empirical-lang VS tail2futhark

Statically typed dataframes are exactly why I created the Empirical programming language:

https://www.empirical-soft.com

It can infer the column names and types from a CSV file at compile time.

Here's an example that misspells the "ask" column as if it were plural:

  let quotes = load("quotes.csv")

I'm interested in languages experimenting with integrating tables/dataframes and the relational model as native parts of the language

With how much of the code I've written that's been around representing entities and doing CRUD on collections of them it seems a bit wild not to have native "Table" types.

There's an HN user who has a neat language called "Empirical" based on this, and there's an active research language called "Flix" that has this as well. APL/K and other array langs feature a similar concept as I understand it.

https://www.empirical-soft.com/

https://flix.dev/

The most used in finance is a few functions in pandas, like merge_asof(). It's pretty industry standard now.

https://pandas.pydata.org/docs/reference/api/pandas.merge_as...

The biggest open-source thing I've ever written is Empirical, a language for time-series analysis.

https://www.empirical-soft.com

I'm also top 1% on Stack Overflow. A lot of companies have found me through there.

https://stackoverflow.com/users/478288/chrisaycock

I started Empirical with the goal of "q like Haskell". The end result went in a radically different direction, but the guiding light has always been to have a statically typed language where tables and queries are a first-class operation.

The source code is publicly available under AGPL with the Commons Clause:

https://github.com/empirical-soft/empirical-lang

Some of the OP's observations are why I wrote Empirical [1].

- I wanted static types that felt dynamic, which meant being able to infer types from a file as long as the file path can be determined at compile time.

- I wanted Dataframes to be a first-class container since the high-level of abstraction leads to great performance when doing analytics.

Therefore, I created a language with statically typed Dataframes. Empirical can infer a CSV's type at compile time, namely because performs compile-time function evaluation automatically. The result is a language that runs easily in a REPL but can be used for long-running scripts without worrying about common programming errors.

[1] https://www.empirical-soft.com

I wanted to hit the runtime repeatedly with as little overhead as possible. So VVM has no type look-up, multiple operands per instruction, and a cache-efficient IR. The sum() operand for the above example is invoked directly in a loop almost as fast as hand-written C++.

VVM has its own assembly language [2]. I have blog post that explains some of the design choices [3].

[1] https://github.com/empirical-soft/empirical-lang/tree/master...

[2] https://github.com/empirical-soft/empirical-lang/tree/master...

[3] https://www.empirical-soft.com/2020/09/03/a-tour-of-the-vect...

In trying to make APL my primary PL, I have been playing with April[2], a subset of APL in Lisp, another old favorite PL. It is a blast. I am honing both my Lisp and APL skills, and they seem to cover each other's weaknesses for easier adoption in the general programming world.

[1] https://github.com/henrikurms/tail2futhark