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Learn Python by Building Data Science Applications

You're reading from   Learn Python by Building Data Science Applications A fun, project-based guide to learning Python 3 while building real-world apps

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789535365
Length 482 pages
Edition 1st Edition
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Authors (2):
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Philipp Kats Philipp Kats
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Philipp Kats
David Katz David Katz
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David Katz
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Table of Contents (26) Chapters Close

Preface 1. Section 1: Getting Started with Python
2. Preparing the Workspace FREE CHAPTER 3. First Steps in Coding - Variables and Data Types 4. Functions 5. Data Structures 6. Loops and Other Compound Statements 7. First Script – Geocoding with Web APIs 8. Scraping Data from the Web with Beautiful Soup 4 9. Simulation with Classes and Inheritance 10. Shell, Git, Conda, and More – at Your Command 11. Section 2: Hands-On with Data
12. Python for Data Applications 13. Data Cleaning and Manipulation 14. Data Exploration and Visualization 15. Training a Machine Learning Model 16. Improving Your Model – Pipelines and Experiments 17. Section 3: Moving to Production
18. Packaging and Testing with Poetry and PyTest 19. Data Pipelines with Luigi 20. Let's Build a Dashboard 21. Serving Models with a RESTful API 22. Serverless API Using Chalice 23. Best Practices and Python Performance 24. Assessments 25. Other Books You May Enjoy

Beyond Beautiful Soup

In this example, we used the BS4 library to parse static HTML for us. Beautiful Soup is an invaluable library for dealing with occasionally messy HTML, but when it comes to large scales and dynamic pages, it simply won't suffice. For production scraping in large quantities, perhaps on a regular basis, it is a good idea to utilize the Scrapy (https://scrapy.org/) package. Scrapy is an entire framework for downloading HTML, parsing data, pulling data, and then storing it. One of its killer features is that it can run asynchronously – for example, while it is waiting for one page to load, it can switch to processing another, automatically. Because of that, Scrapy's scrapers are significantly faster on large lists of websites. At the same time, its interface is more expressive for a developer, as it is explicitly designed for scraping.

Depending...

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