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django all python ruby start learning ruby on rails all ruby game development cloud hosting start learning aws web servers hosting scaling all cloud hosting music get unlimited creative assets home code python python 3 function annotations the function annotations syntax has been a python feature since version 3 0 but the semantics of annotations have been left undefined in this tutorial i ll present this interesting aspect of modern python and explore what it can be used for today scroll to top gigi sayfan jul 13 2023 9 min read copied to clipboard python coding fundamentals in this tutorial i ll show you how to take advantage of general purpose function annotations and combine them with decorators you will also learn about the pros and cons of function annotations when it is appropriate to use them and when it is best to use other mechanisms like docstrings and plain decorators to annotate means to add notes explanations or comments annotations provide additional information about the expected types or other metadata they are not evaluated or treated as regular code statements function annotations function annotations are specified in pep 3107 the main motivation was to provide a standard way to associate metadata to function arguments and return value a lot of community members found novel use cases but used different methods such as custom decorators custom docstring formats and adding custom attributes to the function object it s important to understand that python doesn t bless the annotations with any semantics it purely provides a nice syntactic support for associating metadata as well as an easy way to access it also annotations are totally optional the syntax for annotations looks like this for functions and classes 1 def func arg type return_annotation 2 pass let s take a look at an example here is a function foo that takes three arguments called a b and c and prints their sum note that foo returns nothing the first argument a is not annotated the second argument b is annotated with the string annotating b and the third argument c is annotated with type int the return value is annotated with the type float note the syntax for annotating the return value 1 from typing import newtype 2 3 annotating_b newtype annotating_b str 4 5 def foo a b annotating_b c int float 6 print a b c the annotations have no impact whatsoever on the execution of the function let s call foo twice once with int arguments and once with string arguments in both cases foo does the right thing and the annotations are simply ignored 1 foo hello world 2 hello world 3 foo 1 2 3 4 6 default arguments default arguments are specified after the annotation 1 from typing import newtype 2 3 custom_argument newtype an argument that defaults to 5 str 4 5 def foo x custom_argument 5 6 print x 7 8 9 foo 7 10 7 11 foo 12 5 accessing function annotations the function object has an attribute called annotations it is a mapping that maps each argument name to its annotation the return value annotation is mapped to the key return which can t conflict with any argument name because return is a reserved word that can t serve as an argument name note that it is possible to pass a keyword argument named return to a function 1 def bar args kwargs the keyword arguments dict 2 print kwargs return 3 d return 4 4 bar d 5 4 let s go back to our first example and check its annotations 1 def foo a b str c int float 2 return a b c 3 4 print foo __annotations__ 5 b annotating b c class int return class float this is pretty straightforward if you annotate a function with an arguments array and or keyword arguments array then obviously you can t annotate individual arguments 1 def foo args list of unnamed arguments kwargs dict of named arguments 2 return args kwargs 3 print foo __annotations__ 4 args list of unnamed arguments kwargs dict of named arguments if you read the section about accessing function annotations in pep 3107 it says that you access them through the func_annotations attribute of the function object this is out of date as of python 3 2 don t be confused it s simply the annotations attribute what can you do with annotations this is the big question annotations have no standard meaning or semantics there are several categories of generic uses you can use them as better documentation and move argument and return value documentation out of the docstring for example this function 1 def div a b 2 divide a by b 3 args 4 a the dividend 5 b the divisor must be different than 0 6 return 7 the result of dividing a by b 8 9 return a b can be converted to 1 def div a the dividend 2 b the divisor must be different than 0 the result of dividing a by b 3 divide a by b 4 return a b while the same information is captured there are several benefits to the annotations version if you rename an argument the documentation docstring version may be out of date it is easier to see if an argument is not documented there is no need to come up with a special format of argument documentation inside the docstring to be parsed by tools the annotations attribute provides a direct standard mechanism of access another usage that we will talk about later is optional typing python is dynamically typed which means you can pass any object as an argument of a function but often functions will require arguments to be of a specific type with annotations you can specify the type right next to the argument in a very natural way remember that just specifying the type