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NumPy Essentials电子书

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30人正在读 | 0人评论 9.8

作       者:Leo (Liang-Huan) Chin

出  版  社:Packt Publishing

出版时间:2016-04-01

字       数:36.5万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Boost your scientific and analytic capabilities in no time at all by discovering how to build real-world applications with NumPy About This Book Optimize your Python *s with powerful NumPy modules Explore the vast opportunities to build outstanding scientific/ analytical modules by yourself Packed with rich examples to help you master NumPy arrays and universal functions Who This Book Is For If you are an experienced Python developer who intends to drive your numerical and scientific applications with NumPy, this book is for you. Prior experience or knowledge of working with the Python language is required. What You Will Learn Manipulate the key attributes and universal functions of NumPy Utilize matrix and mathematical computation using linear algebra modules Implement regression and curve fitting for models Perform time frequency / spectral density analysis using the Fourier Transform modules Collate with the distutils and setuptools modules used by other Python libraries Establish Cython with NumPy arrays Write extension modules for NumPy code using the C API Build sophisticated data structures using NumPy array with libraries such as Panda and Scikits In Detail 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. Style and approach This quick guide will help you get to grips with the nitty-gritties of NumPy using with practical programming examples. Each topic is explained in both theoretical and practical ways with hands-on examples providing you efficient way of learning and adequate knowledge to support your professional work.
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NumPy Essentials

NumPy Essentials

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. An Introduction to NumPy

The scientific Python stack

The need for NumPy arrays

Representing of matrices and vectors

Efficiency

Ease of development

NumPy in Academia and Industry

Code conventions used in the book

Installation requirements

Using Python distributions

Using Python package managers

Using native package managers

Summary

2. The NumPy ndarray Object

Getting started with numpy.ndarray

Array indexing and slicing

Memory layout of ndarray

Views and copies

Creating arrays

Creating arrays from lists

Creating random arrays

Other arrays

Array data types

Summary

3. Using NumPy Arrays

Vectorized operations

Universal functions (ufuncs)

Getting started with basic ufuncs

Working with more advanced ufuncs

Broadcasting and shape manipulation

Broadcasting rules

Reshaping NumPy Arrays

Vector stacking

A boolean mask

Helper functions

Summary

4. NumPy Core and Libs Submodules

Introducing strides

Structured arrays

Dates and time in NumPy

File I/O and NumPy

Summary

5. Linear Algebra in NumPy

The matrix class

Linear algebra in NumPy

Decomposition

Polynomial mathematics

Application - regression and curve fitting

Summary

6. Fourier Analysis in NumPy

Before we start

Signal processing

Fourier analysis

Fourier transform application

Summary

7. Building and Distributing NumPy Code

Introducing Distutils and setuptools

Preparing the tools

Building the first working distribution

Adding NumPy and non-Python source code

Testing your package

Distributing your application

Summary

8. Speeding Up NumPy with Cython

The first step toward optimizing code

Setting up Cython

Hello world in Cython

Multithreaded code

NumPy and Cython

Summary

9. Introduction to the NumPy C-API

The Python and NumPy C-API

The basic structure of an extension module

The header segment

The initialization segment

The method structure array

The implementation segment

Creating an array squared function using Python C-API

Creating an array squared function using NumPy C-API

Building and installing the extension module

Summary

10. Further Reading

pandas

scikit-learn

netCDF4

SciPy

Summary

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