Scrapling/docs/tutorials/migrating_from_beautifulsoup.md

Migrating from BeautifulSoup to Scrapling

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If you're already familiar with BeautifulSoup, you're in for a treat. Scrapling is faster, provides similar parsing capabilities, and adds powerful new features for fetching and handling modern web pages. This guide will help you quickly adapt your existing BeautifulSoup code to take advantage of Scrapling's capabilities.

Below is a table that covers the most common operations you'll perform when scraping web pages. Each row shows how to accomplish a specific task in BeautifulSoup and the corresponding way to do it in Scrapling.

You will notice some shortcuts in BeautifulSoup are missing in Scrapling, but that's one of the reasons that makes BeautifulSoup slower than Scrapling. The point is: If the same feature can be used in a short oneliner, there is no need to sacrifice performance to make that short line shorter :)

Task	BeautifulSoup Code	Scrapling Code
Parser import	from bs4 import BeautifulSoup	from scrapling.parser import Adaptor
Parsing HTML from string	soup = BeautifulSoup(html, 'html.parser')	page = Adaptor(html)
Finding a single element	element = soup.find('div', class_='example')	element = page.find('div', class_='example')
Finding multiple elements	elements = soup.find_all('div', class_='example')	elements = page.find_all('div', class_='example')
Finding a single element (Example 2)	element = soup.find('div', attrs={"class": "example"})	element = page.find('div', {"class": "example"})
Finding a single element (Example 3)	element = soup.find(re.compile("^b"))	element = page.find(re.compile("^b")) element = page.find_by_regex(r"^b")
Finding a single element (Example 4)	element = soup.find(lambda e: len(list(e.children)) > 0)	element = page.find(lambda e: len(e.children) > 0)
Finding a single element (Example 5)	element = soup.find(["a", "b"])	element = page.find(["a", "b"])
Find element by its text content	element = soup.find(text="some text")	element = page.find_by_text("some text", partial=False)
Using CSS selectors to find the first matching element	elements = soup.select_one('div.example')	elements = page.css_first('div.example')
Using CSS selectors to find all matching element	elements = soup.select('div.example')	elements = page.css('div.example')
Get a prettified version of the page/element source	prettified = soup.prettify()	prettified = page.prettify()
Get a Non-pretty version of the page/element source	source = str(soup)	source = page.body
Get tag name of an element	name = element.name	name = element.tag
Extracting text content of an element	string = element.string	string = element.text
Extracting all the text in a document or beneath a tag	text = soup.get_text(strip=True)	text = page.get_all_text(strip=True)
Access the dictionary of attributes	attrs = element.attrs	attrs = element.attrib
Extracting attributes	attr = element['href']	attr = element.attrib['href']
Navigating to parent	parent = element.parent	parent = element.parent
Get all parents of an element	parents = list(element.parents)	parents = list(element.iterancestors())
Searching for an element in the parents of an element	target_parent = element.find_parent("a")	target_parent = element.find_ancestor(lambda p: p.tag == 'a')
Get all siblings of an element	N/A	siblings = element.siblings
Get next sibling of an element	next_element = element.next_sibling	next_element = element.next
Searching for an element in the siblings of an element	target_sibling = element.find_next_sibling("a") target_sibling = element.find_previous_sibling("a")	target_sibling = element.siblings.search(lambda s: s.tag == 'a')
Searching for elements in the siblings of an element	target_sibling = element.find_next_siblings("a") target_sibling = element.find_previous_siblings("a")	target_sibling = element.siblings.filter(lambda s: s.tag == 'a')
Searching for an element in the next elements of an element	target_parent = element.find_next("a")	target_parent = element.below_elements.search(lambda p: p.tag == 'a')
Searching for elements in the next elements of an element	target_parent = element.find_all_next("a")	target_parent = element.below_elements.filter(lambda p: p.tag == 'a')
Searching for an element in the previous elements of an element	target_parent = element.find_previous("a")	target_parent = element.path.search(lambda p: p.tag == 'a')
Searching for elements in the previous elements of an element	target_parent = element.find_all_previous("a")	target_parent = element.path.filter(lambda p: p.tag == 'a')
Get previous sibling of an element	prev_element = element.previous_sibling	prev_element = element.previous
Navigating to children	children = list(element.children)	children = element.children
Get all descendants of an element	children = list(element.descendants)	children = element.below_elements
Filtering a group of elements that satisfies a condition	group = soup.find('p', 'story').css.filter('a')	group = page.find_all('p', 'story').filter(lambda p: p.tag == 'a')

One point to remember: BeautifulSoup provides features for modifying and manipulating the page after parsing it. Scrapling focuses more on Scraping the page faster for you, and then you can do what you want with the extracted information. So, two different tools can be used in Web SScraping, but one of them specializes in Web Scraping :)

Putting It All Together

Here's a simple example of scraping a web page to extract all the links using BeautifulSoup and Scrapling.

With BeautifulSoup:

import requests
from bs4 import BeautifulSoup

url = 'http://example.com'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

links = soup.find_all('a')
for link in links:
    print(link['href'])

With Scrapling:

from scrapling import Fetcher

url = 'http://example.com'
page = Fetcher.get(url=url)

links = page.css('a::attr(href)')
for link in links:
    print(link)

As you can see, Scrapling simplifies the process by handling the fetching and parsing in a single step, making your code cleaner and more efficient.

Additional Notes:

• Different parsers: BeautifulSoup allows you to set the parser engine to use, and one of them is lxml. Scrapling doesn't do that and uses the lxml library by default for performance reasons.
• Element Types: In BeautifulSoup, elements are Tag objects, while in Scrapling, they are Adaptor objects. However, they provide similar methods and properties for navigation and data extraction.
• Error Handling: Both libraries return None when an element is not found (e.g., soup.find() or page.css_first()). To avoid errors, Check for None before accessing properties.
• Text Extraction: Scrapling provides additional methods for handling text through TextHandler, such as clean(), which can be helpful for removing extra whitespace or unwanted characters. Please check out the documentation for the complete list.

The documentation provides more details on Scrapling's features and the full list of arguments that can be passed to all methods.

This guide should make your transition from BeautifulSoup to Scrapling smooth and straightforward. Happy scraping!