Iterators vs. Generators in Python: When to Use Which?

Generators vs. Iterators in Python

Python's iterators and generators are both iterables, but they differ in their implementation and use cases.

Iterators

Iterators are iterable objects that provide an interface for traversing a sequence of items. They have two main methods:

• __iter__: Returns the iterator itself, allowing it to be iterated over multiple times.
• __next__: Returns the next item in the sequence. Raising StopIteration when there are no more items left.

Generators

Generators are a special type of iterator that use the yield keyword to generate values on the fly. When called, a generator function returns a generator object that can be iterated over.

Internally, a generator stores a suspended execution state that keeps track of the current position in the iteration. When iterating over a generator, the __next__ method resumes the suspended function and yields the next value. The execution is then suspended again until the next iteration.

Use Cases

• Use Iterators:

  • When you need to maintain state across iterations (e.g., a custom iterator with complex behavior).
  • When you need to expose additional methods besides iteration (e.g., a class with current() and next() methods).

• Use Generators:

  • When simplicity and efficiency are priorities.
  • When you want to generate values lazily, without storing an intermediate list.
  • When you want to pause and resume the iteration (e.g., suspending a computation for later use).

Example

Consider the following function that generates square numbers for a given range:

def squares(start, stop):
    for i in range(start, stop):
        yield i * i

This function creates a generator that yields square numbers one at a time. It's more efficient than a list comprehension or a custom iterator, as it avoids creating an intermediate list of all the squared values.

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