Python语言进阶知识点总结

def select_sort(origin_items, comp=lambda x, y: x < y):
 """简单选择排序"""
 items = origin_items[:]
 for i in range(len(items) - 1):
  min_index = i
  for j in range(i + 1, len(items)):
   if comp(items[j], items[min_index]):
    min_index = j
  items[i], items[min_index] = items[min_index], items[i]
 return items
def bubble_sort(origin_items, comp=lambda x, y: x > y):
 """高质量冒泡排序(搅拌排序)"""
 items = origin_items[:]
 for i in range(len(items) - 1):
  swapped = False
  for j in range(i, len(items) - 1 - i):
   if comp(items[j], items[j + 1]):
    items[j], items[j + 1] = items[j + 1], items[j]
    swapped = True
  if swapped:
   swapped = False
   for j in range(len(items) - 2 - i, i, -1):
    if comp(items[j - 1], items[j]):
     items[j], items[j - 1] = items[j - 1], items[j]
     swapped = True
  if not swapped:
   break
 return items
def merge_sort(items, comp=lambda x, y: x <= y):
 """归并排序(分治法)"""
 if len(items) < 2:
  return items[:]
 mid = len(items) // 2
 left = merge_sort(items[:mid], comp)
 right = merge_sort(items[mid:], comp)
 return merge(left, right, comp)


def merge(items1, items2, comp):
 """合并(将两个有序的列表合并成一个有序的列表)"""
 items = []
 index, index2 = 0, 0
 while index1 < len(items1) and index2 < len(items2):
  if comp(items1[index1], items2[index2]):
   items.append(items1[index1])
   index1 += 1
  else:
   items.append(items2[index2])
   index2 += 1
 items += items1[index1:]
 items += items2[index2:]
 return items
def seq_search(items, key):
 """顺序查找"""
 for index, item in enumerate(items):
  if item == key:
   return index
 return -1
def bin_search(items, key):
 """折半查找"""
 start, end = 0, len(items) - 1
 while start <= end:
  mid = (start + end) // 2
  if key > items[mid]:
   start = mid + 1
  elif key < items[mid]:
   end = mid - 1
  else:
   return mid
 return -1

prices = {
 'AAPL': 191.88,
 'GOOG': 1186.96,
 'IBM': 149.24,
 'ORCL': 48.44,
 'ACN': 166.89,
 'FB': 208.09,
 'SYMC': 21.29
}
# 用股票价格大于100元的股票构造一个新的字典
prices2 = {key: value for key, value in prices.items() if value > 100}
print(prices2)

names = ['关羽', '张飞', '赵云', '马超', '黄忠']
courses = ['语文', '数学', '英语']
# 录入五个学生三门课程的成绩
# 错误 - 参考http://pythontutor.com/visualize.html#mode=edit
# scores = [[None] * len(courses)] * len(names)
scores = [[None] * len(courses) for _ in range(len(names))]
for row, name in enumerate(names):
 for col, course in enumerate(courses):
  scores[row][col] = float(input(f'请输入{name}的{course}成绩: '))
  print(scores)

"""
从列表中找出最大的或最小的N个元素
堆结构(大根堆/小根堆)
"""
import heapq

list1 = [34, 25, 12, 99, 87, 63, 58, 78, 88, 92]
list2 = [
 {'name': 'IBM', 'shares': 100, 'price': 91.1},
 {'name': 'AAPL', 'shares': 50, 'price': 543.22},
 {'name': 'FB', 'shares': 200, 'price': 21.09},
 {'name': 'HPQ', 'shares': 35, 'price': 31.75},
 {'name': 'YHOO', 'shares': 45, 'price': 16.35},
 {'name': 'ACME', 'shares': 75, 'price': 115.65}
]
print(heapq.nlargest(3, list1))
print(heapq.nsmallest(3, list1))
print(heapq.nlargest(2, list2, key=lambda x: x['price']))
print(heapq.nlargest(2, list2, key=lambda x: x['shares']))
"""
迭代工具 - 排列 / 组合 / 笛卡尔积
"""
import itertools

itertools.permutations('ABCD')
itertools.combinations('ABCDE', 3)
itertools.product('ABCD', '123')

