python in - What does the “yield” keyword do?





example 2.7 (26)


To understand what yield does, you must understand what generators are. And before generators come 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 createGenerator():
...    mylist = range(3)
...    for i in mylist:
...        yield i*i
...
>>> mygenerator = createGenerator() # create a generator
>>> print(mygenerator) # mygenerator is an object!
<generator object createGenerator 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 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 an "if/else" anymore.


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 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 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 candidates 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)) 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:

>>> 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 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 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.

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?


1. The code comes from Jochen Schulz (jrschulz), who made a great Python library for metric spaces. This is the link to the complete source: Module mspace.




Many people use return rather than yield, but in some cases yield can be more efficient and easier to work with.

Here is an example which yield is definitely best for:

return (in function)

import random

def return_dates():
    dates = [] # With 'return' you need to create a list then return it
    for i in range(5):
        date = random.choice(["1st", "2nd", "3rd", "4th", "5th", "6th", "7th", "8th", "9th", "10th"])
        dates.append(date)
    return dates

yield (in function)

def yield_dates():
    for i in range(5):
        date = random.choice(["1st", "2nd", "3rd", "4th", "5th", "6th", "7th", "8th", "9th", "10th"])
        yield date # 'yield' makes a generator automatically which works
                   # in a similar way. This is much more efficient.

Calling functions

dates_list = return_dates()
print(dates_list)
for i in dates_list:
    print(i)

dates_generator = yield_dates()
print(dates_generator)
for i in dates_generator:
    print(i)

Both functions do the same thing, but yield uses three lines instead of five and has one less variable to worry about.

This is the result from the code:

As you can see both functions do the same thing. The only difference is return_dates() gives a list and yield_dates() gives a generator.

A real life example would be something like reading a file line by line or if you just want to make a generator.




yield is just like return - it returns whatever you tell it to (as a generator). The difference is that the next time you call the generator, execution starts from the last call to the yield statement. Unlike return, the stack frame is not cleaned up when a yield occurs, however control is transferred back to the caller, so its state will resume the next time the function.

In the case of your code, the function get_child_candidates is acting like an iterator so that when you extend your list, it adds one element at a time to the new list.

list.extend calls an iterator until it's exhausted. In the case of the code sample you posted, it would be much clearer to just return a tuple and append that to the list.




Here is a simple example:

def isPrimeNumber(n):
    print "isPrimeNumber({}) call".format(n)
    if n==1:
        return False
    for x in range(2,n):
        if n % x == 0:
            return False
    return True

def primes (n=1):
    while(True):
        print "loop step ---------------- {}".format(n)
        if isPrimeNumber(n): yield n
        n += 1

for n in primes():
    if n> 10:break
    print "wiriting result {}".format(n)

Output:

loop step ---------------- 1
isPrimeNumber(1) call
loop step ---------------- 2
isPrimeNumber(2) call
loop step ---------------- 3
isPrimeNumber(3) call
wiriting result 3
loop step ---------------- 4
isPrimeNumber(4) call
loop step ---------------- 5
isPrimeNumber(5) call
wiriting result 5
loop step ---------------- 6
isPrimeNumber(6) call
loop step ---------------- 7
isPrimeNumber(7) call
wiriting result 7
loop step ---------------- 8
isPrimeNumber(8) call
loop step ---------------- 9
isPrimeNumber(9) call
loop step ---------------- 10
isPrimeNumber(10) call
loop step ---------------- 11
isPrimeNumber(11) call

I am not a Python developer, but it looks to me yield holds the position of program flow and the next loop start from "yield" position. It seems like it is waiting at that position, and just before that, returning a value outside, and next time continues to work.

It seems to be an interesting and nice ability :D




All great answers, however a bit difficult for newbies.

I assume you have learned the return statement.

As an analogy, return and yield are twins. return means 'return and stop' whereas 'yield` means 'return, but continue'

  1. Try to get a num_list with return.
def num_list(n):
    for i in range(n):
        return i

Run it:

In [5]: num_list(3)
Out[5]: 0

See, you get only a single number rather than a list of them. return never allows you prevail happily, just implements once and quit.

