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html5libvsscrapling

MIT 97 14 1,220
32.8 million (month) Jul 30 2007 1.1(2020-06-22 23:32:36 ago)
36,206 2 7 BSD-3-Clause
Aug 01 2024 397.4 thousand (month) 0.4.5(2026-04-07 04:22:27 ago)

html5lib is a pure-python library for parsing HTML. It is designed to conform to the WHATWG HTML specification, as is implemented by all major web browsers.

As html5lib is implemented in pure-python it is significantly slower than alternatives powered by lxml (like parsel or beautifulsoup). However, html5lib implements a more true html5 parsing which can represent HTML tree more correctly than alternatives.

Scrapling is an adaptive web scraping framework for Python that introduces "self-healing" selectors — selectors that can track and find elements even when the website's DOM structure changes. This solves one of the biggest maintenance headaches in web scraping: broken selectors after website updates.

Key features include:

  • Self-healing selectors Scrapling uses smart element matching that can identify target elements even after the page structure changes. It builds a fingerprint of the element based on multiple attributes (text, position, siblings, attributes) and uses fuzzy matching to relocate it.
  • Multiple parsing backends Supports different parsing engines including lxml (fast) and a custom engine, allowing you to choose the right balance of speed and features.
  • Scrapy-like Spider API Provides a familiar Spider class pattern for organizing crawling logic, similar to Scrapy but with the added benefit of adaptive selectors.
  • CSS and XPath selectors Full support for CSS selectors and XPath, plus the adaptive matching system on top.
  • Type hints and modern Python Built with full type annotations and 92% test coverage for reliability.
  • Async support Supports asynchronous crawling for efficient concurrent scraping.

Scrapling gained massive traction in 2025 as one of the most starred new Python scraping libraries. It is particularly useful for scraping targets that frequently update their HTML structure, where traditional selector-based scrapers would break.

Highlights


css-selectorsxpathfastpopular

Example Use


```python import html5lib from html5lib import parse html_doc = "My Title" parsed = parse(html_doc) title = parsed.getElementsByTagName("title")[0] print(title.childNodes[0].nodeValue) ```
```python from scrapling import Fetcher, StealthFetcher, PlayWrightFetcher # Simple fetching with adaptive parsing fetcher = Fetcher() page = fetcher.get("https://example.com/products") # CSS selectors work as expected products = page.css(".product-card") for product in products: name = product.css_first(".name").text() price = product.css_first(".price").text() print(f"{name}: {price}") # Adaptive selector - finds the element even if DOM changes # Uses element fingerprinting for resilient matching element = page.find("Product Title", auto_match=True) # Stealth fetching with anti-bot bypass stealth = StealthFetcher() page = stealth.get("https://protected-site.com") # Playwright-based fetching for JS-rendered pages pw = PlayWrightFetcher() page = pw.get("https://spa-example.com", headless=True) ```

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