List Comprehensions

Overview

A List Comprehension offers a concise, elegant syntax to create a new list from an existing sequence. In simple words, it replaces multi-line for loops and .append() calls with a single readable line.

Learning Objectives

• Master the syntax: [expression for item in iterable if condition].
• Convert traditional for loops into list comprehensions.
• Use conditional logic inside list transformations.

Concept Explanation

Syntax breakdown:

new_list = [expression for item in iterable if condition]

1. expression: The operation performed on each item (e.g. x * 2).
2. item: The loop variable representing the current item.
3. iterable: The source collection (list, range, tuple).
4. condition (optional): A filter clause (e.g. if x > 10).

Code Examples

Example 1 — Basic Transformation

numbers = [1, 2, 3, 4, 5]

# Traditional loop approach
squares_loop = []
for n in numbers:
    squares_loop.append(n ** 2)

# List comprehension approach
squares_comp = [n ** 2 for n in numbers]
print("Squares:", squares_comp) # [1, 4, 9, 16, 25]

Example 2 — Filtering with Conditions

scores = [45, 88, 72, 90, 30, 65]

# Filter scores >= 60 and convert to passing status
passing_scores = [s for s in scores if s >= 60]
print("Passing Scores:", passing_scores) # [88, 72, 90, 65]

# If-Else transformation
status = ["Pass" if s >= 60 else "Fail" for s in scores]
print("Status:", status)

Common Mistakes

• Overcomplicating comprehensions: Writing deeply nested comprehensions with 3+ loops makes code hard to read. Use regular loops when complexity grows.
• Confusing if position: Filtering condition goes at the end ([x for x in data if x > 0]), while if-else transformations go before for ([x if x > 0 else 0 for x in data]).

Best Practices

• Keep list comprehensions short and readable.