688 lines
19 KiB
Plaintext
688 lines
19 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "19280f5c-8e34-4a64-8dfb-6353cbf597d2",
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"metadata": {},
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"source": [
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"# 体质检测数据处理"
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]
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},
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{
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"cell_type": "markdown",
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"id": "112d80f4-607b-41f9-bb30-ac2eb8f52ee2",
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"metadata": {},
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"source": [
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"## 基础数据管理"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b7dfd50f-a614-497d-8f0a-fa1e4c89168a",
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"metadata": {},
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"source": [
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"### 体测项目标准导入"
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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": "8cd73044-56a8-409b-a873-085feb63af17",
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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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"\n",
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"tcbz ={\n",
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" 'HeightWeigh':'身高体重',\n",
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" 'StepExperiment':'台阶指数',\n",
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" 'Lung':'肺活量',\n",
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" 'Proneness':'坐位体前屈',\n",
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" 'PowerfullGrip':'握力',\n",
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" 'OneMinutePushUp':'一分钟仰卧起坐',\n",
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" 'PushUp':'俯卧撑',\n",
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" 'VerticalJump':'纵跳',\n",
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" 'ReactionTime':'选择反应时',\n",
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" 'FootStand':'单脚站立'\n",
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"}\n",
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"wb = openpyxl.load_workbook('data/体质检测标准.xlsx')\n",
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"sheet = wb.active\n",
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"# sheets = wb.sheetnames\n",
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"dict1 = {}\n",
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"dict3 = {}\n",
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"for k, v in tcbz.items():\n",
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" dict3[v] = k\n",
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"sheet = wb.active\n",
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"data1 =list(sheet.values)\n",
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"del data1[0]\n",
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"for item in data1:\n",
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" sex = item[0][6:]\n",
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" if sex =='男':\n",
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" age_sex = 'M'+item[0][:5]\n",
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" else:\n",
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" age_sex = 'F'+item[0][:5]\n",
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" dict1.setdefault(age_sex,{})\n",
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" if item[1] in dict3.keys():\n",
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" xm = dict3[item[1]]\n",
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" dict1[age_sex].setdefault(xm,[])\n",
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" dict1[age_sex][xm].append(item[3]/item[4])\n",
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"dict2 = {}\n",
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"#print(dict1)\n",
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"for k, v in dict1.items():\n",
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" i1 = int(k[1:3])\n",
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" i2 = int(k[-2:])\n",
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" m_sex = k[:1]\n",
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" for i in range(i1,i2+1):\n",
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" dict2[m_sex+str(i)] = k\n",
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"dict3 = {}\n",
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"dict3['person'] = dict2\n",
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"dict3['criteria'] = dict1\n",
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"filename = 'data/体质检测标准.json'\n",
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"with open(filename, 'w') as fl:\n",
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" json.dump(dict3, fl, ensure_ascii=False)\n",
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"print('ok')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c36641d8-c54b-43d2-ad9d-ff4cb72deba9",
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"metadata": {},
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"source": [
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"### 身高标准导入"
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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": "2ca52d7b-d77e-452f-98f6-95072a5a1934",
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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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"\n",
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"tcbz ={\n",
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" 'HeightWeigh':'身高体重',\n",
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" 'StepExperiment':'台阶指数',\n",
