docs/source/cheat_sheet.rst - third_party/github.com/python/mypy - Git at Google

 .. _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, Iterator

    # 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_class_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
	.. _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, Iterator

	# 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_class_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