martinsmit

2 năm trướcdiscuss

Check out redframes[1] which provides a dplyr-like syntax and is fully interoperable with pandas.

[1]: https://github.com/maxhumber/redframes

martinsmit

3 năm trướcdiscuss

Yes, but no one's made a reverse-mode autodiff system for it yet, so all of the linked examples have hand-written derivatives.

martinsmit

3 năm trướcdiscuss

Similar to Hyukoh's[1], although it is actually multiple documents. P.TM seem to have gone all in, which is neat.

[1]: http://www.hyukoh.com/

martinsmit

3 năm trướcdiscuss

Ironically, sometimes calculating cost-per-use takes more brainpower than it's worth.

Sometimes I get a lot of enjoyment of buying a thing that I know I will love and considering all of the alternatives. Other times, I just defer to what worked in the past.

martinsmit

3 năm trướcdiscuss

Oh trust me, I am. The new code_report solution video on it convinced me to try it.

martinsmit

3 năm trướcdiscuss

The lack of an AD primitive is something I've discussed with the creator of BQN, coming from a JAX world I really miss it and feel that it's such an obvious feature, especially in a language which has a way to turn a tacit function into its AST[1], which has been used for symbolic differentiation[2]. Going from symbolic to reverse-mode AD is not much of a leap and users can define their own primitives with ReBQN[3].

I see what you mean by obfuscation, but I think that it's one of those things that feels really hard and stupid until you start being able to do it really quickly. When you learn a foreign language, you first read letters, then words, then sentences because you become accustomed to larger pieces of the language that you can predict what's coming next without reading it. A similar sort of thing happens with APL/BQN, you read letters (primitives), then you begin to recognise words (small, commonly used groups of primitives), then you see larger patterns which look like magical incantations to an inexperienced user.

These "words" are (typically) tacit phrases, many of them only existing due to specific primitives like swap. Once I used BQN to golf, I started wishing Julia had a swap for operators i.e.

  -(3, 5) = -2
  swap(-)(3, 5) = 2

I won't defend these languages to the death, but they are fun to puzzle your brain with in codegolf. Maybe Dex[4] will go somewhere too.

[1]: https://mlochbaum.github.io/BQN/spec/system.html#operation-p...

[2]: https://saltysylvi.github.io/blog/bqn-macros.html

[3]: https://mlochbaum.github.io/BQN/doc/rebqn.html

[4]: https://github.com/google-research/dex-lang

martinsmit

3 năm trướcdiscuss

Here's a meme that might help: https://www.reddit.com/r/LispMemes/comments/irkm5m/nobody_li...

martinsmit

3 năm trướcdiscuss

I switched permanently from Plots.jl to Makie.jl in order to have backend-agnostic fine-grained control. My publication plots look fantastic and the power given to users is really something. It also has a nicer API than Plots.jl once you get a hang of the figure, axis, plot distinction (plots live inside axes live inside figures) and what goes where.

Unfortunately, as with Plots, the documentation is lacking. The basic tutorial does a good job introducing the aspects of the package at a high level, but the fact that some parts of the documentation uses functions/structs that don't have docstrings in examples makes it very hard to build on the examples in these cases.

I get it, I can do anything with Makie, and most things that I want to do work amazingly. But my code for a single figure can get huge because it's all so low level. See, for example, the Legend documentation[1].

[1]: https://docs.makie.org/stable/examples/blocks/legend/index.h...

martinsmit

3 năm trướcdiscuss

> Tidier

I have not tried it. I like that the project makes broadcasting invisible, I dislike that it tries to completely replicate R's semantics and Tidyverse's syntax. Two examples: firstly, the tuples vs scalars thing doesn't seem very Julia to me. Secondly, I love that DF.jl has :column_name and variable_name as separate syntax. Tidier.jl drops this convention (from what I see in the readme).