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" 'Lung':'肺活量',\n",
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" 'Proneness':'坐位体前屈',\n",
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" 'PowerfullGrip':'握力',\n",
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" 'OneMinutePushUp':'一分钟仰卧起坐',\n",
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" 'PushUp':'俯卧撑',\n",
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" 'VerticalJump':'纵跳',\n",
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" 'ReactionTime':'选择反应时',\n",
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" 'FootStand':'单脚站立'\n",
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"}\n",
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"wb = openpyxl.load_workbook('data/体质检测标准_BMI.xlsx')\n",
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"sheet = wb.active\n",
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"# sheets = wb.sheetnames\n",
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"dict1 = {}\n",
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"dict3 = {}\n",
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"for k, v in tcbz.items():\n",
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" dict3[v] = k\n",
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"sheet = wb.active\n",
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"data1 =list(sheet.values)\n",
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"del data1[0]\n",
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"for item in data1:\n",
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" min_age = int(item[1]/12)\n",
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" max_age = int((item[2]+1)/12) - 1\n",
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" sex = item[0]\n",
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" height = int(item[3])\n",
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" if sex ==0:\n",
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" age_sex = 'M'+str(min_age)+'~'+str(max_age)\n",
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" else:\n",
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" age_sex = 'F'+str(min_age)+'~'+str(max_age)\n",
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" dict1.setdefault(age_sex,{})\n",
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" \n",
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" dict1[age_sex].setdefault(height,[])\n",
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" dict1[age_sex][height].append(item[5]/1000)\n",
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"dict2 = {}\n",
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"#print(dict1)\n",
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"for k, v in dict1.items():\n",
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" i1 = int(k[1:3])\n",
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" i2 = int(k[-2:])\n",
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" m_sex = k[:1]\n",
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" for i in range(i1,i2+1):\n",
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" dict2[m_sex+str(i)] = k\n",
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"dict3 = {}\n",
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"dict3['person'] = dict2\n",
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"dict3['criteria'] = dict1\n",
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"filename = 'data/体质检测标准_BMI.json'\n",
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"with open(filename, 'w') as fl:\n",
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" json.dump(dict3, fl, ensure_ascii=False)\n",
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"print('ok')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "59f5708d-c2d0-4470-b2f4-b9face431865",
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"metadata": {},
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"source": [
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"### 计算分数"
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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": "f32b266a-7e46-4703-ab61-7cbbc81b5ab1",
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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 time\n",
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"import csv\n",
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"\n",
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"filename = 'data/体质检测标准.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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"person = dict1['person']\n",
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"criteria = dict1['criteria']\n",
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"data = {'name':'张三','sex':'M','age':59,'item':'PowerfullGrip','result':51.0}\n",
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"info = data['sex']+str(data['age'])\n",
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"bz = person[info]\n",
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"mx = criteria[bz][data['item']]\n",
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"result = data['result']\n",
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"score = -1\n",
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"print(mx)\n",
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"for bz1 in mx:\n",
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" if result < bz1:\n",
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" print(bz1)\n",
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" score = mx.index(bz1,0)\n",
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" break\n",
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" else:\n",
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" score = 5\n",
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"#if score == -1:\n",
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" # score = 5\n",
