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Work through practical examples to unlock the full potential of web scraping with Python and gain valuable insights from high-quality data Key Features Build an initial portfolio of web scraping projects with detailed explanations Grasp Python programming fundamentals related to web scraping and data extraction Acquire skills to code web scrapers, store data in desired formats, and employ the data professionally Purchase of the print or Kindle book includes a free PDF eBook Book Description Web scraping is a powerful tool for extracting data from the web, but it can be daunting for those without a technical background. Designed for novices, this book will help you grasp the fundamentals of web scraping and Python programming, even if you have no prior experience. Adopting a practical, hands-on approach, this updated edition of Hands-On Web Scraping with Python uses real-world examples and exercises to explain key concepts. Starting with an introduction to web scraping fundamentals and Python programming, you’ll cover a range of scraping techniques, including requests, lxml, pyquery, Scrapy, and Beautiful Soup. You’ll also get to grips with advanced topics such as secure web handling, web APIs, Selenium for web scraping, PDF extraction, regex, data analysis, EDA reports, visualization, and machine learning. This book emphasizes the importance of learning by doing. Each chapter integrates examples that demonstrate practical techniques and related skills. By the end of this book, you’ll be equipped with the skills to extract data from websites, a solid understanding of web scraping and Python programming, and the confidence to use these skills in your projects for analysis, visualization, and information discovery. What you will learn Master web scraping techniques to extract data from real-world websites Implement popular web scraping libraries such as requests, lxml, Scrapy, and pyquery Develop advanced skills in web scraping, APIs, PDF extraction, regex, and machine learning Analyze and visualize data with Pandas and Plotly Develop a practical portfolio to demonstrate your web scraping skills Understand best practices and ethical concerns in web scraping and data extraction Who this book is for This book is for beginners who want to learn web scraping and data extraction using Python. No prior programming knowledge is required, but a basic understanding of web-related concepts such as websites, browsers, and HTML is assumed. If you enjoy learning by doing and want to build a portfolio of web scraping projects and delve into data-related studies and application, then this book is tailored for your needs. Table of Contents Web Scraping Fundamentals Python programming for Data and Web Searching and Processing Web Documents Scraping Using PyQuery, a jQuery-Like Library for Python Scraping the Web with Scrapy and Beautiful Soup Working with the Secure Web Data Extraction Using Web APIs Using Selenium to Scrape the Web Using Regular Expressions and PDFs Data Mining, Analysis, and Visualization Machine Learning and Web Scraping After Scraping – Next Steps and Data Analysis Cover Title page Copyright and Credits Contributors Table of Contents Preface Part 1:Python and Web Scraping Chapter 1: Web Scraping Fundamentals Technical requirements What is web scraping? Understanding the latest web technologies HTTP HTML XML JavaScript CSS Data-finding techniques used in web pages HTML source page Developer tools Summary Further reading Chapter 2: Python Programming for Data and Web Technical requirements Why Python (for web scraping)? Accessing the WWW with Python Setting things up Creating a virtual environment Installing libraries Loading URLs URL handling and operations requests – Python library Implementing HTTP methods GET POST Summary Further reading Part 2: Beginning Web Scraping Chapter 3: Searching and Processing Web Documents Technical requirements Introducing XPath and CSS selectors to process markup documents The Document Object Model (DOM) XPath CSS selectors Using web browser DevTools to access web content HTML elements and DOM navigation XPath and CSS selectors using DevTools Scraping using lxml – a Python library lxml by example Web scraping using lxml Parsing robots.txt and sitemap.xml The robots.txt file Sitemaps Summary Further reading Chapter 4: Scraping Using PyQuery, a jQuery-Like Library for Python Technical requirements PyQuery overview Introducing jQuery Exploring PyQuery Installing PyQuery Loading a web URL Element traversing, attributes, and pseudo-classes Iterating using PyQuery Web scraping using PyQuery Example 1 – scraping book details Example 2 – sitemap to CSV Example 3 – scraping quotes with author details Summary Further reading Chapter 5: Scraping the Web with Scrapy and Beautiful Soup Technical requirements Web parsing using Python Introducing Beautiful Soup Installing Beautiful Soup Exploring Beautiful Soup Web scraping using Beautiful Soup Web scraping using Scrapy Setting up a project Creating an item Implementing the spider Exporting data Deploying a web crawler Summary Further reading Part 3: Advanced Scraping Concepts Chapter 6: Working with the Secure Web Technical requirements Exploring secure web content Form processing Cookies