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Beautiful Soup vs Scrapy: Which Should You Choose in 2024?

Updated on Jan 2
5 min read
Written by
Gulbahar Karatas
Gulbahar Karatas
Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security.

She is a frequent user of the products that she researches. For example, she is part of AIMultiple's web data benchmark team that has been annually measuring the performance of top 9 web data infrastructure providers.

She previously worked as a marketer in U.S. Commercial Service.

Gülbahar has a Bachelor's degree in Business Administration and Management.
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Beautiful Soup and Scrapy are Python libraries commonly used for web scraping. Each has unique strengths and use cases, selecting the right tool for your particular data collection needs crucial.

In this guide, we’ll dive deep into the essential aspects of Beautiful Soup and Scrapy, comparing their features, ease of use, and functionality. Understanding each option’s key differences and unique strengths will allow you to make a well-informed decision that best fits your specific needs and use case.

Beautiful Soup vs Scrapy at a glance

Beautiful Soup and Scrapy are two Python libraries commonly used for web crawling and scraping. However, they have different features and use cases. Here’s a comparison of their key features:

Figure 1: Table comparing Beautiful Soup and Scrapy in terms of key features

Table compare Beautiful Soup and Scrapy based on their purposes, performance and speed.

Beautiful Soup overview

Beautiful Soup is a Python parsing library that helps developers to extract and parse data from HTML and XML documents (Figure 2). It supports various parsers, such as html.parser, lxml, and html5lib for parsing HTML and XML documents.

Figure 2: Showing how Beautiful Soup HTML parser executes

Beautiful Soup is a Python package that allows you to extract and parse HTML and XML documents.
Source: Wikipedia1

You can install Beautiful Soup on Windows, Linux, or any operating system by typing the following command into your prompt or terminal (Figure 3).

Figure 3:  Installing Beautiful Soup using Pip package

You can set up Beautiful Soup using Pip. The image shows the command that enable users to install Beautiful Soup.

Beautiful Soup main features

  • Navigating the parse tree: Beautiful Soup provides a simple way to move from one part of a document to another using CSS or XPath selectors. You can use the “find” and “find_all” methods to search for specific elements or attributes within the document.
  • Modifying the parse tree: You can rename a tag, add new attributes or remove a tag or string from the tree.
  • Encoding Detection: Beautiful Soup can automatically detect the encoding of HTML and XML documents and convert them to Unicode using a sub-library called Unicode.

Advantages of Beautiful Soup

  • Easy to learn: Beautiful Soup has a relatively lower learning curve than Scrapy.
  • Third-Party library integration: Beautiful Soup can be easily integrated with other Python libraries to improve the functionality of web scraping projects. For example, you can use Requests or Selenium for making HTTP requests and then use Beautiful Soup to parse the web page’s content.
  • Parsing HTML and XML: Beautiful Soup currently supports “lxml”, “html5lib”, and “html.parser” (Python’s built-in HTML parser). You can parse HTML and XML documents and convert them into a tree-like structure.
    Each of these parsers may produce different outputs when parsing the same document. The image below shows how the choice of the parser can affect the parsed result  (Figure 4):

Figure 4: With the same input, different parsers produce different results

The results of data parsing vary depending on the type of data parser.
Source: Scrapy – specifying the parser to use2

Downsides of Beautiful Soup

You can use automated data collection tools to automate and scale up your web scraping processes without any programming language knowledge.

Bright Data’s Web Scraper IDE enables businesses and individuals to build web scraping tools with its ready-made scraping functions (Figure 5). Web scraper IDE includes built-in unblocking technology support to help people extract data without getting banned or blocked.

Figure 5: Bright Data’s Web Scraper IDE

Bright Data's web scraper IDE help businesses collect data from websites automatically.
Source: Bright Data

Scrapy overview

Scrapy is a Python-based web scraping library offering powerful goodies for writing web scrapers and crawl websites. It is designed specifically for web scraping and crawling tasks.

