Python allowed them to build a successful company. Now, when their stack is mature and maintenance is more important than rapid prototyping, Python allows them to add type hinting.

Because they are engineers, they built a tool (in Python) that allows them to do it in an automated manner.

And all of this is great!

They are evolving their code to fit their needs; it's nothing like making a octagonal wheel and wishing you'd have gone for a round one in the beginning.

I'm a big fan of rapid prototyping with Python to map out problem domains, and once the domain has been mapped properly, rewriting in a statically typed language if necessary.

Python is much better for prototyping than the other langs I've used. Because the syntax is almost pseudocode, and the duck-typing makes a lot of design patterns and boilerplate obsolete, so I can dedicate my headspace to the problem at hand.

Right tool for the right job, as they say.

Big assumption.

Both because they might have known Python already and also because Python is quite a bit more newbie-friendly, concise and expressive than the mainstream statically typed languages.

But no, ignoring that it is not a big assumption really. The benefits of static typing comes pretty quickly, especially if there are more than one programmer.

The preconcept that dynamic languages are more productive is just an illusion because you can easily take shortcuts that will hamper your progress in the future. A proper typed language with HM type inference has the ability to mostly avoid writing the types with the guarantee that the compiler will catch most of the pitfalls. And if you don't do any logic error pretty much every time your code just works. Saying that in a million line application Python is a better choice than F# or Haskell it's frankly ridiculous in my opinion.

Do you disagree?

People do use ML family languages, and they are better. There are plenty of non-technical reasons they aren't as widespread as dynamic languages or shitty static languages.

Type hints allow external tools to check some things, but at this point you're basically imposing static types so why not use a language with the tooling and optimizations to take advantage of that?

Python is a good choice to prototype, write small (less than a few thousand lines of code) projects with non-trivial complexity, and somewhat larger projects with more boilerplate (e.g. Django webapps). Beyond that its utility diminishes until it starts to become a hindrance.

The empirical evidence of reality is against you: there are successful large (in terms both of codebase and contributors/development team) projects in these awful terrible children's languages, and there are unmaintainable failed piles of crap in even the most grown-up of languages you'd care to name. The choice of language, and choice of type system, seem not to correlate with the success or failure in a meaningful way.

The interesting thing to note is that a language that's perfectly acceptable at the above at small or medium scale might turn into a hindrance at large-scale. An otherwise fast to develop in language like Python won't be so fast if every change has to be painstakingly reviewed and tested due to the complexity of interactions in the code base.

Using a type system to verify assumptions/requirements is not a recipe for success, but it can improve reliability.

You can write spaghetti code in any language, it turns out. Blaming the language for that is not really an indicator of understanding the problem.

Here we're talking about average or best-effort: large code bases are complex in spite of the best intentions of their maintainers, so using tools that can manage that conplexity in an easier way through e.g their type systems could lead to better results.

Python's type system is, imo, currently better than Java's, and the syntax is cleaner than java's or C++s. You get all the benefits of static typing without having to put `auto` and `List<>` everywhere. And at the same time, you get all of the advantages that python has over statically typed languages that aren't haskell (like comprehensions). And, when you need to, if you're doing something that's especially tricky or dynamic or whatnot, you can fall back to untypedness.

I think the closest parallel I can draw is to something like Rust. You get a huge set of guarantees for free, but can opt to do unsafe things when it's absolutely necessary, and better yet, you can start in unsafe land and then go back later and make sure your code is safe.

I'm curious what tooling you feel that say, Java, has over type-annotated python.

This is also possible in traditionally statically typed languages. Nothing stops you from doing unsafe casts or using reflection. Much like its exceedingly unlikely that you'll run across this in "normal" java or C++, its exceedingly unlikely for you to run into any issues with this in python. And, in fact, the typechecker has ways to handle unusual things like dynamically created attributes, for when that comes up.

And yes I mean this quite honestly. I've seen a lot of typechecked code, some of it quite ridiculously dynamic. Typecheckers perform absolutely fine.

>The "tooling" the other languages have includes a compiler that performs these checks in a way that Hints + Checker-of-choice is unlikely (or unable) to.

What way is that? Typechecking is static analysis. There's really no difference between how java or cpp does typechecking and how mypy does, other than that the python typechecker isn't installed by default.

>The answer is: not nearly what a compiler does.

This is not an answer.

Neither of those is "silent" or "unknowing".

> What way is that? Typechecking is static analysis. There's really no difference between how java or cpp does typechecking and how mypy does, other than that the python typechecker isn't installed by default.

The typechecker can't handle un-hinted code (or, rather, it chooses something very permissive, like 'Any' for all hints). It's incomplete at best.

> This is not an answer. It is. That you don't like or agree with it doesn't make it not an answer.

And in Java or c++, un-hinted code couldn't compile. The python type checker can do more than a java or c++ checker in this regard.

>Neither of those is "silent" or "unknowing".

They're exactly as silent or unknowing as you would get in typed python code. You appear to be comparing untyped python. That's an incorrect comparison. Offhand, I actually can't think of anything I could do in typed python that would get around the type checker, that wouldn't be considered reflection or a dynamic cast, and be very obviously so in python too. If you have an example of a silent or unknowing failure of well typed python code that passes on mypy, you should probably file a bug report ;)

>That you don't like or agree with it doesn't make it not an answer.

You're right. Its not an answer not because I disagree with it (I don't), but because it doesn't actually answer anything, which is why I don't disagree with it.

To summarize this:

Python typecheckers are capable of more type inference than Java, and require less syntax than c++ or Java to get well typed code. A typed python codebase can interact cleanly with an untyped python codebase, and within the typed parts of the code, you get equivalent safety guarantees to what the type systems of Java or C++ provide.

Your appear to be ascribing magical powers to compilers in other languages, when those compilers have exactly the same type information as mypy does.

In other words, going back to your first statement:

>Hints don't provide any guarantees.

Hints provide exactly the same guarantees as any other type system: "Assuming you write reasonable code that doesn't attempt to subvert the type system, the type system will catch any dumb mistakes you make."

That's the exact same guarantee you get in any statically typed language.

But you're right, I should have specified "traditional" static language.

There is no reason to to save the literally 0.2s (you can still spend that time reasoning about your code) it takes to write the type. It is better for yourself writing it and for readability to be explicit.

    auto s = "Hello world";
    for (auto c: s) {
        cout << c;
    }

Tell that to any serious Numpy/Scipy/Pandas user.