asyncio和aiohttp

asyncio官网

https://docs.python.org/zh-cn/3/library/asyncio-task.html

下面为伪代码：

import aiohttp

import asyncio

from bs4 import BeautifulSoup

import pandas as pd

# 将数据存入 li=[]或数据库

#获取页面

li=[]
async def fetch(url,session):

    async with session.get(url) as response:

        return await response.text


# 解析网页

async def parse(html):

    soup = BeautifulSoup(html,'lxml')

    # 获取网页中的畅销书
    book_list=soup.find('ul',class_='book_list')('li')

    for book in book_list:
        info =book.find_all('div')
        # 获取每本畅销书的排名，名称，评论数，作者，出版社
        rank = info[0].text[0:-1]
        name = info[2].text
        comments = info[3].text.split('条')[0]
        author = info[4].text
        date_and_publisher = info[5].text.split()
        publisher = date_and_publisher[1] if len(date_and_publisher) >= 2 else ''

        # 将每本畅销书的上述信息加入到table中
        li.append([rank, name, comments, author, publisher])



# 处理页面

async def download(url):

    async with aiohttp.ClientSession as session:

        # 获取页面
        html = await fetch(session, url)

        # 解析页面
        await parse(html)

# 全部网页urls


urls=[url1,url2,url3]

# 利用asycio 模块进行一步IO处理
loop = asyncio.get_event_loop()

# 异步获取任务
tasks= [asyncio.ensure_future(download(url)) for url in urls]

tasks = asyncio.gather(*tasks)

loop.run_until_complete(tasks)


# 将table转化为pandas中的DataFrame并保存为CSV格式的文件
df = pd.DataFrame(li, columns=['rank','name','comments','author','publisher'])

df.to_csv('E://douban/dangdang.csv',index=False)