Book Image

SciPy Recipes

By : V Kishore Ayyadevara, Ruben Oliva Ramos
Book Image

SciPy Recipes

By: V Kishore Ayyadevara, Ruben Oliva Ramos

Overview of this book

With the SciPy Stack, you get the power to effectively process, manipulate, and visualize your data using the popular Python language. Utilizing SciPy correctly can sometimes be a very tricky proposition. This book provides the right techniques so you can use SciPy to perform different data science tasks with ease. This book includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib, and pandas, among others. You will use these libraries to solve real-world problems in linear algebra, numerical analysis, data visualization, and much more. The recipes included in the book will ensure you get a practical understanding not only of how a particular feature in SciPy Stack works, but also of its application to real-world problems. The independent nature of the recipes also ensure that you can pick up any one and learn about a particular feature of SciPy without reading through the other recipes, thus making the book a very handy and useful guide.
Table of Contents (11 chapters)

Choosing the solver used to find the solution of equations

How about having the functions for computing equations and finding the solutions? In this section, we will see how to do it.

Getting ready

This is a collection of general-purpose, non-linear multidimensional solvers. These solvers find x for which F(x) = 0. Both x and F can be multidimensional.

Functions

Parameters

newton_krylov(F, xin[, iter, rdiff, method, ...])

Find a root of a function, using Krylov approximation for inverse Jacobian.

anderson(F, xin[, iter, alpha, w0, M, ...])

Find a root of a function, using (extended) Anderson mixing.

General non-linear solvers:

Functions

Parameters

broyden1(F, xin[, iter, alpha, ...])

Find...