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NumPy Cookbook

Chapter 2. Advanced Indexing and Array Concepts

In this chapter, we will cover:

Installing SciPy
Installing PIL
Resizing images
Comparing views and copies
Flipping Lena
Fancy indexing
Indexing with a list of locations
Indexing with booleans
Stride tricks for Sudoku
Broadcasting arrays

Key benefits

Do high performance calculations with clean and efficient NumPy code

Analyze large sets of data with statistical functions

Execute complex linear algebra and mathematical computations

Description

Today's world of science and technology is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list. NumPy will give you both speed and high productivity. "NumPy Cookbook" will teach you all about NumPy, a leading scientific computing library. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, it is free and open source. "Numpy Cookbook" will teach you to write readable, efficient, and fast code that is as close to the language of Mathematics as much as possible with the cutting edge open source NumPy software library. You will learn about installing and using NumPy and related concepts. At the end of the book, we will explore related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project through examples. "NumPy Cookbook" will help you to be productive with NumPy and write clean and fast code.

What you will learn

Learn advanced Indexing and linear algebra

Know reshaping automatically

Dive into Broadcasting and Histograms

Profile NumPy code and visualize your profiling results

Speed up your code with Cython

Use the array interface to expose foreign memory to NumPy

Use universal functions and interoperability features

Learn about Matplotlib and Scipy which is often used in conjunction with Numpy

What do you get with Print?

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Sujit Pal Jun 22, 2014

Numpy is central to most scientific Python toolkits, and learning to write effective Numpy code can make your code more readable and faster. While the Numpy documentation is quite comprehensive, books provide a more structured learning path, and since there are not too many books on Numpy, this book hits a sweet spot. The book is aimed at intermediate level Python users. You will gain more from the book if you work out the code examples yourself rather than just read the examples. Also the examples are slightly mathy (its a book about arrays and matrices after all), so you may have to do some reading if you don't remember your linear algebra, for example.The book covers examples from famous algorithms (Fibonacci, Sieve of Eratosthenes, etc), finance, etc, mainly to show the usage for various NumPy functions, both simple and advanced. There is a full chapter of recipes on Audio and Image processing techniques. There is also discussion of using memory mapped files, sharing data with the Python Image Library (PIL) through the array interface, converting code to Cython for speed, universal functions (none on vectorize() strangely), masking, etc. There is other information, such as interfacing with R using RPy2, running Numpy on Google App Engine and PiCloud (I didn't pay too much attention to these since I didn't anticipate using them).The format of the recipes were a bit unusual. Generally it tells you what you can do with it (in the title), then gives a quick overview of the approach (in English), followed by the full code to do accomplish the recipe. The recipes in the book flips this around, putting partial code with some explanation first, then the full code, and then the overview. So the reading (or following along with a Python shell) is not linear, reader has to jump back and forth. Not a huge deal once you recognize it, but using a more linear style may enhance the reading and learning experience in future editions.I read this book after I read "Learning Numpy Arrays" by the same author, and there is quite a bit of overlap in the examples. Perhaps not surprising because the subject is mostly identical. However, I think its still worth purchasing both the books because each book has enough unique content.

Amazon Verified review

Tom Jensen Morgan Jan 15, 2013

I read this book during the holidays. [...] I found it quite useful and well-written. I am just learning Python and am primarily interested in how it can be used for geospatial processing. I recommend this book for anyone wanting to grow their Python skillset.

BeckyLEXI Jan 30, 2013

While this book has some advanced examples, the nice thing is that it is accessible for newer to intermediate users of python and NumPy. The recipes are well documented and are concrete, helping with the learning experience. The range of the examples also helps to expand your understanding of what NumPy is capable of - which is enlightening. I will continue to use this book as a reference into the future.

Amazon Customer Dec 28, 2012

When I first mentioned that I was getting this book, a colleague of mine wanted to know why I was even bothering - 'just go straight for Pandas' he said. Actually, I think he is missing the point of both this book and NumPy in general.Not only is this one of those well written cookbooks that sets out the problems and solutions neatly and succinctly, but it is one of those cookbooks that you turn to, not really expecting to find the answer to a problem you are having right now, but rather solutions to problems that give you insight into just how broad and wide the solutions that NumPy can be applied to.The NumPy Cookbook covers everything from getting started with IPython (worth the price of admission alone, trust me) - to re-sizing images, processing audio, performing statistical analysis (obviously), estimating stock returns and, well, err, installing Pandas.The very best cookbooks answer the questions you did not know you had, and show you to do things that you did not know were possible. That is what the 'NumPy Cookbook' does - and it does it exceptionally well.I spend my time working with Python, examining numbers from our data warehouse, and I have been using NumPy for a long while to manipulate the data, to slice, dice and fill in the blanks. But this book showed me some new tricks, some tricks that I will be able to apply directly to my work, and for that I am grateful.So, install Pandas and forget all about NumPy? No. NumPy is a pre-requisite for Pandas, and you really should know all about it, because the two are not mutually exclusive. Read this book - and learn about some of the really cool stuff that you can do with NumPy.

alan1955 Jan 15, 2013

I am a scientist just beginning to use Python, Numpy, Scipy, and Matplotlib on a Linux machine. There are a lot of things to learn to use with anyone of these. This book provides valuable insights for a person just beginning with Numpy that would take a long time to discover on your own through working examples. The examples are quite extensive covering many areas. All areas are not of interest to me, but the techniques used through working example can be easily adapted to whatever problem you are working on, saving a lot of time. I would say for me the examples in chapter 7 on Profiling and debugging chapter 2 on advanced indexing, and chapter 3 on commonly used functions were very useful. I would recommend this to anyone who is wanting to get started with Numpy and is planning to use it regularly. More information can be had at: [...]