best yeoman generators

In the **php5.5.0** version, a new generator * (generators) * feature was added to simplify the complexity of implementing an Iterator interface * (Iterator) * To create a simple iterator. With the generator, we can easily use foreach to iterate over a series of data without having to pre-build the objects in memory to be iterated, greatly reducing memory overhead. When the generator function is called, it returns an iterative object, and when the

(g) , computes g the value of the next element until it is calculated to the last element, no more elements are thrown when the StopIteration error occurs.1g= (x forXinchRange (10))2 3 forIinchg:4 Print(i)5 6 7 The results of the operation are as follows:8 9D:\python35\python.exe D:/python Training/s14/day4/called by the generator. PYTen 0 One1 A2 -3 -4 the5 -6 -7 -8 +9 - +Process finished with exit code 05. for There are several types of data that can be directly acting on the loopA class

}; Octal: {: o}; Decimal: {:d}; "" Hex: {: x}; Hex: {: X}, Percent: {:. 3%} ". Format (*[15, 0.0998, 122344]) print (numb) numb =" binary: {: #b}; Octal: {: #o}; Decimal: {: #d}; "" Hex: {: #x}; Hexadecimal: {: #X}, Percent: {:. 3%} ". Format (*[15, 0.0998, 122344]) print (numb) Second, recursionThe loop executes the same program, knowing that a condition is met to end the call.def func (numb): Numb + = 1print (numb) If numb >= 10:return "END" return func (numb) ret = func (0) print (ret)Study q

GeneratorIn Python, a mechanism that loops one side of the computation, called the generator: Generator.Such as:1 for Xin range2 >>> gobject0x1022ef630>Here g is a generator.IteratorsWe already know that for there are several types of data that can be directly acting on a loop:A class is a collection of data types, such as,,, list tuple , and dict set str so on;One is generator to include the generator and yield the generator function with the band.These objects, which can be directly applied t

):C=consumer ("A")C2=consumer ("B")C.__next__ ()C2.__next__ ()Print ("Lao Tzu began to prepare buns!") ")For I in range (10):Time.sleep (1)Print ("Made 1 buns, in two halves!") ")C.send (i)C2.send (i)Producer ("Alex")Operation Result:A ready to eat steamed buns!B prepare to eat steamed buns!Lao Tzu began to prepare steamed buns!Made 1 buns, in two halves!Bun [0] came and was [A] eatenBun [0] came, was [B] eatenMade 1 buns, in two halves!Bun [1] came and was [A] eatenBun [1] came, was [B] eatenFo

when it is not called.Generator (Generator)The builder is one of the most attractive features of the Python language, and the generator is a special kind of iterator, but it's more elegant. It does not need to write the __iter__ () and __next__ () methods as in the class above, only one yiled keyword is required. The generator must be an iterator (and vice versa), so any generator also generates values in a lazy-loaded pattern. Examples of Fibonacci sequences implemented with

python/iterators and generators one, iterators迭代器的特性： 迭代是Python中最强有力的特性之一，可以把迭代看成是一种处理序列中元素的方式。 可以直接作用于for循环的对象统称为可迭代对象(Iterable)。 可以被next()函数调用并不断返回下一个值的对象称为迭代器(Iterator)。 所有的Iterable均可以通过内置函数iter()来转变为Iterator。 出可迭代对象的个数会报错StopIteration，是一个结束信号迭代器的优点： 1、迭代器提供一种不依赖于索引的取值方式，这样就可以遍历那些没有索引的对象了(字典、集合、文件) 2、迭代器和列表比较，迭代器是惰性计算，更节约内存迭代器的缺点： 1、永远不能获取迭代器的长度，使用不如列表索引取值灵活 2、一次性的，只能往后取值，不能往前，不能像索引一样取得指定位置的值Ii. ways to view iterator

yield and return? return can only be returned once the function is completely finished, and yield can return multiple values What does yield really do: yield the function into a generator (the generator is an iterator) The function pauses and continues the next run when the state is saved by yield The following example is the application of two generators, one for constant input URL, constant parsing, and the other is to imitate the Linux

a type of data that automatically implements the iterator protocol (other data types need to call their own built-in __iter__ method), so the generator is an iterative object.Generator classification and representation in Python: (Python provides generators in two different ways)1. Generator function: general function definition, however, returns the result using the yield statement instead of the return statement. The yield statement returns one res

