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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

Building a web page

While FastAPI is focused on the APIs, it is still entirely possible to serve HTML pages as well. The code will be almost identical to the preceding code—except that our functions need to return this HTML code.

The most common approach to generate HTML in Python is to use the Jinja2 templating engine—that way, you write the template as an HTML code with some injections of Python and later render them by feeding it with the variables; Jinja will execute and hide the injections, returning the resultant page.

For the sake of building a simple example, however, we will use another package: VDOM, which allows us to generate VDOMs (short for Virtual Document Object Models) in Python and then convert them into HTML. Flask is great for smaller projects, but not for large and complex applications.

To separate this page from the main API, let's create...

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