{ "cells": [ { "cell_type": "markdown", "id": "4626da6c-d228-4270-8d8d-c98784a106ad", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [], "toc-hr-collapsed": true }, "source": [ "# 第一次体测" ] }, { "cell_type": "markdown", "id": "ba2deee7-9c63-4115-bcee-ad341175fc02", "metadata": {}, "source": [ "## 人员信息导入" ] }, { "cell_type": "code", "execution_count": null, "id": "dea5e486-6927-469f-aa57-e5efac4670be", "metadata": { "tags": [] }, "outputs": [], "source": [ "import openpyxl\n", "import json\n", "\n", "\n", "wb = openpyxl.load_workbook('data/农业银行测试名单.xlsx')\n", "sheet = wb.active\n", "# sheets = wb.sheetnames\n", "person = {}\n", "\n", "for n in range(2, sheet.max_row+1):\n", " if sheet.cell(n,1).value is not None:\n", " code = int(sheet.cell(n, 2).value)\n", " person.setdefault(code, {})\n", " dict1 = {}\n", " dict1['name'] = sheet.cell(n, 3).value\n", " if sheet.cell(n, 4).value ==1:\n", " sex = '男'\n", " else:\n", " sex = '女'\n", " dict1['sex'] = sex\n", " birth = str(sheet.cell(n, 5).value).split()[0]\n", " dict1['birth'] = birth\n", " dict1['unit'] = sheet.cell(n, 1).value\n", " if sheet.cell(n,10).value is not None:\n", " dict1['phone'] = sheet.cell(n, 10).value \n", " person[code] = dict1\n", "\n", "filename = 'data/北京农行人员名单.json'\n", "with open(filename, 'w') as fl:\n", " json.dump(person, fl, ensure_ascii=False)\n", "print('ok')" ] }, { "cell_type": "markdown", "id": "1c00e1e4-101a-4e9f-a7ed-9c65200d19df", "metadata": {}, "source": [ "## 成绩导入" ] }, { "cell_type": "code", "execution_count": null, "id": "fd73b8be-d3fe-49aa-99d2-3cc6df4a3eb0", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "import time\n", "import csv\n", "\n", "filename = '../item.json'\n", "item = {}\n", "unit = {}\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for k,v in dict1.items():\n", " item[k] = v\n", "re_ta = {}\n", "dict1 = {}\n", "list1 = []\n", "filename = 'data/北京农行人员名单all.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "\n", "filename = 'data/places_result_20230918.csv'\n", "with open(filename,'r',newline='') as csv_file:\n", " fl = csv.reader(csv_file,delimiter=',')\n", " header = next(fl) \n", " for line in fl:\n", " #line = re.sub('[\\r\\n\\f ]{1,}', '', line)\n", " list1.append(line)\n", "#print(list1)\n", "for result in list1:\n", " user = str(result[2])\n", " if user in dict1.keys(): \n", " m_item = str(result[3]) \n", " re_ta.setdefault(user,{}) \n", " re_ta[user]['name'] = dict1[user]['name']\n", " re_ta[user]['sex'] = dict1[user]['sex'] \n", " re_ta[user]['unit'] = dict1[user]['unit']\n", " item_name = item[m_item]['name']\n", " re_ta[user].setdefault(item_name,{}) \n", " score = int(result[4])/item[m_item]['divisor'] \n", " re_ta[user][item_name]['成绩'] = f'{score} {item[m_item][\"unit\"]}'\n", " re_ta[user][item_name]['得分'] = result[5]\n", "print(len(re_ta))\n", "filename = 'data/result_北京农行.json'\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(re_ta, fl, ensure_ascii=False) \n", "print(len(re_ta))" ] }, { "cell_type": "markdown", "id": "928790e6-d8a4-4b2b-852a-3c323380a80f", "metadata": {}, "source": [ "## 成绩导出" ] }, { "cell_type": "code", "execution_count": null, "id": "b870bd6e-f972-4073-b13d-5fda8cc92bb7", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "import openpyxl\n", "\n", "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", "title = ['编号','姓名','性别','单位','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", "\n", "filename = 'data/result_北京农行.