{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length mismatch: Expected axis has 50 elements, new values have 40 elements",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-67eca2aa80c8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[0mfolder_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mr'C:\\Users\\admin\\Desktop\\工作簿1'\u001b[0m  \u001b[1;31m# 替换为你的文件夹路径\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[0moutput_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mr'C:\\Users\\admin\\Desktop\\工作簿11111.xlsx'\u001b[0m  \u001b[1;31m# 输出文件名\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mmerge_excel_files\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfolder_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-2-67eca2aa80c8>\u001b[0m in \u001b[0;36mmerge_excel_files\u001b[1;34m(folder_path, output_file)\u001b[0m\n\u001b[0;32m     15\u001b[0m         \u001b[0mtemp_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m         \u001b[1;31m# 填充列名\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m         \u001b[0mtemp_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m         \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtemp_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'outer'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m   6000\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6001\u001b[0m             \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6002\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6003\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6004\u001b[0m             \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pandas\\_libs\\properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.AxisProperty.__set__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_set_axis\u001b[1;34m(self, axis, labels)\u001b[0m\n\u001b[0;32m    728\u001b[0m         \"\"\"\n\u001b[0;32m    729\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 730\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    731\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    732\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mset_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m    223\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mset_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mAxisInt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_labels\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m         \u001b[1;31m# Caller is responsible for ensuring we have an Index object.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 225\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_set_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    226\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_labels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pandas\\core\\internals\\base.py\u001b[0m in \u001b[0;36m_validate_set_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m     68\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mnew_len\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mold_len\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m             raise ValueError(\n\u001b[0m\u001b[0;32m     71\u001b[0m                 \u001b[1;34mf\"Length mismatch: Expected axis has {old_len} elements, new \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m                 \u001b[1;34mf\"values have {new_len} elements\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Length mismatch: Expected axis has 50 elements, new values have 40 elements"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "def merge_excel_files(folder_path, output_file):\n",
    "    # 获取文件夹中所有的xls文件\n",
    "    files = [f for f in os.listdir(folder_path) if f.endswith('.xls')]\n",
    "\n",
    "    # 读取第一个文件，作为基础数据框\n",
    "    first_file = os.path.join(folder_path, files[0])\n",
    "    df = pd.read_excel(first_file)\n",
    "\n",
    "    # 遍历剩余的文件，并将它们的数据框合并到基础数据框中\n",
    "    for file in files[1:]:\n",
    "        file_path = os.path.join(folder_path, file)\n",
    "        temp_df = pd.read_excel(file_path)\n",
    "        # 填充列名\n",
    "        temp_df.columns = df.columns\n",
    "        df = df.merge(temp_df, how='outer', on=list(df.columns))\n",
    "\n",
    "    # 将合并后的数据框写入到一个新的Excel文件中\n",
    "    df.to_excel(output_file, index=False)\n",
    "\n",
    "# 使用示例\n",
    "folder_path = r'C:\\Users\\admin\\Desktop\\工作簿1'  # 替换为你的文件夹路径\n",
    "output_file = r'C:\\Users\\admin\\Desktop\\工作簿11111.xlsx'  # 输出文件名\n",
    "merge_excel_files(folder_path, output_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-e8345f508062>:44: UserWarning: Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).\n",
      "  result.to_excel(r'C:\\Users\\admin\\Desktop\\工作簿22222.xlsx', index=False)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "def merge_excel_files(folder_path):\n",
    "    # 获取文件夹中的所有文件\n",
    "    files = os.listdir(folder_path)\n",
    "    \n",
    "    # 筛选出所有的Excel文件\n",
    "    excel_files = [file for file in files if file.endswith('.xlsx') or file.endswith('.xls')]\n",
    "    \n",
    "    # 初始化一个空的DataFrame用于存储合并后的数据\n",
