Annotations Best Practices
**************************

author:
   Larry Hastings


Abstract
^^^^^^^^

This document is designed to encapsulate the best practices for
working with annotations dicts.  If you write Python code that
examines "__annotations__" on Python objects, we encourage you to
follow the guidelines described below.

The document is organized into four sections: best practices for
accessing the annotations of an object in Python versions 3.10 and
newer, best practices for accessing the annotations of an object in
Python versions 3.9 and older, other best practices for
"__annotations__" that apply to any Python version, and quirks of
"__annotations__".

Note that this document is specifically about working with
"__annotations__", not uses *for* annotations. If you’re looking for
information on how to use “type hints” in your code, please see the
"typing" module.


Accessing The Annotations Dict Of An Object In Python 3.10 And Newer
====================================================================

Python 3.10 adds a new function to the standard library:
"inspect.get_annotations()".  In Python versions 3.10 through 3.13,
calling this function is the best practice for accessing the
annotations dict of any object that supports annotations.  This
function can also “un-stringize” stringized annotations for you.

In Python 3.14, there is a new "annotationlib" module with
functionality for working with annotations. This includes a
"annotationlib.get_annotations()" function, which supersedes
"inspect.get_annotations()".

If for some reason "inspect.get_annotations()" isn’t viable for your
use case, you may access the "__annotations__" data member manually.
Best practice for this changed in Python 3.10 as well: as of Python
3.10, "o.__annotations__" is guaranteed to *always* work on Python
functions, classes, and modules.  If you’re certain the object you’re
examining is one of these three *specific* objects, you may simply use
"o.__annotations__" to get at the object’s annotations dict.

However, other types of callables–for example, callables created by
"functools.partial()"–may not have an "__annotations__" attribute
defined.  When accessing the "__annotations__" of a possibly unknown
object,  best practice in Python versions 3.10 and newer is to call
"getattr()" with three arguments, for example "getattr(o,
'__annotations__', None)".

Before Python 3.10, accessing "__annotations__" on a class that
defines no annotations but that has a parent class with annotations
would return the parent’s "__annotations__". In Python 3.10 and newer,
the child class’s annotations will be an empty dict instead.


Accessing The Annotations Dict Of An Object In Python 3.9 And Older
===================================================================

In Python 3.9 and older, accessing the annotations dict of an object
is much more complicated than in newer versions. The problem is a
design flaw in these older versions of Python, specifically to do with
class annotations.

Best practice for accessing the annotations dict of other
objects–functions, other callables, and modules–is the same as best
practice for 3.10, assuming you aren’t calling
"inspect.get_annotations()": you should use three-argument "getattr()"
to access the object’s "__annotations__" attribute.

Unfortunately, this isn’t best practice for classes.  The problem is
that, since "__annotations__" is optional on classes, and because
classes can inherit attributes from their base classes, accessing the
"__annotations__" attribute of a class may inadvertently return the
annotations dict of a *base class.* As an example:

   class Base:
       a: int = 3
       b: str = 'abc'

   class Derived(Base):
       pass

   print(Derived.__annotations__)

This will print the annotations dict from "Base", not "Derived".

Your code will have to have a separate code path if the object you’re
examining is a class ("isinstance(o, type)"). In that case, best
practice relies on an implementation detail of Python 3.9 and before:
if a class has annotations defined, they are stored in the class’s
"__dict__" dictionary.  Since the class may or may not have
annotations defined, best practice is to call the "get()" method on
the class dict.

To put it all together, here is some sample code that safely accesses
the "__annotations__" attribute on an arbitrary object in Python 3.9
and before:

   if isinstance(o, type):
       ann = o.__dict__.get('__annotations__', None)
   else:
       ann = getattr(o, '__annotations__', None)

After running this code, "ann" should be either a dictionary or
"None".  You’re encouraged to double-check the type of "ann" using
"isinstance()" before further examination.

Note that some exotic or malformed type objects may not have a
"__dict__" attribute, so for extra safety you may also wish to use
"getattr()" to access "__dict__".


Manually Un-Stringizing Stringized Annotations
==============================================

In situations where some annotations may be “stringized”, and you wish
to evaluate those strings to produce the Python values they represent,
it really is best to call "inspect.get_annotations()" to do this work
for you.

