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

Exploring the dataset

For this chapter, we'll use the dataset on WWII battles we collected earlier in Chapter 7, Scraping Data from the Web with Beautiful Soup 4. As you may remember, the dataset includes dates, results, sides, leaders, and the number of troops and casualties of those battles. But what questions can we answer with this information? Let's start with something simple: which battles took the most casualties on both sides? Where were most of the tanks destroyed? How was the number of casualties distributed over time and geography?

In the previous chapter, we cleaned and processed most of the data; however, given the sensitivity of the subject, we went ahead and cross-checked main values row-by-row, manually, and, indeed, had to correct a few values. This work cannot be completely automated. In this and further chapters, we'll work with the corrected...