增加对书籍销量和书籍日期的爬取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()

这段代码看起来是一个爬虫程序，用来爬取当当网上关于某个关键词的图书信息。其中，程序使用了 requests 库来发送 HTTP 请求，使用了 BeautifulSoup 库来解析 HTML 页面，从而获取书名、作者、价格等信息。为了提高效率，程序使用了多线程的方式来并行爬取多个页面。在主函数中，先创建了多个线程，每个线程负责爬取一定范围内的页面，然后启动线程，最后等待所有线程执行完毕。

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在这段代码中增加对书籍销量和书籍日期的爬取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()

可以在循环中增加对书籍销量和日期的爬取，可以使用类似的方法获取这些信息。例如：

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
    sales = book.find('span', class_='search_sales').text
    date = book.find('span', class_='search_book_publishtime').text
    print(title, author, price, sales, date)

这样就可以将书籍的销量和日期信息一并爬取出来了。

增加代码对评论数量的爬取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=%BF%C6%BB%C3%D0%A1%CB%B5&act=input&page_index=1&sort_type=sort_default#J_tab'.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()

要增加代码对评论数量的爬取，您需要先确定评论数量的位置和元素，然后在循环中找到该元素并提取其文本。以下是一个修改过的爬取代码示例：

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=%BF%C6%BB%C3%D0%A1%CB%B5&act=input&page_index=1&sort_type=sort_default#J_tab'.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
comment = book.find('p', class_='search_star_line').find_all('a')[2].text # 获取评论数量元素并提取文本
print(title, author, price, comment)

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()

在这个示例中，我们使用了`book.find('p', class_='search_star_line').find_all('a')[2].text`来找到评论数量元素并提取文本。您可能需要根据网站的具体结构进行调整。