Book Image

Building Data Science Applications with FastAPI

By : François Voron
5 (1)
Book Image

Building Data Science Applications with FastAPI

5 (1)
By: François Voron

Overview of this book

FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you’ll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you’ll cover best practices relating to testing and deployment to run a high-quality and robust application. You’ll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you’ll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you’ll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you’ll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI.
Table of Contents (19 chapters)
1
Section 1: Introduction to Python and FastAPI
7
Section 2: Build and Deploy a Complete Web Backend with FastAPI
13
Section 3: Build a Data Science API with Python and FastAPI

Chapter 11: Introduction to NumPy and pandas

In recent years, Python has gained a lot of popularity in the data science field. Its very efficient and readable syntax makes the language a very good choice for scientific research, while still being suitable for production workloads: it's very easy to deploy research projects into real applications that will bring value to users. Thanks to this growing interest, a lot of specialized Python libraries have emerged. The most well known are probably NumPy and pandas. Their goal is to provide a set of tools to manipulate a big set of data in an efficient way, much more than what we could actually achieve with standard Python, and we'll show how and why in this chapter. NumPy and pandas are at the heart of most data science applications in Python; knowing them is therefore the first step on your journey into Python for data science.

In this chapter, we're going to cover the following main topics:

  • Getting started with...