Before you start looking into programming languages, you should already have some knowledge of what is web scraping and when should it be used.
In this article, I'll advise you on why, in my opinion, Python is the best language for web scraping. However, I also think the best programming language is usually the one you feel more comfortable using.
I'll also talk about other programming languages you can use for web scraping and what libraries are available for each language. Ultimately, it doesn't matter if a language is more suitable for a particular job if you already know one and can write faster and better code with it.
Why I think Python is the best programming language for Web Scraping
When determining which programming language was best suited for web scraping, I considered a few factors. These include:
- Learning curve
- Ease of finding information and resources
- Third-party libraries with comprehensive documentation
The learning curve for Python is considered gentle, as it is known for its intuitive syntax and clear structure, making it the top choice for beginners to get started and progress quickly.
Python is slower than compiled languages because it is an interpreted language, meaning that the code is executed line by line at runtime. Its dynamic typing and high-level abstractions can also cause slower performance.
However, the trade-off for slower performance is the ease of development and readability of Python code, which can make it a good choice for many applications where speed is not a critical factor.
Speed is crucial in web scraping, especially when scraping large volumes of pages. However, in most cases, the language used to write the scraper is not the main concern, but rather factors such as network delays, slow third-party libraries, and inefficient code.
It's important to note that Python support Multithreading and Multiprocessing. However, you should be aware that due to the Global Interpreter Lock (GIL), Python's multithreading has some limitations.
Ease of finding information and resources
Python has a strong community fully committed to creating and maintaining various libraries and packages that make development in Python easier.
Thanks to the popularity of Python and its adoption for web scraping, you can not only find resources on how to get started writing your own web scraper, but also more advanced techniques and code examples to help you deal with the most common problems that arise during your programming journey.
Third-party libraries with comprehensive documentation
- BeautifulSoup: A popular library for parsing HTML and XML, making it easier to extract data from web pages.
- Scrapy: A fast, open-source framework for web scraping that provides an efficient way to extract and store data.
- Requests: A library that makes it simple to send HTTP requests, a crucial part of web scraping.
- LXML: A library for parsing HTML and XML, offering a high-level API for flexible and efficient data extraction.
- PyQuery: A library that makes it easy to extract data from HTML and XML using jQuery-like queries.
- Parsel: A library for extracting data from HTML and XML using CSS-like selectors.
- Selectolax: A fast HTML parsing library with a low memory footprint, ideal for extracting data from web pages.
Apart from Web Scraping, Python can be used for a wide range of applications. These includes:
- Web Development (Django, Flask, Pyramid)
- Scientific and Numeric Computing (SciPy, NumPy)
- Data Analysis and Visualization (Pandas, Matplotlib, Seaborn)
- Artificial Intelligence and Machine Learning (TensorFlow, PyTorch, Scikit-Learn)
- Desktop GUI Applications (Tkinter, PyQt)
- Games Development (Pygame)
- Network Programming (Twisted)
- Automation and System Administration (Ansible, SaltStack, Fabric)
- Embedded Systems and Internet of Things (MicroPython)
- Financial and Algorithmic Trading (Quantlib, PyAlgoTrade)
What are the best programming languages for Web Scraping other than Python?
Now that we know why Python is the most suitable programming language for building web scrapers, I think it's worth taking a look at the other options. There are many programming languages that can be used for web scraping, thanks to the availability of open-source libraries available for them.
Some of the most popular NodeJS web scraping libraries are:
- Request: A simplified HTTP request library that supports all HTTP methods, automatic compression and decompression, and many other features for making web requests.
- Cheerio: A fast, flexible, and lean implementation of core jQuery designed specifically for the server side.
- Osmosis: A lightweight and flexible web scraping library for Node.js that allows you to extract data from HTML and XML pages with a CSS selector-based syntax.
- Puppeteer: A Node.js library that provides a high-level API for controlling headless Chrome or Chromium. Puppeteer allows you to automate browser tasks, extract data from websites, and create end-to-end tests.
If you're interested in writing a web scraper using NodeJS, then you should know what's the best web scraping framework for NodeJS.
These programming languages are well suited for web scraping due to their raw speed and fine-tuned control over system resources. C++, in particular, is favoured for web scraping and web crawling projects that demand intensive computations and the handling of vast amounts of data. The language's ability to perform low-level system operations enables it to efficiently scrape even the most complex websites.
The most popular C/C++ web scraping libraries are:
- libcurl: A client-side URL transfer library, that supports a range of protocols including HTTP and HTTPS
- gumbo-parser: A C library for parsing HTML5 pages, designed to be fast and flexible
- htmlcxx: A simple CSS and HTML parsing library for C++
- Beautiful Soup: A popular Python library for parsing HTML and XML, that can be used from C++ using the Python/C API
Java is a robust and secure programming language widely used for web scraping projects that demand stability and reliability. With its extensive libraries and platform independence, Java is an excellent choice for projects that require the scraping of websites on multiple platforms. The Java Virtual Machine provides a secure runtime environment for executing code, ensuring the safety of sensitive data during scraping operations.
The most common libraries for web scraping with Java are:
- jsoup: A library for working with HTML. It provides all the tools needed to complete web scraping tasks such as a URL fetching API, CSS selectors and data manipulation
- Apache HttpComponents: a set of low-level Java libraries for HTTP and related protocols, including client-side support for HTTP/HTTPS
- Selenium: A suite of tools for automating web browsers, that can be used for web scraping as well as testing
- Jaunt: a Java library for web-scraping, using a CSS or XPath selector syntax
This programming language is designed with concurrency in mind, making it a top choice for building web scrapers that require the simultaneous handling of multiple requests. Go is also known for its efficient memory management and fast processing speeds, making it ideal for scraping large amounts of data from websites.
The most common libraries for web scraping with Go are:
- GoQuery: A minimalist and fast library for working with HTML documents using a jQuery-like syntax
- Colly: A fast and efficient web scraping library for Go, with support for parallel requests and middleware
- Gocrawl: A flexible and high-performance web crawling library for Go, with a focus on scalable distributed crawling
- GoJSON: A Go library for parsing JSON, useful for working with APIs and data returned in JSON format
- Goroutine: A Go library for working with HTTP requests and responses, including support for sending GET and POST requests and parsing response data
- Puppeteer: A Node.js library for controlling headless Chrome, that can be used from Go using the Node.js/Go bridge