Had I had MyPy back when I was working in data science, I would've saved countless hours and headaches.
No, the code in data science isn't bad because of the lack of typing. The code is "bad" mostly because those writing it are relatively fresh from starting to program. Also there is more pressure to make things possible, often just to run it once, and neglect repeatability or scaling to larger code bases. Different emphasis. That doesn't mean an experienced full stack developer would do Data Science better, because he might lack a lot of skills that matter more in that domain.
I'm not sure if that's true. Doesn't Pandas handle ETL and some anaysis? There is nothing inherent to ETL that makes it a hard problem with compiled languages.
In your opinion, what does Pandas do that's hard to do with compile-time languages?
This even starts with basic Numpy and handling tensor objects. It's not easy for a type checker to understand what operations you can do with what shape of tensor. Worse, most often you don't know (or want to know) some of the dimensions or even dimensionality of some of the objects. Then it is impossible to check all of this at compile time.
That doesn't sound like a Python problem.
Instead, it sounds like the natural consequence of numpy being designed in a way where their data types aren't organized into subtypes, and leave that as runtime properties. This is a natural reflection of numpy's take on vectors, matrices, and tensors, which in terms of types are just big arrays with runtime properties.
To put things in perspective, in C++, Eigen supports static dense vectors and matrices whose size is specified and known at compile-time. I'm sure Python doesn't impose addition static type constraints than C++.
Significant types in ETL-style applications typically come from outside (e.g. a certain CSV column in the input file contains a date in YYYYMMDD format, or maybe YYYYDDMM, figure it out, and don't forget time zones or your accounting will go wrong).
Then types are mostly complex but obvious and easily deducted (e.g. multiplying matrices of compatible shapes necessarily gives a matrix of a certain shape, why should the program say anything more detailed or lower-level than "do a matrix multiplication" or "do a tensor product"?); they are an often dynamic and unpredictable property of the data, not a useful abstraction.
What, mutate data with known structure? What exactly do you imagine is hard about this, let alone notoriously so?
A pandas dataframe types to something like Iterable[SomeKindOfProductType[int, str, str, ... (78 other columns)]]. The formal type of a dataframe in the middle of a transformation is... not very useful to know.
The number and types of the columns aren't necessarily known at compile time. Which leads to runtime errors. Even in a statically typed language, such dataframes are a kind of "dynamic typing escape hatch". As complexity of a software increases, such mechanismus of dynamic typing and throwing runtime errors creep up all over the place.
It's certainly very useful to know, even if that knowledge is indirect via the type of the resulting dataframe after all the transforms.
This resonates with my experience. I had the opportunity to work on a DS codebase written entirely in Scala with all the typing, parallelism, actor model, whatnot. Basically I joined the company because of this technical factor. It was fun until I figured out that DS was "typed IF-THEN-ELSE written by Java devs in Scala returning stuff the users complain about with high reliability within milliseconds". Now I am happy to be back to the single threaded untyped Python world. Still no bugs in production, because we validate all requests to death, have unit tests and integration tests running on real data not on mokups. Basically we follow the principle: if the integration test passes, our typing is just right, or at least good enough. Funnily all the typing errors we catch are caused by wrongly typed data, coming from the productive system written in a typed programming language... what a strange world.
I can see you are enjoying the life outside of a highly regulated industry. Having certain kinds of production data in tests (or feeding that to test environment) would be a major audit finding in any finance or healthcare company.
Makes for both a blessing and a curse.
...thereby destroying the production-like features for which you want it for testing, which you then need to recreate and reintroduce, so you might as well just synthesize test data in the first place, since that's what you end up doing anyway, in effect.
No, in actual practice you don't scrub the stuff you actually need to test.
No, I’ve spent ~20 years in healthcare, with this issue as a frequently recurring issue.
> No, in actual practice you don't scrub the stuff you actually need to test.
In actual practice, the stuff you really need to test often overlaps with the stuff minimally required to scrub to legally deanonymize the data. The most common scenario I’ve seen trying to do this is both creating most of the work of generating synthetic data and failing to legally deidentify the source data.
This is the largest difference. When there's no expectation of code lasting beyond a very short lifespan, why go through the effort to future proof things, improve maintainability, have better ergonomics, etc?
Sounds a lot like F# type providers to me.
https://thesharperdev.com/introduction-to-fsharp-type-provid...
And good luck teaching beginning scientists F#.
The output of most analytics is a report. For longer lived processes it seems easier to just hand off