values - yield in python example




What does the “yield” keyword do? (20)

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.


What does the yield keyword do in Python?

Answer Outline/Summary

  • A function with yield, when called, returns a Generator.
  • Generators are iterators because they implement the iterator protocol, so you can iterate over them.
  • A generator can also be sent information, making it conceptually a coroutine.
  • In Python 3, you can delegate from one generator to another in both directions with yield from.
  • (Appendix critiques a couple of answers, including the top one, and discusses the use of return in a generator.)

Generators:

yield is only legal inside of a function definition, and the inclusion of yield in a function definition makes it return a generator.

The idea for generators comes from other languages (see footnote 1) with varying implementations. In Python's Generators, the execution of the code is frozen at the point of the yield. When the generator is called (methods are discussed below) execution resumes and then freezes at the next yield.

yield provides an easy way of implementing the iterator protocol, defined by the following two methods: __iter__ and next (Python 2) or __next__ (Python 3). Both of those methods make an object an iterator that you could type-check with the Iterator Abstract Base Class from the collections module.

>>> def func():
...     yield 'I am'
...     yield 'a generator!'
... 
>>> type(func)                 # A function with yield is still a function
<type 'function'>
>>> gen = func()
>>> type(gen)                  # but it returns a generator
<type 'generator'>
>>> hasattr(gen, '__iter__')   # that's an iterable
True
>>> hasattr(gen, 'next')       # and with .next (.__next__ in Python 3)
True                           # implements the iterator protocol.

The generator type is a sub-type of iterator:

>>> import collections, types
>>> issubclass(types.GeneratorType, collections.Iterator)
True

And if necessary, we can type-check like this:

>>> isinstance(gen, types.GeneratorType)
True
>>> isinstance(gen, collections.Iterator)
True

A feature of an Iterator is that once exhausted, you can't reuse or reset it:

>>> list(gen)
['I am', 'a generator!']
>>> list(gen)
[]

You'll have to make another if you want to use its functionality again (see footnote 2):

>>> list(func())
['I am', 'a generator!']

One can yield data programmatically, for example:

def func(an_iterable):
    for item in an_iterable:
        yield item

The above simple generator is also equivalent to the below - as of Python 3.3 (and not available in Python 2), you can use yield from:

def func(an_iterable):
    yield from an_iterable

However, yield from also allows for delegation to subgenerators, which will be explained in the following section on cooperative delegation with sub-coroutines.

Coroutines:

yield forms an expression that allows data to be sent into the generator (see footnote 3)

Here is an example, take note of the received variable, which will point to the data that is sent to the generator:

def bank_account(deposited, interest_rate):
    while True:
        calculated_interest = interest_rate * deposited 
        received = yield calculated_interest
        if received:
            deposited += received


>>> my_account = bank_account(1000, .05)

First, we must queue up the generator with the builtin function, next. It will call the appropriate next or __next__ method, depending on the version of Python you are using:

>>> first_year_interest = next(my_account)
>>> first_year_interest
50.0

And now we can send data into the generator. (Sending None is the same as calling next.) :

>>> next_year_interest = my_account.send(first_year_interest + 1000)
>>> next_year_interest
102.5

Cooperative Delegation to Sub-Coroutine with yield from

Now, recall that yield from is available in Python 3. This allows us to delegate coroutines to a subcoroutine:

def money_manager(expected_rate):
    under_management = yield     # must receive deposited value
    while True:
        try:
            additional_investment = yield expected_rate * under_management 
            if additional_investment:
                under_management += additional_investment
        except GeneratorExit:
            '''TODO: write function to send unclaimed funds to state'''
        finally:
            '''TODO: write function to mail tax info to client'''


def investment_account(deposited, manager):
    '''very simple model of an investment account that delegates to a manager'''
    next(manager) # must queue up manager
    manager.send(deposited)
    while True:
        try:
            yield from manager
        except GeneratorExit:
            return manager.close()

And now we can delegate functionality to a sub-generator and it can be used by a generator just as above:

>>> my_manager = money_manager(.06)
>>> my_account = investment_account(1000, my_manager)
>>> first_year_return = next(my_account)
>>> first_year_return
60.0
>>> next_year_return = my_account.send(first_year_return + 1000)
>>> next_year_return
123.6

You can read more about the precise semantics of yield from in PEP 380.

Other Methods: close and throw

The close method raises GeneratorExit at the point the function execution was frozen. This will also be called by __del__ so you can put any cleanup code where you handle the GeneratorExit:

>>> my_account.close()

You can also throw an exception which can be handled in the generator or propagated back to the user:

>>> import sys
>>> try:
...     raise ValueError
... except:
...     my_manager.throw(*sys.exc_info())
... 
Traceback (most recent call last):
  File "<stdin>", line 4, in <module>
  File "<stdin>", line 2, in <module>
ValueError

Conclusion

I believe I have covered all aspects of the following question:

What does the yield keyword do in Python?

It turns out that yield does a lot. I'm sure I could add even more thorough examples to this. If you want more or have some constructive criticism, let me know by commenting below.


Appendix:

Critique of the Top/Accepted Answer**

  • It is confused on what makes an iterable, just using a list as an example. See my references above, but in summary: an iterable has an __iter__ method returning an iterator. An iterator provides a .next (Python 2 or .__next__ (Python 3) method, which is implicitly called by for loops until it raises StopIteration, and once it does, it will continue to do so.
  • It then uses a generator expression to describe what a generator is. Since a generator is simply a convenient way to create an iterator, it only confuses the matter, and we still have not yet gotten to the yield part.
  • In Controlling a generator exhaustion he calls the .next method, when instead he should use the builtin function, next. It would be an appropriate layer of indirection, because his code does not work in Python 3.
  • Itertools? This was not relevant to what yield does at all.
  • No discussion of the methods that yield provides along with the new functionality yield from in Python 3. The top/accepted answer is a very incomplete answer.

