List comprehension in Python is a concise and readable way to create new lists by applying an expression to each item in an existing iterable (like a list, tuple, or range), optionally filtering items based on a condition.

Basic Syntax

[expression for item in iterable]

• expression: The operation applied to each item (e.g., x ** 2).

• item: The variable representing each element in the iterable.

• iterable: The source of data (e.g., range(10), [1, 2, 3]).

With Filtering (if condition)

[expression for item in iterable if condition]

• Only items satisfying the condition are included in the result.

Examples

• Square numbers:

  squares = [x ** 2 for x in range(5)]  # [0, 1, 4, 9, 16]

• Filter even numbers:

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

• Transform strings:

  uppercase = [word.upper() for word in ['hello', 'world']]  # ['HELLO', 'WORLD']

• Flatten nested lists:

  matrix = [[1, 2], [3, 4]]
  flattened = [num for row in matrix for num in row]  # [1, 2, 3, 4]

Benefits

• More readable: Combines looping, filtering, and transformation in one line.

• Faster execution: Often faster than equivalent for loops due to internal optimizations.

• Cleaner code: Eliminates the need for manual append() calls and temporary variables.

When to Avoid

• Avoid deeply nested or complex logic—readability decreases.

• For large datasets, consider generator expressions ( ) instead of list comprehensions [] to save memory.

Related Structures

• Set comprehension: {x for x in range(5)} → {0, 1, 2, 3, 4}

• Dictionary comprehension: {k: v for k, v in [('a', 1), ('b', 2)]} → {'a': 1, 'b': 2}

List comprehensions are a core Pythonic idiom for clean, efficient list creation.