Python Higher Order Functions

As Python programmers, we always strive to make our code concise and reusable. We keep looking for best practices that can help us achieve this goal. In today’s tutorial, we are going to discuss one such idea which is higher-order functions in Python. Let’s understand what are these and how can we use them.

What are Higher-Order Functions in Python?

In Python, a higher-order function is the one that meets one or more of the following criteria:

• Allows other functions to be passed to it as parameters.
• Can send back other functions as a result.
• Creates new functions within its body.

Given the above points, such functions are a powerful tool for abstraction and code reuse. They allow us to write more concise and expressive code, and to avoid duplication of effort.

Examples of Higher-Order Functions in Python

• map() – applies a function to each item in an iterable.
• filter() – creates a new iterable containing only the items from the original iterable that satisfy a given condition.
• reduce() – applies a function to two items at a time and continuously combines the results until it produces a single value.
• sorted() – returns a sorted copy of an iterable.
• min() and max() – return the smallest or largest item in an iterable.
• any() and all() – return True if any or all of the items in an iterable satisfy a given condition.
• enumerate() – returns an iterable of tuples containing the index and value of each item in an iterable.
• zip() – returns an iterable of tuples containing the corresponding items from two or more iterables.

Creating Higher-Order Functions

In addition to the built-in higher-order functions listed above, we can also create our own higher-order functions. For example, the following function takes a function as an argument and returns a new function that applies the given function to each item in an iterable:

Python code:

def apply_to_each(f):
  def inner(iterable):
    for item in iterable:
      yield f(item)
  return inner

We can use this function as follows:

Once more piece of Python code:

import random

def do_square(x):
    return x * x

def do_calc(func, nums):
    new_nums = list(apply_to_each(func)(nums))
    return new_nums

rand_nums = [random.randint(1, 100) for _ in range(5)]

sqrd_nums = do_calc(do_square, rand_nums)

print("Random Numbers:", rand_nums)
print("Squared Numbers:", sqrd_nums)

This will print the following output:

Random Numbers: [52, 25, 56, 87, 78]
Squared Numbers: [2704, 625, 3136, 7569, 6084]

Also Read: Generate Random Images in Python

In this code, we generate random numbers and then use a function (do_square) to calculate their squares. The do_calc function applies this square calculation to each random number, and we get a list of both the original random numbers and their squared values. It’s like a machine that takes random numbers, does some math, and shows you the numbers before and after the math.

Benefits

Higher-order functions offer several benefits, including:

• Abstraction: Higher-order functions allow us to abstract away common patterns in Python code. This can make our code more concise and easier to understand.
• Code reuse: We can reuse higher-order functions in multiple places within our code. This can save us time and effort.
• Expressiveness: Higher-order functions can be used to express complex computations more concisely and elegantly.

Comparison of Different Methods

Which Method is the Most Suitable?

The best method for creating a higher-order function depends on the specific situation. However, in general, decorators are the most concise and expressive way to create them. Nested functions can be useful for creating functions that need to access variables from the outer function. Closures can be useful for creating functions that need to retain state between calls.

Conclusion

Higher-order functions are a powerful tool for abstraction, code reuse, and expressiveness. By understanding how to use higher-order functions, we can write more concise, clean, and manageable code. So, let’s vow to use these as much as possible in our code but with insight.

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Enjoy coding,
TechBeamers.