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Author 1

Tests from PrettyTables.jl

https://github.com/ronisbr/PrettyTables.jl/blob/master/test/general.jl

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5.8 ms
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using Tables
31.1 ms
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begin
struct TestVec{T} <: AbstractArray{T,1}
data::Array{T,1}
end
Base.IndexStyle(::Type{A}) where {A<:TestVec} = Base.IndexCartesian()
Base.size(A::TestVec) = size(getfield(A, :data))
Base.getindex(A::TestVec, index::Int) = getindex(getfield(A, :data), index)
Base.collect(::Type{T}, itr::TestVec) where {T} = TestVec(collect(T, getfield(itr, :data)))
Base.convert(::Type{<:TestVec}, x::Array) = TestVec(x)
end
6.1 ms
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begin
struct MinimalTable
data::Matrix
colnames::TestVec
end

Tables.istable(x::MinimalTable) = true
Tables.columnaccess(::MinimalTable) = true
Tables.columnnames(x::MinimalTable) = getfield(x, :colnames)
Tables.columns(x::MinimalTable) = x
Base.getindex(x::MinimalTable, i1, i2) = getindex(getfield(x, :data), i1, i2)
Base.getproperty(x::MinimalTable, s::Symbol) = getindex(x, :, findfirst(==(s), Tables.columnnames(x)))
end
3.9 ms
3×3 Matrix{Float64}:
 0.212798   0.603537   0.429298
 0.717627   0.0663684  0.959119
 0.0334289  0.692403   0.473176
data = rand(3,3)
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14.6 μs
mt = MinimalTable(data, [Symbol("Col. 1"), Symbol("Col. 2"), Symbol("Col. 3")])
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25.7 μs
table = (A = Int64.(1:10),
B = Float64.(1:10),
C = Int64.(1:10),
D = Float64.(1:10))
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76.1 ms
ABCD
Int64Float64Int64Float64
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t = Tables.rows(table)
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4.3 ms
using DataFrames
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1.3 s
DataFrame
DataFrame
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"Tables.RowIterator{NamedTuple{(:A, :B, :C, :D), Tuple{Vector{Int64}, Vector{Float64}, Vector{Int64}, Vector{Float64}}}}((A = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], B = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0], C = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], D = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]), 10)"
string(t)
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191 ms
rowaccess(x::T) where T in Tables at /home/runner/.julia/packages/Tables/cRTb7/src/Tables.jl:338
@which Tables.rowaccess(t)
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102 ms