"""
找出序列中出现次数最多的元素
"""
from collections import Counter

words = [
 'look', 'into', 'my', 'eyes', 'look', 'into', 'my', 'eyes',
 'the', 'eyes', 'the', 'eyes', 'the', 'eyes', 'not', 'around',
 'the', 'eyes', "don't", 'look', 'around', 'the', 'eyes',
 'look', 'into', 'my', 'eyes', "you're", 'under'
]
counter = Counter(words)
print(counter.most_common(3))

# 公鸡5元一只 母鸡3元一只 小鸡1元三只
# 用100元买100只鸡 问公鸡/母鸡/小鸡各多少只
for x in range(20):
 for y in range(33):
  z = 100 - x - y
  if 5 * x + 3 * y + z // 3 == 100 and z % 3 == 0:
   print(x, y, z)

# A、B、C、D、E五人在某天夜里合伙捕鱼 最后疲惫不堪各自睡觉
# 第二天A第一个醒来 他将鱼分为5份 扔掉多余的1条 拿走自己的一份
# B第二个醒来 也将鱼分为5份 扔掉多余的1条 拿走自己的一份
# 然后C、D、E依次醒来也按同样的方式分鱼 问他们至少捕了多少条鱼
fish = 1
while True:
 total = fish
 enough = True
 for _ in range(5):
  if (total - 1) % 5 == 0:
   total = (total - 1) // 5 * 4
  else:
   enough = False
   break
 if enough:
  print(fish)
  break
 fish += 1

"""
贪婪法：在对问题求解时，总是做出在当前看来是最好的选择，不追求最优解，快速找到满意解。
输入：
20 6
电脑 200 20
收音机 20 4
钟 175 10
花瓶 50 2
书 10 1
油画 90 9
"""
class Thing(object):
 """物品"""

 def __init__(self, name, price, weight):
  self.name = name
  self.price = price
  self.weight = weight

 @property
 def value(self):
  """价格重量比"""
  return self.price / self.weight


def input_thing():
 """输入物品信息"""
 name_str, price_str, weight_str = input().split()
 return name_str, int(price_str), int(weight_str)


def main():
 """主函数"""
 max_weight, num_of_things = map(int, input().split())
 all_things = []
 for _ in range(num_of_things):
  all_things.append(Thing(*input_thing()))
 all_things.sort(key=lambda x: x.value, reverse=True)
 total_weight = 0
 total_price = 0
 for thing in all_things:
  if total_weight + thing.weight <= max_weight:
   print(f'小偷拿走了{thing.name}')
   total_weight += thing.weight
   total_price += thing.price
 print(f'总价值: {total_price}美元')


if __name__ == '__main__':
 main()

"""
快速排序 - 选择枢轴对元素进行划分，左边都比枢轴小右边都比枢轴大
"""
def quick_sort(origin_items, comp=lambda x, y: x <= y):
 items = origin_items[:]
 _quick_sort(items, 0, len(items) - 1, comp)
 return items


def _quick_sort(items, start, end, comp):
 if start < end:
  pos = _partition(items, start, end, comp)
  _quick_sort(items, start, pos - 1, comp)
  _quick_sort(items, pos + 1, end, comp)


def _partition(items, start, end, comp):
 pivot = items[end]
 i = start - 1
 for j in range(start, end):
  if comp(items[j], pivot):
   i += 1
   items[i], items[j] = items[j], items[i]
 items[i + 1], items[end] = items[end], items[i + 1]
 return i + 1

"""
递归回溯法：叫称为试探法，按选优条件向前搜索，当搜索到某一步，发现原先选择并不优或达不到目标时，就退回一步重新选择，比较经典的问题包括骑士巡逻、八皇后和迷宫寻路等。
"""
import sys
import time

SIZE = 5
total = 0


def print_board(board):
 for row in board:
  for col in row:
   print(str(col).center(4), end='')
  print()


def patrol(board, row, col, step=1):
 if row >= 0 and row < SIZE and \
  col >= 0 and col < SIZE and \
  board[row][col] == 0:
  board[row][col] = step
  if step == SIZE * SIZE:
   global total
   total += 1
   print(f'第{total}种走法: ')
   print_board(board)
  patrol(board, row - 2, col - 1, step + 1)
  patrol(board, row - 1, col - 2, step + 1)
  patrol(board, row + 1, col - 2, step + 1)
  patrol(board, row + 2, col - 1, step + 1)
  patrol(board, row + 2, col + 1, step + 1)
  patrol(board, row + 1, col + 2, step + 1)
  patrol(board, row - 1, col + 2, step + 1)
  patrol(board, row - 2, col + 1, step + 1)
  board[row][col] = 0


def main():
 board = [[0] * SIZE for _ in range(SIZE)]
 patrol(board, SIZE - 1, SIZE - 1)


if __name__ == '__main__':
 main()

