优化这段代码，使它能够爬取到全部100页的计算机书籍import requests from bs4 import BeautifulSoup for i in range(1, 101): # 循环爬取100页 url = 'http://search.dangdang.com/?key=%BC%C6%CB%E3%BB%FA&act=input&page_index={}'.format(i) response = requests.get(url) page = response.text soup = BeautifulSoup(page, 'lxml') books = soup.find('ul', class_='bigimg') for book in books.find_all('li'): title = book.find('a', class_='pic').get('title') author = book.find('p', class_='search_book_author').text price = book.find('p', class_='price').find('span', class_='search_now_price').text print(title, author, price)

可以使用多线程或异步进行优化，以加快数据爬取的速度。

以下是使用多线程进行优化的示例代码：

import requests
from bs4 import BeautifulSoup
from threading import Thread

def crawl_books(start, end):
    for i in range(start, end):
        url = 'http://search.dangdang.com/?key=%BC%C6%CB%E3%BB%FA&act=input&page_index={}'.format(i)
        response = requests.get(url)
        page = response.text
        soup = BeautifulSoup(page, 'lxml')
        books = soup.find('ul', class_='bigimg')
        for book in books.find_all('li'):
            title = book.find('a', class_='pic').get('title')
            author = book.find('p', class_='search_book_author').text
            price = book.find('p', class_='price').find('span', class_='search_now_price').text
            print(title, author, price)

threads = []
for i in range(1, 101, 10):
    t = Thread(target=crawl_books, args=(i, i+10))
    threads.append(t)
    t.start()

for t in threads:
    t.join()

以上代码将每10页作为一组，启动一个线程来爬取数据，共启动10个线程，可以大大缩短数据爬取的时间。