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SAVE THE WORLD CODE GENERATOR GENERATOR
SAVE THE WORLD CODE GENERATOR PLUS

To restart the process, we’d need to create another generator object This also means that the iterator can only be iterated through once.

When the function terminates, StopIteration is raised automatically on further calls.

As was mentioned before, yield merely pauses the function and the control is transferred to the calling block this implies that local variables and their values are maintained between successive calls.

We can, as such, iterate through the elements using next()

Methods like _iter_() and _next_() are automatically implemented.

We can use the next() function or for / while loops to iterate over it and only then does the generator function start to execute However, calling the function does not start its execution.

As a generator function, it returns a generator object.

If it did, it would immediately become a generator function A regular function contains no such statements.

The generator function contains one or more yield statements.It maintains the states of the function so that later it can resume from that point, should it be called once again.Ī generator function features some basic differences from a regular function: yield acts like a pause button on a music player.return terminates the function’s execution.The difference arises from the effect these two each have on the function’s execution flow: In terms of values returned, both yield and return act the same. But it does have the twist of using yield statements, as opposed to regular functions, which usually use the return statement. Now, a generator being nothing more than a function, it’s fairly easy to create one. And I guess the simplest way I could describe the generators is this: a function that returns a generator object, which we can then use in our iteration process. Now let’s point out that all of that functionality is achievable with even less effort from our side, via these generators.

SAVE THE WORLD CODE GENERATOR PLUS

We saw how we needed to create and use a class that implemented both _iter_() and _next_() methods, plus we had to keep a close eye on the situation where we run out of elements and to raise a StopIteration exception as a direct consequence. We’ve seen how building an iterator in Python isn’t exactly the most straightforward thing to do. As a natural next step, as we’ll come to see in just a few moments, following the last article where we discovered and went over the topic of iterators, today we’ll take on another very important topic related to Python: generators.