Julia: A fast and elegant way to get a matrix from an array of arrays

Question

There is an array of arrays containing more than 10,000 pairs of Float64 values. Something like this:

v = [[rand(),rand()], ..., [rand(),rand()]]

I want to get a matrix with two columns from it. It is possible to bypass all pairs with a cycle, it looks cumbersome, but gives the result in a fraction of a second:

x = Vector{Float64}()
y = Vector{Float64}()
for i = 1:length(v)
    push!(x, v[i][1])
    push!(y, v[i][2])
end
w = hcat(x,y)

The solution with permutedims(reshape(hcat(v...), (length(v[1]), length(v)))), which I found in this task, looks more elegant but completely suspends Julia, is needed to restart the session. Perhaps it was optimal six years ago, but now it is not working in the case of large arrays. Is there a solution that is both compact and fast?

Answer 1

I hope this is short and efficient enough for you:

 getindex.(v, [1 2])

and if you want something simpler to digest:

[v[i][j] for i in 1:length(v), j in 1:2]

Also the hcat solution could be written as:

permutedims(reshape(reduce(hcat, v), (length(v[1]), length(v))));

and it should not hang your Julia (please confirm - it works for me).

@Antonello: to understand why this works consider a simpler example:

julia> string.(["a", "b", "c"], [1 2])
3×2 Matrix{String}:
 "a1"  "a2"
 "b1"  "b2"
 "c1"  "c2"

I am broadcasting a column Vector ["a", "b", "c"] and a 1-row Matrix [1 2]. The point is that [1 2] is a Matrix. Thus it makes broadcasting to expand both rows (forced by the vector) and columns (forced by a Matrix). For such expansion to happen it is crucial that the [1 2] matrix has exactly one row. Is this clearer now?