Michael Heydt - Python Web Scraping Cookbook

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Michael Heydt is an independent consultant specializing in social, mobile, analytics, and cloud technologies, with an emphasis on cloud native 12-factor applications. Michael has been a software developer and trainer for over 30 years and is the author of books such as D3.js By Example, Learning Pandas, Mastering Pandas for Finance, and Instant Lucene.NET. You can find more information about him on LinkedIn at michaelheydt.

Mei Lu is the founder and CEO of Jobfully, providing career coaching for software
developers and engineering leaders. She is also a Career/Executive Coach for
Carnegie Mellon University Alumni Association, specializing in the software / high-tech
industry.
Previously, Mei was a software engineer and an engineering manager at Qpass,
M.I.T., and MicroStrategy. She received her MS in Computer Science from the
University of Pennsylvania and her MS in Engineering from Carnegie Mellon
University.

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 projects of automation, website scraping, crawling, and exporting data in various formats (CSV, JSON, XML, and TXT) and databases such as (MongoDB, SQLAlchemy, and Postgres). Lazar also has experience of fronted technologies and languages such as HTML, CSS, JavaScript, and jQuery.

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The internet contains a wealth of data. This data is both provided through structured APIs as well as by content delivered directly through websites. While the data in APIs is highly structured, information found in web pages is often unstructured and requires collection, extraction, and processing to be of value. And collecting data is just the start of the journey, as that data must also be stored, mined, and then exposed to others in a value-added form.

With this book, you will learn many of the core tasks needed in collecting various forms of information from websites. We will cover how to collect it, how to perform several common data operations (including storage in local and remote databases), how to perform common media-based tasks such as converting images an videos to thumbnails, how to clean unstructured data with NTLK, how to examine several data mining and visualization tools, and finally core skills in building a microservices-based scraper and API that can, and will, be run on the cloud.

Through a recipe-based approach, we will learn independent techniques to solve specific tasks involved in not only scraping but also data manipulation and management, data mining, visualization, microservices, containers, and cloud operations. These recipes will build skills in a progressive and holistic manner, not only teaching how to perform the fundamentals of scraping but also taking you from the results of scraping to a service offered to others through the cloud. We will be building an actual web-scraper-as-a-service using common tools in the Python, container, and cloud ecosystems.

This book is for those who want to learn to extract data from websites using the process of scraping and also how to work with various data management tools and cloud services. The coding will require basic skills in the Python programming language.