Python Web Scraping

By : Jarmul

3 (2)

Buy this Book

Python Web Scraping

3 (2)

By: Jarmul

Buy this Book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Introduction to Web Scraping

When is web scraping useful?

Is web scraping legal?

Python 3

Background research

Crawling your first website

Summary

Scraping the Data

Analyzing a web page

Three approaches to scrape a web page

CSS selectors and your Browser Console

XPath Selectors

LXML and Family Trees

Comparing performance

Scraping results

Summary

Caching Downloads

When to use caching?

Adding cache support to the link crawler

Disk Cache

Key-value storage cache

Summary

Concurrent Downloading

One million web pages

Sequential crawler

Threaded crawler

How threads and processes work

Performance

Summary

Dynamic Content

An example dynamic web page

Reverse engineering a dynamic web page

Rendering a dynamic web page

The Render class

Summary

Interacting with Forms

The Login form

Extending the login script to update content

Automating forms with Selenium

Summary

Solving CAPTCHA

Registering an account

Optical character recognition

Solving complex CAPTCHAs

Using a CAPTCHA solving service

CAPTCHAs and machine learning

Summary

Scrapy

Installing Scrapy

Starting a project

Different Spider Types

Scraping with the shell command

Visual scraping with Portia

Automated scraping with Scrapely

Summary

Putting It All Together

Google search engine

Facebook

Gap

BMW

Summary

Customer Reviews

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Sequential crawler

We can now use AlexaCallback with a slightly modified version of the link crawler we developed earlier to download the top 500 Alexa URLs sequentially. To update the link crawler, it will now take either a start URL or a list of start URLs:

# In link_crawler function

if isinstance(start_url, list):
    crawl_queue = start_url
else:
    crawl_queue = [start_url]

We also need to update the way the robots.txt is handled for each site. We use a simple dictionary to store the parsers per domain (see: https://github.com/kjam/wswp/blob/master/code/chp4/advanced_link_crawler.py#L53-L72). We also need to handle the fact that not every URL we encounter will be relative, and some of them aren't even URLs we can visit, such as e-mail addresses with mailto: or javascript: event commands. Additionally, due to some sites not having the robots.txt files and other poorly formed URLs, there are some additional...