The iterator and generator

Iterable object iterable

1. An object that satisfies the following conditions can become an iterable object

• Object implements __iter__ method
• __iter__ object becomes iterable object

2. Features of iterable objects:

• You can traverse and read data through for .. in .. statements.
• [The working principle of the for loop: internally call the __iter__ method on the iterable object, after obtaining the iterator object, call the __next__ method through the iterator object again and again to obtain the iteration result]

3. Determine whether the object is iterable:

• isinstance(object, Iterable) Iterable: iterable object
• Iterable objects: strings, lists, tuples, dictionaries, sets

from collections.abc import Iterableprint(isinstance(123, Iterable)) # False means it is not an iterable objectprint(isinstance('python', Iterable)) # True represents an iterable objectprint(isinstance({'name':'zs'}, Iterable)) # True represents an iterable object

Iterator iterator

Iterator has two functions: iter() and next()Obtain the iterator of the iterable object through the iter() function, continuously use the next() function for the obtained iterator object to obtain the next piece of data, and use next() to raise the StopIteration exception after the elements are retrieved.

# Example 1li = ['Confused', 'SpongeBob', 'Xiaobai'] # The list is an iterable object# create iterator objectname_li = iter(li)print(next(name_li))print(next(name_li))print(next(name_li))print(next(name_li)) # StopIterationExample 2: Custom iterator classclass Test2:def __iter__(self): # This method returns an iterator objectself.n = 1return self # Returns self, indicating that the instance object itself is its own iterator objectdef __next__(self):self.n += 1return self.nte2 = Test2()print(te2) # te2 has the same address as my# print(next(te2)) # AttributeError: 'Test2' object has no attribute 'n'# te2 uses the next method to report an error because the iter method is not called, and the self.n instance attribute inside is not generatedmy = iter(te2) # will call the __iter__ methodprint(next(my)) # 2print(next(my)) #3print(next(my)) #4# Use the same effect of the for loop, the for loop will automatically call the iter method, and then call the next methodfor i in te2:if i <=10:print(i)else:break# Example 3:class Test3:def __init__(self):self.n = 1def __iter__(self):return self # returns the instance object of the current iterator classdef __next__(self):self.n += 1if self.n == 10:raise StopIteration('terminated, can't continue running')return self.nte3 = Test3()try:while True:print(te3.__next__())except StopIteration as e:print("what",e)

Generator

1. A generator is an iterator.The definition method is similar to the list comprehension, just change the [] of the list comprehension to ()2. Generator function: In python, a function that uses yield is called a generatorOrdinary functions, use return for return values; generator functions use yield statementsThe yield statement returns one result at a time, and in the middle of each result, suspends the function so that execution continues from where it left off.The yield effect interrupts the function and saves the interrupted state

 list comprehension# li = [i*10 for i in range(3)]# print(li)# Change list comprehension [] to ()li2 = (i*10 for i in range(3)) # generator expressionprint(li2) # at 0x000002105DD1B548>print(next(li2))print(next(li2))print(next(li2))# print(next(li2)) # StopIterationdef funa(val):li = []li.append(val)print('This is a list:', li)funa('a') # This is the list: ['a']funa('b') # This is the list: ['b']funa('c') # This is the list: ['c']# generator functiondef funb():print('---started----')yield 2 # returns a 2 and pauses the functionyield 3yield 4fb = funb() # call the generator function, which returns a generator objectprint(fb) # print(next(fb)) # 2print(next(fb)) #3print(next(fb)) # 4print(next(fb)) # StopIteration