本文目录导读：
我来分享几个Python栈入栈的经典案例,从简单到复杂逐步演示。
基础入栈操作
使用列表实现栈
# 创建一个空栈
stack = []
# 入栈操作 (压入数据)
stack.append(10) # 栈: [10]
stack.append(20) # 栈: [10, 20]
stack.append(30) # 栈: [10, 20, 30]
print("当前栈:", stack) # 输出: [10, 20, 30]
自定义栈类
class Stack:
def __init__(self):
self.items = []
def push(self, item):
"""入栈操作"""
self.items.append(item)
print(f"入栈: {item}")
def pop(self):
"""出栈操作"""
if not self.is_empty():
return self.items.pop()
return None
def is_empty(self):
return len(self.items) == 0
def size(self):
return len(self.items)
def peek(self):
"""查看栈顶元素"""
if not self.is_empty():
return self.items[-1]
return None
# 使用示例
stack = Stack()
stack.push("A") # 入栈: A
stack.push("B") # 入栈: B
stack.push("C") # 入栈: C
print(f"栈的大小: {stack.size()}") # 输出: 3
print(f"栈顶元素: {stack.peek()}") # 输出: C
批量入栈
def batch_push(stack, items):
"""批量将多个元素入栈"""
for item in items:
stack.append(item)
return stack
# 示例
stack = []
batch_push(stack, [1, 2, 3, 4, 5])
print("批量入栈后:", stack) # 输出: [1, 2, 3, 4, 5]
条件入栈（带验证）
def conditional_push(stack, item, max_size=5):
"""
条件入栈：栈满时不能入栈
"""
if len(stack) >= max_size:
print(f"栈已满，无法入栈 {item}")
return False
stack.append(item)
print(f"成功入栈: {item}")
return True
# 示例
stack = []
for i in range(7): # 尝试入栈7个元素
conditional_push(stack, i * 10)
print("最终栈:", stack) # 只包含前5个元素
实际应用场景案例
1 括号匹配检查
def check_brackets(expression):
"""检查括号是否匹配"""
stack = []
brackets = {'(': ')', '[': ']', '{': '}'}
for char in expression:
if char in brackets.keys(): # 左括号入栈
stack.append(char)
print(f"入栈: {char}")
elif char in brackets.values(): # 右括号检查
if not stack:
return False
left = stack.pop()
if brackets[left] != char:
return False
return len(stack) == 0
# 测试
expression = "{[()]}"
print(f"括号匹配: {check_brackets(expression)}") # True
2 浏览器的前进后退
class BrowserHistory:
def __init__(self):
self.back_stack = [] # 后退栈
self.forward_stack = [] # 前进栈
self.current = None
def visit(self, url):
"""访问新页面"""
if self.current:
self.back_stack.append(self.current)
print(f"将 {self.current} 入栈到后退栈")
self.current = url
self.forward_stack.clear() # 清空前进栈
print(f"访问新页面: {url}")
def go_back(self):
"""后退"""
if self.back_stack:
self.forward_stack.append(self.current)
self.current = self.back_stack.pop()
print(f"后退到: {self.current}")
# 测试
browser = BrowserHistory()
browser.visit("google.com")
browser.visit("python.org") # google.com 入栈到后退栈
browser.visit("github.com") # python.org 入栈到后退栈
browser.go_back() # 后退到 python.org
入栈操作性能对比
import time
def performance_test():
"""测试不同入栈方式的性能"""
# 方法1: 列表append
stack1 = []
start = time.time()
for i in range(100000):
stack1.append(i)
print(f"列表append耗时: {time.time() - start:.4f}秒")
# 方法2: 使用collections.deque
from collections import deque
stack2 = deque() # 双端队列也可以当栈用
start = time.time()
for i in range(100000):
stack2.append(i) # 与列表相似的入栈方式
print(f"deque耗时: {time.time() - start:.4f}秒")
performance_test()
- 入栈方法: 使用
list.append()或deque.append() - 验证检查: 添加栈容量限制、类型检查等
- 实际应用: 括号匹配、函数调用、撤销操作等
- 性能考虑: 普通场景用列表即可，大量操作考虑deque
希望这些案例能帮助你理解Python栈的入栈操作！
