Python Data Scraping

https://github.com/PedroooAlmeida/PyDataScrape-Multiprocessed

This Python script is a web scraper that utilizes the multiprocessing library to speed up data collection by running multiple processes in parallel. It fetches HTML content from a list of URLs, parses the content using BeautifulSoup, and stores the scraped data into a CSV file for further analysis.

Features

• Multiprocessing: Uses the multiprocessing library to scrape multiple pages concurrently, reducing the total time needed to gather data.

• Efficient Parsing: Leverages BeautifulSoup and the lxml parser for fast and efficient HTML parsing.

• Data Storage: Outputs the scraped data into a CSV file for easy analysis using Pandas.

• Error Handling: Implements basic error handling to manage network issues and failed requests.

How Multiprocessing Works

• This script uses Python's multiprocessing library to run multiple processes concurrently. Instead of scraping one webpage at a time, the script divides the list of URLs among multiple worker processes. This allows for significantly faster scraping, especially when dealing with a large number of URLs.

• For example, if you have 4 CPU cores, the script can scrape 4 pages simultaneously. You can adjust the number of processes based on your machine's CPU capacity by modifying the processes parameter in the script "AdjustMultiprocessing.py"

• You could also use the multiprocessing.cpu_count() function to get the number of logical CPU cores on your system and set that as the number of processes. This ensures you utilize your available processing power efficiently without overloading the system.