Building Custom Iterators
Explore how to develop custom iterators in Python by following the iterator protocol using __iter__ and __next__. Understand managing iteration state, signaling completion with StopIteration, and when to use class-based iterators versus generators for complex scenarios. This lesson equips you to create iterable objects that integrate seamlessly with Python's for loops.
Iteration in Python relies on two methods defined by the iterator protocol. By implementing these methods, we can create custom objects that maintain their own iteration state while remaining iterable like built-in types such as lists.
At this point, we move beyond merely using Python’s iteration infrastructure. We learn how to extend it, designing objects that integrate naturally with the language itself.
The iterator protocol in classes
To make an object compatible with a for loop, it must follow the iterator protocol. This protocol requires two special dunder methods:
__iter__(): This is the entry point. When we start a loop, Python calls this method to get the iterator object.__next__(): This is the engine. Python calls this repeatedly to fetch the next item in the sequence.
When we write for item in object:, Python is actually performing a specific sequence of actions behind the scenes. It asks the object for an iterator, and then repeatedly asks that iterator for the next value until the iterator signals that it is empty.
Implementing __next__and managing state
Unlike a list, an iterator does not store all of its values in memory. It produces values on demand, one at a time. To do this ...