def f(num1, my_float=3.5):
    # type: (int, float) -> float
    return num1 + my_float

# An argument can be declared positional-only by giving it a name
# starting with two underscores:
def quux(__x):
    # type: (int) -> None
quux(3)  # Fine
quux(__x=3)  # Error

# This is how you annotate a function value.
x = f # type: Callable[[int, float], float]

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

# There's alternative syntax for functions with many arguments.
def send_email(address,     # type: Union[str, List[str]]
               sender,      # type: str
               cc,          # type: Optional[List[str]]
               bcc,         # type: Optional[List[str]]
               body=None    # type: List[str]
    # type: (...) -> bool

When you’re puzzled or when things are complicated

from typing import Union, Any, 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) # -> error: Revealed type is ''

# Use Union when something could be one of a few types.
x = [3, 5, "test", "fun"] # type: List[Union[int, str]]

# Use Any if you don't know the type of something or it's too
# dynamic to write a type for.
x = mystery_function() # type: Any

# This is how to deal with varargs.
# This makes each positional arg and each keyword arg a 'str'.
def call(self, *args, **kwargs):
         # type: (*str, **str) -> str
         request = make_request(*args, **kwargs)
         return self.do_api_query(request)

# Use `ignore` to suppress type-checking 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 #

# cast is a helper function for mypy that allows for guidance of how to convert types.
# it does not cast at runtime
a = [4]
b = cast(List[int], a)  # passes fine
c = cast(List[str], a)  # passes fine (no runtime check)
reveal_type(c)  # -> error: 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`
    def __setattr__(self, name, value):
        # type: (str, int) -> None
        ... = 42  # works = 'Ex-parrot'  # fails type checking

# TODO: explain "Need type annotation for variable" when
# initializing with None or an empty container

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(iterable_of_ints):
    # type: (Iterable[int]) -> List[str]
    return [str(x) for x in iterator_of_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_dict):
    # type: (Mapping[int, str]) -> List[int]
    return list(my_dict.keys())
f({3: 'yes', 4: 'no'})
def f(my_mapping):
    # type: (MutableMapping[int, str]) -> Set[str]
    my_dict[5] = 'maybe'
    return set(my_dict.values())
f({3: 'yes', 4: 'no'})


class MyClass(object):

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

    # 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):
        # type: () -> None

# User-defined classes are written with just their own names.
x = MyClass() # type: MyClass

Other stuff

import sys
# typing.Match describes regex matches from the re module.
from typing import Match, AnyStr, IO
x = re.match(r'[0-9]+', "15") # type: Match[str]

# Use AnyStr for functions that should accept any kind of string
# without allowing different kinds of strings to mix.
def concat(a, b):
    # type: (AnyStr, AnyStr) -> AnyStr
    return a + b
concat(u"foo", u"bar")  # type: unicode
concat(b"foo", b"bar")  # type: bytes

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

# TODO: add TypeVar and a simple generic function