Where to use yield in Python best?

Where to use yield in Python best? - python

I know how yield works. I know permutation, think it just as a math simplicity.
But what's yield's true force? When should I use it? A simple and good example is better.

yield is best used when you have a function that returns a sequence and you want to iterate over that sequence, but you do not need to have every value in memory at once.
For example, I have a python script that parses a large list of CSV files, and I want to return each line to be processed in another function. I don't want to store the megabytes of data in memory all at once, so I yield each line in a python data structure. So the function to get lines from the file might look something like:
def get_lines(files):
for f in files:
for line in f:
#preprocess line
yield line
I can then use the same syntax as with lists to access the output of this function:
for line in get_lines(files):
#process line
but I save a lot of memory usage.

Simply put, yield gives you a generator. You'd use it where you would normally use a return in a function. As a really contrived example cut and pasted from a prompt...
>>> 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>
>>> next(bar)
1
>>> next(bar)
3
>>> next(bar)
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.
Further reading:
python wiki http://wiki.python.org/moin/Generators
PEP on generators http://www.python.org/dev/peps/pep-0255/

Another use is in a network client. Use 'yield' in a generator function to round-robin through multiple sockets without the complexity of threads.
For example, I had a hardware test client that needed to send a R,G,B planes of an image to firmware. The data needed to be sent in lockstep: red, green, blue, red, green, blue. Rather than spawn three threads, I had a generator that read from the file, encoded the buffer. Each buffer was a 'yield buf'. End of file, function returned and I had end-of-iteration.
My client code looped through the three generator functions, getting buffers until end-of-iteration.

I'm reading Data Structures and Algorithms in Python
There is a fibonacci function using yield. I think it's the best moment to use yield.
def fibonacci():
a, b = 0, 1
while True:
yield a
a, b = b, a+b
you can use this like:
gen = fibonacci()
for i, f in enumerate(gen):
print(i, f)
if i >= 100: break
So, I think, maybe, when the next element is depending on previous elements, e.g., digital filters, it's time to use yield.

Related

Evaluation of Functions as arguments [duplicate]

Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
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. This piece of code was written by Jochen Schulz (jrschulz), who made a great Python library for metric spaces. This is the link to the complete source: Module mspace.

To understand what yield does, you must understand what generators are. And before you can understand generators, you must understand iterables.
Iterables
When you create a list, you can read its items one by one. Reading its items one by one is called iteration:
>>> mylist = [1, 2, 3]
>>> for i in mylist:
... print(i)
1
2
3
mylist is an iterable. When you use a list comprehension, you create a list, and so an iterable:
>>> mylist = [x*x for x in range(3)]
>>> for i in mylist:
... print(i)
0
1
4
Everything you can use "for... in..." on is an iterable; lists, strings, files...
These iterables are handy because you can read them as much as you wish, but you store all the values in memory and this is not always what you want when you have a lot of values.
Generators
Generators are iterators, a kind of iterable you can only iterate over once. Generators do not store all the values in memory, they generate the values on the fly:
>>> mygenerator = (x*x for x in range(3))
>>> for i in mygenerator:
... print(i)
0
1
4
It is just the same except you used () instead of []. BUT, you cannot perform for i in mygenerator a second time since generators can only be used once: they calculate 0, then forget about it and calculate 1, and end calculating 4, one by one.
Yield
yield is a keyword that is used like return, except the function will return a generator.
>>> def create_generator():
... mylist = range(3)
... for i in mylist:
... yield i*i
...
>>> mygenerator = create_generator() # create a generator
>>> print(mygenerator) # mygenerator is an object!
<generator object create_generator at 0xb7555c34>
>>> for i in mygenerator:
... print(i)
0
1
4
Here it's a useless example, but it's handy when you know your function will return a huge set of values that you will only need to read once.
To master yield, you must understand that when you call the function, the code you have written in the function body does not run. The function only returns the generator object, this is a bit tricky.
Then, your code will continue from where it left off each time for uses the generator.
Now the hard part:
The first time the for calls the generator object created from your function, it will run the code in your function from the beginning until it hits yield, then it'll return the first value of the loop. Then, each subsequent call will run another iteration of the loop you have written in the function and return the next value. This will continue until the generator is considered empty, which happens when the function runs without hitting yield. That can be because the loop has come to an end, or because you no longer satisfy an "if/else".
Your code explained
Generator:
# Here you create the method of the node object that will return the generator
def _get_child_candidates(self, distance, min_dist, max_dist):
# Here is the code that will be called each time you use the generator object:
# If there is still a child of the node object on its left
# AND if the distance is ok, return the next child
if self._leftchild and distance - max_dist < self._median:
yield self._leftchild
# If there is still a child of the node object on its right
# AND if the distance is ok, return the next child
if self._rightchild and distance + max_dist >= self._median:
yield self._rightchild
# If the function arrives here, the generator will be considered empty
# there 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 <= max_dist and distance >= 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:
You don't need to read the values twice.
You may have a lot of children and you don't want them all stored in memory.
And it works because Python does not care if the argument of a method is a list or not. Python expects iterables so it will work with strings, lists, tuples, and generators! This is called duck typing and is one of the reasons why Python is so cool. But this is another story, for another question...
You can stop here, or read a little bit to see an advanced use of a generator:
Controlling a generator exhaustion
>>> class Bank(): # Let's create a bank, building ATMs
... crisis = False
... def create_atm(self):
... while not self.crisis:
... yield "$100"
>>> hsbc = Bank() # When everything's ok the ATM gives you as much as you 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.