"""
动态规划 - 适用于有重叠子问题和最优子结构性质的问题
使用动态规划方法所耗时间往往远少于朴素解法(用空间换取时间)
"""
def fib(num, temp={}):
 """用递归计算Fibonacci数"""
 if num in (1, 2):
  return 1
 try:
  return temp[num]
 except KeyError:
  temp[num] = fib(num - 1) + fib(num - 2)
  return temp[num]

def main():
 items = list(map(int, input().split()))
 size = len(items)
 overall, partial = {}, {}
 overall[size - 1] = partial[size - 1] = items[size - 1]
 for i in range(size - 2, -1, -1):
  partial[i] = max(items[i], partial[i + 1] + items[i])
  overall[i] = max(partial[i], overall[i + 1])
 print(overall[0])


if __name__ == '__main__':
 main()

items1 = list(map(lambda x: x ** 2, filter(lambda x: x % 2, range(1, 10))))
items2 = [x ** 2 for x in range(1, 10) if x % 2]

def record_time(func):
 """自定义装饰函数的装饰器"""
 
 @wraps(func)
 def wrapper(*args, **kwargs):
  start = time()
  result = func(*args, **kwargs)
  print(f'{func.__name__}: {time() - start}秒')
  return result
  
 return wrapper

from functools import wraps
from time import time


def record(output):
 """自定义带参数的装饰器"""
	
	def decorate(func):
		
		@wraps(func)
		def wrapper(*args, **kwargs):
			start = time()
			result = func(*args, **kwargs)
			output(func.__name__, time() - start)
			return result
   
		return wrapper
	
	return decorate
from functools import wraps
from time import time


class Record():
 """自定义装饰器类(通过__call__魔术方法使得对象可以当成函数调用)"""

 def __init__(self, output):
  self.output = output

 def __call__(self, func):

  @wraps(func)
  def wrapper(*args, **kwargs):
   start = time()
   result = func(*args, **kwargs)
   self.output(func.__name__, time() - start)
   return result

  return wrapper

from functools import wraps


def singleton(cls):
 """装饰类的装饰器"""
 instances = {}

 @wraps(cls)
 def wrapper(*args, **kwargs):
  if cls not in instances:
   instances[cls] = cls(*args, **kwargs)
  return instances[cls]

 return wrapper


@singleton
class President():
 """总统(单例类)"""
 pass

from functools import wraps
from threading import Lock


def singleton(cls):
 """线程安全的单例装饰器"""
 instances = {}
 locker = Lock()

 @wraps(cls)
 def wrapper(*args, **kwargs):
  if cls not in instances:
   with locker:
    if cls not in instances:
     instances[cls] = cls(*args, **kwargs)
  return instances[cls]

 return wrapper

"""
月薪结算系统 - 部门经理每月15000 程序员每小时200 销售员1800底薪加销售额5%提成
"""
from abc import ABCMeta, abstractmethod


class Employee(metaclass=ABCMeta):
 """员工(抽象类)"""

 def __init__(self, name):
  self.name = name

 @abstractmethod
 def get_salary(self):
  """结算月薪(抽象方法)"""
  pass


class Manager(Employee):
 """部门经理"""

 def get_salary(self):
  return 15000.0


class Programmer(Employee):
 """程序员"""

 def __init__(self, name, working_hour=0):
  self.working_hour = working_hour
  super().__init__(name)

 def get_salary(self):
  return 200.0 * self.working_hour


class Salesman(Employee):
 """销售员"""

 def __init__(self, name, sales=0.0):
  self.sales = sales
  super().__init__(name)

 def get_salary(self):
  return 1800.0 + self.sales * 0.05


class EmployeeFactory():
 """创建员工的工厂（工厂模式 - 通过工厂实现对象使用者和对象之间的解耦合）"""