Outline of this section Iterators Generators Decorative Device Basic Decorator Multi-parameter Adorner Recursive Algorithm Basics: Binary lookup, two-dimensional array conversion Regular expressions Common Module Learning Jobs: Calculator Development Implementation of subtraction and extension priority resolution User Input 1-2 * ((60-30 + ( -40/5) * (9-2*5/3 + 7/3*99/4*2998 +10

generators:#-*-coding:utf-8-*-__author__='Gubei'ImportSYS#Generator Functions#implementing the Fibonacci sequencedefFibonacci (N):#Initialize VariablesA, b, Count = 0, 1, 0 whileTrue:ifCount >N:return yieldA A, b= B, A +b Count= Count + 1if __name__=="__main__": #Initialize the generator function to produce a generator functionf = Fibonacci (10) whileTrue:Try: Print(Next (f), end=' ') exceptStopAsyn

in the process of the loop? This eliminates the need to createThe whole list, thus saving a lot of space. In Python, this side loop computes the mechanism, called the Generator (Generator).Python offers two ways to create generators:① Generator Function: defined as regular functions, but with yield instead of return.Yeild will return one result at a time, then hang, and continue execution the next time it is suspended, which resolves the memory limit

Linux has two special device files /dev/random and /de/urandom, which are used to generate a random number. The random number generated by/dev/random is related to the state of the computer hardware currently in use, which improves security and is ideal for scenarios where the quality of random numbers is highly demanding. However, if the hardware status change is insufficient to provide enough information to the random number generator, the program that reads the random number generated by

state3. If the yield is followed by a data, the data will be returned,As next () function or for ... in ... Iteration of the next value4. You can wake the generator with next () to allow the generator to continue executing at the breakpointThe code is as follows:defFibo (n):"""using yield implementation generators to find Fibonacci sequences"""Count= 0#records the position of the current iteration, with an initial value of 0NUM1, num2 = 0, 1#the firs

statement appears, and the generator gives you the yield argument, and the generator does not go down. When you ask him for the next number, he will be from the last state. Start running until the yield statement appears, give you the parameters, and then stop. So repeat until you exit the function.Cases:1 def fib (max): 2 A, B = 1, 13while a Max:4 yield# Generators return an iterator that returns a stream of values. 5 A, B

Definition of an Iterative object forThere are several types of data that can be directly acting on a loop:A class is a collection of data types, such as,,, list tuple , and dict set str so on;One is generator to include the generator and yield the generator function with the band.These objects, which can be directly applied to for the loop, are called iterative objects: Iterable . Generator How to create Generators

True:Try: Print(Next (Iter_dic))#"A" "E" "B" .....exceptStopiteration:#need to catch exceptions manually Break　　And we can iterate through the container with a powerful for loop mechanism in Python.　　4,for Cycle#based on the for loop, we can completely no longer rely on the index to fetch the value.DIC = {'a': 1,'b': 2,'C': 3} forKinchDIC:Print(Dic[k])#how the For loop works#1: The dic.__iter__ () method that executes the in object, the iterator object. __iter, returns the object itself.

(Ten) if I > 5 else i] #没有四元表达式l2=[' egg%s '%i for I in range (Ten) if I l3=[' egg%s '%i for I in range (ten) Else ' haha '] #报错, no elsePrint (L)Print (L1)Print (L2)　　　　　　The last is the generator expression:Similar to a list explanation, but the generator expression returns an object that produces results on demand instead of building a list of results at a time, saving memory compared to the list explanationlaomuji= (' egg%s '%i for I in range) #生成器表达式Print (Laomuji)Print (laomuji.__next__ (

continually returns the next value is called an iterator: Iterator.You can use Isinstance () to determine whether an object is a iterator object:Print (Isinstance ([], Iterator)) print (Isinstance ({}, Iterator)) print (Isinstance (' abc ', Iterator)) print (Isinstance ( (X for X in range), Iterator))Output:Falsefalsefalsetrue　　Generators are Iterator objects, but,, list dict str Though Iterable they are, they are not Iterator .Turn list , dict and s

Recently learned Python iterators and generators, the generator is a feature, is to use the data will be used to fetch! Look at the following code and think about it, and you'll understand what lazy computing is!1 defAdd (S, x):2 returnS +x3 4 5 defGen ():6 forIinchRange (4):7 yieldI8 9 TenBase =Gen () One forNinch[1, 10]: ABase = (Add (i, N) forIinchbase) - Print(List (base))Output: [20, 21, 22, 23] very puzzled, please look downTh