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Python Web Scraping Cookbook
Python Web Scraping Cookbook

Python Web Scraping Cookbook: Over 90 proven recipes to get you scraping with Python, microservices, Docker, and AWS

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Profile Icon Michael Heydt
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Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3 (3 Ratings)
Paperback Feb 2018 364 pages 1st Edition
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Arrow left icon
Profile Icon Michael Heydt
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Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3 (3 Ratings)
Paperback Feb 2018 364 pages 1st Edition
eBook
Can$12.99 Can$44.99
Paperback
Can$55.99
Subscription
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eBook
Can$12.99 Can$44.99
Paperback
Can$55.99
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Python Web Scraping Cookbook

Data Acquisition and Extraction

In this chapter, we will cover:

  • How to parse websites and navigate the DOM using BeautifulSoup
  • Searching the DOM with Beautiful Soup's find methods
  • Querying the DOM with XPath and lxml
  • Querying data with XPath and CSS Selectors
  • Using Scrapy selectors
  • Loading data in Unicode / UTF-8 format

Introduction

The key aspects for effective scraping are understanding how content and data are stored on web servers, identifying the data you want to retrieve, and understanding how the tools support this extraction. In this chapter, we will discuss website structures and the DOM, introduce techniques to parse, and query websites with lxml, XPath, and CSS. We will also look at how to work with websites developed in other languages and different encoding types such as Unicode.

Ultimately, understanding how to find and extract data within an HTML document comes down to understanding the structure of the HTML page, its representation in the DOM, the process of querying the DOM for specific elements, and how to specify which elements you want to retrieve based upon how the data is represented.

How to parse websites and navigate the DOM using BeautifulSoup

When the browser displays a web page it builds a model of the content of the page in a representation known as the document object model (DOM). The DOM is a hierarchical representation of the page's entire content, as well as structural information, style information, scripts, and links to other content.

It is critical to understand this structure to be able to effectively scrape data from web pages. We will look at an example web page, its DOM, and examine how to navigate the DOM with Beautiful Soup.

Getting ready

We will use a small web site that is included in the www folder of the sample code. To follow along, start a web server from within the www folder...

Searching the DOM with Beautiful Soup's find methods

We can perform simple searches of the DOM using Beautiful Soup's find methods. These methods give us a much more flexible and powerful construct for finding elements that are not dependent upon the hierarchy of those elements. In this recipe we will examine several common uses of these functions to locate various elements in the DOM.

Getting ready

ff you want to cut and paste the following into ipython, you can find the samples in 02/02_bs4_find.py.

How to do it...

We will start with a fresh iPython session...

Querying the DOM with XPath and lxml

XPath is a query language for selecting nodes from an XML document and is a must-learn query language for anyone performing web scraping. XPath offers a number of benefits to its user over other model-based tools:

  • Can easily navigate through the DOM tree
  • More sophisticated and powerful than other selectors like CSS selectors and regular expressions
  • It has a great set (200+) of built-in functions and is extensible with custom functions
  • It is widely supported by parsing libraries and scraping platforms

XPath contains seven data models (we have seen some of them previously):

  • root node (top level parent node)
  • element nodes (<a>..</a>)
  • attribute nodes (href="example.html")
  • text nodes ("this is a text")
  • comment nodes (<!-- a comment -->)
  • namespace nodes
  • processing instruction nodes

XPath expressions can...

Querying data with XPath and CSS selectors

CSS selectors are patterns used for selecting elements and are often used to define the elements that styles should be applied to. They can also be used with lxml to select nodes in the DOM. CSS selectors are commonly used as they are more compact than XPath and generally can be more reusable in code. Examples of common selectors which may be used are as follows:

What you are looking for Example
All tags *
A specific tag (that is, tr) .planet
A class name (that is, "planet") tr.planet
A tag with an ID "planet3" tr#planet3
A child tr of a table table tr
A descendant tr of a table table tr
A tag with an attribute (that is, tr with id="planet4") a[id=Mars]

Getting ready

...

Using Scrapy selectors

Scrapy is a Python web spider framework that is used to extract data from websites. It provides many powerful features for navigating entire websites, such as the ability to follow links. One feature it provides is the ability to find data within a document using the DOM, and using the now, quite familiar, XPath.

In this recipe we will load the list of current questions on StackOverflow, and then parse this using a scrapy selector. Using that selector, we will extract the text of each question.

Getting ready

The code for this recipe is in 02/05_scrapy_selectors.py.

How to do it...

...

Loading data in unicode / UTF-8

A document's encoding tells an application how the characters in the document are represented as bytes in the file. Essentially, the encoding specifies how many bits there are per character. In a standard ASCII document, all characters are 8 bits. HTML files are often encoded as 8 bits per character, but with the globalization of the internet, this is not always the case. Many HTML documents are encoded as 16-bit characters, or use a combination of 8- and 16-bit characters.

A particularly common form HTML document encoding is referred to as UTF-8. This is the encoding form that we will examine.

