[kergonath]

Some other languages have attempts to emulate it (like some matrix classes in c++ and numpy), but it is not that great. You can make it look close in some places at the cost of more complexity elsewhere. It also feels bolted on with more or less opaque data structures manipulated by ad hoc functions. There always are strange edge cases where the library fights against the language. The TKR (type, kind, rank) concepts are at the very core of the variable model in Fortran, and n-dimensional matrices are first class variables just like scalars, and are much more natural to use. Elemental functions are very useful and trivial to implement in Fortran. Coupled with the great array syntax and easy user-defined operators, it makes writing complex numerical expressions very satisfying and elegant. Granted, some other parts of Fortran are far from elegant.

I am not saying it makes other languages unusable (I use numpy arrays quite frequently; Python and Fortran are very complementary), but definitely less natural and elegant.

[ogogmad]

With Python+Numpy, my issues are or were:

  1. The matrix multiplication syntax used to be bad. But it became possible at some point to write A @ B for the product of A with B. And that seems fine to me.

  2. The code seems more natural when you write `sin(array([1,2,3]))` instead of `np.sin(np.array([1,2,3]))` -- which you can achieve by running `from numpy import *`, but doing this is discouraged by a lot of people.

  3. The convention in Linear Algebra is that indexing starts from 1, not 0. This sometimes makes translating from a language which follows the more usual convention difficult.

Overall though, I think only 3 is unfixable, but this is usually a minor problem. Usually, Python+Numpy doesn't seem any worse than Matlab syntax-wise.

I find the experience of using Pandas to be much worse. Sympy also feels abrasive because of needing to write `m12 = var('v12')`.