    "    merged_data = pd.DataFrame()\n",
    "    \n",
    "    # 遍历所有Excel文件\n",
    "    for file in excel_files:\n",
    "        file_path = os.path.join(folder_path, file)\n",
    "        \n",
    "        # 读取Excel文件\n",
    "        data = pd.read_excel(file_path)\n",
    "        \n",
    "        # 如果合并后的DataFrame为空，则直接将当前数据赋值给它\n",
    "        if merged_data.empty:\n",
    "            merged_data = data\n",
    "        else:\n",
    "            # 合并数据，保留所有列，如果有重复列名，则在合并时添加后缀以避免冲突\n",
    "            common_columns = set(merged_data.columns).intersection(data.columns)\n",
    "            for col in common_columns:\n",
    "                data.rename(columns={col: col + '_1'}, inplace=True)\n",
    "                merged_data.rename(columns={col: col + '_2'}, inplace=True)\n",
    "            \n",
    "            # 将两个数据集合并在一起\n",
    "            merged_data = pd.concat([merged_data, data], axis=1)\n",
    "    \n",
    "    # 重置索引\n",
    "    merged_data.reset_index(drop=True, inplace=True)\n",
    "    \n",
    "    return merged_data\n",
    "\n",
    "# 调用函数，传入包含Excel文件的文件夹路径\n",
    "folder_path = r'C:\\Users\\admin\\Desktop\\工作簿2' \n",
    "result = merge_excel_files(folder_path)\n",
    "\n",
    "# 保存合并后的数据到新的Excel文件\n",
    "result.to_excel(r'C:\\Users\\admin\\Desktop\\工作簿22222.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\numpy\\.libs\\libopenblas64__v0.3.21-gcc_10_3_0.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "<ipython-input-1-05ed69f5774b>:25: UserWarning: Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).\n",
      "  merged_data.to_excel(output_file, index=False)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "def merge_excel_files(folder_path, output_file):\n",
    "    # 获取文件夹中的所有Excel文件\n",
    "    excel_files = [f for f in os.listdir(folder_path) if f.endswith('.xlsx') or f.endswith('.xls')]\n",
    "\n",
    "    # 初始化一个空的DataFrame用于存储合并后的数据\n",
    "    merged_data = pd.DataFrame()\n",
    "\n",
    "    # 遍历所有Excel文件\n",
    "    for file in excel_files:\n",
    "        file_path = os.path.join(folder_path, file)\n",
    "        # 读取Excel文件内容\n",
    "        data = pd.read_excel(file_path)\n",
    "\n",
    "        # 如果merged_data为空，则直接将当前数据赋值给merged_data\n",
    "        if merged_data.empty:\n",
    "            merged_data = data\n",
    "        else:\n",
    "            # 合并两个表格，对于列名相同的，合并到一列\n",
    "            merged_data = pd.concat([merged_data, data], axis=0, ignore_index=True)\n",
    "\n",
    "    # 将合并后的数据写入新的Excel文件\n",
    "    merged_data.to_excel(output_file, index=False)\n",
    "\n",
    "# 使用示例\n",
    "folder_path = r'C:\\Users\\admin\\Desktop\\江苏中标信息\\附表拆分' \n",
    "output_file = r'C:\\Users\\admin\\Desktop\\附件汇总-0902.xlsx'  # 输出文件名\n",
    "merge_excel_files(folder_path, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\runpy.py\", line 197, in _run_module_as_main\n",
      "    return _run_code(code, main_globals, None,\n",
      "  File \"c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\runpy.py\", line 87, in _run_code\n",
      "    exec(code, run_globals)\n",
      "  File \"c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pip\\__main__.py\", line 21, in <module>\n",
      "    from pip._internal.cli.main import main as _main\n",
      "  File \"c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pip\\_internal\\cli\\main.py\", line 8, in <module>\n",
      "    from pip._internal.cli.autocompletion import autocomplete\n",
      "  File \"c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pip\\_internal\\cli\\autocompletion.py\", line 9, in <module>\n",
      "    from pip._internal.cli.main_parser import create_main_parser\n",
      "  File \"c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pip\\_internal\\cli\\main_parser.py\", line 7, in <module>\n",
      "    from pip._internal.cli import cmdoptions\n",
      "  File \"c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pip\\_internal\\cli\\cmdoptions.py\", line 22, in <module>\n",
      "    from pip._internal.cli.progress_bars import BAR_TYPES\n",
      "  File \"c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pip\\_internal\\cli\\progress_bars.py\", line 9, in <module>\n",
      "    from pip._internal.utils.logging import get_indentation\n",
      "  File \"c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pip\\_internal\\utils\\logging.py\", line 14, in <module>\n",
      "    from pip._internal.utils.misc import ensure_dir\n",
      "  File \"c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pip\\_internal\\utils\\misc.py\", line 29, in <module>\n",
      "    from pip._internal.locations import get_major_minor_version, site_packages, user_site\n",
      "  File \"c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pip\\_internal\\locations\\__init__.py\", line 14, in <module>\n",
      "    from . import _sysconfig\n",
      "  File \"c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pip\\_internal\\locations\\_sysconfig.py\", line 7, in <module>\n",
      "    from pip._internal.exceptions import InvalidSchemeCombination, UserInstallationInvalid\n",
