Iterators and Generators: Fundamental Techniques for Efficient Data Processing in Python

Python iterators and generators are used to handle large or infinite data, avoiding loading all data into memory at once and improving efficiency. An iterator is an object that implements the `__iter__` and `__next__` methods, allowing forward-only iteration (non-repeatable). It can be converted from iterable objects like lists using `iter()`, and elements are obtained with `next()`. Generators are special iterators that are more concise and efficient, divided into generator functions (using the `yield` keyword) and generator expressions (parentheses). For example, a generator function can generate the Fibonacci sequence, while an expression like `(x**2 for x in range(10))` does not generate all elements at once, making it far more memory-efficient than list comprehensions. The core difference is that iterators require manual implementation of iteration logic, whereas generators automate this process; generators also offer higher memory efficiency. They are suitable for scenarios like large data streams and infinite sequences. Mastering them optimizes memory usage, making them a key Python technique for data processing.