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parselvsfeedparser

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parsel is a library for parsing HTML and XML using selectors, similar to beautifulsoup. It is built on top of the lxml library and allows for easy extraction of data from HTML and XML files using selectors, similar to how you would use CSS selectors in web development. It is a light-weight library which is specifically designed for web scraping and parsing, so it is more efficient and faster than beautifulsoup in some use cases.

Some of the key features of parsel include:

  • CSS selector & XPath selector support:
    Two most common html parsing path languages are both supported in parsel. This allows selecting attributes, tags, text and complex matching rules that use regular expressions or XPath functions.
  • Modifying data:
    parsel allows you to modify the contents of an element, remove elements or add new elements to a document.
  • Support for both HTML and XML:
    parsel supports both HTML and XML documents and you can use the same selectors for both formats.

It is easy to use and less verbose than beautifulsoup, so it's quite popular among the developers who are working with Web scraping projects and parse data from large volume of web pages.

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-selectorsxpath-selectors

Example Use


from parsel import Selector

# 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>
"""

selector = Selector(html)

# we can use CSS selectors:
selector.css("#product .price::text").get()
"$10"

# or XPath:
selector.xpath('//span[@class="price"]').get()
"$10"

# or get all matching elements:
print(selector.css("#product p::text").getall())
["paragraph 1", "paragraph2"]

# parsel also comes with utility methods like regular expression parsing:
selector.xpath('//span[@class="price"]').re("\d+")
["10"]
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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