I jumped on the opportunity to receive a review copy of Python for Finance. I’ve been programming in Python for the last 2 years, and I’m particularly interested in technical computing.

Python for Finance is targeted for graduate students in finance, although I believe it is also appropriate for senior-level computer science majors who are interested using Python for financial computing.

Let me start with a few minor quibbles. First, I’ve never cared for how Pakt formats their books; it’s clunky and seems dated. Second, there are a number of places in the book where the English is awkward and could have benefited from some decent editing.

Because Python for Finance is fairly short (about 350 pages of content), don’t expect it to be the only book you’ll need if you want to really learn Python. It will, though, get you started and up to speed with the language. About a third of the way through, the author introduces the NumPy and SciPy modules, which are essential for technical computing. Because of the scope of the book, these are given limited coverage, but enough to provide readers with an understanding of how powerful these tools are.

Python for Finance then moves into plotting, analysis of time series, and then jumps into option modeling, which is where the book really starts to tie things together, including various tools to pull financial information from the web. A chapter on the ins and outs of Python loops precedes what was my favorite chapter – Monte Carlo Simulations and Options. This chapter really highlights the power of Python for technical computing.

Overall, I enjoyed Python for Finance. I learned new tools and techniques in Python as well as a significant amount about financial computing.