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "\n", "filename = 'data/北京农行人员名单all.json'\n", "with open(filename,'r') as fl:\n", " dict2 = json.load(fl)\n", " \n", "list1 = []\n", "for k, v in dict1.items():\n", " list2 = []\n", " list2.append(str(k).rjust(5,'0'))\n", " list2.append(v['name']) \n", " list2.append(dict2[k]['sex'])\n", " list2.append(dict2[k]['unit'])\n", " for item in items:\n", " if item in v.keys():\n", " list2.append(v[item]['成绩']) \n", " list2.append(v[item]['得分']) \n", " elif item =='name':\n", " list2.append(v[item])\n", " else:\n", " list2.append('') \n", " list2.append('') \n", " list1.append(list2)\n", "filename = 'data/北京农行体测情况.xlsx'\n", "wb = openpyxl.Workbook()\n", "sheet = wb.active\n", "sheet.append(title)\n", "for row in list1:\n", " sheet.append(row)\n", " \n", "wb.save(filename)" ] }, { "cell_type": "markdown", "id": "dd2034ca-fe8b-4322-b06d-8c5eaf96373d", "metadata": {}, "source": [ "## 报告按部门分类更名" ] }, { "cell_type": "code", "execution_count": null, "id": "04967ec6-b9a0-4097-9e1f-8beaf8a9e75f", "metadata": { "tags": [] }, "outputs": [], "source": [ "import os,sys,shutil\n", "import json\n", "import math\n", "import glob\n", "from pathlib import Path\n", "\n", "fi_path = 'file/2023-05-23'\n", "old = []\n", "dict2 = {}\n", "\n", "filename = 'data/result_北京农行.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "\n", "for k, v in dict1.items():\n", " m_name = v['name']\n", " m_depart = v['unit'] \n", " dict2[int(k)] = [m_name,m_depart]\n", "\n", "fls = glob.glob(f'{fi_path}/*.pdf')\n", "\n", "for fn in fls:\n", " old.append(os.path.basename(fn).split('.')[0])\n", "\n", "for n in old: \n", " o_name = f'{fi_path}/{n}.pdf'\n", " new_path = Path(fi_path,'new',dict1[n]['unit'])\n", " new_path.mkdir(parents = True, exist_ok = True)\n", " n_name = Path(new_path,f'{str(n).rjust(5,\"0\")}-{dict2[int(n)][0]}.pdf')\n", " if not os.path.exists(n_name):\n", " shutil.copyfile(o_name,n_name)\n", " print(n_name)\n", " \n", "print('ok')" ] }, { "cell_type": "markdown", "id": "515bc854-5745-4c38-8633-f56a078ea9a8", "metadata": { "execution": { "iopub.execute_input": "2023-05-25T09:28:28.615647Z", "iopub.status.busy": "2023-05-25T09:28:28.614774Z", "iopub.status.idle": "2023-05-25T09:28:28.619865Z", "shell.execute_reply": "2023-05-25T09:28:28.618821Z", "shell.execute_reply.started": "2023-05-25T09:28:28.615607Z" } }, "source": [ "## 生成人员电话信息表" ] }, { "cell_type": "code", "execution_count": null, "id": "c925d850-ff06-4be5-bf9f-31f4385c37c0", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "import openpyxl\n", "wb = openpyxl.load_workbook('data/农业银行测试手机号码统计表.xlsx')\n", "sheet = wb.active\n", "# sheets = wb.sheetnames\n", "dict1 = {}\n", "\n", "for n in range(2, sheet.max_row+1):\n", " if sheet.cell(n,5).value is not None:\n", " phone = str(sheet.cell(n, 5).value)\n", " dict1.setdefault(phone,{})\n", " code = str(sheet.cell(n, 2).value)\n", " unit = sheet.cell(n, 1).value\n", " name = sheet.cell(n, 3).value\n", " sex = sheet.cell(n, 4).value\n", " dict1[phone]['code'] = code\n", " dict1[phone]['name'] = name\n", " #dict1[phone]['unit'] = unit\n", " #dict1[phone]['sex'] = sex\n", "filename = 'data/农业银行测试手机号码.json'\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(dict1, fl, ensure_ascii=False) \n", "print(len(dict1))\n" ] }, { "cell_type": "markdown", "id": "0f13e837-15d9-4dc6-b356-78b79bbb5b17", "metadata": {}, "source": [ "## 生成验证码" ] }, { "cell_type": "code", "execution_count": null, "id": "a87384c6-dc46-4d09-b757-b42462ed818d", "metadata": { "tags": [] }, "outputs": [], "source": [ "import random\n", "import json\n", "\n", "code_list = []\n", "for i in range(10): # 0~9\n", " code_list.append(str(i))\n", " \n", "list1 = []\n", "#print(code_num)\n", "i = 1\n", "while i < 165:\n", " code = random.sample(code_list,6) #随机取6位数\n", " code_num = ''.join(code)\n", " if code_num not in list1:\n", " list1.append(code_num)\n", " i +=1\n", "print(len(list1))\n", "print(list1)\n", "i = 0\n", "filename = 'data/农业银行测试手机号码.