Python reduce()函数高级应用案例(累积计算的艺术)

初始值(可选) + 序列: [a, b, c, d]
    ↓ reduce(函数)
计算过程: 函数(函数(函数(a, b), c), d)

from functools import reduce
reduce(function, iterable[, initializer])

from functools import reduce
# 计算列表元素的乘积
product = reduce(lambda x, y: x * y, [1, 2, 3, 4])
print(product)  # 输出: 24 (1*2*3*4)
# 计算阶乘
def factorial(n):
    return reduce(lambda x, y: x * y, range(1, n+1))
print(factorial(5))  # 输出: 120 (1*2*3*4*5)

# 列表求和（带初始值10）
total = reduce(lambda x, y: x + y, [1, 2, 3], 10)
print(total)  # 输出: 16 (10+1+2+3)
# 字符串连接
words = ["Hello", " ", "World", "!"]
sentence = reduce(lambda x, y: x + y, words, "")
print(sentence)  # 输出: "Hello World!"

# 合并多个字典
dicts = [{'a': 1}, {'b': 2}, {'a': 3, 'c': 4}]
merged = reduce(lambda x, y: {**x, **y}, dicts)
print(merged)  # 输出: {'a': 3, 'b': 2, 'c': 4}
# 查找最大值（演示reduce用法，实际应用建议用max()）
max_num = reduce(lambda x, y: x if x > y else y, [5, 2, 8, 1])
print(max_num)  # 输出: 8

# 计算平方和
numbers = [1, 2, 3, 4]
sum_of_squares = reduce(lambda x, y: x + y, map(lambda x: x**2, numbers))
print(sum_of_squares)  # 输出: 30 (1+4+9+16)

# 列表扁平化
nested = [[1, 2], [3, 4], [5, 6]]
flat = reduce(lambda x, y: x + y, nested)
print(flat)  # 输出: [1, 2, 3, 4, 5, 6]
# 模拟投票统计
votes = ["A", "B", "A", "C", "B", "A"]
result = reduce(lambda d, k: {**d, k: d.get(k, 0)+1}, votes, {})
print(result)  # 输出: {'A': 3, 'B': 2, 'C': 1}

计算 reduce(lambda x, y: x+y, [1, 2, 3, 4])
步骤1: 计算1+2 → 3
步骤2: 计算3+3 → 6
步骤3: 计算6+4 → 10
结果: 10

计算 reduce(lambda x, y: x+y, [1, 2, 3], 10)
步骤1: 计算10+1 → 11
步骤2: 计算11+2 → 13
步骤3: 计算13+3 → 16
结果: 16

[a, b, c, d]
   ↓ reduce(f)
f(f(f(a, b), c), d)

# reduce版本
product = reduce(lambda x, y: x * y, [1, 2, 3, 4])
# 循环版本
product = 1
for num in [1, 2, 3, 4]:
    product *= num

# 计算总和
numbers = [1, 2, 3, 4]
# reduce方式
total = reduce(lambda x, y: x + y, numbers)
# 内置函数方式（更快）
total = sum(numbers)

# 组合多个函数：f(g(h(x)))
def compose(*funcs):
    return reduce(lambda f, g: lambda x: f(g(x)), funcs)
# 使用示例
add1 = lambda x: x + 1
mul2 = lambda x: x * 2
square = lambda x: x ** 2
composed = compose(add1, mul2, square)  # 相当于 add1(mul2(square(x)))
print(composed(3))  # 输出: 19 = ((3^2)*2)+1

# 构建数据处理管道
data = [1, 2, 3, 4, 5]
process = reduce(
    lambda value, func: func(value),
    [
        lambda x: filter(lambda i: i%2==0, x),  # 过滤偶数
        lambda x: map(lambda i: i**2, x),       # 平方
        lambda x: list(x),                      # 转为列表
        lambda x: sum(x)                        # 求和
    ],
    data
)
print(process)  # 输出: 20 (2² + 4²)

# 简单状态机
def state_machine(state, event):
    if state == "locked":
        return "unlocked" if event == "coin" else "locked"
    else:
        return "locked" if event == "push" else "unlocked"
events = ["coin", "push", "coin", "push"]
final_state = reduce(state_machine, events, "locked")
print(final_state)  # 输出: locked

# 不佳 - 用sum更好
total = reduce(lambda x,y: x+y, big_list)
# 更佳
total = sum(big_list)

# 不佳 - 每次迭代都创建新lambda
result = reduce(lambda x,y: some_complex_op(x,y), data)
# 更佳
def complex_op(x, y):
    # 复杂计算
    return result
result = reduce(complex_op, data)

# 处理大数据时
result = reduce(op, (x for x in big_data if condition))