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1. Can the generator function take parameters?Yes, the generator function supports all parameter forms, because the generator letter is one of the functionsDer Count (start=0): While True:yield startstart++#指定从0开始的计数器2. Can the generator function return a value with return output?The answer is no, the generator's default return value is the generator, then you have to give him a return value this is not possible, in short, will be an error, do not believe you try.In this case, to say, the differ

If there is no such object as a generator, how can this simple "producer-consumer" model be implemented?Import timeDef producer ():Pro_list = []For I in range (10000):Print "Bun%s made ing"% (i)Time.sleep (0.5)Pro_list.append ("Bun%s"%i)Return pro_listDEF consumer (pro_list):For Index,stuffed_bun in Enumerate (pro_list):Print "%s personal, ate the first%s buns"% (Index,stuffed_bun)Pro_list = producer ()Consumer (pro_list)Above this producer and consumer model has a serious problem, is the very l

　GeneratorThe call to the generator function returns an object that is the generator (the type () function can be used to judge the object), and then the next () method can be used to obtain a sequence from the object.The value passed to yield is returned by the next () method.The next () method pauses the execution of the function when it encounters yield and returns. The execution of the restore function is called next again. You can print information to illustrate the execution of the generat

1. List-generation (comprehensions)In Python, list generation is used to create lists, compared to using loops to make them more concise. For example, generate [1*1, 2*2, ..., 10*10], loop with three lines:1 L = []2 for in range (1,11):3 l.append (i*i)The list generation is only one row, preceded by the build rule, followed by the initial element, and finally the criteria can be added:1 for in range (1, 11)]List generation can also be implemente

Changing a list [] to () creates a generator: Generator,generator is the algorithm that is saved.Data types that can be used for a For loop:1. Collection type: List tuple dict set str2, generatorAn object that can directly act on a for loop is collectively known as: An Iterative object (iterable) from Import iterable Print (Isinstance ([],iterable)) Print (Isinstance ({},iterable)) Print for in range (iterable))print(isinstance (iterable))Results:TruetruetruefalseAn object that can be called b

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, ' = ', v) x = Ay = Bz = C#利用生成器来打印循环出key,value>>> [k + ' = ' +v for k,v in D.items () [' X=a ', ' y=b ', ' z=c ']#在列表生成器中加入对字符串的处理条件 >>> l=[' hello ', ' world ', ' IBM ', ' Apple ']>>> [S.lower () for s in l][' hello ', ' world ' , ' IBM ', ' Apple ']>>> [S[0:1].upper () for S-l][' H ', ' W ', ' I ', ' A ']>>> [S[0:].upper () for S in L] #从0位开 Start with all caps [' Hello ', ' World ', ' IBM ', ' APPLE ']>>> [S.capitalize () for S ' in L] #第一个字母大写其它小写 [' Hello ', ' World ', ' IBM ', ' Ap Ple '

1. IntroductionWith a list build, we can create a list directly, but with limited memory, the list size is bound to be finite.If a list element can be inferred from an algorithm, can we continually extrapolate the subsequent elements in the process of looping?In Python, this side loop computes the mechanism, called the generator: Generator.2. Example2.1 The list-generated [] change to (), a generator is created:1 ( for in range (5)) 2 Print (s) # 3

A mechanism that loops one side of a loop in Python as a generator (generator)Executes at each invocation next() , yield encounters a statement return, and resumes execution from the last statement returned yield .Generate list for in range ()>>>1, 4, 9, 16, 25, 36, 49, 64, 81]Generate generator for in range (+)>>> G两者的区别就在于最外层的[]和（），Lis a list, and g is a generatorYield generator>>>defOdd (): ...Print('Step 1')... yield1... Print('Step 2')... yie

function of two points. One is to return the technical result of the current statement, and the second is to suspend the state of the function, and continue execution from the current position when the next call occurs.About generator functions Let's look at a simple example first.defG_func ():Print(' First') yield11111Print('Second') yield22222Print('Third') yield3333g=G_func ()Print(g)#Print(Next (g))# First11111Print(Next (g))#Second22222Print(Next (g))#Third3333Then let's look at a

# This is a learning note for the Liaoche teacher Python tutorial1 , overviewThe list elements are calculated according to an algorithm, and the subsequent elements are continuously deduced during the loop. This side loop is calculated on the side of the mechanism, called the generator: generator1.1 , creating Generator1) Method OneAs soon as you change the [] form of a list to (), you create a generatorL = [x * x for x in range (Ten)] # This is a l

, Li))Print(Li2) a= [] forIinchRange (10): A.append (i+1)Print(a)#as you can see from the above comparison, the second method code is the simplest, that is, the list-generatingAlthough there are list generation, you can simplify the operation of generating a specific list, but when the list data is too large, it consumes too much memory, and the list is not always used, and there is one side loop in Python that calculates the mechanism of the generato

With a list generation, we can create a list directly. However, the list capacity is certainly limited by the memory limit. And, creating a list of 1 million elements, not only takes up a lot of storage space, but if we just need to access the first few elements, the space behind most of the elements is wasted. So if the list element can be calculated according to some algorithm, can we continue to calculate the subsequent elements in the process of the loop? This eliminates the need to create

typing the TEST_COUNTDOWN_CO () code directly above the idle terminal:>>> C = Countdown_co (5) for in C: print n If 5 = = N: c.send (3) from 553210Can see the same code, execution results are not the same, very strange ah! So far I have not thought to understand why. If anyone knows the reason, please tell me O (∩_∩) o Thank you!All right! This article introduces Coroutine's blog is also written well. The next article will introduce some advanced uses of coroutine in the

What is a generator?The builder is a function that contains a special keyword yield. When invoked, the generator function returns a generator. You can use the Send,throw,close method to enable the generator to interact with the outside world. The generator is also an iterator , but it's not just an iterator, it has the next method and behaves exactly the same as the iterator. So the generator can also be used in the

Generator Generator generator: A mechanism for calculating one side of the loop.A generator is a special program that can be used to control the iterative behavior of loops. The builder in Python is one of the iterators that use the yield return value function, which is paused each time the yield is called, and the generator can be restored using the next () function and the Send () function.The gener

With list generation, we can create a list directly. However, with memory limitations, the list capacity is certainly limited. Also, creating a list of 1 million elements takes up a lot of storage space, and if we just need to access the first few elements, the vast majority of the space behind it is wasted.So, if the list element can be calculated according to an algorithm, can we continue to calculate the subsequent elements in the process of the loop? This eliminates the need to create a comp

can be used only for iterative operations, and simpler to understand that the generator is an iterator.In the process of calling the generator to run, the function pauses and saves all current run information every time the yield is encountered, returns the value of yield, and continues running from its current location the next time the next () method is executed.Invokes a generator function that returns an Iterator object.def fib (max): n=0 A=0 b=1 while max>N: Yield b

accessed and finished. Iterators can only move forward without going backwards, but that's fine, because people seldom retreat in the middle of an iteration. In addition, one of the great advantages of iterators is that they do not require that all elements in the entire iteration be prepared in advance. An iterator computes an element only when it iterates over it, and before or after that, the element may not exist or be destroyed. This feature makes it ideal for traversing large or infinite

#!/usr/bin/env python#-*-Coding:utf-8-*-def xrange (n):num = 0While True:if num > N:returnprint (num)yield numnum + = 1obj = xrange (5) # obj is the generator with only the build capability#迭代器: Access cannot be accessed randomly only from beginning to end# can not be rolled back easy to loop large data collection# n1 = obj.__next__ ()# N2 = obj.__next__ ()# n3 = obj.__next__ ()For N in Xrange (Ten):print (n)Pytho