Scrapy vs BeautifulSoup – Which is Better for You?

James Keenan

The automation and anonymity evangelist at Smartproxy. He believes in data freedom and everyone’s right to become a self-starter. James is here to share knowledge and help you succeed with residential proxies.

You must choose the best scraping tool to mine data effectively. There are many popular scrapers, like ScrapeBox, but a lot of people ask which free Python scraper is better: Scrapy or BeautifulSoup.

To find out, you must first understand that Beautiful Soup only parses and extracts data from HTML files, while Scrapy actually downloads, processes and saves data. Scrapy is very good at automatically following links in a site, no matter what the format of those links is, so you don’t need to predict too many aspects of your operation. In this sense, Beautiful Soup is a content parser, while Scrapy is a full web spider and scraper. BS needs an additional content downloader (like requests) to download those HTML files, first.

Newbie friendliness: Beautiful Soup

Both Scrapy and BeautifulSoup are documented well, so you will not have trouble learning by yourself. Nevertheless, Beautiful Soup is a lot easier to use for new scrapers, while Scrapy’s framework makes it quite hard to learn at first. As we’ve said, since Beautiful Soup only parses content, you will need to download an additional package like requests to help it download an HTML file, but that’s a low barrier to entry.

Scallability: Scrapy

Scrapy is the best Py suite to use if you have a large project, because it is a lot more flexible and fits more versatile projects. Beautiful Soup is good for smaller projects, but scales quite poorly. This is because Scrapy can use concurrent and asynchronous requests, which work great with a rotating residential proxy network to provide an unlimited scale for the project.

Speed: Scrapy

Scrapy’s ability to send asynchronous requests is the thing that makes it hands-down the faster of the two. Beautiful Soup with requests is also a slow scraper, when compared to Scrapy.

Scrapy can manage a larger project with speed, but the learning curve might make BS the better option if you want to do a smaller project.

Proxies: tie

Both Scrapy and Beautiful Soup can use rotating proxies to make scraping undetectable. We have a Scrapy proxy middleware and Beautiful Soup solutions on our Github page.

Python 2.7 and 3: tie

Both Beautiful Soup and Scrapy support Python 2.7 and 3, so you won’t encounter any syntax issues with either one of the two.

Community: Scrapy

Community support might not seem like much, but a good thread on Stack Exchange can make or break your project. In this sense, Scrapy is a lot more advanced than Beautiful Soup, because it has an awesome community. This stems from Scrapy’s functionality – it’s fans use it on a variety of projects and stay with it longer. Beautiful Soup is quite powerful it its niche, but as it scales poorly on its own. Nevertheless, some people advise taking the power of BS and combining it with Scrapy, which is what we recommend.

Verdict: why not use both?

In the end, it’s safe to say that Scrapy is better than Beautiful Soup, BUT, if you are just starting out and not jumping into huge projects, you might want to try Beautiful Soup first, because it is a lot easier to learn. You will have no problem moving over to Scrapy later on, no matter if you use Python 2.7 or 3. On the other hand, if you are up for a challenge, try using Beautiful Soup in Scrapy to get the best of both – import BS to parse the content you get through Scrapy!

Here’s an example I borrowed from Scrapy’s documentation:


from bs4 import BeautifulSoup
import scrapy

class ExampleSpider(scrapy.Spider): name = "example" allowed_domains = ["example.com"] start_urls = ( 'http://www.example.com/', )
def parse(self, response): # use lxml to get decent HTML parsing speed soup = BeautifulSoup(response.text, 'lxml') yield { "url": response.url, "title": soup.h1.string }


And before you start scraping, register on Smartproxy and use only the best proxies to get the job done!