Critique of answer suggesting yield in a generator expression or comprehension.

The grammar currently allows any expression in a list comprehension.

expr_stmt: testlist_star_expr (annassign | augassign (yield_expr|testlist) |
                     ('=' (yield_expr|testlist_star_expr))*)
...
yield_expr: 'yield' [yield_arg]
yield_arg: 'from' test | testlist

Since yield is an expression, it has been touted by some as interesting to use it in comprehensions or generator expression - in spite of citing no particularly good use-case.

The CPython core developers are discussing deprecating its allowance. Here's a relevant post from the mailing list:

On 30 January 2017 at 19:05, Brett Cannon wrote:

On Sun, 29 Jan 2017 at 16:39 Craig Rodrigues wrote:

I'm OK with either approach. Leaving things the way they are in Python 3 is no good, IMHO.

My vote is it be a SyntaxError since you're not getting what you expect from the syntax.

I'd agree that's a sensible place for us to end up, as any code relying on the current behaviour is really too clever to be maintainable.

In terms of getting there, we'll likely want:

  • SyntaxWarning or DeprecationWarning in 3.7
  • Py3k warning in 2.7.x
  • SyntaxError in 3.8

Cheers, Nick.

-- Nick Coghlan | ncoghlan at gmail.com | Brisbane, Australia

Further, there is an outstanding issue (10544) which seems to be pointing in the direction of this never being a good idea (PyPy, a Python implementation written in Python, is already raising syntax warnings.)

Bottom line, until the developers of CPython tell us otherwise: Don't put yield in a generator expression or comprehension.

The return statement in a generator

In Python 2:

In a generator function, the return statement is not allowed to include an expression_list. In that context, a bare return indicates that the generator is done and will cause StopIteration to be raised.

An expression_list is basically any number of expressions separated by commas - essentially, in Python 2, you can stop the generator with return, but you can't return a value.

In Python 3:

In a generator function, the return statement indicates that the generator is done and will cause StopIteration to be raised. The returned value (if any) is used as an argument to construct StopIteration and becomes the StopIteration.value attribute.

Footnotes

  1. The languages CLU, Sather, and Icon were referenced in the proposal to introduce the concept of generators to Python. The general idea is that a function can maintain internal state and yield intermediate data points on demand by the user. This promised to be superior in performance to other approaches, including Python threading, which isn't even available on some systems.

  2. This means, for example, that xrange objects (range in Python 3) aren't Iterators, even though they are iterable, because they can be reused. Like lists, their __iter__ methods return iterator objects.

  3. yield was originally introduced as a statement, meaning that it could only appear at the beginning of a line in a code block. Now yield creates a yield expression. https://docs.python.org/2/reference/simple_stmts.html#grammar-token-yield_stmt This change was proposed to allow a user to send data into the generator just as one might receive it. To send data, one must be able to assign it to something, and for that, a statement just won't work.


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.


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.


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()).


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.


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

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
->

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)

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


Here is an example in plain language. I will provide a correspondence between high-level human concepts to low-level Python concepts.

I want to operate on a sequence of numbers, but I don't want to bother my self with the creation of that sequence, I want only to focus on the operation I want to do. So, I do the following:

  • I call you and tell you that I want a sequence of numbers which is produced in a specific way, and I let you know what the algorithm is.
    This step corresponds to defining the generator function, i.e. the function containing a yield.
  • Sometime later, I tell you, "OK, get ready to tell me the sequence of numbers".
    This step corresponds to calling the generator function which returns a generator object. Note that you don't tell me any numbers yet; you just grab your paper and pencil.
  • I ask you, "tell me the next number", and you tell me the first number; after that, you wait for me to ask you for the next number. It's your job to remember where you were, what numbers you have already said, and what is the next number. I don't care about the details.
    This step corresponds to calling .next() on the generator object.
  • … repeat previous step, until…
  • eventually, you might come to an end. You don't tell me a number; you just shout, "hold your horses! I'm done! No more numbers!"
    This step corresponds to the generator object ending its job, and raising a StopIteration exception The generator function does not need to raise the exception. It's raised automatically when the function ends or issues a return.

This is what a generator does (a function that contains a yield); it starts executing, pauses whenever it does a yield, and when asked for a .next() value it continues from the point it was last. It fits perfectly by design with the iterator protocol of Python, which describes how to sequentially request values.

The most famous user of the iterator protocol is the for command in Python. So, whenever you do a:

for item in sequence:

it doesn't matter if sequence is a list, a string, a dictionary or a generator object like described above; the result is the same: you read items off a sequence one by one.

Note that defining a function which contains a yield keyword is not the only way to create a generator; it's just the easiest way to create one.

For more accurate information, read about iterator types, the yield statement and generators in the Python documentation.


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.


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.


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"

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.


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


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).


There's one extra thing to mention: a function that yields doesn't actually have to terminate. I've written code like this:

def fib():
    last, cur = 0, 1
    while True: 
        yield cur
        last, cur = cur, last + cur

Then I can use it in other code like this:

for f in fib():
    if some_condition: break
    coolfuncs(f);

It really helps simplify some problems, and makes some things easier to work with.


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. :)


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()

Yet another TL;DR

Iterator on list: next() returns the next element of the list

Iterator generator: next() will compute the next element on the fly (execute code)

You can see the yield/generator as a way to manually run the control flow from outside (like continue loop one step), by calling next, however complex the flow.

Note: The generator is NOT a normal function. It remembers the previous state like local variables (stack). See other answers or articles for detailed explanation. The generator can only be iterated on once. You could do without yield, but it would not be as nice, so it can be considered 'very nice' language sugar.





coroutine