I think the author really needs to define scientific computing. Some people would think of that as simulations, clusters, astronomy, epidemiology and fluid dynamics. That maybe typified by a scripting language wrapper(inc Python) to C, C++ or even Fortran programs.
He himself seems to be talking more about standard stats and data analysis or prediction. In this field R is growing at least as fast as Python. R is the no1 Kaggle language and the no1 academic stats language, Python 2nd and stable there. The software carpentry movement to train scientitsts concentrates on both R and Python.
Then the biggest scientific programming field is bioinformatics - which really is a mish mash of Python, Perl, Java, C++ whatever piped CLI and a lot of R again. Here Python is growing mostly at the expense of Perl (there is a big Perl legacy) but as most software is designed for piping together in Bash scripts the diversity is not too big a deal.
My own perusal of Job adverts on this area sees employers asking for "R Perl or Python" which are viewed as interchangeable.
But he also talks about document parsing and web dev. I think Python finds its edge when you have to combine things on the sciencey/mathy side of things with more user-facing software development. As the author said, it's nice not having to code switch between the a dozen best-in-field languages, when Python is highly capable across the board. Besides maybe C# and maybe Java, what other languages are as versatile?
True but have you seen most academic websites? They really aren't that into web dev. They are mainly concerned with static publication.
But yes Python is growing in this area and generally as command line scripting glue. I'm not sure its got all our lunch yet though.
He himself seems to be talking more about standard stats and data analysis or prediction. In this field R is growing at least as fast as Python. R is the no1 Kaggle language and the no1 academic stats language, Python 2nd and stable there. The software carpentry movement to train scientitsts concentrates on both R and Python.
Then the biggest scientific programming field is bioinformatics - which really is a mish mash of Python, Perl, Java, C++ whatever piped CLI and a lot of R again. Here Python is growing mostly at the expense of Perl (there is a big Perl legacy) but as most software is designed for piping together in Bash scripts the diversity is not too big a deal. My own perusal of Job adverts on this area sees employers asking for "R Perl or Python" which are viewed as interchangeable.