Web Scraping

Scraping data from Yelp can provide valuable insights into local restaurants, including details like name, URL, cuisines, and ratings.

Scraping Reddit offers a wealth of information on trending topics, community engagement, and popular posts.

Using Python and Playwright to scrape data from Google Trends enables a detailed examination of keyword popularity and the monitoring of trend shifts over time.

When it comes to travel planning, competition analysis or research purposes, scraping flight-related information from Google Flights could yield significant insights.

Scraping Amazon reviews with Python is useful when conducting competitor analysis, checking reviews, and doing market research.

Extracting real property information from Zillow can offer perfect analysis for the market and investments.

This ecommerce scraping tutorial will show you how to harvest information using a combination of requests and lxml from online stores.

The article analyzes the legality of web scraping in 2026, examining the legal aspects and laws regulating this practice, including GDPR and CCPA.

This article outlines the process for developing a Python script designed to parse data from Google Finance. It includes an example of how to integrate a proxy server and implement error handling to ensure robust data scraping.

In this article we are going to break through the process of: how to scrape Walmart data. We will be using requests for sending HTTP requests and lxml for parsing the returned HTML documents.

This guide outlines how to set up a Python script for scraping Twitter data, collecting details such as the author, content of the post, number of comments, retweets, likes, and reactions to the publication.

How to effectively use cURL through PycURL in Python for performing HTTP requests and web scraping. Examples include executing GET and POST requests, setting custom headers, handling XML responses, and managing HTTP errors.

How to create a data collection script using Python. Examples of using Beautiful Soup and lxml libraries, solutions to common parsing errors. Scraping dynamic content with Playwright.

Using proxy servers during web scraping. A step-by-step guide on how to integrate and perform proxy rotation while scraping data from a website. Functional code to run the scraper.

Explanation of the key principles for implementing retries in Python. Overview of common HTTP status codes and their meanings, along with basic and advanced retry mechanisms for handling failed requests.

The article provides a guide on creating a scraper for data collection on Airbnb using Python. It includes a functional code example for parsing key attributes of listings on the Airbnb website.

Step-by-step creation of a script for parsing Amazon product data in Python. Recommendations for choosing a proxy for scraping on Amazon without blocks and captcha.

What is screen scraping, and what tasks does this tool allow you to solve? How does screen scraping differ from web scraping, and why are proxies necessary for performing screen scraping?

What are web crawling and web scraping, and what are their differences, advantages, and disadvantages? What is parsing used for, how to set up your parser in Python, and what libraries and tools are used for scraping?

This article provides an overview of WebHarvy, a powerful tool for automating the process of collecting data from websites with the option to integrate a proxy server.