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "for k, v in dict1.items():\n", " dict1[k]['yzm'] = list1[i]\n", " i +=1\n", "with open(filename,'w') as fl:\n", " json.dump(dict1, fl, ensure_ascii=False) " ] }, { "cell_type": "markdown", "id": "6efc1c2b-7af1-49ec-9b48-8930a8dd2801", "metadata": {}, "source": [ "## 生成下载链接" ] }, { "cell_type": "code", "execution_count": null, "id": "10845b90-48b4-4418-9dac-dd4a6f0147c0", "metadata": { "tags": [] }, "outputs": [], "source": [ "filename = 'data/农业银行测试手机号码.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "dict2 = {}\n", "for k, v in dict1.items():\n", " dict2.setdefault(v['yzm'],{})\n", " dict2[v['yzm']]['code'] = v['code']\n", " dict2[v['yzm']]['name'] = v['name']\n", "print(len(dict2))\n", "filename = 'data/yzm.json'\n", "with open(filename,'w') as fl:\n", " json.dump(dict2, fl, ensure_ascii=False) \n" ] }, { "cell_type": "markdown", "id": "fba0a0c4-edf2-45c2-9199-c6a86919f191", "metadata": {}, "source": [ "## 发送短信" ] }, { "cell_type": "code", "execution_count": null, "id": "62733f52-62c1-43aa-964a-232eab8b5251", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "from tencentcloud.common import credential\n", "from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException\n", "from tencentcloud.sms.v20210111 import sms_client, models\n", "from tencentcloud.common.profile.client_profile import ClientProfile\n", "from tencentcloud.common.profile.http_profile import HttpProfile\n", "try: \n", " secretId = \"AKID22rVyvSqbikXFFvav31ykc9YmqjN6KYc\"\n", " secretKey = \"51XVL1YxDKI6mJh8au6DDuTskS1RhwJX\"\n", " cred = credential.Credential(secretId, secretKey) \n", " httpProfile = HttpProfile()\n", " httpProfile.reqMethod = \"POST\" # post请求(默认为post请求)\n", " httpProfile.reqTimeout = 30 # 请求超时时间，单位为秒(默认60秒)\n", " httpProfile.endpoint = \"sms.tencentcloudapi.com\" # 指定接入地域域名(默认就近接入)\n", " clientProfile = ClientProfile()\n", " clientProfile.signMethod = \"TC3-HMAC-SHA256\" # 指定签名算法\n", " clientProfile.language = \"en-US\"\n", " clientProfile.httpProfile = httpProfile\n", " client = sms_client.SmsClient(cred, \"ap-guangzhou\", clientProfile)\n", " req = models.SendSmsRequest()\n", " req.SmsSdkAppId = \"1400140089\"\n", " req.SignName = \"坤铭教育\"\n", " req.TemplateId = \"1875765\" \n", " req.TemplateParamSet = [\"北京石油分公司体测者\",\"01740518\"]\n", " req.PhoneNumberSet = [\"+8613793180751\"]\n", " req.SessionContext = \"\"\n", " req.ExtendCode = \"\"\n", " req.SenderId = \"\"\n", " resp = client.SendSms(req)\n", " print(resp.to_json_string(indent=2))\n", "except TencentCloudSDKException as err:\n", " print(err)" ] }, { "cell_type": "code", "execution_count": null, "id": "e924a0a9-65a5-49c4-bdf2-b9152d278ad5", "metadata": { "tags": [] }, "outputs": [], "source": [ "dict1 = json.loads(resp.to_json_string(indent=2))\n", "print(dict1['SendStatusSet'][0][\"Message\"])" ] }, { "cell_type": "markdown", "id": "c55a8af0-4e4d-4bd4-b5b1-0a7d9170958c", "metadata": {}, "source": [ "## 批量发送短信（测试）" ] }, { "cell_type": "code", "execution_count": null, "id": "910d1881-d090-48dc-ac87-685ccaaf9553", "metadata": { "tags": [] }, "outputs": [], "source": [ "\n", "from tencentcloud.common import credential\n", "from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException\n", "from tencentcloud.sms.v20210111 import sms_client, models\n", "from tencentcloud.common.profile.client_profile import ClientProfile\n", "from tencentcloud.common.profile.http_profile import HttpProfile\n", "import json\n", "\n", "filename = 'data/短信测试手机号码.