What does the "yield" keyword do in Python?

By

#python #iterator #generator #yield

Solution 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 after 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".

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 &lt; 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 &gt;= self._median:
    yield self._rightchild

# If the function arrives here, the generator will be considered empty
# There are 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 the distance is ok, then you can fill in the result
if distance &lt;= max_dist and distance &gt;= min_dist:
    result.extend(node._values)

# Add the children of the candidate to the candidate's list
# so the loop will keep running until it has 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 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.

Solution 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 the code.
  5. Compare the 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, and 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 its next() (or __next__() in Python 3) method. Depending on the logic, the code inside the next() method may end up looking very complex and prone to bugs. Here generators provide a clean and easy solution.

Solution 3

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. :)

Solution 4

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, set, range, dictionary view, or any other object with a built-in protocol for visiting each element in a certain order.

In a nutshell: Most commonly, 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. Furthermore, advanced usage lets you use generators as coroutines (see below).

generator = myYieldingFunction(...)  # basically a list (but lazy)
x = list(generator)  # evaluate every element into a list

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

Basically, whenever the yield statement is encountered, the function pauses and saves its state, then emits "the next return value in the 'list'" according to the python iterator protocol (to some syntactic construct like a for-loop that repeatedly calls next() and catches a StopIteration exception, etc.). You might have encountered generators with generator expressions; generator functions are more powerful because you can pass arguments back into the paused generator function, using them to implement coroutines. More on that later.

Basic Example ('list')

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 the example to "just returning a list"

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

# return a list                  #  # return a generator
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
    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, non-deterministic programming, and 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)  # calls x.__next__(); x.next() is deprecated
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...

Coroutines

Coroutine example:

def interactiveProcedure():
    userResponse = yield makeQuestionWebpage()
    print('user response:', userResponse)
    yield 'success'

coroutine = interactiveProcedure()
webFormData = next(coroutine)  # same as .send(None)
userResponse = serveWebForm(webFormData)


…at some point later on web form submit…


successStatus = coroutine.send(userResponse)

A coroutine (generators that generally accept input via the yield keyword e.g. nextInput = yield nextOutput, as a form of two-way communication) is basically a computation that is allowed to pause itself and request input (e.g. to what it should do next). When the coroutine pauses itself (when the running coroutine eventually hits a yield keyword), the computation is paused and control is inverted (yielded) back to the 'calling' function (the frame which requested the next value of the computation). The paused generator/coroutine remains paused until another invoking function (possibly a different function/context) requests the next value to unpause it (usually passing input data to direct the paused logic interior to the coroutine's code).

You can think of Python coroutines as lazy incrementally-pending lists, where the next element doesn't just depend on the previous computation but also on input that you may opt to inject during the generation process.

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 (still works in Python 3) if absolutely necessary, since the copyable iterator Python PEP standards proposal has been deferred.

Solution 5

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__. 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!'

Let's do some introspection:

>>> 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__
True                           # implements the iterator protocol.

The generator type is a sub-type of iterator:

from types import GeneratorType
from collections.abc import Iterator

>>> issubclass(GeneratorType, Iterator)
True

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

>>> isinstance(gen, GeneratorType)
True
>>> isinstance(gen, 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 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 built-in 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):
    # must receive deposited value from .send():
    under_management = yield                   # yield None to start.
    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'''
            raise
        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'''
# must queue up manager:
next(manager)      # <- same as manager.send(None)
# This is where we send the initial deposit to the manager:
manager.send(deposited)
try:
yield from manager
except GeneratorExit:
return manager.close()  # delegate?

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) # -> 60.0

Now simulate adding another 1,000 to the account plus the return on the account (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())

Raises:

Traceback (most recent call last):
  File "<stdin>", line 4, in <module>
  File "<stdin>", line 6, in money_manager
  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 about 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 additionally provides a .__next__ method, which is implicitly called by for loops until it raises StopIteration, and once it does raise StopIteration, it will continue to do so.
  • It then uses a generator expression to describe what a generator is. Since a generator expression 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 (which only works in Python 2), when instead he should use the built-in function, next. Calling next(obj) 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 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.

Historical note, 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.

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 an 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 range objects 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.