will not enforce it and additional work a lot of work will be needed still even just specifying the type can make the intent more readable than specifying the type in the docstring and it can help users understand how to call the function yet another benefit of annotations over docstring is that you can attach different types of metadata as tuples or dicts again you can do that with docstring too but it will be text based and will require special parsing finally you can attach a lot of metadata that will be used by special external tools or at runtime via decorators i ll explore this option in the next section multiple annotations suppose you want to annotate an argument with both its type and a help string this is very easy with annotations you can simply annotate the argument with a dict that has two keys type and help 1 def div a dict type float help the dividend 2 b dict type float help the divisor must be different than 0 3 dict type float help the result of dividing a by b 4 divide a by b 5 return a b 6 7 print div __annotations__ 8 a type class float help the dividend 9 b type class float help the divisor must be different than 0 return type class float 10 help the result of dividing a by b combining python annotations and decorators annotations and decorators go hand in hand for a good introduction to python decorators check out my two tutorials deep dive into python decorators and write your own python decorators first annotations can be fully implemented as decorators you can just define an annotate decorator and have it take an argument name and a python expression as arguments and then store them in the target function s annotations attribute this can be done for python 2 as well however the real power of decorators is that they can act on the annotations this requires coordination of course about the semantics of annotations let s look at an example suppose we want to verify that arguments are in a certain range the annotation will be a tuple with the minimum and maximum value for each argument then we need a decorator that will check the annotation of each keyword argument verify that the value is within the range and raise an exception otherwise let s start with the decorator 1 def check_range f 2 def decorated args kwargs 3 for name range in f __annotations__ items 4 min_value max_value range 5 if not min_value kwargs name max_value 6 msg argument is out of range 7 raise valueerror msg format name min_value max_value 8 return f args kwargs 9 return decorated next define a custom class range that represents the range of values 1 def range min_value int max_value int tuple 2 return min_value max_value now let s define our function and decorate it with the check_range decorators the range in this case acts as a type annotation even though it s not a built in type annotation it serves a similar purpose by providing additional information to the range of expected values we can think of it as a custom annotation 1 check_range 2 def foo a range 0 8 b range 5 9 c range 10 20 3 return a b c let s call foo with different arguments and see what happens when all arguments are within their range there is no problem 1 foo a 4 b 6 c 15 2 9 but if we set c to 100 outside of the 10 20 range then an exception is raised 1 foo a 4 b 6 c 100 2 valueerror argument c is out of range 10 20 when should you use decorators instead of annotations there are several situations where decorators are better than annotations for attaching metadata one obvious case is if your code needs to be compatible with python 2 another case is if you have a lot of a metadata as you saw earlier while it s possible to attach any amount of metadata by using dicts as annotations it is pretty cumbersome and actually hurts readability finally if the metadata is supposed to be operated on by a specific decorator it may be better to associate the metadata as arguments for the decorator itself dynamic annotations annotations are just a dict attribute of a function 1 type foo __annotations__ 2 class dict this means you can modify them on the fly while the program is running what are some use cases suppose you want to find out if a default value of an argument is ever used whenever the function is called with the default value you can increment the value of an annotation or maybe you want to sum up all the return values the dynamic aspect can be done inside the function itself or by a decorator 1 def add a b 0 2 result a b 3 add __annotations__ return result 4 return result 5 6 print add __annotations__ return 7 0 8 add 3 4 9 7 10 print add __annotations__ return 11 7 12 add 5 5 13 10 14 print add __annotations__ return 15 17 conclusion function annotations are versatile and exciting they have the potential to usher in a new era of introspective tools that help developers master more and more complex systems they also offer the more advanced developer a standard and readable way to associate metadata directly with arguments and return value in order to create custom tools and interact with decorators but it takes some work to benefit from them and utilize their potential learn python learn python with our complete python tutorial guide whether you re just getting started or you re a seasoned coder looking to learn new skills python coding fundamentals by gigi sayfan like your all in one creative asset toolkit start now unlimited downloads from 16 50 month get access to over one million creative assets on envato over 9 million 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