Book Image

Learn Python by Building Data Science Applications

By : Philipp Kats, David Katz
Book Image

Learn Python by Building Data Science Applications

By: Philipp Kats, David 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)
Free Chapter
1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Functions

At the end of the previous chapter, we solved a simple conversion problem, and while the problem was solved, it can be argued that the code we used wasn't exactly perfect; of course, it allowed us to change variable names (for instance, update hotel pricing), but it was still hard to read and error-prone. In addition, some particular elements of the code were repetitive, as we performed the same operations on different values.

This is exactly the opposite of one measure of code quality employed by programmers—Don't Repeat Yourself! (DRY) code—code that has no repeating parts. In other words, operations that we use multiple times should be articulated and defined once. This will allow us to keep the code short, concise, and expressive. It will be easier to maintain, debug, and change when needed. But how can this be achieved? First of all, it is...