.. _cheat-sheet-py2:

Type hints 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.

.. 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.


Built-in types
**************

.. code-block:: python

   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 = {6, 7}  # type: Set[int]

   # For mappings, we need the types of both keys and values
   x = {'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
   # ("Text" means  "unicode" in Python 2 and "str" in Python 3)
   x = [u"one", u"two"]  # type: List[Text]

   # Use Optional[] for values that could be None
   x = some_function()  # type: Optional[str]
   # 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()

Functions
*********

.. code-block:: python

   from typing import Callable, Iterator, Union, Optional, List

   # 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 arguments with default values as though they
   # had no defaults
   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
       pass

   quux(3)  # Fine
   quux(__x=3)  # Error

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

   # 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):
       # type: (int) -> Iterator[int]
       i = 0
       while i < n:
           yield i
           i += 1

   # There's an 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]]
                  subject='',
                  body=None    # type: List[str]
                  ):
       # type: (...) -> bool
       ...

When you're puzzled or when things are complicated
**************************************************

.. code-block:: python

   from 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 = [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

   # 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 = []  # type: List[str]
   x = None  # type: Optional[str]

   # 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 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, value):
           # type: (str, int) -> None
           ...

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


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.

.. code-block:: python

   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_mapping[5] = 'maybe'
       return set(my_mapping.values())

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


Classes
*******

.. code-block:: python

   class MyClass(object):
       # For instance methods, omit type for "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
           pass

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


Miscellaneous
*************

.. code-block:: python

   import sys
   import re
   from typing import Match, AnyStr, IO

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

   # 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='w'):
       # type: (str) -> IO[str]
       if mode == 'w':
           return sys.stdout
       elif mode == 'r':
           return sys.stdin
       else:
           return sys.stdout


Decorators
**********

Decorator functions can be expressed via generics. See
:ref:`declaring-decorators` for the more details.

.. code-block:: python

    from typing import Any, Callable, TypeVar

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

    def bare_decorator(func):  # type: (F) -> F
        ...

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