PEP 585 – Type Hinting Generics In Standard Collections
- Łukasz Langa <lukasz at python.org>
- Typing-SIG list
- Standards Track
- Python-Dev thread
Table of Contents
- Rationale and Goals
- Backwards compatibility
- Reference implementation
- Rejected alternatives
- Note on the initial draft
Static typing as defined by PEPs 484, 526, 544, 560, and 563 was built
incrementally on top of the existing Python runtime and constrained by
existing syntax and runtime behavior. This led to the existence of
a duplicated collection hierarchy in the
typing module due to
generics (for example
typing.List and the built-in
This PEP proposes to enable support for the generics syntax in all
standard collections currently available in the
Rationale and Goals
This change removes the necessity for a parallel type hierarchy in the
typing module, making it easier for users to annotate their programs
and easier for teachers to teach Python.
Generic (n.) – a type that can be parameterized, typically a container.
Also known as a parametric type or a generic type. For example:
parameterized generic – a specific instance of a generic with the
expected types for container elements provided. Also known as
a parameterized type. For example:
Tooling, including type checkers and linters, will have to be adapted to recognize standard collections as generics.
On the source level, the newly described functionality requires Python 3.9. For use cases restricted to type annotations, Python files with the “annotations” future-import (available since Python 3.7) can parameterize standard collections, including builtins. To reiterate, that depends on the external tools understanding that this is valid.
Starting with Python 3.7, when
from __future__ import annotations is
used, function and variable annotations can parameterize standard
collections directly. Example:
from __future__ import annotations def find(haystack: dict[str, list[int]]) -> int: ...
Usefulness of this syntax before PEP 585 is limited as external tooling
like Mypy does not recognize standard collections as generic. Moreover,
certain features of typing like type aliases or casting require putting
types outside of annotations, in runtime context. While these are
relatively less common than type annotations, it’s important to allow
using the same type syntax in all contexts. This is why starting with
Python 3.9, the following collections become generic using
__class_getitem__() to parameterize contained types:
re.Pattern# typing.Pattern, typing.re.Pattern
re.Match# typing.Match, typing.re.Match
Importing those from
typing is deprecated. Due to PEP 563 and the
intention to minimize the runtime impact of typing, this deprecation
will not generate DeprecationWarnings. Instead, type checkers may warn
about such deprecated usage when the target version of the checked
program is signalled to be Python 3.9 or newer. It’s recommended to
allow for those warnings to be silenced on a project-wide basis.
The deprecated functionality will be removed from the
in the first Python version released 5 years after the release of
Parameters to generics are available at runtime
Preserving the generic type at runtime enables introspection of the type which can be used for API generation or runtime type checking. Such usage is already present in the wild.
Just like with the
typing module today, the parameterized generic
types listed in the previous section all preserve their type parameters
>>> list[str] list[str] >>> tuple[int, ...] tuple[int, ...] >>> ChainMap[str, list[str]] collections.ChainMap[str, list[str]]
This is implemented using a thin proxy type that forwards all method calls and attribute accesses to the bare origin type with the following exceptions:
__repr__shows the parameterized type;
__origin__attribute points at the non-parameterized generic class;
__args__attribute is a tuple (possibly of length 1) of generic types passed to the original
__parameters__attribute is a lazily computed tuple (possibly empty) of unique type variables found in
__getitem__raises an exception to disallow mistakes like
dict[str][str]. However it allows e.g.
dict[str, T][int]and in that case returns
This design means that it is possible to create instances of parameterized collections, like:
>>> l = list[str]()  >>> list is list[str] False >>> list == list[str] False >>> list[str] == list[str] True >>> list[str] == list[int] False >>> isinstance([1, 2, 3], list[str]) TypeError: isinstance() arg 2 cannot be a parameterized generic >>> issubclass(list, list[str]) TypeError: issubclass() arg 2 cannot be a parameterized generic >>> isinstance(list[str], types.GenericAlias) True
Objects created with bare types and parameterized types are exactly the same. The generic parameters are not preserved in instances created with parameterized types, in other words generic types erase type parameters during object creation.
