One of the most critical assets for data-driven organisations is the kind of tools used by their data science professionals. Web crawler and other such web scraping tools are few of those tools that are used to gain meaningful insights. Web scraping allows efficient extraction of data from several web services and helps in converting raw and unstructured data into a structured whole.  

There are several tools available for web scraping, such as lxml, BeautifulSoup, MechanicalSoup, Scrapy, Python Requests and others. Among these, Scrapy and Beautiful Soup are popular among developers. 

In this article, we will compare these two web scraping tools, and try to understand the differences between them. Before diving deep into the tools, let us first understand what these tools are.

Scrapy

Scrapy is an open-source and collaborative framework for extracting the data you need from websites in a fast and simple manner. This tool can be used for extracting data using APIs. It can also be used as a general-purpose web crawler. Thus, Scrapy is an application framework, which can be used for writing web spiders that crawl websites and extract data from them.

The framework provides a built-in mechanism for extracting data – known as selectors – and can be used for data mining, automated testing, etc. Scrapy is supported under Python 3.5+ under CPython and PyPy starting with PyPy 5.9.

Features of Scrapy:

  • Scrapy provides built-in support for selecting and extracting data from HTML/XML sources using extended CSS selectors and XPath expressions
  • An interactive shell console for trying out the CSS and XPath expressions to scrape data
  • Built-in support for generating feed exports in multiple formats (JSON, CSV, XML) and storing them in multiple backends (FTP, S3, local filesystem)

Beautiful Soup

Beautiful Soup is one of the most popular Python libraries which helps in parsing HTML or XML documents into a tree structure to find and extract data. This tool features a simple, Pythonic interface and automatic encoding conversion to make it easy to work with website data. 

This library provides simple methods and Pythonic idioms for navigating, searching, and modifying a parse tree, and automatically converts incoming documents to Unicode and outgoing documents to UTF-8. 

Features of Beautiful Soup:

  • This Python library provides a few simple methods, as well as Pythonic idioms for navigating, searching, and modifying a parse tree
  • The library automatically converts incoming and outgoing documents to Unicode and UTF-8, respectively
  • This library sits on top of popular Python parsers like lxml and html5lib, allowing you to try out different parsing strategies or trade speed for flexibility

Scrapy VS Beautiful Soup

Structure

Scrapy is an open-source framework, whereas Beautiful Soup is a Python library designed for quick turnaround projects like screen-scraping. A framework inverts the control of the program and informs the developer what they need. Whereas in the case of a library, the developer calls the library where and when they need it.

Performance

Due to the built-in support for generating feed exports in multiple formats, as well as selecting and extracting data from various sources, the performance of Scrapy can be said to be faster than Beautiful Soup. Working with Beautiful Soup can speed up with the help of Multithreading process.

Extensibility

Beautiful Soup works best when working on smaller projects. On the other hand, Scrapy may be the better choice for larger projects with more complexities, as this framework can add custom functionalities and can develop pipelines with flexibility and speed.  

Beginner-Friendly

For a beginner who is trying hands-on web scraping for the first time, Beautiful Soup is the best choice to start with. Scrapy can be used for scraping, but it is comparatively more complex than the former. 

Community

The developer’s community of Scrapy is stronger and vast compared to that of Beautiful Soup. Also, developers can use Beautiful Soup for parsing HTML responses in Scrapy callbacks by feeding the response’s body into a BeautifulSoup object and extracting whatever data they need from it.

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