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Learn how to use proxies in Python for web scraping to bypass restrictions, avoid IP bans, and gather data efficiently.

Discover how to build a Scrapy spider to download PDFs from a website, implement date tracking to resume interrupted scraping sessions, and configure the Files Pipeline for efficient file storage. This tutorial provides step-by-step instructions, code explanations, and tips for handling dynamic date extraction and error management.

Learn how I built a Scrapy spider to extract product data from Daraz, including names, prices, stock status, and URLs. This step-by-step guide covers handling AJAX and JSON, Perfect for beginners.

Learn how to handle CAPTCHAs and other anti-scraping measures in an ethical way. This guide covers common website protections, CAPTCHA-solving techniques, and best practices to avoid getting blocked while scraping data legally.

Selenium is a powerful tool for scraping JavaScript-heavy websites by automating browser interactions. It allows users to extract dynamic content, handle scrolling, and interact with web elements efficiently.

Proxies play a crucial role in web scraping by masking IP addresses, rotating requests, and bypassing geo-restrictions to prevent detection and bans. This article explores how proxies help scrapers extract data efficiently while avoiding blocks and CAPTCHAs.

Web data extraction enables businesses to efficiently scrape, analyze, and process vast amounts of online information.

Web scraping automates data extraction from websites, helping businesses with market research, price tracking, and lead generation.

Data extraction is the process of retrieving valuable information from various sources for analysis, automation, and decision-making.

Browser automation streamlines repetitive web tasks like testing, data scraping, and form submissions, boosting efficiency and productivity.