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NumPy Essentials

NumPy Essentials

By : Jaidev Deshpande, Chin, Tanmay Dutta, Shane Holloway
3.3 (3)
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NumPy Essentials

NumPy Essentials

3.3 (3)
By: Jaidev Deshpande, Chin, Tanmay Dutta, Shane Holloway

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 (11 chapters)
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SciPy


SciPy is a well-known Python library focusing on scientific computing (it contains modules for optimization, linear algebra, integration, interpolation, and special functions such as FFT, signal, and image processing). It builds on the NumPy Array object, and NumPy is part of the whole SciPy stack (remember that we introduced the Scientific Python family in Chapter 1An Introduction to NumPy). However, the SciPy module contains various topics that we can't cover in just one section. Let's look at an example of image processing (noise removal) to help you get some idea of what SciPy can do:

In [1]: from scipy.misc import imread, imsave, ascent 
In [2]: import matplotlib.pyplot as plt 
In [3]: image_data = ascent() 

First, we import three functions from SciPy's miscellaneous routines: imreadimsave, and ascent. In the following example, we use the built-in image ascent, which is a 512 by 512 greyscale image. Of course, you may use your own image; simply call imread('your_image_name...

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NumPy Essentials
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