Decorators Function in Python

Decorators function in Python are used to wrap or decorate any kind of function. Learn how to use them here.

Parameter Details

The function to be decorated (wrapped)

Decorators Function in Python

Decorator functions are software design patterns. They dynamically alter the functionality of a function, method, or class without having to directly use subclasses or change the source code of the decorated function. When used correctly, decorators can become powerful tools in the development process. This topic covers implementation and applications of decorator functions in Python. Decorator function

Decorators augment the behavior of other functions or methods. Any function that takes a function as a parameter and returns an augmented function can be used as a decorator.

This simplest decorator does nothing to the function being decorated. Such
minimal decorators can occasionally be used as a kind of code markers. def super_secret_function(f):
return f
def my_function():
print("This is my secret function.")

The @-notation is syntactic sugar that is equivalent to the following:

my_function = super_secret_function(my_function)

It is important to bear this in mind in order to understand how the decorators work. This “unsugared” syntax makes it clear why the decorator function takes a function as an argument, and why it should return another function. It also demonstrates what would happen if you don’t return a function:

def disabled(f):
This function returns nothing, and hence removes the decorated function
from the local scope.
def my_function():
print("This function can no longer be called…")

TypeError: ‘NoneType’ object is not callable

Thus, we usually define a new function inside the decorator and return it. This new function would first do something that it needs to do, then call the original function, and finally process the return value. Consider this simple decorator function that prints the arguments that the original function receives, then calls it.

This is the decorator

def print_args(func):
def inner_func(*args, **kwargs):
return func(*args, **kwargs) #Call the original function with its arguments.
return inner_func
def multiply(num_a, num_b):
return num_a * num_b
print(multiply(3, 5))


Decorators function in Python: Decorator class

(3,5) - This is actually the 'args' that the function receives.
{} - This is the 'kwargs', empty because we didn't specify keyword arguments.
15 - The result of the function.

As mentioned in the introduction, a decorator is a function that can be applied to another function to augment its behavior. The syntactic sugar is equivalent to the following: my_func = decorator(my_func). But what if the decorator was instead a class? The syntax would still work, except that now my_func gets replaced with an instance of the decorator class. If this class implements the call() magic method, then it would still be possible to use my_func as if it was a function:

class Decorator(object):
"""Simple decorator class."""
def init(self, func):
self.func = func
def call(self, *args, *kwargs): print('Before the function call.') res = self.func(args, **kwargs)
print('After the function call.')
return res
def testfunc():
print('Inside the function.')
Before the function call.
Inside the function.
After the function call.

Note that a function decorated with a class decorator will no longer be considered a “function” from type-checking perspective:

import types
isinstance(testfunc, types.FunctionType)



Decorators function in Python: Decorating Methods

For decorating methods you need to define an additional get-method:

from types import MethodType
class Decorator(object):
def init(self, func):
self.func = func
def call(self, *args, *kwargs): print('Inside the decorator.') return self.func(args, **kwargs)
def get(self, instance, cls):

Return a Method if it is called on an instance

return self if instance is None else MethodType(self, instance)
class Test(object):
def init(self):
a = Test()

Inside the decorator.


Class Decorators only produce one instance for a specific function so decorating a method with a class decorator will share the same decorator between all instances of that class:

from types import MethodType
class CountCallsDecorator(object):
def init(self, func):
self.func = func
self.ncalls = 0 # Number of calls of this method
def call(self, *args, *kwargs): self.ncalls += 1 # Increment the calls counter return self.func(args, **kwargs)
def get(self, instance, cls):
return self if instance is None else MethodType(self, instance)
class Test(object):
def init(self):
def do_something(self):
return 'something was done'
a = Test()
a.do_something.ncalls # 1
b = Test()
b.do_something.ncalls # 2

Decorators function in Python: Decorator with arguments (decorator factory)

A decorator takes just one argument: the function to be decorated. There is no way to pass other arguments. But additional arguments are often desired. The trick is then to make a function which takes arbitrary arguments

and returns a decorator.

Decorator functions

def decoratorfactory(message):
def decorator(func):
def wrapped_func(*args, *kwargs): print('The decorator wants to tell you: {}'.format(message)) return func(args, **kwargs)
return wrapped_func
return decorator
@decoratorfactory('Hello World')
def test():

The decorator wants to tell you: Hello World

Important Note:

With such decorator factories you must call the decorator with a pair of parentheses:

@decoratorfactory # Without parentheses
def test():

TypeError: decorator() missing 1 required positional argument: ‘func’

Decorator classes

def decoratorfactory(*decorator_args, **decorator_kwargs):
class Decorator(object):
def init(self, func):
self.func = func
def call(self, *args, *kwargs): print('Inside the decorator with arguments {}'.format(decorator_args)) return self.func(args, **kwargs)
return Decorator
def test():

Inside the decorator with arguments (10,)

Decorators function in Python: Making a decorator look like the decorated function

Decorators normally strip function metadata as they aren’t the same. This can cause problems when using meta-programming to dynamically access function metadata. Metadata also includes function’s docstrings and its name. functools.wraps makes the decorated function look like the original function by copying several attributes to the wrapper function.

from functools import wraps

The two methods of wrapping a decorator are achieving the same thing in hiding that the original function has been decorated. There is no reason to prefer the function version to the class version unless you’re already using one over the other.

As a function

def decorator(func):
Copies the docstring, name, annotations and module to the decorator @wraps(func)
def wrapped_func(*args, *kwargs): return func(args, **kwargs)
return wrapped_func
def test():

As a class

class Decorator(object):
def init(self, func):
Copies name, module, annotations and docstring to the instance. self._wrapped = wraps(func)(self)
def call(self, *args, *kwargs): return self._wrapped(args, **kwargs)
def test():
"""Docstring of test."""

‘Docstring of test.’

Decorators function in Python: Using a decorator to time a function

import time
def timer(func):
def inner(*args, **kwargs):
t1 = time.time()
f = func(*args, **kwargs)
t2 = time.time()
print 'Runtime took {0} seconds'.format(t2-t1)
return f
return inner
def example_function():

do stuff


Decorators function in Python:: Create singleton class with a decorator

A singleton is a pattern that restricts the instantiation of a class to one instance/object. Using a decorator, we can define a class as a singleton by forcing the class to either return an existing instance of the class or create a new instance (if it doesn’t exist).

def singleton(cls):
instance = [None]
def wrapper(*args, *kwargs): if instance[0] is None: instance[0] = cls(args, **kwargs)
return instance[0]
return wrapper

This decorator can be added to any class declaration and will make sure that at most one instance of the class is created. Any subsequent calls will return the already existing class instance.

class SomeSingletonClass:
x = 2
def init(self):
instance = SomeSingletonClass() # prints: Created!
instance = SomeSingletonClass() # doesn't print anything
print(instance.x) # 2
instance.x = 3
print(SomeSingletonClass().x) # 3
So it doesn't matter whether you refer to the class instance via your local variable or whether you create another "instance", you always get the same object


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