using GLPK in Python (Linear programming)

# import PuLP's functions

from pulp import *

# create new task of Linear programming (LP) with maximize of the goal functions
prob = LpProblem("Knapsack problem", LpMaximize)

# variable (integer)
x1 = LpVariable("x1", 0, 10, 'Integer')
x2 = LpVariable("x2", 0, 10, 'Integer')
x3 = LpVariable("x3", 0, 10, 'Integer')

# goal function ("the cost of all in backpack")

prob += 17*x1 + 30*x2 + 75*x3, "obj"

# constraints ("weight of the backpack")
prob += 1.5*x1 + 2.5*x2 + 6*x3 <= 20, "c1"

# run solver
prob.solve()

# print task status
print "Status:", LpStatus[prob.status]

# print optimized variable result
for v in prob.variables():
    print v.name, "=", v.varValue

# print value of goal function
print ("objective = %s$" % value(prob.objective))

Alternate:
we can create LP-file (CPLEX) and use it with GLPK:

    cmd=['glpsol.exe','--cpxlp',outputname,'-o',solfile]

    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, stdin=subprocess.PIPE)

    for line in p.stdout:

        print(">>>>> " + str(line.decode()).rstrip())