No, I want to see the actual matrix, what it looks like when printed at the REPL. Or a subset of it if it’s too big.
Here it is @pdeffebach
5×76 DataFrame
Row │ x1 x2 x3 x4 x5 x6 x7 ⋯
│ Any Any Any Any Any Any Any ⋯
─────┼──────────────────────────────────────────────────────────────────────────
1 │ FC211 FC645 FC6325 FC6328 FC122 FC221 FC212 ⋯
2 │ -0.1 -19.5536 -24.0226 -26.1236 -26.3944 -28.3969 -28.7
3 │ 0.0 19.5536 24.0226 26.1236 26.3944 28.3969 28.79
4 │ 10.0 8.2803 7.88725 7.70247 7.67866 7.50255 7.467
5 │ 0.0274848 0.0227582 0.0216779 0.0211701 0.0211046 0.0206206 0.020 ⋯
70 columns omitted
without converting into DataFrames it looks like
5×76 Array{Any,2}:
"FC211" "FC645" "FC6325" … "FC6414" "FC531"
-0.1 -19.5536 -24.0226 -30.9518 -29.4389
0.0 19.5536 24.0226 94.6948 102.333
10.0 8.2803 7.88725 1.67175 1.0
0.0274848 0.0227582 0.0216779 0.00459478 0.00274848
And how does it print out to CSV?
You aren’t constructing your DataFrames very well. As you can see, the column names are treated as data. Do df = DataFrame(K2Score[2:end, :], K2Score[1,:]); CSV.write("myfilename.csv", df) and see what changes.
2 Likes
I need the column name as data for further analysis, since they are codified purposly.
But my problem is not column names, the problem happening in the row below it.
They will still be printed. Did you run the code I gave you? You will see that the column names are in the DataFrame.
It’s difficult to help you when you don’t give the information we need to diagnose the problem.
1 Like
Sorry @pdeffebach for not being very clear with my doubt.
I ran your suggested code like
Result = [permutedims(Names); K2Score; ShortestPath; score; cum_weight;]
# Making heirarchy by arranging the result in ascending order.
xs = Result
Result = sortslices(xs; dims=2, by=x->x[3])
# Analysis finshed, time to export result.
# the ´Result´ is my desired output needs to be written in csv.
df = DataFrame(Result[2:end, :], Result[1,:])
CSV.write("myfilename.csv", df)
I hope it is correct. but I am getting following error while running it.
MethodError: no method matching DataFrame(::Array{Any,2}, ::Array{Any,1})
Closest candidates are:
DataFrame(::AbstractArray{T,2} where T) at deprecated.jl:70
DataFrame(::AbstractArray{T,2} where T, ::AbstractArray{Symbol,1}; makeunique) at C:\Users\tecnico2\.julia\packages\DataFrames\3mEXm\src\dataframe\dataframe.jl:322
DataFrame(::AbstractArray{T,2} where T, ::AbstractArray{var"#s45",1} where var"#s45"<:AbstractString; makeunique) at C:\Users\tecnico2\.julia\packages\DataFrames\3mEXm\src\dataframe\dataframe.jl:327
...
Stacktrace:
[1] top-level scope at In[127]:6
[2] include_string(::Function, ::Module, ::String, ::String) at .\loading.jl:1091
I guess the resean behind , Result[1, :] is 76-element Array{Any,1}: and Result[2:end, :] is 4×76 Array{Any,2}:
I am sorry again if I made you pissed of.
Thanks for taking time to write.
Sorry, i missed that you have to convert the names to a vector of Strings. Here is a full MWE that should help you construct a DataFrame and write to a CSV without problems.
julia> using DataFrames, CSV
julia> x = [ "FC211" "FC645" "FC6325"
-0.1 -19.5536 -24.0226
0.0 19.5536 24.0226
10.0 8.2803 7.88725
0.0274848 0.0227582 0.0216779]
5×3 Matrix{Any}:
"FC211" "FC645" "FC6325"
-0.1 -19.5536 -24.0226
0.0 19.5536 24.0226
10.0 8.2803 7.88725
0.0274848 0.0227582 0.0216779
julia> nms = string.(x[1,:]);
julia> vals = Float64.(x[2:end, :]);
julia> df = DataFrame(vals, nms)
4×3 DataFrame
Row │ FC211 FC645 FC6325
│ Float64 Float64 Float64
─────┼──────────────────────────────────────
1 │ -0.1 -19.5536 -24.0226
2 │ 0.0 19.5536 24.0226
3 │ 10.0 8.2803 7.88725
4 │ 0.0274848 0.0227582 0.0216779
julia> io = IOBuffer();
julia> CSV.write(io, df);
julia> csv_str = (String(take!(io)));
julia> println(csv_str)
FC211,FC645,FC6325
-0.1,-19.5536,-24.0226
0.0,19.5536,24.0226
10.0,8.2803,7.88725
0.0274848,0.0227582,0.0216779
4 Likes