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SciPy Recipes

You're reading from SciPy Recipes A cookbook with over 110 proven recipes for performing mathematical and scientific computations

Product type Paperback

Published in Dec 2017

Publisher Packt

ISBN-13 9781788291460

Length 386 pages

Edition 1st Edition

Languages

Python

Tools

SciPy

Concepts

Data Analysis

Authors (3):

Martins

Ruben Oliva Ramos

V Kishore Ayyadevara

View More author details

Table of Contents (17) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Getting to Know the Tools FREE CHAPTER

2. Getting Started with NumPy

3. Using Matplotlib to Create Graphs

4. Data Wrangling with pandas

5. Matrices and Linear Algebra

6. Solving Equations and Optimization

7. Constants and Special Functions

8. Calculus, Interpolation, and Differential Equations

9. Statistics and Probability

10. Advanced Computations with SciPy

Integration

In the next section, we will get ourselves acquainted with the methods of integration.

Getting ready

To get involved in the following recipe, we need to know about the following instructions and requirements:

To achieve the definite integration of functions on suitable domains, we have mainly two methods—numerical integration and symbolic integration.
Numerical integration refers to the approximation of a definite integral via a quadrature process. Depending on how the function f(x) is given, the domain of integration, the knowledge of its singularities, and the choice of quadrature is the main part of this section.
In many cases, it is also possible to perform exact integration, even for non-bounded domains, with the aid of symbolic computation. In the SciPy stack, to this effect, we have an implementation of the Risch algorithm for elementary functions, and Meijer G-functions for non-elementary integrals. Both methods are housed in the SymPy libraries. Unfortunately, these symbolic...

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Authors (3)

Martins

Martins

Luiz Felipe Martins holds a PhD in applied mathematics from Brown University and has worked as a researcher and educator for more than 20 years. His research is mainly in the field of applied probability. He has been involved in developing code for the open source homework system, WeBWorK, where he wrote a library for the visualization of systems of differential equations. He was supported by an NSF grant for this project. Currently, he is an Associate Professor in the Department of Mathematics at Cleveland State University, Cleveland, Ohio, where he has developed several courses in applied mathematics and scientific computing. His current duties include coordinating all first-year calculus sessions.

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Ruben Oliva Ramos

Ruben Oliva Ramos

Ruben Oliva Ramos is a computer systems engineer from Tecnologico de Leon Institute, with a master's degree in computer and electronic systems engineering and a specialization in teleinformatics and networking from the University of Salle Bajio in Leon, Guanajuato, Mexico. He has more than 5 years of experience of developing web applications to control and monitor devices connected with Arduino and Raspberry Pi, using web frameworks and cloud services to build the Internet of Things applications. He is a mechatronics teacher at the University of Salle Bajio and teaches students of the master's degree in design and engineering of mechatronics systems. Ruben also works at Centro de Bachillerato Tecnologico Industrial 225 teaching subjects such as electronics, robotics and control, automation, and microcontrollers. He is a consultant and developer for projects in areas such as monitoring systems and datalogger data using technologies (such as Android, iOS, HTML5, and ASP.NET), databases (such as SQlite, MongoDB, and MySQL), web servers, hardware programming, and control and monitor systems for data acquisition and programming.

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V Kishore Ayyadevara

V Kishore Ayyadevara

Kishore Ayyadevara is an entrepreneur and a hands-on leader working at the intersection of technology, data, and AI to identify and solve business problems. With over a decade of experience in leadership roles, Kishore has established and grown successful applied data science teams at American Express and Amazon, as well as a top health insurance company. In his current role, he is building a start-up focused on making AI more accessible to healthcare organizations. Outside of work, Kishore has shared his knowledge through his five books on ML/AI, is an inventor with 12 patents, and has been a speaker at multiple AI conferences.

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