Web scraping 是否有任何具体的声明,以使美丽的团队能够正确地进行刮除?

Web scraping 是否有任何具体的声明,以使美丽的团队能够正确地进行刮除?,web-scraping,beautifulsoup,Web Scraping,Beautifulsoup,我正试图从维基百科上刮桌子。我试图传递('div',class='mw parser output'),它返回了一个文本。但为什么表标记返回了一个空列表?请解释一下。谢谢。要从wiki页面中删除第二个表,可以使用以下示例: import requests from bs4 import BeautifulSoup url = 'https://en.wikipedia.org/wiki/Makati' soup = BeautifulSoup(requests.get(url).conten

我正试图从维基百科上刮桌子。我试图传递('div',class='mw parser output'),它返回了一个文本。但为什么表标记返回了一个空列表?请解释一下。谢谢。

要从wiki页面中删除第二个表,可以使用以下示例:

import requests
from bs4 import BeautifulSoup

url = 'https://en.wikipedia.org/wiki/Makati'

soup = BeautifulSoup(requests.get(url).content, 'html.parser')

second_table = soup.select('.wikitable')[1]
for tr in second_table.select('tr'):
    print('{:<25} {:<25} {:<25} {:<25} {:<25}'.format(*[t.get_text(strip=True) for t in tr.select('th, td')]))
Barangay                  Population (2004)         Population (2010)[51]     Area (km2)                District                 
Bangkal                   22,433                    23,378                    0.74                      1st                      
Bel-Air                   9,330                     18,280                    1.71                      1st                      
Carmona                   3,699                     3,096                     0.34                      1st                      
Cembo                     25,815                    27,998                    0.22                      2nd                      
Comembo                   14,174                    14,433                    0.27                      2nd                      
Dasmariñas                5,757                     5,654                     1.90                      1st                      
East Rembo                23,902                    26,433                    0.44                      2nd                      
Forbes Park               3,420                     2,533                     2.53                      1st                      
Guadalupe Nuevo           22,493                    18,271                    0.57                      2nd                      
Guadalupe Viejo           13,632                    16,411                    0.62                      2nd                      

... and so on.