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python - 使用 BeautifulSoup 从表中提取某些列

问题描述

您好，我正在尝试使用 html 表从本网站确定在 ebay 上购买商品的日期：https ://offer.ebay.com/ws/eBayISAPI.dll?ViewBidsLogin&item=173653442617&rt=nc&_trksid=p2047675.l2564

我的python代码：

def soup_creator(url):
  # Downloads the eBay page for processing
  res = requests.get(url)
  # Raises an exception error if there's an error downloading the website
  res.raise_for_status()
  # Creates a BeautifulSoup object for HTML parsing
  return BeautifulSoup(res.text, 'lxml')

soup = soup_creator(item_link)      
purchases = soup.find('div', attrs={'class' : 'BHbidSecBorderGrey'})
purchases = purchases.findAll('tr', attrs={'bgcolor' : '#ffffff'})
for purchase in purchases:
    date = purchase.findAll("td", {"align": "left"})
    date = date[2].get_text()
    print(purchase)

当我运行它时，打印语句不返回任何内容，我认为这意味着它没有找到任何东西。我希望它打印出这样的内容：

Jul-02-19 18:22:28 PDT
Jun-27-19 16:12:59 PDT
Jun-23-19 06:46:23 PDT
...

标签： pythonhtmlxmlweb-scrapingbeautifulsoup

解决方案

我使用列表解包和切片从表格行中选择正确的单元格，然后提取它们的文本。使用切片列列表可以解决[2:5]问题。

import requests
from bs4 import BeautifulSoup
import re

def make_soup(url: str) -> BeautifulSoup:
    res = requests.get(url)
    res.raise_for_status()
    return BeautifulSoup(res.text, 'html.parser')

def extract_purchases(soup: BeautifulSoup) -> list:
    table = soup.find('th', text=re.compile('Date of Purchase')).find_parent('table')
    purchases = []
    for row in table.find_all('tr')[1:]:
        price_cell, qty_cell, date_cell = row.find_all('td')[2:5]
        p = {
            'price': price_cell.text.strip(),
            'quantity': qty_cell.text.strip(),
            'date': date_cell.text.strip()
        }
        purchases.append(p)
    return purchases

if __name__ == '__main__':
    url = 'https://offer.ebay.com/ws/eBayISAPI.dll?ViewBidsLogin&item=173653442617&rt=nc&_trksid=p2047675.l2564'
    soup = make_soup(url)
    purchases = extract_purchases(soup)

    from pprint import pprint
    pprint(purchases)

输出：

[{'date': 'Jul-02-19 18:22:28 PDT', 'price': 'US $54.08', 'quantity': '1'},
 {'date': 'Jun-27-19 16:12:59 PDT', 'price': 'US $54.08', 'quantity': '1'},
 {'date': 'Jun-23-19 06:46:23 PDT', 'price': 'US $54.08', 'quantity': '1'},
 {'date': 'Jun-20-19 09:14:07 PDT', 'price': 'US $54.08', 'quantity': '1'},
 {'date': 'May-23-19 09:48:59 PDT', 'price': 'US $63.04', 'quantity': '1'},
 {'date': 'May-20-19 06:05:24 PDT', 'price': 'US $63.04', 'quantity': '1'},
 {'date': 'May-17-19 13:10:38 PDT', 'price': 'US $63.04', 'quantity': '1'},
 {'date': 'May-04-19 17:11:32 PDT', 'price': 'US $55.36', 'quantity': '1'},
 {'date': 'Apr-24-19 15:27:42 PDT', 'price': 'US $55.36', 'quantity': '1'},
 {'date': 'Apr-07-19 17:03:05 PDT', 'price': 'US $54.08', 'quantity': '1'},
 {'date': 'Apr-06-19 21:20:17 PDT', 'price': 'US $54.08', 'quantity': '1'},
 {'date': 'Apr-06-19 13:29:45 PDT', 'price': 'US $54.08', 'quantity': '1'},
 {'date': 'Apr-05-19 14:42:23 PDT', 'price': 'US $54.08', 'quantity': '1'},
 {'date': 'Apr-03-19 21:37:14 PDT', 'price': 'US $54.08', 'quantity': '1'},
 {'date': 'Apr-02-19 18:23:45 PDT', 'price': 'US $54.08', 'quantity': '1'},
 {'date': 'Mar-31-19 06:01:36 PDT', 'price': 'US $54.08', 'quantity': '1'},
 {'date': 'Mar-25-19 14:37:27 PDT', 'price': 'US $56.64', 'quantity': '1'},
 {'date': 'Feb-12-19 10:57:22 PST', 'price': 'US $53.94', 'quantity': '1'}]

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