55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
import pandas as pd
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import numpy as np
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# 设置随机种子
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np.random.seed(42)
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num_companies = 170 # 企业ID范围
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# 生成企业和设备数据
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num_rows = 220 # 每个表的行数
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company_ids = np.arange(num_companies)
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# 第二步：生成剩余的随机企业ID
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remaining_ids = np.random.randint(0, num_companies, size=num_rows - num_companies)
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# 合并两部分的企业ID
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all_company_ids = np.concatenate([company_ids, remaining_ids])
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# 第三步：对企业ID进行升序排序
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all_company_ids.sort()
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device_ids = np.random.randint(51, 107, size=num_rows)
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material_ids = np.random.randint(0, 51, size=num_rows)
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product_ids = np.random.randint(0, 107, size=num_rows)
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device_quantities = np.random.randint(50, 200, size=num_rows)
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material_quantities = np.random.randint(100,200, size=num_rows)
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product_quantities = np.random.randint(20, 100, size=num_rows)
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# 创建三个表格的数据框
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df_devices = pd.DataFrame({
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'Firm_Code': all_company_ids,
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'设备id': device_ids,
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'设备数量': device_quantities
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})
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df_materials = pd.DataFrame({
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'Firm_Code': all_company_ids,
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'材料id': material_ids,
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'材料数量': material_quantities
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})
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df_products = pd.DataFrame({
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'Firm_Code': all_company_ids,
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'产品id': product_ids,
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'产品数量': product_quantities
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})
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# 保存为CSV文件
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df_devices.to_csv('测试数据 companies_devices.csv', index=False)
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df_materials.to_csv('测试数据 companies_materials.csv', index=False)
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df_products.to_csv('测试数据 companies_products.csv', index=False)
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