Blogspam, copied (without author's permission?) from http://www.johndcook.com/blog/2012/10/24/python-for-data-ana... . [EDITED to add: actually, it looks like it was with the author's position. Still blogspam. Incidentally, John Cook's blog is frequently updated and frequently interesting, at least for anyone with an interest in mathematics and software.]
Given that the posting has risen quite high on the front page, can the link be changed to the URL of the original?
Not that there was much meat in the article, anyway, but at least the following is mildly insightful:
I prefer Python to R for mathematical computing because mathematical computing doesn’t exist in a vacuum; there’s always other stuff to do. I find doing mathematical programming in a general-purpose language is easier than doing general-purpose programming in a mathematical language.
Instead of following the Amazon link, you can go to O'reilly page and use the code CFSTNY to get 50% off. (almost no difference with Amazon.com Kindle version, but you also get the pdf)
I had seen Pandas referenced online and had been meaning to look at it at some point. Reading this book has forced me to pay attention. The Pandas dataframe type is well explained here and is now firing my mind, I'm just spinning off into various applications that hadn't occurred to me before reading this book.
In summary, I would definitely recommend it so far.
This is great; I've been looking forward to this book for a while. I'd recommend the author's blog, Quant Pythonista (http://blog.wesmckinney.com/), where he posts details of his various quantitative Python projects. It's great for a beginner such as myself to see real-world applications outside of my own projects.
Hmmm I use python for most scientific computing applications, but I do a lot of statistical things in R. I've been interested in trying pandas for a while, might be time to give it a shot . . .
I have been using Pandas and R for sometime now. I found Pandas is a little rough and fragile around the edges (for example, it was really hard to figure out how to get the median of a distribution in a dataframe as compared to R - fairly basic stuff really). Things do not quite work they way you want them. As for R inspite of the arcane syntax, the quality and number of packages, the help and the IDE (rstudio) is really good.
I bought this book when it was in beta form. Thanks for reminding me that I need to update my version. I don't know Python but this book's existence has piqued my interest into moving into it.
From a novice's perspective, this book is incredible. The fact that it's on the O'Reilly network (pay once -- get it in any format, DRM-free) is icing on the cake.
