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.
max()– return the smallest or largest item in an iterable.
Trueif 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:
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.
Higher-order functions offer a number of 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 in a more concise and elegant way.
Comparison of Different Methods
|Decorators||Decorators are a Python syntax feature that allows us to wrap a function in another function. The outer function can then modify the behavior of the inner function.|
|Nested functions||Nested functions are functions that are defined within the body of another function. Nested functions can access variables and functions from the outer function.|
|Closures||Closures are functions that remember the values of variables from the scope in which they were defined. This allows closures to retain state between calls.|
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.
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.