Practice Data Analysis 100 Knock 61

import numpy as np
import pandas aspd
from itertools import product
from pull import LpVariable, lpSum, value
from toolpy import model_min, addvars, addvals

df_tc=pd.read_csv("trans_cost.csv", index_col="Factory")
df_demand=pd.read_csv("demand.csv")
df_supply=pd.read_csv("supply.csv")

np.random.seed(1)
nw = len(df_tc.index)
nf=len(df_tc.columns)
pr=list(product(range(nw), range(nf)))))

m1 = model_min()
v1 = {(i,j): LpVariable("v%d_%d"%(i,j), lowBound=0)for i,jinpr}

m1+=lpSum(df_tc.iloc[i][j]*v1[i,j] for i, jinpr)
for i in range (nw):
    m1+=lpSum(v1[i,j]) for jin range(nf))<=df_supply.iloc[0][i]
for jin range (nf):
    m1+=lpSum(v1[i,j]) for i in range(nw))>=df_demand.iloc[0][i]
m1.solve()

df_tr_sol=df_tc.copy()
total_cost = 0
fork, x in v1.items():
    i,j = k[0], k[1]
    df_tr_sol.iloc[i][j] = value(x)
    total_cost+=df_tc.iloc[i][j]*value(x)

print(df_tr_sol)
print("Total shipping cost:" + str(total_cost))