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Hands-On Web Scraping with Python

Hands-On Web Scraping with Python - Second Edition

By : Chapagain
5 (10)
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Hands-On Web Scraping with Python

Hands-On Web Scraping with Python

5 (10)
By: Chapagain

Overview of this book

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.
Table of Contents (20 chapters)
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1
Part 1:Python and Web Scraping
4
Part 2:Beginning Web Scraping
8
Part 3:Advanced Scraping Concepts
13
Part 4:Advanced Data-Related Concepts
16
Part 5:Conclusion

Data processing

Data processing, in the context of web scraping, refers to storing, handling, managing, and analyzing the data that is generated from scraping. In previous chapters of the book, we focused on the concept of effective and efficient scraping with code examples.

As the demand for data is growing, technologies are also evolving and adapting to new changes. Currently, as there has been a boom in AI/ML-based systems, there is competition to provide easy and quick solutions to problems without compromising on quality.

In the coming sections, we will introduce some technologies that help with data processing.

PySpark

The Python library for Apache Spark, pyspark (https://spark.apache.org/), is used to process and analyze data, especially of a large volume. Spark is a framework that is used to handle big data (data with variety, volume, and velocity) and is more effective than Hadoop (https://hadoop.apache.org/), a framework for parallel processing, scheduling, and...

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