As someone who has used numpy for many years and written a great deal of production code using it, I was surprised when I read through this and saw some numpy tricks that I didn't know regarding the speeds of various operations! This is really a fantastic reference that provides a deeper level of understanding of what numpy does under the hood.
One thing I will highlight that the author just touched on briefly, is that numpy combined with numba is really a phenomenal combination for dealing with very computationally intensive problems.
The folks at Continuum Analytics have really done a fantastic job building numba (numba.pydata.org), which JIT compiles a subset of python functions using LLVM, and is designed to work seamlessly with numpy arrays. Numba makes it much easier to speed up performance bottlenecks and allows you to easily create numpy ufuncs which can take advantage of array broadcasting.