Python web scraping : fetching data from the web

Successfully scrape data from any website with the power of Python 3.x About This Book • A hands-on guide to web scraping using Python with solutions to real-world problems • Create a number of different web scrapers in Python to extract information • This book includes practical examples on using the popular and well-maintained libraries in Python for your web scraping needs Who This Book Is For This book is aimed at developers who want to use web scraping for legitimate purposes. Prior programming experience with Python would be useful but not essential. Anyone with general knowledge of programming languages should be able to pick up the book and understand the principals involved. What You Will Learn • Extract data from web pages with simple Python programming • Build a concurrent crawler to process web pages in parallel • Follow links to crawl a website • Extract features from the HTML • Cache downloaded HTML for reuse • Compare concurrent models to determine the fastest crawler • Find out how to parse JavaScript-dependent websites • Interact with forms and sessions In Detail 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. Style and approach This hands-on guide is full of real-life examples and solutions starting simple and then progressively becoming more complex. Each chapter in this book introduces a problem and then provides one or more possible solutions. Content: Cover Credits Copyright About the Authors About the Reviewers www.PacktPub.com Customer Feedback Table of Contents Preface Chapter 1: 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 featuresParsing robots.txt Supporting proxies Throttling downloads Avoiding spider traps Final version Using the requests library Summary Chapter 2: 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 Chapter 3: Caching Downloads When to use caching? Adding cache support to the link crawler Disk Cache. Implementing DiskCacheTesting 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 Chapter 4: 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 [Python multiprocessing and the GIL] Python multiprocessing and the GIL. Optical character recognitionFurther 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 Chapter 8: 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. Cover -- Credits -- Copyright -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: 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 -- Chapter 2: 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 -- Chapter 3: 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 -- Chapter 4: 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 Chapter 5: 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; Chapter 6: Interacting with Forms ; The Login form; Loading cookies from the web browser; Extending the login script to update content; Automating forms with Selenium; ; Summary; Chapter 7: Solving CAPTCHA ; Registering an account; Loading the CAPTCHA image. Read more... Abstract: Chapter 5: 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; Chapter 6: Interacting with Forms ; The Login form; Loading cookies from the web browser; Extending the login script to update content; Automating forms with Selenium; ; Summary; Chapter 7: Solving CAPTCHA ; Registering an account; Loading the CAPTCHA image About the Author 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) 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