127 lines
3.3 KiB
Python
127 lines
3.3 KiB
Python
import pandas as pd
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# ====== 填入你的数据 ======
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names = [
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"集成电路制造",
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"晶圆测试",
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"功率半导体器件",
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"二极管",
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"碳化硅外延晶片",
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"氮化镓外延片",
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"晶闸管",
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"氮化铝外延片",
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"磷化铟外延片",
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"LED外延片",
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"晶体管",
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"硅外延片",
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"整流桥",
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"蚀刻液",
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"砷化镓单晶片",
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"多晶硅片",
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"碳化硅单晶和单晶片",
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"磷化铟单晶和单晶片",
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"氮化镓晶体和单晶片",
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"单晶硅片",
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"氮化镓衬底",
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"碳化硅衬底",
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"磷化铟衬底",
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"硅衬底",
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"氮化铝衬底",
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"深紫外LED衬底",
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"氟化硅",
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"显影液",
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"稀释剂",
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"硅原材料",
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"聚羧酸减水剂",
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"表面活性剂",
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"碳化硅",
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"高纯金属有机化合物",
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"半导体电镀设备",
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"晶硅切片机",
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"薄膜生长设备",
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"硅片倒角机",
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"等离子去胶机",
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"晶圆清洗机",
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"熔炼矿热炉",
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"光刻胶及其配套试剂",
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"离子注入设备",
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"剥离液",
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"芯片设计验证",
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"金属保护液",
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"化学机械抛光设备",
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"高纯硼酸（核电）",
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"电子级环氧树脂",
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"光刻机",
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"通用湿电子化学品",
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"单晶生长炉",
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"晶圆测量设备",
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"电子级阻燃材料及化学品",
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"液晶取向剂及配套化学品",
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"功能湿电子化学品",
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"砷化镓",
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"氮化镓",
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"氮化硅",
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"磁性载体",
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"研磨液及配套化学品、研磨垫材料",
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"电子级酚醛树脂",
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"钝化液",
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"电镀化学品及配套材料",
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"涂胶显影设备",
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"硅片研磨机",
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"刻蚀机",
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"氧化/扩散炉",
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"磷化铟",
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"氮化铝",
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"晶圆检测设备",
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"多晶硅切削液"
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]
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counts = [
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3726,2171,1915,1423,1141,1132,1127,1113,1111,1104,1092,1082,813,642,558,555,551,535,
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526,520,429,425,419,398,365,351,226,90,30,30,30,30,24,20,20,20,
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20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,
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18,16,16,10
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]
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# 检查长度是否一致
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if len(names) != len(counts):
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raise ValueError(f"名称数量 ({len(names)}) 与 count 数量 ({len(counts)}) 不一致！")
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# 创建 DataFrame
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df = pd.DataFrame({"名称": names, "count": counts})
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# ====== 定义类别划分规则 ======
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def categorize(name):
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if any(x in name for x in ["制造","设计验证"]):
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return "芯片制造与设计"
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elif any(x in name for x in ["晶圆","外延片","硅片","单晶","多晶"]):
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return "晶圆及外延片"
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elif any(x in name for x in ["器件","二极管","晶闸管","晶体管","整流桥"]):
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return "半导体器件"
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elif any(x in name for x in ["衬底"]):
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return "衬底材料"
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elif any(x in name for x in ["液","试剂","化学品","材料","金属有机化合物","活性剂","减水剂","环氧树脂"]):
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return "化学品与材料"
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elif any(x in name for x in ["机","设备","炉","薄膜","测量","光刻"]):
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return "制造设备"
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else:
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return "其他材料与辅助"
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# 应用分类
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df["类别"] = df["名称"].apply(categorize)
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# ====== 按类别统计 ======
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stats = df.groupby("类别")["count"].agg(['min','max','mean','median','sum']).reset_index()
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stats.rename(columns={
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"min":"最小值",
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"max":"最大值",
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"mean":"均值",
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"median":"中位数",
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"sum":"总和"
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}, inplace=True)
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# 输出结果
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print(stats)
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# 如果需要保存为 Excel
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stats.to_excel("产业类别统计分析.xlsx", index=False) |