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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Veröffentlichungsjahr: 2017
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Title Page
Second Edition
BIRMINGHAM - MUMBAI
Copyright
Python Web Scraping
Second Edition
Copyright © 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: October 2015
Second edition: May 2017
Production reference: 1240517
ISBN 978-1-78646-258-9
www.packtpub.com
Credits
Authors
Katharine Jarmul
Richard Lawson
Copy Editor
Manisha Sinha
Reviewers
Dimitrios Kouzis-Loukas
Lazar Telebak
Project Coordinator
Nidhi Joshi
Commissioning Editor
Veena Pagare
Proofreader
Safis Editing
Acquisition Editor
Varsha Shetty
Indexer
Francy Puthiry
Content Development Editor
Cheryl Dsa
Production Coordinator
Shantanu Zagade
Technical Editor
Danish Shaikh
About the Authors
Katharine Jarmul is a data scientist and Pythonista based in Berlin, Germany. She runs a data science consulting company, Kjamistan, that provides services such as data extraction, acquisition, and modelling for small and large companies. She has been writing Python since 2008 and scraping the web with Python since 2010, and has worked at both small and large start-ups who use web scraping for data analysis and machine learning. When she's not scraping the web, you can follow her thoughts and activities via Twitter (@kjam) or on her blog: https://blog.kjamistan.com.
Richard Lawson is from Australia and studied Computer Science at the University of Melbourne. Since graduating, he built a business specializing in web scraping while travelling the world, working remotely from over 50 countries. He is a fluent Esperanto speaker, conversational in Mandarin and Korean, and active in contributing to and translating open source software. He is currently undertaking postgraduate studies at Oxford University and in his spare time enjoys developing autonomous drones. You can find him on LinkedIn at https://www.linkedin.com/in/richardpenman.
About the Reviewers
Dimitrios Kouzis-Loukas has over fifteen years of experience providing software systems to small and big organisations. His most recent projects are typically distributed systems with ultra-low latency and high-availability requirements. He is language agnostic, yet he has a slight preference for C++ and Python. A firm believer in open source, he hopes that his contributions will benefit individual communities as well as all of humanity.
Lazar Telebak is a freelance web developer specializing in web scraping, crawling, and indexing web pages using Python libraries/frameworks.
He has worked mostly on a projects that deal with automation and website scraping, crawling and exporting data to various formats including: CSV, JSON, XML, TXT and databases such as: MongoDB, SQLAlchemy, Postgres.
Lazar also has experience of fronted technologies and languages: HTML, CSS, JavaScript, jQuery.
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Table of Contents
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
Introduction to Web Scraping
When is web scraping useful?
Is web scraping legal?
Python 3
Background research
Checking robots.txt
Examining the Sitemap
Estimating the size of a website
Identifying the technology used by a website
Finding the owner of a website
Crawling your first website
Scraping versus crawling
Downloading a web page
Retrying downloads
Setting a user agent
Sitemap crawler
ID iteration crawler
Link crawlers
Advanced features
Parsing robots.txt
Supporting proxies
Throttling downloads
Avoiding spider traps
Final version
Using the requests library
Summary
Scraping the Data
Analyzing a web page
Three approaches to scrape a web page
Regular expressions
Beautiful Soup
Lxml
CSS selectors and your Browser Console
XPath Selectors
LXML and Family Trees
Comparing performance
Scraping results
Overview of Scraping
Adding a scrape callback to the link crawler
Summary
Caching Downloads
When to use caching?
Adding cache support to the link crawler
Disk Cache
Implementing DiskCache
Testing the cache
Saving disk space
Expiring stale data
Drawbacks of DiskCache
Key-value storage cache
What is key-value storage?
