Python迭代器与生成器：从入门到精通

好的，我们将通过以下结构逐步理解Python迭代器与生成器：

一、理解基础循环

Python的for循环本质上是通过迭代协议实现的：

my_list = [1, 2, 3]
for item in my_list:
print(item)

等价于：

iterator = iter(my_list)
while True:
try:
item = next(iterator)
print(item)
except StopIteration:
break

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二、迭代器协议

符合迭代器协议的对象需满足两个条件：

class Counter:
def __init__(self, n):
self.n = n
self.current = 0

def __iter__(self):
return self

def __next__(self):
if self.current < self.n:
self.current += 1
return self.current
raise StopIteration

# 使用示例
for num in Counter(5):
print(num) # 输出1到5

三、生成器精要

生成器是创建迭代器的便捷工具，使用yield关键字：

def fibonacci(max_len):
a, b = 0, 1
count = 0
while count < max_len:
yield a
a, b = b, a + b
count += 1

# 实际应用
for num in fibonacci(10):
print(num)

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四、执行流程精解

graph LR
A[调用生成器函数] –> B[创建生成器对象]
B –> C[调用next时运行]
C –> D[执行至yield暂停]
D –> E[返回yield值]
E –> C

五、应用对比场景

# 生成器表达式示例
squared_gen = (x**2 for x in range(1000000)) # 仅需数KB内存

六、进阶实战

处理无限流数据：

def real_time_data_stream():
while True:
data = get_network_data() # 假设的获取函数
yield processed_data

通过逐步理解这些核心概念，您将掌握Python迭代模型的精髓，并能灵活运用生成器处理海量数据流等现代应用场景。