empower generators

):ifUser_info.get ('User_type', None) = = 2: Ret= Func (*args, * *Kwargs)returnretElse: return('no permission to view') returnInnerdeflogin ():"""Login function: return:"""username= Input ("Please enter user name:") Password= Input ("Please enter your password:") ifUsername = ='Ordinary' andPassword = ='123': user_info['Is_login'] =True user_info['User_type'] = 1return("Welcome to visit") elifUsername = ='Admin' andPassword = ='456': user_info['Is_login'] =True user_info['U

conditional end call is met.def func (N): + = 1 # (1) func is 1,n is 2 (3) func is 2,n is 3 (5) func is 3,n is 4 if n >=4 : return ' End ' # (6) greater than or equal to 4 o'clock, return ' End ' return func (n) # (2) Here is 2 (4) Here is 3 r = func (1)print( R) # (7) Final return ' End 'Using recursion to achieve factorial:1*2*3*4*5*6*7def func (N): if or n==1: return 1 return n*func (n-1) # 7*func (6 then Func (6) is 6*func (5) and then always recur

offset in a non-exhaustive traversal, but not an element at offset if both the offset and offset elements are required, you can use the enumerate () function to return a generator object with this built-in function‘www.baidu.com‘>>> enumerate(url) #发现是个内存对象证明这个内置函数是一个生成器对象object at 0x000000000217D3A8>>>> g1 = enumerate(url)>>> g1.next()(0, ‘w‘)Generator function: The function body contains the yield keyword, and the result of the function is the generator function #生成器本身是迭代器#yield的功能: 1. Sim

) returns a 5-length data, and if it is a range (1000) it takes up a 1000-size array space, and if we use the ' generator ' to generate a number when needed, the space occupancy will be reduced. Here we can use the xrange () function to implement.Xrange Function Description: The usage is exactly the same as the range, and the difference is that it is not an array, but a generator. xrange example: "' >>> xrange (5) xrange (5) >>> list (xrange (5)) [0, 1, 2, 3, 4]>>> xrange (1,5) xrange ( 1, 5)

function contains the yield syntax, the function becomes the generatorCode:defCash_money (amount): whileAmount>0:amount-=100yield100#as long as yield is the generator, the value returned by the generator is an iterator Print("We 're taking the money again.") ATM=cash_money (500)Print(Type (ATM))Print(ATM.__next__())Print(ATM.__next__())Print('Other thing')#To build the benefits of iterators, the function executes half after the other work, and after the work is done, proceed with the fun

definition contains a yield keyword, then the function is no longer a normal function, but a generator:f = fib (a) Print F----------------Generator and functions do not have the same execution flow. The function is executed sequentially, the statement is encountered return or the last line of the function is returned. The function that becomes generator, executes at each invocation next() , encounters a yield statement return, and executes again from the last statement returned yield .python--l

Iterators作用:不依靠索引遍历,可遍历无索引对象特性:分批次取值,比有索引对象更节省内存缺点:无法获取迭代器的长度 使用不如列表索引灵活 一次性的,只能从头到尾取值判断:只要本身有__iter__方法,就是可迭代的try : #异常退出,for 循环自带检测 dadadaexcept StopIteration: breakGenerator简单定义:生成器就是一个函数 包含 yield 语句特性:分步运行函数,能够保存函数当前状态与return的区别 :return 只能返回一次,yield可以返回多次值Yield把函数变成生成器(生成器本身也是迭代器)用yield 可以返回多次函数在暂停以及下一次继续下一次运行状态有yield保存Python-based iterators and generators

()returnSelf.a#returns the next value4. Reverse IterationUsing the built-in reversed enables reverse iterations, provided the object size can be determined, or the __reversed__ method is implemented.A = [1, 2, 3, 4] for in reversed (a): print xUsing the __reversed__ methodclassCountdown:def __init__(self, start): Self.start=Start#Forward iterator def __iter__(self): n=Self.start whilen >0:yieldN N-= 1#Reverse iterator def __reversed__(self): n= 1 whileN Self.start:yieldN N+ = 1 forRrin

( Let detail of map.values ()) { 8 Console.log (detail); 9 } 1 let set = new set ([Ten, 30 ] ); 2 for ( Let num of Set.keys ()) { 3 Console.log (num ); 4 5 6 let map = new map ([[' Name ', ' Ghostwu '], [' Age ', 22 7 for ( Let detail of Map.keys ()) { 8 Console.log ( detail); 9 } Default iterator:1Let userlist = [' Ghostwu ', ' Goku ', ' eight commandments ' ];2 3 //equivalent to calling values4 for(let name of UserList) {5 Console.log (name);6 }7 8Let set =NewSet

normal objects, and can be assigned to other variables, which can be used as return values and can be defined within another function.1 Import Time2 3 defTiming (fun):4 5 defDeco (*ARG, * *kwarg):6 7Start_time =time.time ()8 9Fun (*arg, * *Kwarg)Ten OneStop_time =time.time () A - Print('The func run time is:%s'% (Stop_time-start_time)) - the returndeco - - @Timing - defTar (name, age): + -Time.sleep (2) + A Print('In the tar:', name, age) at -Tar'HK', 18)Operation Res

