The definition of "basics" depends on your domain. For the fluid dynamics and geological data that Enthought (the sponsor and primary developer of SciPy) works with, SciPy and NumPy provides all the basics.
One could argue that if you're going to call something "SciPy", it ought to cover all sorts of scientific computation...but, it is Open Source under a very open license. Contributing is very easy, and the team are very friendly to outsiders sending patches, and it's not hard to no longer be considered an "outsider". So, if you need it and SciPy doesn't provide it, why not develop it and contribute?
What I'm trying to say is that complaining about missing functions you need from an Open Source project just wastes your time and annoys the pig.
Hey, without going into details I have contributed a fair amount of code to R.
Scipy (and the whole Python numerical computation stack) has nontrivial competition out there and here's what it needs to do to compete...if its developers care about adoption, which most do.
Definitely not saying scipy == crap, just that for stats/machine learning (which is a big percentage of a lot of applications today) it is not mature.
One could argue that if you're going to call something "SciPy", it ought to cover all sorts of scientific computation...but, it is Open Source under a very open license. Contributing is very easy, and the team are very friendly to outsiders sending patches, and it's not hard to no longer be considered an "outsider". So, if you need it and SciPy doesn't provide it, why not develop it and contribute?
What I'm trying to say is that complaining about missing functions you need from an Open Source project just wastes your time and annoys the pig.