Web scraping is a very powerful technique that can be used to extract data from the internet. This practice has become one of the most popular ways for companies to gather information about their competitors, customers, and the market in general.
When it comes to web scraping, Python is the most popular language among the many tools, libraries, and frameworks available for scraping web data. But there are some strong contenders that have been gaining in popularity over the years.
In this blog, we’re going to take a look at what libraries and frameworks are used for web scraping in different programming languages. We’ll start with Python and then move on to other languages like Javascript, Java, Ruby, R, and Golang.
Web scraping libraries for Python developers
Python is an excellent tool for web scraping because it offers a wide range of libraries that make it easy to get started with this task.
We’ve identified 13 Python libraries that help with web scraping, and we are going to discuss each of them one by one.
1. Requests
Requests is the most popular python library for fetching URLs. It is also used by many web scraping tools, which means that if you want to skip reinventing the wheel and save yourself some time, you should start with requests.
It is easy to use and allows you to fetch the URL of any webpage. If you are a beginner learning web scraping, start with requests. It is the first step in developing a web scraper.
2. BeautifulSoup
BeautifulSoup is a Python library for extracting data from HTML or XML documents. The developer can use beautifulsoup for navigating, searching, and modifying the parse tree. Beautiful Soup supports the default HTML parser in Python’s standard library, but it also supports several third-party Python parsers. The combination of requests and BeautifulSoup works great for simple web scrapers.
See a nice tutorial here on how to scrape data using requests and BeautifulSoup
3. Lxml
lxml is the most feature-rich and easy-to-use library for processing XML and HTML using the Python programming language. You can use lxml with BeautifulSoup by replacing the default parser with the lxml parser. Lxml is a relatively fast library however - it has a dependency on the external C libraries.
See a tutorial here on how to scrape data with Python and lxml
4. html5lib
Unlike lxml, which has C dependencies, Html5lib is a pure-python library for parsing HTML. It is designed to conform to the WHATWG HTML specification, as is implemented by all major web browsers. This means it parses the data the same way modern browsers do. Since it uses an HTML5 parsing algorithm, it even fixes lots of broken HTML and adds several missing tags to complete the text and make it look like an HTML doc. Compared to lxml - html5lib is slow, the speed difference is noticeable on large-scale web scraping jobs. however, for small use cases - it is fast enough.
5. Scrapy
Scrapy is a mature framework for web scraping and web crawling. Scrapy provides an asynchronous mechanism that processes multiple requests in parallel. If you’re building web scrapers internally using an open-source technology - scrapy is a good choice. The learning curve of scrapy is a little high - but it comes with batteries included that help handle most common web scraping problems.
6. PySpider
pyspider is a web scraping framework that is written in Python. The best thing about Pyspider is it comes with a UI that is really helpful in monitoring the crawlers. Pyspider comes with Puppeteer built-in to help render javascript websites. Even though it can be used - it is not actively maintained by the community. However - it will be interesting to check it out.
7. Requests-HTML
The requests-HTML library is an HTML parser that lets you use CSS Selectors and XPath Selectors to scrape from a web page. This library comes with full javascript support, user agent mocking (Tricking the target website into believing you are a real human), and many more. Requests-HTML providers ease of use as in beautifulsoup and good enough to be used for dynamic, javascript websites where it would be overkill to use a browser automation library like selenium.
8. MechanicalSoup
MechanicalSoup is a python library for automating interaction with websites. Under the hood, it uses the requests and BeautifulSoup libraries to do the web scraping part. The biggest limitation of MEchanicalSoup is the inability to handle Javascript web pages. MechanicalSoup is a descendant of Mechanize, another library that we will discuss below.
9. Selenium
Selenium is a complete web automation framework that can be used for web scraping. It was originally developed for testing purposes, and then developers started using it for web scraping as well. Selenium has multiple language support ( Java, c#, Python, Kotlin, Ruby, Javascript ), which means you can use it with any language it is supported. Python is the popular choice for web scraping with selenium. Even with all these nice features - selenium has three critical problems that make it undesirable for large-scale projects.
Selenium requires more system resources compared to alternatives. This could mean spending a lot of money on servers and other resources for large projects.
Selenium is notoriously slow.
It is easier to detect Selenium-based web scraping
10. Playwright
Playwright is a type of headless browser like Puppeteer and Selenium used for web scraping. Playwright gained popularity recently as more developers started replacing selenium with playwright for web scraping tasks. It is a library based on Node.js that allows fast and efficient data extraction.
Playwright outperformed Selenium in several ways:
The execution speed for Playwright is faster and more reliable than Selenium.
It has a lower learning curve
Excellent documentation
11. Chomp JS
chompjs is used for web scrapping heavy Javascript web pages into valid Python dictionaries. When you scrape data - it is often not present directly inside HTML but instead provided as an embedded JavaScript object that is later used to initialize the page. Chompjs converts a javascript string into something that Pythons json.load can read and convert into a dictionary-based format. It can be used within Scrapy.
12. Phantomjs
Until a few years ago, PhantomJS used to be a star in the web scraping world for dealing with javascript-heavy websites. Selenium, combined with PhantomJS, was a choice developers used, but when other tools came to the market, PhantomJS lost its dominance.
Here is an old tutorial on how to use PhantomJS with BeautifulSoup and Selenium
13. Pandas
Pandas is a data manipulation library of Python. It would not come to your mind a web scraping tool. However - Pandas works great extracting Tabular data from websites. See a tutorial here.
