Python Data Extraction

Python Web Scraping Tutorial

Python is the go-to language for web scraping thanks to libraries like BeautifulSoup, Requests, Scrapy, and Selenium. This tutorial covers setup, common patterns, handling dynamic content, and scaling your scrapers.

  • Requests + BeautifulSoup

    HTTP / Parsing

  • Scrapy

    Crawling

  • Selenium / Playwright

    Browser automation

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Python scraping flow

  1. Website

  2. HTTP / Browser

  3. Python scraper

  4. HTML parsing

  5. Extracted data

This tutorial covers setup, common patterns, handling dynamic content, and scaling your scrapers.

  1. 01

    setup

  2. 02

    common patterns

  3. 03

    handling dynamic content

  4. 04

    scaling your scrapers

  1. Website
  2. Python scraper
  3. Parse
  4. Extract

Key Libraries

For enterprise-scale extraction without managing infrastructure, explore our web scraping services and web scraping tools overview.

HTTP / Parsing

Requests + BeautifulSoup

Simple HTTP requests and HTML parsing. Best for static pages.

Crawling

Scrapy

Full-featured framework for large-scale crawling.

Browser automation

Selenium / Playwright

Browser automation for JavaScript-rendered content.

Static page

  1. HTTP request
  2. HTML parsing

Requests + BeautifulSoup — Simple HTTP requests and HTML parsing. Best for static pages.

Dynamic page

  1. Browser automation
  2. JavaScript-rendered content
  3. Extraction

Selenium / Playwright — Browser automation for JavaScript-rendered content.

From source to extracted data

Illustrative workflow

Python
# Illustrative workflow
# HTTP request or browser load
# → HTML parsing
# → extracted fields

source = "website"
method = "python scraping"
Extracted data
{
  "source": "website",
  "method": "python scraping",
  "status": "extracted"
}

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  1. Website
  2. Python scraper
  3. Extracted data