列表推导式

Python教程12：列表进阶与推导式高级 “熟能生巧，巧能生精。” 在第8课我们学习了列表推导式的基础，今天我们深入探讨列表推导式的高级技巧和列表的进阶操作，让你的代码更加Pythonic和高效。 1. 回顾：列表推导式基础 1# 基础语法 2squares = [x**2 for x in range(10)] 3 4# 带条件 5evens = [x for x in range(10) if x % 2 == 0] 6 7# if-else 8result = [x if x > 0 else 0 for x in [-1, 2, -3, 4]] 2. 嵌套列表推导式二维列表展平 1# 传统方法 2matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] 3flat = [] 4for row in matrix: 5 for num in row: 6 flat.append(num) 7 8# 列表推导式 9flat = [num for row in matrix for num in row] 10print(flat) # [1, 2, 3, 4, 5, 6, 7, 8, 9] 理解技巧：从左到右阅读，就像嵌套的for循环。创建二维列表 1# 创建3×3矩阵 2matrix = [[0 for _ in range(3)] for _ in range(3)] 3print(matrix) 4# [[0, 0, 0], [0, 0, 0], [0, 0, 0]] 5 6# 创建乘法表 7table = [[i*j for j in range(1, 10)] for i in range(1, 10)] 8 9# 注意：不要这样创建二维列表 10# bad = [[0] * 3] * 3 # 错误！所有行是同一个对象多重嵌套with条件 1# 找出两个列表的所有组合（有条件） 2a = [1, 2, 3] 3b = [3, 4, 5] 4 5# 找出和大于5的组合 6result = [(x, y) for x in a for y in b if x + y > 5] 7print(result) # [(2, 4), (2, 5), (3, 3), (3, 4), (3, 5)] 3. 列表推导式vs传统循环 1# 性能对比示例 2import time 3 4# 方法1：传统for循环 5start = time.time() 6result1 = [] 7for i in range(100000): 8 result1.append(i**2) 9time1 = time.time() - start 10 11# 方法2：列表推导式 12start = time.time() 13result2 = [i**2 for i in range(100000)] 14time2 = time.time() - start 15 16print(f"传统循环：{time1:.4f}秒") 17print(f"列表推导式：{time2:.4f}秒") 18# 列表推导式通常快20-30% 4. 字典和集合推导式进阶字典推导式高级用法 1# 统计字符出现次数 2text = "hello world" 3char_count = {char: text.count(char) for char in set(text) if char != ' '} 4 5# 从列表创建索引字典 6fruits = ['apple', 'banana', 'cherry'] 7fruit_index = {fruit: i for i, fruit in enumerate(fruits)} 8 9# 嵌套字典推导式 10students = ['Alice', 'Bob'] 11subjects = ['Math', 'English'] 12grades = { 13 student: {subject: 0 for subject in subjects} 14 for student in students 15} 集合推导式妙用 1# 去重并转换 2numbers = [1, -2, 3, -4, 5] 3abs_unique = {abs(n) for n in numbers} # {1, 2, 3, 4, 5} 4 5# 找差异 6list1 = [1, 2, 3, 4, 5] 7list2 = [4, 5, 6, 7, 8] 8diff = {x for x in list1 if x not in list2} # {1, 2, 3} 5. 生成器表达式深入 1# 列表推导式：立即生成，占内存 2squares_list = [x**2 for x in range(1000000)] 3 4# 生成器表达式：按需生成，省内存 5squares_gen = (x**2 for x in range(1000000)) 6 7# 使用生成器 8total = sum(x**2 for x in range(1000000)) 9 10# 生成器只能遍历一次 11gen = (x for x in range(5)) 12print(list(gen)) # [0, 1, 2, 3, 4] 13print(list(gen)) # []（已耗尽） 6. 列表的高级操作 zip和enumerate进阶 1# zip并行遍历 2names = ['Alice', 'Bob', 'Charlie'] 3ages = [25, 30, 35] 4cities = ['Beijing', 'Shanghai', 'Guangzhou'] 5 6# 创建字典 7people = [ 8 {'name': n, 'age': a, 'city': c} 9 for n, a, c in zip(names, ages, cities) 10] 11 12# enumerate with start 13for i, name in enumerate(names, start=1): 14 print(f"{i}. {name}") filter和map结合推导式 1# 虽然有filter和map，但推导式更清晰 2numbers = range(1, 11) 3 4# filter + map方式 5result1 = list(map(lambda x: x**2, filter(lambda x: x % 2 == 0, numbers))) 6 7# 推导式方式（更清晰） 8result2 = [x**2 for x in numbers if x % 2 == 0] 7. 实战案例案例1：矩阵转置 1matrix = [ 2 [1, 2, 3], 3 [4, 5, 6], 4 [7, 8, 9] 5] 6 7# 转置 8transposed = [[row[i] for row in matrix] for i in range(len(matrix[0]))] 9print(transposed) 10# [[1, 4, 7], [2, 5, 8], [3, 6, 9]] 11 12# 或使用zip 13transposed = [list(col) for col in zip(*matrix)] 案例2：笛卡尔积 1colors = ['红', '黑'] 2sizes = ['S', 'M', 'L'] 3products = [f"{color}-{size}" for color in colors for size in sizes] 4# ['红-S', '红-M', '红-L', '黑-S', '黑-M', '黑-L'] 案例3：数据清洗 1# 清洗CSV数据 2raw_data = [ 3 " Alice, 25 ", 4 "Bob,30", 5 " Charlie, 35 " 6] 7 8cleaned = [ 9 [item.strip() for item in row.split(',')] 10 for row in raw_data 11] 8. 何时不用推导式虽然推导式简洁，但有时不适合： ...