Typing Python Libraries

Much of Python’s popularity can be attributed to the rich collection of Python libraries available to developers. Authors of these libraries play an important role in improving the experience for Python developers. This document provides some recommendations and guidance for Python library authors.

Why provide type annotations?

Providing type annotations has the following benefits:

  1. Type annotations help provide users of libraries a better coding experience by enabling fast and accurate completion suggestions, class and function documentation, signature help, hover text, auto-imports, etc.

  2. Users of libraries are able to use static type checkers to detect issues with their use of libraries.

  3. Type annotations allow library authors to specify an interface contract that is enforced by tools. This lets the library implementation evolve with less fear that users are depending on implementation details. In the event of changes to the library interface, type checkers are able to warn users when their code is affected.

  4. Library authors are able to use static type checking themselves to help produce high-quality, bug-free implementations.

How to provide type annotations?

PEP 561 documents several ways type information can be provided for a library:

  • inline type annotations (preferred)

  • type stub files included in the package

  • a separate companion type stub package

  • type stubs in the typeshed repository

Inline type annotations simply refers to the use of annotations within your .py files. In contrast, with type stub files, type information lives in separate .pyi files; see Type Stubs and Writing and Maintaining Stub Files for more details.

We recommend using the inline type annotations approach, since it has the following benefits:

  • Typically requires the least effort to add and maintain

  • Users don’t have to download additional packages

  • Always remains consistent with the implementation

  • Allows library authors to type check their own code

  • Allows language servers to show users relevant details about the implementation, such as docstrings and default parameter values

However, there are cases where inlined type annotations are not possible — most notably when a library’s functionality is implemented in a language other than Python.

If you are not interested in providing type annotations for your library, you could suggest users to contribute type stubs to the typeshed project.

Marking a package as providing type information

As specified in PEP 561, tools will not treat your package as providing type information unless it includes a special py.typed marker file.


Before marking a package as providing type information, it is best to ensure that the library’s interface is fully annotated. See How much of my library needs types? for more details.

Inline type annotations

A typical directory structure would look like:


It’s important to ensure that the py.typed marker file is included in the distributed package. If using setuptools, this can be achieved like so:

from setuptools import setup

   package_data={"my_great_package": ["py.typed"]},

Type stub files included in the package

It’s possible to include a mix of type stub files (.pyi) and inline type annotations (.py). One use case for including type stub files in your package is to provide types for extension modules in your library. A typical directory structure would look like:


If using setuptools, we can ensure the .pyi and py.typed files are included like so:

from setuptools import setup

   package_data={"my_great_package": ["py.typed", "stuff.pyi"]},

The presence of .pyi files does not affect the Python interpreter at runtime in any way. However, static type checkers will only look at the .pyi file and ignore the corresponding .py file.

Companion type stub package

These are often referred to as “stub-only” packages. The name of the stub package should be the name of the runtime package suffixed with -stubs. The py.typed marker file is not necessary for stub-only packages. This approach can be useful to develop type stubs independently from your library.

For example:

from setuptools import setup

   package_data={"my_great_package-stubs": ["__init__.pyi", "stuff.pyi"]},

Users are then able to install the stubs-only package separately to provide types for the original library.

Inclusion in sdist

Note that to ensure inclusion of .pyi and py.typed files in an sdist (.tar.gz archive), you may also need to modify the inclusion rules in your MANIFEST.in (see the packaging guide for more details on MANIFEST.in). For example:

global-include *.pyi
global-include py.typed

How much of my library needs types?

A “py.typed” library should aim to be type complete so that type checking and inspection can work to their full extent. Here we say that a library is “type complete” if all of the symbols that comprise its interface have type annotations that refer to types that are fully known. Private symbols are exempt.

Library interface (public and private symbols)

If a py.typed module is present, a type checker will treat all modules within that package (i.e. all files that end in .py or .pyi) as importable unless the file name begins with an underscore. These modules comprise the supported interface for the library.

Each module exposes a set of symbols. Some of these symbols are considered “private” — implementation details that are not part of the library’s interface. Type checkers can use the following rules to determine which symbols are visible outside of the package.

  • Symbols whose names begin with an underscore (but are not dunder names) are considered private.

