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What you will learn

Install NumPy and discover its arrays and features

Perform data analysis and complex array operations with NumPy

Analyze time series and perform signal processing

Understand NumPy modules and explore the scientific Python ecosystem

Improve the performance of calculations with clean and efficient NumPy code

Analyze large data sets using statistical functions and execute complex linear algebra and mathematical computations

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Edward Grefenstette Aug 07, 2014

The following review was produced after being sent a free copy of the book by Packt Publishing. I have endeavoured to be objective and state my view of the book, unaltered by this."Learning NumPy Array" by Ivan Idris is an excellent book which covers a range of use cases for the Python NumPy library across different aspects of scientific computing. It gives a short introduction to NumPy classes and core functions, discusses data analysis with NumPy data structures, and then using the pandas library (built on top of NumPy, amongst other libraries), provides a succinct overview of signal processing techniques with NumPy, followed by a short tutorial of profiling and debugging NumPy code with iPython, UnitTest, and Nose. The book ends with a helpful chapter on other tools scientific computing programmers may wish to consider alongside NumPy, most importantly cython and sklearn.While the book is entitled "Learning NumPy Array", it is not exactly aimed at a beginner audience. The reader is assumed to have a decent knowledge of linear algebra, statistics, and some cursory experience with similar statistics or matrix libraries/languages such as Matlab or R. While it does introduce NumPy basics, the reader should expect to have access to the NumPy documentation while reading the book to get the most out of it. Chapters 3 through 5 are quite dense, and readers without experience with the relevant areas (statistical analysis for chapters 3-4 and signal processing for chapter 5) will need to spend time and perhaps read third-party sources to get the most out of the material. All this is to say, this is a fairly technical book, but the examples are many, are well spelled-out, and coherently explained. Readers with a bit of experience in the domains it covers, or readers who are willing to put in the extra effort to read around the topics, will get a lot out of this book, and be able to implement solutions to similar problems quickly.In addition to the core material covered in chapters 3-5, I particularly enjoyed the fairly clear tutorials for profiling, debugging and testing in chapter six. Some of this material could easily have been excluded from the book in favour of expanding the explanations and giving some background in chapters 3-5, but I found the explanations to be well written and helpful, so the inclusion in this book is appreciated.Furthermore, the final chapter is a nice starting point for newcomers to the python scientific computing world, as it presents some helpful pointers (and use examples) to other resources available. Even as a researcher who has used these tools before once or twice, at least, this chapter was a good read and reminded me of how easily these libraries play with each other.Overall, this is a good read. It will take some small effort for most readers to get through the more technical sections of this book, but there is a lot to get out of reading it. While the same material can be found in several tutorials scattered around the web, it is good to see that someone took the effort to distil all the material into one volume, provide excellent example code, and enough explanation to feed intuitions as to how to best apply the knowledge presented in it.

Amazon Verified review

alan1955 Sep 17, 2014

This is an excellent book for learning NumPy. It is for someone who has experience with python and numerical coding. What I liked was chapter two where the book covers the basics of NumPy to get you quickly up and running. All in one chapter. So if you are already trying to do some basic things with NumPy, it will save you a lot of time. The rest of the book is filled with examples. The examples illustrate things you can do with NumPy, and are arranged topically. The topics cover basic data analysis, predictive analysis, smoothing, moving averages, and sifting. The other plus is that it introduces Scipy, the scientific python programming package. If you are not familiar with it, you will get to see what it can do in a few of the examples. It is also a short book. Being short it assumes you have a working knowledge of Python and some numerical experience. Too many computer books are so long, you can never get through them. Because of this you can quickly get up to speed. Also the code for examples can be downloaded, which is very helpful.

