How do iterators and generators improve performance in Python?

How do iterators and generators improve performance in Python?

Iterators and generators allow for lazy evaluation, meaning they generate items on-the-fly rather than storing them in memory. This can significantly reduce memory usage and improve performance, especially with large datasets.

Can you give me an example of a generator in Python?

Sure! Here's a simple generator that yields squares of numbers:

def square_generator(n):
    for i in range(n):
        yield i ** 2

You can use it like this:

for square in square_generator(5):
    print(square)

What are the differences between iterators and generators?

Iterators are objects that implement the iterator protocol, which consists of the __iter__() and __next__() methods. Generators, on the other hand, are a simpler way to create iterators using the yield keyword, which automatically handles the state for you.

When should I use a generator instead of a list?

You should use a generator when dealing with large datasets or when you want to save memory. Generators produce items one at a time and only when requested, while lists store all items in memory.