Parsing

​​HTML structure shifts constantly, but the visible text on a page tends to remain more stable. That stability is what makes text-based selectors useful in web scraping. This guide covers the core functions you need to work with text in XPath: text(), contains(), starts-with(), normalize-space(), and translate(), including where each one breaks and how to combine them to build selectors that survive page updates.

Python is usually the first choice for web scraping, but it can struggle in high-throughput scenarios where you’re fetching many pages concurrently or need stronger reliability. That’s where Rust comes in. In this tutorial, you’ll build a Hacker News scraper in Rust, covering setup, JSON output, and scaling, along with where Rust excels, where it adds friction, and when to offload to a managed scraping API.

Ever wished you could make the web work for you? JavaScript web scraping allows you to gather valuable information from websites in an automated way, unlocking insights that would be difficult to collect manually. In this guide, you'll learn the key tools, techniques, and best practices to scrape data efficiently, whether you're a beginner or a developer looking to streamline data collection.

If you're tired of duct-taping complicated scripts just to grab web data, this n8n web scraping tutorial is for you. You'll see how to use n8n for web scraping, why it beats DIY scrapers, and what you need to get started. Perfect for developers and coding beginners looking to automate data extraction without the headaches.

If you’re here, you already know Amazon constantly tweaks product prices. The eCommerce giant makes around 2.5 million price changes daily, resulting in the average item seeing new pricing roughly every ten minutes. For sellers, marketers, and savvy shoppers, that creates both a challenge and an opportunity.

This comprehensive guide walks you through proven methods – from Excel's built-in tools to powerful scraping APIs that can simplify your Amazon price monitoring workflow.

Web scraping with Python is a powerful technique for extracting valuable data from the web, enabling automation, analysis, and integration across various domains. Using libraries like Beautiful Soup and Requests, developers can efficiently parse HTML and XML documents, transforming unstructured web data into structured formats for further use. This guide explores essential tools and techniques to navigate the vast web and extract meaningful insights effortlessly.

They say you’ll never have time to read all the books or watch all the movies in your entire lifetime – but what if you could at least gather all their titles, ratings, and reviews in seconds? That’s the magic of web scraping: automating the impossible, collecting large amounts of data, and uncovering hidden insights from all across the internet. In this article, we’ll explore valuable web scraping ideas that you can create even with little to no experience – completely free of charge.

Keeping up with everything happening around the world can feel overwhelming. With countless news sites competing for your attention using catchy headlines, it’s hard to find what you need among celebrity tea and what the Kardashians were up to this week. Fortunately, there’s a handy tool called Google News that makes it easier to stay informed by helping you filter out the noise and focus on essential information. Let’s explore how you can use Google News together with Python to get the key updates delivered right to you.

Scraping static web pages can be challenging, but Cheerio makes it fast and efficient. Cheerio is a lightweight Node.js library that parses and manipulates HTML using a syntax similar to jQuery. This guide covers key concepts, practical code examples, and essential techniques to help you extract web data with ease—no matter your experience level.

Beautiful Soup is a widely used Python library that plays a vital role in data extraction. It offers powerful tools for parsing HTML and XML documents, making it possible to extract valuable data from web pages effortlessly. This library simplifies the often complex process of dealing with the unstructured content found on the internet, allowing you to transform raw web data into a structured and usable format.

HTML document parsing plays a pivotal role in the world of information. The HTML data can be used further for data integration, analysis, and automation, covering everything from business intelligence to research and beyond. The web is a massive place full of valuable information; therefore, in this guide, we’ll employ various tools and scripts to explore the vast seas and teach them to bring back all the data.

This one’s gonna be serious. But not scary. We know how frightening the word “programming” could be for a newbie or a person with a little technical background. But hey, don’t worry, we’ll make your trip in Python smooth and pleasant. Deal? Then, let’s go!

Python is widely known for its simple syntax. On the other hand, when learning Python for the first time or coming to Python after having worked with other programming languages, you may face some difficulties. If you’ve ever got a syntax error when running your Python code, then you’re in the right place.

In this guide, we’ll analyze common cases of parsing errors in Python. The cherry on the cake is that by the end of this article, you’ll have learnt how to resolve such issues.

Keepin’ it short and sweet: data parsing is a process of computer software converting unstructured and often unreadable data into structured and readable format. Parsing offers a lot of benefits, some of which include work optimization, saving time, reducing costs, and many more; in addition, you can use parsed data in plenty of different situations.

Even tho that sounds epic, parsing itself can be quite complicated. But hold on, buddy, and get ready to explore a step-by-step process on how to parse HTML and XML documents using lxml.

If you're fresh-new to data scraping, you may not be familiar with selectors yet. Let us introduce ya – selectors are objects that find and return web items on a page. These pieces are an essential part of a scraper, as they affect your tests' outcome, efficiency, and speed.

Yep, understanding the idea of a selector isn't that complicated. Finding the right selector itself might be. To be honest, even the two languages that define them, XPath and CSS, have their own pros and cons. So it can quickly become a headache to choose one of them. But here's some good news – we're here to help! Let's explore it together.