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"print(score)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "755f8b66-c71c-4138-bd12-dff24e85e36a",
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"metadata": {},
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"source": [
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"### 计算BMI分数"
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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": 53,
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"id": "b32fbbdd-eb0a-4f0d-8901-e39a727c3ff8",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-04-30T01:48:23.869481Z",
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"iopub.status.busy": "2023-04-30T01:48:23.868619Z",
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"iopub.status.idle": "2023-04-30T01:48:23.882339Z",
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"shell.execute_reply": "2023-04-30T01:48:23.881272Z",
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"shell.execute_reply.started": "2023-04-30T01:48:23.869438Z"
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},
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"tags": []
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},
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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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"M50~59\n",
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"177.0,81.1\n",
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"5\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 = 'data/体质检测标准_BMI.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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"person = dict1['person']\n",
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"criteria = dict1['criteria']\n",
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"data = {'name':'张三','sex':'M','age':59,'item':'HeightWeight','result':'177.0,81.1'}\n",
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"info = data['sex']+str(data['age'])\n",
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"\n",
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"bz = person[info]\n",
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"print(bz)\n",
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"result = data['result']\n",
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"print(result)\n",
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"height = int(float(result.split(',')[0]))\n",
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"weight = float(result.split(',')[1])\n",
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"mx = criteria[bz][str(height)]\n",
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"if weight < mx[0]:\n",
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" score = 1\n",
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"elif weight < mx[1]:\n",
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" score = 3\n",
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"elif weight < mx[2]:\n",
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" score = 5 \n",
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"elif weight <= mx[3]:\n",
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" score = 3 \n",
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"elif weight > mx[3]:\n",
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" score = 1\n",
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"print(score)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "732161bb-98f2-4861-ab66-7a374678bcd8",
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"metadata": {},
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"source": [
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"# 体质检测数据管理"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bb2db5e8-5dc6-4366-a39c-a3600ee5007c",
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"metadata": {},
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"source": [
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"## 数据导入Mycrm"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bc98d772-5841-41a3-ad8f-f7d1ff3b5fde",
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"metadata": {},
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"source": [
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"### 导入系统人员信息"
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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": "2cd93f45-6613-4a3f-97e4-5236068e1860",
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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 time\n",
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"import csv\n",
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"\n",
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"list1 = []\n",
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"filename = 'data/person_134.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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"dict1 = {}\n",
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"for result in list1:\n",
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" code = str(result[4])\n",
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" if int(result[1]) == 0:\n",
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" sex = '男'\n",
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" else:\n",
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" sex = '女'\n",
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" \n",
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" m_item = str(result[3]) \n",
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" dict1.setdefault(code,{}) \n",
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" dict1[code]['name'] = result[0]\n",
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" dict1[code]['sex'] = sex\n",
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" dict1[code]['birth'] = result[2]\n",
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"filename = 'data/天津石化人员清单.json'\n",
