result = 。
- 耶 - 没有更多的
yield实际情况与基于列表的方法发生的情况明显不同。 在很多情况下，收益率方法的记忆效率会更高，速度更快。 在其他情况下，这个技巧会让你陷入无限循环，尽管原始函数工作得很好。 请继续阅读以了解更多信息...
首先， 迭代器协议 - 当你写
for x in mylist: ...loop body...
事实是，只要Python想循环对象的内容，Python就会执行上述两个步骤 - 所以它可能是一个for循环，但它也可以是像
__iter__()方法以使您的类的实例可迭代。 这个方法应该返回一个迭代器 。 迭代器是带有
self 。 这将适用于简单的情况，但不是当你想让两个迭代器同时在同一个对象上循环时。
for循环并不知道它处理的是什么类型的对象 - 它只是遵循迭代器协议，并且很高兴在item调用
next()获取item。 内置列表逐个返回它们的项目，字典逐个返回键 ，文件逐个返回行 ，等等。并且生成器返回......那么这就是
def f123(): yield 1 yield 2 yield 3 for item in f123(): print item
f123() ，它不会返回yield语句中的任何值！ 它返回一个生成器对象。 此外，函数并不真正退出 - 它进入暂停状态。 当
所以生成器对象有点像适配器 - 一方面它展示了迭代器协议，通过暴露
通常你可以编写不使用生成器但实现相同逻辑的代码。 一种选择是使用我之前提到的临时列表“技巧”。 这在所有情况下都不起作用，例如，如果你有无限循环，或者当你有一个很长的列表时，它可能会无效地使用内存。 另一种方法是实现一个新的可迭代的类
SomethingIter ，它保存实例成员中的状态，并在Python 3中的
__next__() ）方法中执行下一个逻辑步骤。 取决于逻辑，
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
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
_get_child_candidates时会发生什么？ 是否返回列表？ 单个元素？ 它是否再次被调用？ 随后的通话何时停止？
1.代码来自Jochen Schulz（jrschulz），他为度量空间创建了一个伟大的Python库。 这是完整源代码的链接： 模块mspace 。
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
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 3 lines instead of 5 and has one less variable to worry about.
This is the result from the code:
As you can see both functions do the same thing, the only difference is
return_dates() gives a list and
yield_dates() gives a generator
A real life example would be something like reading a file line by line or if you just want to make a generator
From a programming viewpoint, the iterators are implemented as thunks
To implement iterators/generators/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".
" next " is a message sent to a closure, created by " 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 as in python, it's the same model of computation, only a change in syntax is required to rewrite it in python.
Welcome to Racket v220.127.116.11. -> (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 ->
All great answers whereas a bit difficult for newbies.
I assume you have learned
As an analogy,
yield are twins.
return means 'Return and Stop' whereas 'yield` means 'Return but Continue'
- Try to get a num_list with
def num_list(n): for i in range(n): return i
In : num_list(3) Out: 0
See, you get only a single number instead of a list of them,.
return never allow you happy to prevail. It implemented once and quit.
- There comes
In : def num_list(n): ...: for i in range(n): ...: yield i ...: In : num_list(3) Out: <generator object num_list at 0x10327c990> In : list(num_list(3)) Out: [0, 1, 2]
Now, you win to get all the numbers.
return which runs once and stops,
yield runs times you planed.
You can interpret
return one of them ,
return all of them . This is called
- One more step we can rewrite
In : def num_list(n): ...: result =  ...: for i in range(n): ...: result.append(i) ...: return result In : num_list(3) Out: [0, 1, 2]
It's the core about
The difference between a list
return outputs and the object
yield output is:
You can get [0, 1, 2] from a list object always whereas can only retrieve them from 'the object
yield output' once.
So, it has a new name
generator object as displayed in
Out: <generator object num_list at 0x10327c990> .
In conclusion as a metaphor to grok it,
yield are twins,
generator are twins.
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
Yet another TL;DR
iterator on list :
next() returns the next element of the list
iterator generator :
next() will compute the next element on the fly (execute code)
You can see the yield/generator as a way to manually run the control flow from outside (like continue loop 1 step), by calling next, however complex the flow.
NOTE: the generator is NOT a normal function, it remembers previous state like local variables (stack), see other answers or articles for detailed explanation, the generator can only be iterated on once . You could do without
yield but it would not be as nice, so it can be considered 'very nice' language sugar.