Installing Redis
Overview of Redis
Redis cache implementation
Compression
Testing the cache
Exploring requests-cache
Summary
Concurrent Downloading
One million web pages
Parsing the Alexa list
Sequential crawler
Threaded crawler
How threads and processes work
Implementing a multithreaded crawler
Multiprocessing crawler
Performance
Summary
Dynamic Content
An example dynamic web page
Reverse engineering a dynamic web page
Edge cases
Rendering a dynamic web page
PyQt or PySide
Debugging with Qt
Executing JavaScript
Website interaction with WebKit
Waiting for results
The Render class
Selenium
Selenium and Headless Browsers
Summary
Interacting with Forms
The Login form
Loading cookies from the web browser
Extending the login script to update content
Automating forms with Selenium
"Humanizing" methods for Web Scraping
Summary
Solving CAPTCHA
Registering an account
Loading the CAPTCHA image
Optical character recognition
Further improvements
Solving complex CAPTCHAs
Using a CAPTCHA solving service
Getting started with 9kw
The 9kw CAPTCHA API
Reporting errors
Integrating with registration
CAPTCHAs and machine learning
Summary
Scrapy
Installing Scrapy
Starting a project
Defining a model
Creating a spider
Tuning settings
Testing the spider
Different Spider Types
Scraping with the shell command
Checking results
Interrupting and resuming a crawl
Scrapy Performance Tuning
Visual scraping with Portia
Installation
Annotation
Running the Spider
Checking results
Automated scraping with Scrapely
Summary
Putting It All Together
Google search engine
The website
Facebook API
Gap
BMW
Summary
Preface
The internet contains the most useful set of data ever assembled, largely publicly accessible for free. However this data is not easily re-usable. It is embedded within the structure and style of websites and needs to be extracted to be useful. This process of extracting data from webpages is known as web scraping and is becoming increasingly useful as ever more information is available online.
All code used has been tested with Python 3.4+ and is available for download at https://github.com/kjam/wswp.
What this book covers
Chapter 1, Introduction to Web Scraping, introduces what is web scraping and how to crawl a website.
Chapter 2, Scraping the Data, shows you how to extract data from webpages using several libraries.
Chapter 3, Caching Downloads, teaches how to avoid re downloading by caching results.
Chapter 4, Concurrent Downloading, helps you how to scrape data faster by downloading websites in parallel.
Chapter 5, Dynamic Content, learn about how to extract data from dynamic websites through several means.
Chapter 6, Interacting with Forms, shows how to work with forms such as inputs and navigation for search and login.
Chapter 7, Solving CAPTCHA, elaborates how to access data protected by CAPTCHA images.
Chapter 8, Scrapy, learn how to use Scrapy crawling spiders for fast and parallelized scraping and the Portia web interface to build a web scraper.
Chapter 9, Putting It All Together, an overview of web scraping techniques you have learned via this book.
What you need for this book
To help illustrate the crawling examples we have created a sample website at http://example.webscraping.com. The source code used to generate this website is available at http://bitbucket.org/WebScrapingWithPython/website, which includes instructions how to host the website yourself if you prefer.
We decided to build a custom website for the examples instead of scraping live websites so we have full control over the environment. This provides us stability - live websites are updated more often than books and by the time you try a scraping example it may no longer work. Also a custom website allows us to craft examples that illustrate specific skills and avoid distractions. Finally a live website might not appreciate us using them to learn about web scraping and might then block our scrapers. Using our own custom website avoids these risks, however the skills learnt in these examples can certainly still be applied to live websites.
Who this book is for
This book assumes prior programming experience and would most likely not be suitable for absolute beginners. The web scraping examples require competence with Python and installing modules with pip. If you need a brush-up there is an excellent free online book by Mark Pilgrim available at http://www.diveintopython.net. This is the resource I originally used to learn Python.
The examples also assume knowledge of how webpages are constructed with HTML and updated with JavaScript. Prior knowledge of HTTP, CSS, AJAX, WebKit, and Redis would also be useful but not required, and will be introduced as each technology is needed. Detailed references for many of these topics are available at https://developer.mozilla.org/.
Reader feedback
Feedback from our readers is always welcome. Let us know what you think about this book—what you liked or may have disliked. Reader feedback is important for us to develop titles that you really get the most out of.
To send us general feedback, simply send an e-mail to [email protected], and mention the book title through the subject of your message.
If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide on www.packtpub.com/authors.
Customer support
Now that you are the proud owner of a Packt book, we have a number of things to help you to get the most from your purchase.
Downloading the example code
You can download the example code files for this book from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.
You can download the code files by following these steps:
Log in or register to our website using your e-mail address and password.
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Select the book for which you're looking to download the code files.
Choose from the drop-down menu where you purchased this book from.
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You can also download the code files by clicking on the Code Files button on the book's webpage at the Packt Publishing website. This page can be accessed by entering the book's name in the Search box. Please note that you need to be logged in to your Packt account.
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Python-Web-Scraping-Second-Edition. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Errata
Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our books—maybe a mistake in the text or the code—we would be grateful if you could report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this book. If you find any errata, please report them by visiting http://www.packtpub.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details of your errata. Once your errata are verified, your submission will be accepted and the errata will be uploaded to our website or added to any list of existing errata under the Errata section of that title.