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"with open(filename,'w') as fl:\n",
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" json.dump(dict1, fl, ensure_ascii=False) \n",
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"print(len(dict1)) "
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]
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},
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{
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"cell_type": "markdown",
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"id": "2eb50f19-c15f-4a36-87b7-cd02551d248d",
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"metadata": {},
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"source": [
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"### 导入测试项目item"
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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": "0eb37435-9c15-40ef-bd4c-2dffc026f59e",
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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 psycopg2\n",
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"from psycopg2 import extras as ex\n",
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"\n",
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"filename = '../item.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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"data_list = []\n",
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"for k, v in dict1.items():\n",
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" data_list.append((int(k),v['name'])) \n",
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"conn = psycopg2.connect(database=\"mycrm\", user=\"postgres\", password=\"songyi\", host=\"localhost\", port=\"5432\")\n",
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"cursor = conn.cursor()\n",
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"sql = 'insert into tianjin_item (id,name) values %s'\n",
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"ex.execute_values(cursor, sql, data_list, page_size=10000)\n",
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"conn.commit()\n",
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"cursor.close()\n",
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"conn.close()\n",
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"print('ok!')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2ba97298-1e7e-4192-8d69-ea59f98e071a",
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"metadata": {},
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"source": [
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"### 导入一级部门"
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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": "9f7ed380-95f3-47d4-9eb5-74937af7811f",
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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 psycopg2\n",
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"from psycopg2 import extras as ex\n",
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"\n",
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"conn = psycopg2.connect(database=\"mycrm\", user=\"postgres\", password=\"songyi\", host=\"localhost\", port=\"5432\")\n",
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"cursor = conn.cursor()\n",
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"filename = 'data/天津石化人员名单.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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"unit = set()\n",
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"for k, v in dict1.items():\n",
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" #if 'sub_unit' not in v.keys():\n",
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" # print(k,v['name'])\n",
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" unit.add(v['unit'])\n",
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"print(unit) \n",
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"i = 1\n",
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"dict2 = {}\n",
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"for item in unit:\n",
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" dict2[item] = i\n",
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" i+=1\n",
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"data_list = []\n",
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"for k, v in dict2.items():\n",
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" data_list.append((v,k)) \n",
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" \n",
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"sql = 'insert into tianjin_unit (id,name) values %s'\n",
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"ex.execute_values(cursor, sql, data_list, page_size=10000)\n",
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"conn.commit()\n",
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"cursor.close()\n",
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"conn.close()\n",
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"print('ok!')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b8a1fa7f-08ff-49bb-8052-9f86b1f2ac18",
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"metadata": {},
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"source": [
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"### 导入二级部门"
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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": "a13e81f2-467f-42c7-bb46-b547ef7bdd6c",
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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 psycopg2\n",
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"from psycopg2 import extras as ex\n",
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"\n",
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"conn = psycopg2.connect(database=\"mycrm\", user=\"postgres\", password=\"songyi\", host=\"localhost\", port=\"5432\")\n",
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"cursor = conn.cursor()\n",