You can start using Scrapy by running the following command:

Figure 6: Installing Scrapy using Pip

Scrapy main features

  • CSS and XPath Support: Provides built-in support for selecting and extracting data from HTML/XML sources using CSS or XPath expressions.
    • XPath selectors: XPath selectors are widely used in web scraping projects. They enable users to select elements based on their position in the document tree.
    • CSS selectors: CSS selectors are easier to learn and use than XPath selectors. They are widely used in web development.
  • Scrapy shell: The Scrapy shell is an interactive tool that allows people to test and debug their web scraping code.
  • Feed exports: This feature allows you to save the scraped data to a file in various formats, including JSON, CSV, or XML. Additionally, you can extend the supported format through the FEED_EXPORTERS setting.
  • Downloading files and images: You can automatically download images attached to the scraped data using Files Pipeline or the Images Pipeline. It is possible to use both the Files and Images Pipeline simultaneously. For example, after downloading the media, the image pipeline allows you to:
    • Convert all downloaded images to a common format
    • Generate thumbnails
    • Filter the images based on their size
  • Telnet Console: It is a built-in tool for inspecting and controlling a Scrapy running process from the command line. Here are some of the capabilities of Scrapy Telnet Console:
    • Inspect the spider’s state and view the engine status
    • Modify the spider’s settings and behavior
    • Inspect and export scraped data

Using Telnet Console over public networks or insecure connections is not recommended. Telnet does not provide any transport-layer security. To mitigate this security risk, you can use it over a local network or an SSH tunnel.

To connect the console you need to type the following command:

Figure 7: Showing how to connect Telnet Console

Scrapy provides function called Telnet Console to control scraper running process.
Source: Scrapy-How to access the telnet console3

Advantages of Scrapy

  • Asynchronous scraping: Scrapy is built on top of the powerful Twisted networking framework, which allows users to send multiple requests concurrently. You don’t have to wait for each response before sending the next request. This makes Scrapy preferable for large-scale web scraping projects.
  • Robustness: Scrapy provides various built-in features to handle web scraping challenges such as cookies, user agents, proxies, and CAPTCHAs. Here are some of the built-in supports provided by Scrapy for handling anti-scraping measures:

Figure 8:  Demonstrating how a rotating IP address performs when making a request

Scrapy does not include built-in support for CAPTCHA challenges. You can use a third-party CAPTCHA-solving service to bypass CAPTCHAs.

Downsides of Scrapy

  • Learning Curve: Scrapy has a steep learning curve than Beautiful Soup, especially for Python beginners. It is a complex framework with many features and functions. This may make it more challenging to use and configure.

Beautiful Soup vs Scrapy: Which should you choose?

The choice between Beautiful Soup and Scrapy depends on your specific needs and use case. When comparing these two options, Beautiful Soup is much more beginner friendly and a lightweight library used for parsing HTML and XML documents. Scrapy, on the other hand, is a more robust and feature-rich web scraping framework capable of handling more complex data collection tasks.

1. Beautiful Soup is a more suitable option if:

  • You have limited coding experience.
  • Your data collection task is simple and does not require handling JavaScript or dynamic content and scraping data from APIs.
  • You are working on small to medium-sized projects that don’t require concurrent requests.

2. Scrapy is a more suitable option if:

  • You have a background in data experts, data analysis, or IT.
  • You must  handle more complex scraping tasks since it allows for concurrent scraping of multiple web pages.
  • You are working on large-scale web scraping projects.

3. Combining both libraries might be beneficial if:

If you want to use Scrapy for its advanced web scraping features while using Beautiful Soup for parsing and extracting required data.

Further reading

If you have more questions, do not hesitate contacting us:

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Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security. She is a frequent user of the products that she researches. For example, she is part of AIMultiple's web data benchmark team that has been annually measuring the performance of top 9 web data infrastructure providers. She previously worked as a marketer in U.S. Commercial Service. Gülbahar has a Bachelor's degree in Business Administration and Management.

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