本文目录导读：
我来给你详细讲解Python多层循环的各种案例和技巧。
1️⃣ 基础多层循环结构
双重循环基础
# 2x3的矩阵遍历
for i in range(2): # 外层：行
for j in range(3): # 内层：列
print(f"({i},{j})", end=" ")
print() # 换行
# 输出：(0,0) (0,1) (0,2)
# (1,0) (1,1) (1,2)
三重循环示例
# 三维坐标遍历
for x in range(2):
for y in range(2):
for z in range(2):
print(f"({x},{y},{z})", end=" ")
print()
print("---")
2️⃣ 实用案例
案例1：乘法表（双重循环）
# 打印99乘法表
for i in range(1, 10):
for j in range(1, i + 1): # 内层循环范围随外层变化
print(f"{j}×{i}={i*j:2d}", end=" ")
print()
案例2：打印菱形（多重循环控制）
def print_diamond(n):
# 上半部分
for i in range(n):
# 打印空格
for j in range(n - i - 1):
print(" ", end="")
# 打印星号
for k in range(2 * i + 1):
print("*", end="")
print()
# 下半部分
for i in range(n - 2, -1, -1):
for j in range(n - i - 1):
print(" ", end="")
for k in range(2 * i + 1):
print("*", end="")
print()
print_diamond(5)
案例3：矩阵转置（双重循环）
matrix = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
]
rows = len(matrix)
cols = len(matrix[0])
# 转置矩阵
transposed = []
for i in range(cols): # 外层遍历列
row = []
for j in range(rows): # 内层遍历行
row.append(matrix[j][i])
transposed.append(row)
print("原矩阵:", matrix)
print("转置后:", transposed)
3️⃣ 高级技巧
列表推导式中的多层循环
# 双重循环的列表推导式 pairs = [(x, y) for x in range(3) for y in range(3)] print(pairs) # 带条件的双重循环 matrix = [[i*j for j in range(1, 4)] for i in range(1, 4)] print(matrix) # 三维列表 cube = [[[i*j*k for k in range(2)] for j in range(2)] for i in range(2)] print(cube)
使用itertools.product
from itertools import product
# 等价于三层嵌套循环
for x, y, z in product(range(2), range(3), range(2)):
print(f"({x},{y},{z})", end=" ")
4️⃣ 实际应用场景
场景1：学生成绩统计
# 多个班级、多个学生的成绩统计
classes = ["A班", "B班", "C班"]
students = ["小明", "小红", "小华"]
scores = [
[[85, 90, 78], [92, 88, 95], [76, 82, 89]], # A班
[[79, 85, 92], [88, 90, 86], [91, 87, 93]], # B班
[[95, 88, 82], [77, 83, 90], [84, 86, 91]] # C班
]
for i, class_name in enumerate(classes):
print(f"\n=== {class_name} ===")
for j, student in enumerate(students):
total = sum(scores[i][j])
avg = total / len(scores[i][j])
print(f"{student}: 总分={total}, 平均分={avg:.1f}")
场景2：图像处理（像素遍历）
import numpy as np
# 模拟一个5x5的灰度图像
image = np.random.randint(0, 256, (5, 5))
# 应用3x3均值滤波器
def apply_filter(img, kernel_size=3):
height, width = img.shape
pad = kernel_size // 2
result = np.zeros_like(img)
for i in range(pad, height - pad):
for j in range(pad, width - pad):
# 3层循环：遍历邻域
total = 0
count = 0
for ki in range(-pad, pad + 1):
for kj in range(-pad, pad + 1):
total += img[i + ki][j + kj]
count += 1
result[i][j] = total // count
return result
print("原图：\n", image)
filtered = apply_filter(image)
print("滤波后：\n", filtered)
场景3：递归替代多层循环（N皇后问题）
def solve_n_queens(n):
def is_safe(board, row, col):
# 检查列
for i in range(row):
if board[i] == col:
return False
# 检查对角线
if abs(board[i] - col) == abs(i - row):
return False
return True
def backtrack(board, row):
if row == n:
solutions.append(board[:])
return
for col in range(n): # 这一层相当于内层循环
if is_safe(board, row, col):
board[row] = col
backtrack(board, row + 1)
solutions = []
backtrack([-1] * n, 0)
return solutions
# 4皇后问题
solutions = solve_n_queens(4)
for sol in solutions:
print(sol)
5️⃣ 性能优化技巧
循环展开和优化
import time
# 不好的方式：频繁计算
def bad_loop(n):
result = 0
for i in range(n):
for j in range(n):
result += i * j # 每次计算i*j
# 好的方式：提前计算
def good_loop(n):
result = 0
for i in range(n):
i_times = i # 提前计算
for j in range(n):
result += i_times * j
# 使用局部变量加速
def fast_loop(n):
result = 0
local_range = range # 局部变量引用
for i in local_range(n):
for j in local_range(n):
result += i * j
# 测试性能
n = 500
start = time.time()
bad_loop(n)
print(f"坏方法: {time.time() - start:.3f}s")
start = time.time()
good_loop(n)
print(f"好方法: {time.time() - start:.3f}s")
使用break和continue控制流程
# 提前跳出多层循环
found = False
for i in range(10):
for j in range(10):
if i * j == 42:
print(f"找到{i}×{j}=42")
found = True
break
if found:
break
# 或者使用else子句
for i in range(10):
for j in range(10):
if i * j == 42:
print(f"找到{i}×{j}=42")
break
else:
continue # 内层未找到，继续外层
break # 内层找到，跳出外层
多层循环的关键点：
- 清晰的结构：保持缩进规范，添加注释
- 合理使用range：注意内外层循环的范围关系
- 性能优化：减少不必要的计算，使用局部变量
- 控制流程：善用break、continue、else
- 替代方案：考虑列表推导式、递归或itertools
多层循环虽然强大，但超过3层就要考虑是否可以用其他方式实现,避免代码可读性太差。
