本文目录导读：
我来为你详细介绍Python中常用的遍历算法案例,这些是处理数据的基础操作。
线性遍历（顺序遍历）
数组遍历
# 基本数组遍历
arr = [1, 2, 3, 4, 5]
# 方法1：直接遍历
for item in arr:
print(item, end=' ') # 输出: 1 2 3 4 5
# 方法2：索引遍历
for i in range(len(arr)):
print(f"索引{i}: {arr[i]}")
# 方法3：枚举遍历
for index, value in enumerate(arr):
print(f"位置{index}: {value}")
链表遍历
class ListNode:
def __init__(self, val=0, next=None):
self.val = val
self.next = next
# 创建链表: 1 -> 2 -> 3
head = ListNode(1, ListNode(2, ListNode(3)))
# 遍历链表
current = head
while current:
print(current.val, end=' ') # 输出: 1 2 3
current = current.next
二维数据遍历
矩阵遍历
matrix = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
]
# 逐行遍历
print("逐行遍历:")
for row in matrix:
for element in row:
print(element, end=' ')
print()
# 逐列遍历
print("\n逐列遍历:")
for col in range(len(matrix[0])):
for row in range(len(matrix)):
print(matrix[row][col], end=' ')
print()
# 对角线遍历
print("\n对角线遍历:")
for i in range(len(matrix)):
print(matrix[i][i], end=' ') # 输出: 1 5 9
树结构遍历
二叉树遍历
class TreeNode:
def __init__(self, val=0, left=None, right=None):
self.val = val
self.left = left
self.right = right
# 创建二叉树:
# 1
# / \
# 2 3
# / \
# 4 5
root = TreeNode(1,
TreeNode(2, TreeNode(4), TreeNode(5)),
TreeNode(3))
# 前序遍历（根-左-右）
def preorder(node):
if node:
print(node.val, end=' ')
preorder(node.left)
preorder(node.right)
print("前序遍历:")
preorder(root) # 输出: 1 2 4 5 3
# 中序遍历（左-根-右）
def inorder(node):
if node:
inorder(node.left)
print(node.val, end=' ')
inorder(node.right)
print("\n中序遍历:")
inorder(root) # 输出: 4 2 5 1 3
# 后序遍历（左-右-根）
def postorder(node):
if node:
postorder(node.left)
postorder(node.right)
print(node.val, end=' ')
print("\n后序遍历:")
postorder(root) # 输出: 4 5 2 3 1
广度优先遍历（层序遍历）
from collections import deque
def level_order(root):
if not root:
return []
result = []
queue = deque([root])
while queue:
level_size = len(queue)
current_level = []
for _ in range(level_size):
node = queue.popleft()
current_level.append(node.val)
if node.left:
queue.append(node.left)
if node.right:
queue.append(node.right)
result.append(current_level)
return result
print("层序遍历:", level_order(root)) # 输出: [[1], [2, 3], [4, 5]]
图结构遍历
深度优先搜索 (DFS)
graph = {
'A': ['B', 'C'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F'],
'D': ['B'],
'E': ['B', 'F'],
'F': ['C', 'E']
}
def dfs(graph, start, visited=None):
if visited is None:
visited = set()
visited.add(start)
print(start, end=' ')
for neighbor in graph[start]:
if neighbor not in visited:
dfs(graph, neighbor, visited)
print("DFS遍历:")
dfs(graph, 'A') # 输出: A B D E F C
广度优先搜索 (BFS)
from collections import deque
def bfs(graph, start):
visited = {start}
queue = deque([start])
while queue:
vertex = queue.popleft()
print(vertex, end=' ')
for neighbor in graph[vertex]:
if neighbor not in visited:
visited.add(neighbor)
queue.append(neighbor)
print("BFS遍历:")
bfs(graph, 'A') # 输出: A B C D E F
高级遍历技巧
带条件的过滤遍历
# 遍历并过滤
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# 只遍历偶数
even_numbers = [num for num in numbers if num % 2 == 0]
print("偶数:", even_numbers) # 输出: [2, 4, 6, 8, 10]
# 带条件的累加
sum_of_large = sum(num for num in numbers if num > 5)
print("大于5的和:", sum_of_large) # 输出: 40
并行遍历多个序列
# 使用zip并行遍历
names = ['Alice', 'Bob', 'Charlie']
scores = [85, 92, 78]
grades = ['B', 'A', 'C']
for name, score, grade in zip(names, scores, grades):
print(f"{name}: {score}分, 等级{grade}")
反向遍历
# 反向遍历
arr = [1, 2, 3, 4, 5]
# 方法1：reversed()
for item in reversed(arr):
print(item, end=' ') # 输出: 5 4 3 2 1
print()
# 方法2：负步长
for i in range(len(arr)-1, -1, -1):
print(arr[i], end=' ') # 输出: 5 4 3 2 1
实际应用案例
文件系统遍历
import os
def walk_directory(path):
"""遍历目录结构"""
for root, dirs, files in os.walk(path):
level = root.replace(path, '').count(os.sep)
indent = ' ' * 4 * level
print(f'{indent}{os.path.basename(root)}/')
subindent = ' ' * 4 * (level + 1)
for file in files:
print(f'{subindent}{file}')
# 使用示例
# walk_directory('./') # 遍历当前目录
JSON数据遍历
def traverse_json(data, indent=0):
"""递归遍历JSON结构"""
prefix = ' ' * indent
if isinstance(data, dict):
for key, value in data.items():
print(f"{prefix}{key}:", end=' ')
if isinstance(value, (dict, list)):
print()
traverse_json(value, indent + 2)
else:
print(value)
elif isinstance(data, list):
for index, item in enumerate(data):
print(f"{prefix}[{index}]:", end=' ')
if isinstance(item, (dict, list)):
print()
traverse_json(item, indent + 2)
else:
print(item)
# 示例数据
data = {
"name": "产品目录",
"items": [
{"id": 1, "name": "手机", "price": 2999},
{"id": 2, "name": "电脑", "price": 5999}
],
"metadata": {
"version": "1.0",
"updated": "2024-01-01"
}
}
print("JSON遍历:")
traverse_json(data)
这些遍历算法覆盖了Python中常见的数据结构操作，通过灵活运用这些方法，你可以高效地处理各种类型的数据,选择哪种遍历方式取决于你的具体需求和数据结构的特性。
