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cascadiavsfeedparser

BSD-2-Clause 1 1 675
58.1 thousand (month) Feb 20 2018 Start(6 years ago)
1,821 8 83 BSD-2-Clause
6.0.11(3 months ago) Jun 15 2007 3.4 million (month)

cascadia is a library for Go that provides a CSS selector engine, allowing you to use CSS selectors to select elements from an HTML document.

It is built on top of the html package in the Go standard library, and provides a more efficient and powerful way to select elements from an HTML document.

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


package main

import (
  "fmt"
  "github.com/andybalholm/cascadia"
  "golang.org/x/net/html"
  "strings"
)

func main() {
  // Create an HTML string
  html := `<html>
        <body>
          <div id="content">
            <p>Hello, World!</p>
            <a href="http://example.com">Example</a>
          </div>
        </body>
      </html>`

  // Parse the HTML string into a node tree
  doc, err := html.Parse(strings.NewReader(html))
  if err != nil {
    fmt.Println("Error:", err)
    return
  }

  // Compile the CSS selector
  sel, err := cascadia.Compile("p")
  if err != nil {
    fmt.Println("Error:", err)
    return
  }

  // Use the Selector.Match method to select elements from the document
  matches := sel.Match(doc)
  if len(matches) > 0 {
    fmt.Println(matches[0].FirstChild.Data)
    // > Hello, World!
  }
}
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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