The Most Powerful Language for Data Extraction

What We Do With Python Web Scraping

• Handle JavaScript-rendered websites with Playwright & Selenium
• Scrape millions of pages per day with Scrapy distributed crawlers
• Auto-retry on failures with exponential backoff
• Rotating proxies and user-agent management built-in
• Clean and transform data with Pandas before delivery
• Export to CSV, Excel, JSON, SQL databases, or REST APIs

Python Web Scraping Tech Stack

import scrapy

class ProductSpider(scrapy.Spider):
    name = 'amazon_products'
    
    def start_requests(self):
        urls = ['https://example.com/products']
        for url in urls:
            yield scrapy.Request(url, callback=self.parse)
    
    def parse(self, response):
        for product in response.css('.product-card'):
            yield {
                'title': product.css('h2::text').get(),
                'price': product.css('.price::text').get(),
                'rating': product.css('.rating::text').get(),
                'asin': product.attrib.get('data-asin'),
            }

* This is a simplified example. Production scrapers include error handling, proxies, and rate limiting.

Common Use Cases

• 1
  E-commerce price monitoring (Amazon, Flipkart, Nykaa)
• 2
  Real estate listing extraction (MagicBricks, 99acres)
• 3
  Social media data collection (Twitter, Instagram)
• 4
  News and media content aggregation
• 5
  Lead generation from business directories
• 6
  Financial market data collection