Python 迭代器与生成器详解

迭代器基础

迭代器是 Python 中用于顺序访问集合元素的对象，需实现__iter__和__next__方法。

class NumberIterator:
    def __init__(self, limit):
        self.limit = limit
        self.counter = 0

    def __iter__(self):
        return self

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

numbers = NumberIterator(3)
for num in numbers:
    print(num)

生成器实现

生成器通过 yield 语句实现延迟计算，按需生成数据。

def fibonacci_generator(limit):
    a, b = 0, 1
    count = 0
    while count < limit:
        yield a
        a, b = b, a + b
        count += 1

fib = fibonacci_generator(5)
for number in fib:
    print(number)

文件读取示例

生成器在处理大文件时特别有用。

def read_large_file(file_path):
    with open(file_path, 'r') as file:
        for line in file:
            yield line.strip()

def process_file(file_path):
    for line in read_large_file(file_path):
        processed_data = line.upper()
        yield processed_data

无限序列生成

生成器可以创建无限序列，按需获取数据。

def infinite_counter():
    num = 0
    while True:
        yield num
        num += 1

counter = infinite_counter()
for _ in range(5):
    print(next(counter))

生成器表达式

类似列表推导式的简洁语法创建生成器。

squares_gen = (x * x for x in range(5))
print(next(squares_gen))
print(next(squares_gen))

生成器进阶特性

send 方法允许向生成器发送值。

def number_generator():
    while True:
        received = yield
        yield received * 2

gen = number_generator()
next(gen)
print(gen.send(10))
next(gen)

迭代器链式操作

通过迭代器实现数据处理管道。

def generate_numbers():
    for i in range(5):
        yield i

def square_numbers(numbers):
    for num in numbers:
        yield num * num

def filter_even(numbers):
    for num in numbers:
        if num % 2 == 0:
            yield num

numbers = generate_numbers()
squared = square_numbers(numbers)
result = filter_even(squared)

for num in result:
    print(num)