High-performance HTML parsing library for Python

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  • License: Apache Software License, MIT License (MIT OR Apache-2.0)
  • Tags html , parser , scraping , css-selectors , dom
  • Requires: Python >=3.10
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Project description

fast-scrape

PyPI Python License

10-50x faster HTML parsing for Python. Rust-powered, BeautifulSoup-compatible API.

Installation

pip install fast-scrape
Alternative package managers 
# uv (recommended - 10-100x faster)
uv pip install fast-scrape

# Poetry
poetry add fast-scrape

# Pipenv
pipenv install fast-scrape

[!IMPORTANT] Requires Python 3.10 or later.

Quick start

from scrape_rs import Soup

soup = Soup("<html><body><div class='content'>Hello, World!</div></body></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")

text = element.text          # Get text content
html = element.inner_html    # Get inner HTML
href = element.get("href")   # Get attribute
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. Uses all CPU cores automatically.

Type hints

Full IDE support with type stubs:

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:

Operation Speedup
Parse (1 KB) 9.7x faster
Parse (5.9 MB) 10.6x faster
find(".class") 132x faster
select(".class") 40x faster

Related packages

Platform Package
Rust scrape-core
Node.js @fast-scrape/node
WASM @fast-scrape/wasm

License

MIT OR Apache-2.0

Project details

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  • License: Apache Software License, MIT License (MIT OR Apache-2.0)
  • Tags html , parser , scraping , css-selectors , dom
  • Requires: Python >=3.10
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0.2.6

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0.1.4

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