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Pandas vs. Julia – cheat sheet and comparison

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Re: Pandas vs. Julia – cheat sheet and comparison

#131

Post by jakobnissen »

DNF2 wrote: post #115
jakobnissen wrote: post #110

Earlier quoted context omitted.

Julia does this for exponentiation, i.e. a literal exponent is parsef differently that exponentiation by a variable. I think it was a mistake. Invariably, a new user discovers the discrepancy and is thoroughly confused. Let's not repeat the same mistake with big nums.

I don't think this has anything to do with whether 1e-300 and 10.0^-300 is parsed differently (and perhaps that is a mistake). The poster seems to want to parse 1e-300 directly as a BigFloat in the call `BigFloat(1e-300)`, because of the function it is passed to.

Yeah what I mean is - we already have "magic parsing" with literal_pow, and it's confusing and unnecessary. It was a mistake. We should not make the same mistake when parsing float literals

Re: Pandas vs. Julia – cheat sheet and comparison

#132

Post by tpoacher »

joeman1000 wrote: post #56

The thing that keeps me coming back to Julia is the ability to pipe (or whatever you want to call it). It makes DataFrame operations a lot cleaner since I don't need to modify in place or create new DFs at intermediate steps in a process. Here's a video showing this sort of workflow in R: https://youtu.be/W3e8qMBypSE

I just use a simple chaining function for python like so https://sr.ht/~tpapastylianou/chain-ops-python/

Re: Pandas vs. Julia – cheat sheet and comparison

#133

Post by dillydogg »

joeman1000 wrote: post #56

The thing that keeps me coming back to Julia is the ability to pipe (or whatever you want to call it). It makes DataFrame operations a lot cleaner since I don't need to modify in place or create new DFs at intermediate steps in a process. Here's a video showing this sort of workflow in R: https://youtu.be/W3e8qMBypSE

> The thing that keeps me coming back to Julia is the ability to pipe

> Provides link to R.

Is there an example of this in Julia? I use R now, and every time I give Julia a shot I go back to R because of the insane TTFP. I don't use anything remotely close to big data, and the 90-120s compile times just to replot my small data (using AlgebraOfGraphics.jl in a Pluto notebook) just kill me.

Re: Pandas vs. Julia – cheat sheet and comparison

#134

Post by joeman1000 »

tpoacher wrote: post #132
joeman1000 wrote: post #56

The thing that keeps me coming back to Julia is the ability to pipe (or whatever you want to call it). It makes DataFrame operations a lot cleaner since I don't need to modify in place or create new DFs at intermediate steps in a process. Here's a video showing this sort of workflow in R: https://youtu.be/W3e8qMBypSE

I just use a simple chaining function for python like so https://sr.ht/~tpapastylianou/chain-ops-python/

A neat solution, but you can’t alter the position of the argument per function.

Re: Pandas vs. Julia – cheat sheet and comparison

#135

Post by tpoacher »

joeman1000 wrote: post #134
tpoacher wrote: post #132

Earlier quoted context omitted.

I just use a simple chaining function for python like so https://sr.ht/~tpapastylianou/chain-ops-python/

A neat solution, but you can’t alter the position of the argument per function.

Of course you can. In fact I'm doing just that in two places in the example.

(Yes I know what you mean, but yes you know what I mean!)

In the end, chains are about readability and logical flow; even if you don't like pre-wrapping in more meaningfully named functionals like the example, and accept the slight readability cost of using the occasional in-spot lambda or partial, I feel that this still becomes a lot more readable than "treat this symbol unconventionally in this context as a positional placeholder" hacky syntax stuff.

Re: Pandas vs. Julia – cheat sheet and comparison

#136

Post by ChrisRackauckas »

dillydogg wrote: post #133
joeman1000 wrote: post #56

The thing that keeps me coming back to Julia is the ability to pipe (or whatever you want to call it). It makes DataFrame operations a lot cleaner since I don't need to modify in place or create new DFs at intermediate steps in a process. Here's a video showing this sort of workflow in R: https://youtu.be/W3e8qMBypSE

> The thing that keeps me coming back to Julia is the ability to pipe > Provides link to R. Is there an example of this in Julia? I use R now, and every time I give Julia a shot I go back to R because of the insane TTFP. I don't use anything remotely close to big data, and the 90-120s compile times just to replot my small data (using AlgebraOfGraphics.jl in a Pluto notebook) just kill me.

Did you try v1.9 or v1.10 yet? From others I'm hearing that the code caching changed Makie from about 70 seconds down to 10 in v1.9, and then the loading time improvements brought it to like 5 (unreleased of course, though v1.10 should be branching in a few weeks). Makie load times were of course one of the ones highlighted in the release notes of v1.9: https://julialang.org/blog/2023/04/julia-1.9-highlights/. So while Makie won't be "instant" by v1.10 (<1 second), it was one of the worst offenders before and has gone from "wtf" to "bad but manageable".

Re: Pandas vs. Julia – cheat sheet and comparison

#137

Post by Max-Limelihood »

time_to_smile wrote: post #90
joeman1000 wrote: post #56

The thing that keeps me coming back to Julia is the ability to pipe (or whatever you want to call it). It makes DataFrame operations a lot cleaner since I don't need to modify in place or create new DFs at intermediate steps in a process. Here's a video showing this sort of workflow in R: https://youtu.be/W3e8qMBypSE

I'm still not entirely convinced that pipes aren't an anti-pattern. Absolutely an improvement over nested function calls: a(b(c(d))) vs d |> c |> b |> a but I'm not convinced pipes are better than more verbose code that explains each step: step1 = c(d) step2 = b(step1) result = a(step2) I've written a lot of tidy R and do understand the specific use cases where it really doesn't make sense to use the more verbose for…

If nothing else, you can just pipe the code and then write comments explaining what's left after each step. But the verbose code can be substantially slower (which happens when piping can be used to perform all these operations lazily).

Re: Pandas vs. Julia – cheat sheet and comparison

#138

Post by dillydogg »

ChrisRackauckas wrote: post #136
dillydogg wrote: post #133

Earlier quoted context omitted.

> The thing that keeps me coming back to Julia is the ability to pipe > Provides link to R. Is there an example of this in Julia? I use R now, and every time I give Julia a shot I go back to R because of the insane TTFP. I don't use anything remotely close to big data, and the 90-120s compile times just to replot my small data (using AlgebraOfGraphics.jl in a Pluto notebook) just kill me.

Did you try v1.9 or v1.10 yet? From others I'm hearing that the code caching changed Makie from about 70 seconds down to 10 in v1.9, and then the loading time improvements brought it to like 5 (unreleased of course, though v1.10 should be branching in a few weeks). Makie load times were of course one of the ones highlighted in the release notes of v1.9: https://julialang.org/blog/2023/04/julia-1.9-highlights/ . So wh…

I haven't! I didn't realize that code caching was part of 1.9. Looks like I'll have to check it out. Thanks
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