List, Dict, and Set Comprehensions¶

Comprehensions provide a concise way to create new sequences (such as lists, dictionaries, and sets) from existing collections without writing verbose for loops and .append() calls.

1. List Comprehensions¶

A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses.

Syntax¶

[expression for item in iterable if condition]

# Traditional loop:
squares = []
for x in range(1, 6):
    squares.append(x ** 2)

# Equivalent list comprehension:
comp_squares = [x ** 2 for x in range(1, 6)]

print(comp_squares)

Filtering with if Clause¶

numbers = [12, 5, 8, 19, 22, 7, 30]
evens = [n for n in numbers if n % 2 == 0]
print(evens)  # [12, 8, 22, 30]

Nested List Comprehensions¶

Flatten a 2-dimensional matrix into a single list:

matrix = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]

flattened = [val for row in matrix for val in row]
print(flattened)  # [1, 2, 3, 4, 5, 6, 7, 8, 9]

2. Dictionary Comprehensions¶

Dictionary comprehensions construct dictionaries from iterables:

Syntax¶

{key_expression: value_expression for item in iterable if condition}

# Number to cube mapping
cubes = {n: n ** 3 for n in range(1, 6)}
print("Cubes:", cubes)

# Inverting a dictionary (swapping keys and values)
original = {"a": 1, "b": 2, "c": 3}
inverted = {v: k for k, v in original.items()}
print("Inverted:", inverted)

3. Set Comprehensions¶

Set comprehensions create sets, automatically deduplicating values:

Syntax¶

{expression for item in iterable if condition}

words = ["apple", "banana", "avocado", "cherry", "apricot"]
first_letters = {word[0].upper() for word in words}
print(first_letters)  # {'A', 'B', 'C'}

4. Generator Expressions¶

If you are dealing with large datasets and do not need the entire list loaded into memory at once, use a generator expression by replacing square brackets [...] with parentheses (...):

# Generator expression (lazy evaluation)
sum_of_squares = sum(x ** 2 for x in range(1_000_000))
print("Sum:", sum_of_squares)