(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.)
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 intuitive 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, ie
the logic how the data is computed .
There are 2 approaches to wrap such metadata.
- The OO approach, we wrap the metadata
as a class. This is the so-called
iteratorwho implements the iterator protocol (ie the
__iter__()methods). This is also the commonly seen iterator design pattern .
- The functional approach, we wrap the metadata
as a function. This is the so-called
generator function. But under the hood, the returned
IS-Aiterator because it also implements the iterator protocol.
Either way, an iterator is created, ie 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.
Here is a mental image of what
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.
yield function, when its code begins to run (ie 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 (ie the particular
So it's a kind of a frozen function that the generator is hanging onto.
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>
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']
gi_frame fields are where the frozen state is stored. Exploring them with
dir(..) , we can confirm that our mental model above is credible.
yield is just like
return - it returns whatever you tell it to. The only difference is that the next time you call the function, execution starts from the last call to the
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.
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.
There's an IBM article which explains it reasonably well (for Python) as far as I can see.
The key idea is that the compiler/interpreter/whatever does some trickery so that as far as the caller is concerned, they can keep calling next() and it will keep returning values - as if the generator method was paused . Now obviously you can't really "pause" a method, so the compiler builds a state machine for you to remember where you currently are and what the local variables etc look like. This is much easier than writing an iterator yourself.
Yield is an Object
return in a function will return a single value.
If you want function to return huge set of values use
yield is a barrier
like Barrier in Cuda Language, it will not transfer control until it gets completed.
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 is no value to return.
When you find yourself building a
list from scratch...
def squares_list(n): the_list =  # Replace for x in range(n): y = x * x the_list.append(y) # these return the_list # lines
yield each piece instead
def squares_the_yield_way(n): for x in range(n): y = x * x yield y # with this
This was my first "aha" moment with yield.
yield is a sugary way to say
build a series of stuff
>>> for square in squares_list(4): ... print(square) ... 0 1 4 9 >>> for square in squares_the_yield_way(4): ... print(square) ... 0 1 4 9
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. That's revealing. We lose the convenience of a container, but gain the power of an arbitrarily long series.
Yield is lazy , it puts off computation. A function with a yield in it doesn't actually execute at all when you call it. The iterator object it returns uses magic to maintain the function's internal context. Each time you call
next() on the iterator (this happens in a for-loop) execution inches forward to the next yield. (
StopIteration and ends the series.)
Yield is versatile . It can do infinite loops:
>>> 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(squares_the_yield_way(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.
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).
>>> 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()
return。 相反 ，它会立即返回一个名为生成器的懒惰“待处理列表”对象
- 一个生成器是可迭代的。 什么是可迭代的 ？ 它像
简而言之： 生成器是一个懒惰的递增列表 ，并且
generator = myYieldingFunction(...) x = list(generator) generator v [x, ..., ???] generator v [x, x, ..., ???] generator v [x, x, x, ..., ???] StopIteration exception [x, x, x] done list==[x, x, x]
range 。 调用
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>
>>> list(makeRange(5)) [0, 1, 2, 3, 4]
# 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]
# _ITERABLE_ >>> [x+10 for x in makeRange(5)] [10, 11, 12, 13, 14]
chain ）。 例如，你甚至可以使用生成器来实现像
def enumerate(iterable): zip(count(), iterable) ，或者在while循环中用
list(makeRange(5)) 。 这是我之前描述的“懒惰，增量列表”。
>>> 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
[1,2,3] ， 迭代器是请求的for循环的特定实例，如
[1,2,3].__iter__() 。 生成器与任何迭代器完全相同，除了它的写法（使用函数语法）。
> x = myRange(5) > list(x) [0, 1, 2, 3, 4] > list(x) 
...然后记住一个生成器是一个迭代器 ; 也就是说，这是一次性使用。 如果你想重用它，你应该再次调用
myRange(...) 。 如果您需要使用两次结果，请将结果转换为列表并将其存储在变量
x = list(myRange(5)) 。 那些绝对需要克隆一个生成器的人（例如，可怕的元编程人员）可以在绝对必要的情况下使用
itertools.tee ，因为可复制的迭代器Python PEP标准提议已被推迟。
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.