Explain generators and iterators and when they should be used.

In Python, an iterator is any object that implements the __iter__() and __next__() methods, allowing you to traverse a sequence one element at a time. A generator is a special type of iterator created using a function with the yield statement. Generators produce values lazily, generating each item only when requested, which saves memory and can handle infinite or very large sequences. Use generators when you need to process large datasets, stream data, or implement pipelines where intermediate results are not needed all at once. They are ideal for reading large files line by line, streaming network data, or creating lazy sequences in functional programming. Generators also simplify code by eliminating the need to create and manage explicit iterator objects. In contrast, regular iterators (like lists or tuples) load all data into memory, which can be inefficient for large collections. Therefore, choose generators for memory‑efficient, on‑demand iteration, and use standard iterables when the entire collection is needed or when random access is required.