20231018
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+135
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@@ -10,26 +10,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"id": "bbba6efc-73cd-4db6-bae7-014724fee731",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-14T10:59:21.792059Z",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ok\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import openpyxl\n",
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"import json\n",
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@@ -65,27 +51,12 @@
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},
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{
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"cell_type": "code",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ok\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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@@ -110,23 +81,23 @@
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},
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{
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"cell_type": "markdown",
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"id": "699c6a40-a6ae-4300-9646-708cb85aa5e8",
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"id": "6bcd45c2-10af-4d5f-9e0b-5cd1df4f7a7f",
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"metadata": {},
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"source": [
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"## 手工数据导入数据库"
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"## 获取人员测试成绩"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "def3f47a-24ef-4815-b63b-2a16b79b4c15",
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"execution_count": 7,
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"id": "970e171e-1360-448f-a28c-520ccb8f314a",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-15T06:15:31.143651Z",
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"iopub.execute_input": "2023-10-18T02:17:23.733390Z",
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"tags": []
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},
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@@ -135,10 +106,130 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[(496534, 6, 5, 32, '2023-10-15', '2023-10-15 14:00:00'), (496534, 39, 5, 12, '2023-10-15', '2023-10-15 14:00:00'), (496534, 105, 5, 22, '2023-10-15', '2023-10-15 14:00:00'), (496534, 136, 5, 20, '2023-10-15', '2023-10-15 14:00:00'), (496534, 110, 5, 53, '2023-10-15', '2023-10-15 14:00:00'), (496534, 144, 5, 11, '2023-10-15', '2023-10-15 14:00:00'), (496534, 87, 5, 45, '2023-10-15', '2023-10-15 14:00:00'), (496534, 71, 5, 22, '2023-10-15', '2023-10-15 14:00:00'), (496534, 153, 5, 16, '2023-10-15', '2023-10-15 14:00:00'), (496534, 158, 5, 23, '2023-10-15', '2023-10-15 14:00:00'), (496534, 150, 5, 28, '2023-10-15', '2023-10-15 14:00:00'), (496534, 140, 5, 16, '2023-10-15', '2023-10-15 14:00:00'), (496534, 138, 5, 35, '2023-10-15', '2023-10-15 14:00:00'), (496534, 137, 5, 30, '2023-10-15', '2023-10-15 14:00:00'), (496534, 377, 5, 59, '2023-10-15', '2023-10-15 14:00:00'), (496534, 131, 5, 55, '2023-10-15', '2023-10-15 14:00:00'), (496534, 76, 5, 52, '2023-10-15', '2023-10-15 14:00:00'), (496534, 14, 5, 25, '2023-10-15', '2023-10-15 14:00:00'), (496534, 88, 5, 29, '2023-10-15', '2023-10-15 14:00:00'), (496534, 115, 5, 30, '2023-10-15', '2023-10-15 14:00:00'), (496534, 78, 5, 57, '2023-10-15', '2023-10-15 14:00:00'), (496534, 103, 5, 67, '2023-10-15', '2023-10-15 14:00:00'), (496534, 148, 5, 10, '2023-10-15', '2023-10-15 14:00:00'), (496534, 54, 5, 57, '2023-10-15', '2023-10-15 14:00:00'), (496534, 35, 5, 21, '2023-10-15', '2023-10-15 14:00:00'), (496534, 42, 5, 39, '2023-10-15', '2023-10-15 14:00:00'), (496534, 45, 5, 50, '2023-10-15', '2023-10-15 14:00:00'), (496534, 95, 5, 36, '2023-10-15', '2023-10-15 14:00:00'), (496534, 122, 5, 51, '2023-10-15', '2023-10-15 14:00:00'), (496534, 74, 5, 27, '2023-10-15', '2023-10-15 14:00:00'), (496534, 63, 5, 62, '2023-10-15', '2023-10-15 14:00:00'), (496534, 100, 5, 20, '2023-10-15', '2023-10-15 14:00:00'), (496534, 162, 5, 30, '2023-10-15', '2023-10-15 14:00:00'), (496534, 43, 5, 50, '2023-10-15', '2023-10-15 14:00:00'), (496534, 149, 5, 49, '2023-10-15', '2023-10-15 14:00:00'), (496534, 127, 5, 16, '2023-10-15', '2023-10-15 14:00:00'), (496534, 62, 5, 21, '2023-10-15', '2023-10-15 14:00:00'), (496534, 40, 5, 10, '2023-10-15', '2023-10-15 14:00:00'), (496534, 53, 5, 24, '2023-10-15', '2023-10-15 14:00:00')]\n"
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"141\n",
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"141\n"
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]
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}
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],
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"source": [
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"import json\n",
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"import time\n",
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"import csv\n",
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"\n",
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"filename = '../item.json'\n",
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"item = {}\n",
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"unit = {}\n",
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"with open(filename,'r') as fl:\n",
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" dict1 = json.load(fl) \n",
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"for k,v in dict1.items():\n",
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" item[k] = v\n",
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"re_ta = {}\n",
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"dict1 = {}\n",
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"list1 = []\n",
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"filename = 'data/北京党校.json'\n",
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"with open(filename,'r') as fl:\n",
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" dict1 = json.load(fl) \n",
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"\n",