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"filename = 'data/天津石化人员名单.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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"unit = set()\n",
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"for k, v in dict1.items():\n",
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" #if 'sub_unit' not in v.keys():\n",
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" # print(k,v['name'])\n",
|
|
" unit.add(v['unit'])\n",
|
|
"print(unit) \n",
|
|
"i = 1\n",
|
|
"dict2 = {}\n",
|
|
"for item in unit:\n",
|
|
" dict2[item] = i\n",
|
|
" i+=1\n",
|
|
"data_list = []\n",
|
|
"dict3 = {}\n",
|
|
"for k, v in dict1.items():\n",
|
|
" dict3.setdefault(v['unit'],set())\n",
|
|
" dict3[v['unit']].add(v['sub_unit'])\n",
|
|
"i = 1 \n",
|
|
"for k, v in dict3.items():\n",
|
|
" unit_id = dict2[k]\n",
|
|
" for item in v:\n",
|
|
" data_list.append((i,item,unit_id))\n",
|
|
" i+=1\n",
|
|
"sql = 'insert into tianjin_sub_unit (id,name,unit_id) values %s'\n",
|
|
"ex.execute_values(cursor, sql, data_list, page_size=10000)\n",
|
|
"conn.commit()\n",
|
|
"cursor.close()\n",
|
|
"conn.close()\n",
|
|
"print('ok!')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "53bf98fe-0ae9-402f-b8d8-495e8f31df3d",
|
|
"metadata": {},
|
|
"source": [
|
|
"### 导入人员信息"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "6af82fd2-b1d6-44b7-8b13-58883b58f8b0",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import json\n",
|
|
"import psycopg2\n",
|
|
"from psycopg2 import extras as ex\n",
|
|
"\n",
|
|
"conn = psycopg2.connect(database=\"mycrm\", user=\"postgres\", password=\"songyi\", host=\"localhost\", port=\"5432\")\n",
|
|
"cursor = conn.cursor()\n",
|
|
"filename = 'data/天津石化人员清单.json'\n",
|
|
"with open(filename,'r') as fl:\n",
|
|
" person = json.load(fl)\n",
|
|
" \n",
|
|
"filename = 'data/天津石化人员名单.json'\n",
|
|
"with open(filename,'r') as fl:\n",
|
|
" dict1 = json.load(fl)\n",
|
|
"unit = set()\n",
|
|
"for k, v in dict1.items():\n",
|
|
" #if 'sub_unit' not in v.keys():\n",
|
|
" # print(k,v['name'])\n",
|
|
" unit.add(v['unit'])\n",
|
|
"print(unit) \n",
|
|
"i = 1\n",
|
|
"dict2 = {}\n",
|
|
"for item in unit:\n",
|
|
" dict2[item] = i\n",
|
|
" i+=1\n",
|
|
"data_list = []\n",
|
|
"dict3 = {}\n",
|
|
"dict4 = {}\n",
|
|
"for k, v in dict1.items():\n",
|
|
" dict3.setdefault(v['unit'],set())\n",
|
|
" dict3[v['unit']].add(v['sub_unit']) \n",
|
|
"i = 1 \n",
|
|
"for k, v in dict3.items():\n",
|
|
" unit_id = dict2[k]\n",
|
|
" for item in v:\n",
|
|
" data_list.append((i,item,unit_id))\n",
|
|
" i+=1\n",
|
|
"for item in data_list:\n",
|
|
" sub_id = item[0]\n",
|
|
" dict4[sub_id] = {'name':item[1],'unit':item[2]}\n",
|
|
"dict5 = {}\n",
|
|
"for k, v in dict1.items():\n",
|
|
" id_unit = dict2[v['unit']]\n",
|
|
" sub_name = v['sub_unit']\n",
|
|
" id_sub = 0\n",
|
|
" for k1, v1 in dict4.items():\n",
|
|
" if v1['name'] == sub_name and v1['unit'] == id_unit:\n",
|
|
" id_sub = int(k1)\n",
|
|
" if id_sub >0:\n",
|
|
" dict5[k] = {'unit':id_unit,'sub_unit':id_sub}\n",
|
|
" else:\n",
|
|
" print(k,'not fund')\n",
|
|
"data_list = []\n",
|
|
"for k, v in dict5.items():\n",
|
|
" id = int(k)\n",
|
|
" name = person[k]['name']\n",
|
|
" sex = person[k]['sex']\n",
|
|
" birth = person[k]['birth']\n",
|
|
" phone = ''\n",
|
|
" note = ''\n",
|
|
" unit = v['sub_unit']\n",
|
|
" data_list.append((id,name,sex,birth,unit))\n",
|
|
"sql = 'insert into tianjin_person (id,name,sex,birth,unit_id) values %s'\n",
|
|
"ex.execute_values(cursor, sql, data_list, page_size=10000)\n",
|
|
"conn.commit()\n",
|
|
"cursor.close()\n",
|
|
"conn.close()\n",
|
|
"print('ok!') \n",
|
|
" "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "fd95a45a-c6df-47c3-9941-2d0579b2c5d9",
|
|
"metadata": {},
|
|
"source": [
|
|
"### 导入测试成绩及得分"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 50,
|
|
"id": "360e61e9-7895-484b-a9d6-333bdefa1a86",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-04-29T14:29:07.198753Z",
|
|
"iopub.status.busy": "2023-04-29T14:29:07.197888Z",
|
|
"iopub.status.idle": "2023-04-29T14:29:08.464741Z",
|
|
"shell.execute_reply": "2023-04-29T14:29:08.463607Z",
|
|
"shell.execute_reply.started": "2023-04-29T14:29:07.198711Z"
|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"ok!\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import json\n",
|
|
"import psycopg2\n",
|
|
"from psycopg2 import extras as ex\n",
|
|
"\n",
|
|
"filename = '../item.json'\n",
|
|
"with open(filename,'r') as fl:\n",
|
|
" dict1 = json.load(fl) \n",
|
|
"dict2 = {}\n",
|
|
"for k, v in dict1.items():\n",
|
|
" dict2[v['name']] = int(k)\n",
|
|
"conn = psycopg2.connect(database=\"mycrm\", user=\"postgres\", password=\"songyi\", host=\"localhost\", port=\"5432\")\n",
|
|
"cursor = conn.cursor()\n",
|
|
"data_list = []\n",
|
|
"filename = 'data/result_天津.json'\n",
|
|
"with open(filename,'r') as fl:\n",
|
|
" dict1 = json.load(fl) \n",
|
|
"for k, v in dict1.items():\n",
|
|
" for item in v.keys():\n",
|
|
" if item in dict2.keys():\n",
|
|
" item_id = dict2[item]\n",
|
|
" if v[item]['得分'] == '\\\\N':\n",
|
|
" score = None\n",
|
|
" else:\n",
|
|
" score = int(v[item]['得分'])\n",
|
|
" \n",
|
|
" data_list.append((v[item]['成绩'],score,int(k),item_id))\n",
|
|
" \n",
|
|
"sql = 'insert into tianjin_records (performance,score,avatar_id_id,item_id_id) values %s'\n",
|
|
"ex.execute_values(cursor, sql, data_list, page_size=10000)\n",
|
|
"conn.commit()\n",
|
|
"cursor.close()\n",
|
|
"conn.close()\n",
|
|
"print('ok!') \n",
|
|
" "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "cb747368-d3ac-4b29-ae4f-ce207d5fcd43",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import json\n",
|
|
"\n",
|
|
"filename = 'data/天津石化人员名单.json'\n",
|
|
"with open(filename,'r') as fl:\n",
|
|
" dict1 = json.load(fl)\n",
|
|
"for k, v in dict1.items():\n",
|
|
" if 'num_id' not in v.keys():\n",
|
|
" print(k,v['name'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "51dc35a5-dc07-40a4-9ea7-375ca7f983c3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.10.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|