  1. There comes yield

Replace return with yield:

In [10]: def num_list(n):
    ...:     for i in range(n):
    ...:         yield i
    ...:

In [11]: num_list(3)
Out[11]: <generator object num_list at 0x10327c990>

In [12]: list(num_list(3))
Out[12]: [0, 1, 2]

Now, you win to get all the numbers.

Comparing to return which runs once and stops, yield runs times you planed. You can interpret return as return one of them, and yield as return all of them. This is called iterable.

  1. One more step we can rewrite yield statement with return
In [15]: def num_list(n):
    ...:     result = []
    ...:     for i in range(n):
    ...:         result.append(i)
    ...:     return result

In [16]: num_list(3)
Out[16]: [0, 1, 2]

It's the core about yield.

The difference between a list return outputs and the object yield output is:

You will always get [0, 1, 2] from a list object but only could retrieve them from 'the object yield output' once. So, it has a new name generator object as displayed in Out[11]: <generator object num_list at 0x10327c990>.

In conclusion, as a metaphor to grok it:

  • return and yield are twins
  • list and generator are twins



While a lot of answers show why you'd use a yield to create a generator, there are more uses for yield. It's quite easy to make a coroutine, which enables the passing of information between two blocks of code. I won't repeat any of the fine examples that have already been given about using yield to create a generator.

To help understand what a yield does in the following code, you can use your finger to trace the cycle through any code that has a yield. Every time your finger hits the yield, you have to wait for a next or a send to be entered. When a next is called, you trace through the code until you hit the yield… the code on the right of the yield is evaluated and returned to the caller… then you wait. When next is called again, you perform another loop through the code. However, you'll note that in a coroutine, yield can also be used with a send… which will send a value from the caller into the yielding function. If a send is given, then yield receives the value sent, and spits it out the left hand side… then the trace through the code progresses until you hit the yield again (returning the value at the end, as if next was called).

For example:

>>> def coroutine():
...     i = -1
...     while True:
...         i += 1
...         val = (yield i)
...         print("Received %s" % val)
...
>>> sequence = coroutine()
>>> sequence.next()
0
>>> sequence.next()
Received None
1
>>> sequence.send('hello')
Received hello
2
>>> sequence.close()



Shortcut to Grokking 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 original definition.

This trick may give you an idea of the logic behind the function, but what actually happens with yield is significantly different that 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 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.




Like every answer suggests, yield is used for creating a sequence generator. It's used for generating some sequence dynamically. For example, while reading a file line by line on a network, you can use the yield function as follows:

def getNextLines():
   while con.isOpen():
       yield con.read()

You can use it in your code as follows:

for line in getNextLines():
    doSomeThing(line)

Execution Control Transfer gotcha

The execution control will be transferred from getNextLines() to the for loop when yield is executed. Thus, every time getNextLines() is invoked, execution begins from the point where it was paused last time.

Thus in short, a function with the following code

def simpleYield():
    yield "first time"
    yield "second time"
    yield "third time"
    yield "Now some useful value {}".format(12)

for i in simpleYield():
    print i

will print

"first time"
"second time"
"third time"
"Now some useful value 12"



Here are some Python examples of how to actually implement generators as if Python did not provide syntactic sugar for them:

As a Python generator:

from itertools import islice

def fib_gen():
    a, b = 1, 1
    while True:
        yield a
        a, b = b, a + b

assert [1, 1, 2, 3, 5] == list(islice(fib_gen(), 5))

Using lexical closures instead of generators

def ftake(fnext, last):
    return [fnext() for _ in xrange(last)]

def fib_gen2():
    #funky scope due to python2.x workaround
    #for python 3.x use nonlocal
    def _():
        _.a, _.b = _.b, _.a + _.b
        return _.a
    _.a, _.b = 0, 1
    return _

assert [1,1,2,3,5] == ftake(fib_gen2(), 5)

Using object closures instead of generators (because ClosuresAndObjectsAreEquivalent)

class fib_gen3:
    def __init__(self):
        self.a, self.b = 1, 1

    def __call__(self):
        r = self.a
        self.a, self.b = self.b, self.a + self.b
        return r

assert [1,1,2,3,5] == ftake(fib_gen3(), 5)



There is another yield use and meaning (since Python 3.3):

yield from <expr>

From PEP 380 -- Syntax for Delegating to a Subgenerator:

A syntax is proposed for a generator to delegate part of its operations to another generator. This allows a section of code containing 'yield' to be factored out and placed in another generator. Additionally, the subgenerator is allowed to return with a value, and the value is made available to the delegating generator.

