Learning NumPy Array

by Ivan Idris (Author)

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ISBN-13: 978-1783983902

ISBN-10: 1783983906

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

About This Book

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
Perform complex array operations in a simple manner

Who This Book Is For

This book is for programmers, scientists, or engineers, who have basic Python knowledge and would like to be able to do numerical computations with Python.

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

In Detail

NumPy is an extension of Python, which provides highly optimized arrays and numerical operations. NumPy replaces a lot of the functionality of Matlab and Mathematica specifically vectorized operations, but in contrast to those products is free and open source. In today's world of science and technology, it is all about speed and flexibility.

This book will teach you about NumPy, a leading scientific computing library. This book enables you to write readable, efficient, and fast code, which is closely associated to the language of mathematics. Save thousands of dollars on expensive software, while keeping all the flexibility and power of your favorite programming language.

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. Learning NumPy Array will help you be productive with NumPy and write clean and fast code.

Publisher ‏ : ‎ Packt Publishing (June 13, 2014)
Language ‏ : ‎ English
Paperback ‏ : ‎ 164 pages
ISBN-10 ‏ : ‎ 1783983906
ISBN-13 ‏ : ‎ 978-1783983902
Item Weight ‏ : ‎ 10.4 ounces
Dimensions ‏ : ‎ 7.5 x 0.37 x 9.25 inches

Best Sellers Rank: #3,752,400 in Books (See Top 100 in Books)
- #976 in SQL
- #2,899 in Database Storage & Design
- #9,373 in Computer Programming Languages

Customer Reviews:
9 ratings

About the author

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Ivan Idris

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Ivan Idris was born in Bulgaria from Indonesian parents. He moved to the Netherlands in the 1990s, where he graduated from high school and got a MSc in Experimental Physics.

His graduation thesis had a strong emphasis on Applied Computer Science. After graduating he worked for several companies as Java Developer, Datawarehouse Developer and QA Analyst.

His main professional interests are Business Intelligence, Big Data and Cloud Computing. Ivan Idris enjoys writing clean testable code and interesting technical articles.

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Arvin Parsaei

Poor Sample codes

Reviewed in the United States on November 9, 2019

Verified Purchase

Book is well-written and comprehensive. A good portion of the code samples don’t work and also good luck signing up at their website and download the sample codes. It’s interesting how a tech-related publisher doesn’t have a functioning website.

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Daniel R. Vallejo

Five Stars

Reviewed in the United States on September 28, 2014

Verified Purchase

great

One person found this helpful

Helpful

Sujit Pal

Very comprehensive tour of Numpy, good intro to Time Series Analysis and Signal Processing

Reviewed in the United States on June 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.

3 people found this helpful

Helpful

Roberto Avilés

a must have for those interested in learning data analysis techniques and tools

Reviewed in the United States on July 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.

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David Gavin

No table of Contents

Reviewed in Japan on March 11, 2017

Verified Purchase

I just got this book and opened it up in Kindle for PC (what I use for viewing all kindle textbooks) and found that it has no pull out table of contents. Every Kindle book I have ever bought has had this.

There is one at the very beginning of the text, but that isn't very useful when you're trying to reference something from a different section to help you understand what you're working through at the moment.

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Learning NumPy Array

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About This Book

Who This Book Is For

What You Will Learn

In Detail

Product details

About the author

Ivan Idris

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