¿Qué hace el & # 8220; rendimiento & # 8221; palabra clave hacer?

StackOverflow https://stackoverflow.com/questions/231767

Pregunta

¿Cuál es el uso de la palabra clave yield en Python? ¿Qué hace?

Por ejemplo, estoy tratando de entender este código 1 : 

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

Y esta es la persona que llama: 

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

¿Qué sucede cuando se llama al método _get_child_candidates ? ¿Se devuelve una lista? ¿Un solo elemento? ¿Se llama de nuevo? ¿Cuándo se detendrán las llamadas subsiguientes?

1. Este fragmento de código fue escrito por Jochen Schulz (jrschulz), quien hizo una gran biblioteca de Python para espacios métricos. Este es el enlace a la fuente completa: Módulo mspace .

¿Fue útil?

Solución

Para comprender qué hace yield , debe comprender qué son los generadores . Y antes de que pueda comprender los generadores, debe entender iterables .

Iterables

Cuando creas una lista, puedes leer sus elementos uno por uno. La lectura de sus elementos uno por uno se llama iteración: 

>>> mylist = [1, 2, 3]
>>> for i in mylist:
...    print(i)
1
2
3

mylist es un iterable . Cuando utiliza una comprensión de lista, crea una lista, y por lo tanto una iterable: 

>>> mylist = [x*x for x in range(3)]
>>> for i in mylist:
...    print(i)
0
1
4

Todo lo que puedes usar " para ... en ... " en es un iterable; lists , strings , files ...

Estos iterables son útiles porque puede leerlos tanto como desee, pero almacena todos los valores en la memoria y esto no siempre es lo que desea cuando tiene muchos valores.

Generadores

Los

generadores son iteradores, una especie de iterable que solo puede iterar una vez . Los generadores no almacenan todos los valores en la memoria, generan los valores sobre la marcha : 

>>> mygenerator = (x*x for x in range(3))
>>> for i in mygenerator:
...    print(i)
0
1
4

Es igual, excepto que usó () en lugar de [] . PERO, usted no puede ejecutar for i in mygenerator por segunda vez, ya que los generadores solo se pueden usar una vez: calculan 0, luego se olvidan y calculan 1 y terminan de calcular 4 , uno por uno.

Rendimiento

yield es una palabra clave que se usa como return , excepto que la función devolverá un generador. 

>>> def createGenerator():
...    mylist = range(3)
...    for i in mylist:
...        yield i*i
...
>>> mygenerator = createGenerator() # create a generator
>>> print(mygenerator) # mygenerator is an object!
<generator object createGenerator at 0xb7555c34>
>>> for i in mygenerator:
...     print(i)
0
1
4

Aquí es un ejemplo inútil, pero es útil cuando sabes que tu función devolverá un gran conjunto de valores que solo necesitarás leer una vez.

Para dominar el rendimiento , debe comprender que cuando llama a la función, el código que ha escrito en el cuerpo de la función no se ejecuta. La función solo devuelve el generador objeto, esto es un poco complicado :-)

Luego, su código continuará desde donde lo dejó cada vez que for use el generador.

Ahora la parte difícil:

La primera vez que for llama al objeto generador creado a partir de su función, ejecutará el código en su función desde el principio hasta que llegue a yield , y luego ' devolverá el primer valor del bucle. Luego, cada una de las otras llamadas ejecutará el bucle que ha escrito en la función una vez más y devolverá el siguiente valor, hasta que no haya ningún valor que devolver.

El generador se considera vacío una vez que se ejecuta la función, pero ya no alcanza el rendimiento . Puede ser porque el ciclo ha llegado a su fin, o porque ya no satisface un " if / else " .

