I know there is CSV.jl, but I am wondering what the easiest way to access the data by their column names would be if I just use DelimitedFiles.jl.
My first approach would look like this, but maybe there is a more elegant solution.
using DelimitedFiles
data,header = readdlm(path,header=true)
coldata = data[:,header .== "column name"]
You can convert the header and columns to a dict with, for example:
The data:
shell> more test.dat
A B C
1 2 3
1 2 3
1 2 3
julia> data, header = readdlm("./test.dat",header=true)
([1.0 2.0 3.0; 1.0 2.0 3.0; 1.0 2.0 3.0], AbstractString["A" "B" "C"])
julia> d = Dict(header[i] => data[:,i] for i in 1:length(header))
Dict{SubString{String},Array{Float64,1}} with 3 entries:
"B" => [2.0, 2.0, 2.0]
"A" => [1.0, 1.0, 1.0]
"C" => [3.0, 3.0, 3.0]
julia> d["A"]
3-element Array{Float64,1}:
1.0
1.0
1.0
That copies the data. If that is not desirable, you can use dictionary only associating the header with the column indexes:
julia> hind = Dict(header[i] => i for i in 1:length(header))
Dict{SubString{String},Int64} with 3 entries:
"B" => 2
"A" => 1
"C" => 3
julia> data[:,hind["A"]]
3-element Array{Float64,1}:
1.0
1.0
Or if you are just willing to do this once in a while:
julia> data[:, findfirst(==("A"),header)]
3-element Array{Float64,1}:
1.0
1.0
1.0
The DIct option looks very nice.
Maybe views will remove the need to copy the data
Dict(header[i]=>view(data,:,i) for i in 1:length(header))
or a named tuple(although the code looks a bit messy)
(;((Symbol.(header[i]),view(data,:,i)) for i in 1:length(header))...)
indeed