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PEP: 526
Title: Syntax for Variable Annotations
Version: $Revision$
Last-Modified: $Date$
Author: Ryan Gonzalez <>, Philip House <>, Ivan Levkivskyi <>, Lisa Roach <>, Guido van Rossum <>
Status: Final
Type: Standards Track
Content-Type: text/x-rst
Created: 09-Aug-2016
Python-Version: 3.6
Post-History: 30-Aug-2016, 02-Sep-2016
This PEP has been provisionally accepted by the BDFL.
See the acceptance message for more color:
Notice for Reviewers
This PEP was drafted in a separate repo:
There was preliminary discussion on python-ideas and at
Before you bring up an objection in a public forum please at least
read the summary of `rejected`_ ideas listed at the end of this PEP.
PEP 484 introduced type hints, a.k.a. type annotations. While its
main focus was function annotations, it also introduced the notion of
type comments to annotate variables::
# 'primes' is a list of integers
primes = [] # type: List[int]
# 'captain' is a string (Note: initial value is a problem)
captain = ... # type: str
class Starship:
# 'stats' is a class variable
stats = {} # type: Dict[str, int]
This PEP aims at adding syntax to Python for annotating the types of variables
(including class variables and instance variables),
instead of expressing them through comments::
primes: List[int] = []
captain: str # Note: no initial value!
class Starship:
stats: ClassVar[Dict[str, int]] = {}
PEP 484 explicitly states that type comments are intended to help with
type inference in complex cases, and this PEP does not change this
intention. However, since in practice type comments have also been
adopted for class variables and instance variables, this PEP also
discusses the use of type annotations for those variables.
Although type comments work well enough, the fact that they're
expressed through comments has some downsides:
- Text editors often highlight comments differently from type annotations.
- There's no way to annotate the type of an undefined variable; one needs to
initialize it to ``None`` (e.g. ``a = None # type: int``).
- Variables annotated in a conditional branch are difficult to read::
if some_value:
my_var = function() # type: Logger
my_var = another_function() # Why isn't there a type here?
- Since type comments aren't actually part of the language, if a Python script
wants to parse them, it requires a custom parser instead of just using
- Type comments are used a lot in typeshed. Migrating typeshed to use
the variable annotation syntax instead of type comments would improve
readability of stubs.
- In situations where normal comments and type comments are used together, it is
difficult to distinguish them::
path = None # type: Optional[str] # Path to module source
- It's impossible to retrieve the annotations at runtime outside of
attempting to find the module's source code and parse it at runtime,
which is inelegant, to say the least.
The majority of these issues can be alleviated by making the syntax
a core part of the language. Moreover, having a dedicated annotation syntax
for class and instance variables (in addition to method annotations) will
pave the way to static duck-typing as a complement to nominal typing defined
by PEP 484.
While the proposal is accompanied by an extension of the ``typing.get_type_hints``
standard library function for runtime retrieval of annotations, variable
annotations are not designed for runtime type checking. Third party packages
will have to be developed to implement such functionality.
It should also be emphasized that **Python will remain a dynamically typed
language, and the authors have no desire to ever make type hints mandatory,
even by convention.** Type annotations should not be confused with variable
declarations in statically typed languages. The goal of annotation syntax is
to provide an easy way to specify structured type metadata
for third party tools.
This PEP does not require type checkers to change their type checking
rules. It merely provides a more readable syntax to replace type
Type annotation can be added to an assignment statement or to a single
expression indicating the desired type of the annotation target to a third
party type checker::
my_var: int
my_var = 5 # Passes type check.
other_var: int = 'a' # Flagged as error by type checker,
# but OK at runtime.
This syntax does not introduce any new semantics beyond PEP 484, so that
the following three statements are equivalent::
var = value # type: annotation
var: annotation; var = value
var: annotation = value
Below we specify the syntax of type annotations
in different contexts and their runtime effects.