To view the previously submitted errata, go to https://www.packtpub.com/books/content/support and enter the name of the book in the search field. The required information will appear under the Errata section.
Piracy
Piracy of copyrighted material on the Internet is an ongoing problem across all media. At Packt, we take the protection of our copyright and licenses very seriously. If you come across any illegal copies of our works in any form on the Internet, please provide us with the location address or website name immediately so that we can pursue a remedy.
Please contact us at [email protected] with a link to the suspected pirated material.
We appreciate your help in protecting our authors and our ability to bring you valuable content.
Questions
If you have a problem with any aspect of this book, you can contact us at [email protected], and we will do our best to address the problem.
Introduction to Web Scraping
Welcome to the wide world of web scraping! Web scraping is used by many fields to collect data not easily available in other formats. You could be a journalist, working on a new story, or a data scientist extracting a new dataset. Web scraping is a useful tool even for just a casual programmer, if you need to check your latest homework assignments on your university page and have them emailed to you. Whatever your motivation, we hope you are ready to learn!
In this chapter, we will cover the following topics:
Introducing the field of web scraping
Explaining the legal challenges
Explaining Python 3 setup
Performing background research on our target website
Progressively building our own advanced web crawler
Using non-standard libraries to help scrape the Web
When is web scraping useful?
Suppose I have a shop selling shoes and want to keep track of my competitor's prices. I could go to my competitor's website each day and compare each shoe's price with my own; however this will take a lot of time and will not scale well if I sell thousands of shoes or need to check price changes frequently. Or maybe I just want to buy a shoe when it's on sale. I could come back and check the shoe website each day until I get lucky, but the shoe I want might not be on sale for months. These repetitive manual processes could instead be replaced with an automated solution using the web scraping techniques covered in this book.
In an ideal world, web scraping wouldn't be necessary and each website would provide an API to share data in a structured format. Indeed, some websites do provide APIs, but they typically restrict the data that is available and how frequently it can be accessed. Additionally, a website developer might change, remove, or restrict the backend API. In short, we cannot rely on APIs to access the online data we may want. Therefore we need to learn about web scraping techniques.
Is web scraping legal?
Web scraping, and what is legally permissible when web scraping, are still being established despite numerous rulings over the past two decades. If the scraped data is being used for personal and private use, and within fair use of copyright laws, there is usually no problem. However, if the data is going to be republished, if the scraping is aggressive enough to take down the site, or if the content is copyrighted and the scraper violates the terms of service, then there are several legal precedents to note.
In Feist Publications, Inc. v. Rural Telephone Service Co., the United States Supreme Court decided scraping and republishing facts, such as telephone listings, are allowed. A similar case in Australia, Telstra Corporation Limited v. Phone Directories Company Pty Ltd, demonstrated that only data with an identifiable author can be copyrighted. Another scraped content case in the United States, evaluating the reuse of Associated Press stories for an aggregated news product, was ruled a violation of copyright in Associated Press v. Meltwater. A European Union case in Denmark, ofir.dk vs home.dk, concluded that regular crawling and deep linking is permissible.
There have also been several cases in which companies have charged the plaintiff with aggressive scraping and attempted to stop the scraping via a legal order. The most recent case, QVC v. Resultly, ruled that, unless the scraping resulted in private property damage, it could not be considered intentional harm, despite the crawler activity leading to some site stability issues.
These cases suggest that, when the scraped data constitutes public facts (such as business locations and telephone listings), it can be republished following fair use rules. However, if the data is original (such as opinions and reviews or private user data), it most likely cannot be republished for copyright reasons. In any case, when you are scraping data from a website, remember you are their guest and need to behave politely; otherwise, they may ban your IP address or proceed with legal action. This means you should make download requests at a reasonable rate and define a user agent to identify your crawler. You should also take measures to review the Terms of Service of the site and ensure the data you are taking is not considered private or copyrighted.
If you have doubts or questions, it may be worthwhile to consult a media lawyer regarding the precedents in your area of residence.
You can read more about these legal cases at the following sites:
Feist Publications Inc. v. Rural Telephone Service Co.
