Transportation Problem : how to addresing variable from range(len()) for constraint? (transitioning from excel)

I'm new on using phyton for optimization (usually I simple use Ms.Excel) and this is my first time asking on stackoverflow. Usually in Ms. Excel you can add constraint from selecting cells, now i'm struggling to do the same in python. The code that I use is on the end of the page.

I want to express that warehouse(1) + warehouse(2) = 15. warehouse 1 and warehouse 2 need 15 to be delivered in total, but the codes using range for the name of the warehouse so I don't know how to do that. my demand is on array so python must have assign the warehouse name in order. So, how do I express those? My friend say If.. elif... else.. might be help, but how? ( he don't know either )

I might using gp.multidict instead so it's possible to address the variable manually, but my actual data is around 2000 so it's too much to do that one by one.

Sorry for my bad english :") Any idea? Any idea or help will very much appreciated. Thank you very much.

gp.multidict :

production, a = gp.multidict({
'A1': 7, 'A2': 4, 'A3': 9}) # Place of Origin 
sales, b = gp.multidict({
'B1': 3, 'B2': 6, 'B3': 5, 'B4': 6}) # place of sales 
route, cost = gp.multidict({

('A1', 'B1'): 3,
('A1', 'B2'): 11,
('A1', 'B3'): 3,a
('A1', 'B4'): 10,
('A2', 'B1'): 1,
('A2', 'B2'): 9,
('A2', 'B3'): 2,
('A2', 'B4'): 8,
('A3', 'B1'): 7,
('A3', 'B2'): 4,
('A3', 'B3'): 10,
('A3', 'B4'): 5}) 

my code :

import gurobipy as gp
from gurobipy import GRB

# Warehouse demand in thousands of units
demand = [15, 18, 14, 20]

# Plant capacity in thousands of units
capacity = [20, 22, 17, 19, 18]

# Fixed costs for each plant
fixedCosts = [12000, 15000, 17000, 13000, 16000]

# Transportation costs per thousand units
transCosts = [[4000, 2000, 3000, 2500, 4500],
              [2500, 2600, 3400, 3000, 4000],
              [1200, 1800, 2600, 4100, 3000],
              [2200, 2600, 3100, 3700, 3200]]

# Range of plants and warehouses
plants = range(len(capacity))
warehouses = range(len(demand))

# Model
m = gp.Model("facility")

# Plant open decision variables: open[p] == 1 if plant p is open.
open = m.addVars(plants,
                 vtype=GRB.BINARY,
                 obj=fixedCosts,
                 name="open")


# The objective is to minimize the total fixed and variable costs
m.ModelSense = GRB.MINIMIZE

# Production constraints
m.addConstrs(
    (transport.sum('*', p) <= capacity[p]*open[p] for p in plants), "Capacity")

# Demand constraints
m.addConstrs(
    (transport.sum(w) == demand[w] for w in warehouses),
    "Demand")

# Save model
m.write('facilityPY.lp')

# First open all plants
for p in plants:
    open[p].Start = 1.0

# Now close the plant with the highest fixed cost
print('Initial guess:')
maxFixed = max(fixedCosts)
for p in plants:
    if fixedCosts[p] == maxFixed:
        open[p].Start = 0.0
        print('Closing plant %s' % p)
        break
print('')

# Use barrier to solve root relaxation
m.Params.Method = 2

# Solve
m.optimize()

# Print solution
print('\nTOTAL COSTS: %g' % m.ObjVal)
print('SOLUTION:')
for p in plants:
    if open[p].X > 0.99:
        print('Plant %s open' % p)
        for w in warehouses:
            if transport[w, p].X > 0:
                print('  Transport %g units to warehouse %s' %
                      (transport[w, p].X, w))
    else:
        print('Plant %s closed!' % p)