and sessions User authentication HTML processing using Python User authentication and cookies Using proxies Summary Further reading Chapter 7: Data Extraction Using Web APIs Technical requirements Introduction to web APIs Types of API Benefits of web APIs Data formats and patterns in APIs Example 1 – sunrise and sunset Example 2 – GitHub emojis Example 3 – Open Library Web scraping using APIs Example 1 – holidays from the US calendar Example 2 – Open Library book details Example 3 – US cities and time zones Summary Further reading Chapter 8: Using Selenium to Scrape the Web Technical requirements Introduction to Selenium Advantages and disadvantages of Selenium Use cases of Selenium Components of Selenium Using Selenium WebDriver Setting things up Exploring Selenium Scraping using Selenium Example 1 – book information Example 2 – forms and searching Summary Further reading Chapter 9: Using Regular Expressions and PDFs Technical requirements Overview of regex Regex with Python re (search, match, and findall) re.split re.sub re.compile Regex flags Using regex to extract data Example 1 – Yamaha dealer information Example 2 – data from sitemap Example 3 – Godfrey’s dealer Data extraction from a PDF The PyPDF2 library Extraction using PyPDF2 Summary Further reading Part 4: Advanced Data-Related Concepts Chapter 10: Data Mining, Analysis, and Visualization Technical requirements Introduction to data mining Predictive data mining Descriptive data mining Handling collected data Basic file handling JSON CSV SQLite Data analysis and visualization Exploratory Data Analysis using ydata_profiling pandas and plotly Summary Further reading Chapter 11: Machine Learning and Web Scraping Technical requirements Introduction to ML ML and Python programming Types of ML ML using scikit-learn Simple linear regression Multiple linear regression Sentiment analysis Summary Further reading Part 5: Conclusion Chapter 12: After Scraping – Next Steps and Data Analysis Technical requirements What happens after scraping? Web requests pycurl Proxies Data processing PySpark polars Jobs and careers Summary Further reading Index Other Books You May Enjoy Work through practical examples to unlock the full potential of web scraping with Python and gain valuable insights from high-quality data Key Features* Build an initial portfolio of web scraping projects with detailed explanations * Grasp Python programming fundamentals related to web scraping and data extraction * Acquire skills to code web scrapers, store data in desired formats, and employ the data professionally * Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWeb scraping is a powerful tool for extracting data from the web, but it can be daunting for those without a technical background. Designed for novices, this book will help you grasp the fundamentals of web scraping and Python programming, even if you have no prior experience. Adopting a practical, hands-on approach, this updated edition of Hands-On Web Scraping with Python uses real-world examples and exercises to explain key concepts. Starting with an introduction to web scraping fundamentals and Python programming, you’ll cover a range of scraping techniques, including requests, lxml, pyquery, Scrapy, and Beautiful Soup. You’ll also get to grips with advanced topics such as secure web handling, web APIs, Selenium for web scraping, PDF extraction, regex, data analysis, EDA reports, visualization, and machine learning. This book emphasizes the importance of learning by doing. Each chapter integrates examples that demonstrate practical techniques and related skills. By the end of this book, you’ll be equipped with the skills to extract data from websites, a solid understanding of web scraping and Python programming, and the confidence to use these skills in your projects for analysis, visualization, and information discovery. What you will learn* Master web scraping techniques to extract data from real-world websites * Implement popular web scraping libraries such as requests, lxml, Scrapy, and pyquery * Develop advanced skills in web scraping, APIs, PDF extraction, regex, and machine learning * Analyze and visualize data with Pandas and Plotly * Develop a practical portfolio to demonstrate your web scraping skills * Understand best practices and ethical concerns in web scraping and data extraction Who this book is forThis book is for beginners who want to learn web scraping and data extraction using Python. No prior programming knowledge is required, but a basic understanding of web-related concepts such as websites, browsers, and HTML is assumed. If you enjoy learning by doing and want to build a portfolio of web scraping projects and delve into data-related studies and application, then this book is tailored for your needs. Table of Contents1. Web Scraping Fundamentals 2. Python programming for Data and Web 3. Searching and Processing Web Documents 4. Scraping Using PyQuery, a jQuery-Like Library for Python 5. Scraping the Web with Scrapy and Beautiful Soup 6. Working with the Secure Web 7. Data Extraction Using Web APIs 8. Using Selenium to Scrape the Web 9. Using Regular Expressions and PDFs 10. Data Mining, Analysis, and Visualization 11. Machine Learning and Web Scraping 12. After Scraping – Next Steps and Data Analysis