Concise Iteration: List Comprehensions

Simple for loops to create lists can be verbose. We can leverage list comprehensions to define the list contents directly within square brackets, obtaining a more compact syntax.

# Example: Double items using a for loop
old_items = [1, 2, 3, 4]
doubled_items = []

for item in old_items:
    doubled_items.append(item * 2)

print(doubled_items)

# Example: Double items using list comprehension
doubled_items_with_comprehension = [item * 2 for item in old_items]
print(doubled_items_with_comprehension)

List Comprehension Syntax

• Syntax: [<expression> for <item> in <iterable>]
• [] indicates a new list is created eagerly.
• <expression> is applied to each item.
• for <item> in <iterable> defines the loop.

servers = ["web", "db", "backend"]
uppercase_servers = [server.upper() for server in servers]
print(uppercase_servers)

Filtering with if in Comprehensions

• Purpose: Include only items meeting a condition.
• Syntax: [<expression> for <item> in <iterable> if <condition>].
• The condition filters items before expression is evaluated.

numbers = [1, 5, 10, 8, 2, 15]
even_numbers = [num + 1 for num in numbers if num % 2 == 0]
print(even_numbers)

Set and Dictionary Comprehensions

• Set comprehension uses {} and produces unique items.
• Dictionary comprehension uses {key: value ...}.
• Both evaluated eagerly like list comprehensions.

numbers = [1, 2, 3, 2, 4, 1, 3]
unique_squares = {x * x for x in numbers}
print(unique_squares)

servers = ["web", "backend"]
server_ips = {server: f"192.168.1.{i}" for i, server in enumerate(servers)}
print(server_ips)

Conditional Expression (Ternary Operator)

• Purpose: Apply different expressions based on a condition within the comprehension.
• Syntax: <value_if_true> if <condition> else <value_if_false> inside the comprehension.
• Places the ternary before the for clause.

numbers = [1, 5, 10, 8, 2, 15]
categories = ["PASS" if num >= 8 else "FAIL" for num in numbers]
print(categories)