Here's an HTML document I'll be using as an example throughout this document. It's part of a story from "Alice in Wonderland":
html_doc = """<html><head><title>The Dormouse's story</title></head>
<body>
<p class="title"><b>The Dormouse's story</b></p>
<p class="story">Once upon a time there were three little sisters; and their names were
<a href="http://example.com/elsie" class="sister" id="link1">Elsie</a>,
<a href="http://example.com/lacie" class="sister" id="link2">Lacie</a> and
<a href="http://example.com/tillie" class="sister" id="link3">Tillie</a>;
and they lived at the bottom of a well.</p>
<p class="story">...</p>
"""
Running the "three sisters" document through Beautiful Soup gives us a BeautifulSoup object, which represents the document as a nested data structure:
from bs4 import BeautifulSoup
soup = BeautifulSoup(html_doc, 'html.parser')
print(soup.prettify())
# <html>
# <head>
# <title>
# The Dormouse's story
# </title>
# </head>
# <body>
# <p class="title">
# <b>
# The Dormouse's story
# </b>
# </p>
# <p class="story">
# Once upon a time there were three little sisters; and their names were
# <a class="sister" href="http://example.com/elsie" id="link1">
# Elsie
# </a>
# ,
# <a class="sister" href="http://example.com/lacie" id="link2">
# Lacie
# </a>
# and
# <a class="sister" href="http://example.com/tillie" id="link3">
# Tillie
# </a>
# ; and they lived at the bottom of a well.
# </p>
# <p class="story">
# ...
# </p>
# </body>
# </html>
Here are some simple ways to navigate that data structure:
One common task is extracting all the URLs found within a page's <a> tags:
for link in soup.find_all('a'):
print(link.get('href'))
# http://example.com/elsie
# http://example.com/lacie
# http://example.com/tillie
Another common task is extracting all the text from a page:
print(soup.get_text())
# The Dormouse's story
#
# The Dormouse's story
#
# Once upon a time there were three little sisters; and their names were
# Elsie,
# Lacie and
# Tillie;
# and they lived at the bottom of a well.
#
# ...
Example
The following program scrapes the 247ctf.com scoreboard:
import requests
from bs4 import BeautifulSoup
page = requests.get('https://247ctf.com/scoreboard')
soup = BeautifulSoup(page.content, 'html.parser')
table = soup.find('table')
table_body = table.find('tbody')
rows = table_body.find_all('tr')
for row in rows:
print('------------------------------------------------------')
cols = [x.text.strip() for x in row.find_all('td')]
print(f"{cols[2]} is in {cols[0]} place with {cols[4]}.")
