Python List Comprehension: Syntax, Usage, and Examples

Python list comprehension provides a concise way to create lists using a single line of code. It allows you to generate new lists by applying an operation to each item in an existing iterable, often replacing traditional loops for better readability and efficiency.

How to Use List Comprehension in Python

The syntax of a list comprehension follows this structure:

[expression for item in iterable]

• expression: Defines the operation to perform on each item.
• item: Represents each element from the iterable.
• iterable: The source data structure (list, tuple, range, etc.).

Example: Creating a List with List Comprehension

numbers = [1, 2, 3, 4, 5]
squared_numbers = [n**2 for n in numbers]
print(squared_numbers)  # Output: [1, 4, 9, 16, 25]

This example squares each number in the numbers list and stores the results in squared_numbers.

When to Use List Comprehension in Python

List comprehension is useful when you need to:

1. Transform data efficiently
   • When applying an operation to each element in an iterable (e.g., squaring numbers, converting strings to lowercase).
2. Filter elements in a list
   • When keeping only specific elements based on a condition (e.g., extracting even numbers).
3. Replace loops with a more readable approach
   • When improving code readability and reducing the number of lines needed to generate lists.

Examples of List Comprehension in Python

Filtering Elements with a Condition

You can use list comprehension with if statements to filter elements that meet certain conditions.

numbers = [1, 2, 3, 4, 5, 6]
even_numbers = [n for n in numbers if n % 2 == 0]
print(even_numbers)  # Output: [2, 4, 6]

This example selects only even numbers from the numbers list.

Using if-else in List Comprehension

You can include both if and else to modify values based on a condition.

numbers = [1, 2, 3, 4, 5]
squared_or_halved = [n**2 if n % 2 == 0 else n / 2 for n in numbers]
print(squared_or_halved)  # Output: [0.5, 4, 1.5, 16, 2.5]

Here, even numbers are squared, and odd numbers are divided by 2.

Using Nested List Comprehension in Python

You can create multidimensional lists with nested list comprehensions.

matrix = [[j for j in range(3)] for i in range(3)]
print(matrix)
# Output: [[0, 1, 2], [0, 1, 2], [0, 1, 2]]

This creates a 3x3 matrix where each row contains numbers from 0 to 2.

Using List Comprehension with Dictionaries

List comprehension also works when creating or modifying dictionaries.

keys = ['a', 'b', 'c']
values = [1, 2, 3]
dictionary = {k: v for k, v in zip(keys, values)}
print(dictionary)  # Output: {'a': 1, 'b': 2, 'c': 3}

This example pairs keys with values using zip().

Learn More About List Comprehension in Python

List Comprehension vs. filter and lambda

List comprehensions often replace filter and lambda for readability.

Using filter and [lambda](https://mimo.org/glossary/python/lambda-function:

numbers = [1, 2, 3, 4, 5]
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers)  # Output: [2, 4]

Using list comprehension (more readable):

even_numbers = [n for n in numbers if n % 2 == 0]

Double List Comprehension in Python

You can flatten a nested list using double list comprehension.

nested_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flattened = [num for sublist in nested_list for num in sublist]
print(flattened)  # Output: [1, 2, 3, 4, 5, 6, 7, 8, 9]

Using List Comprehension with enumerate

If you need both index and value, use enumerate.

words = ["apple", "banana", "cherry"]
indexed_words = [(i, word) for i, word in enumerate(words)]
print(indexed_words)  # Output: [(0, 'apple'), (1, 'banana'), (2, 'cherry')]

Using List Comprehension with a Dictionary

You can swap keys and values using dictionary comprehension.

original_dict = {'a': 1, 'b': 2, 'c': 3}
swapped = {v: k for k, v in original_dict.items()}
print(swapped)  # Output: {1: 'a', 2: 'b', 3: 'c'}

List Comprehension and Generators

If you need an iterable instead of a list, use generator expressions.

numbers = (n**2 for n in range(5))
print(list(numbers))  # Output: [0, 1, 4, 9, 16]

Best Practices for List Comprehension

• Use list comprehension for readability, but avoid making expressions too long.
• Prefer list comprehension over loops when creating new lists.
• Use dictionary comprehension when working with key-value pairs.
• Use generator expressions when you don’t need to store the full list in memory.

Python list comprehension simplifies data transformations and makes code more expressive. Whether you need to filter, modify, or generate lists dynamically, list comprehension offers a concise and efficient solution.