我尝试从 Dataframe 中选择一个行子集，以满足某些约束条件，同时最大化目标。这是我目前所拥有的：

import pandas as pd
import numpy as np
from scipy.optimize import minimize

df = pd.DataFrame({'a': np.random.randint(0, 100, size=100),
                   'b': np.random.randint(0, 100, size=100),
                   'c': np.random.randint(0, 10, size=100),
                   'd': np.random.randint(0, 5, size=100)})

# constraint functions

def a_constraint(x0, df):
    return 1000 - df[x0].a.sum()

def c_constraint(x0, df):
    return 3 - df[x0].c.value_counts().max()

def d_constraint(x0, df):
    return 3 - df[(x0) & (df.d == 1)].shape[0]

def n_constraint(x0, df):
    return 15 - df[x0].shape[0]

# objective function

def objective(x0, df):
    return -df[x0].b.sum()

# initial guess

x0 = np.full((df.shape[0]), True, dtype=bool)

# optimization connstraints as list of dicts

constraints = [{'type': 'ineq', 'fun': a_constraint, 'args': (df,)},
               {'type': 'ineq', 'fun': d_constraint, 'args': (df,)},
               {'type': 'eq', 'fun': c_constraint, 'args': (df,)},
               {'type': 'eq', 'fun': n_constraint, 'args': (df,)},
               ]

result = minimize(objective, x0=x0, method='SLSQP', args=(df,), constraints=constraints)

然而，我得到了一个KeyError。看起来它把我的布尔数组当作列标签而不是索引掩码。有人能告诉我哪里出错了吗？
为了回应下面hpaulj的评论，回溯为：

KeyError                                  Traceback (most recent call last)
d:\Users\github\fpl\fpl_stackoverflow.py in <module>
     38                ]
     39 
---> 40 result = minimize(objective, x0=x0, method='SLSQP', args=(df,), constraints=constraints)

d:\Users\.conda\envs\test_env\lib\site-packages\scipy\optimize\_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
    626     elif meth == 'slsqp':
    627         return _minimize_slsqp(fun, x0, args, jac, bounds,
--> 628                                constraints, callback=callback,**options)
    629     elif meth == 'trust-constr':
    630         return _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp,

d:\Users\.conda\envs\test_env\lib\site-packages\scipy\optimize\slsqp.py in _minimize_slsqp(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, finite_diff_rel_step,**unknown_options)
    327     # meq, mieq: number of equality and inequality constraints
    328     meq = sum(map(len, [atleast_1d(c['fun'](x, *c['args']))
--> 329               for c in cons['eq']]))
    330     mieq = sum(map(len, [atleast_1d(c['fun'](x, *c['args']))
    331                for c in cons['ineq']]))

d:\Users\.conda\envs\test_env\lib\site-packages\scipy\optimize\slsqp.py in <listcomp>(.0)
    327     # meq, mieq: number of equality and inequality constraints
    328     meq = sum(map(len, [atleast_1d(c['fun'](x, *c['args']))
--> 329               for c in cons['eq']]))
...
-> 1308                 raise KeyError(f"None of [{key}] are in the [{axis_name}]")
   1309 
   1310             ax = self.obj._get_axis(axis)

KeyError: "None of [Float64Index([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n              1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n              1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n              1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n              1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n              1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n              1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n              1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n             dtype='float64')] are in the [columns]"

该错误在目标函数中的print(x0)之前抛出。