No, they didn't. This article is narrow minded and prejudiced. Of cause if we're talking about programming in general - static typing languages are often a better option. But programming doesn't ends there, there is technical computing, statistical computing, data analysis and so on. How about beating Python + numpy + pandas in data crunching or maybe R in statistics? In this fields underlying data types is almost al…
I'd think this sentiment misses a small bit of context.
In the above combination, Python is the glue language, which merely makes it easy to dispatch the heavy lifting. Numpy is a damn good convenience wrapper around the under-the-hood number crunching engines - which themselves are written in static languages.
I mean, have you ever tried to _build_ numpy from scratch? That thing pulls in both BLAS and LAPACK, some of the most heavily optimised libraries in the world. These fortran(!) libraries can trace their ancestry all the way to 1970's, and have benefited from aggressive optimisations done over the course of 4 decades. [0,1]
So, invoking numpy as a testament to power of dynamic languages misses a pretty important point. And I say that as someone who loves Python, and uses it as a first-choice tool in perhaps 90% of use cases.
0: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprogra...