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

Best Practices and Python Performance

After going through the preceding chapters and learning various things about Python, we have come to the last chapter. Here, we want to discuss some general strategies that you can implement and how to write code that works faster, is cleaner, and is easier to maintain. These approaches can be used for data-oriented codeor any other type of code, for that matter.

This chapter is split into three parts. The first section will discuss how you can analyze and speed up your code, the second section will cover best practices for maintaining your code so that you'll code faster and cleaner, and in the third and final section, we'll go through a brief overview of the non-Python technologies that you might find useful for your projects.

The following topics will be covered in this chapter:

  • Ways to monitor performance and identify...