Website Scraping with Python
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: https://amzn.to/2Dkl4gI
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:
- 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)
Table of Contents
- What will I do exactly?
- About the programming language
- Some extra feature
- Length of the book
- 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
- The sources
- Next step
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