[Python] What Python function should I use to perform this discrete maximization function that...

Please see the optimization problem in this image, and let me know what the best way to implement this in Python. I have all the available data needed for this. This is my first time posting on stack overflow, so sorry if I'm unaware of built in LaTeX--hopefully the handwriting isn't too bad.

def access_equity(p:int, M:gpd.GeoDataFrame=homes):

zsum = 0
curr_constraints = []
results = []
for index, row in M.iterrows():
g_i = row['transit_dependent_population']
c_i = row['PRT_ground_coverage']
if row['no_access_routes']:
Y = 0
else:
Y = 1
zsum += g_i * Y * c_i
constraint = LinearConstraint([[-1], [1]], [-Y, p])
results.append(milp(c=(1/zsum), bounds=(0,1), integrality=1, constraints=constraint).success)
return results

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