# Function Annotations in Python

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Function annotations introduced in Python 3.0 adds a feature that allows you to add arbitrary metadata to function parameters and return value. Since python 3, function annotations have been officially added to python (PEP-3107). The primary purpose was to have a standard way to link metadata to function parameters and return value.

## Basics of Function Annotations

Let’s understand some basics of function annotations −

• Function annotations are completely optional both for parameters and return value.

• Function annotations provide a way of associating various parts of a function with arbitrary python expressions at compile time.

• The PEP-3107 makes no attempt to introduce any kind of standard semantics, even for the built-in types. All this work left to the third-party libraries.

## Syntax

Annotations of simple parameters

The annotations for parameters take the following form −

def foo(x: expression, y: expression = 20):
….

Whereas the annotations for excess parameters are as −

def foo(**args: expression, **kwargs: expression):
…..

In the case of nested parameters, annotations always follow the name of the parameters and not till the last parenthesis. It is not required to annotate all the parameters of a nested parameter.

def foo(x1, y1: expression), (x2: expression, y2: expression)=(None, None)):
……

It’s important to understand that python doesn’t provide any semantics with annotations. It only provides nice syntactic support for associating metadata as well as an easy way to access it. Also, annotations are not mandatory.

>>> def func(x:'annotating x', y: 'annotating y', z: int) -> float: print(x + y + z)

In the above example, function func() takes three parameters called x,y and z, finally prints their sum. The first argument x is annotated with string ‘annotating x’, second argument y is annotated with the string ‘annotating y’, and the third argument z is annotated with type int. The return value is annotated with the type float. Here the ‘->’ syntax for annotating the return value.

## Output

>>> func(2,3,-4)
1
>>> func('Function','-','Annotation')
Function-Annotation

Above we call func() twice, once with int arguments and once with string arguments. In both cases, func() does the right thing and annotations are simply ignored. So, we see the annotations have no effect on the execution of the function func().

## Accessing Function Annotations

All the annotations are stored in a dictionary called __annotations__, which itself is an attribute of the function −

>>> def func(x:'annotating x', y: 'annotating y', z: int) -> float: print(x + y + z)
>>> func.__annotations__
{'x': 'annotating x', 'y': 'annotating y', 'z': <class 'int'>, 'return': <class 'float'>}

As we can see in the preceding code example, annotations are not typed declarations, though they could certainly be used for that purpose and they resemble the typing syntax used in some other languages, as shown below −

>>> def func(a: 'python', b: {'category: ' 'language'}) -> 'yep':
pass
>>> func.__annotations__
{'a': 'python', 'b': {'category: language'}, 'return': 'yep'}
>>>

They are arbitrary expressions, which means that arbitrary values can be stored in the __annotations__ dictionary. Although, they don’t add much significance to the python itself, except that it should store the values. That said, defining the parameter and return types is a common use of function annotations.

## The @no_type_check decorator

If you find yourselves using a tool that assumes annotations are type declarations but you want to use them for some other purpose, use the standard @no_type_check decorator to exempt your function from such processing, as shown here −

>>> from typing import no_type_check
>>> @no_type_check
def func(a: 'python', b: {'category: ' 'language'}) -> 'yep':
pass
>>>

Normally, this isn’t required as most tools that use annotations have a way of recognizing the ones meant for them. The decorator is for protecting corner cases where things are ambiguous.

## Annotations as input to function decorators

Annotations combine well with decorators because annotation values make a good way to provide input to a decorator, and decorator-generated wrappers are a good place to put code that gives meaning to annotations.

from functools import wraps
@wraps(func)
def wrapper(**kwargs):
final_args = {}
for name, value in kwargs. items():
else:
final_args[name] = value
result = func(**final_args)
return result
return wrapper
def func(a: int, b: repr) -> str:
return a

So, the adapted decorator encloses the function in a wrapper. This wrapper only accepts keyword arguments, which means that even, if the original function could accept positional arguments, they have to be specified by name.

Once the function is wrapped, wrapper also looks for adapters in the function's parameter annotations and applies them before passing the arguments to the real function.

Once the function returns, the wrapper checks for a return value adapter; if it finds one, it applies it to the return value before finally returning it.

When we consider the implications of what's happening here, they're pretty impressive. We've actually modified what it means to pass a parameter to a function or return a value.

## Keyword arguments

Sometimes, one or more of a method's parameters don't require any processing, except assigning them to an attribute of self. Can we use decorators and annotations to make this happen automatically? Of course, we can.

from functools import wraps
def store_args(func):
@wraps(func)
def wrapper(self, **kwargs):
for name, value in kwargs.items():
attrib = func.__annotations__.get(name)
if attrib is True:
attrib = name
if isinstance(attrib, str):
setattr(self, attrib, value)
return func(self, **kwargs)
return wrapper
class A:
@store_args
def __init__(self, first: True, second: 'example'):
pass
a = A(first = 5, second = 6)
assert a.first == 5
assert a.example == 6
Published on 19-Feb-2019 17:50:06