Skip to content


MIT License - - -
98.3 million (month) Jul 26 2019 4.12.3(4 months ago)
152 1 3 MIT
Dec 22 2019 1.1 thousand (month) 2.2.4(3 years ago)

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.

ralger is a small web scraping framework for R based on rvest and xml2.

It's goal to simplify basic web scraping and it provides a convenient and easy to use API.

It offers functions for retrieving pages, parsing HTML using CSS selectors, automatic table parsing and auto link, title, image and paragraph extraction.



Example Use

from bs4 import BeautifulSoup

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

soup = BeautifulSoup(html)

# we can iterate using dot notation:
"Hello World"

# or use find method to recursively find matching elements:

# 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

# bs4 also contains various utility functions like HTML formatting
   Hello World!
  <div id="product">
    Product Title
    paragraph 1
   <span class="price">

url <- ""

# retrieve HTML and select elements using CSS selectors:
best_uni <- scrap(link = url, node = "a span", clean = TRUE)
head(best_uni, 5)
#>  [1] "Harvard University"
#>  [2] "Stanford University"
#>  [3] "University of Cambridge"
#>  [4] "Massachusetts Institute of Technology (MIT)"
#>  [5] "University of California, Berkeley"

# ralger can also parse HTML attributes
attributes <- attribute_scrap(
  link = "",
  node = "a", # the a tag
  attr = "class" # getting the class attribute

head(attributes, 10) # NA values are a tags without a class attribute
#>  [1] "navbar-brand logo" "nav-link"          NA
#>  [4] NA                  NA                  "nav-link"
#>  [7] NA                  "nav-link"          NA
#> [10] NA

# ralger can automatically scrape tables:
data <- table_scrap(link ="")

#> # A tibble: 6 × 4
#>    Rank Title                                      `Lifetime Gross`  Year
#>   <int> <chr>                                      <chr>            <int>
#> 1     1 Avatar                                     $2,847,397,339    2009
#> 2     2 Avengers: Endgame                          $2,797,501,328    2019
#> 3     3 Titanic                                    $2,201,647,264    1997
#> 4     4 Star Wars: Episode VII - The Force Awakens $2,069,521,700    2015
#> 5     5 Avengers: Infinity War                     $2,048,359,754    2018
#> 6     6 Spider-Man: No Way Home                    $1,901,216,740    2021

Alternatives / Similar

Was this page helpful?