What does the "yield" keyword do?

Question

What is the use of the yield keyword in Python? What does it do?

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?

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_{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.}

Answer 1

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 = [1, 2, 3] >>> for i in mylist: ...    print(i) 1 2 3 

mylist is an iterable. When you use a list comprehension, you create a list, and so an iterable:

>>> mylist = [x*x for x in range(3)] >>> for i in mylist: ...    print(i) 0 1 4 

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:

>>> mygenerator = (x*x for x in range(3)) >>> for i in mygenerator: ...    print(i) 0 1 4 

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

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 generator >>> print(mygenerator) # mygenerator is an object! <generator object create_generator at 0xb7555c34> >>> for i in mygenerator: ...     print(i) 0 1 4 

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 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 "if/else".

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Your code explained

Generator:

# Here you create the method of the node object that will return the generator def _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 

Caller:

# Create an empty list and a list with the current object reference result, 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 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:

>>> a = [1, 2] >>> b = [3, 4] >>> a.extend(b) >>> print(a) [1, 2, 3, 4] 

But in your code, it gets a generator, which is good because:

1. You don't need to read the values twice.
2. 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 want >>> corner_street_atm = hsbc.create_atm() >>> print(corner_street_atm.next()) $100 >>> print(corner_street_atm.next()) $100 >>> print([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 ATMs >>> print(wall_street_atm.next()) <type 'exceptions.StopIteration'> >>> hsbc.crisis = False # The trouble is, even post-crisis the ATM remains empty >>> print(corner_street_atm.next()) <type 'exceptions.StopIteration'> >>> brand_new_atm = hsbc.create_atm() # Build a new one to get back in business >>> for cash in brand_new_atm: ...    print cash $100 $100 $100 $100 $100 $100 $100 $100 $100 ... 

Note: For Python 3, useprint(corner_street_atm.__next__()) or print(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:

>>> horses = [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)] 

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.

Answer 2

Shortcut to understanding yield

When you see a function with yield statements, apply this easy trick to understand what will happen:

1. Insert a line result = [] at the start of the function.
2. Replace each yield expr with result.append(expr).
3. Insert a line return result at the bottom of the function.
4. Yay - no more yield statements! Read and figure out code.
5. 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:

1. Gets an iterator for mylist:

   Call iter(mylist) -> this returns an object with a next() method (or __next__() in Python 3).

   [This is the step most people forget to tell you about]

2. 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 x and the loop body is executed. If an exception StopIteration is 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.extend(mylist) (where otherlist is a Python list).

Here 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 __iter__() and next() on the same class, and have __iter__() return 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:

1. Built-in lists, dictionaries, tuples, sets, files.
2. User-defined classes that implement __iter__().
3. Generators.

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 3  for item in f123():     print item 

Instead of 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 __iter__() and 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() (or __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.