Updating the number of variables and Constraints through For Loop

Good afternoon!

I'm using Gurobi in Python and I was hoping to update the number of variables using a for loop. For example, this is how I initially defined yDHT:

yDHT = m.addVars(D, H, S, vtype=GRB.CONTINUOUS, name='yDHT')

where S is a list containing 3 scenarios

Now, I want to increase the number of scenarios to 10 by using a for loop. I also want to solve the optimization model each time a scenario is added. This way, I'll have the initial run of the model containing 3 scenarios + 7 more runs of the models with the updated list of scenarios. For example

Initial optimization run: [S1, S2, S8]

Run optimization with the first scenario added: [S1, S2, S8, S3], while solutions from the previous run being the warm-start 

Run optimization with the second scenario added: [S1, S2, S8, S3, S5], while solutions from the previous run being the warm-start 

Run optimization with the third scenario added: [S1, S2, S8, S3, S5, S7], while solutions from the previous run being the warm-start 

This is how I have done so far:

for scenario_update in range(len(scenarios1.Index)):
            entering_scenario = scenarios1.take([scenario_update])
            scenarios = scenarios.append(entering_scenario)
            scenario = scenarios.Index.tolist()

Which returns the right sequence of scenarios:

[S1, S2, S8] -> [S1, S2, S8, S3] -> [S1, S2, S8, S3, S5] -> [S1, S2, S8, S3, S5, S7]

But I wasn't sure how I should go about updating the constarints for each of the scenarios added, and updating the number of variables. 

Should I do something like this?

#Updating the number of variables

YDHT.update({depot, entering_scenario, m.addVar(vtype = GRB.BINARY, name= 'yDHT')})

#Updating Constraints

for s in entering_scenario.Index.tolist(): 
                for d in depot:                    
                        m.addConstr(gp.quicksum(yDBT[d, b, s] for b in DC) +
                                    gp.quicksum(yDHT[d, h, s] + yDHV[d, h, s] + yDHU[d, h, s] for h in shelter)
                                    <= PD[d] * xD[d], name='Constraint 4') 

#Warm-starting

yDHT[depot[d], shelter[h], scenario[s]].start = yDHT[depot[d], shelter[h], scenario[s]].x

#Re-run the model

m.optimize(callback = data_cb)

Or is there a better approach? 

Thank you so much. Any help will be truly appreciated. 

Sincerely,

Min Lee