Web Scraping Libraries for Javascript Developers
1. Puppeteer
Puppeteer is a Node JS library that provides a high-level API to control headless Chrome or Chromium. Built by google, Puppeteer is the preferred choice of Javascript developers for web scraping. Node JS is not a programming language but a server runtime that uses Javascript as the main programming interface. If you’re into Javascript and want to try web scraping with it - give puppeteer a try. See a nice tutorial here.
2. Cheerio
Cheerio is a tool for parsing HTML and XML using Node.js. Cheerio is fast, flexible, and easy to get started with. Cheerio alone can’t accomplish web scraping, you need to get the HTML to Cheerio using Axios or similar http client libraries. If you’re coming from a Python background - think of Cheerio as a BeautifulSoup alternative but in Javascript.
3. Playwright
Playwright (explained above) has multiple programming language supports and one of them is Javascript. If you have tried Playwright with Python - try the Javascript version as well.
4. Simple web crawler for node.js
Simplecrawler provides a basic, flexible, and robust API for scraping websites. It is a straightforward library to start with if you have Javascript experience. The best thing about Simple crawler is that it is highly customizable.
5. Crawlee
Crawlee is the successor to Apify SDK. It is fully written in Typescript. The goal of Crawlee is to provide a toolbox for generic web scraping, crawling, and automation tasks in JavaScript like Scrapy in Python. See details of Crawlee here
6. Axios
Axios is a simple promise-based HTTP client for the browser and node.js. If you’re from a Python background - think of it as a requests alternative. Built-in Javascript and fully open source.
7. Selenium
Selenium (Explained above ) supports Javascript for interfacing using the Javascript API. More about Selenium for Javascript here
8. Nightmare
Nightmare is a high-level browser automation library built by the awesome people Segment. Nightmare and Cheerio together is an amazing combination to scrape data, especially in cases where you need to mimic the user actions to get to the data. Nightmare was originally developed for automating tasks across websites that don't have APIs but is most often used for web scraping these days.
9. Osmosis
Osmosis is an HTML/XML parser written in Node Js. It uses native libxml C bindings under the hood and has support for CSS selectors and Xpaths. The biggest advantage is the faster parsing, faster searching, and minimal system resource usage. This will come in handy when data is extracted from websites on a very large scale.
Web Scraping Libraries for Java Developers
1. Heritrix
Heritrix is a web scraper library written in Java. It is known for its high extensibility and is designed for web archiving. You can interface with a web browser and control it using Heritrix. The crawler behind the internet archive is built using Heritrix.
2. Web Harvest
Web-Harvest is an open-source web scraping framework written in Java. It can collect useful data from specified pages. It uses XSLT, XQuery, and Regular Expressions for parsing and XML manipulations. Custom Java libraries could easily supplement it to augment its extraction capabilities. It is one of the oldest web scraping tools.
3. Apache Nutch
Separating crawling from scraping is an industry best practice for large use cases. Nutch is a highly extensible, highly scalable, matured, production-ready Web crawler. You can save the crawler output into a database and then pass it on to Solr and use other libraries to get information out of it.
4. Gecco
Gecco is also a lightweight web crawler built in Java language. Gecco framework is known for its remarkable scalability. They achieve it using distributed crawling using Redis at the backend. Gecco supports Asynchronous Ajax requests and has ways to manage proxies.
5. Jaunt
Jaunt is a web scraping library built with Java. The library provides a fast, ultra-light headless browser for web scraping. The browser provides web-scraping functionality, access to the DOM, and control over each HTTP Request/Response. However, Jaunt does not support Javascript* rendering.
6. Jauntium
Jauntium was built to add the javascript rending functionality to Jaunt, its predecessor. It supports all major browsers and is built on top of Jaunt and Selenium. Jauntium is free and is available under the Apache license
7. Stormcrawler
StormCrawler is a mature open-source web scraping library written mostly in Java. It is used for building low-latency, scalable and optimized web scraping solutions in Java. Stormcrawler is the preferred choice of Java developers over the other alternatives because of its scalability and extendability. The learning curve for stormcrawler is relatively low if you’ve worked with Jave before.
8. Jsoup
jsoup is a Java library for scraping and parsing HTML. jsoup implements the WHATWG HTML5 specification and parses HTML to the same DOM as modern browsers do. Jsoup provides a very convenient API for fetching URLs and extracting and manipulating data using the HTML5 DOM methods and CSS selectors.
Web Scraping libraries in other programming languages
1. Nokogiri - Ruby
Nokogiri is a web scraping library written in Ruby programming language. It helps extract data from XML and HTML documents. Nokogiri is built on top of libxml2 and libxslt and supports using CSS and XPath selectors. However, for enterprise use cases, Nokogiri is not really preferred as it lacks functionalities provided by other mature frameworks. Also, Ruby's ecosystem of machine learning and ML tools is minimal, and people prefer Python as the absolute master in those.
2. Colly - Golang
Colly is a web scraping tool written in Golang. It is a good choice for golang developers and has a following of 17.2K developers on Github. Colly is built in a way that is scalable and fast. Using a single core, it can support more than a thousand requests per second. Another useful feature is the automatic cookie and session handling. If you want to enhance the capabilities of Colly - use extensions already available or build your own.
3. rvest - R
R is one of the most popular programming languages used for statistical computing and machine learning. rvest is an R package that makes it easy to scrape data from web pages. rvest is inspired by beautifulsoup and has a decent performance. However, it doesn't work very well with websites having Javascript rendering.
Wrapping up
There are plenty of web scraping tools out there, and they're all great. But we've narrowed the list down to the top 33 and made a handy infographic so that you can easily compare them all at once.
Use this guide to help you find the one that's right for your project!
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