>Are those all in standard libraries, fully documented, backed by 60 year old, fully debugge code (LAPACK, etc) ... yes, my np.mean() might be slower than your

>Are those all in standard libraries, fully documented, backed by 60 year old, fully debugge code (LAPACK, etc) ... yes, my np.mean() might be slower than your map(). I almost always don't care. Hmm ... LAPACK itself is only 23 years old (1992). Its was implemented to be a faster replacement of the "standard" LINPACK library, by effectively exploiting the caches on modern processors. For most serious users of numerical computing, performance is extremely important. Also, I think you might be putting far too much faith in an arbitrary assortment of Python libraries. If you think that NumPy is just a thin wrapper on LAPACK, and you are using it in mission or life critical operations, then I think you have some code vetting to do. Even if some of your chosen libraries do use LAPACK, and even if there is some code in LAPACK that is 60 years old, that does not suddenly mean that all of your chosen Python libraries (and your code on top of them) is all correct. Most serious users of many of the routines that you mention (e.g. "machine learning" and "financial calculations") implement and test these routines themselves in order to ensure their correctness. In very serious model vetting operations, it would be customary to even invest in two different independent implementations of the same routine. > I reliably email to anyone across the world and they can immediately run and modify my code because it is such a standard? I don't think "reliable" and "emailing scripts" normally go together. It would be more customary to use a source code control system in a high reliability environment. Personally I have found reproducing Python installations to be very tricky and I would take extreme care in any critical operations that assume two different Python installations produce the exactly the same results (high reliability, like some of the 60+year old codes run by NASA, would even require bug for bug reproducibility). "Python/Numpy/Scipy/Pandas/Matplotlib" are not the last word on anything, theyt are not standardized as you imply, and they leave vast room for improvement in all dimensions (performance, correctness, reliability, productivity, etc).