Shortcut to understanding yield
When you see a function with yield statements, apply this easy trick to understand what will happen:
Insert a line result = [] at the start of the function.
Replace each yield expr with result.append(expr).
Insert a line return result at the bottom of the function.
Yay - no more yield statements! Read and figure out the code.
Compare function to the original definition.
This trick may give you an idea of the logic behind the function, but what actually happens with yield is significantly different than what happens in the list-based approach. In many cases, the yield approach will be a lot more memory efficient and faster too. In other cases, this trick will get you stuck in an infinite loop, even though the original function works just fine. Read on to learn more...
Don't confuse your Iterables, Iterators, and Generators
First, the iterator protocol - when you write
for x in mylist:
...loop body...
Python performs the following two steps:
Gets an iterator for 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]
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:
Built-in lists, dictionaries, tuples, sets, and files.
User-defined classes that implement __iter__().
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.

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. :)

The yield keyword is reduced to two simple facts:
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
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: 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 example to "just returning a list"
The above example can be thought of as merely creating a list which 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 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) # 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 which generally accept input via the yield keyword e.g. nextInput = yield nextOutput, as a form of two-way communication) is basically a computation which 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 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.

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 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):
# 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 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 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 builtin 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
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.
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.
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.

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 is called.
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.

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.

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

TL;DR
Instead of this:
def square_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 square_yield(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 square_list(4):
... print(square)
...
0
1
4
9
>>> for square in square_yield(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. Those terms are revealing. We lose the convenience of a container, but gain the power of a series that's computed as needed, and arbitrarily long.
Yield is lazy, it puts off computation. A function with a yield in it doesn't actually execute at all when you call it. It returns an iterator object that remembers where it left off. 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 (this is the natural end of a for-loop).
Yield is versatile. Data doesn't have to be stored all together, it can be made available one at a time. It can be infinite.
>>> 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(square_yield(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.

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.

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.

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.

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 are calculated 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(generator) on the generator object.
(In Python 2, .next was a method of the generator object; in Python 3, it is named .__next__, but the proper way to call it is using the builtin next() function just like len() and .__len__)
… 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 on the first next(), pauses whenever it does a yield, and when asked for the 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.

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()

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).

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'
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.
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.
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, one uses messages sent to a closure object, which has a dispatcher, and the dispatcher answers to "messages".
"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 possible to do this kind of computation without mutation, by returning the current value and the next yielder (making it referential transparent). Racket uses a sequence of transformations of the initial program in some intermediary languages, one of such rewriting making the yield operator to be transformed in some language with simpler operators.
Here is a demonstration of how yield could be rewritten, which uses the structure of R6RS, but the semantics is identical to Python's. It's the same model of computation, and only a change in syntax is required to rewrite it using yield of 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)

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.

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 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.

