Python函数式编程lambda、map、filter与reduce的实战应用

```python

# Python函数式编程实战：lambda、map、filter与reduce的深度应用

# 1. lambda匿名函数的实战应用

# 场景：快速定义简单函数

square = lambda x: x2

add = lambda x, y: x + y

# 示例：在排序中使用lambda

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

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

# 2. map函数的实战应用

# 场景：批量处理数据转换

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

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

# 示例：多序列映射

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函数的实战应用

# 场景：数据筛选

numbers = range(1, 11)

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

# 示例：复杂条件筛选

products = [

{'name': 'laptop', 'price': 1200, 'category': 'electronics'},

{'name': 'book', 'price': 15, 'category': 'education'},

{'name': 'phone', 'price': 800, 'category': 'electronics'}

]

expensive_electronics = list(filter(

lambda product: product['price'] > 500 and product['category'] == 'electronics',

products

))

# 4. reduce函数的实战应用

from functools import reduce

# 场景：累积计算

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

product = reduce(lambda x, y: x y, numbers)

# 示例：复杂数据聚合

sales = [100, 200, 150, 300, 250]

total_sales = reduce(lambda acc, sale: acc + sale, sales, 0)

# 5. 组合使用实战案例

# 场景：数据处理管道

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

# 筛选偶数 -> 平方 -> 求和

result = reduce(

lambda acc, x: acc + x,

map(

lambda x: x2,

filter(lambda x: x % 2 == 0, data)

),

0

)

# 6. 实际项目应用：文本处理

texts = ['hello world', 'python programming', 'functional programming']

# 统计所有文本中单词的总数

total_words = reduce(

lambda acc, text: acc + len(text.split()),

texts,

0

)

# 7. 实际项目应用：数据分析

sales_data = [

{'month': 'Jan', 'revenue': 10000},

{'month': 'Feb', 'revenue': 12000},

{'month': 'Mar', 'revenue': 8000},

{'month': 'Apr', 'revenue': 15000}

]

# 计算平均收入

total_revenue = reduce(lambda acc, data: acc + data['revenue'], sales_data, 0)

average_revenue = total_revenue / len(sales_data)

# 8. 性能优化技巧

# 使用生成器表达式与函数式编程结合

large_data = range(1000000)

# 惰性求值，节省内存

processed_data = map(lambda x: x2, filter(lambda x: x % 2 == 0, large_data))

# 9. 错误处理实践

def safe_divide(x, y):

try:

return x / y

except ZeroDivisionError:

return float('inf')

numbers = [10, 5, 2, 0, 8]

divisions = list(map(lambda x: safe_divide(100, x), numbers))

# 10. 实际业务场景：用户数据处理

users = [

{'name': 'Alice', 'age': 25, 'active': True},

{'name': 'Bob', 'age': 17, 'active': True},

{'name': 'Charlie', 'age': 30, 'active': False},

{'name': 'David', 'age': 22, 'active': True}

]

# 获取活跃成年用户的姓名列表

active_adult_users = list(

map(

lambda user: user['name'],

filter(

lambda user: user['active'] and user['age'] >= 18,

users

)

)

)

# 总结：函数式编程的优势

# - 代码简洁明了

# - 易于测试和调试

# - 支持惰性求值

# - 便于并行处理

# - 提高代码可读性

print(函数式编程实战示例执行完成！)

```