A short tutorial about streamlining your code with Decorators and Meta-classes

Meta-programming in Python is a tough, albeit interesting, nut to crack.  This article is a guide for those who want to learn about two crucial features of meta-programming – decorators and meta classes.

What Is Meta-Programming?

In short, meta-programming is the act of writing code that manipulates code. However, without understanding the concept, the above one-liner probably won’t excite the grey cells. Let’s delve deeper to understand meta-programming in the context of Python.

Meta-programming in Python can be stated as: “Meta-programming is an act of building functions and classes that can manipulate code by modifying, wrapping existing code, or generating code.” However, this may seem really difficult to understand at one go. Therefore, it’s necessary to get familiar with the relevant terms.

There are two vital elements to achieve meta-programming in Python:

• Decorators
• Meta-classes

Decorators:

A decorator is a way of adding new functionality to an existing function without modifying its original structure.

Consider the following three functions:

def add(x, y):

Broadly speaking, a decorator helps to avoid repetition when printing the function name and parameter values each time the function is called.

With reference to the code block above, consider a situation in which we need to print the function name and parameter values when the function is called. This should be applicable to all three functions above.

The native way is to add print/log statements to all three functions. However, this is repetitive work and we would also need to modify each function body.

Here’s what that would look like:

def add(x, y):

It is clear that the same output can be achieved in a more efficient manner. To do this, we need to write a decorator function. The proper use of a decorator can cut down the series of mundane tasks of modifying each function body.

def my_decorator(func):

In the above code snippet, my_decorator is a decorator function. We decorate all three functions with @my_decorator and we have not touched the existing function body to add this print functionality.

Therefore, a decorator function is, at its core, a higher-order function that take a function as an argument and returns another function.

In the above code, my_decorator takes a function as an argument and returns wrapper_function as a result. wrapper_function adds print functionality to func.

Now, let’s switch to another crucial element of meta-programming.

Meta-Classes:

A special class in itself, a meta-class would be best described as the class of a class. Instead of defining the behaviour of its own instances like an ordinary class, a meta-class defines the behaviour of an ordinary class and its instance. A class, in this case, would thus be an instance of a meta-class.

Using a meta-class, methods or fields can be added or subtracted from an ordinary class. Python has one special class, the type class, which is by default a meta-class. All custom type classes must inherit from the type class .In this example, the class calchas three class methods. A meta-class can be used to provide a functionality such as debug, to all the methods at once.

class Calc():

To do this, First, we need to create a meta-class MetaClassDebug, with debug functionality, and make the Calc class inherit from MetaClassDebug. When we call any method from the Calc class, it will be invoked with our debug_function.

def debug_function(func):

def wrapper(*args, **kwargs):
print(“{0} is called with parameter {1}”.format(func.__qualname__, args[1:]))
return func(*args, **kwargs)

return wrapper

def debug_all_methods(cls):

for key, val in vars(cls).items():
if callable(val):
setattr(cls, key, debug_function(val))
return cls

class MetaClassDebug(type):

def __new__(cls, clsname, bases, clsdict):
obj = super().__new__(cls, clsname, bases, clsdict)
obj = debug_all_methods(obj)
return obj

class Calc(metaclass=MetaClassDebug):
def add(self, x, y):
return x + y

def sub(self, x, y):
return x – y

def mul(self, x, y):
return x * y

calc = Calc()
print(calc.add(2, 3))
print(calc.sub(2, 3))
print(calc.mul(2, 3))

**************** output ****************

Calc.add is called with parameter (2, 3)
5
Calc.sub is called with parameter (2, 3)
-1
Calc.mul is called with parameter (2, 3)
6

In the above code, the

With meta-classes, not only can new behaviour be added to all methods within a class, but the instance creation of a class can also be controlled. With meta-classes, methods and fields can also be added or removed from classes.

Conclusion:

This article described decorators and meta-classes, two types of meta-programming in Python. If you need some guidance with fine-tuning your organization’s programming capabilities, please reach out to us at marketing@nitorinfotech.com. Nitor Infotech’s dedicated technology experts and programmers can help throughout your journey, from initial assessment to monitoring.

The article portrayed the relationship among instances, classes, and, meta-classes in Python.  Are you contemplating implementation of custom meta-classes by sub classing the default type meta-class of Python? If you are then, rely on Nitor Infotech’s dedicated technology experts and programmers, who can help throughout your entire programming and modernization journey, from initial assessment to implementation and monitoring.