personally Im surprised R is still in active development when the main use case for people to use R (at least when I was using it) was for statistical analysis. Python with its libraries (a lot I believe ported from R) just does is nicer, and faster.

Nah, it's not nicer. dplyr is way better than pandas. But there is no end to the supply of Python fanbois who only know Python and assume that whatever's in Python just has to be better

hated

Pandas is used in some top 10 banks for analytics. Its performance is abysmal at the scale used there. Nobody wants to invest resources in training analysts to write high performance code so here we are. I have never viewed SQL more highly after seeing the mess that analysts make when writing imperative code.

smabie

Pandas/python is amazingly prevalent at trading firms. And everyday, we bitch about the performance, we bitch about the stupid API, we bitch about the GIL, the lack of expressiveness. The list goes on and on. But for some braindead reason, we never switch to Julia. It's masochistic.

ivirshup

I do think Julia is a far better language for numerics than python, but compared to DataFrames.jl, pandas can be quite fast. I know, "but it's easier to make it faster in Julia". Last I checked `sort(df, :col)` was significantly slower than `df[sortperm(df[:col])]`. Someone actually has to go through and make these libraries fast.

Second issue, in my field (bioinformatics) the script is still a pretty common unit of code. Without cached compilation being a simple flag, Julia often is slower.

smabie

Yeah, that's a good point. DataFrames.jl starts to really shine what the cookie cutter pandas functions arent adequate for what you need to do. DataFrames.jl can certainly be slower in some cases, but you should expect a consistent level of performance no matter what you do. This is a farcry from Pandas, which tanks by large factors when you start calling Python code vs C code.

In regards to Julia's compilation problem, you can use https://github.com/JuliaLang/PackageCompiler.jl to precompile an image, allowing you to avoid paying the JIT performance penalty over and over again.