Release date slated for October 2017.
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, when it made sense to still target Python 2, as relevant to today's argument. The new version is updated for Python 3.5.
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.
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...
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.
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.
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