html-to-markdown
A modern, fully typed Python library for converting HTML to Markdown. This library is a completely rewritten fork of markdownify with a modernized codebase, strict type safety and support for Python 3.9+.
Features:
⭐️ Full HTML5 Support: Comprehensive support for all modern HTML5 elements including semantic, form, table, ruby, interactive, structural, SVG, and math elements
⭐️ Enhanced Table Support: Advanced handling of merged cells with rowspan/colspan support for better table representation
⭐️ Type Safety: Strict MyPy adherence with comprehensive type hints
Metadata Extraction: Automatic extraction of document metadata (title, meta tags) as comment headers
⭐️ Streaming Support: Memory-efficient processing for large documents with progress callbacks
⭐️ Highlight Support: Multiple styles for highlighted text (<mark> elements)
⭐️ Task List Support: Converts HTML checkboxes to GitHub-compatible task list syntax
nstallation
Optional lxml Parser
For improved performance, you can install with the optional lxml parser:
The lxml parser offers:
🆘 ~30% faster HTML parsing compared to the default html.parser
🆘 Better handling of malformed HTML
🆘 More robust parsing for complex documents
Quick Start
Convert HTML to Markdown with a single function call:
Working with BeautifulSoup:
If you need more control over HTML parsing, you can pass a pre-configured BeautifulSoup instance:
Github: https://github.com/Goldziher/html-to-markdown
https://t.iss.one/DataScienceN⭐️
A modern, fully typed Python library for converting HTML to Markdown. This library is a completely rewritten fork of markdownify with a modernized codebase, strict type safety and support for Python 3.9+.
Features:
Metadata Extraction: Automatic extraction of document metadata (title, meta tags) as comment headers
nstallation
pip install html-to-markdown
Optional lxml Parser
For improved performance, you can install with the optional lxml parser:
pip install html-to-markdown[lxml]
The lxml parser offers:
Quick Start
Convert HTML to Markdown with a single function call:
from html_to_markdown import convert_to_markdown
html = """
<!DOCTYPE html>
<html>
<head>
<title>Sample Document</title>
<meta name="description" content="A sample HTML document">
</head>
<body>
<article>
<h1>Welcome</h1>
<p>This is a <strong>sample</strong> with a <a href="https://example.com">link</a>.</p>
<p>Here's some <mark>highlighted text</mark> and a task list:</p>
<ul>
<li><input type="checkbox" checked> Completed task</li>
<li><input type="checkbox"> Pending task</li>
</ul>
</article>
</body>
</html>
"""
markdown = convert_to_markdown(html)
print(markdown)
Working with BeautifulSoup:
If you need more control over HTML parsing, you can pass a pre-configured BeautifulSoup instance:
from bs4 import BeautifulSoup
from html_to_markdown import convert_to_markdown
# Configure BeautifulSoup with your preferred parser
soup = BeautifulSoup(html, "lxml") # Note: lxml requires additional installation
markdown = convert_to_markdown(soup)
Github: https://github.com/Goldziher/html-to-markdown
https://t.iss.one/DataScienceN
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LangExtract
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
GitHub: https://github.com/google/langextract
https://t.iss.one/DataScience4🖕
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
GitHub: https://github.com/google/langextract
https://t.iss.one/DataScience4
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Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
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2️⃣ Machine Learning
3️⃣ Data Visualization
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7️⃣ Deep Learning
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Researchers trained the model on 70 hours of Minecraft gameplay and achieved impressive results:
GameFactory can create procedural game worlds — from volcanoes to cherry blossom forests, just like in the iconic simulator.
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python-docx: Create and Modify Word Documents #python
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
Example
https://t.iss.one/DataScienceN🚗
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
pip install python-docx
Example
from docx import Document
document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")
document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'
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Data scientists, this is for you — I dug up LeetCode for DS
DataLemur — a powerful platform that collects real interview problems from Tesla, Facebook, Twitter, Microsoft, and other top companies
Inside: practical tasks on SQL, statistics, Python, and ML. You can filter by difficulty level and company
Top-notch for those preparing for interviews for Data Scientist / Data Analyst roles. Get it here🍯
👉 https://t.iss.one/DataScienceN 👍
DataLemur — a powerful platform that collects real interview problems from Tesla, Facebook, Twitter, Microsoft, and other top companies
Inside: practical tasks on SQL, statistics, Python, and ML. You can filter by difficulty level and company
Top-notch for those preparing for interviews for Data Scientist / Data Analyst roles. Get it here
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📝 Description: :satellite: Organized Resources for Deep Learning Researchers and Developers
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📝 Description: A curated list of NLP resources focused on Transformer networks, attention mechanism, GPT, BERT, ChatGPT, LLMs, and transfer learning.
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🔗 Repository URL: https://github.com/Lets-DevOps/awesome-learning
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📝 Description: A curated list for DevOps learning resources. Join the slack channel to discuss more.
🔗 Repository URL: https://github.com/Lets-DevOps/awesome-learning
📖 Readme: https://github.com/Lets-DevOps/awesome-learning#readme
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📝 Description: Resources for learning and using recursion schemes.
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📝 Description: :fish::fish::fish: 机器学习面试复习资源
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📖 Readme: https://github.com/sudharsan13296/Awesome-Meta-Learning#readme
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📝 Description: A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.
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📖 Readme: https://github.com/sudharsan13296/Awesome-Meta-Learning#readme
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📝 Description: This is a list of links to different freely available learning resources about computer programming, math, and science.
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📝 Description: This is a list of links to different freely available learning resources about computer programming, math, and science.
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