What does the "yield" keyword do?
To understand what
yield does, you must understand what generators are. And before you can understand generators, you must understand iterables.
When you create a list, you can read its items one by one. Reading its items one by one is called iteration:
1, 2, 3]for i in mylist: print(i)123mylist = [
mylist is an iterable. When you use a list comprehension, you create a list, and so an iterable:
for x in range(3)]for i in mylist: print(i)014mylist = [x*x
Everything you can use "
for... in..." on is an iterable;
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, 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:
for x in range(3))for i in mygenerator: print(i)014mygenerator = (x*x
It is just the same except you used
() instead of
. BUT, you cannot 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.
def create_generator(): mylist = range(3) for i in mylist: yield i*i... mygenerator = create_generator() # create a generatorprint(mygenerator) # mygenerator is an object!<generator object create_generator at 0xb7555c34>for i in mygenerator: print(i)014
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.
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 hits
yield, 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 hitting
yield. That can be because the loop has come to an end, or because you no longer satisfy an
Your code explained
# Here you create the method of the node object that will return the generatordef _get_child_candidates(self, distance, min_dist, max_dist): # Here is the code that will be called each time you use the generator object: # If there is still a child of the node object on its left # AND if the distance is ok, return the next child if self._leftchild and distance - max_dist < self._median: yield self._leftchild # If there is still a child of the node object on its right # AND if the distance is ok, return the next child if self._rightchild and distance + max_dist >= self._median: yield self._rightchild # If the function arrives here, the generator will be considered empty # there is no more than two values: the left and the right children
# Create an empty list and a list with the current object referenceresult, candidates = list(), [self]# Loop on candidates (they contain only one element at the beginning)while candidates: # Get the last candidate and remove it from the list node = candidates.pop() # Get the distance between obj and the candidate distance = node._get_dist(obj) # If distance is ok, then you can fill the result if distance <= max_dist and distance >= min_dist: result.extend(node._values) # Add the children of the candidate in the candidate's list # so the loop will keep running until it will have looked # at all the children of the children of the children, etc. of the candidate candidates.extend(node._get_child_candidates(distance, min_dist, max_dist))return result
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))exhaust all the values of the generator, but
whilekeeps creating new generator objects which will produce different values from the previous ones since it's not applied on the same node.
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:
1, 2] b = [3, 4] a.extend(b)print(a)[1, 2, 3, 4]a = [
But in your code, it gets a generator, which is good because:
- You don't need to read the values twice.
- You may 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 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
class Bank(): # Let's create a bank, building ATMs crisis = False def create_atm(self): while not self.crisis: yield "$100"hsbc = Bank() # When everything's ok the ATM gives you as much as you wantcorner_street_atm = hsbc.create_atm()print(corner_street_atm.next())$100print(corner_street_atm.next())$100print([corner_street_atm.next() for cash in range(5)])['$100', '$100', '$100', '$100', '$100'] hsbc.crisis = True # Crisis is coming, no more money!print(corner_street_atm.next())<type 'exceptions.StopIteration'> wall_street_atm = hsbc.create_atm() # It's even true for new ATMsprint(wall_street_atm.next())<type 'exceptions.StopIteration'> hsbc.crisis = False # The trouble is, even post-crisis the ATM remains emptyprint(corner_street_atm.next())<type 'exceptions.StopIteration'> brand_new_atm = hsbc.create_atm() # Build a new one to get back in businessfor cash in brand_new_atm: print cash$100$100$100$100$100$100$100$100$100...
Note: For Python 3, use
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?
An example? Let's see the possible orders of arrival for a four-horse race:
1, 2, 3, 4] races = itertools.permutations(horses)print(races)<itertools.permutations object at 0xb754f1dc>print(list(itertools.permutations(horses)))[(1, 2, 3, 4), (1, 2, 4, 3), (1, 3, 2, 4), (1, 3, 4, 2), (1, 4, 2, 3), (1, 4, 3, 2), (2, 1, 3, 4), (2, 1, 4, 3), (2, 3, 1, 4), (2, 3, 4, 1), (2, 4, 1, 3), (2, 4, 3, 1), (3, 1, 2, 4), (3, 1, 4, 2), (3, 2, 1, 4), (3, 2, 4, 1), (3, 4, 1, 2), (3, 4, 2, 1), (4, 1, 2, 3), (4, 1, 3, 2), (4, 2, 1, 3), (4, 2, 3, 1), (4, 3, 1, 2), (4, 3, 2, 1)]horses = [
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.