 @staticmethod
 def create(emp_type, *args, **kwargs):
  """创建员工"""
  emp_type = emp_type.upper()
  emp = None
  if emp_type == 'M':
   emp = Manager(*args, **kwargs)
  elif emp_type == 'P':
   emp = Programmer(*args, **kwargs)
  elif emp_type == 'S':
   emp = Salesman(*args, **kwargs)
  return emp


def main():
 """主函数"""
 emps = [
  EmployeeFactory.create('M', '曹操'), 
  EmployeeFactory.create('P', '荀彧', 120),
  EmployeeFactory.create('P', '郭嘉', 85), 
  EmployeeFactory.create('S', '典韦', 123000),
 ]
 for emp in emps:
  print('%s: %.2f元' % (emp.name, emp.get_salary()))


if __name__ == '__main__':
 main()

"""
经验：符号常量总是优于字面常量，枚举类型是定义符号常量的最佳选择
"""
from enum import Enum, unique

import random


@unique
class Suite(Enum):
 """花色"""

 SPADE, HEART, CLUB, DIAMOND = range(4)

 def __lt__(self, other):
  return self.value < other.value


class Card():
 """牌"""

 def __init__(self, suite, face):
  """初始化方法"""
  self.suite = suite
  self.face = face

 def show(self):
  """显示牌面"""
  suites = ['♠️', '♥️', '♣️', '♦️']
  faces = ['', 'A', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'J', 'Q', 'K']
  return f'{suites[self.suite.value]} {faces[self.face]}'

 def __str__(self):
  return self.show()

 def __repr__(self):
  return self.show()


class Poker():
 """扑克"""

 def __init__(self):
  self.index = 0
  self.cards = [Card(suite, face)
      for suite in Suite
      for face in range(1, 14)]

 def shuffle(self):
  """洗牌（随机乱序）"""
  random.shuffle(self.cards)
  self.index = 0

 def deal(self):
  """发牌"""
  card = self.cards[self.index]
  self.index += 1
  return card

 @property
 def has_more(self):
  return self.index < len(self.cards)


class Player():
 """玩家"""

 def __init__(self, name):
  self.name = name
  self.cards = []

 def get_one(self, card):
  """摸一张牌"""
  self.cards.append(card)

 def sort(self, comp=lambda card: (card.suite, card.face)):
  """整理手上的牌"""
  self.cards.sort(key=comp)


def main():
 """主函数"""
 poker = Poker()
 poker.shuffle()
 players = [Player('东邪'), Player('西毒'), Player('南帝'), Player('北丐')]
 while poker.has_more:
  for player in players:
    player.get_one(poker.deal())
 for player in players:
  player.sort()
  print(player.name, end=': ')
  print(player.cards)


if __name__ == '__main__':
 main()

typedef struct_object {
 /* 引用计数 */
 int ob_refcnt;
 /* 对象指针 */
 struct_typeobject *ob_type;
} PyObject;
/* 增加引用计数的宏定义 */
#define Py_INCREF(op) ((op)->ob_refcnt++)
/* 减少引用计数的宏定义 */
#define Py_DECREF(op) \ //减少计数
 if (--(op)->ob_refcnt != 0) \
  ; \
 else \
  __Py_Dealloc((PyObject *)(op))

# 循环引用会导致内存泄露 - Python除了引用技术还引入了标记清理和分代回收
# 在Python 3.6以前如果重写__del__魔术方法会导致循环引用处理失效
# 如果不想造成循环引用可以使用弱引用
list1 = []
list2 = [] 
list1.append(list2)
list2.append(list1)

class SetOnceMappingMixin():
 """自定义混入类"""
 __slots__ = ()

 def __setitem__(self, key, value):
  if key in self:
   raise KeyError(str(key) + ' already set')
  return super().__setitem__(key, value)


class SetOnceDict(SetOnceMappingMixin, dict):
 """自定义字典"""
 pass


my_dict= SetOnceDict()
try:
 my_dict['username'] = 'jackfrued'
 my_dict['username'] = 'hellokitty'
except KeyError:
 pass
print(my_dict)

import threading


class SingletonMeta(type):
 """自定义元类"""

 def __init__(cls, *args, **kwargs):
  cls.__instance = None
  cls.__lock = threading.Lock()
  super().__init__(*args, **kwargs)

 def __call__(cls, *args, **kwargs):
  if cls.__instance is None:
   with cls.__lock:
    if cls.__instance is None:
     cls.__instance = super().__call__(*args, **kwargs)
  return cls.__instance


class President(metaclass=SingletonMeta):
 """总统(单例类)"""
 pass

class StreamHasher():
 """哈希摘要生成器(策略模式)"""

