Which is better for web scraping, BeautifulSoup and Scrapy?

BeautifulSoup and Scrapy
One of the most important assets of data-driven companies is the tools used by data science specialists. Some of the technologies used to obtain useful information include a web crawler and web scraping tools. Web scraping allows you to fetch data from a variety of web services and convert unstructured data into structured data.
Scraping the web can be done with XML, BeautifulSoup, MechanicalSoup, Scrapy, Python Requests, and others. Scrapy and Beautiful Soup are two of the most popular options.
We will examine the differences between these two web scraping technologies in this post. We’ll also discuss the advantages and disadvantages of Scrapy and BeautifulSoup scraping, as well as how Proxycrawl can work around these issues. Before we go into details, let’s define what they are.


Beautiful Soup is a popular Python library for parsing HTML or XML texts into a tree structure in order to find and extract data. With a simple Python interface and automated encoding conversion, this application simplifies working with website data.
In addition to simple Python methods and idioms for traversing, searching, and changing parse trees, the library will convert incoming and outgoing documents to Unicode and UTF-8 automatically.
Beautiful Soup features:
  • A Python library allows you to navigate, search, modify, and modify a parse tree using Pythonic idioms.
  • Upon entering or leaving the library, documents are automatically converted to Unicode and UTF-8.
  • It allows you to experiment with different parsing strategies or trade-off speed and flexibility by using popular Python parsers such as lxml and html5lib.


Scrapy is an open-source web crawling and scraping framework that allows you to crawl various online pages and download, parse, and store data you scrape. It can handle all tasks independently. Despite the fact that Scrapy comes with everything you need to get started, it also supports third-party extensions and middleware. A significant benefit of this is that it allows the consumer to modify and ensure that he is getting exactly what he needs. This is a great relief for those who work with Scrapy since it does not support JavaScript on its own. By combining Scrapy with a headless browser such as Selenium, Puppeteer, or Splash, you can unlock JavaScript.
Scrapy is also incredibly fast and powerful. In addition, it can handle asynchronous requests, which allows you to scrape multiple pages at the same time while maintaining full control over the information gathered.
Features of Scrapy:
  • Scrapy allows you to select and extract data from HTML/XML sources using extended CSS selectors and XPath expressions.
  • Scrapy allows users to toy out CSS and XPath expressions to scrape data.
  • Provides support for exporting feeds in multiple formats (JSON, CSV, XML) and storing them in multiple backends.

Is Scrapy better than BeautifulSoup?

It is not enough to know the differences between Scrapy and BeautifulSoup. One must also take into account their disadvantages.

The disadvantages of using Scrapy

Scrapy is a powerful tool for web scrapers, but it has some serious limitations:
JavaScript is not supported: Scrapy will not be able to scrape dynamic pages that use JavaScript.
Installation is difficult: Scrapy is not the easiest to set up if you are unfamiliar with web scraping.
Light documentation for beginners: Scrapy comes with rather basic documentation. If you don’t know how to code, this can be difficult for beginners.

The disadvantages of using BeautifulSoup

Designed specifically for programmers, BeautifulSoup is a flexible scraping tool. In contrast to Scrapy, it comes with a lot of documentation, making it easy to learn once you have mastered the basics. On the other hand, BeautifulSoup has a number of drawbacks, including:
Dependencies: BeautifulSoup cannot function as a parser on its own. First, you need to install the prerequisites.
Laggy: It can be slow when used with dependencies, especially when compared to Scrapy and other full-suite solutions.
Requires Python knowledge: If you aren’t proficient in Python and don’t know how to scrape using libraries, you might find it difficult to use.
Inefficient for larger jobs: BeautifulSoup is best suited for modest web scraping tasks.
Minimal proxy support: Unlike Scrapy, BeautifulSoup does not support the easy use of proxies. Thus, it is difficult to use BeautifulSoup to extract large volumes of data from the same server without having your IP address restricted.

Scrapy vs BeautifulSoup


Beautiful Soup is a Python library designed for projects that require rapid turnaround, such as screen scraping. Scrapy is an open-source framework, whereas Beautiful Soup is a Python library designed for fast turnaround projects, such as screen scraping. Frameworks are used to invert the control of the program and tell the developer what they are required to do. In contrast, a library is called by the developer whenever and wherever it is required.


Due to built-in support for generating feed exports in many formats and the ability to select and extract data from a variety of sources, Scrapy’s speed can be considered faster than Beautiful Soup’s. You can increase the speed of your work with Beautiful Soup by using Multithreading.


When it comes to smaller projects, Beautiful Soup excels. Scrapy, on the other hand, maybe a better option for larger projects with greater complexity, as it can add custom functionality and construct pipelines more quickly and efficiently.


A beginner learning web scraping for the first time should start with Beautiful Soup. You can use Scrapy to scrape, but it’s a lot more complicated.


As a result, Scrapy has a much larger and more active developer community than Beautiful Soup. Developers may also use Beautiful Soup in Scrapy callbacks to parse HTML responses by putting the response’s body into a BeautifulSoup object and extracting the data they require.

Proxycrawl: An alternative to Scrapy and BeautifulSoup

Scrapy and BeautifulSoup, as previously mentioned, have substantial disadvantages that make them difficult to use for scraping large volumes of data. These two tools can be very difficult to use if you are scraping dynamic websites, large amounts of data, or real-time information. Proxycrawl can be helpful. It is a complete scraping solution for programmers and non-programmers alike. The application is robust, adaptable, and stylish.

We offer the following features and more:

Hassle-free scraping: You won’t have to worry about managing servers, managing and rotating proxy servers, answering CAPTCHAs, scalability of the browser, or checking for anti-scraping updates if you use Proxycrawl. Proxycrawl will handle everything so you can concentrate on gathering useful information from source sites.
JavaScript rendering: Proxycrawl is unlike many other scraping tools in that it can scrape data from JavaScript sites. As a result, you can extract data from a wide variety of dynamic websites.
Ample documentation: Proxycrawl comes with extensive documentation to help you get started. You can also use our Postman documentation if you are a programmer. Our APIs provide structured JSON output of the metadata from processed sites.
24/7/365 customer support: Our customer service representatives are available to assist you at any time.
Frequent module and improvement updates:
Proxycrawl, unlike many scraping solutions, is subject to the frequent module and enhancement upgrades. As well as adding modules on a regular basis, we also respond to user requests. Do not hesitate to contact us if you have a module concept or need bespoke solutions for your project.


Choosing the right scraping tools, such as BeautifulSoup versus Scrapy, can be difficult, especially if you’re new to scraping. Many scraping programs, like BeautifulSoup and Scrapy, don’t provide a complete scraping solution and may be difficult to use and install for beginners. A better option would be Proxycrawl. This simple scraping solution lets you scrape a wide range of sites and data, including dynamic sites, huge data, and real-time data.


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