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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
Languages
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Authors (2):
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Philipp Kats Philipp Kats
Author Profile Icon Philipp Kats
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

Shell, Git, Conda, and More – at Your Command

In this chapter, we will review a bunch of non-Pythonic tools that will make your life as a developer significantly easier, and that will help you communicate on the same terms with other developers. While this book is focused on teaching Python, nothing exists in a vacuum. Any developer (Python or not) uses multiple technologies and languages in their daily routine, knowing that at least some of them will improve their productivity significantly. Before we move on to building ever more complex substances, let's briefly review some tools beyond Python itself.

In this chapter, we will cover the following topics:

  • Shell
  • Running Python scripts and designing command-line interfaces
  • Git and GitHub
  • Reproducible virtual environments with conda
  • Structuring your workflow with a Cookiecutter
...
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