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

Caching with decorators

As you can see, geocoding takes time—working with a server takes time, as does being nice and waiting between requests. Thus, we probably don't want to waste time asking the same questions over and over again. For example, if many records within the same sessions have the same address, it makes sense to pull that data once, and then reuse it. Specifics may depend on the nature of the data. Namely, if we're checking air ticket availability, we shouldn't cache the results—the data might change any second. But for geolocation, we don't anticipate any changes any time soon.

The process of storing data we've pulled locally and then using it instead of getting the same data again is called caching. For example, all modern browsers do this—they cache some secondary elements of the web page for you to use and they&apos...