We also suggest how type checkers might interpret annotations, but
compliance to these suggestions is not mandatory. (This is in line
with the attitude towards compliance in PEP 484.)
Global and local variable annotations
The types of locals and globals can be annotated as follows::
some_number: int # variable without initial value
some_list: List[int] = [] # variable with initial value
Being able to omit the initial value allows for easier typing of variables
assigned in conditional branches::
sane_world: bool
if 2+2 == 4:
sane_world = True
sane_world = False
Note that, although the syntax does allow tuple packing, it does *not* allow
one to annotate the types of variables when tuple unpacking is used::
# Tuple packing with variable annotation syntax
t: Tuple[int, ...] = (1, 2, 3)
# or
t: Tuple[int, ...] = 1, 2, 3 # This only works in Python 3.8+
# Tuple unpacking with variable annotation syntax
header: str
kind: int
body: Optional[List[str]]
header, kind, body = message
Omitting the initial value leaves the variable uninitialized::
a: int
print(a) # raises NameError
However, annotating a local variable will cause the interpreter to always make
it a local::
def f():
a: int
print(a) # raises UnboundLocalError
# Commenting out the a: int makes it a NameError.
as if the code were::
def f():
if False: a = 0
print(a) # raises UnboundLocalError
Duplicate type annotations will be ignored. However, static type
checkers may issue a warning for annotations of the same variable
by a different type::
a: int
a: str # Static type checker may or may not warn about this.
.. _classvar:
Class and instance variable annotations
Type annotations can also be used to annotate class and instance variables
in class bodies and methods. In particular, the value-less notation ``a: int``
allows one to annotate instance variables that should be initialized
in ``__init__`` or ``__new__``. The proposed syntax is as follows::
class BasicStarship:
captain: str = 'Picard' # instance variable with default
damage: int # instance variable without default
stats: ClassVar[Dict[str, int]] = {} # class variable
Here ``ClassVar`` is a special class defined by the typing module that
indicates to the static type checker that this variable should not be
set on instances.
Note that a ``ClassVar`` parameter cannot include any type variables, regardless
of the level of nesting: ``ClassVar[T]`` and ``ClassVar[List[Set[T]]]`` are
both invalid if ``T`` is a type variable.
This could be illustrated with a more detailed example. In this class::
class Starship:
captain = 'Picard'
stats = {}
def __init__(self, damage, captain=None):
self.damage = damage
if captain:
self.captain = captain # Else keep the default
def hit(self):
Starship.stats['hits'] = Starship.stats.get('hits', 0) + 1
``stats`` is intended to be a class variable (keeping track of many different
per-game statistics), while ``captain`` is an instance variable with a default
value set in the class. This difference might not be seen by a type
checker: both get initialized in the class, but ``captain`` serves only
as a convenient default value for the instance variable, while ``stats``
is truly a class variable -- it is intended to be shared by all instances.
Since both variables happen to be initialized at the class level, it is
useful to distinguish them by marking class variables as annotated with
types wrapped in ``ClassVar[...]``. In this way a type checker may flag
accidental assignments to attributes with the same name on instances.
For example, annotating the discussed class::
class Starship:
captain: str = 'Picard'
damage: int
stats: ClassVar[Dict[str, int]] = {}
def __init__(self, damage: int, captain: str = None):
self.damage = damage
if captain:
self.captain = captain # Else keep the default
def hit(self):
Starship.stats['hits'] = Starship.stats.get('hits', 0) + 1
enterprise_d = Starship(3000)
enterprise_d.stats = {} # Flagged as error by a type checker
Starship.stats = {} # This is OK
As a matter of convenience (and convention), instance variables can be
annotated in ``__init__`` or other methods, rather than in the class::
from typing import Generic, TypeVar
T = TypeVar('T')
class Box(Generic[T]):
def __init__(self, content):
self.content: T = content
Annotating expressions
The target of the annotation can be any valid single assignment
target, at least syntactically (it is up to the type checker what to
do with this)::
class Cls:
c = Cls()
c.x: int = 0 # Annotates c.x with int.
c.y: int # Annotates c.y with int.
d = {}
d['a']: int = 0 # Annotates d['a'] with int.
d['b']: int # Annotates d['b'] with int.