Imagine that you have created a remarkable machine that is capable of generating thousands and thousands of lightbulbs per day. The machine generates these lightbulbs in boxes with a unique serial number. You don't have enough space to store all of these lightbulbs at the same time, so you would like to adjust it to generate lightbulbs on-demand.
Python generators don't differ much from this concept. Imagine that you have a function called barcode_generator that generates unique serial numbers for the boxes. Obviously, you can have a huge number of such barcodes returned by the function, subject to the hardware (RAM) limitations. A wiser, and space efficient, option is to generate those serial numbers on-demand.
Machine's code:
def barcode_generator():
serial_number = 10000 # Initial barcode
while True:
yield serial_number
serial_number += 1
barcode = barcode_generator()
while True:
number_of_lightbulbs_to_generate = int(input("How many lightbulbs to generate? "))
barcodes = [next(barcode) for _ in range(number_of_lightbulbs_to_generate)]
print(barcodes)
# function_to_create_the_next_batch_of_lightbulbs(barcodes)
produce_more = input("Produce more? [Y/n]: ")
if produce_more == "n":
break
Note the next(barcode) bit.
As you can see, we have a self-contained “function” to generate the next unique serial number each time. This function returns a generator! As you can see, we are not calling the function each time we need a new serial number, but instead we are using next() given the generator to obtain the next serial number.
Lazy Iterators
To be more precise, this generator is a lazy iterator! An iterator is an object that helps us traverse a sequence of objects. It's called lazy because it does not load all the items of the sequence in memory until they are needed. The use of next in the previous example is the explicit way to obtain the next item from the iterator. The implicit way is using for loops:
for barcode in barcode_generator():
print(barcode)
This will print barcodes infinitely, yet you will not run out of memory.
In other words, a generator looks like a function but behaves like an iterator.
Real-world application?
Finally, real-world applications? They are usually useful when you work with big sequences. Imagine reading a huge file from disk with billions of records. Reading the entire file in memory, before you can work with its content, will probably be infeasible (i.e., you will run out of memory).

An easy example to understand what it is: yield
def f123():
for _ in range(4):
yield 1
yield 2
for i in f123():
print (i)
The output is:
1 2 1 2 1 2 1 2

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"

(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 naive 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.
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.
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.

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.

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.

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 +=

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()).

How do you call on a function that has yield in repl in python? [duplicate]

Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
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. This piece of code was written by Jochen Schulz (jrschulz), who made a great Python library for metric spaces. This is the link to the complete source: Module mspace.

To understand what yield does, you must understand what generators are. And before you can understand generators, you must understand iterables.
Iterables
When you create a list, you can read its items one by one. Reading its items one by one is called iteration:
>>> mylist = [1, 2, 3]
>>> for i in mylist:
... print(i)
1
2
3
mylist is an iterable. When you use a list comprehension, you create a list, and so an iterable:
>>> mylist = [x*x for x in range(3)]
>>> for i in mylist:
... print(i)
0
1
4
Everything you can use "for... in..." on is an iterable; lists, strings, files...
These iterables are handy because you can read them as much as you wish, but you store all the values in memory and this is not always what you want when you have a lot of values.
Generators
Generators are iterators, a kind of iterable you can only iterate over once. Generators do not store all the values in memory, they generate the values on the fly:
>>> mygenerator = (x*x for x in range(3))
>>> for i in mygenerator:
... print(i)
0
1
4
It is just the same except you used () instead of []. BUT, you cannot perform for i in mygenerator a second time since generators can only be used once: they calculate 0, then forget about it and calculate 1, and end calculating 4, one by one.
Yield
yield is a keyword that is used like return, except the function will return a generator.
>>> def create_generator():
... mylist = range(3)
... for i in mylist:
... yield i*i
...
>>> mygenerator = create_generator() # create a generator
>>> print(mygenerator) # mygenerator is an object!
<generator object create_generator at 0xb7555c34>
>>> for i in mygenerator:
... print(i)
0
1
4
Here it's a useless example, but it's handy when you know your function will return a huge set of values that you will only need to read once.
To master yield, you must understand that when you call the function, the code you have written in the function body does not run. The function only returns the generator object, this is a bit tricky.
Then, your code will continue from where it left off each time for uses the generator.
Now the hard part:
The first time the for calls the generator object created from your function, it will run the code in your function from the beginning until it hits yield, then it'll return the first value of the loop. Then, each subsequent call will run another iteration of the loop you have written in the function and return the next value. This will continue until the generator is considered empty, which happens when the function runs without hitting yield. That can be because the loop has come to an end, or because you no longer satisfy an "if/else".
Your code explained
Generator:
# Here you create the method of the node object that will return the generator
def _get_child_candidates(self, distance, min_dist, max_dist):
# Here is the code that will be called each time you use the generator object:
# If there is still a child of the node object on its left
# AND if the distance is ok, return the next child
if self._leftchild and distance - max_dist < self._median:
yield self._leftchild
# If there is still a child of the node object on its right
# AND if the distance is ok, return the next child
if self._rightchild and distance + max_dist >= self._median:
yield self._rightchild
# If the function arrives here, the generator will be considered empty
# there 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 <= max_dist and distance >= 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:
You don't need to read the values twice.
You may have a lot of children and you don't want them all stored in memory.
And it works because Python does not care if the argument of a method is a list or not. Python expects iterables so it will work with strings, lists, tuples, and generators! This is called duck typing and is one of the reasons why Python is so cool. But this is another story, for another question...
You can stop here, or read a little bit to see an advanced use of a generator:
Controlling a generator exhaustion
>>> class Bank(): # Let's create a bank, building ATMs
... crisis = False
... def create_atm(self):
... while not self.crisis:
... yield "$100"
>>> hsbc = Bank() # When everything's ok the ATM gives you as much as you 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.

