SlackBuildsOrg/development/numexpr

The numexpr package evaluates multiple-operator array expressions many times
faster than NumPy can. It accepts the expression as a string, analyzes it,
rewrites it more efficiently, and compiles it to faster Python code on the
fly. It's the next best thing to writing the expression in C and compiling
it with a specialized just-in-time (JIT) compiler, i.e. it does not require
a compiler at runtime.

Also, and since version 1.4, numexpr implements support for multi-threading
computations straight into its internal virtual machine, written in C. This
allows to bypass the GIL in Python, and allows near-optimal parallel
performance in your vector expressions, most specially on CPU-bounded
operations (memory-bounded were already the strong point of Numexpr).

This requires numpy.