使用Pandas将Excel Solver转换为Python

Question

我已经在Excel Solver中建立了一个示例，只是为了演示我希望从Python开始的内容。我在Python中尝试了PuLP，但始终出现错误。我正在尝试最小化df ['AP']与输入变量的差异，或者只是使利润最大化。

idx = df.index

#Define Inclusion Variable as Boolean
x1 = LpVariable.dicts("idx", idx, lowBound=-1, upBound = 1)

#Objective
prob += sum([x1[l] for l in idx]), 'Max of Profit'
#prob += lpSum([costs[i]*ingredient_vars[i] for i in Ingredients]), "Total Cost of Ingredients per can"

#Constraints
prob += lpSum([df['CalcVar'][l] for l in idx]) <= curDy + 0.01, "CalcVar"
prob += lpSum([df['CalcVar'][l] for l in idx]) >= curDy - 0.01, "CalcVar"

#Wrap up & Solve
LpSolverDefault.msg = 1
prob.writeLP('LaneOpt.lp')
prob.solve()

错误

Traceback (most recent call last):
  File "solver.py", line 84, in <module>
    prob.solve()
  File "PATH", line 1671, in solve
    status = solver.actualSolve(self, **kwargs)
  File "PATH", line 1362, in actualSolve
    return self.solve_CBC(lp, **kwargs)
  File "PATH", line 1427, in solve_CBC
    raise PulpSolverError("Pulp: Error while executing "+self.path)
pulp.solvers.PulpSolverError: Pulp: Error while executing ...\cbc.exe