Credit to the D developers for providing a concise, carefully-designed library for N-D array processing. The chained method invocations demonstrate D's UFCS (Uniform Function Call Syntax) nicely. And it's a definite bonus that you can use underscore like a comma separator in long integer literals (eg, `100_000`).

But if you use Python + Numpy/Scipy/Matplotlib and you're looking for a modern, compiled language for execution speedups or greater flexibility than what Numpy broadcasting operations provide by default, I would recommend Nim. It's as fast as C++ or D, it has Pythonic syntax, and it already includes many of D's best features (including type inference, UFCS, and underscores in integer literals).

And best of all, you don't need to rewrite all your existing Python+Numpy code into a new language to start using Nim.

The Pymod library we've created allows you to write Nim functions, compile them as standard CPython extension modules, and simply drop them into your existing Python code: https://github.com/jboy/nim-pymod

The Pymod library even includes a type `ptr PyArrayObject` that provides native Nim access to Numpy ndarrays via the Numpy C-API [ https://github.com/jboy/nim-pymod#pyarrayobject-type ]. So you can bounce back and forth between your Python code and your Nim code for the cost of a Python extension module function call. All of Numpy, Scipy & Matplotlib are still available to you in Python, in addition to statically-typed C++-like iterators in Nim+Pymod [ https://github.com/jboy/nim-pymod#pyarrayiter-types , https://github.com/jboy/nim-pymod#pyarrayiter-loop-idioms ]. The Nim for-loops will be compiled to C code that the C compiler can then auto-vectorize.