NumPy Beginner's Guide - Second Edition et plus d'un million d'autres livres sont disponibles pour le Kindle d'Amazon. En savoir plus

Identifiez-vous pour activer la commande 1-Click.
Amazon Rachète votre article
Recevez un chèque-cadeau de EUR 7,54
Amazon Rachète cet article
Plus de choix
Vous l'avez déjà ? Vendez votre exemplaire ici
Désolé, cet article n'est pas disponible en
Image non disponible pour la
couleur :
Image non disponible

Commencez à lire NumPy Beginner's Guide - Second Edition sur votre Kindle en moins d'une minute.

Vous n'avez pas encore de Kindle ? Achetez-le ici ou téléchargez une application de lecture gratuite.

NumPy Beginner's Guide - Second Edition [Anglais] [Broché]

Ivan Idris

Prix : EUR 37,44 Livraison à EUR 0,01 En savoir plus.
  Tous les prix incluent la TVA
o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o
En stock.
Expédié et vendu par Amazon. Emballage cadeau disponible.


Prix Amazon Neuf à partir de Occasion à partir de
Format Kindle EUR 15,13  
Broché EUR 37,44  
Vendez cet article - Prix de rachat jusqu'à EUR 7,54
Vendez NumPy Beginner's Guide - Second Edition contre un chèque-cadeau d'une valeur pouvant aller jusqu'à EUR 7,54, que vous pourrez ensuite utiliser sur tout le site Les valeurs de rachat peuvent varier (voir les critères d'éligibilité des produits). En savoir plus sur notre programme de reprise Amazon Rachète.

Description de l'ouvrage

25 avril 2013

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

In Detail

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 beginner’s 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.

Offres spéciales et liens associés

Les clients ayant acheté cet article ont également acheté

Descriptions du produit

Biographie de l'auteur

Ivan Idris

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

Détails sur le produit

Commentaires en ligne 

Il n'y a pas encore de commentaires clients sur
5 étoiles
4 étoiles
3 étoiles
2 étoiles
1 étoiles
Commentaires client les plus utiles sur (beta) 4.3 étoiles sur 5  11 commentaires
8 internautes sur 8 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 About "NumPy Beginner's Guide" (2nd Edition) 30 juin 2013
Par JGM - Publié sur
When one is dealing with numerical methods, there are many good reasons to do so using free/open numerical tools ... But, whether you happen to be doing "real" work for a company or to be a PhD candidate, too often you are confronted with the dilemma of investing your time in learning alternative and more productive ways of doing your work (i.e. the promising combination python/NumPy) and actually having your work done by the due date.

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.
4 internautes sur 4 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Beginners Guide and Handy Reference 3 juillet 2013
Par ReaddyEddy - Publié sur
Who is it for:
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.

What's covered:
The coverage is goes from creating vectors and multi-dimensional matrices through calculating Eigenvectors, the FFT, complex numbers, polynomial fitting and many others.

Example Code:
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.
1 internautes sur 1 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Excellent introductory book 5 août 2013
Par Matthieu Brucher - Publié sur
The first chapter introduces the reader to the scientific ecosystem. The main modules are covered, but there are some small mistakes (ipython --pylab does not import Matplotlib, Numpy ans Scipy, just a subset of the first that gives a Matlab-like interface aith some renamed Numpy functions) and what I think is a bad habit (importing everything from Pylab and using it as is). Nothing is lost because in the second chapter, Numpy is properly introduced and imported explicitly. There is a link missing between the two chapters because I didn't understand why Numpy was used this way and not with pylab.

So the second chapter is about the core Numpy data structure: the multidimensional array. The author browses through different ways of creating them, by stacking them, flattening them... The next chapter deals with universal functions, or put it in an other way, functions that run on array element by elements. There are many different way to do so, with specifics that are tackled properly.

Before the chapter of matrices, there is a useful chapter on correlation, convolution and polynomials. Although one may want to go up to Scipy for signal processing, more often than not, Numpy is enough. There enough examples to understand how everything work. Then, the much hated matrix class is introduced. There are many discussions online on whether this class is actually useful, and I won't delve on this. Suffice to say that the power of this class can be seen in the examples.

The following chapter is about the different submodules inside Numpy: linear algebra, fft, random number. The proper pointers to Scipy are provided, as well as explicit samples. I only can regret the time shifting example is not perfect, as a filter is applied on the amplitude and the inverse FFT is applied on the amplitudes only. So the transformed signal loses all the phase information, which may be why the image is similar but not that similar to the original one.

The seventh chapter tackles different extra functions, mainly finance-related, and I have to say that I don't know their use enough to comment. Of course, this is why they are in the middle of the book and not in the first pages as the other Numpy functions. Still, there are some useful functions here (some I didn't know about), as sorting, searching...

When one code scientific code, one often forget about testing. And Numpy has a nice module for scientific testing. It is nice to know that this aspect of science is not forgotten here and has a proper introduction. (also don't forget about version control!)

The last three chapters introduces additional modules. The first one was partial addressed before, Matplotlib, and if you need something more advanced, there is always the Matplotlib book also published by Packt. Then there are some examples with Scipy, Scikits (soon a new book on Machine Learning with scikit-learn will be available, also by Packt, for which I was a technical reviewer, and it is really great) and other tools. The final chapter is about Pygame, but I don't code games 100% in Python, so I didn't really read it!

