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rvestvsbeautifulsoup

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rvest is a popular R library for web scraping and parsing HTML and XML documents. It is built on top of the xml2 and httr libraries and provides a simple and consistent API for interacting with web pages.

One of the main advantages of using rvest is its simplicity and ease of use. It provides a number of functions that make it easy to extract information from web pages, even for those who are not familiar with web scraping. The html_nodes and html_node functions allow you to select elements from an HTML document using CSS selectors, similar to how you would select elements in JavaScript.

rvest also provides functions for interacting with forms, including html_form, set_values, and submit_form functions. These functions make it easy to navigate through forms and submit data to the server, which can be useful when scraping sites that require authentication or when interacting with dynamic web pages.

rvest also provides functions for parsing XML documents. It includes xml_nodes and xml_node functions, which also use CSS selectors to select elements from an XML document, as well as xml_attrs and xml_attr functions to extract attributes from elements.

Another advantage of rvest is that it provides a way to handle cookies, so you can keep the session alive while scraping a website, and also you can handle redirections with handle_redirects

beautifulsoup is a Python library for pulling data out of HTML and XML files. It creates parse trees from the source code that can be used to extract data from HTML, which is useful for web scraping. With beautifulsoup, you can search, navigate, and modify the parse tree. It sits atop popular Python parsers like lxml and html5lib, allowing users to try out different parsing strategies or trade speed for flexibility.

beautifulsoup has a number of useful methods and attributes that can be used to extract and manipulate data from an HTML or XML document. Some of the key features include:

  • Searching the parse tree
    You can search the parse tree using the various search methods that beautifulsoup provides, such as find(), find_all(), and select(). These methods take various arguments to search for specific tags, attributes, and text, and return a list of matching elements.
  • Navigating the parse tree
    You can navigate the parse tree using the various navigation methods that beautifulsoup provides, such as next_sibling, previous_sibling, next_element, previous_element, parent, and children. These methods allow you to move up, down, and around the parse tree.
  • Modifying the parse tree
    You can modify the parse tree using the various modification methods that beautifulsoup provides, such as append(), extend(), insert(), insert_before(), and insert_after(). These methods allow you to add new elements to the parse tree, or to change the position of existing elements.
  • Accessing tag attributes
    You can access the attributes of a tag using the attrs property. This property returns a dictionary of the tag's attributes and their values.
  • Accessing tag text
    You can access the text within a tag using the string property. This property returns the text as a string, with any leading or trailing whitespace removed.

With the above feature one can easily extract data out of HTML or XML files. It is widely used in web scraping and other data extraction projects.

It also has features for parsing XML files, special methods for dealing with HTML forms, pretty printing HTML and a few other functionalities.

Highlights


css-selectorsdsl-selectorshttp2

Example Use


library("rvest")

# Rvest can use basic HTTP client to download remote HTML:
tree <- read_html("http://webscraping.fyi/lib/r/rvest")
# or read from string:
tree <- read_html('
<div class="products">
  <a href="/product/1">Cat Food</a>
  <a href="/product/2">Dog Food</a>
</div>
')

# to parse HTML trees with rvest we use r pipes (the %>% symbol) and html_element function:
# we can use css selectors:
print(tree %>% html_element(".products>a") %>% html_text())
# "[1] "\nCat Food\nDog Food\n""

# or XPath:
print(tree %>% html_element(xpath="//div[@class='products']/a") %>% html_text())
# "[1] "\nCat Food\nDog Food\n""

# Additionally rvest offers many quality of life functions:
# html_text2 - removes trailing and leading spaces and joins values
print(tree %>% html_element("div") %>% html_text2())
# "[1] "Cat Food Dog Food""

# html_attr - selects element's attribute:
print(tree %>% html_element("div") %>% html_attr('class'))
# "products"
from bs4 import BeautifulSoup

# this is our HTML page:
html = """
<head>
  <title>Hello World!</title>
</head>
<body>
  <div id="product">
    <h1>Product Title</h1>
    <p>paragraph 1</p>
    <p>paragraph2</p>
    <span class="price">$10</span>
  </div>
</body>
"""

soup = BeautifulSoup(html)

# we can iterate using dot notation:
soup.head.title
"Hello World"

# or use find method to recursively find matching elements:
soup.find(class_="price").text
"$10"

# the selected elements can be modified in place:
soup.find(class_="price").string = "$20"

# beautifulsoup also supports CSS selectors:
soup.select_one("#product .price").text
"$20"

# bs4 also contains various utility functions like HTML formatting
print(soup.prettify())
"""
<html>
 <head>
  <title>
   Hello World!
  </title>
 </head>
 <body>
  <div id="product">
   <h1>
    Product Title
   </h1>
   <p>
    paragraph 1
   </p>
   <p>
    paragraph2
   </p>
   <span class="price">
    $20
   </span>
  </div>
 </body>
</html>
"""

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