> I'm not sure if someone is looking directly at the data.table parts

I believe there was some effort to make an i-j-by syntax in Julia but it fell through or stopped getting worked on. By this syntax I mean something like:

  # An example of using i, j, and by
  @dt flights [
    carrier == "AA",
    (mean(:arr_delay), mean(:dep_delay)),
    by = (:origin, :dest, :month)]

  # An example of expressions in by
  @dt flights [_, nrows, by = (:dep_delay > 0, :arr_delay > 0)]

The idea of ijby (as I understand it) is that it has a consistent structure: row selection/filtering comes before column selection/filtering, and is optionally followed by "by" and then other keyword arguments which augment the data that the core "ij" operations act upon.

data.table also has some nifty syntax like

  data[, x := x + 1] # update in place
  data[, x := x/nrows(.SD), by = y] # .SD =  references data subset currently being worked on

which make it more concise than dplyr.

The conciseness and structure that comes from data.table and its tendency to be much less code than comparable tidyverse transformations through some well-informed choices and reservations of syntax make it nicer for me to use.

martinsmit

3 năm trướcdiscuss

I agree with your conclusion but want to add that switching from Julia may not make sense either.

According to these benchmarks: https://h2oai.github.io/db-benchmark/, DF.jl is the fastest library for some things, data.table for others, polars for others. Which is fastest depends on the query and whether it takes advantage of the features/properties of each.

For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.

martinsmit

3 năm trướcdiscuss

BQN[1] has higher order functions. Of the array languages I've used, it's by far my favourite. That said, I mostly solve small problems for fun in them.

[1] https://mlochbaum.github.io/BQN/index.html

martinsmit

3 năm trướcdiscuss

Context: Coming from a statistics background, I learned a bit of R, then a bit of Python for data analysis/science, then found Julia as the language I invested my time in. Over time I keep up with R and Python enough to know what's different since I learned them, but don't use them daily.

What I always tell people is the following:

If you are writing code using existing libraries then use whichever language has those languages. The NN stack(s) in Python are great, the statistical ML stack(s) in R are simple and include SOTA techniques.

If you are writing a package yourself, then I assume you know the core of the idea well enough to be able to write your code from the "top down" i.e. you're not experimenting with how to solve the problem at hand, you're implementing something concretely defined.

In this case, and tailored to your use, I would argue that Julia has more advantages than disadvantages, especially compared to R or Python. Here are a few comments:

1. Environments, dependencies, and distribution can all be handled by Pkg.jl, the built in package manager. There is no 3rd party tool involved, there is no disagreement in the community on which is better. This is my biggest pain point with Python.

2. Julia's type system both exists and is more powerful than that of Python (types or classes) and R (even Hadley's new S7(?) system). By powerful I mean generics/parametric types and overloading/dispatch built in. You can code without them, but certain problems are solved elegantly by them. Since working heavily with types in recent years, I find this to be my biggest pain point in R and I wouldn't want to write a package in R, although I like to use it as an end user.

3. New developments in scientific programming, programming ergonomics, hardware generic code (as in this post), and other cool features happen in Julia. New developments in statistics happen in R (and increasingly Julia), new developments funded by big companies happen in Python.

4. The Python and R interpreter start up faster than Julia. The biggest problem here is when you are redefining types, which is the only thing in Julia that can't currently be "hot reloaded" i.e. you need to restart Julia to redefine types.

5. Working with tabular data is (currently) far more ergonomic and effortless in R than Python and Julia.

6. Plotting is not a solved problem in Julia. Plots.jl is pretty easy and pretty powerful, Makie.jl is powerful but very manual. Time to first plot is longer than R or Python.

7. Julia has almost zero technical debt, R and Python have a lot. Backwards compatibility is guaranteed for Julia code written in >v1.0 and Pkg.jl handles package compatibility. If I send you code I wrote 4 years ago along with a Project.toml containing [compat] information then you could run the code with zero effort. (This is the theory, in practice Julia programmers are typically scientists first and coders second, ymmv.)

8. You can choose how low level you want your code to be. Prototyping can be done in Julia, rewriting to be faster can be done in Julia, production code can be done in Julia. Translating Python to C++ production might mean thinking about types for the first time in the dev process. In Julia, going to production just means making sure your code is type stable.