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分析.py

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