Python Functools Module

How to Use Python Functools Module?

When it comes to how to use functools module, then we just need to follow a simple step. We need to import this module into our program, and then we can use it anywhere. To start using functools module, we need to write a simple import statement:

import functools

Classes in Python Functools Module

There are two classes in the functools module:

partial Class

The partial class in the functools module allows us to create a new function with some of the original function's arguments that are already set. This can be helpful when you want to simplify function calls by fixing specific arguments.

Let us look at the example of the partial class.

• Python

Python

from functools import partial as partialClass



# This is our original function

def power(baseNum, exponentNum):

    return baseNum ** exponentNum


# Invoking the original function

res = power(2, 3)

print("The result of original function is",res)


print("After applying partialClass...")

# Creating a new function with fixed base as 3

cube = partialClass(power, 3)


# Calculating the cube of a number using our new function

ans = cube(3)

print("Cube of the number is",ans)

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Output

The result of original function is 8
After applying partialClass...
Cube of the number is 27

partialmethod Class

The partialmethod class is similar to the partial class, but it is designed specifically for methods within classes. 

Let us understand this with the help of an example.

• Python

Python

from functools import partialmethod



class ImageProcessor:

    def __init__(self):

        self.filter = 'Original'



    def _filter(self, filter_type):

        self.filter = filter_type



    grayscale = partialmethod(_filter, filter_type='Grayscale')

    sepia = partialmethod(_filter, filter_type='Sepia')



# Creating an ImageProcessor object

img = ImageProcessor()



# Original image

print(img.filter)



# Applying filters

img.grayscale()

print(img.filter)



img.sepia()

print(img.filter)

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Output

Original
Grayscale
Sepia

Explanation

In this example, we are building an image-processing application. So, we have created an ImageProcessor class. This class represents an image. As we know, the image can have multiple filters, such as grayscale, sepia, and invert. So, we have created methods to apply these filters. We have fixed the filter type for each method using partialmethod class.

Functions in Python Functools Module

There are several functions that are provided by the functools module. Let us discuss a few of them:

reduce() Function

The reduce() function in the functools module is used to apply a binary function cumulatively to the items of an iterable from left to right. Then it produces a single reduced result. We can see the steps as well to understand the reduce() function’s working:

Let us discuss it with the help of an example.

• Python

Python

from functools import reduce



# Defining a multiply function

def multiply(x, y):

    return x * y



# Calculating the product of all elements in a list using reduce

num = [4, 2, 3, 6, 5]

ans = reduce(multiply, num)

print("The multiplication of the numbers in the list is", ans)

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Output

The multiplication of the numbers in the list is 720  

lru_cache() Function

The lru_cache() function is used for memoization. It helps to cache the results of expensive function calls to improve the performance. 

Let us discuss it with the help of an example.

• Python

Python

from functools import lru_cache


# Finding Fibonacci using memoization

@lru_cache(maxsize=None)

def fib(n):

    if n <= 1:

        return n

    else:

        return fib(n-1) + fib(n-2)


# Calculating the Fibonacci 

ans = fib(9)

print("The 9th Fibonacci is",ans) 

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Output 

The 9th Fibonacci is 34

Explanation

In this example, we have used memoization to find the 9th Fibonacci number efficiently. The @lru_cache decorator from the functools module is applied to the Fibonacci function. It has enabled the memoization for this recursive function. Using memoization with lru_cache effectively turned the recursive Fibonacci function into a dynamic programming solution. That’s why the results of subproblems are stored and reused when needed. This is how lru_cache has improved the performance of the recursive Fibonacci function. 

wraps() Function

The wraps() function is a decorator. It preserves the metadata of the original function when creating a new decorator. When we use the wraps decorator, it ensures that when we create a new decorator, it doesn't lose the important properties of the original function. In simple words, it acts like a wrapper around the new decorator. This makes sure that it looks and behaves like the original function.

Let us discuss it with the help of an example.

• Python

Python

from functools import wraps



def exampleDeco(func):

    @wraps(func)

    def wrapper(*args, **kwargs):

        # Print a message before the wrapped function is executed

        print("Loading…. before the function is called.") 

        

        # Call the original function and store its result

        ans = func(*args, **kwargs)  

        

        # Print a message after the wrapped function is executed

        print("Loading ended…. after the function is called.")  

        

        # Return the answer of the original function

        return ans  

        

    # Return the decorated wrapper function

    return wrapper 



@exampleDeco

def greeting():

    print("Hello! Ninjas")  



# Calling the decorated function

greeting() 

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Output

Loading…. before the function is called.
Hello! Ninjas
Loading ended…. after the function is called.

Explanation

In this example, we have defined a decorator exampleDeco. It uses the wraps() function from the functools module. Then we have applied the exampleDeco decorator to the greeting() function. When we call the decorated greeting() function, the decorator prints loading messages before and after the original function is executed.

Frequently Asked Questions

What do you mean by functional programming in Python?

Functional programming treats computation as the evaluation of mathematical functions. It promotes code reusability, modularity, and ease of debugging.

Can we use the functools module with custom decorators?

Yes, we can use the functools module with custom decorators. We can apply the wraps decorator.

When should we use the lru_cache decorator?

We can use the lru_cache decorator when we have a computationally expensive function that is called with the same arguments repeatedly. Memoization provided by this decorator can significantly improve performance by caching results.

How does the reduce() function differ from map() and filter()?

The reduce() function aggregates the elements of an iterable using a binary function, Then it produces a single reduced result. The map() and filter() transform and filter elements, respectively, in an iterable, producing a new iterable.

Conclusion

In this blog, we have discussed about Python Functools module. We have covered classes and functions that come with functools module. We have also discussed several examples. If you want to learn more about the Python modules, then you can check out our blogs:

• Import and Modules in Python
   
• What is the Difference between Module and Package in Python?
   
• Collections Module in Python
   

We hope this blog helps you to get knowledge about the Python Functools module. You can refer to our guided paths on the Codestudio platform. You can also consider our paid courses such as DSA in Python to give your career an edge over others! 

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