  • Imported symbols are considered private by default. If they use the import A as A (a redundant module alias), from X import A as A (a redundant symbol alias), or from . import A forms, symbol A is not private unless the name begins with an underscore. If a file __init__.py uses form from .A import X, symbol A is treated likewise. If a wildcard import (of the form from X import *) is used, all symbols referenced by the wildcard are not private.

  • A module can expose an __all__ symbol at the module level that provides a list of names that are considered part of the interface. This overrides all other rules above, allowing imported symbols or symbols whose names begin with an underscore to be included in the interface.

  • Local variables within a function (including nested functions) are always considered private.

The following idioms are supported for defining the values contained within __all__. These restrictions allow type checkers to statically determine the value of __all__.

  • __all__ = ('a', b')

  • __all__ = ['a', b']

  • __all__ += ['a', b']

  • __all__ += submodule.__all__

  • __all__.extend(['a', b'])

  • __all__.extend(submodule.__all__)

  • __all__.append('a')

  • __all__.remove('a')

Type Completeness

The following are best practice recommendations for how to define “type complete”:


  • All class variables, instance variables, and methods that are “visible” (not overridden) are annotated and refer to known types

  • If a class is a subclass of a generic class, type arguments are provided for each generic type parameter, and these type arguments are known types

Functions and Methods:

  • All input parameters have type annotations that refer to known types

  • The return parameter is annotated and refers to a known type

  • The result of applying one or more decorators results in a known type

Type Aliases:

  • All of the types referenced by the type alias are known


  • All variables have type annotations that refer to known types

Type annotations can be omitted in a few specific cases where the type is obvious from the context:

  • Constants that are assigned simple literal values (e.g. RED = '#F00' or MAX_TIMEOUT = 50 or room_temperature: Final = 20). A constant is a symbol that is assigned only once and is either annotated with Final or is named in all-caps. A constant that is not assigned a simple literal value requires explicit annotations, preferably with a Final annotation (e.g. WOODWINDS: Final[List[str]] = ['Oboe', 'Bassoon']).

  • Enum values within an Enum class do not require annotations because they take on the type of the Enum class.

  • Type aliases do not require annotations. A type alias is a symbol that is defined at a module level with a single assignment where the assigned value is an instantiable type, as opposed to a class instance (e.g. Foo = Callable[[Literal["a", "b"]], Union[int, str]] or Bar = Optional[MyGenericClass[int]]).

  • The “self” parameter in an instance method and the “cls” parameter in a class method do not require an explicit annotation.

  • The return type for an __init__ method does not need to be specified, since it is always None.

  • The following module-level symbols do not require type annotations: __all__,__author__, __copyright__, __email__, __license__, __title__, __uri__, __version__.

  • The following class-level symbols do not require type annotations: __class__, __dict__, __doc__, __module__, __slots__.

Examples of known and unknown types

# Variable with unknown type
a = [3, 4, 5]

# Variable with known type
a: List[int] = [3, 4, 5]

# Type alias with partially unknown type (because type
# arguments are missing for list and dict)
DictOrList = Union[list, dict]

# Type alias with known type
DictOrList = Union[List[Any], Dict[str, Any]]

# Generic type alias with known type
_T = TypeVar("_T")
DictOrList = Union[List[_T], Dict[str, _T]]

# Function with known type
def func(a: Optional[int], b: Dict[str, float] = {}) -> None:

# Function with partially unknown type (because type annotations
# are missing for input parameters and return type)
def func(a, b):

# Function with partially unknown type (because of missing
# type args on Dict)
def func(a: int, b: Dict) -> None:

# Function with partially unknown type (because return type
# annotation is missing)
def func(a: int, b: Dict[str, float]):

# Decorator with partially unknown type (because type annotations
# are missing for input parameters and return type)
def my_decorator(func):
    return func

# Function with partially unknown type (because type is obscured
# by untyped decorator)
def func(a: int) -> str:

# Class with known type
class MyClass:
    height: float = 2.0

    def __init__(self, name: str, age: int):
        self.age: int = age

    def name(self) -> str:

# Class with partially unknown type
class MyClass:
    # Missing type annotation for class variable
    height = 2.0

    # Missing input parameter annotations
    def __init__(self, name, age):
        # Missing type annotation for instance variable
        self.age = age

    # Missing return type annotation
    def name(self):

# Class with partially unknown type
class BaseClass:
    # Missing type annotation
    height = 2.0

    # Missing type annotation
    def get_stuff(self):

# Class with known type (because it overrides all symbols
# exposed by BaseClass that have incomplete types)
class DerivedClass(BaseClass):
    height: float

    def get_stuff(self) -> str:

# Class with partially unknown type because base class
# (dict) is generic, and type arguments are not specified.
class DictSubclass(dict):

Best Practices for Inlined Types

Wide vs. Narrow Types

In type theory, when comparing two types that are related to each other, the “wider” type is the one that is more general, and the “narrower” type is more specific. For example, Sequence[str] is a wider type than List[str] because all List objects are also Sequence objects, but the converse is not true. A subclass is narrower than a class it derives from. A union of types is wider than the individual types that comprise the union.

In general, a function input parameter should be annotated with the widest possible type supported by the implementation. For example, if the implementation requires the caller to provide an iterable collection of strings, the parameter should be annotated as Iterable[str], not as List[str]. The latter type is narrower than necessary, so if a user attempts to pass a tuple of strings (which is supported by the implementation), a type checker will complain about a type incompatibility.

As a specific application of the “use the widest type possible” rule, libraries should generally use immutable forms of container types instead of mutable forms (unless the function needs to modify the container). Use Sequence rather than List, Mapping rather than Dict, etc. Immutable containers allow for more flexibility because their type parameters are covariant rather than invariant. A parameter that is typed as Sequence[Union[str, int]] can accept a List[int], Sequence[str], and a Sequence[int]. But a parameter typed as List[Union[str, int]] is much more restrictive and accepts only a List[Union[str, int]].


If a function or method can return multiple different types and those types can be determined based on the presence or types of certain parameters, use the @overload mechanism defined in PEP 484. When overloads are used within a “.py” file, they must appear prior to the function implementation, which should not have an @overload decorator.

Keyword-only Parameters

If a function or method is intended to take parameters that are specified only by name, use the keyword-only separator (*).

def create_user(age: int, *, dob: Optional[date] = None):

Annotating Decorators

Decorators modify the behavior of a class or a function. Providing annotations for decorators is straightforward if the decorator retains the original signature of the decorated function.

_F = TypeVar("_F", bound=Callable[..., Any])

def simple_decorator(_func: _F) -> _F:
     Simple decorators are invoked without parentheses like this:
       def my_function(): ...

def complex_decorator(*, mode: str) -> Callable[[_F], _F]:
     Complex decorators are invoked with arguments like this:
       def my_function(): ...

Decorators that mutate the signature of the decorated function present challenges for type annotations. The ParamSpec and Concatenate mechanisms described in PEP 612 provide some help here, but these are available only in Python 3.10 and newer. More complex signature mutations may require type annotations that erase the original signature, thus blinding type checkers and other tools that provide signature assistance. As such, library authors are discouraged from creating decorators that mutate function signatures in this manner.

Generic Classes and Functions

Classes and functions that can operate in a generic manner on various types should declare themselves as generic using the mechanisms described in PEP 484. This includes the use of TypeVar symbols. Typically, a TypeVar should be private to the file that declares it, and should therefore begin with an underscore.

Type Aliases

Type aliases are symbols that refer to other types. Generic type aliases (those that refer to unspecialized generic classes) are supported by most type checkers.

PEP 613 provides a way to explicitly designate a symbol as a type alias using the new TypeAlias annotation.

