Python for Finance: Analyze Big Financial Data Paperback – 27 Dec. 2014

This book gives an excellent all-round and practical insight into what it says in the title, “Python for Finance”. It will not make you into an expert of Python nor quantitative finance. However, it provides some real gems of insight of how to do things efficiently and succinctly; Yves provides a holistic theme of using several Python frameworks in a consistent way, and illustrates ideas using progressively evolving examples.Really great thing about this approach is that one can be productive learning concepts (if you’re new to quant finance), learn how to implement these concepts in python (if you’re new to Python), and make use of some great frameworks of Python (if you’re new to frameworks; eg: numpy, Pandas, IPython notebook…) via building practical examples that you can test real-world problems from the day one. I’ve been in quant finance for many years, but relatively new to Pandas and some of the Python frameworks; without a doubt, Yves’ book managed to save me many weeks of effort having to Google, read other analysis/blogs/opinions etc...In summary, the book provides you tons of useful insights, and other references for you to read about, and to consolidate your knowledge of Python and quantitative methods for finance; whether you’re an expert in either areas, or neither areas…. If you’re an expert in both, the book will convince you of your know-how, and will still give you useful references to keep your knowledge up to date. A brilliant buy.

Great book, do make use of the online tools as well and work through the examples. A great way to pick up python, from a finance background and in a hurry. Good core reading.Covers much of Numpy, Pandas, Scipy, Matplotlib.

It is a good book for the price and kind of a standard in the matter, good place to start, do not expect truly deep things though,from the finance point of view it is just the essentials

Decent book with lots of examples of python in use with ipython notebooks, using appropriate libraries, etc. Many graphics examples. Nothing, however, is particularly deep. It is a good intermediate programming book with almost no programming in it. Also some of the examples jump quite a bit. From PCA to kernel PCA in just a sentence. No discussion of what most people have to do with PCA (analyze loadings and scores, look at scree plots, determine appropriate rotations etc), then jump right away to kernel PCA (which is used for what exactly? Not that much in reality really--interesting method but doesn't need so much airtime).This is a good overview and I don't know all of the material myself. At the same time, I am compelled to look to other books for more detail.

Amazing and hugely useful book. I use it as a ref all the time now.

If you know some quantitative finance and would like to switch from using Excel or VBA this is a very helpful book. It covers all of the essential topics such as data types, visualisation and numerical Python (NumPy). Unusually for a beginners book it covers Pandas very early on. Again, this is helpful as Pandas is such a powerful tool for financial time series analysis and puts Python on a level with R. Some of the explanations of Python syntax are a bit shallow but combined with online resources at the Python Software Foundation you'll get working with Python for finance quickly. I also found the layout of the book off-putting at times: it has pages of code in some parts with no explanation. Surely this could have been placed on a website? In the best part of the book Hilpisch shows the reader how to use the Pandas DataReader for downloading financial data from the web.

A very good read for anyone keen on using Python for quantative finance.

For a book named "Python for Finance" you would expect some real insight and practitioner examples. I found none! The author, Yves Hilpisch, writes a book on finance but has no actual finance experience therefore the examples look like they were given for an undergrad class entitled "financial engineering 101".Also I noticed some reviews look suspicious on the UK and US amazon.