Re:Polars
by Galanwe on Tuesday January 09, 2024 @09:47AM
(#38926760)
> Many axis=1 operations in pandas do a transpose under the hood, mind you
Sure, but many others are natively axis=1-aware and avoid full transposition.
> Axis=1 belongs in matrices, not in heterogeneous data.
I'm not sure to understand what that means. Care to elaborate?
> They are a performance footgun.
You don't get to only solve the problems that are efficient to solve...
> We make the transpose explicit.
Yes, but when you do mixed time series / cross sectional computations, you cannot always untangle both dimensions and transpose once. Sometimes your computation intrinsicely interleaves cross sectional and time series. In these case, which happen a lot in financial computations, then explicitly fully transposing is very slow.
Sure, but many others are natively axis=1-aware and avoid full transposition.
> Axis=1 belongs in matrices, not in heterogeneous data.
I'm not sure to understand what that means. Care to elaborate?
> They are a performance footgun.
You don't get to only solve the problems that are efficient to solve...
> We make the transpose explicit.
Yes, but when you do mixed time series / cross sectional computations, you cannot always untangle both dimensions and transpose once. Sometimes your computation intrinsicely interleaves cross sectional and time series. In these case, which happen a lot in financial computations, then explicitly fully transposing is very slow.