The win with Python (and other dynamic languages) is that you can experiment quickly with ideas when you're formulating a solution, that's a big part of exploratory data science.
If you're curious about high-speed work in Python - Radim did a blog series on how he sped up word2vec to be faster than Google's original C code: http://radimrehurek.com/2013/09/deep-learning-with-word2vec-...
I'll also note [self promo!] that I wrote on book on High Performance Python, if that's your cup of tea (and Radim wrote a section in it): http://shop.oreilly.com/product/0636920028963.do
It is clunky but I'm hoping we'll get better control as nbconvert evolves, so we're experimenting with this approach.
Most of the code examples are not 'live', they're pasted in along with analysis results (the book is about high performance and parallel computing: http://shop.oreilly.com/product/0636920028963.do ), as lots of the examples are best run from a fresh VM.
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