python迭代器,生成器

from collections.abc import Iterable
a: int = 1
print(isinstance(a, Iterable))  # False
b: str = "lalalalala" 
print(isinstance(b, Iterable))  # True
c: set = set([1, 2])
print(isinstance(c, Iterable))  # True

class MyIterable:
	def __iter__(self):
		pass

print(isinstance(MyIterable(), Iterable)) # True

from collections.abc import Iterator, Iterable
class MyIterator:
	def __init__(self, array_list):
		self.array_list = array_list
		self.index = 0
	def __iter__(self):
		return self
	def __next__(self):
		if self.index < len(self.array_list):
			val = self.array_list[self.index]
			self.index += 1
			return val
		else:
			raise StopIteration
# 父类如果是迭代器，子类也将是迭代器
class MySubIterator(MyIterator):
	def __init__(self):
		pass

myIterator = MyIterator([1, 2, 3, 4])
# 判断是否为可迭代对象
print(isinstance(myIterator, Iterable))  # True
# 判断是否为迭代器
print(isinstance(myIterator, Iterator))  # True
# 子类实例化
mySubIterator = MySubIterator()
print(isinstance(mySubIterator, Iterator))  # True
# 进行迭代
print(next(myIterator))  # 1
print(next(myIterator))  # 2
print(next(myIterator))  # 3
print(next(myIterator))  # 4
print(next(myIterator))  # raise StopIteration

 - 优点：迭代器对象表示的是一个数据流，可以在需要时才去调用next来获取一个值；因而本身在内存中始终只保留一个值，对于内存占用小可以存放无限数据流。优于其他容器需要一次将所有元素都存放进内存，如：列表、集合、字典...等
 - 缺点：1.无法获取存放的元素长度，除非取完计数。2.只能向后取值，next()永远返回的是下一个值。取值不灵活，无法取出指定值(无法像字典的key,或列表的下标)，而且迭代器的生命周期是一次性的元素被迭代完则生命周期结束

# 列表生成式
_list = [i for i in range(10)]
print(type(_list))  # <class 'list'>
print(_list)  # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
# 生成器
_generator = (i for i in range(10))
print(type(_generator))  # <class 'generator'>
print(_generator)  # <generator object <genexpr> at 0x7fbcd92c9ba0>
# 生成器对象取值
print(_generator.__next__())  # 0
print(next(_generator)) # 1
# 注意从第三个元素开始了！
for x in _generator:
	print(x)  # 2，3，4，5，6，7，8，9

def gen_generator():
	yield 1

def generator():
	return 1

print(gen_generator(), type(gen_generator()))  
# <generator object gen_generator at 0x7fe68b2c8b30> <class 'generator'>
print(generator(), type(generator()))  
# 1 <class 'int'>

def gen_generator2():
	yield [1, 2, 3]

s = gen_generator2()
print(next(s))  # [1, 2, 3]

def consumer():
	r = ''
	while True:
		n = yield r
		if not n:
			return
		print(f'[CONSUMER] Consuming get params.. ({n})')
		if n == 3:
			r = '500 Error'
		else:
			r = '200 OK'

def produce(c):
	c.send(None)  # 启动生成器
	n = 0
	while n < 5:
		n = n + 1
		print(f'[PRODUCER] Producing with params.. ({n})')
		r = c.send(n)  # 一旦n有值，则切换到consumer执行
		print(f'[PRODUCER] Consumer return : [{r}]')
		if not r.startswith('200'):
			print("消费者返回服务异常，则结束生产，并关闭消费者")
			c.close()  # 关闭生成器
			break

consume = consumer()
produce(consume)
# [PRODUCER] Producing with params.. (1)
# [CONSUMER] Consuming get params.. (1)
# [PRODUCER] Consumer return : [200 OK]
# [PRODUCER] Producing with params.. (2)
# [CONSUMER] Consuming get params.. (2)
# [PRODUCER] Consumer return : [200 OK]
# [PRODUCER] Producing with params.. (3)
# [CONSUMER] Consuming get params.. (3)
# [PRODUCER] Consumer return : [500 Error]
# 消费者返回服务异常，则结束生产，并关闭消费者

def my_generator(n, end_case):
	for i in range(n):
		if i == end_case:
			return f'当 i==`{i}`时，中断程序。'
		else:
			yield i

g = my_generator(5, 2)  # 调用
for _i in g:  # for循环不会显式触发异常，故而无法获取到return的值
	print(_i)
# 输出：
# 0
# 1

def my_generator2(n, end_case):
	for i in range(n):
		if i == end_case:
			return f'当 i==`{i}`时，中断程序。'
		else:
			yield i

g = my_generator2(5, 2)  # 调用
try:
	print(next(g))  # 0
	print(next(g))  # 1
	print(next(g))  # 此处要触发end_case了
except StopIteration as exc:
	print(exc.value)  # 当 i==`2`时，中断程序。

def my_generator3(n, end_case):
	for i in range(n):
		if i == end_case:
			return f'当 i==`{i}`时，中断程序。'
		else:
			yield i

def wrap_my_generator(generator):  # 将my_generator的返回值包装成一个生成器
	result = yield from generator
	yield result

g = my_generator3(5, 2)  # 调用
for _ in wrap_my_generator(g):
	print(_)
# 输出：
# 0
# 1
# 当 i==`2`时，中断程序。

# 子生成器
def average_gen():
	total = 0
	count = 0
	average = 0
	while True:
		new_num = yield average
		if new_num is None:
			break
		count += 1
		total += new_num
		average = total / count
	# 每一次return，都意味着当前协程结束。
	return total, count, average

# 委托生成器
def proxy_gen():
	while True:
		# 只有子生成器要结束（return）了，yield from左边的变量才会被赋值，后面的代码才会执行。
		total, count, average = yield from average_gen()
		print("总共传入 {} 个数值， 总和：{}，平均数：{}".format(count, total, average))

# 调用方
def main():
	calc_average = proxy_gen()
	next(calc_average)  # 激活协程
	calc_average.send(10)  # 传入：10
	calc_average.send(None)  # 结束协程
	print("================== 重开协程 ===================")
	calc_average.send(20)  # 传入：20
	calc_average.send(30)  # 传入：30
	calc_average.send(None)  # 结束协程

if __name__ == '__main__':
	main()
# 输出：
# 总共传入 1 个数值， 总和：10，平均数：10.0
# ================== 重开协程 ===================
# 总共传入 2 个数值， 总和：50，平均数：25.0