```python

# Python函数式编程：lambda、map与filter的巧妙应用

# 1. lambda匿名函数的灵活运用

# 创建简单的数学运算函数

add = lambda x, y: x + y

square = lambda x: x 2

is_even = lambda x: x % 2 == 0

# 在排序中的使用

students = [('Alice', 85), ('Bob', 92), ('Charlie', 78)]

sorted_students = sorted(students, key=lambda x: x[1], reverse=True)

# 2. map函数的高效数据处理

# 批量处理数据

numbers = [1, 2, 3, 4, 5]

squared_numbers = list(map(lambda x: x2, numbers))

# 多序列并行处理

names = ['alice', 'bob', 'charlie']

ages = [25, 30, 35]

user_info = list(map(lambda name, age: f'{name.title()} is {age} years old', names, ages))

# 3. filter函数的精准筛选

# 筛选偶数

numbers = range(1, 11)

even_numbers = list(filter(lambda x: x % 2 == 0, numbers))

# 筛选特定条件的字符串

words = ['python', 'java', 'javascript', 'c++', 'go']

long_words = list(filter(lambda word: len(word) > 4, words))

# 4. 组合应用的强大威力

# 链式处理：筛选后转换

mixed_numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

result = list(map(lambda x: x2, filter(lambda x: x > 5, mixed_numbers)))

# 复杂数据处理

data = [{'name': 'Alice', 'score': 85},

{'name': 'Bob', 'score': 92},

{'name': 'Charlie', 'score': 78}]

# 筛选高分学生并提取姓名

high_scorers = list(map(lambda x: x['name'],

filter(lambda x: x['score'] >= 90, data)))

# 5. 实际应用场景

# 数据清洗

raw_data = [' hello ', 'WORLD', ' python ']

cleaned_data = list(map(lambda x: x.strip().lower(), raw_data))

# 条件计算

temperatures = [20, 25, 30, 15, 35]

hot_days = list(filter(lambda temp: temp > 28, temperatures))

# 6. 性能优化技巧

# 使用生成器表达式提高效率

large_dataset = range(1000000)

# 使用map和filter的组合

processed_data = map(lambda x: x2,

filter(lambda x: x % 3 == 0, large_dataset))

# 7. 函数组合的优雅实现

def compose(functions):

return lambda x: reduce(lambda acc, f: f(acc), functions, x)

# 示例：先平方，再筛选，最后转换为字符串

pipeline = compose(

lambda x: x2,

lambda x: x if x > 10 else None,

lambda x: str(x) if x else 'Too small'

)

# 应用管道

results = list(filter(None, map(pipeline, range(1, 6))))

# 总结应用要点

def demonstrate_usage():

# lambda的简洁性

quick_calc = lambda x: x 2 + 10

# map的批量处理能力

prices = [100, 200, 300]

discounted = list(map(lambda price: price 0.9, prices))

# filter的精确筛选

numbers = [15, 20, 25, 30, 35]

divisible_by_5 = list(filter(lambda x: x % 5 == 0, numbers))

return quick_calc(5), discounted, divisible_by_5

# 调用演示

demo_result = demonstrate_usage()

```