When you create a list, you can read its items one by one. Reading its items one by one is called iteration:
mylist is an iterable. When you use a list comprehension, you create a list, and so an iterable:
Everything you can use "for. in. " on is an iterable: lists. strings. files.
These iterables are handy because you can read them as much as you wish, but you store all the values in memory and this is not always what you want when you have a lot of values.
Generators are iterators, but you can only iterate over them once. It's because they do not store all the values in memory, they generate the values on the fly :
It is just the same except you used () instead of . BUT, you can not perform for i in mygenerator a second time since generators can only be used once: they calculate 0, then forget about it and calculate 1, and end calculating 4, one by one.
Yield is a keyword that is used like return. except the function will return a generator.
Here it's a useless example, but it's handy when you know your function will return a huge set of values that you will only need to read once.
To master yield. you must understand that when you call the function, the code you have written in the function body does not run. The function only returns the generator object, this is a bit tricky :-)
Then, your code will be run each time the for uses the generator.
Now the hard part:
The first time the for calls the generator object created from your function, it will run the code in your function from the beginning until it hits yield. then it'll return the first value of the loop. Then, each other call will run the loop you have written in the function one more time, and return the next value, until there is no value to return.
The generator is considered empty once the function runs but does not hit yield anymore. It can be because the loop had
come to an end, or because you do not satisfy a "if/else" anymore.
Your code explained
This code contains several smart parts:
The loop iterates on a list but the list expands while the loop is being iterated :-) It's a concise way to go through all these nested data even if it's a bit dangerous since you can end up with an infinite loop. In this case, candidates.extend(node._get_child_candidates(distance, min_dist, max_dist)) exhausts all the values of the generator, but while keeps creating new generator objects which will produce different values from the previous ones since it's not applied on the same node.
The extend() method is a list object method that expects an iterable and adds its values to the list.
Usually we pass a list to it:
But in your code it gets a generator, which is good because:
- You don't need to read the values twice.
- You can have a lot of children and you don't want them all stored in memory.
And it works because Python does not care if the argument of a method is a list or not. Python expects iterables so it will work with strings, lists, tuples and generators! This is called duck typing and is one of the reason why Python is so cool. But this is another story, for another question.
You can stop here, or read a little bit to see a advanced use of generator:
Controlling a generator exhaustion
It can be useful for various things like controlling access to a resource.
Itertools, your best friend
The itertools module contains special functions to manipulate iterables. Ever wish to duplicate a generator? Chain two generators? Group values in a nested list with a one liner? Map / Zip without creating another list?
Understanding the inner mechanisms of iteration
Iteration is a process implying iterables (implementing the __iter__() method) and iterators (implementing the __next__() method). Iterables are any objects you can get an iterator from. Iterators are objects that let you iterate on iterables.
More about it in this article about how does the for loop work .