Shortcut to understanding yield
When you see a function with yield statements, apply this easy trick to understand what will happen:
Insert a line result = [] at the start of the function.
Replace each yield expr with result.append(expr).
Insert a line return result at the bottom of the function.
Yay - no more yield statements! Read and figure out the code.
Compare function to the original definition.
This trick may give you an idea of the logic behind the function, but what actually happens with yield is significantly different than what happens in the list-based approach. In many cases, the yield approach will be a lot more memory efficient and faster too. In other cases, this trick will get you stuck in an infinite loop, even though the original function works just fine. Read on to learn more...
Don't confuse your Iterables, Iterators, and Generators
First, the iterator protocol - when you write
for x in mylist:
...loop body...
Python performs the following two steps:
Gets an iterator for 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]
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:
Built-in lists, dictionaries, tuples, sets, and files.
User-defined classes that implement __iter__().
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.

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. :)

The yield keyword is reduced to two simple facts:
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
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: 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 example to "just returning a list"
The above example can be thought of as merely creating a list which 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 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) # 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 which generally accept input via the yield keyword e.g. nextInput = yield nextOutput, as a form of two-way communication) is basically a computation which 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 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.

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 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):
# 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 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 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 builtin 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
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.
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.
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.

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 is called.
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.

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.

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

TL;DR
Instead of this:
def square_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 square_yield(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 square_list(4):
... print(square)
...
0
1
4
9
>>> for square in square_yield(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. Those terms are revealing. We lose the convenience of a container, but gain the power of a series that's computed as needed, and arbitrarily long.
Yield is lazy, it puts off computation. A function with a yield in it doesn't actually execute at all when you call it. It returns an iterator object that remembers where it left off. 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 (this is the natural end of a for-loop).
Yield is versatile. Data doesn't have to be stored all together, it can be made available one at a time. It can be infinite.
>>> 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(square_yield(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.

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.

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.

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.

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 are calculated 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(generator) on the generator object.
(In Python 2, .next was a method of the generator object; in Python 3, it is named .__next__, but the proper way to call it is using the builtin next() function just like len() and .__len__)
… 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 on the first next(), pauses whenever it does a yield, and when asked for the 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.

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()

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).

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'
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.
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.
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, one uses messages sent to a closure object, which has a dispatcher, and the dispatcher answers to "messages".
"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 possible to do this kind of computation without mutation, by returning the current value and the next yielder (making it referential transparent). Racket uses a sequence of transformations of the initial program in some intermediary languages, one of such rewriting making the yield operator to be transformed in some language with simpler operators.
Here is a demonstration of how yield could be rewritten, which uses the structure of R6RS, but the semantics is identical to Python's. It's the same model of computation, and only a change in syntax is required to rewrite it using yield of 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)

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.

