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     "end_time": "2025-04-23T08:34:19.513552Z",
     "start_time": "2025-04-23T08:34:19.451573Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "df = pd.read_excel(r\"C:\\Users\\Administrator.DESKTOP-7IC2USJ\\Desktop\\新建文件夹\\2025年计划产值汇总.xlsx\",sheet_name=\"2025年计划台次汇总\")\n",
    "\n",
    "df_melted = pd.melt(\n",
    "    df,\n",
    "    id_vars=[\"公司\", \"分类\"],  # 固定列\n",
    "    var_name=\"月份\",          # 新的月份列名\n",
    "    value_name=\"值\"           # 新的值列名\n",
    ")\n",
    "\n",
    "df_melted = df_melted.sort_values(by=[\"公司\", \"分类\", \"月份\"]).reset_index(drop=True)\n",
    "\n",
    "# 定义年份\n",
    "year = 2025\n",
    "print(df_melted[\"月份\"])\n",
    "# 提取月份中的数字并拼接年份\n",
    "df_melted[\"月份\"] = df_melted[\"月份\"].apply(lambda x: f\"{year}-{re.findall(r'\\d+', x)[0]}-01\")\n",
    "\n",
    "print(\"\\n转换后的数据：\")\n",
    "print(df_melted)\n",
    "    \n",
    "df_melted.to_excel(r\"C:\\Users\\Administrator.DESKTOP-7IC2USJ\\Desktop\\新建文件夹\\2025年计划台次汇总.xlsx\",index=False)\n",
    "    "
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     10月\n",
      "1     11月\n",
      "2     12月\n",
      "3      1月\n",
      "4      2月\n",
      "     ... \n",
      "91     5月\n",
      "92     6月\n",
      "93     7月\n",
      "94     8月\n",
      "95     9月\n",
      "Name: 月份, Length: 96, dtype: object\n",
      "\n",
      "转换后的数据：\n",
      "       公司  分类          月份       值\n",
      "0   四元桥奔驰  机修  2025-10-01  833000\n",
      "1   四元桥奔驰  机修  2025-11-01  943000\n",
      "2   四元桥奔驰  机修  2025-12-01  973000\n",
      "3   四元桥奔驰  机修   2025-1-01  918000\n",
      "4   四元桥奔驰  机修   2025-2-01  745500\n",
      "..    ...  ..         ...     ...\n",
      "91    张家口  理赔   2025-5-01  641087\n",
      "92    张家口  理赔   2025-6-01  756578\n",
      "93    张家口  理赔   2025-7-01  820006\n",
      "94    张家口  理赔   2025-8-01  820006\n",
      "95    张家口  理赔   2025-9-01  795607\n",
      "\n",
      "[96 rows x 4 columns]\n"
     ]
    }
   ],
   "execution_count": 15
  }
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