High-performance HTML parsing library for Python
Project description
scrape-rs (Python)
Python bindings for scrape-rs, a high-performance HTML parsing library.
Installation
pip install scrape-rs
Alternative package managers:
# uv (recommended - 10-100x faster)
uv pip install scrape-rs
# Poetry
poetry add scrape-rs
# Pipenv
pipenv install scrape-rs
[!IMPORTANT] Requires Python 3.10 or later.
Quick start
from scrape_rs import Soup
html = "<html><body><div class='content'>Hello, World!</div></body></html>"
soup = Soup(html)
div = soup.find("div")
print(div.text)
# Hello, World!
Usage
Find elements
from scrape_rs import Soup
soup = Soup(html)
# Find first element by tag
div = soup.find("div")
# Find all elements
divs = soup.find_all("div")
# CSS selectors
for el in soup.select("div.content > p"):
print(el.text)
Element properties
element = soup.find("a")
# Get text content
text = element.text
# Get inner HTML
html = element.inner_html
# Get attribute
href = element.get("href")
Batch processing
from scrape_rs import Soup
# Process multiple documents in parallel
documents = [html1, html2, html3]
soups = Soup.parse_batch(documents)
for soup in soups:
print(soup.find("title").text)
[!TIP] Use
parse_batch()for processing multiple documents. It uses all CPU cores automatically.
Type hints
This package includes type stubs for full IDE support:
from scrape_rs import Soup, Tag
def extract_links(soup: Soup) -> list[str]:
return [a.get("href") for a in soup.select("a[href]")]
Performance
Compared to BeautifulSoup on the same HTML documents:
| Operation | Speedup |
|---|---|
| Parse (1 KB) | 9.7x faster |
| Parse (219 KB) | 9.2x faster |
| Parse (5.9 MB) | 10.6x faster |
find(".class") |
132x faster |
select(".class") |
40x faster |
[!TIP] Run
python benches/compare_python.pyfrom the project root to benchmark on your hardware.
Related packages
Part of the scrape-rs project:
scrape-core— Rust core libraryscrape-rs(npm) — Node.js bindings@scrape-rs/wasm— Browser/WASM bindings
License
Licensed under either of Apache License, Version 2.0 or MIT License at your option.
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