Amazon Verified review

Roberto Avilés Jul 30, 2014

This book is a 7 chapters, +140 page hands-on introduction to the power of Python’s Library, NumPy.In Chapter 1, we learn to install Python, SciPy, Matplotlib, IPython and NumPy on Windows, Linux and Macintosh machines and start writing NumPy code.Chapter 2 reviews the basics on NumPy: Data Types, Array Types, Type Conversions, Creating, Indexing, Slicing and Manipulating array Shapes. Advantages of NumPy arrays: we know items in the array are of the same type (example, dtype!) plus, NumPy arrays can perform vectorized operations on the whole array: better than using lists; NumPy uses an optimized C API for those operations which make them especially fast. We learn how to transform a multidimensional array into a one dimensional array, how to stack, split, convert, copy and view them by playing with images, doing tricks with Sudoku and audio arrays.In Chapter 3 we are ready to learn Basic Data Analysis by working on a genuine (and quite completely) data set by looking for evidence of planetary heating.Chapter 4 is about Predictive Analysis and the use of the ‘pandas’ library. Pandas have plotting subroutines and in this chapter data of previous chapter is re-examined and extended to correlating weather and stocks!Now, in Chapter 5 we focus on ‘Signal Processing Techniques’ and analyze time series. The example data set will be sunspot data which we sift and plot to show the extremes of Sun activity. Tools as ‘moving averages’ and smoothing functions are introduced and we are ready to do a forecasting using an ARMA (autoregressive moving average) model. Then we learn how to design and use a filter and the “cointegration”, a better metric to define the relatedness of two time series.In Chapter 6 the book moves into Profiling, Debugging and Testing. NumPy adds the numpy.testing package (and its utility functions) to help NumPy code the unit testing. Later we met Nose, a Python framework that eases unit testing by organizing it.Chapter 7 relates to the Scientific Python Ecosystem. Scipy is built on NumPy and adds functionality as numerical integration, interpolation, optimization, statistics, clustering with scikit-learn, the detection of corners (all with examples), the use of Cython with NumPy and compares NumPy to Blaze (a collection of libraries being built towards the goal of generalizing NumPy ‘s data model and working on distributed data.)This book is a complete hands-on guide on the use of NumPy, through worked examples and ideas, a must have for those interested in Data Analysis, Forecasting and Signal Processing Techniques.

Amazon Verified review

Sujit Pal Jun 21, 2014

Numpy is a fast matrix library that is at the core of scientific computing toolkits in Python - whether you are doing statistics, machine learning or signal processing (to name just a few possibilities), chances are you are using Numpy under the covers. Because Numpy is optimized for speed and because you are often dealing with large datasets, knowing the right function to call can mean the difference between run times of a few seconds or a few hours. This book can help you write cleaner and faster Numpy code.After a quick introduction to Numpy in Chapter 1, Chapter 2 takes the reader through a quick tour of Numpy functions, describing the major ones, including differently named functions which do the same thing, one updating in place and the other creating a copy. Chapter 3-5 on is all case studies, mostly about Time Series Analysis and Signal Processing, perhaps because they are good vehicles for demonstrating array handling techniques, but a nice side effect is that it gives the reader a quick introduction to these subjects as well. Chapter 6 describes Numpy's unit testing functionality, and also covers Python unit testing frameworks (unittest package as well as nose). It also describes how to profile and debug Python code from the IPython shell. Chapter 7 quickly covers various other players in the scientific Python ecosystem, and describes how to optimize Python code by rewriting them to Cython.I found the book immensely informative and relatively easy to read. It helps to actually work through the code in a Python shell as you are reading the book, in some cases the author condenses multiple steps into a single one, obviously expecting the reader to follow along, so being able to break the steps into smaller ones can help in understanding. Using the shell also gives you access to Python's help system, so you can read about new functions as you encounter them. One minor nit - it would have been more convenient if the data used for the analysis could have been packaged with the code for the book (for people reading the book offline) - but perhaps there are copyright restrictions on such distribution.DISCLAIMER: I didn't purchase this book, a PackT representative in my social network was offering free copies to review, and I asked for one because (a) I use Numpy and wanted to learn more and (b) as a Numpy user, I felt qualified to review the book objectively.

Amazon Verified review

Daniel R. Vallejo Sep 28, 2014

great

Amazon Verified review

Learning NumPy Array: Supercharge your scientific Python computations by understanding how to use the NumPy library effectively

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