(
http://caselaw.lp.findlaw.com/scripts/getcase.pl?court=US&vol=499&invol=340
)
Telstra Corporation Limited v. Phone Directories Company Pvt Ltd
(
http://www.austlii.edu.au/au/cases/cth/FCA/2010/44.html
)
Associated Press v.Meltwater
(
http://www.nysd.uscourts.gov/cases/show.php?db=special&id=279
)
ofir.dk vs home.dk
(
http://www.bvhd.dk/uploads/tx_mocarticles/S_-_og_Handelsrettens_afg_relse_i_Ofir-sagen.pdf
)
QVC v. Resultly
(
https://www.paed.uscourts.gov/documents/opinions/16D0129P.pdf
)
Python 3
Throughout this second edition of Web Scraping with Python, we will use Python 3. The Python Software Foundation has announced Python 2 will be phased out of development and support in 2020; for this reason, we and many other Pythonistas aim to move development to the support of Python 3, which at the time of this publication is at version 3.6. This book is complaint with Python 3.4+.
If you are familiar with using Python Virtual Environments or Anaconda, you likely already know how to set up Python 3 in a new environment. If you'd like to install Python 3 globally, we recommend searching for your operating system-specific documentation. For my part, I simply use Virtual Environment Wrapper (https://virtualenvwrapper.readthedocs.io/en/latest/) to easily maintain many different environments for different projects and versions of Python. Using either Conda environments or virtual environments is highly recommended, so that you can easily change dependencies based on your project needs without affecting other work you are doing. For beginners, I recommend using Conda as it requires less setup. The Conda introductory documentation (https://conda.io/docs/intro.html) is a good place to start!
Background research
Before diving into crawling a website, we should develop an understanding about the scale and structure of our target website. The website itself can help us via the robots.txt and Sitemap files, and there are also external tools available to provide further details such as Google Search and WHOIS.
Checking robots.txt
Most websites define a robots.txt file to let crawlers know of any restrictions when crawling their website. These restrictions are just a suggestion but good web citizens will follow them. The robots.txt file is a valuable resource to check before crawling to minimize the chance of being blocked, and to discover clues about the website's structure. More information about the robots.txt protocol is available at http://www.robotstxt.org. The following code is the content of our example robots.txt, which is available at http://example.webscraping.com/robots.txt:
# section 1 User-agent: BadCrawler Disallow: / # section 2 User-agent: * Crawl-delay: 5 Disallow: /trap # section 3 Sitemap: http://example.webscraping.com/sitemap.xml
In section 1, the robots.txt file asks a crawler with user agent BadCrawler not to crawl their website, but this is unlikely to help because a malicious crawler would not respect robots.txt anyway. A later example in this chapter will show you how to make your crawler follow robots.txt automatically.
Section 2 specifies a crawl delay of 5 seconds between download requests for all user-agents, which should be respected to avoid overloading their server(s). There is also a /trap link to try to block malicious crawlers who follow disallowed links. If you visit this link, the server will block your IP for one minute! A real website would block your IP for much longer, perhaps permanently, but then we could not continue with this example.
Section 3 defines a Sitemap file, which will be examined in the next section.
Examining the Sitemap
Sitemap files are provided bywebsites to help crawlers locate their updated content without needing to crawl every web page. For further details, the sitemap standard is defined at http://www.sitemaps.org/protocol.html. Many web publishing platforms have the ability to generate a sitemap automatically. Here is the content of the Sitemap file located in the listed robots.txt file:
<?xml version="1.0" encoding="UTF-8"?> <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"> <url><loc>http://example.webscraping.com/view/Afghanistan-1</loc></url> <url><loc>http://example.webscraping.com/view/Aland-Islands-2</loc></url> <url><loc>http://example.webscraping.com/view/Albania-3</loc></url> ... </urlset>
This sitemap provides links to all the web pages, which will be used in the next section to build our first crawler. Sitemap files provide an efficient way to crawl a website, but need to be treated carefully because they can be missing, out-of-date, or incomplete.
Estimating the size of a website
The size of the target website will affect how we crawl it. If the website is just a few hundred URLs, such as our example website, efficiency is not important. However, if the website has over a million web pages, downloading each sequentially would take months. This problem is addressed later in Chapter 4 , Concurrent Downloading, on distributed downloading.
A quick way to estimate the size of a website is to check the results of Google's crawler, which has quite likely already crawled the website we are interested in. We can access this information through a Google search with the site keyword to filter the results to our domain. An interface to this and other advanced search parameters are available at http://www.google.com/advanced_search.
Here are the site search results for our example website when searching Google for site:example.webscraping.com:
As we can see, Google currently estimates more than 200 web pages (this result may vary), which is around the website size. For larger websites, Google's estimates may be less accurate.
We can filter these results to certain parts of the website by adding a URL path to the domain. Here are the results for site:example.webscraping.com/view
