Decorators are one of the most beneficial and effective tools of Python. These are used to alter the conduct of the function. Decorators grant the flexibility to wrap some other feature to make bigger the working of wrapped function, without permanently modifying it.
In Decorators, features are surpassed as an argument into every other feature and then called interior the wrapper function.
It is also known as meta programming the place a part of the software tries to exchange any other section of application at bring together time.
Before understanding the Decorator, we need to recognize some vital ideas of Python.
What are the functions in Python?
Python has the most fascinating characteristic that the whole thing is dealt with as an object even lessons or any variable we outline in Python is also assumed as an object. Functions are satisfactory objects in the Python due to the fact they can reference to, exceeded to a variable and returned from other functions as well. The example is given below:
def func1(msg):
print(msg)
func1("Hii")
func2 = func1
func2("Hii")
Output:
Hii
HiiIn the above program, when we run the code it give the equal output for both functions. The func2 referred to feature func1 and act as function. We want to apprehend the following concept of the function:
The function can be referenced and passed to a variable and returned from other functions as well.
The functions can be declared inside another function and passed as an argument to another function.
Inner Function
Python gives the facility to define the feature interior another function. These kinds of functions are known as inner functions. Consider the following example:
def func():
print("We are in first function")
def func1():
print("This is first child function")
def func2():
print(" This is second child function")
func1()
func2()
func()
Output:
We are in first function
This is first child function
This is second child functionIn the above program, it would not depend how the baby features are declared. The execution of the infant characteristic makes effect on the output. These toddler features are domestically bounded with the func() so they can’t be known as separately.
A function that accepts other function as an argument is also referred to as higher order function. Consider the following example:
def add(x):
return x+1
def sub(x):
return x-1
def operator(func, x):
temp = func(x)
return temp
print(operator(sub,10))
print(operator(add,20))
Output:
9
21In the above program, we have surpassed the sub() characteristic and add() feature as argument in operator() function.
A characteristic can return any other function. Consider the below example:
def hello():
def hi():
print("Hello")
return hi
new = hello()
new()
Output:
HelloIn the above program, the hi() characteristic is nested inner the hello() function. It will return each time we name hi().
Decorating functions with parameters
Let’s have an instance to understand the parameterized decorator function:
def divide(x,y):
print(x/y)
def outer_div(func):
def inner(x,y):
if(x<y):
x,y = y,x
return func(x,y)
return inner
divide1 = outer_div(divide)
divide1(2,4)
Output:
2.0Syntactic Decorator
In the above program, we have adorned out_div() that is little bit bulky. Instead of the use of above method, Python approves to use decorator in convenient way with @symbol. Sometimes it is known as “pie” syntax.
def outer_div(func):
def inner(x,y):
if(x<y):
x,y = y,x
return func(x,y)
return inner
# syntax of generator
@outer_div
def divide(x,y):
print(x/y)
Output:
2.0Reusing Decorator
We can reuse the decorator as nicely by means of recalling that decorator function. Let’s make the decorator to its personal module that can be used in many other functions. Creating a file known as mod_decorator.py with the following code:
def do_twice(func):
def wrapper_do_twice():
func()
func()
return wrapper_do_twice
We can import mod_decorator.py in other file.
from decorator import do_twice
@do_twice
def say_hello():
print("Hello There")
say_hello()
Output:
Hello There
Hello TherePython Decorator with Argument
We desire to pass some arguments in function. Let’s do it in following code:
from decorator import do_twice
@do_twice
def display(name):
print(f"Hello {name}")
display()
Output:
TypeError: display() missing 1 required positional argument: 'name'As we can see that, the characteristic did not receive the argument. Running this code raises an error. We can restore this error by using *args and **kwargs in the inner wrapper function. Modifying the decorator.py as follows:
def do_twice(func):
def wrapper_function(*args,**kwargs):
func(*args,**kwargs)
func(*args,**kwargs)
return wrapper_function
Now wrapper_function() can accept any variety of argument and bypass them on the function.