The new syntax also opens up some opportunities for optimisation when one generator re-yields values produced by another.

Moreover this will introduce (since Python 3.5):

async def new_coroutine(data):
   ...
   await blocking_action()

to avoid coroutines being confused with a regular generator (today yield is used in both).




The yield keyword simply collects returning results. Think of yield like return +=




I was going to post "read page 19 of Beazley's 'Python: Essential Reference' for a quick description of generators", but so many others have posted good descriptions already.

Also, note that yield can be used in coroutines as the dual of their use in generator functions. Although it isn't the same use as your code snippet, (yield) can be used as an expression in a function. When a caller sends a value to the method using the send() method, then the coroutine will execute until the next (yield) statement is encountered.

Generators and coroutines are a cool way to set up data-flow type applications. I thought it would be worthwhile knowing about the other use of the yield statement in functions.




It's returning a generator. I'm not particularly familiar with Python, but I believe it's the same kind of thing as C#'s iterator blocks if you're familiar with those.

The key idea is that the compiler/interpreter/whatever does some trickery so that as far as the caller is concerned, they can keep calling next() and it will keep returning values - as if the generator method was paused. Now obviously you can't really "pause" a method, so the compiler builds a state machine for you to remember where you currently are and what the local variables etc look like. This is much easier than writing an iterator yourself.




(My below answer only speaks from the perspective of using Python generator, not the underlying implementation of generator mechanism, which involves some tricks of stack and heap manipulation.)

When yield is used instead of a return in a python function, that function is turned into something special called generator function. That function will return an object of generator type. The yield keyword is a flag to notify the python compiler to treat such function specially. Normal functions will terminate once some value is returned from it. But with the help of the compiler, the generator function can be thought of as resumable. That is, the execution context will be restored and the execution will continue from last run. Until you explicitly call return, which will raise a StopIteration exception (which is also part of the iterator protocol), or reach the end of the function. I found a lot of references about generator but this one from the functional programming perspective is the most digestable.

(Now I want to talk about the rationale behind generator, and the iterator based on my own understanding. I hope this can help you grasp the essential motivation of iterator and generator. Such concept shows up in other languages as well such as C#.)

As I understand, when we want to process a bunch of data, we usually first store the data somewhere and then process it one by one. But this intuitive approach is problematic. If the data volume is huge, it's expensive to store them as a whole beforehand. So instead of storing the data itself directly, why not store some kind of metadata indirectly, i.e. the logic how the data is computed.

There are 2 approaches to wrap such metadata.

  1. The OO approach, we wrap the metadata as a class. This is the so-called iterator who implements the iterator protocol (i.e. the __next__(), and __iter__() methods). This is also the commonly seen iterator design pattern.
  2. The functional approach, we wrap the metadata as a function. This is the so-called generator function. But under the hood, the returned generator object still IS-A iterator because it also implements the iterator protocol.

Either way, an iterator is created, i.e. some object that can give you the data you want. The OO approach may be a bit complex. Anyway, which one to use is up to you.




The yield keyword is reduced to two simple facts:

  1. If the compiler detects the yield keyword anywhere inside a function, that function no longer returns via the return statement. Instead, it immediately returns a lazy "pending list" object called a generator
  2. A generator is iterable. What is an iterable? It's anything like a list or set or range or dict-view, with a built-in protocol for visiting each element in a certain order.

In a nutshell: a generator is a lazy, incrementally-pending list, and yield statements allow you to use function notation to program the list values the generator should incrementally spit out.

generator = myYieldingFunction(...)
x = list(generator)

   generator
       v
[x[0], ..., ???]

         generator
             v
[x[0], x[1], ..., ???]

               generator
                   v
[x[0], x[1], x[2], ..., ???]

