5 PEP 318: Decorators for Functions and Methods

Python 2.2 extended Python's object model by adding static methods and class methods, but it didn't extend Python's syntax to provide any new way of defining static or class methods. Instead, you had to write a def statement in the usual way, and pass the resulting method to a staticmethod() or classmethod() function that would wrap up the function as a method of the new type. Your code would look like this:

class C:
   def meth (cls):
       ...
   
   meth = classmethod(meth)   # Rebind name to wrapped-up class method

If the method was very long, it would be easy to miss or forget the classmethod() invocation after the function body.

The intention was always to add some syntax to make such definitions more readable, but at the time of 2.2's release a good syntax was not obvious. Today a good syntax still isn't obvious but users are asking for easier access to the feature; a new syntactic feature has been added to meet this need.

The new feature is called ``function decorators''. The name comes from the idea that classmethod, staticmethod, and friends are storing additional information on a function object; they're decorating functions with more details.

The notation borrows from Java and uses the "@" character as an indicator. Using the new syntax, the example above would be written:

class C:

   @classmethod
   def meth (cls):
       ...

The @classmethod is shorthand for the meth=classmethod(meth) assignment. More generally, if you have the following:

@A @B @C
def f ():
    ...

def f(): ...
f = A(B(C(f)))

Decorators must come on the line before a function definition, and can't be on the same line, meaning that @A def f(): ... is illegal. You can only decorate function definitions, either at the module level or inside a class; you can't decorate class definitions.

A decorator is just a function that takes the function to be decorated as an argument and returns either the same function or some new callable thing. It's easy to write your own decorators. The following simple example just sets an attribute on the function object:

>>> def deco(func):
...    func.attr = 'decorated'
...    return func
...
>>> @deco
... def f(): pass
...
>>> f
<function f at 0x402ef0d4>
>>> f.attr
'decorated'
>>>

As a slightly more realistic example, the following decorator checks that the supplied argument is an integer:

def require_int (func):
    def wrapper (arg):
        assert isinstance(arg, int)
        return func(arg)

    return wrapper

@require_int
def p1 (arg):
    print arg

@require_int
def p2(arg):
    print arg*2

An example in PEP 318 contains a fancier version of this idea that lets you both specify the required type and check the returned type.

Decorator functions can take arguments. If arguments are supplied, your decorator function is called with only those arguments and must return a new decorator function; this function must take a single function and return a function, as previously described. In other words, @A @B @C(args) becomes:

def f(): ...
_deco = C(args)
f = A(B(_deco(f)))

A small related change makes the func_name attribute of functions writable. This attribute is used to display function names in tracebacks, so decorators should change the name of any new function that's constructed and returned.