NumPy operations are mostly done element-wise, which requires two arrays in an operation to have the same shape; however, this doesn't mean that NumPy operations can't take two differently shaped arrays (refer to the first example we looked at with scalars). NumPy provides the flexibility to broadcast a smaller-sized array across a larger one. But we can't broadcast the array to just about any shape. It needs to follow certain constrains; we will be covering them in this section. One key idea to keep in mind is that broadcasting involves performing meaningful operations over two differently shaped arrays. However, inappropriate broadcasting might lead to an inefficient use of memory that slows down computation.
NumPy Essentials
By :
NumPy Essentials
By:
Overview of this book
In today’s world of science and technology, it’s all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy gives you both the speed and high productivity you need.
This book will walk you through NumPy using clear, step-by-step examples and just the right amount of theory. We will guide you through wider applications of NumPy in scientific computing and will then focus on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples.
You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier Transform; solving linear systems of equations, interpolation, extrapolation, regression, and curve fitting; and evaluating integrals and derivatives. We will also introduce you to using Cython with NumPy arrays and writing extension modules for NumPy code using the C API. This book will give you exposure to the vast NumPy library and help you build efficient, high-speed programs using a wide range of mathematical features.
Table of Contents (16 chapters)
NumPy Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
An Introduction to NumPy
The NumPy ndarray Object
Using NumPy Arrays
NumPy Core and Libs Submodules
Linear Algebra in NumPy
Fourier Analysis in NumPy
Building and Distributing NumPy Code
Speeding Up NumPy with Cython
Introduction to the NumPy C-API
Further Reading
Customer Reviews