What functionality does the yield
keyword in Python provide?
For example, I'm trying to understand this code1:
def _get_child_candidates(self, distance, min_dist, max_dist):
if self._leftchild and distance - max_dist < self._median:
yield self._leftchild
if self._rightchild and distance + max_dist >= self._median:
yield self._rightchild
And this is the caller:
result, candidates = [], [self]
while candidates:
node = candidates.pop()
distance = node._get_dist(obj)
if distance <= max_dist and distance >= min_dist:
result.extend(node._values)
candidates.extend(node._get_child_candidates(distance, min_dist, max_dist))
return result
What happens when the method _get_child_candidates
is called?
Is a list returned? A single element? Is it called again? When will subsequent calls stop?
1. This piece of code was written by Jochen Schulz (jrschulz), who made a great Python library for metric spaces. This is the link to the complete source: Module mspace.
Best Answer
To understand what
yield
does, you must understand what generators are. And before you can understand generators, you must understand iterables.Iterables
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
Generators are iterators, a kind of iterable you can only iterate over once. Generators 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 cannot performfor 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 after calculating 4, one by one.Yield
yield
is a keyword that is used likereturn
, 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 continue from where it left off each time
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 hitsyield
, then it'll return the first value of the loop. Then, each subsequent call will run another iteration of the loop you have written in the function and return the next value. This will continue until the generator is considered empty, which happens when the function runs without hittingyield
. That can be because the loop has come to an end, or because you no longer satisfy an"if/else"
.Your code explained
Generator:
Caller:
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, butwhile
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:
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 reasons why Python is so cool. But this is another story, for another question...
You can stop here, or read a little bit to see an advanced use of a generator:
Controlling a generator exhaustion
Note: For Python 3, use
print(corner_street_atm.__next__())
orprint(next(corner_street_atm))
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?Then just
import itertools
.An example? Let's see the possible orders of arrival for a four-horse race:
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.There is more about it in this article about how
for
loops work.