One important consequence of this is that the interpreter does not attempt to type check operations on the collection created with a parameterized type. This provides symmetry between:
l: list[str] = 
l = list[str]()
For accessing the proxy type from Python code, it will be exported
types module as
Pickling or (shallow- or deep-) copying a
will preserve the type, origin, attributes and parameters.
Future standard collections must implement the same behavior.
A proof-of-concept or prototype implementation exists.
Keeping the status quo forces Python programmers to perform book-keeping
of imports from the
typing module for standard collections, making
all but the simplest annotations cumbersome to maintain. The existence
of parallel types is confusing to newcomers (why is there both
The above problems also don’t exist in user-built generic classes which share runtime functionality and the ability to use them as generic type annotations. Making standard collections harder to use in type hinting from user classes hindered typing adoption and usability.
It would be easier to implement
__class_getitem__ on the listed
standard collections in a way that doesn’t preserve the generic type,
in other words:
>>> list[str] <class 'list'> >>> tuple[int, ...] <class 'tuple'> >>> collections.ChainMap[str, list[str]] <class 'collections.ChainMap'>
This is problematic as it breaks backwards compatibility: current
equivalents of those types in the
typing module do preserve
the generic type:
>>> from typing import List, Tuple, ChainMap >>> List[str] typing.List[str] >>> Tuple[int, ...] typing.Tuple[int, ...] >>> ChainMap[str, List[str]] typing.ChainMap[str, typing.List[str]]
As mentioned in the “Implementation” section, preserving the generic type at runtime enables runtime introspection of the type which can be used for API generation or runtime type checking. Such usage is already present in the wild.
Additionally, implementing subscripts as identity functions would make Python less friendly to beginners. Say, if a user is mistakenly passing a list type instead of a list object to a function, and that function is indexing the received object, the code would no longer raise an error.
>>> l = list >>> l[-1] TypeError: 'type' object is not subscriptable
__class_getitem__ as an identity function:
>>> l = list >>> l[-1] list
The indexing being successful here would likely end up raising an exception at a distance, confusing the user.
Disallowing instantiation of parameterized types
Given that the proxy type which preserves
__args__ is mostly useful for runtime introspection purposes,
we might have disallowed instantiation of parameterized types.
In fact, forbidding instantiation of parameterized types is what the
typing module does today for types which parallel builtin
collections (instantiation of other parameterized types is allowed).
The original reason for this decision was to discourage spurious parameterization which made object creation up to two orders of magnitude slower compared to the special syntax available for those builtin collections.
This rationale is not strong enough to allow the exceptional treatment
of builtins. All other parameterized types can be instantiated,
including parallels of collections in the standard library. Moreover,
Python allows for instantiation of lists using
list() and some
builtin collections don’t provide special syntax for instantiation.
isinstance(obj, list[str]) perform a check ignoring generics
An earlier version of this PEP suggested treating parameterized generics
list[str] as equivalent to their non-parameterized variants
list for purposes of
This would be symmetrical to how
list[str]() creates a regular list.
This design was rejected because
checks with parameterized generics would read like element-by-element
runtime type checks. The result of those checks would be surprising,
>>> isinstance([1, 2, 3], list[str]) True
Note the object doesn’t match the provided generic type but
isinstance() still returns
True because it only checks whether
the object is a list.
If a library is faced with a parameterized generic and would like to
isinstance() check using the base type, that type can
be retrieved using the
__origin__ attribute on the parameterized
isinstance(obj, list[str]) perform a runtime type check
This functionality requires iterating over the collection which is a destructive operation in some of them. This functionality would have been useful, however implementing the type checker within Python that would deal with complex types, nested type checking, type variables, string forward references, and so on is out of scope for this PEP.
Naming the type
GenericType instead of
We considered a different name for this type, but decided
GenericAlias is better – these aren’t real types, they are
aliases for the corresponding container type with some extra metadata
Note on the initial draft
An early version of this PEP discussed matters beyond generics in standard collections. Those unrelated topics were removed for clarity.
Thank you to Guido van Rossum for his work on Python, and the implementation of this PEP specifically.
This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive.
Last modified: 2022-02-27 22:46:36 GMT