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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 FREE CHAPTER
2. Preparing the Workspace 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

Summary

We've done a lot in this chapter. First, we learned about geocoding in general, including geocoding services and their web APIs. We also discussed how you can interact with web APIs programmatically, from Python, using the requests library. Then, we experimented with a specific API from Nominatim and wrote a thin wrapper function that geocodes any arbitrary address. On top of that, we wrote another function to geocode addresses in bulk that keeps working even if a specific request fails or no location was found for some addresses. We used the built-in csv library both to read data from and write to CSV files. Finally, as the code we used seemed as though it might be useful in the future, we moved it from a notebook into a dedicated Python file, which can be used as a standalone script with its own interface or as a module to import functions from.

In the next chapter...

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