Thu 15 Dec 2011
This is an unorthodox review of Numpy 1.5 Beginner’s Guide by Ivan Idris. I have to say two things right off before we get into the review:
- This book was given to me in ebook and physical form directly from Packt Publishing
- I actually don’t think I have enough math to review this
On that second one, I took college Calculus, but this book talks about terms I either don’t remember or they just weren’t covered. I had Statistics I and II as well, but the author deals more with matrix manipulation and linear algebra. I think my old Finance and Accounting classes helped the most, but that was at the end of the book.
The Book’s Audience
So who is this book actually aimed at? I think it’s aimed at high level mathematicians, scientists and stock market number crunchers. The prose is pretty good, if a bit dry. Most of the book is made up of a section introduction, a problem, how to solve it with NumPy / Matplotlib and some code examples. The code examples are snippets instead of full fledged runnable code, but you should be able to piece together most of it easily. The author doesn’t spend time importing libraries or creating fancy classes, so all the examples are very straight-forward, especially if you already understand the math equations. Note that the equations are not explained, so if you don’t know them, you’ll have to do some digging yourself.
What is Covered
Oodles and oodles of equations and math terms. For example, you’ll learn how to do various moving averages, Bollinger bands, trend lines, factorials, matrices (lots and lots of ‘em), hanning, hamming, ufuncs, Lissajous curves, determinants, Fourier transforms, various logarithms, matrice sorting and lots more. All of that is within NumPy with occasional SciPy stuff thrown in. The examples focused on the stock market and finance and seemed to work well within that context.
Near the end of the book, in chapter 9, the author switches gears slightly and discusses Matplotlib a little more in depth. He had been using it off and on in previous chapters, but he covers a lot more of its basic functions in this chapter. Then in the 10th and final chapter, he delves in SciPy and even manages to mention SciKits.
I noticed a few minor grammatical or sentence structure issues here and there, but this is one of the better written Packt books.
As I mentioned, I don’t really understand a lot of this book due to the high level math. It saddens me that I either didn’t cover this when I was in high school or college or that I’ve managed to forget so much of it. However, while the author doesn’t spend much time explaining the examples, I think that the quick nature in which it is written, works. Feel free to download Chapter 3 to get a taste of what it’s like. If you’re into this sort of thing or want to learn how to apply this sort of thing in Python, than I think this book may be right up your alley.
Numpy 1.5 Beginner’s Guide
By Ivan Idris