How to Scrape Google Flights Data with Python

Google Flights offers public flight data, but ethical scraping is crucial, respecting terms of service and robots.txt. Companies like Hopper leverage scraped flight data for price prediction services, generating significant savings and profits. Extractable data includes flight details, times, duration, price, stops, and CO2 emissions. Scraping Google Flights faces challenges like IP blocks, CAPTCHAs, dynamic website structure, and rate limiting. Scrapeless, a Python library, overcomes these obstacles by automating IP rotation, CAPTCHA solving, and data extraction. Setting up a Python environment with PyCharm and pip is the first step for scraping. The process involves creating a project, writing a script, and utilizing the Scrapeless library. The output provides JSON data containing comprehensive flight information. Scrapeless offers a reliable, scalable, and legally compliant solution for Google Flights data scraping, handling various challenges and providing real-time data. The Scrapeless API efficiently manages high-frequency scraping demands.