The book mainly covers chapter by chapter "all you need" to do Finance with Python. From data structures, performance Python, (Bayesian) statistics, stochastics to Excel integration and Web technologies.
It also provides -- in addition to many smaller examples and use cases -- a larger case study about a complete, integrated derivatives pricing library.
Here the table of contents as it stands now (work still in progress! Early Release covers chapters 4-7, 1-3 and 8 will be added soon):
Note, I am Continuum Analytics alumni, of which is Yves J. Hilpisch is the European director and whose CEO was the lead contributer to NumPy for many years.
So my endorsement is not against Packt's Python for Finance but rather for O'Reillys Python for Finance based on previous work and experience of the Author.
In addition to the many, many libraries included with Anaconda, it installs ipython (and ipython notebook). I believe using Anaconda is among the easiest ways to get ipython on Windows, and you get all the other libs too.