Skip to content

cssselectvsfeedparser

NOASSERTION 21 8 291
9.7 million (month) Apr 14 2012 1.2.0(2 years ago)
1,992 9 92 NOASSERTION
Jun 15 2007 5.4 million (month) 6.0.11(a year ago)

cssselect is a BSD-licensed Python library to parse CSS3 selectors and translate them to XPath 1.0 expressions.

XPath 1.0 expressions can be used in lxml or another XPath engine to find the matching elements in an XML or HTML document.

cssselect is used by other popular Python packages like parsel and scrapy but can also be used on it's own to generate valid XPath 1.0 expressions for parsing HTML and XML documents in other tools.

Note that because XPath selectors are more powerful than CSS selectors this translation is only possible one way. Converting XPath to CSS selectors is impractical and not supported by cssselect.

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.

Example Use


from cssselect import GenericTranslator, SelectorError

translator = GenericTranslator()
try:
    expression = translator.css_to_xpath('div.content')
    print(expression)
    'descendant-or-self::div[@class and contains(concat(' ', normalize-space(@class), ' '), ' content ')]'
except SelectorError as e:
    print(f'Invalid selector {e}')
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']

Alternatives / Similar


Was this page helpful?