- Taschenbuch: 150 Seiten
- Verlag: Packt Publishing (22. Februar 2013)
- Sprache: Englisch
- ISBN-10: 1782161627
- ISBN-13: 978-1782161622
- Größe und/oder Gewicht: 19 x 0,9 x 23,5 cm
- Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
- Amazon Bestseller-Rang: Nr. 202.256 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Learning SciPy for Numerical and Scientific Computing (Englisch) Taschenbuch – 22. Februar 2013
|Neu ab||Gebraucht ab|
Dieses Buch gibt es in einer neuen Auflage:
Wird oft zusammen gekauft
Kunden, die diesen Artikel gekauft haben, kauften auch
Es wird kein Kindle Gerät benötigt. Laden Sie eine der kostenlosen Kindle Apps herunter und beginnen Sie, Kindle-Bücher auf Ihrem Smartphone, Tablet und Computer zu lesen.
Geben Sie Ihre E-Mail-Adresse oder Mobiltelefonnummer ein, um die kostenfreie App zu beziehen.
Über den Autor und weitere Mitwirkende
Francisco J. Blanco-Silva
The owner of a scientific consulting companyTizona Scientific Solutionsand adjunct faculty in the Department of Mathematics of the University of South Carolina, Dr. Blanco-Silva obtained his formal training as an applied mathematician at Purdue University. He enjoys problem solving, learning, and teaching. An avid programmer and blogger, when it comes to writing he relishes finding that common denominator among his passions and skills, and making it available to everyone.
He coauthored Chapter 5 of the book Modeling Nanoscale Imaging in Electron Microscopy, Springer by Peter Binev, Wolfgang Dahmen, and Thomas Vogt.
Welche anderen Artikel kaufen Kunden, nachdem sie diesen Artikel angesehen haben?
In diesem Buch(Mehr dazu)
Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)
My favorite thing about it this book is that when a module is introduced, the author gives a list of many relevant functions when appropriate. For example, when he introduces the linear algebra module (scipy.linalg) in Chapter 3, he goes through many of the matrix creation and operations functions that I didn't even know existed, and I'm an intermediate-level Python/NumPy user. He discusses solving large linear systems, eigenvalue problems, and FIVE different matrix decompositions as well as the corresponding module functions for each type of problem. This book is worth the price for Chapter 3 alone.
But thankfully, it goes on to discuss solving various common ODEs, optimization, the Runge-Kutta method, and numerical integration. And that's just Chapter 4. Again, the important detail here is how the author links each topic and problem to the corresponding SciPy module and relevant functions that do the vast majority of the work for you. He also shows how to use matplotlib for graphical purposes when a problem calls for it. Chapter 5 is about signal processing, which I didn't really understand but I think the gist of it is how to extrapolate from incomplete data and how to separate the signal from the noise.
I'm currently working as a data miner, which is the topic of Chapter 6. This is a nice introduction to the data analysis modules for SciPy: scipy.stats, scipy.spatial, and scipy.cluster. The data analysis examples were good, and the breakdown of hierarchical clustering was excellent, but I wished the chapter was a little longer. It is a great complement to McKinney's book on using Python for data analysis, which I also own.
All in all, I strongly recommend this book to anyone who has a computational problem to solve.
I'm writing this review after spending 3 hours with this book. I am so angry that I felt I needed to let other people know.
There are two major errors in the first two pieces of code in the book. If you are new to SciPy, as I was, that means wasting 2 hours ploughing through the SciPy online documentation to figure out the correct code (it is not easy!). Since the main reason for buying the book is that the online documentation makes absolutely no sense to newcomers, it rather defeats the purpose of the book.
So, being a good citizen, I did what was requested at the front of the book and attempted to submit an errata form with the correct code, or at least see what others had submitted, but the site has been abandoned by its owner.
I recommend you never buy a book from PACKT publishing, it is a complete rip off.
As to finding a good introduction to SciPy online or elsewhere, good luck, I'm still looking.
Further details at my website: [...]
who aims to make Python his/her primary tool for developing and testing his/her algorithms. And this is in itself a very good idea, given the power and versatility of Python + NumPy + SciPy, and that they are free software.
The style of the book is clear, concise and easy to follow. Furthermore, it guides the reader through examples which are central in the practice of scientific computing, making these examples good starting points for the reader's own developments using Python.