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  • Book Overview & Buying NumPy Essentials
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NumPy Essentials

NumPy Essentials

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

NumPy Essentials

3.3 (3)
By: Jaidev Deshpande, Leo (Liang-Huan) 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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Application - regression and curve fitting


Since we are talking about the application of linear algebra, our experience comes from real-world cases. Let's begin with linear regression. So, let's say we are curious about the relationship between the age of a person and his/her sleeping quality. We'll use the data available online from the Great British Sleep Survey 2012 (https://www.sleepio.com/2012report/).

There were 20,814 people who took the survey, in an age range from under 20 to over 60 years old, and they evaluated their sleeping quality by scores from 4 to 6.

In this practice, we will just use 100 as our total population and simulate the age and sleeping scores followed the same distribution as the survey results. We want to know whether their age grows, sleep quality (scores) increases or decreases? As you already know, this is a hidden linear regression practice. Once we drew the regression line of the age and sleeping scores, by looking at the slope of the line, the answer will...

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NumPy Essentials
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