import pandas as pd import itertools

读取数据

data = pd.read_csv('data4.csv')

计算每种单品的收益

data['单品收益'] = data['销量'] * data['成本加成定价'] * (1 - data['单品损耗'] * 0.01) - data['销量'] * data['批发价格']

按分类名称进行分组，并计算每种分类的收益之和

grouped_data = data.groupby('分类名称')['单品收益'].sum()

动态规划算法

def max_profit(data): n = len(data) dp = [[0 for _ in range(n+1)] for _ in range(n+1)] for i in range(1, n+1): for j in range(i, n+1): dp[i][j] = max(dp[i][j-1], dp[i-1][j-1] + data[j-1]) return dp[n][n]

获取单品名称列表

items = data['单品名称'].tolist()

初始化最大收益和对应的单品名称组合

max_profit = 0 max_profit_combination = []

遍历进货单品名称数的范围

for r in range(27, 34): # 获取所有可能的单品名称组合 combinations = itertools.combinations(items, r) for combination in combinations: # 根据单品名称组合筛选数据 selected_data = data[data['单品名称'].isin(combination)] # 计算商超收益 total_profit = selected_data.groupby('分类名称')['单品收益'].sum().sum() # 更新最大收益和对应的单品名称组合 if total_profit > max_profit: max_profit = total_profit max_profit_combination = combination

输出结果

print('进货量最大利润的单品名称组合是：', max_profit_combination) print('商超的最大收益是：', max_profit)