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It's more to do with developer ergonomics. Knowing what something is or returns (and having some guarantees about it) make it much easier to reason about code.

It's more to do with developer ergonomics. Knowing what something is or returns (and having some guarantees about it) make it much easier to reason about code. Then, knowing (with guarantees) what a variable/object is, knowing right away what I can do with it. > What exact typesystem do you prefer over Python's dynamic typing? I just want to know what various objects are laying around in my code so I don't have to keep it in my head. For example, if you are using a database library like psycopg, you run a query and execute it. What kind of object is returned from the query execution function? How do you check if it was null? You have to go looking at documentation, and often what you find is examples of what you can do. But those examples often don't include absolutely everything, so you have to go looking at the code itself. But library code in python is often pretty arcane. With Fortran/C++/Rust/etc, you get all of that basically for free in your editor's autocomplete. And the compiler will check to make sure you didn't do anything truly dumb. Dynamic typing also encourages some frustratingly bad habits. Lots of code out there will have functions that change return type depending on what arguments are provided. And occasionally someone will forget to return something in some branch and you won't know until you hit an edge case (during runtime) and suddenly you have a None coming from somewhere. My projects tend to be in Python now, but I recently started a side project in C++ again and found it very refreshing. > probably e.g. NumPy/SciPy infrastructure would be about impossible Partly true, but maybe not as true as you think. I've been using the nlohmann JSON library for C++, and it is amazing. The code almost looks pythonic, and its type conversions are done automatically (although at runtime of course). For example, for(const auto &[key, value] : some_json["x"].items()) double d = value["other_key"]; EDIT: Also, I didn't realize how much I missed function overloading until I started the C++ project. Wow is that handy.

jampekka 3y ago
I find that "what can I do with the value" is a lot more important than "what the object is". Especially in scientific context this is usually "can I iterate this" "can I do arithmetic with this" etc. NumPy's broadcasting and support for "any" iterables is a great example of this. And very difficult to accomplish with at least the typesystems you listed. E.g. going from a nested list in a JSON to a Eigen matrix is quite a pain even with (awesome) libraries like nlohmann/json. I often have to resort to C++ for performance or libraries. I make a lot more bugs than in Python. And the bugs are often really tricky and often pass the typesystem (e.g. both nlohmann and Eigen have to really hack the templates and these cause a lot of edge cases). Also serializing stuff in C++ is a real pain even with nlohmann/json because for god's sake there's still no reflection in C++ in 2024. Of course you can abuse the power of dynamic typing. But you can just as well abuse anything else that's remotely powerful (see e.g. Boost).

jampekka 3y ago

I find that "what can I do with the value" is a lot more important than "what the object is". Especially in scientific context this is usually "can I iterate this" "can I do arithmetic with this" etc. NumPy's broadcasting and support for "any" iterables is a great example of this. And very difficult to accomplish with at least the typesystems you listed. E.g. going from a nested list in a JSON to a Eigen matrix is quite a pain even with (awesome) libraries like nlohmann/json. I often have to resort to C++ for performance or libraries. I make a lot more bugs than in Python. And the bugs are often really tricky and often pass the typesystem (e.g. both nlohmann and Eigen have to really hack the templates and these cause a lot of edge cases). Also serializing stuff in C++ is a real pain even with nlohmann/json because for god's sake there's still no reflection in C++ in 2024. Of course you can abuse the power of dynamic typing. But you can just as well abuse anything else that's remotely powerful (see e.g. Boost).