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?
yield
When you see a function with yield statements, apply this easy trick to understand what will happen:
result = [] at the start of the function.yield expr with result.append(expr).return result at the bottom of the function.yield statements! Read and figure out the code.
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...
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:
__iter__().
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.
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):
self.count = -1
def __iter__(self):
return self
def next(self):
self.count += 1
if self.count < 4:
return self.count
else:
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:
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 generatorlist 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(...)
x = list(generator)
generator
v
[x[0], ..., ???]
generator
v
[x[0], x[1], ..., ???]
generator
v
[x[0], x[1], x[2], ..., ???]
StopIteration exception
[x[0], x[1], x[2]] done
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.
Let's define a function makeRange that's just like Python's range. Calling makeRange(n) RETURNS A GENERATOR:
def makeRange(n):
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]
The above example can be thought of as merely creating a list which you append to and return:
def makeRange(n):
"""return [0,1,2,...,n-1]"""
TO_RETURN = []
i = 0
while i < n:
TO_RETURN += [i]
i += 1
return TO_RETURN
>>> makeRange(5)
[0, 1, 2, 3, 4]
There is one major difference, though; see the last section.
An iterable is the last part of a list comprehension, and all generators are iterable, so they're often used like so:
>>> [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.
This is how the "Python iteration protocol" works. That is, what is going on when you do list(makeRange(5)). This is what I describe earlier as a "lazy, incremental list".
>>> x=iter(range(5))
>>> next(x)
0
>>> next(x)
1
>>> next(x)
2
>>> next(x)
3
>>> next(x)
4
>>> next(x)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration
The built-in function next() just calls the objects .__next__() function, which is a part of the "iteration protocol" and is found on all iterators. You can manually use the next() function (and other parts of the iteration protocol) to implement fancy things, usually at the expense of readability, so try to avoid doing that...
Coroutine example:
def interactiveProcedure():
userResponse = yield makeQuestionWebpage()
print('user response:', userResponse)
yield 'success'
coroutine = interactiveProcedure()
webFormData = next(coroutine)
userResponse = serveWebForm(webFormData)
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.
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
yieldkeyword do in Python?
yield, when called, returns a Generator.yield from.return in a generator.)
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)
<type 'function'>
>>> gen = func()
>>> type(gen)
<type 'generator'>
>>> hasattr(gen, '__iter__')
True
>>> hasattr(gen, '__next__')
True
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.
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
yield from
Now, recall that yield from is available in Python 3. This allows us to delegate coroutines to a subcoroutine:
def money_manager(expected_rate):
under_management = yield
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'''
next(manager)
manager.send(deposited)
try:
yield from manager
except GeneratorExit:
return manager.close()
And now we can delegate functionality to a sub-generator and it can be used by a generator just as above:
my_manager = money_manager(.06)
my_account = investment_account(1000, my_manager)
first_year_return = next(my_account)
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
You can read more about the precise semantics of yield from in PEP 380.
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
I believe I have covered all aspects of the following question:
What does the
yieldkeyword 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.
__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.yield part..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.yield does at all.yield provides along with the new functionality yield from in Python 3.The top/accepted answer is a very incomplete answer.
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.
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.
TL;DR
def square_list(n):
the_list = []
for x in range(n):
y = x * x
the_list.append(y)
return the_list
def square_yield(n):
for x in range(n):
y = x * x
yield y
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.
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.
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.
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:
defining the generator function, i.e. the function containing a yield.next(generator) on the generator object..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__)StopIteration exception.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()
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 ->
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__',
...
'close',
'gi_code',
'gi_frame',
'gi_running',
'next',
'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.
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.
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.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
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.