Website Scraping with Python


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Website Scraping with Python

How to use BeautifulSoup and Scrapy in practice

About the Book

New version by Apress

In 2018 I teamed-up with Apress and we released an updated version of this book. You can find it on Amazon:

This book is the follow-up of my previous one: "XML processing and website scraping in Java". There I looked at ways and tools to process XML and HTML in Java, did some performace comparisons and introduced some new programming concepts to make things even better.

In this book I take a closer look at website scraping with the two tools used nowadays: BeautifulSoup and Scrapy.

I create the sample application from the Java book -- now in Python, use the two tools for parsing, show examples how to export CSV files in Python.

As a bonus I will compare the two tools for their runtime, try to tweak where possible and I will give a quick introduction on plotting the runtimes as charts.

Until it is finished, you can buy the book for a discounted price. The final book will be around $35.

I will write about the following topics in this book:

  • BeautifulSoup
  • Scrapy
  • Performance comparison
  • Plotting in Python
  • Functional programming with Python
  • Parallel code execution with Python
  • Sample application to gather Amazon data
  • Other real-life projects (source code coming soon into the package)
  • Update for Scrapy's release and Python 3 (coming soon)
  • Share this book

  • Categories

    • Software
    • Computers and Programming
    • Python
    • Data Science
  • Feedback

    Email the Author(s)

About the Author

Gábor László Hajba
Gábor László Hajba

Gábor László Hajba, a seasoned Senior Software Developer at ProLion GmbH in Wiener Neustadt, Austria, brings a wealth of expertise in Java and Python to his role. His primary responsibility involves crafting innovative solutions while also imparting valuable knowledge to his colleagues, ensuring they excel in their respective roles.

A testament to his proficiency, Gábor's book "Website Scraping with Python - Using BeautifulSoup and Scrapy" was published by Apress in 2018. This publication had its humble beginnings as a LeanPub book in 2014, reflecting his passion for sharing insights and empowering fellow developers.

Beyond his professional endeavors, Gábor finds immense joy in his personal life. He cherishes his role as a devoted husband and a proud father to a delightful little girl and an exuberant young boy. Additionally, he nurtures a keen interest in music and aspires to become a proficient bass player, demonstrating his relentless pursuit of growth and fulfillment.

Gábor's multifaceted journey showcases his dedication to both his craft and his loved ones, embodying the spirit of a well-rounded individual who excels in various domains.

Table of Contents

    • Preface
      • What will I do exactly?
      • About the programming language
      • Some extra feature
      • Prerequisites
      • Length of the book
      • LeanPub
    • Some brief words about the project
    • 1. BeautifulSoup – The ancestor of JSoup
      • 1.1 Some words about BeautifulSoup
      • 1.2 Configuring the download timeout
      • 1.3 Configuring the proxy
      • 1.4 Changing the XML-parser behind BeautifulSoup
      • 1.5 Some of the errors that happened
      • 1.6 Bits and pieces of the solution
      • 1.7 Printing a CSV line
      • 1.8 Conclusion
    • 2. Scrapy – another way to gather data
      • 2.1 Some words about Scrapy
      • 2.2 Setting up the project
      • 2.3 Configuring the download timeout
      • 2.4 Configuring the proxy
      • 2.5 Working with offline data
      • 2.6 Bits and pieces of the solution
      • 2.7 Errors which happened
      • 2.8 Exporting the data as a CSV
      • 2.9 Accessing settings
      • 2.10 Scrapy as a library
      • 2.11 Conclusion
    • 3. Performance of the solutions
      • 3.1 The dataset
      • 3.2 Introducing the test environments
      • 3.3 The test scenarios
      • 3.4 BeautifulSoup compared with itself
      • 3.5 Comparing Scrapy with itself
      • 3.6 Comparing both tools
    • 4. Creating plots with Python
      • 4.1 Simple examples
      • 4.2 Display multiple data ranges
      • 4.3 Displaying the averages
      • 4.4 Displaying the legend
      • 4.5 Formatting the plot
      • 4.6 Conclusion
    • 5. Some thoughts on functional programming
      • 5.1 The idea behind functional programming
      • 5.2 First class functions
      • 5.3 Currying
      • 5.4 Writing declaratively
      • 5.5 Using map and reduce
      • 5.6 Using recursion
      • 5.7 Pipelining
      • 5.8 Applying functional programming
      • 5.9 Conclusion
    • 6. Parallel working
      • 6.1 Why Should I care, I use Scrapy?
      • 6.2 Parallelism in Python
      • 6.3 An example
      • 6.4 CPU-bound tasks
      • 6.5 Possible errors you can encounter
      • 6.6 Conclusion
      • 6.7 Sources
    • 7. Two real-life projects
      • 7.1 Sport clubs spider
      • 7.2 Bloso scraper
      • 7.3 Conclusion
    • Extra! Extra! Read all about it!
      • Introducing the requirements
      • Starting the project
      • The items
      • Defining the spider
      • Exporting the data into a database
      • Offline data
      • Performance
      • The sources
      • Next step
  • Notes

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