Python Iterators vs. Generators: When Should I Use Which?

Understanding the Distinction between Python's Iterators and Generators

In the world of Python programming, iterators and generators are two closely related yet distinct concepts. Let's delve into their differences and explore when each of these constructs proves most useful.

Iterators: A General Abstraction for Traversing Data

An iterator is a more general abstraction that encompasses any object with a next method and an iter method that returns itself. This means that iterators can represent various data structures, such as lists, tuples, or custom classes, and provide a consistent way to traverse them.

Generators: A Special Type of Iterator with Yield Magic

Every generator is an iterator, but not vice versa. A generator is constructed by calling a function containing one or more yield expressions. These yield expressions temporarily pause the function's execution and return values. The resulting object possesses the characteristics of an iterator, but its implementation is unique.

When to Use Iterators vs. Generators

Custom Iterators: Opt for a custom iterator when you require a custom class with intricate state-tracking or need to expose additional methods beyond __next__.

Generators: Generators are typically more suitable and simpler to implement for most scenarios, especially when the state maintenance is minimal. Yield expressions handle frame suspension and resumption, making it effortless to manage state.

Practical Example: Generating Square Numbers

Consider the task of generating square numbers within a given range.

Generator Implementation:

def squares(start, stop):
    for i in range(start, stop):
        yield i * i  # Use yield to pause and return values

Custom Iterator Implementation:

class Squares(object):
    def __init__(self, start, stop):
        self.start = start
        self.stop = stop

    def __iter__(self):
        return self

    def __next__(self):
        if self.start >= self.stop:
            raise StopIteration
        current = self.start * self.start
        self.start += 1
        return current

While the generator approach requires less code, the custom iterator gives more flexibility with additional methods.

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