It is a great compliment to Wear McKinney's "Python for Data Analysis" it is more like a recipe book than the internals as Wes' book is. Also, JVP includes more than just Pandas and NumPy goodies.
Highly Recommend, and fork to create your own curated handbook.
I find it hard to believe you're citing a book that was published in 2012[1], when it made sense to still target Python 2, as relevant to today's argument. The new version is updated for Python 3.5[2].
[1] http://shop.oreilly.com/product/0636920023784.do
[2] https://www.safaribooksonline.com/library/view/python-for-da...
Don't get me wrong, I'm grateful for all the work and I know I haven't contributed much but I think the online could be improved with more examples and recipes.
Edit: There really is no excuse, getting started is easy[2].
Other than that I'd recommend that you find a domain you are particularily interested in and get some books on the specific aspects of that, for example Python for Data Analysis. http://shop.oreilly.com/product/0636920023784.do
This allows you to focus on what to do with python and the eco system of frameworks and so on.
If you want to learn how to use Python effectively, I believe you should decide what domain to apply it in. Python is no end in itself, as is the case with most other tools...
http://shop.oreilly.com/product/0636920023784.do
I have the book and its been great reading so far. Ch 4 gives a nice introduction to Numpy (about 30 pages). Concise but also useful for immediate real-world usage.
http://shop.oreilly.com/product/0636920023784.do?code=CFSTNY
One caveat I would mention about data analysis would be that statistics is not just number crunching. It is really a bit of an art to making sure you are looking at the right sample of data in the right way, and ensure you are accounting for all potential biases. Surprisingly, I have noticed as I've gotten more experience doing data analysis, it takes me longer to do and I make less confident assertions. But on the other hand, I now very rarely make assertions which were incorrect, which is extremely important. I believe that incorrect data analysis is significantly worse than no data analysis.
So, the advice I would give to people getting started is whenever you come to a conclusion by analyzing a particular piece of data, ask your "if I look at the data differently, can I come to the opposite conclusion?". You would be surprised how often the answer to this question is yes, and that is a good indicator that you a) need more data or b) cannot make a significant conclusion. This can be especially difficult when you are already sure you know the answer to a question even before you do the data analysis, but you really have to be disciplined about it.
Anyone reading this who wants to get started with Pandas: The early release of "Python for Data Analysis" (http://shop.oreilly.com/product/0636920023784.do) is already very helpful.
http://shop.oreilly.com/product/0636920050896.do
Release date slated for October 2017.