Python is getting widely used to quickly prototype numerical kernels, thanks to the numpyscipymatplotlibipython team. But when it comes to performance, it still lags behind equivalent native code. The Pythran compiler proposes a solution to this problem, by statically compiling and optimizing high-level Pythonnumpy kernels into parallel, vectorized C++11 code. The leitmotiv is to take high-level Numpy code, without the need of explicit loops, and rely on the semantic of the numpy operations to generate efficient code.