Andy Dienes
Andy Dienes
I found myself wanting to find the keys of the DataFrame, ordered by some comparator of their columns. As it stands, it seems that the most straightforward way to do...
example pasted from slack: ``` julia> a = "PREVIOUS" "PREVIOUS" julia> a = 1 1 julia> julia> # navigates to `a =` but should be `a = 1` ``` description...
when I train a machine on a (columns) subset of a dataframe, I can predict on the full dataframe but it will take the wrong columns. this should either 1....
I understand the rationale for the very elaborate `missing` logic in that it forces the user to be explicit about how to handle missing values and potentially avoids sneaky statistical...
I know there is `ShiftedArrays.jl` , but (and not to sound needy) I think that adding a new package dependency, typing out the 12-character `ShiftedArray` instead of just `shift`, and...
pasted from slack: consider the following type of expr ``` select(groupby(df, :x4), :x1 => first => :first_in_group) ``` this is fine in isolation, but is a little unwieldy when embedded...
https://github.com/JuliaStats/StatsBase.jl/blob/77e63df55233d9fc3119bd10144d5b8cc1b9f0ad/src/sampling.jl#L729C9-L729C54 by changing that to `ifelse`, a la https://github.com/JuliaStats/Distributions.jl/pull/1831
from slack: regarding https://github.com/JuliaStats/Distributions.jl/pull/1831/files I realized that the portion ``` # Use an alias table fill!(x, zero(eltype(x))) a = s.alias for i = 1:n x[rand(rng, a)] += 1 end ```...
``` julia> y = Vector{Union{Float64, Missing}}() Union{Missing, Float64}[] julia> append!(y, [1,2,3]) 3-element Vector{Union{Missing, Float64}}: 1.0 2.0 3.0 julia> X = rand(3,3) 3×3 Matrix{Float64}: 0.830955 0.821036 0.235061 0.028721 0.326948 0.560919 0.905771...
there is ellipsis truncation past max depth, but if the tree has very large fan-out then it will print the entire level, resulting in far too much output printed