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)

Performance

To further understand how increasing the number of threads and processes affects the time required when downloading, here is a table of results for crawling 500 web pages:

Script Number of threads Number of processes Time Comparison with sequential Errors Seen?
Sequential 1 1 1349.798s 1 N
Threaded 5 1 361.504s 3.73 N
Threaded 10 1 275.492s 4.9 N
Threaded 20 1 298.168s 4.53 Y
Processes 2 2 726.899s 1.86 N
Processes 2 4 559.93s 2.41 N
Processes 2 8 451.772s 2.99 Y
Processes 5 2 383.438s 3.52 N
Processes 5 4 156.389s 8.63 Y
Processes 5 8 296.610s 4.55 Y

The fifth column shows the proportion of time in comparison to the base case of sequential downloading. We can see that the increase in performance is not linearly proportional to the number of threads and processes but appears logarithmic, that is, until adding more threads actually decreases performance. For example, one process...