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"filename = 'data/places_result_20231018.csv'\n",
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"with open(filename,'r',newline='') as csv_file:\n",
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" fl = csv.reader(csv_file,delimiter=',')\n",
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" header = next(fl) \n",
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" for line in fl:\n",
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" #line = re.sub('[\\r\\n\\f ]{1,}', '', line)\n",
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" list1.append(line)\n",
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"#print(list1)\n",
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"for result in list1:\n",
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" user = str(result[2])\n",
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" if user in dict1.keys(): \n",
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" m_item = str(result[3]) \n",
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" re_ta.setdefault(user,{}) \n",
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" re_ta[user]['name'] = dict1[user]['name']\n",
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" re_ta[user]['sex'] = dict1[user]['sex'] \n",
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" re_ta[user]['unit'] = dict1[user]['unit']\n",
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" item_name = item[m_item]['name']\n",
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" re_ta[user].setdefault(item_name,{}) \n",
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" score = int(result[4])/item[m_item]['divisor'] \n",
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" re_ta[user][item_name]['成绩'] = f'{score} {item[m_item][\"unit\"]}'\n",
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"print(len(re_ta))\n",
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"filename = 'data/result_北京党校.json'\n",
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"\n",
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"with open(filename,'w') as fl:\n",
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" json.dump(re_ta, fl, ensure_ascii=False) \n",
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"print(len(re_ta))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d034a61d-1fbd-417b-99bd-277e43ebb678",
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"metadata": {},
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"source": [
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"## 导出测试人员信息"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "867ad6b0-9e9d-48bc-a10a-3b9cb6125890",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import json\n",
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"import openpyxl\n",
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"\n",
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"items = ['身高','体重','肺活量','握力','坐位体前屈','纵跳','俯卧撑','一分钟仰卧起坐','单脚站立','选择反应时','台阶指数']\n",
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"title = ['编号','姓名','性别','单位','部门','身高','体重','肺活量','握力','坐位体前屈','纵跳','俯卧撑','一分钟仰卧起坐','单脚站立','选择反应时','台阶指数']\n",
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"\n",
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"filename = 'data/result_北京党校.json'\n",
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"with open(filename,'r') as fl:\n",
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" dict1 = json.load(fl)\n",
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"\n",
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"filename = 'data/北京党校.json'\n",
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"with open(filename,'r') as fl:\n",
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" dict2 = json.load(fl)\n",
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" \n",
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"list1 = []\n",
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"for k, v in dict1.items():\n",
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" list2 = []\n",
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" list2.append(str(k).rjust(5,'0'))\n",
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" list2.append(v['name']) \n",
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" list2.append(dict2[k]['sex'])\n",
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" list2.append(dict2[k]['unit'])\n",
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" for item in items:\n",
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" if item in v.keys():\n",
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" list2.append(v[item]['成绩']) \n",
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" elif item =='name':\n",
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" list2.append(v[item])\n",
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" else:\n",
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" list2.append('') \n",
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" list1.append(list2)\n",
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"filename = 'data/北京党校.xlsx'\n",
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"wb = openpyxl.Workbook()\n",
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"sheet = wb.active\n",
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"sheet.append(title)\n",
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"for row in list1:\n",
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" sheet.append(row)\n",
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" \n",
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"wb.save(filename)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "699c6a40-a6ae-4300-9646-708cb85aa5e8",
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"metadata": {},
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"source": [
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"## 手工数据导入数据库"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "def3f47a-24ef-4815-b63b-2a16b79b4c15",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import openpyxl\n",
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"import json\n",
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