Whilst O'Reilly books just keep getting worse. Seriously, "Scipy and Numpy" is typo ridden and so simplistic it's irrelevant and "Python for Data Analysis" should really be called "Mainly Pandas 'cos I wrote it, plus a chapter about IPython".
The sales numbers already show that the book was timely and relevant to a huge (> 10,000 so far) number of people.
I can definitely see why some think that the title is misleading. The book clearly does not aim to survey the field of data analysis in Python. However, it does teach you everything you need to know to start doing data analysis in Python using one particular (good) tool chain.
As to the merits, per se, of having some third party write the book: I'm doubtful. I followed wesm's development through blog posts, twitter updates, etc. Writing pandas forced him to consider a lot aspects of dealing with data in python, both deep and practical. In the process, I'm confident he became one of the foremost experts in not only his own software but in the current state of Data Analysis in Python. Who better to write this book?
Another piece of advice for people: about 80% of Python for Data Analysis is the same as the online documentation for Pandas, the other 20% some examples and other notes. So if you're buying it you should buy it to support Wes McKinney, not to learn a ton of new stuff. Which wouldn't be the worst thing you could do, pandas is pretty awesome and I don't know what else you would use in Python to do statistical data analysis. (Of course there are separate tools for more specialized analyses and techniques like ML and NLP, but I mean "plain ol' stats".)
I would have loved if the book had included sections on the statsmodels and scikit libraries, especially since they are integrated with pandas.
While not directly covering the python libraries mentioned such as scikits learn this is a good into to some concepts in machine learning. https://www.udacity.com/course/cs373