python

# 1. 列表推导式：生成0-9的平方
squares = [x**2 for x in range(10)]
print(squares)  # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

# 等价于传统写法
squares_traditional = []
for x in range(10):
    squares_traditional.append(x**2)

# 2. 带条件的列表推导式：只取偶数
evens = [x for x in range(10) if x % 2 == 0]
print(evens)  # [0, 2, 4, 6, 8]

# 3. 字典推导式：快速构建映射
names = ['Alice', 'Bob', 'Charlie']
name_len = {name: len(name) for name in names}
print(name_len)  # {'Alice': 5, 'Bob': 3, 'Charlie': 7}

# 4. 集合推导式：自动去重
nums = [1, 2, 2, 3, 3, 3]
unique = {x for x in nums}
print(unique)  # {1, 2, 3}

python

# 手动使用迭代器
my_list = [1, 2, 3]
iterator = iter(my_list)  # 获取迭代器

print(next(iterator))  # 1
print(next(iterator))  # 2
print(next(iterator))  # 3
# print(next(iterator))  # 这里会抛出 StopIteration 异常

# for 循环本质上就是不断调用 next()
for item in my_list:
    print(item)  # 等价于上面的手动调用

# 判断是否可迭代
from collections.abc import Iterable, Iterator

print(isinstance([1, 2, 3], Iterable))  # True，列表是可迭代对象
print(isinstance([1, 2, 3], Iterator))  # False，列表本身不是迭代器
print(isinstance(iter([1, 2, 3]), Iterator))  # True，iter() 转换后是迭代器

python

# 方式1：生成器函数
def count_up_to(n):
    """生成从 0 到 n 的数字"""
    i = 0
    while i <= n:
        yield i  # 产出 i，函数暂停，下次 next() 时继续
        i += 1

# 使用生成器
counter = count_up_to(3)
print(next(counter))  # 0
print(next(counter))  # 1
print(next(counter))  # 2
print(next(counter))  # 3
# print(next(counter))  # StopIteration

# 可以用 for 循环遍历
for num in count_up_to(3):
    print(num)  # 0, 1, 2, 3

# 方式2：生成器表达式
gen = (x**2 for x in range(10))
print(gen)  # <generator object <genexpr> at 0x...>
print(next(gen))  # 0
print(next(gen))  # 1
print(list(gen))  # 剩余的：[4, 9, 16, 25, 36, 49, 64, 81]

# 对比列表推导式的内存占用（重点！）
import sys

list_comp = [x for x in range(1000000)]  # 列表：立即生成，占用内存大
gen_expr = (x for x in range(1000000))   # 生成器：几乎不占内存

print(sys.getsizeof(list_comp))  # 大约 8MB
print(sys.getsizeof(gen_expr))   # 大约 112 字节！