It's hard to be mad at the author for the import issue. But I find it also difficult not to, as the philosophy changes depending on the chapter without saying why. Still, for an introductory book to Numpy, it is great if not excellent. A lot of simple examples, a lot of checks, the good pointers to write efficient code...
1 internautes sur 1 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 5 stars for material, 3 for presentation 10 juillet 2013
Par Kelly Summerlin - Publié sur
Format:Format Kindle|Achat vérifié
The material covered is pretty good considering the breadth of the NumPy library. I would have like more coverage of the NumPy record types. The only real problem I had with this book, I wasn't happy with the style of the material presented. The style tries to present a problem to solve with NumPy, then shows a segment of programming steps to solve the problem, and finally shows the completed code. I think I prefer the cookbook style of O'Reilly books better.

This style almost presents the solution twice once with a step by step, and then again with full code. It seems redundant.
5.0 étoiles sur 5 NumPy Beginners Guide, A sure path to Success. 2 juillet 2013
Par Paul A. Courchene - Publié sur
I wish to comment on the excellent book by Ivan Idris. The text entitled NumPy Beginner's Guide, Second Edition is an outstanding book for a broad range of computer enthusiasts. While not all computer nerds are necessarily interested in Programming per se', in light of the growth and momentum of digital media, it is now a fact of life that many fields of employment require some basic introduction to computer programming. Whether you are a business or financial analyst, or perhaps a serious computer wanabee, or even a College student, it is now time to take the first step.
With the momentum seen in the computer language of Python, many people who would otherwise not be inclined to learn this skill, never the less are posing some interest and saying "I wonder if I could do that?" This book will show you how.
Since this book covers programming with Python from A to Z, and provides an easy path of self-learning, a number of people with a broad range of computer and math skills can accomplish these tasks.
Yes, sitting in the classroom somewhere and listening to someone lecture on the fine points of computer programming may be the road to success for lots of people, but it is not beyond achieving this same goal of learning new skills by self-study ...
In the first few pages of this book, the notion of interactive python is introduced (called IPython). That is, one would take a canned script or snippet of sample code and run it in a command line interpreter or interactive shell. That is a fancy name for entering code at a prompt, essentially using a cut and paste routine. How simple can it be ...
Since the subject text has a bazillion examples or python routines and coding structure, one can view a broad range of examples from the simplistic to the quite advanced level. With this book, following along step by step or page by page, you can almost become a computer (read python) programmer, overnight. I. E., one enters the example code, then follows the book and reviews the resulting output, along with subtle hints about the interpreter operation. The shell (or debugger) will allow you to step into the code line by line and view the resultant computer operations. Thus, not only do you have visual feedback (to check your work) but one of the books highlights are the numerous "On-line resources and help" that will provide you with support material at a click of a mouse.
As an aside, I regularly read the Yahoo email Group entitled "the Python Community"
([...] each day where someone asks the basic question about "Where can I learn Python by way of self-study?"

This book provides that answer and opportunity. So Chapter 1 of this book is appropriately entitled
"NumPy Quick Start".
As you begin Chapter 2, you will soon recognize the format that follows, and gives the reader (or student) an introduction to key Python topics. For example, to program a machine to organize and otherwise manipulate data, be it text or a set of integers, we need to know some basic objects such as data structures. So after a few words about a data structure like an "array", one needs to create an array using examples (code) from the book. With further comments about "What happened?", while creating an array, you will be provided a mini quiz or "Pop quiz" that will test your comprehension and understanding of the concept at hand. Obviously, the quiz is a good reinforcement tool to aid learning.
Next, the author uses additional examples to lead you through some more variations of the objects that you are studying. That is: the "Time for Action" is now an opportunity to alter or create alternate arrays by manipulating their dimensions etc. This Time for Action is actually a number of requests (or examples) for "you to try it". Now, not only are you learning new skills and techniques but in fact are "gaining experience" with Python.
As a computer wanabee, I now recommend that you take a pencil in hand and copy some of the examples on paper (that you may be interested in). This may be seen as a proactive learning experience and will help you understand some rudimentary things like the syntax (the ways words combine to form phrases or code) of the Python language.
Let me clarify a point here, about this text. It is true that this text is focused on mathematical techniques that the average beginner may not be quite ready for, I. E. basic Statistics and Linear Models as seen in Chapter 3, but I would argue that as we discussed above, the layout of the book and its associated (read: excellent) format allows one to learn and gain experience, one step at a time.
Chapter 4 of the book lays out additional functions that start to exhibit the power of NumPy. For example (page 95) starts a discussion of Polynomials, in layman's terms, that of finding a function the fits a set of data. I am of course assuming that you have some interest in math and "numbers" per se', if in fact you have searched for this book at
Other Chapters go on to highlight such programming techniques as related to:
(3). CSV files, Simple Statistics, Linear Models and Trend Lines, (4). Convenient Functions,
(5). Matrices and Universal Functions, (6) Linear Algebra, Random Numbers, Distributions,
(7). Special Routines for Sorting, Searching, Financial Functions. (8) Quality and Testing [of software],
(9). Plotting (results such as Graphs, Histograms, Scatter Plots etc.),
(10.) SciPy., a Python based system for Scientists, Engineering and Mathematics. and
(11). PyGames. What could be more fun than learning to program games in Python.

As you inspect this book, you will find something of interest to everyone who is acquainted with the word Python.

It is an excellent book in a format that is easy to find a keyword or idea and will remain a valuable resource to you, for a long time.
Ces commentaires ont-ils été utiles ?   Dites-le-nous

Discussions entre clients

Le forum concernant ce produit
Discussion Réponses Message le plus récent
Pas de discussions pour l'instant

Posez des questions, partagez votre opinion, gagnez en compréhension
Démarrer une nouvelle discussion
Première publication:
Aller s'identifier

Rechercher parmi les discussions des clients
Rechercher dans toutes les discussions Amazon

Rechercher des articles similaires par rubrique


Souhaitez-vous compléter ou améliorer les informations sur ce produit ? Ou faire modifier les images?