Book Image

Python Web Scraping - Second Edition

By : Katharine Jarmul
Book Image

Python Web Scraping - Second Edition

By: Katharine Jarmul

Overview of this book

The Internet contains the most useful set of data ever assembled, most of which is publicly accessible for free. However, this data is not easily usable. It is embedded within the structure and style of websites and needs to be carefully extracted. Web scraping is becoming increasingly useful as a means to gather and make sense of the wealth of information available online. This book is the ultimate guide to using the latest features of Python 3.x to scrape data from websites. In the early chapters, you'll see how to extract data from static web pages. You'll learn to use caching with databases and files to save time and manage the load on servers. After covering the basics, you'll get hands-on practice building a more sophisticated crawler using browsers, crawlers, and concurrent scrapers. You'll determine when and how to scrape data from a JavaScript-dependent website using PyQt and Selenium. You'll get a better understanding of how to submit forms on complex websites protected by CAPTCHA. You'll find out how to automate these actions with Python packages such as mechanize. You'll also learn how to create class-based scrapers with Scrapy libraries and implement your learning on real websites. By the end of the book, you will have explored testing websites with scrapers, remote scraping, best practices, working with images, and many other relevant topics.
Table of Contents (10 chapters)

Summary

This chapter covered two approaches to scraping data from dynamic web pages. It started with reverse engineering a dynamic web page using browser tools, and then moved on to using a browser renderer to trigger JavaScript events for us. We first used WebKit to build our own custom browser, and then reimplemented this scraper with the high-level Selenium framework.

A browser renderer can save the time needed to understand how the backend of a website works; however, there are some disadvantages. Rendering a web page adds overhead and is much slower than just downloading the HTML or using API calls. Additionally, solutions using a browser renderer often require polling the web page to check whether the resulting HTML has loaded, which is brittle and can fail when the network is slow.

I typically use a browser renderer for short-term solutions where the long-term performance and reliability is less important...