                       StopIteration exception
[x[0], x[1], x[2]]     done

list==[x[0], x[1], x[2]]

Example

Let's define a function makeRange that's just like Python's range. Calling makeRange(n) RETURNS A GENERATOR:

def makeRange(n):
    # return 0,1,2,...,n-1
    i = 0
    while i < n:
        yield i
        i += 1

>>> makeRange(5)
<generator object makeRange at 0x19e4aa0>

To force the generator to immediately return its pending values, you can pass it into list() (just like you could any iterable):

>>> list(makeRange(5))
[0, 1, 2, 3, 4]

Comparing example to "just returning a list"

The above example can be thought of as merely creating a list which you append to and return:

# list-version                   #  # generator-version
def makeRange(n):                #  def makeRange(n):
    """return [0,1,2,...,n-1]""" #~     """return 0,1,2,...,n-1"""
    TO_RETURN = []               #>
    i = 0                        #      i = 0
    while i < n:                 #      while i < n:
        TO_RETURN += [i]         #~         yield i
        i += 1                   #          i += 1  ## indented
    return TO_RETURN             #>

>>> makeRange(5)
[0, 1, 2, 3, 4]

There is one major difference, though; see the last section.


How you might use generators

An iterable is the last part of a list comprehension, and all generators are iterable, so they're often used like so:

#                   _ITERABLE_
>>> [x+10 for x in makeRange(5)]
[10, 11, 12, 13, 14]

To get a better feel for generators, you can play around with the itertools module (be sure to use chain.from_iterable rather than chain when warranted). For example, you might even use generators to implement infinitely-long lazy lists like itertools.count(). You could implement your own def enumerate(iterable): zip(count(), iterable), or alternatively do so with the yield keyword in a while-loop.

Please note: generators can actually be used for many more things, such as implementing coroutines or non-deterministic programming or other elegant things. However, the "lazy lists" viewpoint I present here is the most common use you will find.


Behind the scenes

This is how the "Python iteration protocol" works. That is, what is going on when you do list(makeRange(5)). This is what I describe earlier as a "lazy, incremental list".

>>> x=iter(range(5))
>>> next(x)
0
>>> next(x)
1
>>> next(x)
2
>>> next(x)
3
>>> next(x)
4
>>> next(x)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration

The built-in function next() just calls the objects .next() function, which is a part of the "iteration protocol" and is found on all iterators. You can manually use the next() function (and other parts of the iteration protocol) to implement fancy things, usually at the expense of readability, so try to avoid doing that...


Minutiae

Normally, most people would not care about the following distinctions and probably want to stop reading here.

In Python-speak, an iterable is any object which "understands the concept of a for-loop" like a list [1,2,3], and an iterator is a specific instance of the requested for-loop like [1,2,3].__iter__(). A generator is exactly the same as any iterator, except for the way it was written (with function syntax).

When you request an iterator from a list, it creates a new iterator. However, when you request an iterator from an iterator (which you would rarely do), it just gives you a copy of itself.

Thus, in the unlikely event that you are failing to do something like this...

> x = myRange(5)
> list(x)
[0, 1, 2, 3, 4]
> list(x)
[]

... then remember that a generator is an iterator; that is, it is one-time-use. If you want to reuse it, you should call myRange(...) again. If you need to use the result twice, convert the result to a list and store it in a variable x = list(myRange(5)). Those who absolutely need to clone a generator (for example, who are doing terrifyingly hackish metaprogramming) can use itertools.tee if absolutely necessary, since the copyable iterator Python PEP standards proposal has been deferred.




Yield gives you a generator.

def get_odd_numbers(i):
    return range(1, i, 2)
def yield_odd_numbers(i):
    for x in range(1, i, 2):
       yield x
foo = get_odd_numbers(10)
bar = yield_odd_numbers(10)
foo
[1, 3, 5, 7, 9]
bar
<generator object yield_odd_numbers at 0x1029c6f50>
bar.next()
1
bar.next()
3
bar.next()
5

As you can see, in the first case foo holds the entire list in memory at once. It's not a big deal for a list with 5 elements, but what if you want a list of 5 million? Not only is this a huge memory eater, it also costs a lot of time to build at the time that the function is called. In the second case, bar just gives you a generator. A generator is an iterable--which means you can use it in a for loop, etc, but each value can only be accessed once. All the values are also not stored in memory at the same time; the generator object "remembers" where it was in the looping the last time you called it--this way, if you're using an iterable to (say) count to 50 billion, you don't have to count to 50 billion all at once and store the 50 billion numbers to count through. Again, this is a pretty contrived example, you probably would use itertools if you really wanted to count to 50 billion. :)

This is the most simple use case of generators. As you said, it can be used to write efficient permutations, using yield to push things up through the call stack instead of using some sort of stack variable. Generators can also be used for specialized tree traversal, and all manner of other things.




