This is a fantastic step forward for Julia, and array-oriented computing in general. Routines that smoothly generalize to arrays with an arbitrary number of di

This is a fantastic step forward for Julia, and array-oriented computing in general. Routines that smoothly generalize to arrays with an arbitrary number of dimensions is also a strength of NumPy -- it's the core idea behind NumPy's "universal functions" [1] like sin or exp. Writing custom ufuncs in C is not easy, but Numba makes it straightforward to write fast ufuncs in Python [2]. For many of the use cases described here, it's a pretty solid alternative. In general, I think Numba's ufuncs are under-appreciated -- they make it easy to write fast code that works on any number of array dimensions, and even automatically respect metadata when called on array-like objects from third party libraries like pandas. Unfortunately, we don't have abstractions quite as powerful as eachindex and CartesianRange in NumPy/Numba. This makes for some awkward work-arounds and optimization constraints (e.g., see my project Numbagg [3], and discussions on the Numba issue tracker [4]). That said, I don't think there's any reason why these abstractions couldn't also be implemented in Numba, and I would love to see that happen. [1] http://docs.scipy.org/doc/numpy/reference/ufuncs.html http://docs.scipy.org/doc/numpy/reference/ufuncs.html [2] http://numba.pydata.org/numba-doc/0.23.1/user/vectorize.html http://numba.pydata.org/numba-doc/0.23.1/user/vectorize.html [3] https://github.com/shoyer/numbagg https://github.com/shoyer/numbagg [4] e.g., https://github.com/numba/numba/issues/841 https://github.com/numba/numba/issues/841