Python中常用的数据结构与算法优化技巧指南

# 使用字典来统计字符出现次数
text = "hello world"
char_count = {}
for char in text:
    if char in char_count:
        char_count[char] += 1
    else:
        char_count[char] = 1
print(char_count)

# 使用链表实现队列
class Node:
    def __init__(self, value):
        self.value = value
        self.next = None
 
class Queue:
    def __init__(self):
        self.head = None
        self.tail = None
 
    def enqueue(self, value):
        new_node = Node(value)
        if not self.head:
            self.head = new_node
            self.tail = new_node
        else:
            self.tail.next = new_node
            self.tail = new_node
 
    def dequeue(self):
        if not self.head:
            return None
        value = self.head.value
        self.head = self.head.next
        return value
 
# 使用队列
q = Queue()
q.enqueue(1)
q.enqueue(2)
q.enqueue(3)
print(q.dequeue())  # 输出 1

# 使用生成器生成斐波那契数列
def fibonacci():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b
 
fib = fibonacci()
for _ in range(10):
    print(next(fib))

from collections import Counter
 
text = "hello world"
char_count = Counter(text)
print(char_count)

# 使用快速排序对列表进行排序
def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    return quick_sort(left) + middle + quick_sort(right)
 
arr = [3, 6, 8, 10, 1, 2, 1]
print(quick_sort(arr))

# 使用列表推导式创建新列表
numbers = [1, 2, 3, 4, 5]
squared_numbers = [x**2 for x in numbers]
print(squared_numbers)
 
# 使用生成器表达式创建生成器
even_numbers = (x for x in range(10) if x % 2 == 0)
for num in even_numbers:
    print(num)

from functools import lru_cache
 
@lru_cache(maxsize=None)
def fib(n):
    if n < 2:
        return n
    return fib(n-1) + fib(n-2)
 
print(fib(10))

from concurrent.futures import ThreadPoolExecutor
 
def square(n):
    return n**2
 
numbers = [1, 2, 3, 4, 5]
with ThreadPoolExecutor() as executor:
    results = executor.map(square, numbers)
    print(list(results))

import asyncio
 
async def fetch_data(url):
    # 模拟异步IO操作
    await asyncio.sleep(1)
    return f"Data from {url}"
 
async def main():
    urls = ["url1", "url2", "url3"]
    tasks = [fetch_data(url) for url in urls]
    results = await asyncio.gather(*tasks)
    print(results)
 
asyncio.run(main())

import numpy as np
import pandas as pd
 
# 使用NumPy进行数组操作
arr = np.array([1, 2, 3, 4, 5])
print(arr.mean())  # 计算平均值
 
# 使用Pandas进行数据分析
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'Salary': [50000, 60000, 70000]}
df = pd.DataFrame(data)
print(df.describe())  # 统计描述

import networkx as nx
 
# 创建一个简单的图形
G = nx.Graph()
G.add_edge('A', 'B')
G.add_edge('B', 'C')
G.add_edge('C', 'D')
print(nx.shortest_path(G, 'A', 'D'))  # 输出最短路径

# 使用生成器计算斐波那契数列
def fibonacci():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b
 
fib = fibonacci()
for _ in range(1000000):
    next(fib)