If you’re using Python 3.9 or older, or if for some reason you can’t
use "inspect.get_annotations()", you’ll need to duplicate its logic.
You’re encouraged to examine the implementation of
"inspect.get_annotations()" in the current Python version and follow a
similar approach.

In a nutshell, if you wish to evaluate a stringized annotation on an
arbitrary object "o":

* If "o" is a module, use "o.__dict__" as the "globals" when calling
  "eval()".

* If "o" is a class, use "sys.modules[o.__module__].__dict__" as the
  "globals", and "dict(vars(o))" as the "locals", when calling
  "eval()".

* If "o" is a wrapped callable using "functools.update_wrapper()",
  "functools.wraps()", or "functools.partial()", iteratively unwrap it
  by accessing either "o.__wrapped__" or "o.func" as appropriate,
  until you have found the root unwrapped function.

* If "o" is a callable (but not a class), use "o.__globals__" as the
  globals when calling "eval()".

However, not all string values used as annotations can be successfully
turned into Python values by "eval()". String values could
theoretically contain any valid string, and in practice there are
valid use cases for type hints that require annotating with string
values that specifically *can’t* be evaluated.  For example:

* **PEP 604** union types using "|", before support for this was added
  to Python 3.10.

* Definitions that aren’t needed at runtime, only imported when
  "typing.TYPE_CHECKING" is true.

If "eval()" attempts to evaluate such values, it will fail and raise
an exception.  So, when designing a library API that works with
annotations, it’s recommended to only attempt to evaluate string
values when explicitly requested to by the caller.


Best Practices For "__annotations__" In Any Python Version
==========================================================

* You should avoid assigning to the "__annotations__" member of
  objects directly.  Let Python manage setting "__annotations__".

* If you do assign directly to the "__annotations__" member of an
  object, you should always set it to a "dict" object.

* You should avoid accessing "__annotations__" directly on any object.
  Instead, use "annotationlib.get_annotations()" (Python 3.14+) or
  "inspect.get_annotations()" (Python 3.10+).

* If you do directly access the "__annotations__" member of an object,
  you should ensure that it’s a dictionary before attempting to
  examine its contents.

* You should avoid modifying "__annotations__" dicts.

* You should avoid deleting the "__annotations__" attribute of an
  object.


"__annotations__" Quirks
========================

In all versions of Python 3, function objects lazy-create an
annotations dict if no annotations are defined on that object.  You
can delete the "__annotations__" attribute using "del
fn.__annotations__", but if you then access "fn.__annotations__" the
object will create a new empty dict that it will store and return as
its annotations.  Deleting the annotations on a function before it has
lazily created its annotations dict will throw an "AttributeError";
using "del fn.__annotations__" twice in a row is guaranteed to always
throw an "AttributeError".

Everything in the above paragraph also applies to class and module
objects in Python 3.10 and newer.

In all versions of Python 3, you can set "__annotations__" on a
function object to "None".  However, subsequently accessing the
annotations on that object using "fn.__annotations__" will lazy-create
an empty dictionary as per the first paragraph of this section.  This
is *not* true of modules and classes, in any Python version; those
objects permit setting "__annotations__" to any Python value, and will
retain whatever value is set.

If Python stringizes your annotations for you (using "from __future__
import annotations"), and you specify a string as an annotation, the
string will itself be quoted.  In effect the annotation is quoted
*twice.*  For example:

   from __future__ import annotations
   def foo(a: "str"): pass

   print(foo.__annotations__)

This prints "{'a': "'str'"}".  This shouldn’t really be considered a
“quirk”; it’s mentioned here simply because it might be surprising.

If you use a class with a custom metaclass and access
"__annotations__" on the class, you may observe unexpected behavior;
see **749** for some examples. You can avoid these quirks by using
"annotationlib.get_annotations()" on Python 3.14+ or
"inspect.get_annotations()" on Python 3.10+. On earlier versions of
Python, you can avoid these bugs by accessing the annotations from the
class’s "__dict__" (for example, "cls.__dict__.get('__annotations__',
None)").

In some versions of Python, instances of classes may have an
"__annotations__" attribute. However, this is not supported
functionality. If you need the annotations of an instance, you can use
"type()" to access its class (for example,
"annotationlib.get_annotations(type(myinstance))" on Python 3.14+).