from decorator import do_twice
@do_twice
def display(name):
print(f"Hello {name}")
display("John")
Output:
Hello John
Hello JohnReturning Values from Decorated Functions
We can manipulate the return type of the adorned function. The instance is given below:
from decorator import do_twice
@do_twice
def return_greeting(name):
print("We are created greeting")
return f"Hi {name}"
hi_adam = return_greeting("Adam")
Output:
We are created greeting
We are created greetingFancy Decorators
Let’s recognize the fancy decorators by using the following topic:
Class Decorators
Python provides two methods to decorate a class. Firstly, we can enhance the approach internal a class; there are built-in decorators like @classmethod, @staticmethod and @property in Python. The @classmethod and @staticmethod define techniques inner category that is now not related to any different occasion of a class. The @property is typically used to modify the getters and setters of a category attributes. Let’s recognize it by way of the following example:
Example: 1- @property decorator – By the usage of it, we can use the category function as an attribute. Consider the following code:
class Student:
def __init__(self,name,grade):
self.name = name
self.grade = grade
@property
def display(self):
return self.name + " got grade " + self.grade
stu = Student("John","B")
print("Name:", stu.name)
print("Grade:", stu.grade)
print(stu.display)
Output:
Name: John
Grade: B
John got grade BExample:2 – @staticmethod decorator- The @staticmethod is used to define a static technique in the class. It is known as by way of the usage of the category name as well as instance of the class. Consider the following code:
class Person:
@staticmethod
def hello():
print("Hello Peter")
per = Person()
per.hello()
Person.hello()
Output:
Hello Peter
Hello PeterSingleton Class
A singleton type solely has one instance. There are many singletons in Python consisting of True, None, etc.
Nesting Decorators
We can use multiple decorators with the aid of using them on top of each other. Let’s reflect onconsideration on the following example:
@function1
@function2
def function(name):
print(f "{name}")
In the above code, we have used the nested decorator through stacking them onto one another.
Decorator with Arguments
It is continually beneficial to omit arguments in a decorator. The decorator can be performed quite a few times according to the given value of the argument. Let us think about the following example:
Import functools
def repeat(num):
#Creating and returning a wrapper function
def decorator_repeat(func):
@functools.wraps(func)
def wrapper(*args,**kwargs):
for _ in range(num):
value = func(*args,**kwargs)
return value
return wrapper
return decorator_repeat
#Here we are passing num as an argument which repeats the print function
@repeat(num=5)
def function1(name):
print(f"{name}")
Output:
JavatPoint
JavatPoint
JavatPoint
JavatPoint
JavatPointIn the above example, @repeat refers to a characteristic object that can be called in some other function. The @repeat(num = 5) will return a characteristic which acts as a decorator.
The above code may also appear complicated but it is the most typically used decorator pattern where we have used one extra def that handles the arguments to the decorator.
Note: Decorator with argument is no longer frequently used in programming, but it presents flexibility. We can use it with or without argument.
Stateful Decorators
Stateful decorators are used to maintain music of the decorator state. Let us consider the example the place we are growing a decorator that counts how many times the function has been called.
Import functools
def count_function(func):
@functools.wraps(func)
def wrapper_count_calls(*args, **kwargs):
wrapper_count_calls.num_calls += 1
print(f"Call{wrapper_count_calls.num_calls} of {func.__name__!r}")
return func(*args, **kwargs)
wrapper_count_calls.num_calls = 0
return wrapper_count_calls
@count_function
def say_hello():
print("Say Hello")
say_hello()
say_hello()
Output:
Call 1 of 'say_hello'
Say Hello
Call 2 of 'say_hello'
Say HelloIn the above program, the nation represented the range of calls of the feature saved in .num_calls on the wrapper function. When we call say_hello() it will show the wide variety of the call of the function.
Classes as Decorators
The lessons are the first-rate way to maintain state. In this section, we will learn how to use a class as a decorator. Here we will create a classification that incorporates __init__() and take func as an argument. The classification desires to be callable so that it can stand in for the decorated function.
To making a category callable, we implement the one-of-a-kind __call__() method.
import functools
class Count_Calls:
def __init__(self, func):
functools.update_wrapper(self, func)
self.func = func
self.num_calls = 0
def __call__(self, *args, **kwargs):
self.num_calls += 1
print(f"Call{self.num_calls} of {self.func.__name__!r}")
return self.func(*args, **kwargs)
@Count_Calls
def say_hello():
print("Say Hello")
say_hello()
say_hello()
say_hello()
Output:
Call 1 of 'say_hello'
Say Hello
Call 2 of 'say_hello'
Say Hello
Call 3 of 'say_hello'
Say HelloThe __init__() method shops a reference to the feature and can do any other required initialization.