Su código explicado

Generador: 

# Here you create the method of the node object that will return the generator
def _get_child_candidates(self, distance, min_dist, max_dist):

    # Here is the code that will be called each time you use the generator object:

    # If there is still a child of the node object on its left
    # AND if distance is ok, return the next child
    if self._leftchild and distance - max_dist < self._median:
        yield self._leftchild

    # If there is still a child of the node object on its right
    # AND if distance is ok, return the next child
    if self._rightchild and distance + max_dist >= self._median:
        yield self._rightchild

    # If the function arrives here, the generator will be considered empty
    # there is no more than two values: the left and the right children

Llamador: 

# Create an empty list and a list with the current object reference
result, candidates = list(), [self]

# Loop on candidates (they contain only one element at the beginning)
while candidates:

    # Get the last candidate and remove it from the list
    node = candidates.pop()

    # Get the distance between obj and the candidate
    distance = node._get_dist(obj)

    # If distance is ok, then you can fill the result
    if distance <= max_dist and distance >= min_dist:
        result.extend(node._values)

    # Add the children of the candidate in the candidates list
    # so the loop will keep running until it will have looked
    # at all the children of the children of the children, etc. of the candidate
    candidates.extend(node._get_child_candidates(distance, min_dist, max_dist))

return result

Este código contiene varias partes inteligentes:

  • El bucle se repite en una lista, pero la lista se expande mientras se repite el bucle :-) Es una forma concisa de revisar todos estos datos anidados, aunque sea un poco peligroso, ya que puede terminar con una Bucle infinito. En este caso, candidatos.extend (node._get_child_candidates (distance, min_dist, max_dist)) agota todos los valores del generador, pero mientras continúa creando nuevos objetos generadores que produce valores diferentes de los anteriores ya que no se aplica en el mismo nodo.

  • El método extend () es un método de objeto de lista que espera un iterable y agrega sus valores a la lista.

Por lo general, le pasamos una lista: 

>>> a = [1, 2]
>>> b = [3, 4]
>>> a.extend(b)
>>> print(a)
[1, 2, 3, 4]

Pero en su código recibe un generador, lo cual es bueno porque:

  1. No necesita leer los valores dos veces.
  2. Es posible que tenga muchos hijos y no quiera que todos se almacenen en la memoria.

Y funciona porque a Python no le importa si el argumento de un método es una lista o no. Python espera iterables por lo que trabajará con cadenas, listas, tuplas y generadores. Esto se llama tipificación de pato y es una de las razones por las que Python es tan genial. Pero esta es otra historia, para otra pregunta ...

Puede detenerse aquí o leer un poco para ver un uso avanzado de un generador:

Controlando el agotamiento del generador 

>>> 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
...

Nota: Para Python 3, use print (corner_street_atm .__ next __ ()) o print (next (corner_street_atm))

Puede ser útil para varias cosas como controlar el acceso a un recurso.

Itertools, tu mejor amigo

El módulo itertools contiene funciones especiales para manipularlo. ¿Alguna vez quisiste duplicar un generador? Cadena de dos generadores? ¿Agrupar valores en una lista anidada con una línea? Map / Zip sin crear otra lista?

Luego, solo importa itertools .

¿Un ejemplo? Veamos las posibles órdenes de llegada para una carrera de cuatro caballos: 

>>> 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)]

Comprender los mecanismos internos de la iteración

Iteración es un proceso que implica iterables (implementación del método __iter __ () ) e iteradores (implementación del método __next __ () ). Iterables son los objetos de los que puede obtener un iterador. Los iteradores son objetos que le permiten iterar en iterables.

Hay más información al respecto en este artículo sobre cómo for los bucles funcionan .

Otros consejos

Atajo para entender yield

Cuando vea una función con declaraciones yield , aplique este sencillo truco para comprender lo que sucederá:

  1. Inserta una línea result = [] al inicio de la función.
  2. Reemplace cada yield expr con result.append (expr) .
  3. Inserte una línea resultado de retorno en la parte inferior de la función.
  4. Yay - ¡no más declaraciones de rendimiento ! Lea y descubra el código.
  5. Compare la función con la definición original.

Este truco puede darle una idea de la lógica detrás de la función, pero lo que realmente sucede con yield es significativamente diferente de lo que sucede en el enfoque basado en listas. En muchos casos, el enfoque de rendimiento será mucho más eficiente en memoria y también más rápido. En otros casos, este truco te dejará atrapado en un bucle infinito, aunque la función original funcione bien. Siga leyendo para obtener más información ...

No confunda sus iteradores, iteradores y generadores

Primero, el protocolo de iterador - cuando escribes 

for x in mylist:
    ...loop body...

