In object-oriented programming, the decorator pattern is a design pattern that allows behavior to be added to an individual object, dynamically, without affecting the behavior of other objects from the same class.[1] The decorator pattern is often useful for adhering to the Single Responsibility Principle, as it allows functionality to be divided between classes with unique areas of concern.[2] The decorator pattern is structurally nearly identical to the chain of responsibility pattern, the difference being that in a chain of responsibility, exactly one of the classes handles the request, while for the decorator, all classes handle the request.

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The discussion continued on and off on python-dev from February 2002 through July 2004. Hundreds and hundreds of posts were made, with people proposing many possible syntax variations. Guido took a list of proposals to EuroPython 2004 [7], where a discussion took place. Subsequent to this, he decided that we'd have the Java-style [10] @decorator syntax, and this appeared for the first time in 2.4a2. Barry Warsaw named this the 'pie-decorator' syntax, in honor of the Pie-thon Parrot shootout which occurred around the same time as the decorator syntax, and because the @ looks a little like a pie. Guido outlined his case [8] on Python-dev, including this piece [9] on some of the (many) rejected forms.
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The .__init__() method must store a reference to the function and can do any other necessary initialization. The .__call__() method will be called instead of the decorated function. It does essentially the same thing as the wrapper() function in our earlier examples. Note that you need to use the functools.update_wrapper() function instead of @functools.wraps.

In Python 2.4a3 (to be released this Thursday), everything remains as currently in CVS. For 2.4b1, I will consider a change of @ to some other single character, even though I think that @ has the advantage of being the same character used by a similar feature in Java. It's been argued that it's not quite the same, since @ in Java is used for attributes that don't change semantics. But Python's dynamic nature makes that its syntactic elements never mean quite the same thing as similar constructs in other languages, and there is definitely significant overlap. Regarding the impact on 3rd party tools: IPython's author doesn't think there's going to be much impact; Leo's author has said that Leo will survive (although it will cause him and his users some transitional pain). I actually expect that picking a character that's already used elsewhere in Python's syntax might be harder for external tools to adapt to, since parsing will have to be more subtle in that case. But I'm frankly undecided, so there's some wiggle room here. I don't want to consider further syntactic alternatives at this point: the buck has to stop at some point, everyone has had their say, and the show must go on.
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The container must inject a delegate object to the delegate injection point. The delegate object implements the delegate type and delegates method invocations to remaining uninvoked decorators and eventually to the bean. When the container calls a decorator during business method interception, the decorator may invoke any method of the delegate object.
Just take a look at the code again. In the if/else clause we are returning greet and welcome, not greet() and welcome(). Why is that? It’s because when you put a pair of parentheses after it, the function gets executed; whereas if you don’t put parenthesis after it, then it can be passed around and can be assigned to other variables without executing it. Did you get it? Let me explain it in a little bit more detail. When we write a = hi(), hi() gets executed and because the name is yasoob by default, the function greet is returned. If we change the statement to a = hi(name = "ali") then the welcome function will be returned. We can also do print hi()() which outputs now you are in the greet() function.
The decorator pattern, also known as the wrapper pattern, is when you wrap an object within another object, thus providing a means of enhancing or overriding certain behavior. The wrapper object will delegate any incoming method calls to the original object, unless it defines a new method to enhance or replace the original object’s behavior. By using the decorator pattern, you can dynamically create as many decorated objects as you want, each enhancing the behavior of the original object in a unique way — and all without mutating the original object. In this manner, you can effectively add, remove, or extend behaviors at runtime.
Did you get it? We just applied the previously learned principles. This is exactly what the decorators do in Python! They wrap a function and modify its behaviour in one way or the another. Now you might be wondering that we did not use the @ anywhere in our code? That is just a short way of making up a decorated function. Here is how we could have run the previous code sample using @.
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