Python: List Comprehension Explained with Examples

If you’ve spent any time with Python, you’ve likely heard the term “Pythonic.” It refers to code that is not just functional, but also clean, readable, and idiomatic to the Python language. One of the most powerful tools for writing Pythonic code is the list comprehension, a feature that allows you to build lists in a single, elegant line.

While traditional for loops are perfectly capable of creating lists, list comprehensions offer a more concise and often more efficient alternative. Let’s explore how they work and why you should be using them.

From for Loop to Comprehension

At its core, a list comprehension is a syntactic shortcut for a for loop that builds a list. Imagine you want to create a list containing the squares of the first ten numbers.

Here’s how you would do it with a standard for loop:

squares = []
for i in range(1, 11):
  squares.append(i * i)

print(squares)
# Output: [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]

This code is straightforward: it initializes an empty list, loops through the numbers, and appends the square of each number to the list.

Now, let’s achieve the exact same result with a list comprehension:

squares = [i * i for i in range(1, 11)]

print(squares)
# Output: [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]

The difference is immediate. The list comprehension is more compact and, once you’re familiar with the syntax, arguably easier to read. It encapsulates the entire logic of creating the list in one readable line.

Adding Conditional Logic for Filtering

The power of list comprehensions extends beyond simple transformations. You can also embed conditional logic to filter the elements you want to include in your new list.

Suppose you only want the squares of the even numbers from the same range.

Using a for loop, you would add an if statement:

even_squares = []
for i in range(1, 11):
  if i % 2 == 0:
    even_squares.append(i * i)

print(even_squares)
# Output: [4, 16, 36, 64, 100]

With a list comprehension, the if condition is simply added to the end:

even_squares = [i * i for i in range(1, 11) if i % 2 == 0]

print(even_squares)
# Output: [4, 16, 36, 64, 100]

Notice how the order of the for and if statements remains the same in both snippets, making the transition intuitive.

More List Comprehension Examples

The following represents some more examples of list comprehension.

Converting a list of strings to uppercase

words = ["hello", "world", "python"]
upper_words = [word.upper() for word in words]
# Output: ['HELLO', 'WORLD', 'PYTHON']

Extracting numbers from a string

sentence = "The price is $50 and the tax is $5."
numbers = [int(char) for char in sentence if char.isdigit()]
# Output: [5, 0, 5]

Creating a flattened list from a list of lists (nested comprehension)

matrix = [[1, 2], [3, 4], [5, 6]]
flat_list = [num for row in matrix for num in row]
# Output: [1, 2, 3, 4, 5, 6]

Why Bother? The Advantages

So, why should you make list comprehensions a regular part of your Python toolkit?

1. Readability and Conciseness: They reduce the amount of boilerplate code, making your intentions clearer and your scripts shorter.
2. Performance: In many cases, list comprehensions are faster than their for loop counterparts. This is because the iteration is handled at the C-language level in Python’s interpreter, which is more optimized.

While they are incredibly useful, it’s wise to avoid overly complex, multi-line comprehensions that become difficult to understand. For highly complex logic, a traditional for loop might still be the more readable choice.

By mastering list comprehensions, you take a significant step towards writing more elegant, efficient, and truly “Pythonic” code.