You're reading from Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python

Product type Paperback

Published in Oct 2021

Publisher Packt

ISBN-13 9781801079211

Length 426 pages

Edition 1st Edition

Languages

Python

Tools

FastAPI

Concepts

Data Science

Author (1):

François Voron

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Table of Contents (19) Chapters

Preface

1. Section 1: Introduction to Python and FastAPI

2. Chapter 1: Python Development Environment Setup FREE CHAPTER

3. Chapter 2: Python Programming Specificities

4. Chapter 3: Developing a RESTful API with FastAPI

5. Chapter 4: Managing Pydantic Data Models in FastAPI

6. Chapter 5: Dependency Injections in FastAPI

7. Section 2: Build and Deploy a Complete Web Backend with FastAPI

8. Chapter 6: Databases and Asynchronous ORMs

9. Chapter 7: Managing Authentication and Security in FastAPI

10. Chapter 8: Defining WebSockets for Two-Way Interactive Communication in FastAPI

11. Chapter 9: Testing an API Asynchronously with pytest and HTTPX

12. Chapter 10: Deploying a FastAPI Project

13. Section 3: Build a Data Science API with Python and FastAPI

14. Chapter 11: Introduction to NumPy and pandas

15. Chapter 12: Training Machine Learning Models with scikit-learn

16. Chapter 13: Creating an Efficient Prediction API Endpoint with FastAPI

17. Chapter 14: Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV

18. Other Books You May Enjoy

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:

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You're reading from Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python

Table of Contents (19) Chapters

Chapter 11: Introduction to NumPy and pandas

Authors (1)

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You're reading from Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python

Table of Contents (19) Chapters

Chapter 11: Introduction to NumPy and pandas

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