NumPy Beginner's Guide - Second Edition (Anglais) Broché – 25 avril 2013
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Descriptions du produit
Présentation de l'éditeur
An action packed guide using real world examples of the easy to use, high performance, free open source NumPy mathematical library
- Perform high performance calculations with clean and efficient NumPy code
- Analyze large data sets with statistical functions
- Execute complex linear algebra and mathematical computations
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.
What you will learn from this book
- Install NumPy
- NumPy arrays
- Universal functions
- NumPy matrices
- NumPy modules
- Plot with Matplotlib
- Test NumPy code
- Relation to SciPy
The book is written in beginners guide style with each aspect of NumPy demonstrated with real world examples and required screenshots.
Who this book is written for
If you are a programmer, scientist, or engineer who has basic Python knowledge and would like to be able to do numerical computations with Python, this book is for you. No prior knowledge of NumPy is required.
Biographie de l'auteur
Ivan Idris has an MSc in Experimental Physics. His graduation thesis had a strong emphasis on Applied Computer Science. After graduating, he worked for several companies as a Java Developer, Data warehouse 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. Ivan Idris is the author of NumPy 1.5 Beginner's Guide and NumPy Cookbook by Packt Publishing. You can find more information and a blog with a few NumPy examples at ivanidris.net.
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Commentaires en ligne
Commentaires client les plus utiles sur Amazon.com (beta)
This style almost presents the solution twice once with a step by step, and then again with full code. It seems redundant.
As a PhD student myself, article reviewing, code debugging, data analysis and other obligations and deadlines have been so far the reason not to get the grips with NumPy ... until I found Mr. Idris's "NumPy - Beginner's guide"!
Personally, I find the most remarkable feature of the book to be the good compromise the author has found between:
* the amount and relevance of the information offered,
* the clarity of the exposition and
* the immediate applicability of the information provided.
As a first remark, the book covers many of the most recurrent techniques I need to use during my research activity, and thus the book can very well serve as a reference. However, do not mistake the book as yet another "How To" guide, or a simple "Cook-Book": far from that, you see an evident and conscious effort to lead the reader through different capabilities of NumPy in a bottom-up, constructive manner: this is a book you can actually learn from.
Another highlight of the book is the early focus on data processing from text files. Instead of presenting this feature in an arcane manner detached from other features (as is often the case in many programming guides), the author presents briefly but in enough detail the text-file-processing capabilities of NumPy intertwined with several statistical analysis tools.
Of course, there is a space devoted to most common procedures for linear algebra, signal processing, efficient sorting algorithms, ...
Yet another success of the book concerns the graphical representation of information; the book devotes a full chapter to matplotlib and to explain how to produce the most common graphs needed to effectively communicate one's work . This does not prevent the author to use matplotlib if needed in previous chapters, offering in any of such occasions at least the minimal explanation of what is being done.
To conclude, I believe this book can help users/developers of numerical methods to become independent and proficient users of NumPy: a reader minimally familiar with the python syntax will be able, in very short time, to port her/his existing numerical tools into NumPy, thus acquiring the experience needed to devise new, more efficient tools taking advantage of the advantages of the python/NumPy duo.
This is an excellent book for the Python programmer who wants to extend their knowledge into mathematical programming and those with a mathematical or engineering background who want to leverage open source alternatives to commercial tools by using the NumPy with Python.
Installation and other packages:
Installation on the main platforms and the relationship between NumPy and SciPy, and using the library with Matplotlib and other Python modules is well covered.
The coverage is goes from creating vectors and multi-dimensional matrices through calculating Eigenvectors, the FFT, complex numbers, polynomial fitting and many others.
The examples I've followed are well thought through and illustrate the use the relevant parts of the NumPy API required in a clear and concise manner. An increasing amount of mathematical background is needed and for a beginner the book should be read in conjunction with a text book covering the relevant material.
I would heartily recommend the book both as tool for learning and as a working reference for the NumPy library and wish it had been available when I was going up the learning curve.
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