What are generators and how do they differ from lists?

A generator is a function that returns items one by one using yield, rather than creating the entire list in memory all at once.

Generator function:

def count_up_to(n):
    i = 1
    while i <= n:
        yield i
        i += 1

for num in count_up_to(5):
    print(num)  # 1, 2, 3, 4, 5

Generator expression:

Similar to a list comprehension, but uses parentheses:

# List comprehension — creates the whole list in memory
squares_list = [x ** 2 for x in range(1000000)]

# Generator expression — evaluates one by one
squares_gen = (x ** 2 for x in range(1000000))

Key differences from a list:

• Memory: a generator only stores the current item, not the entire collection.
• Single use: a generator can be iterated over only once.
• Laziness: items are evaluated on demand, not in advance.

gen = (x for x in range(3))
print(list(gen))  # [0, 1, 2]
print(list(gen))  # [] — already exhausted

When to use:

• Generator — when working with large datasets, streams, or when there is no need to keep all items.
• List — when multiple access, indexing is required, or the dataset is known to be small.