The rapid growth of digital data has necessitated the development of automated tools for information retrieval. This research presents a web scraping system designed to extract location-based business data from Google Maps using Python, Playwright for browser automation, and supporting libraries such as Pandas, urllib, and asyncio. Traditionally, manual data entry from Google Maps into spreadsheets is time-consuming and inefficient. Our proposed scraper automates this process, reducing human effort while ensuring faster and more accurate data collection. The system enables users to define search keywords (e.g., "gym", "grocery store") and locations (e.g., "Nagpur", "Mumbai"), retrieves business details programmatically, and stores them in an Excel sheet. The methodology incorporates asynchronous processing for efficiency, error handling for robust data retrieval, and optimization techniques to enhance CPU and RAM performance, particularly for bulk data extraction. Key challenges, such as dynamic scrolling and duplicate data issues, have been addressed. The results demonstrate significant time savings and improved accuracy in data acquisition. This research contributes to the field of web automation, presenting a scalable approach for extracting structured business data from online platforms.
Web Scraping, Browser Automation, Google Maps Data Extraction, Playwright, Asynchronous Processing, Data Optimization, Python Automation, Business Intelligence, Data Mining, Large-Scale Data Collection
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