      "ImportError: cannot import name 'InvalidSchemeCombination' from 'pip._internal.exceptions' (c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\pip\\_internal\\exceptions.py)\n"
     ]
    }
   ],
   "source": [
    "pip install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "c:\\Users\\admin\\.conda\\envs\\F6processing\\lib\\site-packages\\numpy\\.libs\\libopenblas64__v0.3.21-gcc_10_3_0.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'version': '5.1.6-r1@1f8eeb2#8e663c5',\n",
       " 'code': 0,\n",
       " 'msg': 'success',\n",
       " 'data': {'id': 271710,\n",
       "  'datasetId': 1,\n",
       "  'fieldName': 'c1',\n",
       "  'appId': 1142,\n",
       "  'createdBy': 144,\n",
       "  'createdAt': '2024-09-05 09:40:44',\n",
       "  'updatedBy': 144,\n",
       "  'updatedAt': '2024-09-05 09:40:44',\n",
       "  'isMetric': False,\n",
       "  'originType': 'integer',\n",
       "  'label': '测试2',\n",
       "  'type': 'number',\n",
       "  'config': {'dialectName': 'MysqlDialect'},\n",
       "  'visible': True,\n",
       "  'nativeType': 'BIGINT',\n",
       "  'hideValue': False,\n",
       "  'formula': '-5327531844512980082',\n",
       "  'expr': {'kind': 'formula',\n",
       "   'op': '-5327531844512980082',\n",
       "   'type': 'number',\n",
       "   'value': '222222222222222222222222222222222222'},\n",
       "  'detectedType': 'integer',\n",
       "  'suggestedTypes': ['number', 'string', 'date'],\n",
       "  'defaultAggrType': 'sum',\n",
       "  'basicType': 'number'},\n",
       " 'chartDataStartTimeMillis': 0}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "from pprint import pprint\n",
    "cookies_str = 'gr_user_id=7c87d03e-506e-4da1-88a9-cdb29122c33f; sensorsdata2015jssdkcross=%7B%22distinct_id%22%3A%2210979894419862155492%22%2C%22first_id%22%3A%221750b696378f-0c7f49ca6d76a6-3d634f03-1327104-1750b6963799e7%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E5%BC%95%E8%8D%90%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC%22%2C%22%24latest_referrer%22%3A%22https%3A%2F%2Fyunxiu.f6car.cn%2F%22%7D%2C%22%24device_id%22%3A%221750b696378f-0c7f49ca6d76a6-3d634f03-1327104-1750b6963799e7%22%7D; sid=e1371e36-dfcd-4574-b565-f555490a417e; csrf=1c2cf922-8784-4d67-bfc6-bf9e4a2ca019; _USER_SESSION_ID=23cbc128-2d76-4acc-8d5d-02d956200f32; pac4jCsrfToken=c7e538c4ac3f442c87433d58f7ce9968'\n",
    "\n",
    "cookies_str = cookies_str.encode('utf-8').decode('latin-1')\n",
    "\n",
    "cookie_dict = {item.split('=')[0]: item.split('=')[1]\n",
    "               for item in cookies_str.split('; ')}\n",
    "\n",
    "headers = {\n",
    "  'accept':'application/json',\n",
    "  'accept-encoding':'gzip, deflate, br, zstd',\n",
    "  'accept-language':'zh-CN',\n",
    "  'content-length':'498',\n",
    "  'content-type':'application/json',\n",
    "  'cookie':'gr_user_id=7c87d03e-506e-4da1-88a9-cdb29122c33f; sensorsdata2015jssdkcross=%7B%22distinct_id%22%3A%2210979894419862155492%22%2C%22first_id%22%3A%221750b696378f-0c7f49ca6d76a6-3d634f03-1327104-1750b6963799e7%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E5%BC%95%E8%8D%90%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC%22%2C%22%24latest_referrer%22%3A%22https%3A%2F%2Fyunxiu.f6car.cn%2F%22%7D%2C%22%24device_id%22%3A%221750b696378f-0c7f49ca6d76a6-3d634f03-1327104-1750b6963799e7%22%7D; sid=e1371e36-dfcd-4574-b565-f555490a417e; csrf=1c2cf922-8784-4d67-bfc6-bf9e4a2ca019; _USER_SESSION_ID=23cbc128-2d76-4acc-8d5d-02d956200f32; pac4jCsrfToken=c7e538c4ac3f442c87433d58f7ce9968',\n",
    "  'origin':'https://insight.f6car.com',\n",
    "  'priority':'u=1, i',\n",
    "  'referer':'https://insight.f6car.com/datamart/1142/dataset/1/fields',\n",
    "  'sec-ch-ua':'\"Chromium\";v=\"128\", \"Not;A=Brand\";v=\"24\", \"Google Chrome\";v=\"128\"',\n",
    "  'sec-ch-ua-mobile':'?0',\n",
    "  'sec-ch-ua-platform':'\"Windows\"',\n",
    "  'sec-fetch-dest':'empty',\n",
    "  'sec-fetch-mode':'cors',\n",
    "  'sec-fetch-site':'same-origin',\n",
    "  'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36',\n",
    "  'x-csrf-token':'1c2cf922-8784-4d67-bfc6-bf9e4a2ca019',\n",
    "  'x-request-domain':'https://insight.f6car.com',\n",
    "  'x-request-hash-url':'https://insight.f6car.com/datamart/1142/dataset/1/fields',\n",
    "  'x-request-proxy':'http://localhost:3003'\n",
    "\n",
    "}\n",
    "\n",
    "json1={\n",
    "  \"options\": {\n",
    "    \"label\": \"测试2\",\n",
    "    \"visible\": 'true',\n",
    "    \"expr\": {\n",
    "      \"kind\": \"formula\",\n",
    "      \"op\": \"222222222222222222222222222222222222\",\n",
    "      \"value\": \"222222222222222222222222222222222222\"\n",
    "    }\n",
    "  }\n",
    "}\n",
    "# request_id = generate_request_id()\n",
    "url = 'https://insight.f6car.com/api/apps/1142/datasets/1/fields?01J6ZWYWDSXH08K0Q7C85JPA9K'\n",
    "res = requests.post(url,cookies=cookie_dict,headers=headers,json=json1)\n",
    "strele = res.json()\n",
    "strele"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "F6processing",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}