Note that even a parenthesized name is considered an expression,
not a simple name::
(x): int # Annotates x with int, (x) treated as expression by compiler.
(y): int = 0 # Same situation here.
Where annotations aren't allowed
It is illegal to attempt to annotate variables subject to ``global``
or ``nonlocal`` in the same function scope::
def f():
global x: int # SyntaxError
def g():
x: int # Also a SyntaxError
global x
The reason is that ``global`` and ``nonlocal`` don't own variables;
therefore, the type annotations belong in the scope owning the variable.
Only single assignment targets and single right hand side values are allowed.
In addition, one cannot annotate variables used in a ``for`` or ``with``
statement; they can be annotated ahead of time, in a similar manner to tuple
a: int
for a in my_iter:
f: MyFile
with myfunc() as f:
Variable annotations in stub files
As variable annotations are more readable than type comments, they are
preferred in stub files for all versions of Python, including Python 2.7.
Note that stub files are not executed by Python interpreters, and therefore
using variable annotations will not lead to errors. Type checkers should
support variable annotations in stubs for all versions of Python. For example::
# file lib.pyi
ADDRESS: unicode = ...
class Error:
cause: Union[str, unicode]
Preferred coding style for variable annotations
Annotations for module level variables, class and instance variables,
and local variables should have a single space after corresponding colon.
There should be no space before the colon. If an assignment has right hand
side, then the equality sign should have exactly one space on both sides.
- Yes::
code: int
class Point:
coords: Tuple[int, int]
label: str = '<unknown>'
- No::
code:int # No space after colon
code : int # Space before colon
class Test:
result: int=0 # No spaces around equality sign
Changes to Standard Library and Documentation
- A new covariant type ``ClassVar[T_co]`` is added to the ``typing``
module. It accepts only a single argument that should be a valid type,
and is used to annotate class variables that should not be set on class
instances. This restriction is ensured by static checkers,
but not at runtime. See the
`classvar`_ section for examples and explanations for the usage of
``ClassVar``, and see the `rejected`_ section for more information on
the reasoning behind ``ClassVar``.
- Function ``get_type_hints`` in the ``typing`` module will be extended,
so that one can retrieve type annotations at runtime from modules
and classes as well as functions.
Annotations are returned as a dictionary mapping from variable or arguments
to their type hints with forward references evaluated.
For classes it returns a mapping (perhaps ``collections.ChainMap``)
constructed from annotations in method resolution order.
- Recommended guidelines for using annotations will be added to the
documentation, containing a pedagogical recapitulation of specifications
described in this PEP and in PEP 484. In addition, a helper script for
translating type comments into type annotations will be published
separately from the standard library.
Runtime Effects of Type Annotations
Annotating a local variable will cause
the interpreter to treat it as a local, even if it was never assigned to.
Annotations for local variables will not be evaluated::
def f():
x: NonexistentName # No error.
However, if it is at a module or class level, then the type *will* be
x: NonexistentName # Error!
class X:
var: NonexistentName # Error!
In addition, at the module or class level, if the item being annotated is a
*simple name*, then it and the annotation will be stored in the
``__annotations__`` attribute of that module or class (mangled if private)
as an ordered mapping from names to evaluated annotations.
Here is an example::
from typing import Dict
class Player:
players: Dict[str, Player]
__points: int
# prints: {'players': typing.Dict[str, __main__.Player],
# '_Player__points': <class 'int'>}
``__annotations__`` is writable, so this is permitted::
__annotations__['s'] = str
But attempting to update ``__annotations__`` to something other than an
ordered mapping may result in a TypeError::
class C:
__annotations__ = 42
x: int = 5 # raises TypeError
(Note that the assignment to ``__annotations__``, which is the
culprit, is accepted by the Python interpreter without questioning it
-- but the subsequent type annotation expects it to be a
``MutableMapping`` and will fail.)
