When should I use BeautifulSoup vs Scrapy?
BeautifulSoup and Scrapy serve different purposes in the scraping ecosystem.
BeautifulSoup:
A parsing library, not a scraping framework:
- Parses HTML and extracts data
- Use with Requests library for HTTP
- Best for: Simple scripts, one-off scraping, learning, small projects
- Pros: Easy to learn, flexible, lightweight
- Cons: No built-in crawling, session management, or data pipelines
Scrapy:
A complete scraping framework:
- Built-in spider classes for crawling
- Concurrent requests out of the box
- Item pipelines for data processing
- Built-in middlewares and extensions
- Best for: Large-scale scraping, production systems, crawling entire sites
- Pros: Feature-rich, scalable, production-ready
- Cons: Steeper learning curve, more complex setup
Code comparison:
BeautifulSoup (simple):
import requests
from bs4 import BeautifulSoup
response = requests.get(url)
soup = BeautifulSoup(response.text, 'lxml')
title = soup.find('h1').text
Scrapy (structured):
class MySpider(scrapy.Spider):
name = 'myspider'
start_urls = [url]
def parse(self, response):
yield {'title': response.css('h1::text').get()}
When to use BeautifulSoup:
- Scraping 10-100 pages
- One-time data extraction
- Learning web scraping
- Quick prototypes
- Integrating scraping into larger Python applications
When to use Scrapy:
- Scraping 1,000+ pages
- Recurring scraping jobs
- Complex crawling logic (following links, pagination)
- Need for data pipelines (validation, storage, export)
- Production deployments with monitoring
- Rate limiting and retry logic required
Can you combine them?
Yes! Some developers use Scrapy for crawling/requests and BeautifulSoup for parsing when they prefer its API.
Recommendation:
Start with BeautifulSoup to learn fundamentals. Migrate to Scrapy when scaling up or when your script becomes too complex to maintain.