Python iterators and generators

One, iterator

1, the object can be iterated: the object that can directly act on the For loop is called an iterative object, namely iterable.

Common Iteration objects: str, list, tuple, dict, set, range

Methods for detecting iterative objects:

　　1), detection method: Dir (the object being measured)-if it contains __iter__, then this object is called an iterative object.

#返回True表示是可可迭代对象, false is not str1 = ' Hello ' print (' __iter__ ' in Dir (str1))  #Truenum = 2018print (' __iter__ ' in Dir (num))   #False

　　2), test whether it is an iterator or an iterative object

#检测可迭代对象与迭代器的方法lis = [1,2,3]from collections import iterable    #Iterable can iterate objects from collections import Iterator    # Iterator iterators
Print (Isinstance (lis,iterable))     #Trueprint (Isinstance (lis,iterator))     #False # isinstance () is a python built-in function, Returns whether an object is an instance of a class or its child class. If true, returns false instead.

2, Iterators: An object that can remember where to traverse

Iterator contains: File handle

2.1, the value of the iterator

#取值方式 use __next__ () S1 = ' ABCD ' s3 = s1.__iter__ () print (s3.__next__ ()) print (s3.__next__ ()) print (s3.__next__ ()) Print (S3 . __next__ ()) #输出结果A B C D

2.2, the meaning of iterators:

1), iterators save memory.2), the iterator inertia mechanism.3), iterators can not be repeated, always down execution.

2.3, for loop mechanism:

1), internal contains the __iter__ method, which will iterate the object first into an iterator, and then call the __next__ method.2), it has a method of exception handling.

The #实例1 for loop perfectly implements the iteration for I in [end=]:    print (I, M, ")                       #1 2 3# instance 2 while loop requires exception handling statement to implement iteration   L = [1,2,3]l_iter = l.__i TER__ () while True:    try:        print (l_iter.__next__ (), end= ")   #1 2 3      except stopiteration:        break

Second, generator

1.1. Definition: The function using yield is called the generator (generator), which is essentially an iterator, which is an iterator written in Python code.

1.2. Characteristics:1), you can use the generator function.2), you can use a variety of derivation to build an iterator.3), can be converted by data.

#普通函数def func ():    return 222ret = func () print (ret)          #111 # Generator Function--generator def gener ():    yield 111    yield 222g = Gener () print (g)            #<generator object Gener at 0x000001c8e10a43b8>print (g.__next__ ()) #111print (g.__next__ () ) #222 the difference between #return and yield #return returns to the caller value and ends the function. #yiled  returns to the caller value and pauses the pointer over the current position.

1.3. Send function1), send and Next functions2), send a value to the previous yiled as a wholeNote: Send cannot send a value to the first and last yield, only with next

Def gener ():    yield 111    count = Yield 222    print ('---', count)    yield 333    yield 444g = Gener () Print (g.__next__ ())     print (G.send (' AAA '))    print (G.send (' BBB '))    #count--Bbbprint (G.send (' Last '))   #输出结果111222-BBB333444

　　

Python iterators and generators