For those who prefer a minimal working example, meditate on this interactive Python session:

>>> def f():
...   yield 1
...   yield 2
...   yield 3
... 
>>> g = f()
>>> for i in g:
...   print i
... 
1
2
3
>>> for i in g:
...   print i
... 
>>> # Note that this time nothing was printed



Yield is an object

A return in a function will return a single value.

If you want a function to return a huge set of values, use yield.

More importantly, yield is a barrier.

like barrier in the CUDA language, it will not transfer control until it gets completed.

That is, 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, every other call will run the loop you have written in the function one more time, returning the next value until there isn't any value to return.




Here is a mental image of what yield does.

I like to think of a thread as having a stack (even when it's not implemented that way).

When a normal function is called, it puts its local variables on the stack, does some computation, then clears the stack and returns. The values of its local variables are never seen again.

With a yield function, when its code begins to run (i.e. after the function is called, returning a generator object, whose next() method is then invoked), it similarly puts its local variables onto the stack and computes for a while. But then, when it hits the yield statement, before clearing its part of the stack and returning, it takes a snapshot of its local variables and stores them in the generator object. It also writes down the place where it's currently up to in its code (i.e. the particular yield statement).

So it's a kind of a frozen function that the generator is hanging onto.

When next() is called subsequently, it retrieves the function's belongings onto the stack and re-animates it. The function continues to compute from where it left off, oblivious to the fact that it had just spent an eternity in cold storage.

Compare the following examples:

def normalFunction():
    return
    if False:
        pass

def yielderFunction():
    return
    if False:
        yield 12

When we call the second function, it behaves very differently to the first. The yield statement might be unreachable, but if it's present anywhere, it changes the nature of what we're dealing with.

>>> yielderFunction()
<generator object yielderFunction at 0x07742D28>

Calling yielderFunction() doesn't run its code, but makes a generator out of the code. (Maybe it's a good idea to name such things with the yielder prefix for readability.)

>>> gen = yielderFunction()
>>> dir(gen)
['__class__',
 ...
 '__iter__',    #Returns gen itself, to make it work uniformly with containers
 ...            #when given to a for loop. (Containers return an iterator instead.)
 'close',
 'gi_code',
 'gi_frame',
 'gi_running',
 'next',        #The method that runs the function's body.
 'send',
 'throw']

The gi_code and gi_frame fields are where the frozen state is stored. Exploring them with dir(..), we can confirm that our mental model above is credible.




yield is like a return element for a function. The difference is, that the yield element turns a function into a generator. A generator behaves just like a function until something is 'yielded'. The generator stops until it is next called, and continues from exactly the same point as it started. You can get a sequence of all the 'yielded' values in one, by calling list(generator()).




TL;DR

Instead of this:

def squares_list(n):
    the_list = []                         # Replace
    for x in range(n):
        y = x * x
        the_list.append(y)                # these
    return the_list                       # lines

do this:

def squares_the_yield_way(n):
    for x in range(n):
        y = x * x
        yield y                           # with this one.

Whenever you find yourself building a list from scratch, yield each piece instead.

This was my first "aha" moment with yield.


yield is a sugary way to say

build a series of stuff

Same behavior:

>>> for square in squares_list(4):
...     print(square)
...
0
1
4
9
>>> for square in squares_the_yield_way(4):
...     print(square)
...
0
1
4
9

Different behavior:

Yield is single-pass: you can only iterate through once. When a function has a yield in it we call it a generator function. And an iterator is what it returns. That's revealing. We lose the convenience of a container, but gain the power of an arbitrarily long series.