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 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.

Imagine that you have created a remarkable machine that is capable of generating thousands and thousands of lightbulbs per day. The machine generates these lightbulbs in boxes with a unique serial number. You don't have enough space to store all of these lightbulbs at the same time, so you would like to adjust it to generate lightbulbs on-demand.
Python generators don't differ much from this concept. Imagine that you have a function called barcode_generator that generates unique serial numbers for the boxes. Obviously, you can have a huge number of such barcodes returned by the function, subject to the hardware (RAM) limitations. A wiser, and space efficient, option is to generate those serial numbers on-demand.
Machine's code:
def barcode_generator():
serial_number = 10000 # Initial barcode
while True:
yield serial_number
serial_number += 1
barcode = barcode_generator()
while True:
number_of_lightbulbs_to_generate = int(input("How many lightbulbs to generate? "))
barcodes = [next(barcode) for _ in range(number_of_lightbulbs_to_generate)]
print(barcodes)
# function_to_create_the_next_batch_of_lightbulbs(barcodes)
produce_more = input("Produce more? [Y/n]: ")
if produce_more == "n":
break
Note the next(barcode) bit.
As you can see, we have a self-contained “function” to generate the next unique serial number each time. This function returns a generator! As you can see, we are not calling the function each time we need a new serial number, but instead we are using next() given the generator to obtain the next serial number.
Lazy Iterators
To be more precise, this generator is a lazy iterator! An iterator is an object that helps us traverse a sequence of objects. It's called lazy because it does not load all the items of the sequence in memory until they are needed. The use of next in the previous example is the explicit way to obtain the next item from the iterator. The implicit way is using for loops:
for barcode in barcode_generator():
print(barcode)
This will print barcodes infinitely, yet you will not run out of memory.
In other words, a generator looks like a function but behaves like an iterator.
Real-world application?
Finally, real-world applications? They are usually useful when you work with big sequences. Imagine reading a huge file from disk with billions of records. Reading the entire file in memory, before you can work with its content, will probably be infeasible (i.e., you will run out of memory).

An easy example to understand what it is: yield
def f123():
for _ in range(4):
yield 1
yield 2
for i in f123():
print (i)
The output is:
1 2 1 2 1 2 1 2

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"

(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 naive 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.
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.
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.

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.

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.

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 +=

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()).

for loop save to array but skip saving elements

Basically, I want a fancy oneliner that doesn't read all of the files I'm looking at into memory, but still processes them all, and saves a nice sample of them.
The oneliner I would like to do is:
def foo(findex):
return [bar(line) for line in findex] # but skip every nth term
But I would like to be able to not save every nth line in that. i.e., I still want it to run (for byte position purposes), but I don't want to save the image, because I don't have enough memory for that.
So, if the output of bar(line) is 1,2,3,4,5,6,... I would like it to still run on 1,2,3,4,5,6,... but I would like the return value to be [1,3,5,7,9,...] or something of the sort.

use enumerate to get the index, and a filter using modulo to take every other line:
return [bar(line) for i,line in enumerate(findex) if i%2]
Generalize that with i%n so everytime that the index is divisible by n then i%n==0 and bar(line) isn't issued into the listcomp.
enumerate works for every iterable (file handle, generator ...), so it's way better than using range(len(findex))
Now the above is incorrect if you want to call bar on all the values (because you need the side effect generated by bar), because the filter prevents execution. So you have to do that in 2 passes, for instance using map to apply your function to all items of findex and pick only the results you're interested in (but it guarantees that all of the lines are processed) using the same modulo filter but after the execution:
l = [x for i,x in enumerate(map(bar,findex)) if i%n]

If findex is subscriptable (accepts [] operator with indices), you can try this way :
def foo(findex):
return [bar(findex[i]) for i in range (0, len(findex), 2) ]

How to slice a generator object or iterator?

