FLOSS Project Planets

Real Python: When to Use a List Comprehension in Python

Planet Python - Sat, 2024-12-07 09:00

List comprehensions in Python provide a concise way to create lists by embedding a loop and optional conditional logic in a single line. You use a list comprehension to transform and filter elements from an iterable efficiently. It allows you to replace complex loops and map() functions with more readable and often faster expressions. By understanding list comprehensions, you can optimize your code for better performance and clarity.

You can add conditional logic to a list comprehension in Python by appending an if statement to the end of the expression. This allows you to filter elements based on a condition, effectively replacing the need for filter() in many cases. Additionally, you can use conditional expressions at the beginning to choose between different outcomes, enhancing the versatility of your list comprehensions.

By the end of this tutorial, you’ll understand that:

A list comprehension in Python is a tool for creating lists by iterating over an iterable and optionally applying a condition.
You should use list comprehensions instead of loops when you want concise, readable code that performs transformations or filtering.
You add conditional logic to a list comprehension by including an if statement within the comprehension.
A list comprehension can be faster than a for loop because it’s optimized for performance by Python’s internal mechanisms.
A Python list comprehension is not lazy—it generates and stores the entire list in memory eagerly.
The difference between list comprehensions and map() is that the former creates a list, while the latter returns a map object, which is iterable.
To optimize performance with list comprehensions, use them for small to medium-sized lists and profile different approaches to choose the fastest one.

In this tutorial, you’ll explore how to leverage list comprehensions to simplify your code. You’ll also gain an understanding of the trade-offs that come with using them so that you can determine when other approaches are preferable.

Get Your Code: Click here to download the free code that shows you how and when to use list comprehensions in Python.

Take the Quiz: Test your knowledge with our interactive “When to Use a List Comprehension in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

When to Use a List Comprehension in Python

In this quiz, you'll test your understanding of Python list comprehensions. You'll revisit how to rewrite loops as list comprehensions, how to choose between comprehensions and loops, and how to use conditional logic in your comprehensions.

Transforming Lists in Python

There are a few different ways to create and add items to a lists in Python. In this section, you’ll explore for loops and the map() function to perform these tasks. Then, you’ll move on to learn about how to use list comprehensions and when list comprehensions can benefit your Python program.

Use for Loops

The most common type of loop is the for loop. You can use a for loop to create a list of elements in three steps:

Instantiate an empty list.
Loop over an iterable or range of elements.
Append each element to the end of the list.

If you want to create a list containing the first ten perfect squares, then you can complete these steps in three lines of code:

Python >>> squares = [] >>> for number in range(10): ... squares.append(number * number) ... >>> squares [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] Copied!

Here, you instantiate an empty list, squares. Then, you use a for loop to iterate over range(10). Finally, you multiply each number by itself and append the result to the end of the list.

Work With map Objects

For an alternative approach that’s based in functional programming, you can use map(). You pass in a function and an iterable, and map() will create an object. This object contains the result that you’d get from running each iterable element through the supplied function.

As an example, consider a situation in which you need to calculate the price after tax for a list of transactions:

Python >>> prices = [1.09, 23.56, 57.84, 4.56, 6.78] >>> TAX_RATE = .08 >>> def get_price_with_tax(price): ... return price * (1 + TAX_RATE) ... >>> final_prices = map(get_price_with_tax, prices) >>> final_prices <map object at 0x7f34da341f90> >>> list(final_prices) [1.1772000000000002, 25.4448, 62.467200000000005, 4.9248, 7.322400000000001] Copied!

Here, you have an iterable, prices, and a function, get_price_with_tax(). You pass both of these arguments to map() and store the resulting map object in final_prices. Finally, you convert final_prices into a list using list().

Leverage List Comprehensions

List comprehensions are a third way of making or transforming lists. With this elegant approach, you could rewrite the for loop from the first example in just a single line of code:

Python >>> squares = [number * number for number in range(10)] >>> squares [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] Copied!

Rather than creating an empty list and adding each element to the end, you simply define the list and its contents at the same time by following this format:

Python Syntax new_list = [expression for member in iterable] Copied! Read the full article at https://realpython.com/list-comprehension-python/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Search form

Tag cloud

You are here

FLOSS Project Planets

Pages

Recent Publications

FLOSS Project Planets

FLOSS Research