Yield is lazy, it puts off computation. A function with a yield in it doesn't actually execute at all when you call it. The iterator object it returns uses magic to maintain the function's internal context. Each time you call next() on the iterator (this happens in a for-loop) execution inches forward to the next yield. (return raises StopIteration and ends the series.)

Yield is versatile. It can do infinite loops:

>>> def squares_all_of_them():
...     x = 0
...     while True:
...         yield x * x
...         x += 1
...
>>> squares = squares_all_of_them()
>>> for _ in range(4):
...     print(next(squares))
...
0
1
4
9

If you need multiple passes and the series isn't too long, just call list() on it:

>>> list(squares_the_yield_way(4))
[0, 1, 4, 9]

Brilliant choice of the word yield because both meanings apply:

yield — produce or provide (as in agriculture)

...provide the next data in the series.

yield — give way or relinquish (as in political power)

...relinquish CPU execution until the iterator advances.




There is one type of answer that I don't feel has been given yet, among the many great answers that describe how to use generators. Here is the programming language theory answer:

The yield statement in Python returns a generator. A generator in Python is a function that returns continuations (and specifically a type of coroutine, but continuations represent the more general mechanism to understand what is going on).

Continuations in programming languages theory are a much more fundamental kind of computation, but they are not often used, because they are extremely hard to reason about and also very difficult to implement. But the idea of what a continuation is, is straightforward: it is the state of a computation that has not yet finished. In this state, the current values of variables, the operations that have yet to be performed, and so on, are saved. Then at some point later in the program the continuation can be invoked, such that the program's variables are reset to that state and the operations that were saved are carried out.

Continuations, in this more general form, can be implemented in two ways. In the call/cc way, the program's stack is literally saved and then when the continuation is invoked, the stack is restored.

In continuation passing style (CPS), continuations are just normal functions (only in languages where functions are first class) which the programmer explicitly manages and passes around to subroutines. In this style, program state is represented by closures (and the variables that happen to be encoded in them) rather than variables that reside somewhere on the stack. Functions that manage control flow accept continuation as arguments (in some variations of CPS, functions may accept multiple continuations) and manipulate control flow by invoking them by simply calling them and returning afterwards. A very simple example of continuation passing style is as follows:

def save_file(filename):
  def write_file_continuation():
    write_stuff_to_file(filename)

  check_if_file_exists_and_user_wants_to_overwrite(write_file_continuation)

In this (very simplistic) example, the programmer saves the operation of actually writing the file into a continuation (which can potentially be a very complex operation with many details to write out), and then passes that continuation (i.e, as a first-class closure) to another operator which does some more processing, and then calls it if necessary. (I use this design pattern a lot in actual GUI programming, either because it saves me lines of code or, more importantly, to manage control flow after GUI events trigger.)

The rest of this post will, without loss of generality, conceptualize continuations as CPS, because it is a hell of a lot easier to understand and read.


Now let's talk about generators in Python. Generators are a specific subtype of continuation. Whereas continuations are able in general to save the state of a computation (i.e., the program's call stack), generators are only able to save the state of iteration over an iterator. Although, this definition is slightly misleading for certain use cases of generators. For instance:

def f():
  while True:
    yield 4

This is clearly a reasonable iterable whose behavior is well defined -- each time the generator iterates over it, it returns 4 (and does so forever). But it isn't probably the prototypical type of iterable that comes to mind when thinking of iterators (i.e., for x in collection: do_something(x)). This example illustrates the power of generators: if anything is an iterator, a generator can save the state of its iteration.

To reiterate: Continuations can save the state of a program's stack and generators can save the state of iteration. This means that continuations are more a lot powerful than generators, but also that generators are a lot, lot easier. They are easier for the language designer to implement, and they are easier for the programmer to use (if you have some time to burn, try to read and understand this page about continuations and call/cc).