 def __init__(self, alg='md5', size=4096):
  self.size = size
  alg = alg.lower()
  self.hasher = getattr(__import__('hashlib'), alg.lower())()

 def __call__(self, stream):
  return self.to_digest(stream)

 def to_digest(self, stream):
  """生成十六进制形式的摘要"""
  for buf in iter(lambda: stream.read(self.size), b''):
   self.hasher.update(buf)
  return self.hasher.hexdigest()

def main():
 """主函数"""
 hasher1 = StreamHasher()
 with open('Python-3.7.1.tgz', 'rb') as stream:
  print(hasher1.to_digest(stream))
 hasher2 = StreamHasher('sha1')
 with open('Python-3.7.1.tgz', 'rb') as stream:
  print(hasher2(stream))


if __name__ == '__main__':
 main()

def fib(num):
 """生成器"""
 a, b = 0, 1
 for _ in range(num):
  a, b = b, a + b
  yield a
 
 
class Fib(object):
 """迭代器"""
 
 def __init__(self, num):
  self.num = num
  self.a, self.b = 0, 1
  self.idx = 0
 
 def __iter__(self):
  return self

 def __next__(self):
  if self.idx < self.num:
   self.a, self.b = self.b, self.a + self.b
   self.idx += 1
   return self.a
  raise StopIteration()

"""
面试题：进程和线程的区别和联系？
进程 - 操作系统分配内存的基本单位 - 一个进程可以包含一个或多个线程
线程 - 操作系统分配CPU的基本单位
并发编程（concurrent programming）
1. 提升执行性能 - 让程序中没有因果关系的部分可以并发的执行
2. 改善用户体验 - 让耗时间的操作不会造成程序的假死
"""
import glob
import os
import threading

from PIL import Image

PREFIX = 'thumbnails'


def generate_thumbnail(infile, size, format='PNG'):
 """生成指定图片文件的缩略图"""
	file, ext = os.path.splitext(infile)
	file = file[file.rfind('/') + 1:]
	outfile = f'{PREFIX}/{file}_{size[0]}_{size[1]}.{ext}'
	img = Image.open(infile)
	img.thumbnail(size, Image.ANTIALIAS)
	img.save(outfile, format)


def main():
 """主函数"""
	if not os.path.exists(PREFIX):
		os.mkdir(PREFIX)
	for infile in glob.glob('images/*.png'):
		for size in (32, 64, 128):
   # 创建并启动线程
			threading.Thread(
				target=generate_thumbnail, 
				args=(infile, (size, size))
			).start()
			

if __name__ == '__main__':
	main()

"""
多线程程序如果没有竞争资源处理起来通常也比较简单
当多个线程竞争临界资源的时候如果缺乏必要的保护措施就会导致数据错乱
说明：临界资源就是被多个线程竞争的资源
"""
import time
import threading

from concurrent.futures import ThreadPoolExecutor


class Account(object):
 """银行账户"""

 def __init__(self):
  self.balance = 0.0
  self.lock = threading.Lock()

 def deposit(self, money):
  # 通过锁保护临界资源
  with self.lock:
   new_balance = self.balance + money
   time.sleep(0.001)
   self.balance = new_balance


class AddMoneyThread(threading.Thread):
 """自定义线程类"""

 def __init__(self, account, money):
  self.account = account
  self.money = money
  # 自定义线程的初始化方法中必须调用父类的初始化方法
  super().__init__()

 def run(self):
  # 线程启动之后要执行的操作
  self.account.deposit(self.money)

def main():
 """主函数"""
 account = Account()
 # 创建线程池
 pool = ThreadPoolExecutor(max_workers=10)
 futures = []
 for _ in range(100):
  # 创建线程的第1种方式
  # threading.Thread(
  #  target=account.deposit, args=(1, )
  # ).start()
  # 创建线程的第2种方式
  # AddMoneyThread(account, 1).start()
  # 创建线程的第3种方式
  # 调用线程池中的线程来执行特定的任务
  future = pool.submit(account.deposit, 1)
  futures.append(future)
 # 关闭线程池
 pool.shutdown()
 for future in futures:
  future.result()
 print(account.balance)


if __name__ == '__main__':
 main()

