An Easy Guide to List Comprehension in Python


Welcome to the world of Python programming! Today, we’ll explore a powerful concept called “list comprehension.” Don’t worry if you’re new to programming – this comprehensive guide will explain everything in a simple and understandable manner. List comprehension allows us to create lists in a concise and elegant way, making our code more efficient and readable. Let’s dive in and explore the wonders of list comprehension!

What is List Comprehension?

List comprehension is a compact way to create lists in Python by combining elements from existing sequences or performing operations on them. It follows a simple syntax that allows you to create new lists using a single line of code, making your programs shorter and more expressive.

Basic Syntax

The basic syntax of list comprehension consists of three parts: expression, iteration, and optional condition. Here’s how it looks:

new_list = [expression for item in iterable if condition]

  • Expression: The expression defines the operation to be performed on each item in the iterable. It can be a calculation, transformation, or any other operation that produces a value.
  • Iteration: The iteration part specifies the source of elements for the new list. It can be a list, tuple, string, or any other iterable object.
  • Condition (optional): The condition allows you to filter elements from the source based on specific criteria. It is optional and can be omitted if not required

Creating a Simple List

Let’s start with a simple example to understand the basic structure of list comprehension. Suppose we want to create a list containing the squares of numbers from 1 to 5. Here’s how we can do it using list comprehension:

squares = [x**2 for x in range(1, 6)]
print(squares) # Output: [1, 4, 9, 16, 25]

In this example, the expression x**2 calculates the square of each number x in the range from 1 to 5.

Filtering Elements

List comprehension allows us to filter elements from the source based on certain conditions. Let’s say we want to create a new list containing only the even numbers from the range 1 to 10:

evens = [x for x in range(1, 11) if x % 2 == 0]
print(evens) # Output: [2, 4, 6, 8, 10]

In this example, the condition x % 2 == 0 checks if a number is divisible by 2 (i.e., even). Only the numbers satisfying this condition are included in the new list.

Nested List Comprehension

List comprehension can also be nested, allowing us to work with multi-dimensional data structures. Let’s say we have a matrix and want to create a flattened list containing all its elements:

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

In this example, we use two levels of iteration: the outer loop(for row in matrix) iterates over each row in the matrix, and the inner loop(for num in row) iterates over each element within the row.

Conditional Expression

List comprehension allows the use of conditional expressions to perform different operations based on specific conditions. Let’s say we want to create a list containing the squares of even numbers and cubes of odd numbers from 1 to 5:

numbers = [x**2 if x % 2 == 0 else x**3 for x in range(1, 6)]
print(numbers) # Output: [1, 4, 27, 16, 125]

In this example, the conditional expression x**2 if x % 2 == 0 else x**3 calculates the square of even numbers and the cube of odd numbers.

Applying a Function to All Elements of a List

List comprehension allows us to apply a function to all elements of a list in a concise and elegant manner. This feature is particularly useful when we want to perform the same operation on each element of a list. Let’s explore an example of finding the length of all strings in a list:

strings = [“Hello”, “Python”, “List”, “Comprehension”]
lengths = [ len(s) for s in strings ]
print(lengths) # Output: [5, 6, 4, 14]

In this example, we use list comprehension to iterate over each string s in the strings list. The expression len(s) calculates the length of each string, and the resulting lengths are stored in the lengths list.


Congratulations! You’ve now learned the fundamentals of list comprehension in Python. By leveraging this powerful technique, you can create new lists effortlessly, filter elements, work with multi-dimensional structures, and perform different operations based on conditions. Keep practicing and exploring more complex scenarios to enhance your Python programming skills. Happy coding!