On Wed, Sep 30 2015, Tomas Lycken <[email protected]> wrote: > You can get Python-like performance without getting computer-sciency. > > Again, the problem is not reaching Python-like performance, but rather *not > beating Python by enough*.
Raw speed is a red herring. For many applications it is not the most relevant factor --- scientific computing is one of the exceptions, but even there a lot of code is used just a few times, for solving a specific problem, where coding time dominates. Python has the following key strengths: 1. you can prototype code very quickly, 2. you can rely on a lot of existing mature libraries, 3. with very little effort, you can _write_ your own libraries. Of course these all interact, but 3. is probably the most important. R is much more quirky when it comes to writing robust library code, but still very widespread in the statistics community because of 1. and 2. Don't get me wrong, speed is important too. But I get the impression that people discuss speed benchmarks a lot because this is something that can be boiled down to numbers (how meaningful they are is, of course, another question), while 1-3. above are very hard to quantify. Best, Tamas