I would like to loop over a "slice" of an iterator. I'm not sure if this is possible as I understand that it is not possible to slice an iterator. What I would like to do is this:
def f():
for i in range(100):
yield(i)
x = f()
for i in x[95:]:
print(i)
This of course fails with:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-37-15f166d16ed2> in <module>()
4 x = f()
5
----> 6 for i in x[95:]:
7 print(i)
TypeError: 'generator' object is not subscriptable
Is there a pythonic way to loop through a "slice" of a generator?
Basically the generator I'm actually concerned with reads a very large file and performs some operations on it line by line. I would like to test slices of the file to make sure that things are performing as expected, but it is very time consuming to let it run over the entire file.
Edit:
As mentioned I need to to this on a file. I was hoping that there was a way of specifying this explicitly with the generator for instance:
import skbio
f = 'seqs.fna'
seqs = skbio.io.read(f, format='fasta')
seqs is a generator object
for seq in itertools.islice(seqs, 30516420, 30516432):
#do a bunch of stuff here
pass
The above code does what I need, however is still very slow as the generator still loops through the all of the lines. I was hoping to only loop over the specified slice

In general, the answer is itertools.islice, but you should note that islice doesn't, and can't, actually skip values. It just grabs and throws away start values before it starts yield-ing values. So it's usually best to avoid islice if possible when you need to skip a lot of values and/or the values being skipped are expensive to acquire/compute. If you can find a way to not generate the values in the first place, do so. In your (obviously contrived) example, you'd just adjust the start index for the range object.
In the specific cases of trying to run on a file object, pulling a huge number of lines (particularly reading from a slow medium) may not be ideal. Assuming you don't need specific lines, one trick you can use to avoid actually reading huge blocks of the file, while still testing some distance in to the file, is the seek to a guessed offset, read out to the end of the line (to discard the partial line you probably seeked to the middle of), then islice off however many lines you want from that point. For example:
import itertools
with open('myhugefile') as f:
# Assuming roughly 80 characters per line, this seeks to somewhere roughly
# around the 100,000th line without reading in the data preceding it
f.seek(80 * 100000)
next(f) # Throw away the partial line you probably landed in the middle of
for line in itertools.islice(f, 100): # Process 100 lines
# Do stuff with each line
For the specific case of files, you might also want to look at mmap which can be used in similar ways (and is unusually useful if you're processing blocks of data rather than lines of text, possibly randomly jumping around as you go).
Update: From your updated question, you'll need to look at your API docs and/or data format to figure out exactly how to skip around properly. It looks like skbio offers some features for skipping using seq_num, but that's still going to read if not process most of the file. If the data was written out with equal sequence lengths, I'd look at the docs on Alignment; aligned data may be loadable without processing the preceding data at all, by e.g by using Alignment.subalignment to create new Alignments that skip the rest of the data for you.

islice is the pythonic way
from itertools import islice
g = (i for i in range(100))
for num in islice(g, 95, None):
print num

You can't slice a generator object or iterator using a normal slice operations.
Instead you need to use itertools.islice as #jonrsharpe already mentioned in his comment.
import itertools
for i in itertools.islice(x, 95)
print(i)
Also note that islice returns an iterator and consume data on the iterator or generator. So you will need to convert you data to list or create a new generator object if you need to go back and do something or use the little known itertools.tee to create a copy of your generator.
from itertools import tee
first, second = tee(f())

