NumPy Cookbook, 2nd Edition
By Ivan Idris
Publisher: Packt Publishing
Final Release Date: April 2015
Pages: 258

NumPy has the ability to give you speed and high productivity. High performance calculations can be done easily with clean and efficient code, and it allows you to execute complex algebraic and mathematical computations in no time.

This book will give you a solid foundation in NumPy arrays and universal functions. Starting with the installation and configuration of IPython, you'll learn about advanced indexing and array concepts along with commonly used yet effective functions. You will then cover practical concepts such as image processing, special arrays, and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project with the help of examples. At the end of the book, you will study how to explore atmospheric pressure and its related techniques. By the time you finish this book, you'll be able to write clean and fast code with NumPy.

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(3 of 4 customers found this review helpful)


A complete lack of focus

By varkenvarken

from The Netherlands

About Me Designer, Developer, Educator

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    • No Focus
    • Not A Cookbook

    Best Uses

    • Doorstop

    Comments about oreilly NumPy Cookbook, 2nd Edition:

    this book is more about how to use Numpy in various contexts like IPython and interface it with all sorts of other software like R or Matlab than it is a cookbook on core Numpy concepts.
    As a Numpy cookbook it fails completely: yes it gives step by step instructions on how to accomplish certain tasks but it does not ever gives insight in the reasoning behind the recipe.
    One example shows how to flip an array around the vertical by indexing it with [:,::-1]. While this works there is no explanation whatsoever on why this works although slicing and indexing is fundamental to Numpy. So you might learn a trick, you gain no understanding.
    This is fact one of the worst books I ever bought from O'reilly and I bought scores of them! As soon as I find out how I will ask my money back because the item's description does not match it's content: this isn't a Numpy cookbook but a a collection of faits divers.

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