Python

Modern websites rely heavily on JavaScript and anti-bot measures, making data extraction a challenge. Basic tools fail with dynamic content loaded after the initial page, but Selenium with Python can automate browsers to execute JavaScript and interact with pages like a user. In this tutorial, you'll learn to build scrapers that collect clean, structured data from even the most complex websites.

Google Lens has revolutionized how we interact with visual content – it allows users to search the web using images rather than text queries. This powerful visual search engine can identify objects, text, landmarks, products, and much more from uploaded images. In this guide, we'll explore the types of data that can be scraped from Google Lens, examine various methods for extracting this information, and demonstrate how to efficiently collect visual search results using our Web Scraping API.

Google Finance is one of the most comprehensive financial data platforms, offering real-time stock prices, market analytics, and company insights. Scraping Google Finance provides access to valuable data streams that can transform your analysis capabilities. In this guide, we'll walk through building a robust Google Finance scraper using Python, handling anti-bot measures, and implementing best practices for reliable data extraction.

Google Shopping is a product search engine that aggregates listings from thousands of online retailers. Businesses scrape it to track competitor pricing, spot trends, and gather valuable eCommerce insights. Using APIs, no-code tools, or custom scripts, you can extract data like product titles, prices, ratings, and more. In this guide, we’ll build a custom scraping script using Python and Playwright!

Google Scholar is a free search engine for academic articles, books, and research papers. If you're gathering academic data for research, analysis, or application development, this blog post will give you a reliable foundation. In this guide, you'll learn how to scrape Google Scholar with Python, set up proxies to avoid IP bans, build a working scraper, and explore advanced tips for scaling your data collection.

This comprehensive guide will teach you how to scrape Amazon product data using Python. Whether you’re an eCommerce professional, researcher, or developer, you’ll learn to create a solution to extract valuable insights from Amazon’s marketplace. By following this guide, you’ll acquire practical knowledge on setting up your scraping environment, overcoming common challenges, and efficiently collecting the needed data.

Welcome to 2026! What better way to celebrate than by mastering Python? If you’re new to web scraping, don’t worry – this guide starts from the basics, guiding you step-by-step on collecting data from websites. Whether you’re curious about automating simple tasks or diving into more significant projects, Python makes it easy and fun to start. Let’s slither into the world of web scraping and see how powerful this tool can be!

Online marketplaces are beloved for offering everything from items we don’t need to treasures we didn’t know we were missing. For music lovers and collectors, Discogs stands out as the go-to destination – think of it as the IMDb of music records. Whether you're analyzing vinyl market trends, tracking the value of rare releases, or building a personal archive, Discogs is an unmatched resource. In this tutorial, you’ll learn how to scrape Discogs using Python, step by step, to unlock the data behind the music.

When using Python's Requests library, proxies can help with tasks like web scraping, interacting with APIs, or accessing geo-restricted content. Proxies route HTTP requests through different IP addresses, helping you avoid IP bans, maintain anonymity, and bypass restrictions. This guide covers how to set up and use proxies with the Requests library. Let’s get started!

Standards are a means to clear and define communication between people and things in the world. For example, the human language, USB sockets on computers, or the fact that you must add cereal before pouring milk. When it comes to computer applications and systems, one standard stands out above the rest as the most popular choice for developers – XML (eXtensible Markup Language). In this article, we’ll explore how you can parse data from XML files using Python’s built-in libraries, see the best methods to do so, and understand the importance of effectively reading information.

In this article, we’ll explore the different kinds of errors and exceptions, what causes them, and provide solutions to solving them. No more headaches and cursing your code until it gets scared and starts working – master the language of Python to understand precisely what it wants from you.

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.

Social media scraping can look like a tough nut to crack due to strong anti-bot systems. Gladly, it’s not a rule, at least in Telegram’s case. This platform supports various Telegram bot automation, making the scraping process easier. 

There’re a lot of ready-built solutions for that, but you can easily make one yourself with a bit of coding and the Telegram API. Yes, this platform even has its own API! Dope, innit?

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.

Selenium is the perfect web development and testing tool. It lets you use every major browser and access any site or service you want to test. This versatility makes Selenium indispensable for more than just testing. For example, you can use Selenium with Python to scrape websites. Of course, you will need a proxy service to not get blocked. This is why we are doing this short introduction about how a Selenium proxy network can help you.