A List Comprehension is a syntactic construct in Python that enables the creation of new lists in a concise, expressive, and computationally efficient way. It combines an expression, one or more for loops, and optionally an if condition, all written in a single line of code. This technique replaces traditional iterative loops, improving code readability and reducing structural complexity.
![Minimalist DeGalaLab-style educational icon representing the Python concept List Comprehension, showing the syntax '[expression for element in sequence if condition]' with a soft grey background and fuchsia iteration elements.](https://sp-ao.shortpixel.ai/client/to_webp,q_glossy,ret_img,w_150,h_150/https://degalalab.com/wp-content/uploads/2025/10/listComp-150x150.jpg)
The general structure is as follows:
[expression for element in sequence if condition]Each component serves a specific role:
expression→ defines how each element of the input sequence is transformed.for element in sequence→ iterates through the elements of the original collection.if condition(optional) → filters elements that satisfy a given logical condition.
Basic Example
# Create a list containing the squares of numbers from 0 to 9
squares = [x**2 for x in range(10)]
print(squares)
# ➜ [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]It is also possible to include a conditional statement to refine the result:
# Generate a list with the squares of even numbers only
even_squares = [x**2 for x in range(10) if x % 2 == 0]
print(even_squares)
# ➜ [0, 4, 16, 36, 64]List comprehensions can be extended to other Python collections such as dictionaries (dict comprehensions) and sets (set comprehensions), which follow a similar syntax and enable declarative construction of data structures.
From a pedagogical and best-practice perspective, List Comprehensions are recommended when the transformation logic is short and easily interpretable, as they promote a more functional and expressive programming style. They contribute to writing Pythonic code—that is, code that is clear, elegant, and consistent with Python’s design philosophy.