Python realiza los siguientes dos pasos:

  1. Obtiene un iterador para mylist :

    Llame a iter (mylist) - > esto devuelve un objeto con un método next () (o __next __ () en Python 3).

    [Este es el paso que la mayoría de la gente olvida para informarle sobre]

  2. Utiliza el iterador para recorrer en bucle los elementos:

    Siga llamando al método next () en el iterador devuelto desde el paso 1. El valor de retorno de next () se asigna a x y se ejecuta el cuerpo del bucle. Si se genera una excepción StopIteration desde next () , significa que no hay más valores en el iterador y se sale del bucle.

La verdad es que Python realiza los dos pasos anteriores en el momento en que recorre en bucle el contenido de un objeto, por lo que podría ser un bucle for, pero también podría ser un código como otherlist .extend (mylist) (donde otherlist es una lista de Python).

Aquí mylist es un iterable porque implementa el protocolo iterador. En una clase definida por el usuario, puede implementar el método __iter __ () para hacer que las instancias de su clase sean iterables. Este método debe devolver un iterador . Un iterador es un objeto con un método next () . Es posible implementar tanto __iter __ () como next () en la misma clase, y tener __iter __ () return self . Esto funcionará para casos simples, pero no cuando desea que dos iteradores se repitan sobre el mismo objeto al mismo tiempo.

Así que ese es el protocolo del iterador, muchos objetos implementan este protocolo:

  1. Listas incorporadas, diccionarios, tuplas, conjuntos, archivos.
  2. Clases definidas por el usuario que implementan __iter __ () .
  3. Generadores.

Tenga en cuenta que un bucle for no sabe con qué tipo de objeto se trata: simplemente sigue el protocolo iterador y se complace en obtener elemento tras elemento, ya que llama a siguiente ( ) . Las listas incorporadas devuelven sus elementos uno por uno, los diccionarios devuelven las teclas una por una, los archivos devuelven las líneas una por una, etc. Y los generadores devuelven ... bueno ahí es donde entra yield : 

def f123():
    yield 1
    yield 2
    yield 3

for item in f123():
    print item

En lugar de rendir , si tuviera tres return en f123 () solo se ejecutaría la primera, y la función salida. Pero f123 () no es una función ordinaria. Cuando se llama a f123 () , no devuelve ninguno de los valores en las declaraciones de rendimiento. Devuelve un objeto generador. Además, la función realmente no sale, entra en un estado suspendido. Cuando el bucle for intenta recorrer el objeto generador, la función se reanuda desde su estado suspendido en la línea siguiente después del rendimiento desde el que regresó previamente, ejecuta la siguiente línea de código, en este caso una declaración de yield , y la devuelve como el siguiente elemento. Esto sucede hasta que la función finaliza, momento en el cual el generador genera StopIteration y el ciclo finaliza.

Entonces, el objeto generador es algo así como un adaptador: en un extremo muestra el protocolo del iterador, al exponer los métodos __iter __ () y next () para mantener el for loop feliz. Sin embargo, en el otro extremo, ejecuta la función lo suficiente como para obtener el siguiente valor y la vuelve a poner en modo suspendido.

¿Por qué usar generadores?

Por lo general, puede escribir código que no usa generadores pero implementa la misma lógica. Una opción es usar el truco de la lista temporal que mencioné anteriormente. Eso no funcionará en todos los casos, por ejemplo si tiene bucles infinitos, o puede hacer un uso ineficiente de la memoria cuando tiene una lista realmente larga. El otro enfoque es implementar una nueva clase iterable SomethingIter que mantiene el estado en los miembros de la instancia y realiza el siguiente paso lógico en su next () (o __next __ () en Python 3) método. Dependiendo de la lógica, el código dentro del método next () puede terminar pareciendo muy complejo y propenso a errores. Aquí los generadores proporcionan una solución limpia y fácil.

