Book Image

NumPy Essentials

By : Leo (Liang-Huan) Chin, Tanmay Dutta, Shane Holloway
Book Image

NumPy Essentials

By: 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 (16 chapters)
NumPy Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface

Helper functions


Besides the help() and dir() functions in Python and other online documentation, NumPy also provides a helper function, numpy.lookfor(), to help you find the right function you need. The argument is a string, and it can be in the form of a function name or anything related to it. Let's try to find out more about operations related to resize, which we took a look at in an earlier section:

In [71]: np.lookfor('resize') 
Search results for 'resize' 
--------------------------- 
numpy.ma.resize 
    Return a new masked array with the specified size and shape. 
numpy.chararray.resize 
    Change shape and size of array in-place. 
numpy.oldnumeric.ma.resize 
    The original array's total size can be any size. 
numpy.resize 
    Return a new array with the specified shape.