" Run results, will appear fengzi111, because import test2, "test2.py" was executed once.Why can't I print the fengzi222?"test2.py" is introduced into the "test1.py". In "test2.py" there is the if judgment, the result of judgment: they are two not the same name.Look at the code print (test2.__name__) in the "test1.py" file, which deliberately shows what the name "test2.py" is. The result returned is test2, but now the "test1.py" file is executed! "Test1" = = "Test2"? Obviously false, then there

, tuple, dictionary, collection, file object) that these types of data must be an iterative object? But why do I define a list of a = [1, 2, 3, 4] without the A.next () method, punch the face?(strings, lists, tuples, dictionaries, collections, file objects) These are not iterative objects, but in the for loop, they call their internal __iter__ method, turn them into an iterative object, and the For loop invokes the __next__ method of an iterative object to fetch the value, And the For loop catch

Generates a random numeric password of the specified lengthGenerates a random letter password of the specified lengthGenerates a mix of random numbers and letters of a specified length#encoding =utf-8Import RandomImport stringClass Password_generator:Password_time=0def __init__ (self,length):Self.length=lengthdef digital_password (self):Password_generator.password_time+=1S= ""For I in Range (self.length):S+=str (Random.randint (0,9))return s@classmethoddef letter_password (cls,length):Password_g

Personally feel that iterator and yield achieve the same function, but iterator need to implement in the class, yield is implemented in the real function, both will save the stateThe generator is also implemented by iterators#!/usr/bin/env python#coding:utf-8# definition three functions def Lee (name,age): Return ' I am%s,i am%d old '% (name,age) def Ma Rlon (): Return ' I am Marlon ' Def Allen (): Return ' I am Allen ' function_list= [Lee,marlon,allen] #有三个函数的列表 # definition A generator def myg

=fname5Self.max_sentence_length =Max_sentence_length6 7 def __iter__(self):8 #The entire corpus is one gigantic line--there be no sentence marks at all9 #So just split the sequence of tokens arbitrarily:1 sentence = tokensTenSentence, rest = [], b"' One With Utils.smart_open (self.fname) as Fin: A whileTrue: -Text = rest + fin.read (8192)#avoid loading the entire file (=1 line) into RAM - ifText = = Rest:#EOF theSentence.extend (Rest.split ())#re

is encountered.Invokes a generator function that returns an Iterator object.Compared with yield and not the caseYield execution ResultsNo yield execution resultsUnder what circumstances will yield be used?A function f,f returns a list, which is dynamically computed (either mathematically or logically, and the list is large (either fixed or enlarged with the increase in input parameters), and this is the time at which the It is useful to have a single list element instead of a complete list to s

] Traversal mode# [variable (processed variable) for variable in iteration object if judgment] Filter mode# L1 = [I for I in range (1, 101) if I% 2 = = 0]# Print (L1)#1, filter, all the odd numbers within 100. # L2 = [I for I in range (1, 101) if I% 2 = = 1]# L2 = [I for I in range (1, 101, 2)]# Print (L2)#2, the square of all the numbers within 10. [1,4,9,16 ... []# Print ([I*i for I in range (1, one)])#3, leave the number in the list that is divisible by three within 100. # Print ([i-I in rang

0Next (G)#Output 1Next (I1)#Output 4 #(2) The generator does not retain the result after the iterationGen = (i forIinchRange (4)) inchGen#returns TrueinchGen#returns TrueinchGen#return False, in fact, when detecting 2, 1 is not in the generator, that is, 1 has been iterated, the same 2, 3 is not2. Generator expressionsLimitations: only suitable for simple algorithmsExample: for in range (1,10))print(next Test)print(test). __next__())3. Generator function yieldExample of the Fibonacci func

One, List generation: 1, generate a list: lists = [i*2 for i in range] #使用列表生成式生成一个列表, occupy memory space, when the amount of large can cause a lot of waste. Print (list) 2, using the generator to generate the list: (generator) List1 = (i*2 for me in range) #将生成列表的公式存在变量中, take it when needed, (Note: The disadvantage is that you can only take the next one) print (list1.__next__ ()) #使用__next__取数据, take one number at a time and throw a stopiteration error at the end of the fetch fo

Always thought that Python generator is a list generation, OK, I read less. In fact, the generator is the generator object that implements the iterator protocol using yield returns. As follows: Class Data (object): def __init__ (self, *args): self._data = list (args) def __iter__ (self): for x in Self._data: yield x d = data () >>> d.__iter__ () Use D.next () in Python2 to return the next value of D,In Python3, you need to use Dnext() to return the next v