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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

Product type Paperback

Published in Aug 2019

Publisher Packt

ISBN-13 9781789535365

Length 482 pages

Edition 1st Edition

Languages

Python

Tools

Pygame

Concepts

Application Development

Authors (2):

Philipp Kats

David Katz

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Table of Contents (26) Chapters

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

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Technical requirements

In this chapter, we'll use the following libraries:

FastAPI
pydantic
uvicorn
locust

Make sure to install them, if you haven't done so already. To test our API, we'll use the curl command-line tool. On Windows, you can install curl or use the built-in Invoke-Webrequest tool, aliased to wget.

Alternatively, you can use Postman, https://www.getpostman.com, a standalone and free application for testing and exploring web APIs with a nice graphical interface. To install Postman, go to the website and hit Get Started, then select Download. Postman has versions for Windows and Linux. Its interface is quite clean and easy to learn, so we won't cover it here.

All of the code is available in the GitHub repository, in the Chapter18 folder (https://github.com/PacktPublishing/Learn-Python-by-Building-Data-Science-Applications).

...

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Authors (2)

Kats

Philipp Kats is a researcher at the Urban Complexity Lab, NYU CUSP, a research fellow at Kazan Federal University, and a data scientist at StreetEasy, with many years of experience in software development. His interests include data analysis, urban studies, data journalism, and visualization. Having a bachelor's degree in architectural design and a having followed the rocky path (at first) of being a self-taught developer, Philipp knows the pain points of learning programming and is eager to share his experience.

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Katz

Doron Katz, originally from Sydney, Australia, completed his bachelor's in internet science (the University of Wollongong), before pursuing a master's in management (Charles Sturt University), including a certification in Microsoft Solutions Development. On moving to San Francisco, Doron has worked with various companies, from start-ups to larger organizations, as a software engineer and project manager. Additionally, he is a regular contributor to various distinguished technical publications and has published numerous white papers and also coauthored Developing an iOS Edge.

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