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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 bett

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 always very simple, and because of this you simply can't benefit much from static typing. This is not about expressiveness (the article author is wrong here IMO). It's all about flexibility and (more important) interactivity. I can do useful things in ipython console or in ipython notebook in the browser. It's like a tiny lab for small and moderately sized things, data munging, analysis and visualization, prototyping and all this stuff. There is dynamic languages like Python, R and even MATLAB that's not going to disappear anytime soon. There is a new dynamic language called Julia that's only starts getting traction but IMO it's already more interesting then Rust, Go and Nim combined together.

bostik 11y ago
> How about beating Python + numpy + pandas in data crunching 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_Subprograms https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprogra... 1: https://en.wikipedia.org/wiki/LAPACK https://en.wikipedia.org/wiki/LAPACK
- Veedrac 11y ago
  > So, invoking numpy as a testament to power of dynamic languages misses a pretty important point. I don't think so. People build Numpy in a different language to the one they expose it in because those languages have different strengths. If Numpy's source languages (including BLAS and LAPACK) were suitable for front-end usage, Python would have never entered the equation. I think it's more a testament that despite the base libraries being so freely accessible, only dynamic languages have actually built front-ends that people want to use. Even Julia is dynamic.

bostik 11y ago

> How about beating Python + numpy + pandas in data crunching 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_Subprograms https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprogra... 1: https://en.wikipedia.org/wiki/LAPACK https://en.wikipedia.org/wiki/LAPACK

Veedrac 11y ago
> So, invoking numpy as a testament to power of dynamic languages misses a pretty important point. I don't think so. People build Numpy in a different language to the one they expose it in because those languages have different strengths. If Numpy's source languages (including BLAS and LAPACK) were suitable for front-end usage, Python would have never entered the equation. I think it's more a testament that despite the base libraries being so freely accessible, only dynamic languages have actually built front-ends that people want to use. Even Julia is dynamic.