Sure, below is a small example. I have commented out the parts that are not central to the discussion. To elaborate on my earlier query, I am trying to convert the two-dimensional shadow price results obtained from the energy balance constraint (EB_nodal) into a dataframe.

```
#using JuMP, DataFrames, CSV, GLPK
generators = DataFrame(index = ["OIL_A", "GAS_B", "NUC_C"],
node = ["zonea", "zoneb", "zonec"],
cost = [30,10,5], g_max = [50,60,70],
plant_type = ["conventional","conventional","conventional"])
demand = [0 0 50; 0 0 100; 0 0 150]
T = convert(Array{Int64}, 1:3) #Time
N = convert(Array{Int64}, 1:3) #node
L = convert(Array{Int64}, 1:3)
C = generators.index
locName = Dict(1 => "zonea", 2 => "zoneb", 3 => "zonec")
incidence = [1 -1 0; 1 0 -1; 0 1 -1]
```

```
model = Model(GLPK.Optimizer)
#variables
#@variable(model, vGEN[T, c in C]>=0)
#@variable(model, vFLOW[T, L])
#Objective function
#@expression(model, DispCost[t in T], sum(vGEN[t, c]*generators[findfirst(==(c), generators.index), :].cost for c in C))
#@objective(model, Min, sum(Dispatch_Model[:DispCost]))
#Constraints
#@constraint(model, cMaxPower[t in T, c in C], vGEN[t,c] <= generators[findfirst(==(c), C), :].g_max)
#@expression(model, convGEN[t in T, n in N], sum(vGEN[t, c]* (locName[n] == generators[findfirst(==(c), C), :node] ? 1.0 : 0.0) for c in C))
@constraint(model,
EB_nodal[t in T, n in N],
demand[t,n] == convGEN[t,n]
- sum(incidence[l, n]*vFLOW[t, l] for l in L)
) #main constraint
```

```
optimize!(model)
nodal_pr = shadow_price.(EB_nodal) * -1 #How do i convert a DenseAxisArray results into a dataframe
```

Shadow price results from EB_nodal constraint

```
2-dimensional DenseAxisArray{Float64,2,...} with index sets:
Dimension 1, [1, 2, 3]
Dimension 2, [1, 2, 3]
And data, a 3×3 Matrix{Float64}:
5.0 5.0 5.0
10.0 10.0 10.0
30.0 30.0 30.0
```

Thanks!