buckaroo VS coalton

I created buckaroo [1] as a better dataframe viewer for jupyter with built in summary stats. It's built to bring a better dataframe experience to people already using pandas/polars. All of it is extensible [2] so that you can customize stats and transformations to your workflow.

[1] https://github.com/paddymul/buckaroo

Great tables has done some really nice work on python/jupyter tables. It looks like they are almost building a "grammar of tables" similar to a grammar of graphics. More projects should write about their philosophy and aims like this.

I have built a different table library for jupyter called buckaroo. My approach has been different. Buckaroo aims to allow you to interactively cycle through different formats and post-processing functions to quickly glean important insights from a table while working interactively. I took the view that I type the same commands over and over to perform rudimentary exploratory data analysis, those commands and insights should be built into a table.

Great tables seems built so that you can manually format a table for presentation.

https://github.com/paddymul/buckaroo

https://youtu.be/GPl6_9n31NE

JLisp.

3. It was very easy to define new lowcode commands, and have the frontend add them to the palette. Each command defines two methods "transform" which manipulates the dataframe, and "transform_to_py" which takes the same arguments but emits python code.

Adoption of my library in general, and the low code UI specifically has been very limited. I'm in the middle of plumbing the lowcode support back in after a refactor of other parts.

I would like to build a whole ecosystem around JLisp and Buckaroo. Specifically I have some "auto-cleaning" functionality that emits JLisp cleaning and normalization commands, these commands can then be editted in the UI (delete, edit parameters). It's easier to emit JLisp than raw python syntax, it's also much easier to make a UI to manipulate it.

Do you have a repo to look at? What usecase did you have in mind when you were building it?

If I were evaluating a low-code backend builder I'd be interested in the examples, and tests. Hopefully the tests would double as examples. For a Workflow type low-code-builder I'd be most interested in the cron functionality.

[1] https://github.com/paddymul/buckaroo

[2] http://norvig.com/lispy2.html

I used Norvig’s lisp2.py to build a low code UI. I modified the interpreter to accept JSON flavored lisp, basically replace parens with brackets. The upside is that it was very very easy to make a react front end that manipulates JSON (JLisp). My thinking was, I need a serialization format for operations from the front end, and a way to interpret them. I could write my own language that no one has heard of, or use lisp, which few have used.

https://github.com/paddymul/buckaroo/blob/main/buckaroo/jlis...

Location: Boston

Remote: Yes

Willing to relocate: Yes

Technologies: talking to users, python, pandas/numpy, jupyter, js/ts

Résumé/CV: https://www.linkedin.com/in/paddymullen/

Email: [email protected]

In my next role, I want a broad mandate to make a meaningful impact within an organization by developing products that address genuine business challenges, with a preference for data related problems.

Recently I built the data table for Jupyter/Pandas that I have wanted for over a decade. The open source Buckaroo (https://github.com/paddymul/buckaroo) data table combines a performant table, summary statistics, and a low code UI to expedite common data analysis tasks.

wanted for over a decade. The open source Buckaroo https://github.com/paddymul/buckaroo data table combines a performant table, summary statistics, and a low code UI to

This morning I added a "Related Projects" [3] Section to the Buckaroo docs. If Buckaroo doesn't solve your problem, look at one of the other linked projects (like Mito).

[1] https://github.com/approximatelabs/sketch

[2] https://github.com/paddymul/buckaroo

[3] https://buckaroo-data.readthedocs.io/en/latest/FAQ.html

It's still… not the same. In CL (and specially with SBCL), we get compile time (type) errors and warnings at the blink of an eye, when we compile a single function with a keystroke (typically C-c C-c in Slime).

And there's also been improvement, see Coalton for a ML on top of CL. (https://github.com/coalton-lang/coalton/)

For the parenthetically inclined among us, there's also an implementation in Coalton: <https://github.com/coalton-lang/coalton/tree/main/examples/t...>

Common Lisp has bad marketing (even OCaml has Twitch streamers and "influencers" now), and bad support for general editors, both of which make it a non-starter for most curious people who have an afternoon to try something. But behind all that is magnificent activity for those who got over the initial potential energy barrier. Just to give some examples:

1. SBCL, the most popular open source implementation of Lisp, is seeing potentially two new garbage collectors. One of them is a parallel collector written by a university student (!!) which blows my mind.