The recommended way of getting annotations at runtime is by using
``typing.get_type_hints`` function; as with all dunder attributes,
any undocummented use of ``__annotations__`` is subject to breakage
without warning::
from typing import Dict, ClassVar, get_type_hints
class Starship:
hitpoints: int = 50
stats: ClassVar[Dict[str, int]] = {}
shield: int = 100
captain: str
def __init__(self, captain: str) -> None:
assert get_type_hints(Starship) == {'hitpoints': int,
'stats': ClassVar[Dict[str, int]],
'shield': int,
'captain': str}
assert get_type_hints(Starship.__init__) == {'captain': str,
'return': None}
Note that if annotations are not found statically, then the
``__annotations__`` dictionary is not created at all. Also the
value of having annotations available locally does not offset
the cost of having to create and populate the annotations dictionary
on every function call. Therefore annotations at function level are
not evaluated and not stored.
Other uses of annotations
While Python with this PEP will not object to::
alice: 'well done' = 'A+'
bob: 'what a shame' = 'F-'
since it will not care about the type annotation beyond "it evaluates
without raising", a type checker that encounters it will flag it,
unless disabled with ``# type: ignore`` or ``@no_type_check``.
However, since Python won't care what the "type" is,
if the above snippet is at the global level or in a class, ``__annotations__``
will include ``{'alice': 'well done', 'bob': 'what a shame'}``.
These stored annotations might be used for other purposes,
but with this PEP we explicitly recommend type hinting as the
preferred use of annotations.
.. _rejected:
Rejected/Postponed Proposals
- **Should we introduce variable annotations at all?**
Variable annotations have *already* been around for almost two years
in the form of type comments, sanctioned by PEP 484. They are
extensively used by third party type checkers (mypy, pytype,
PyCharm, etc.) and by projects using the type checkers. However, the
comment syntax has many downsides listed in Rationale. This PEP is
not about the need for type annotations, it is about what should be
the syntax for such annotations.
- **Introduce a new keyword:**
The choice of a good keyword is hard,
e.g. it can't be ``var`` because that is way too common a variable name,
and it can't be ``local`` if we want to use it for class variables or
globals. Second, no matter what we choose, we'd still need
a ``__future__`` import.
- **Use** ``def`` **as a keyword:**
The proposal would be::
def primes: List[int] = []
def captain: str
The problem with this is that ``def`` means "define a function" to
generations of Python programmers (and tools!), and using it also to
define variables does not increase clarity. (Though this is of
course subjective.)
- **Use function based syntax**:
It was proposed to annotate types of variables using
``var = cast(annotation[, value])``. Although this syntax
alleviates some problems with type comments like absence of the annotation
in AST, it does not solve other problems such as readability
and it introduces possible runtime overhead.
- **Allow type annotations for tuple unpacking:**
This causes ambiguity: it's not clear what this statement means::
x, y: T
Are ``x`` and ``y`` both of type ``T``, or do we expect ``T`` to be
a tuple type of two items that are distributed over ``x`` and ``y``,
or perhaps ``x`` has type ``Any`` and ``y`` has type ``T``? (The
latter is what this would mean if this occurred in a function
signature.) Rather than leave the (human) reader guessing, we
forbid this, at least for now.
- **Parenthesized form** ``(var: type)`` **for annotations:**
It was brought up on python-ideas as a remedy for the above-mentioned
ambiguity, but it was rejected since such syntax would be hairy,
the benefits are slight, and the readability would be poor.
- **Allow annotations in chained assignments:**
This has problems of ambiguity and readability similar to tuple
unpacking, for example in::
x: int = y = 1
z = w: int = 1
it is ambiguous, what should the types of ``y`` and ``z`` be?
Also the second line is difficult to parse.
