I could start this review by trying to define as accurately as possible the book’s audience. Is it for Python programmers eager to apply their knowledge to personal finance applications? Or is it for people involved, one way or another, in the realm of small and medium businesses, eager to see how using Python and its libraries can improve decision processes?

If you are a Python beginner, this book is just a collection of programs to be taken as is and put in your Jupyter Notebook, without any pretensions to understanding anything. Though, I must say, any of these programs works perfectly well (except for two typographical errors: a missing quote on page 57, along with a missing import os line on page 95). But, if you want to get real taste, you need some working knowledge of functions and classes, as well as some familiarity with the way Python uses its libraries. Such readers should know at least some pandas to have a clear understanding of the data frames.

Also, this book is definitely not a manual for building a Python-based business. Several financial aspects are considered, and for each of them a program illustrates how to use Python mechanisms and libraries. Of course, the book does not exhaust the topic; it just gives readers an idea of the path to follow:

• Profit: return on investment (ROI), internal rate of return (IRR/XIRR), and net present value (NPV/XNPV)--here, Excel would be sufficient; however, using Python and pandas provides a solid foundation for the rest.
• Currency conversions: the Open Exchange Rates application programming interface (API) can be accessed via the Requests library, and can be robustly encapsulated using classes or Lambda mechanisms.
• Debt amortization: highlight the relevant values (payment, interest, principal and balance) for each month using the pandas and NumPy libraries.
• Budgeting: here, visualization through Matplotlib is applied over the YAML and Recurrent libraries, providing users with a holistic picture of the cash flow.
• Investment: design portfolios, update values, generate buy and sell orders, retrieve stock quotes, and simulate back testing--again, the pandas and Requests libraries are heavily used.
• Spending: here, the book makes use of the Prophet library. I tried to install it on my Windows 10 machine and I must confess that I was only partially successful, that is, I was able to get only some of the results presented in the book, not all of them; but, as the old saying goes, nobody’s perfect.

The book is neither a Python tutorial nor a business manual; its beauty resides elsewhere. The author is a Python enthusiast, both of its libraries and mechanisms and of the Lambda calculus, and his enthusiasm is contagious. Note that the book is not only a love letter to pandas--each financial aspect treated in the book becomes a love letter for Requests, Matplotlib, Recurrent, NumPy, YAML, and the like.

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