Julia mixed-type matrix 2 df conversion benchmark

using DataFrames, DataStructures

m = [
    "parName"   "region"    "forType"       "value";
    "vol"       "AL"        "broadL_highF"  3.3055628012;
    "vol"       "AL"        "con_highF"     2.1360975151;
    "vol"       "AQ"        "broadL_highF"  5.81984502;
    "vol"       "AQ"        "con_highF"     8.1462998309;
]

function toDf1(m)
    h    = [Symbol(c) for c in m[1,:]]
    vals = m[2:end, :]
    df   = convert(DataFrame,OrderedDict(zip(h,[vals[:,i] for i in 1:size(vals,2)])))
    for c in names(df)
        # Try to convert df from Any to In64, Float64 or String (in that order)
        try
          df[c] = convert(DataArrays.DataArray{Int64,1},df[c])
        catch
            try
              df[c] = convert(DataArrays.DataArray{Float64,1},df[c])
            catch
                try
                  df[c] = convert(DataArrays.DataArray{String,1},df[c])
                catch
                end
            end
        end
    end
    return df
end

function toDf2(m)
    s = join([join([m[i,j] for j in indices(m, 2)], '\t') for i in indices(m, 1)], '\n')
    df = DataFrames.inlinetable(s; separator='\t', header=true)
    return df
end

function toDf3(m)
    df = DataFrame()
    for (ind,s) in enumerate(Symbol.(m[1,:])) # convert first row to symbols and iterate through them.
        # check all types the same else assign to Any
        T = typeof(m[2,ind])
        T = all(typeof.(m[2:end,ind]).==T) ? T : Any
        # convert to type of second element then add to data frame
        df[s] = T.(m[2:end,ind])
    end
    return df
end

# second time for compilation.. further times ~ results
@time toDf1(m) # 0.000946 seconds (336 allocations: 19.811 KiB)
@time toDf2(m) # 0.000194 seconds (306 allocations: 17.406 KiB)
@time toDf3(m) # 0.001820 seconds (445 allocations: 35.297 KiB)