- **Allow annotations in** ``with`` **and** ``for`` **statement:**
This was rejected because in ``for`` it would make it hard to spot the actual
iterable, and in ``with`` it would confuse the CPython's LL(1) parser.
- **Evaluate local annotations at function definition time:**
This has been rejected by Guido because the placement of the annotation
strongly suggests that it's in the same scope as the surrounding code.
- **Store variable annotations also in function scope:**
The value of having the annotations available locally is just not enough
to significantly offset the cost of creating and populating the dictionary
on *each* function call.
- **Initialize variables annotated without assignment:**
It was proposed on python-ideas to initialize ``x`` in ``x: int`` to
``None`` or to an additional special constant like Javascript's
``undefined``. However, adding yet another singleton value to the language
would needed to be checked for everywhere in the code. Therefore,
Guido just said plain "No" to this.
- **Add also** ``InstanceVar`` **to the typing module:**
This is redundant because instance variables are way more common than
class variables. The more common usage deserves to be the default.
- **Allow instance variable annotations only in methods:**
The problem is that many ``__init__`` methods do a lot of things besides
initializing instance variables, and it would be harder (for a human)
to find all the instance variable annotations.
And sometimes ``__init__`` is factored into more helper methods
so it's even harder to chase them down. Putting the instance variable
annotations together in the class makes it easier to find them,
and helps a first-time reader of the code.
- **Use syntax** ``x: class t = v`` **for class variables:**
This would require a more complicated parser and the ``class``
keyword would confuse simple-minded syntax highlighters. Anyway we
need to have ``ClassVar`` store class variables to
``__annotations__``, so a simpler syntax was chosen.
- **Forget about** ``ClassVar`` **altogether:**
This was proposed since mypy seems to be getting along fine without a way
to distinguish between class and instance variables. But a type checker
can do useful things with the extra information, for example flag
accidental assignments to a class variable via the instance
(which would create an instance variable shadowing the class variable).
It could also flag instance variables with mutable defaults,
a well-known hazard.
- **Use** ``ClassAttr`` **instead of** ``ClassVar``:
The main reason why ``ClassVar`` is better is following: many things are
class attributes, e.g. methods, descriptors, etc. But only specific
attributes are conceptually class variables (or maybe constants).
- **Do not evaluate annotations, treat them as strings:**
This would be inconsistent with the behavior of function annotations that
are always evaluated. Although this might be reconsidered in future,
it was decided in PEP 484 that this would have to be a separate PEP.
- **Annotate variable types in class docstring:**
Many projects already use various docstring conventions, often without
much consistency and generally without conforming to the PEP 484 annotation
syntax yet. Also this would require a special sophisticated parser.
This, in turn, would defeat the purpose of the PEP --
collaborating with the third party type checking tools.
- **Implement** ``__annotations__`` **as a descriptor:**
This was proposed to prohibit setting ``__annotations__`` to something
non-dictionary or non-None. Guido has rejected this idea as unnecessary;
instead a TypeError will be raised if an attempt is made to update
``__annotations__`` when it is anything other than a mapping.
- **Treating bare annotations the same as global or nonlocal:**
The rejected proposal would prefer that the presence of an
annotation without assignment in a function body should not involve
*any* evaluation. In contrast, the PEP implies that if the target
is more complex than a single name, its "left-hand part" should be
evaluated at the point where it occurs in the function body, just to
enforce that it is defined. For example, in this example::
def foo(self): str
the name ``slef`` should be evaluated, just so that if it is not
defined (as is likely in this example :-), the error will be caught
at runtime. This is more in line with what happens when there *is*
an initial value, and thus is expected to lead to fewer surprises.
(Also note that if the target was ```` (this time correctly
spelled :-), an optimizing compiler has no obligation to evaluate
``self`` as long as it can prove that it will definitely be
Backwards Compatibility
This PEP is fully backwards compatible.
An implementation for Python 3.6 is found on GitHub repo at
This document has been placed in the public domain.
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