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

Speeding up with asynchronous calls

Now, let's turn to the question of performance. Once in a while, our application will need to be constantly monitored and, if needed, scaled and optimized. There are a few ways to speed things up incrementally, for example, by installing the ujson package, which works exactly like built-in json but is more performant (because it is written in C). In that case, FastAPI will automatically switch to using this library instead.

Potentially, more significant improvement in performance is built into FastAPI, Uvicorn, and based on the new features of Python 3.4 and later versions, asynchronous calls. We did spend some time discussing this feature in Chapter 3, Functions. In a nutshell, all of the code we generally write in Python is executed sequentiallyonce one line is executed, Python will go to the next, and so on. It means that, when...