Getting ready

We will read a file named unicode.html from our local web server, located at http...

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Key benefits

  • Hands-on recipes for advancing your web scraping skills to expert level
  • One-stop solution guide to address complex and challenging web scraping tasks using Python
  • Understand web page structures and collect data from a website with ease

Description

Python Web Scraping Cookbook is a solution-focused book that will teach you techniques to develop high-performance scrapers and deal with crawlers, sitemaps, forms automation, Ajax-based sites, caches, and more. You'll explore a number of real-world scenarios where every part of the development/product life cycle will be fully covered. You will not only develop the skills needed to design and develop reliable performance data flows, but also deploy your codebase to AWS. If you are involved in software engineering, product development, or data mining (or are interested in building data-driven products), you will find this book useful as each recipe has a clear purpose and objective. Right from extracting data from the websites to writing a sophisticated web crawler, the book's independent recipes will be a godsend. This book covers Python libraries, requests, and BeautifulSoup. You will learn about crawling, web spidering, working with Ajax websites, paginated items, and more. You will also learn to tackle problems such as 403 errors, working with proxy, scraping images, and LXML. By the end of this book, you will be able to scrape websites more efficiently and able to deploy and operate your scraper in the cloud.

Who is this book for?

This book is ideal for Python programmers, web administrators, security professionals, and anyone who wants to perform web analytics. Familiarity with Python and basic understanding of web scraping will be useful to make the best of this book.

What you will learn

  • Use a variety of tools to scrape any website and data, including BeautifulSoup, Scrapy, Selenium and many more
  • Master expression languages, such as XPath and CSS, and regular expressions to extract web data
  • Deal with scraping traps such as hidden form fields, throttling, pagination, and different status codes
  • Build robust scraping pipelines with SQS and RabbitMQ
  • Scrape assets like image media and learn what to do when Scraper fails to run
  • Explore ETL techniques of building a customized crawler, parser, and convert structured and unstructured data from websites
  • Deploy and run your scraper as a service in AWS Elastic Container Service

Product Details

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Publication date : Feb 09, 2018
Length: 364 pages
Edition : 1st
Language : English
ISBN-13 : 9781787285217
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Product Details

Publication date : Feb 09, 2018
Length: 364 pages
Edition : 1st
Language : English
ISBN-13 : 9781787285217
Category :
Languages :
Concepts :
Tools :

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Table of Contents

12 Chapters
Getting Started with Scraping Chevron down icon Chevron up icon
Data Acquisition and Extraction Chevron down icon Chevron up icon
Processing Data Chevron down icon Chevron up icon
Working with Images, Audio, and other Assets Chevron down icon Chevron up icon
Scraping - Code of Conduct Chevron down icon Chevron up icon
Scraping Challenges and Solutions Chevron down icon Chevron up icon
Text Wrangling and Analysis Chevron down icon Chevron up icon
Searching, Mining and Visualizing Data Chevron down icon Chevron up icon
Creating a Simple Data API Chevron down icon Chevron up icon
Creating Scraper Microservices with Docker Chevron down icon Chevron up icon
Making the Scraper as a Service Real Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3
(3 Ratings)
5 star 33.3%
4 star 0%
3 star 0%
2 star 0%
1 star 66.7%
Tonya Oliver Mar 26, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It's probably worth the read. I don't like the fact that Amazon is forcing me to write this review with no less than 18 words. It's too bad because the book isn't being reviewed here it's Amazon. How's that for 18 words Amazon?
Amazon Verified review Amazon
John Ewers Apr 17, 2018
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
I bought book trying to learn how I could download a few tables from the web into python. I had 2 issues that I needed help with: 1) sites that i need data from require passwords 2) sites have javascript that needs to run before I can grab data. After 4 hours, I got absolutely nothing out of this book and went to youtube / stack overflow (w/ these tools, I figured out my problem in less time than I spent w/ this book)The book starts off by going over a few details on many different scraping libraries. There isn't enough detail to do anything useful w/ webscraping, you just become aware of the existence of this libraries. The 2nd 2/3rds of the book focus exclusively with 'scrappy'. This appears to be a good resource for crawling (finding new websites to go onto); however, not so good for scraping known sites (certainly not for beginner / intermediate python users). If you want to go crawling, this may be a good book for you. I was stunned that reading HTML behind the sites you want to scrape was barely mentioned. This is a key element of any "how to" you can find on youtube and wo a lot of html experience, one of the more challenging parts of scraping.One of my biggest issues was w/ passwords. Book only offered 1/2 a page on this w/ an extremely simple example. Solution did not work on any of the 3 sites I tried it on. Also, I coudl not find 1 mention of what to do w/ javascript.Overall, useless book for me
Amazon Verified review Amazon
Patrick Klein Jan 08, 2022
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
This "book" feels like a collection of Stack Overflow answers to very basic topics with the added disadvantages that it's harder to navigate and you can't just copy-paste.I'm sending this one back.
Amazon Verified review Amazon
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