Piénsalo de esta manera:

Un iterador es solo un término elegante para un objeto que tiene un método next () . Así que una función de rendimiento termina siendo algo como esto:

Versión original: 

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

for i in some_function():
    print i

Esto es básicamente lo que hace el intérprete de Python con el código anterior: 

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

Para obtener más información sobre lo que sucede detrás de escena, el bucle for puede reescribirse en este: 

iterator = some_func()
try:
    while 1:
        print iterator.next()
except StopIteration:
    pass

¿Tiene más sentido o simplemente te confunde más? :)

Debo señalar que esto es una simplificación excesiva para fines ilustrativos. :)

La palabra clave yield se reduce a dos hechos simples:

  1. Si el compilador detecta la palabra clave yield en cualquier lugar dentro de una función, esa función ya no regresa a través de la declaración return . En su lugar , inmediatamente devuelve una perezosa " lista pendiente " objeto llamado generador
  2. Un generador es iterable. ¿Qué es un iterable ? Es algo así como una list o set o range o dict-view, con un protocolo incorporado para visitar cada elemento en un cierto orden .

En pocas palabras: un generador es una lista perezosa, con pendientes incrementales , y rendimiento le permite usar la notación de funciones para programar los valores de la lista el generador debería escupir gradualmente. 

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

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

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

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

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

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

Ejemplo

Definamos una función makeRange que sea como el range de Python. Llamar a makeRange (n) DEVUELVE UN GENERADOR: 

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>

Para forzar al generador a devolver de inmediato sus valores pendientes, puede pasarlo a list () (tal como lo haría con cualquier iterable): 

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

Ejemplo de comparación para "devolver una lista"

El ejemplo anterior puede considerarse simplemente como la creación de una lista a la que se agrega y devuelve: 

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

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

Sin embargo, hay una diferencia importante; ver la última sección.

Cómo puedes usar generadores

Un iterable es la última parte de la comprensión de una lista, y todos los generadores son iterables, por lo que a menudo se usan así: 

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

Para tener una mejor idea de los generadores, puede jugar con el módulo itertools (asegúrese de usar chain.from_iterable en lugar de chain cuando se justifique). Por ejemplo, incluso podría usar generadores para implementar listas perezosas infinitamente largas como itertools.count () . Puede implementar su propio def enumeración (iterable): zip (count (), iterable) , o alternativamente hacerlo con la palabra clave yield en un bucle while.

Tenga en cuenta: los generadores se pueden usar para muchas más cosas, como implementación de coroutines o programación no determinista u otras cosas elegantes. Sin embargo, las " listas perezosas " El punto de vista que presento aquí es el uso más común que encontrará.

Detrás de las escenas

Así es como el " protocolo de iteración de Python " trabajos. Es decir, lo que sucede cuando haces list (makeRange (5)) . Esto es lo que describí anteriormente como una "lista perezosa e incremental". 

>>> 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

La función incorporada next () solo llama a los objetos .next () , que forma parte del " protocolo de iteración " y se encuentra en todos los iteradores. Puede usar manualmente la función next () (y otras partes del protocolo de iteración) para implementar cosas sofisticadas, generalmente a expensas de la legibilidad, así que trate de evitar hacer eso ...

Minutiae

Normalmente, a la mayoría de las personas no les importarían las siguientes distinciones y probablemente quieran dejar de leer aquí.

En Python-speak, un iterable es cualquier objeto que "entiende el concepto de un ciclo for" como una lista [1,2,3] , y un iterador es una instancia específica del bucle for solicitado como [1,2,3]. __iter __ () . Un generador es exactamente igual a cualquier iterador, excepto por la forma en que fue escrito (con la sintaxis de la función).

Cuando solicita un iterador de una lista, crea un nuevo iterador. Sin embargo, cuando solicita un iterador de un iterador (lo cual rara vez haría), solo le da una copia de sí mismo.

Por lo tanto, en el improbable caso de que no esté haciendo algo como esto ... 

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

... luego recuerda que un generador es un iterador ; es decir, es de un solo uso. Si desea reutilizarlo, debe llamar a myRange (...) nuevamente. Si necesita usar el resultado dos veces, conviértalo en una lista y guárdelo en una variable x = list (myRange (5)) . Aquellos que absolutamente necesitan clonar un generador (por ejemplo, que están haciendo una metaprogramación terriblemente hack) pueden usar itertools.tee si es absolutamente necesario, ya que el iterador copiable Python La propuesta de estándares PEP ha sido diferida.