2. SBCL has better and better support for deploying Liwp as a C-compatible shared library, using SBCL-LIBRARIAN. It makes it play nicer with other applications in C and Python.

3. Coalton is another exciting development that allows a Haskell type system and "Lisp-1" functional programming in Common Lisp. That means type classes (or traits), something Lisp hasn't really had a proper notion of, and full type inference. Persistent sequences based off of RRB-trees were recently merged, and interestingly, they're implemented purely in Coalton [1]. That means Clojure-like seqs.

It's interesting to see users of Lisp generating the above ideas and libraries, not a special in-group of committees, "official" developers, etc.

[1] https://github.com/coalton-lang/coalton/blob/main/library/se...

Use an editor that auto-inserts parens and that indents the code correctly. Now nothing bad can happen. And the parens are used to edit code structurally.

re typing: Coalton brings Haskell-like typing on top of CL. https://github.com/coalton-lang/coalton/ Other lisps are typed: typed racket, Carp… and btw, SBCL's compiler brings some welcome type warnings and errors (unlike Python, for instance).

If you like type safety, this project would be perfect for using https://coalton-lang.github.io/ so your REPL supported Common Lisp out of the gate.

Agree that you can use types to express and prove logical properties via compiler; it can be a fun way to solve a problem though too much of it tends to frustrate coworkers. It's also not exactly "low cost"; here's an old quip I have in my quotes file:

"With Scala you feel smart having just got something to work in a beautiful way but when you look around the room to tell your clojure colleague how clever you are, you notice he left 3 hours ago and there is a post-it saying use a Map." --Daniel Worthington-Bodart

> On the contrary, they're still the most effective technique we've found for improving program correctness at low cost.

This is not borne out by research, such as there is any of any quality: https://danluu.com/empirical-pl/ The best intervention to improve correctness, if not already being done, is code review: https://twitter.com/hillelogram/status/1120495752969641986 This doesn't necessarily mean dynamic types are better, just that if static types are better, they aren't tremendously so to obviously show in studies, unlike code review benefit studies.

My own bias is in favor of dynamic types, though I think the way Common Lisp does it is a lot better than Python (plus Lisp is flexible enough in other ways to let static type enthusiasts have their cake and eat it too https://github.com/coalton-lang/coalton), and Python better than PHP, and PHP better than JS. Just like not all static type systems are C, not all dynamic type systems are JS. Untyped langs like assembly or Forth are interesting but I don't have enough experience.

I don't find the argument that valuable though, since I think just focusing on dynamic vs static is one of the least interesting division points when comparing languages or practices, and if we're trading experience takes I think Clojure's immutable-by-default prevents more bugs than any statically typed language that is mutable by default. It's not exactly a low cost intervention though, and when you really need to optimize you'll be encouraged by the profiler to replace some things with Java native arrays and so on. I don't think changing to static types would make a quality difference (especially when things like spec exist to get many of the same or more benefits) and would also not be a low cost intervention.

Last quip to reflect on. "What's true of every bug found in the field? ... It passed the type checker. ... It passed all the tests. Okay. So now what do you do? Right? I think we're in this world I'd like to call guardrail programming. Right? It's really sad. We're like: I can make change because I have tests. Who does that? Who drives their car around banging against the guardrail saying, "Whoa! I'm glad I've got these guardrails because I'd never make it to the show on time."" --Rich Hickey (https://www.infoq.com/presentations/Simple-Made-Easy/)

Coalton seems great, I love the idea. This issue seems problematic, though: https://github.com/coalton-lang/coalton/issues/84

> Lisps can be very flexible, but they usually lack static type safety, opening a wide and horrible door to run-time errors.

People should do basic research before writing something silly like this. Qualifying your statement with 'usually' is just a chicken sh*t approach. Common Lisp and Racket have optional strong typing, leaving the responsibility and choice to the developer. Common Lisp is great for implementing compilers. You also have thing like Typed Racket and Coalton. The latter is comletely statically typed ala MLTON

https://github.com/coalton-lang/coalton

Have you checked out Coalton? It allows static typing a la Haskell within Common Lisp. Fully interoperable with CL, including through SLIME etc.

I've not regretted using Common Lisp for large, professional projects. However, I started Coalton so that some parts of a Common Lisp project can have strong, static, strict types—reaping benefits of compile-time errors and increased efficiency when I need it, without having to rewrite everything.