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That's the point. Let Python be Python. Don't ruin what Python is by trying to make it be everything / things it is not. > for critical applications with high
by njharman 8y ago
That's the point. Let Python be Python. Don't ruin what Python is by trying to make it be everything / things it is not.
> for critical applications with high correctness demands
If you have that use case, pick an appropriate tool. There's more than one programming language ya know?
- carlmr 8y agoExactly, for high correctness I like to use F#, which is also reasonably fast (almost C# speed, with a lot of ML goodies). For high correctness & high performance I think Rust is a great choice, although some of the libraries aren't there yet in terms of maturity. So I usually only use Rust for programs with a small scope where I know that everything I need is available. Or make a DLL with a C interface that I can then call from F#.
- Someone 8y agoSo, your manager says “the data scientists demoed this. Put it into production”. Do you a) pick an appropriate tool and rewrite their code. b) pick an appropriate tool (python type annotations, this verifier), use it to make the python code robust, and deploy the python code in production? Even if doing a) is as easy as doing b) and produces faster, more efficient code, doing a) again will be more expensive than doing b) again when the data scientists update their python code (even if the typed python and the untyped python are in separate repositories, but ideally, the data scientists would continue working with the ‘solidified’ code, adding duct-typed code at will)