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beautifulsoupvsfeedparser

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98.3 million (month) Jul 26 2019 4.12.3(4 months ago)
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Jun 15 2007 3.2 million (month) 6.0.11(6 months 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.

feedparser is a Python module for downloading and parsing syndicated feeds. It can handle RSS 0.90, Netscape RSS 0.91, Userland RSS 0.91, RSS 0.92, RSS 0.93, RSS 0.94, RSS 1.0, RSS 2.0, Atom 0.3, Atom 1.0, and CDF feeds. It also parses several popular extension modules, including Dublin Core and Appleā€™s iTunes extensions.

To use Universal Feed Parser, you will need Python 3.6 or later. Universal Feed Parser is not meant to run standalone; it is a module for you to use as part of a larger Python program.

feedparser can be used to scrape data feeds as it can download them and parse the XML structured data.

Highlights


css-selectorsdsl-selectorshttp2

Example Use


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>
"""
import feedparser

# the feed can be loaded from a remote URL
data = feedparser.parse('http://feedparser.org/docs/examples/atom10.xml')
# local path
data = feedparser.parse('/home/user/data.xml')
# or raw string
data = feedparser.parse('<xml>...</xml>')

# the result dataset is a nested python dictionary containing feed data:
data['feed']['title']

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