But you could easily implement (and conceptualize) generators as a simple, specific case of continuation passing style:

Whenever yield is called, it tells the function to return a continuation. When the function is called again, it starts from wherever it left off. So, in pseudo-pseudocode (i.e., not pseudocode, but not code) the generator's next method is basically as follows:

class Generator():
  def __init__(self,iterable,generatorfun):
    self.next_continuation = lambda:generatorfun(iterable)

  def next(self):
    value, next_continuation = self.next_continuation()
    self.next_continuation = next_continuation
    return value

where the yield keyword is actually syntactic sugar for the real generator function, basically something like:

def generatorfun(iterable):
  if len(iterable) == 0:
    raise StopIteration
  else:
    return (iterable[0], lambda:generatorfun(iterable[1:]))

Remember that this is just pseudocode and the actual implementation of generators in Python is more complex. But as an exercise to understand what is going on, try to use continuation passing style to implement generator objects without use of the yield keyword.




In summary, the yield statement transforms your function into a factory that produces a special object called a generator which wraps around the body of your original function. When the generator is iterated, it executes your function until it reaches the next yield then suspends execution and evaluates to the value passed to yield. It repeats this process on each iteration until the path of execution exits the function. For instance,

def simple_generator():
    yield 'one'
    yield 'two'
    yield 'three'

for i in simple_generator():
    print i

simply outputs

one
two
three

The power comes from using the generator with a loop that calculates a sequence, the generator executes the loop stopping each time to 'yield' the next result of the calculation, in this way it calculates a list on the fly, the benefit being the memory saved for especially large calculations

Say you wanted to create a your own range function that produces an iterable range of numbers, you could do it like so,

def myRangeNaive(i):
    n = 0
    range = []
    while n < i:
        range.append(n)
        n = n + 1
    return range

and use it like this;

for i in myRangeNaive(10):
    print i

But this is inefficient because

  • You create an array that you only use once (this wastes memory)
  • This code actually loops over that array twice! :(

Luckily Guido and his team were generous enough to develop generators so we could just do this;

def myRangeSmart(i):
    n = 0
    while n < i:
       yield n
       n = n + 1
    return

for i in myRangeSmart(10):
    print i

Now upon each iteration a function on the generator called next() executes the function until it either reaches a 'yield' statement in which it stops and 'yields' the value or reaches the end of the function. In this case on the first call, next() executes up to the yield statement and yield 'n', on the next call it will execute the increment statement, jump back to the 'while', evaluate it, and if true, it will stop and yield 'n' again, it will continue that way until the while condition returns false and the generator jumps to the end of the function.




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:

Original version:

def some_function():
    for i in xrange(4):
        yield i

for 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 StopIteration

def 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. :)




From a programming viewpoint, the iterators are implemented as thunks.

To implement iterators, generators, and thread pools for concurrent execution, etc. as thunks (also called anonymous functions), one uses messages sent to a closure object, which has a dispatcher, and the dispatcher answers to "messages".

http://en.wikipedia.org/wiki/Message_passing

"next" is a message sent to a closure, created by the "iter" call.

There are lots of ways to implement this computation. I used mutation, but it is easy to do it without mutation, by returning the current value and the next yielder.

Here is a demonstration which uses the structure of R6RS, but the semantics is absolutely identical to Python's. It's the same model of computation, and only a change in syntax is required to rewrite it in Python.

Welcome to Racket v6.5.0.3.

-> (define gen
     (lambda (l)
       (define yield
         (lambda ()
           (if (null? l)
               'END
               (let ((v (car l)))
                 (set! l (cdr l))
                 v))))
       (lambda(m)
         (case m
           ('yield (yield))
           ('init  (lambda (data)
                     (set! l data)
                     'OK))))))
-> (define stream (gen '(1 2 3)))
-> (stream 'yield)
1
-> (stream 'yield)
2
-> (stream 'yield)
3
-> (stream 'yield)
'END
-> ((stream 'init) '(a b))
'OK
-> (stream 'yield)
'a
-> (stream 'yield)
'b
-> (stream 'yield)
'END
-> (stream 'yield)
'END
->



Tells the iterator that it's reached the end.

As an example:

public interface INode
{
    IEnumerable<Node> GetChildren();
}

public class NodeWithTenChildren : INode
{
    private Node[] m_children = new Node[10];

    public IEnumerable<Node> GetChildren()
    {
        for( int n = 0; n < 10; ++n )
        {
            yield return m_children[ n ];
        }
    }
}

public class NodeWithNoChildren : INode
{
    public IEnumerable<Node> GetChildren()
    {
        yield break;
    }
}






python iterator generator yield coroutine