The Power of Decorators in Python [Meetup]

Decorators in Python
Haim Michael
May 19th
, 2020
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© 2020 life michael
Decorators Basics

Introduction
 The decorator allows us to add new functionality to existing
object without modifying its structure.
 When decorating a function, the decoration is invoked
before the definition of the function takes place.
@decorator
def decorated(): pass
is indirectly changed into
decorator(decorated)

Jump Start
 The simples way to develop a decorator would be to
develop a function that has one parameter to which a
function is passed over.
 Whenever our decorator is invoked (enough to have in our
code @ourdecorator to invoke the decorator) the decorated
function will be passed over to our decorator.

Jump Start
 The decorator function should include the definition of
another function, the decorator function returns. That other
inner function usually invokes the decorated function.
 The function returned by our decorator will take the place of
the decorated function. Every call to the decorated function
in our code will be replaced by a call to the function returned
by our decorator.

Jump Start
def uppercase(func):
print("2")
def inner():
print("4")
data = func()
return data.upper()
return inner
print("9")
@uppercase
def getGreeting():
print("13")
return "good morning"
print("16")
print(getGreeting())

Decorating Function with Parameters
 In order to decorate a function with parameter(s) we just
need make sure the function returned by the decorator has
the same parameter(s) accordingly.

Decorating Function with Parameters
print("2")
def inner(nickname):
print("4")
data = func(nickname)
return data.upper()
return inner
print("9")
@uppercase
def getGreeting(nickname):
print("13")
return "good morning " + nickname
print("16")
print(getGreeting("dave"))

Using a Class as Decorator
 The decorator should be a callable object. It can be a
function. I can also be a class we define together with the
__init__ and the __call__.

Using a Class as Decorator
class uppercase:
def __init__(self, f):
self.f = f
def __call__(self, nickname):
return self.f(nickname).upper()
@uppercase
def greet(nickname):
print(greet("danny"))

Decorators Nesting
 We can place together more than one decorator. Doing so,
we should imagine the decorators execution one after the
other in the order in which they are listed (top to bottom).
 The output of each decorator execution is the input for the
decorator above.

Decorators Nesting
def inner(nickname):
data = func(nickname)
return data.upper()
return inner
def stars(func):
def inner(text):
input = func(text)
return "*** "+input+" ***"
return inner
@stars
@uppercase
def getGreeting(nickname):
print(getGreeting("dave"))

Class Decorators
 We can develop a decorator for classes. The object that
represents a class is also a factory function that is called
whenever a new object is created.
def bang(func):
def inner():
print("bang!!!")
ob = func()
return ob
return inner
@bang
class Rectangle(): pass
ob1 = Rectangle()
ob2 = Rectangle()

Decorators with Arguments
 We can develop a decorator that takes arguments when
called.
def bang(text):
def f(func):
def inner():
print(text)
ob = func()
return ob
return inner
return f
@bang(text="Picaso")
def Rectangle(): pass
ob1 = Rectangle()
ob2 = Rectangle()

Stateful Decorators
 We can develop a decorator that keeps tracking of a state
by using attributes we add to the object that represents the
decorator.
def counter(text):
if not hasattr(counter,"classes"):
counter.classes = dict()
if text not in counter.classes:
counter.classes[text] = 0
def f(func):
def inner():
counter.classes[text] = int(counter.classes[text]) + 1
ob = func()
return ob
return inner
return f

Stateful Decorators
@counter("Rectangle")
class Rectangle(): pass
@counter("Circle")
class Circle(): pass
rec1 = Rectangle()
rec2 = Rectangle()
rec3 = Rectangle()
circ1 = Circle()
circ2 = Circle()
print(counter.classes["Rectangle"])
print(counter.classes["Circle"])

Real World Examples

Measuring Execution Time
 We can easily develop a decorator for the purpose of
measuring the execution time of specific functions.
import time
def stopper(func):
def inner(times):
start = time.perf_counter()
value = func(times)
end = time.perf_counter()
run = end - start
print("running_time=",run)
return value
return inner
@stopper
def doSomething(times):
total = 0
for i in range(times):
total += i
return total
doSomething(99999)

Debugging & Logging
 We can easily develop a decorator that will track each and
every execution of a specific function.
def tinylog(func):
def f(*args, **kwargs):
args1 = [repr(a) for a in args]
args2 = [repr(k)+":"+repr(v) for k, v in kwargs.items()]
value = func(*args, **kwargs)
print("calling ", func.__name__, " ", repr(args1), " ",
repr(args2), " returns ", value)
return value
return f
@tinylog
def total(a,b):
return a+b
print(total(3,4))
print(total(5,5))
print(total(8,7))

Libraries Development
 We can easily create decorators for functions and classes
as part of a library for other developers.
 There are many libraries that employ the power of
decorators in their service, including ORM libraries (e.g.
PonyORM), Unit Testing libraries (e.g. Testify) and others.

Caching Data
 Assuming that we have a function that fetches data from the
server we can develop a decorator that will provide us with
an caching mechanism.

Questions & Answers
 Thanks for attending this meetup.

The Power of Decorators in Python [Meetup]

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