integer linear program in pulp package generate error - linear-programming

Question

I'm trying to run sample code for transportation problem in Jupyter book, but generate error
TypeError: list indices must be integers or slices, not str. What's problem here? How to solve it? Thanks!
from pulp import *
# Creates a list of all the supply nodes
Warehouses = ["A","B"]
# Creates a dictionary for the number of units of supply for each supply node
supply = {"A": 1000,
"B": 4000}
# Creates a list of all demand nodes
Bars = ["1", "2", "3", "4", "5"]
# Creates a dictionary for the number of units of demand for each demand node
demand = {"1": 500,
"2": 900,
"3": 1800,
"4": 200,
"5": 700}
costs = [ #Bars
#1 2 3 4 5
[2,4,5,2,1],#A Warehouses
[3,1,3,2,3] #B
]
# Creates the prob variable to contain the problem data
prob = LpProblem("Beer Distribution Problem", LpMinimize)
# Creates a list of tuples containing all the possible routes for transport
Routes = [(w,b) for w in Warehouses for b in Bars]
# A dictionary called route_vars is created to contain the referenced variables (the routes)
route_vars = LpVariable.dicts("Route",(Warehouses,Bars),0,None,LpInteger)
# The objective function is added to prob first
prob += lpSum([route_vars[w][b]*costs[w][b] for (w,b) in Routes]), "Sum of Transporting Costs"
# The supply maximum constraints are added to prob for each supply node (warehouse)
for w in Warehouses:
prob += lpSum([route_vars[w][b] for b in Bars]) <= supply[w], "Sum of Products out of Warehouse %s"%w
# The demand minimum constraints are added to prob for each demand node (bar)
for b in Bars:
prob += lpSum([route_vars[w][b] for w in Warehouses]) >= demand[b], "Sum of Products into Bars %s"%b

Answer 1

This is more of a basic Python question than a PuLP question.
w,b are strings. So in your code, you are evaluating costs['A']['1']. If you type this in, you see the same error message. To be able to use string indices, you need to use a dict instead of a list (array).
Solution: make costs a dict
One way to do this is:
costs = {'A': {'1':2,'2':4,'3':5,'4':2,'5':1},
'B': {'1':3,'2':1,'3':3,'4':2,'5':3}}