# Simple type alias
FamilyPet = Union[Cat, Dog, GoldFish]

# Generic type alias
ListOrTuple = Union[List[_T], Tuple[_T, ...]]

# Recursive type alias
TreeNode = Union[LeafNode, List["TreeNode"]]

# Explicit type alias using PEP 613 syntax
StrOrInt: TypeAlias = Union[str, int]

Abstract Classes and Methods

Classes that must be subclassed should derive from ABC, and methods or properties that must be overridden should be decorated with the @abstractmethod decorator. This allows type checkers to validate that the required methods have been overridden and provide developers with useful error messages when they are not. It is customary to implement an abstract method by raising a NotImplementedError exception.

from abc import ABC, abstractmethod

class Hashable(ABC):
   def hash_value(self) -> int:
      """Subclasses must override"""
      raise NotImplementedError()

   def print(self) -> str:
      """Subclasses must override"""
      raise NotImplementedError()

Final Classes and Methods

Classes that are not intended to be subclassed should be decorated as @final as described in PEP 591. The same decorator can also be used to specify methods that cannot be overridden by subclasses.


Type annotations should make use of the Literal type where appropriate, as described in PEP 586. Literals allow for more type specificity than their non-literal counterparts.


Constant values (those that are read-only) can be specified using the Final annotation as described in PEP 591.

Type checkers will also typically treat variables that are named using all upper-case characters as constants.

In both cases, it is OK to omit the declared type of a constant if it is assigned a literal str, int, float, bool or None value. In such cases, the type inference rules are clear and unambiguous, and adding a literal type annotation would be redundant.

# All-caps constant with inferred type

# All-caps constant with explicit type
COLOR_FORMAT_RGB: Literal["rgb"] = "rgb"
LATEST_VERSION: Tuple[int, int] = (4, 5)

# Final variable with inferred type
ColorFormatRgb: Final = "rgb"

# Final variable with explicit type
ColorFormatRgb: Final[Literal["rgb"]] = "rgb"
LATEST_VERSION: Final[Tuple[int, int]] = (4, 5)

Typed Dictionaries, Data Classes, and Named Tuples

If your library runs only on newer versions of Python, you are encouraged to use some of the new type-friendly classes.

NamedTuple (described in PEP 484) is preferred over namedtuple.

Data classes (described in PEP 557) is preferred over untyped dictionaries.

TypedDict (described in PEP 589) is preferred over untyped dictionaries.

Compatibility with Older Python Versions

Each new version of Python from 3.5 onward has introduced new typing constructs. This presents a challenge for library authors who want to maintain runtime compatibility with older versions of Python. This section documents several techniques that can be used to add types while maintaining backward compatibility.

Quoted Annotations

Type annotations for variables, parameters, and return types can be placed in quotes. The Python interpreter will then ignore them, whereas a type checker will interpret them as type annotations.

# Older versions of Python do not support subscripting
# for the OrderedDict type, so the annotation must be
# enclosed in quotes.
def get_config(self) -> "OrderedDict[str, str]":
   return self._config

Type Comment Annotations

Python 3.0 introduced syntax for parameter and return type annotations, as specified in PEP 484. Python 3.6 introduced support for variable type annotations, as specified in PEP 526.

If you need to support older versions of Python, type annotations can still be provided as “type comments”. These comments take the form # type: .

class Foo:
   # Variable type comments go at the end of the line
   # where the variable is assigned.
   timeout = None # type: Optional[int]

   # Function type comments can be specified on the
   # line after the function signature.
   def send_message(self, name, length):
      # type: (str, int) -> None

   # Function type comments can also specify the type
   # of each parameter on its own line.
   def receive_message(
      name, # type: str
      length # type: int
      # type: () -> Message


New type features that require runtime support are typically included in the stdlib typing module. Where possible, these new features are back-ported to a runtime library called typing_extensions that works with older Python runtimes.


The typing module exposes a variable called TYPE_CHECKING which has a value of False within the Python runtime but a value of True when the type checker is performing its analysis. This allows type checking statements to be conditionalized.

Care should be taken when using TYPE_CHECKING because behavioral changes between type checking and runtime could mask problems that the type checker would otherwise catch.

Non-Standard Type Behaviors

Type annotations provide a way to annotate typical type behaviors, but some classes implement specialized, non-standard behaviors that cannot be described using standard type annotations. For now, such types need to be annotated as Any, which is unfortunate because the benefits of static typing are lost.


Docstrings should be provided for all classes, functions, and methods in the interface. They should be formatted according to PEP 257.

There is currently no single agreed-upon standard for function and method docstrings, but several common variants have emerged. We recommend using one of these variants.