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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

By : Kats, Katz
2.8 (4)
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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

2.8 (4)
By: Kats, Katz

Overview of this book

Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.
Table of Contents (26 chapters)
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1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Testing the code so far

How would we know whether the code is good, anyway? The only good way is to rigorously test your code. While it may sound like a lot of somewhat unnecessary work, it is a practice that will repay you many times over in the future—once you're sure your code behaves as intended, it is much easier to add new features and be sure that they didn't break any of the existing ones. Furthermore, you can upgrade dependencies or compare different implementations, all being sure that your code behaves as intended.

As for many other things, Python has a standard library for testing—unittest. In contrast to most of the standard libraries, however, unittest is fairly unpopular. Instead, another library, pytest, is considered the de facto industry standard for Python testing, as it provides a clean and reusable pattern of code and has support...

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