Shortcut to understanding
When you see a function with
yield statements, apply this easy trick to understand what will happen:
- Insert a line
result = at the start of the function.
- Replace each
- Insert a line
return resultat the bottom of the function.
- Yay - no more
yieldstatements! Read and figure out code.
- Compare function to the original definition.
This trick may give you an idea of the logic behind the function, but what actually happens with
yield is significantly different than what happens in the list based approach. In many cases, the yield approach will be a lot more memory efficient and faster too. In other cases, this trick will get you stuck in an infinite loop, even though the original function works just fine. Read on to learn more...
Don't confuse your Iterables, Iterators, and Generators
First, the iterator protocol - when you write
for x in mylist: ...loop body...
Python performs the following two steps:
Gets an iterator for
iter(mylist)-> this returns an object with a
__next__()in Python 3).
[This is the step most people forget to tell you about]
Uses the iterator to loop over items:
Keep calling the
next()method on the iterator returned from step 1. The return value from
next()is assigned to
xand the loop body is executed. If an exception
StopIterationis raised from within
next(), it means there are no more values in the iterator and the loop is exited.
The truth is Python performs the above two steps anytime it wants to loop over the contents of an object - so it could be a for loop, but it could also be code like
otherlist is a Python list).
mylist is an iterable because it implements the iterator protocol. In a user-defined class, you can implement the
__iter__() method to make instances of your class iterable. This method should return an iterator. An iterator is an object with a
next() method. It is possible to implement both
next() on the same class, and have
self. This will work for simple cases, but not when you want two iterators looping over the same object at the same time.
So that's the iterator protocol, many objects implement this protocol:
- Built-in lists, dictionaries, tuples, sets, files.
- User-defined classes that implement
Note that a
for loop doesn't know what kind of object it's dealing with - it just follows the iterator protocol, and is happy to get item after item as it calls
next(). Built-in lists return their items one by one, dictionaries return the keys one by one, files return the lines one by one, etc. And generators return... well that's where
yield comes in:
def f123(): yield 1 yield 2 yield 3for item in f123(): print item
yield statements, if you had three
return statements in
f123() only the first would get executed, and the function would exit. But
f123() is no ordinary function. When
f123() is called, it does not return any of the values in the yield statements! It returns a generator object. Also, the function does not really exit - it goes into a suspended state. When the
for loop tries to loop over the generator object, the function resumes from its suspended state at the very next line after the
yield it previously returned from, executes the next line of code, in this case, a
yield statement, and returns that as the next item. This happens until the function exits, at which point the generator raises
StopIteration, and the loop exits.
So the generator object is sort of like an adapter - at one end it exhibits the iterator protocol, by exposing
next() methods to keep the
for loop happy. At the other end, however, it runs the function just enough to get the next value out of it, and puts it back in suspended mode.
Why Use Generators?
Usually, you can write code that doesn't use generators but implements the same logic. One option is to use the temporary list 'trick' I mentioned before. That will not work in all cases, for e.g. if you have infinite loops, or it may make inefficient use of memory when you have a really long list. The other approach is to implement a new iterable class SomethingIter that keeps the state in instance members and performs the next logical step in it's
__next__() in Python 3) method. Depending on the logic, the code inside the
next() method may end up looking very complex and be prone to bugs. Here generators provide a clean and easy solution.
Think of it this way:
An iterator is just a fancy sounding term for an object that has a
next() method. So a yield-ed function ends up being something like this:
def some_function(): for i in xrange(4): yield ifor i in some_function(): print i
This is basically what the Python interpreter does with the above code:
class it: def __init__(self): # Start at -1 so that we get 0 when we add 1 below. self.count = -1 # The __iter__ method will be called once by the 'for' loop. # The rest of the magic happens on the object returned by this method. # In this case it is the object itself. def __iter__(self): return self # The next method will be called repeatedly by the 'for' loop # until it raises StopIteration. def next(self): self.count += 1 if self.count < 4: return self.count else: # A StopIteration exception is raised # to signal that the iterator is done. # This is caught implicitly by the 'for' loop. raise StopIterationdef some_func(): return it()for i in some_func(): print i
For more insight as to what's happening behind the scenes, the
for loop can be rewritten to this:
iterator = some_func()try: while 1: print iterator.next()except StopIteration: pass
Does that make more sense or just confuse you more? :)
I should note that this is an oversimplification for illustrative purposes. :)