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

Getting started with pandas

In the previous section, we introduced NumPy and its ability to efficiently store and work with a large array of data. We'll now introduce another widely used library in data science: pandas. This library is built on top of NumPy to provide convenient data structures able to efficiently store large datasets with labeled rows and columns. This is, of course, especially handy when working with most datasets representing real-world data that we want to analyze and use in data science projects.

To get started, we will, of course, install the library with the usual command:

$ pip install pandas

Once done, we can start to use it in a Python interpreter:

$ python
>>> import pandas as pd

Just like we alias numpy as np, the convention is to alias pandas as pd when importing it.

Using pandas Series for one-dimensional data

The first pandas data structure we'll introduce is Series. This data structure behaves very similarly to...