43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
import pandas as pd
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import numpy as np
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# 设置随机种子，确保结果可重复
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np.random.seed(42)
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# 定义产业数量
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num_industries = 10
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# 创建产业ID列表
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industry_ids = [i for i in range(0, num_industries + 1)]
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# 为每个产业生成随机的材料id、消耗量、产品id和制造量
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consumed_materials_data = []
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produced_products_data = []
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for industry in industry_ids:
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# 每个产业消耗的材料（生成1到3个随机材料ID和消耗量）
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num_materials = np.random.randint(1, 4)
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for _ in range(num_materials):
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material_id = np.random.randint(0, 100)
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consumption_quantity = np.random.randint(50, 500)
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consumed_materials_data.append([industry, material_id, consumption_quantity])
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# 每个产业制造的产品（生成1到3个随机产品ID和制造量）
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num_products = np.random.randint(1, 4)
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for _ in range(num_products):
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product_id = np.random.randint(100, 201)
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production_quantity = np.random.randint(100, 1000)
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produced_products_data.append([industry, product_id, production_quantity])
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# 创建两个数据框
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df_consumed_materials = pd.DataFrame(consumed_materials_data, columns=['产业ID', '消耗材料ID', '消耗量'])
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df_produced_products = pd.DataFrame(produced_products_data, columns=['产业ID', '制造产品ID', '制造量'])
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# 保存两个数据框为CSV文件
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file_path_consumed = '测试数据 consumed_materials.csv'
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file_path_produced = '测试数据 produced_products.csv'
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df_consumed_materials.to_csv(file_path_consumed, index=False)
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df_produced_products.to_csv(file_path_produced, index=False)
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