"""
多个线程竞争一个资源 - 保护临界资源 - 锁（Lock/RLock）
多个线程竞争多个资源（线程数>资源数） - 信号量（Semaphore）
多个线程的调度 - 暂停线程执行/唤醒等待中的线程 - Condition
"""
from concurrent.futures import ThreadPoolExecutor
from random import randint
from time import sleep

import threading


class Account():
 """银行账户"""

 def __init__(self, balance=0):
  self.balance = balance
  lock = threading.Lock()
  self.condition = threading.Condition(lock)

 def withdraw(self, money):
  """取钱"""
  with self.condition:
   while money > self.balance:
    self.condition.wait()
   new_balance = self.balance - money
   sleep(0.001)
   self.balance = new_balance

 def deposit(self, money):
  """存钱"""
  with self.condition:
   new_balance = self.balance + money
   sleep(0.001)
   self.balance = new_balance
   self.condition.notify_all()


def add_money(account):
 while True:
  money = randint(5, 10)
  account.deposit(money)
  print(threading.current_thread().name, 
    ':', money, '====>', account.balance)
  sleep(0.5)


def sub_money(account):
 while True:
  money = randint(10, 30)
  account.withdraw(money)
  print(threading.current_thread().name, 
    ':', money, '<====', account.balance)
  sleep(1)


def main():
 account = Account()
 with ThreadPoolExecutor(max_workers=10) as pool:
  for _ in range(5):
   pool.submit(add_money, account)
   pool.submit(sub_money, account)


if __name__ == '__main__':
 main()

"""
多进程和进程池的使用
多线程因为GIL的存在不能够发挥CPU的多核特性
对于计算密集型任务应该考虑使用多进程
time python3 example22.py
real 0m11.512s
user 0m39.319s
sys  0m0.169s
使用多进程后实际执行时间为11.512秒，而用户时间39.319秒约为实际执行时间的4倍
这就证明我们的程序通过多进程使用了CPU的多核特性，而且这台计算机配置了4核的CPU
"""
import concurrent.futures
import math

PRIMES = [
 1116281,
 1297337,
 104395303,
 472882027,
 533000389,
 817504243,
 982451653,
 112272535095293,
 112582705942171,
 112272535095293,
 115280095190773,
 115797848077099,
 1099726899285419
] * 5


def is_prime(n):
 """判断素数"""
 if n % 2 == 0:
  return False

 sqrt_n = int(math.floor(math.sqrt(n)))
 for i in range(3, sqrt_n + 1, 2):
  if n % i == 0:
   return False
 return True


def main():
 """主函数"""
 with concurrent.futures.ProcessPoolExecutor() as executor:
  for number, prime in zip(PRIMES, executor.map(is_prime, PRIMES)):
   print('%d is prime: %s' % (number, prime))


if __name__ == '__main__':
 main()

"""
异步I/O - async / await
"""
import asyncio


def num_generator(m, n):
 """指定范围的数字生成器"""
 yield from range(m, n + 1)


async def prime_filter(m, n):
 """素数过滤器"""
 primes = []
 for i in num_generator(m, n):
  flag = True
  for j in range(2, int(i ** 0.5 + 1)):
   if i % j == 0:
    flag = False
    break
  if flag:
   print('Prime =>', i)
   primes.append(i)

  await asyncio.sleep(0.001)
 return tuple(primes)


async def square_mapper(m, n):
 """平方映射器"""
 squares = []
 for i in num_generator(m, n):
  print('Square =>', i * i)
  squares.append(i * i)

  await asyncio.sleep(0.001)
 return squares


def main():
 """主函数"""
 loop = asyncio.get_event_loop()
 future = asyncio.gather(prime_filter(2, 100), square_mapper(1, 100))
 future.add_done_callback(lambda x: print(x.result()))
 loop.run_until_complete(future)
 loop.close()


if __name__ == '__main__':
 main()

import asyncio
import re

import aiohttp

PATTERN = re.compile(r'\<title\>(?P<title>.*)\<\/title\>')


async def fetch_page(session, url):
 async with session.get(url, ssl=False) as resp:
  return await resp.text()


async def show_title(url):
 async with aiohttp.ClientSession() as session:
  html = await fetch_page(session, url)
  print(PATTERN.search(html).group('title'))


def main():
 urls = ('https://www.python.org/',
   'https://git-scm.com/',
   'https://www.jd.com/',
   'https://www.taobao.com/',
   'https://www.douban.com/')
 loop = asyncio.get_event_loop()
 tasks = [show_title(url) for url in urls]
 loop.run_until_complete(asyncio.wait(tasks))
 loop.close()


if __name__ == '__main__':
 main()