let's clarify something first.
Spouse you want to extract the first values from your generator, based on the number of arguments you specified to the left of the expression. Starting from this moment, we have a problem, because in Python there are two alternatives to unpack something.
Let's discuss these alternatives using the following example. Imagine you have the following list l = [1, 2, 3]
1) The first alternative is to NOT use the "start" expression
a, b, c = l # a=1, b=2, c=3
This works great if the number of arguments at the left of the expression (in this case, 3 arguments) is equal to the number of elements in the list.
But, if you try something like this
a, b = l # ValueError: too many values to unpack (expected 2)
This is because the list contains more arguments than those specified to the left of the expression
2) The second alternative is to use the "start" expression; this solve the previous error
a, b, c* = l # a=1, b=2, c=[3]
The "start" argument act like a buffer list.
The buffer can have three possible values:
a, b, *c = [1, 2] # a=1, b=2, c=[]
a, b, *c = [1, 2, 3] # a=1, b=2, c=[3]
a, b, *c = [1, 2, 3, 4, 5] # a=1, b=2, c=[3,4,5]
Note that the list must contain at least 2 values (in the above example). If not, an error will be raised
Now, jump to your problem. If you try something like this:
a, b, c = generator
This will work only if the generator contains only three values (the number of the generator values must be the same as the number of left arguments). Elese, an error will be raise.
If you try something like this:
a, b, *c = generator
If the number of values in the generator is lower than 2; an error will be raise because variables "a", "b" must have a value
If the number of values in the generator is 3; then a=, b=(val_2>, c=[]
If the numeber of values in the generator is greater than 3; then a=, b=(val_2>, c=[, ... ]
In this case, if the generator is infinite; the program will be blocked trying to consume the generator
What I propose for you is the following solution
# Create a dummy generator for this example
def my_generator():
i = 0
while i < 2:
yield i
i += 1
# Our Generator Unpacker
class GeneratorUnpacker:
def __init__(self, generator):
self.generator = generator
def __iter__(self):
return self
def __next__(self):
try:
return next(self.generator)
except StopIteration:
return None # When the generator ends; we will return None as value
if __name__ == '__main__':
dummy_generator = my_generator()
g = GeneratorUnpacker(dummy_generator )
a, b, c = next(g), next(g), next(g)

Why does len() not support iterators?

Many of Python's built-in functions (any(), all(), sum() to name some) take iterables but why does len() not?
One could always use sum(1 for i in iterable) as an equivalent, but why is it len() does not take iterables in the first place?

Many iterables are defined by generator expressions which don't have a well defined len. Take the following which iterates forever:
def sequence(i=0):
while True:
i+=1
yield i
Basically, to have a well defined length, you need to know the entire object up front. Contrast that to a function like sum. You don't need to know the entire object at once to sum it -- Just take one element at a time and add it to what you've already summed.
Be careful with idioms like sum(1 for i in iterable), often it will just exhaust iterable so you can't use it anymore. Or, it could be slow to get the i'th element if there is a lot of computation involved. It might be worth asking yourself why you need to know the length a-priori. This might give you some insight into what type of data-structure to use (frequently list and tuple work just fine) -- or you may be able to perform your operation without needing calling len.

This is an iterable:
def forever():
while True:
yield 1
Yet, it has no length. If you want to find the length of a finite iterable, the only way to do so, by definition of what an iterable is (something you can repeatedly call to get the next element until you reach the end) is to expand the iterable out fully, e.g.:
len(list(the_iterable))
As mgilson pointed out, you might want to ask yourself - why do you want to know the length of a particular iterable? Feel free to comment and I'll add a specific example.
If you want to keep track of how many elements you have processed, instead of doing:
num_elements = len(the_iterable)
for element in the_iterable:
...
do:
num_elements = 0
for element in the_iterable:
num_elements += 1
...
If you want a memory-efficient way of seeing how many elements end up being in a comprehension, for example:
num_relevant = len(x for x in xrange(100000) if x%14==0)
It wouldn't be efficient to do this (you don't need the whole list):
num_relevant = len([x for x in xrange(100000) if x%14==0])
sum would probably be the most handy way, but it looks quite weird and it isn't immediately clear what you're doing:
num_relevant = sum(1 for _ in (x for x in xrange(100000) if x%14==0))
So, you should probably write your own function:
def exhaustive_len(iterable):
length = 0
for _ in iterable: length += 1
return length
exhaustive_len(x for x in xrange(100000) if x%14==0)
The long name is to help remind you that it does consume the iterable, for example, this won't work as you might think:
def yield_numbers():
yield 1; yield 2; yield 3; yield 5; yield 7
the_nums = yield_numbers()
total_nums = exhaustive_len(the_nums)
for num in the_nums:
print num
because exhaustive_len has already consumed all the elements.
EDIT: Ah in that case you would use exhaustive_len(open("file.txt")), as you have to process all lines in the file one-by-one to see how many there are, and it would be wasteful to store the entire file in memory by calling list.

Develop Reference

Python is a programming language that lets you work quickly and integrate systems more effectively.