.. _cheat-sheet-py2:

Mypy syntax cheat sheet (Python 2)
==================================

This document is a quick cheat sheet showing how the `PEP 484 <https://www.python.org/dev/peps/pep-0484/>`_ 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 = set([6, 7]) # type: Set[int]

   # 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


Functions
*********

.. code-block:: python

   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
       pass
   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]]
                  subject='',
                  body=None    # type: List[str]
                  ):
       # type: (...) -> bool
        <code>


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

.. code-block:: python

   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 '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

   # 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 # https://github.com/python/mypy/issues/1167

   # 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

   # 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.

.. 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_dict[5] = 'maybe'
       return set(my_dict.values())
   f({3: 'yes', 4: 'no'})


Classes
*******

.. code-block:: python

   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
           pass

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


Other stuff
***********

.. code-block:: python

   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: AnyStr, b: 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') -> IO[str]:
       if mode == 'w':
           return sys.stdout
       elif mode == 'r':
           return sys.stdin
       else:
           return sys.stdout

   # TODO: add TypeVar and a simple generic function

