List & Dict Comprehensions

What Are Comprehensions?

Comprehensions are Python's way of creating new collections from existing ones — in a single, readable line. Once you learn them, you'll wonder how you lived without them.

Without comprehension:

squares = []
for i in range(10):
    squares.append(i ** 2)

With comprehension:

squares = [i ** 2 for i in range(10)]

Same result, but cleaner and often faster!

List Comprehensions

The basic syntax: [expression for item in iterable if condition]

Dictionary Comprehensions

Same idea, but creates dictionaries: {key_expr: value_expr for item in iterable}

Set Comprehensions

Sets use curly braces: {expression for item in iterable}

When NOT to Use Comprehensions

Comprehensions are great, but they shouldn't make your code unreadable:

# BAD — too complex, hard to understand
result = [x*y for x in range(10) for y in range(x) if x % 2 == 0 if y > 2]

# BETTER — use a regular loop for complex logic
result = []
for x in range(10):
    if x % 2 == 0:
        for y in range(x):
            if y > 2:
                result.append(x * y)

Performance Note

Comprehensions are implemented in C and are generally faster than equivalent for-loops with .append(). For large datasets, this can be significant:

# Loop:      ~0.15s
# List comp: ~0.08s  (≈2× faster)

Key Takeaways

• List comprehension: [expr for item in iterable if cond]
• Dict comprehension: {key: val for item in iterable}
• Set comprehension: {expr for item in iterable}
• They're faster and more readable than loops (when used appropriately)
• Don't over-nest — if it's hard to read, use a regular loop
• Comprehensions are one of Python's most "Pythonic" features