Book Image

NumPy Beginner's Guide - Second Edition

By : Ivan Idris
Book Image

NumPy Beginner's Guide - Second Edition

By: Ivan Idris

Overview of this book

NumPy is an extension to, and the fundamental package for scientific computing with Python. In today's world of science and technology, it is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list. NumPy Beginner's Guide will teach you about NumPy, a leading scientific computing library. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, is free and open source. Write readable, efficient, and fast code, which is as close to the language of mathematics as is currently possible with the cutting edge open source NumPy software library. Learn all the ins and outs of NumPy that requires you to know basic Python only. Save thousands of dollars on expensive software, while keeping all the flexibility and power of your favourite programming language.You will learn about installing and using NumPy and related concepts. At the end of the book we will explore some related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. Through examples, you will also learn about plotting with Matplotlib and the related SciPy project. NumPy Beginner's Guide will help you be productive with NumPy and have you writing clean and fast code in no time at all.
Table of Contents (19 chapters)
Numpy Beginner's Guide Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Time for action – applying the ufunc methods on add


Let's call the four methods on add function.

  1. The input array is reduced by applying the universal function recursively along a specified axis on consecutive elements. For the add function, the result of reducing is similar to calculating the sum of an array. Call the reduce method:

    a = np.arange(9)
    print "Reduce", np.add.reduce(a)

    The reduced array should be as follows:

    Reduce 36
    
  2. The accumulate method also recursively goes through the input array. But, contrary to the reduce method, it stores the intermediate results in an array and returns that. The result, in the case of the add function, is equivalent to calling the cumsum function. Call the accumulate method on the add function:

    print "Accumulate", np.add.accumulate(a)

    The accumulated array:

    Accumulate [ 0  1  3  6 10 15 21 28 36]
    
  3. The reduceat method is a bit complicated to explain, so let's call it and go through its algorithm, step-by-step. The reduceat method requires as arguments, an...