What does the yield keyword do in Python?

Answer Outline/Summary

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

Generators:

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

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

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

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

The generator type is a sub-type of iterator:

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

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

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

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

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

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

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

One can yield data programmatically, for example:

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

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

def func(an_iterable):
    yield from an_iterable

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

Coroutines:

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

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

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


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

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

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

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

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

Cooperative Delegation to Sub-Coroutine with yield from

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

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


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

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

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

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

Other Methods: close and throw

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

>>> my_account.close()

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

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

Conclusion

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

What does the yield keyword do in Python?

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

Appendix:

Critique of the Top/Accepted Answer**

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

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

The grammar currently allows any expression in a list comprehension. 

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

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

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

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

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

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

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

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

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

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

Cheers, Nick.

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

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

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

The return statement in a generator

In Python 2:

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

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

In Python 3:

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

Footnotes

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

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

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

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.

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:

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

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

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

for item in sequence:

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

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

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

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'

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

Run it:

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

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

  1. There comes yield

Replace return with yield:

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

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

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

Now, you win to get all the numbers.

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

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

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

It's the core about yield.

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

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

In conclusion, as a metaphor to grok it:

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

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.

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

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

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

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

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

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

Welcome to Racket v6.5.0.3.

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

Here 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.

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"

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.

(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.

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

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

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.

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:

Output

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

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

yield is 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()).

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

Here's a simple yield based approach, to compute the fibonacci series, explained:

def fib(limit=50):
    a, b = 0, 1
    for i in range(limit):
       yield b
       a, b = b, a+b

When you enter this into your REPL and then try and call it, you'll get a mystifying result:

>>> fib()
<generator object fib at 0x7fa38394e3b8>

This is because the presence of yield signaled to Python that you want to create a generator, that is, an object that generates values on demand.

So, how do you generate these values? This can either be done directly by using the built-in function next, or, indirectly by feeding it to a construct that consumes values.

Using the built-in next() function, you directly invoke .next/__next__, forcing the generator to produce a value:

>>> g = fib()
>>> next(g)
1
>>> next(g)
1
>>> next(g)
2
>>> next(g)
3
>>> next(g)
5

Indirectly, if you provide fib to a for loop, a list initializer, a tuple initializer, or anything else that expects an object that generates/produces values, you'll "consume" the generator until no more values can be produced by it (and it returns):

results = []
for i in fib(30):       # consumes fib
    results.append(i) 
# can also be accomplished with
results = list(fib(30)) # consumes fib

Similarly, with a tuple initializer: 

>>> tuple(fib(5))       # consumes fib
(1, 1, 2, 3, 5)

A generator differs from a function in the sense that it is lazy. It accomplishes this by maintaining it's local state and allowing you to resume whenever you need to.

When you first invoke fib by calling it:

f = fib()

Python compiles the function, encounters the yield keyword and simply returns a generator object back at you. Not very helpful it seems.

When you then request it generates the first value, directly or indirectly, it executes all statements that it finds, until it encounters a yield, it then yields back the value you supplied to yield and pauses. For an example that better demonstrates this, let's use some print calls (replace with print "text" if on Python 2):

def yielder(value):
    """ This is an infinite generator. Only use next on it """ 
    while 1:
        print("I'm going to generate the value for you")
        print("Then I'll pause for a while")
        yield value
        print("Let's go through it again.")

Now, enter in the REPL:

>>> gen = yielder("Hello, yield!")

you have a generator object now waiting for a command for it to generate a value. Use next and see what get's printed:

>>> next(gen) # runs until it finds a yield
I'm going to generate the value for you
Then I'll pause for a while
'Hello, yield!'

The unquoted results are what's printed. The quoted result is what is returned from yield. Call next again now:

>>> next(gen) # continues from yield and runs again
Let's go through it again.
I'm going to generate the value for you
Then I'll pause for a while
'Hello, yield!'

The generator remembers it was paused at yield value and resumes from there. The next message is printed and the search for the yield statement to pause at it performed again (due to the while loop).

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
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