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MIT 10 9 182
297 (month) Dec 11 2020 2.0.7(1 year, 6 months ago)
1,595 8 85 GNU GPL 3
Dec 14 2008 1.5 million (month) 2024.2.26(2 months ago)

ExtractNet is an automated web data extraction tool using machine learning to parse HTML and text data.

This tool can be used in web scraping to automatically extract details from scraped HTML documents. While it's not as accurate as structured extraction using HTML parsing tools like CSS selectors or XPath it can still parse a lot of details.

html2text is a Python library that allows developers to convert HTML code into plain text. It is designed to be easy to use, and it provides several options to customize the output.

The package uses the python's built-in html.parser to parse the HTML and then convert it to plain text.

html2text also comes with a CLI tool that can convert HTML files to text:

Usage: html2text [filename [encoding]]

Option  Description
--version   Show program's version number and exit
-h, --help  Show this help message and exit
--ignore-links  Don't include any formatting for links
--escape-all    Escape all special characters. Output is less readable, but avoids corner case formatting issues.
--reference-links   Use reference links instead of links to create markdown
--mark-code Mark preformatted and code blocks with [code]...[/code]

Example Use

import requests
from extractnet import Extractor

raw_html = requests.get('').text
results = Extractor().extract(raw_html)
{'phone_number': '555-555-5555', 'email': ''}
import html2text

h = html2text.HTML2Text()

# Ignore converting links from HTML
h.ignore_links = True
print h.handle("<p>Hello, <a href=''>world</a>!")
"Hello, world!"

print(h.handle("<p>Hello, <a href=''>world</a>!"))

"Hello, world!"

# Don't Ignore links anymore, I like links
h.ignore_links = False
print(h.handle("<p>Hello, <a href=''>world</a>!"))
"Hello, [world](!"

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