Numpy Essentials by Chin, Liang-Hua -- Read -- Imperial Library of Trantor

Index

NumPy Essentials

NumPy Essentials Credits About the Authors About the Reviewers www.PacktPub.com

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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