InMemoryDatasets.jl
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Multithreaded package for working with tabular data in Julia
Hi there, Would it be possible for you to create a discord server to answers simple questions about `InMemoryDatasets` and `DLMReader`? You can put its link on the first page.
Many features has been added to `IMD` overtime, and I basically have added some docs about them here and there. Proofreading, adding more help, complete docstrings, ... are sort of...
https://github.com/sl-solution/InMemoryDatasets.jl/blob/4220d4bdc03e95098c498ed4012b6bee8fe50eb1/test/join.jl#L811 I cannot reproduce it, however, it must be checked - e.g. make sure that it is not related to data race. Perhaps, it is due to `cumsum!`??? The problem...
``` ds = Dataset(A = ["a", "b","a", "b"],B=[1,2,3,4]) julia> ds.A==ds[:,:A] true julia> typeof(ds.A) DatasetColumn{Dataset, Vector{Union{Missing, String}}} julia> typeof(ds[:,:A]) Vector{Union{Missing, String}} (alias for Array{Union{Missing, String}, 1}) julia> ds.A 4-element Vector{Union{Missing, String}}:...
I wonder if the result of the flatten function in these cases is the most expected one. Are there any contraindications to (or is this notoriously preferable rather than) treating...
I don't explain the reason for the following differences ``` julia> modify(compare(ds[!, r"lim"], ds[!, Not(r"lim")], on = 1:3 .=> 1:3, eq = !isless), 1:3=>byrow(x->x.*1)) 6×4 Dataset Row │ a_lim=>a b_lim=>b...
Trying to follow some examples from the tutorial, I found different outputs than expected(as showed in the documentation). ``` julia> ds = Dataset(g = [2, 1, 1, 2, 2], x1_int...
We should check if can improve the performance of `sort` using other algorithms
to track the issue