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "for k, v in dict1.items():\n", " phone = '+86'+str(k)\n", " yzm = v['yzm']\n", " try: \n", " secretId = \"AKID22rVyvSqbikXFFvav31ykc9YmqjN6KYc\"\n", " secretKey = \"51XVL1YxDKI6mJh8au6DDuTskS1RhwJX\"\n", " cred = credential.Credential(secretId, secretKey) \n", " httpProfile = HttpProfile()\n", " httpProfile.reqMethod = \"POST\" # post请求(默认为post请求)\n", " httpProfile.reqTimeout = 30 # 请求超时时间，单位为秒(默认60秒)\n", " httpProfile.endpoint = \"sms.tencentcloudapi.com\" # 指定接入地域域名(默认就近接入)\n", " clientProfile = ClientProfile()\n", " clientProfile.signMethod = \"TC3-HMAC-SHA256\" # 指定签名算法\n", " clientProfile.language = \"en-US\"\n", " clientProfile.httpProfile = httpProfile\n", " client = sms_client.SmsClient(cred, \"ap-guangzhou\", clientProfile)\n", " req = models.SendSmsRequest()\n", " req.SmsSdkAppId = \"1400140089\"\n", " req.SignName = \"坤铭教育\"\n", " req.TemplateId = \"1808914\" \n", " req.TemplateParamSet = [yzm]\n", " req.PhoneNumberSet = [phone]\n", " req.SessionContext = \"\"\n", " req.ExtendCode = \"\"\n", " req.SenderId = \"\"\n", " #resp = client.SendSms(req)\n", " dict2 = json.loads(resp.to_json_string(indent=2))\n", "\n", " print(v['name'],dict2['SendStatusSet'][0][\"Message\"])\n", " except TencentCloudSDKException as err:\n", " print(v['name'],err)" ] }, { "cell_type": "markdown", "id": "59d38404-e097-4f08-a3fd-565e351f09bb", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [], "toc-hr-collapsed": true }, "source": [ "# 第二次体测" ] }, { "cell_type": "markdown", "id": "deeb7606-5752-4337-9117-4ba9edc762e9", "metadata": {}, "source": [ "## 人员信息导入" ] }, { "cell_type": "code", "execution_count": null, "id": "d803a2b2-4a61-42ba-84a3-e9846e3e9f5c", "metadata": {}, "outputs": [], "source": [ "import openpyxl\n", "import json\n", "\n", "\n", "wb = openpyxl.load_workbook('data/北京分行员工体质检测情况表.xlsx')\n", "sheet = wb.active\n", "# sheets = wb.sheetnames\n", "person = {}\n", "\n", "for n in range(2, sheet.max_row+1):\n", " if sheet.cell(n,1).value is not None:\n", " code = int(sheet.cell(n, 1).value)\n", " person.setdefault(code, {})\n", " dict1 = {}\n", " dict1['name'] = sheet.cell(n, 3).value\n", " if sheet.cell(n, 4).value ==1:\n", " sex = '男'\n", " else:\n", " sex = '女'\n", " dict1['sex'] = sex\n", " birth = str(sheet.cell(n, 5).value).split()[0]\n", " dict1['birth'] = birth\n", " dict1['unit'] = sheet.cell(n, 2).value\n", " if sheet.cell(n,6).value is not None:\n", " dict1['phone'] = sheet.cell(n, 6).value \n", " person[code] = dict1\n", "\n", "filename = 'data/北京农行人员名单2308.json'\n", "with open(filename, 'w') as fl:\n", " json.dump(person, fl, ensure_ascii=False)\n", "print('ok')" ] }, { "cell_type": "markdown", "id": "ff97823f-fd67-45ae-bd78-f209317be6b9", "metadata": {}, "source": [ "## 成绩导入" ] }, { "cell_type": "code", "execution_count": null, "id": "4adf86dd-addf-490a-9954-5dcac4693868", "metadata": {}, "outputs": [], "source": [ "import json\n", "import time\n", "import csv\n", "\n", "filename = '../item.json'\n", "item = {}\n", "unit = {}\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for k,v in dict1.items():\n", " item[k] = v\n", "re_ta = {}\n", "dict1 = {}\n", "list1 = []\n", "filename = 'data/北京农行人员名单2308.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "\n", "filename = 'data/places_result_20230825.csv'\n", "with open(filename,'r',newline='') as csv_file:\n", " fl = csv.reader(csv_file,delimiter=',')\n", " header = next(fl) \n", " for line in fl:\n", " #line = re.sub('[\\r\\n\\f ]{1,}', '', line)\n", " list1.append(line)\n", "#print(list1)\n", "for result in list1:\n", " user = str(result[2])\n", " if user in dict1.keys(): \n", " m_item = str(result[3]) \n", " re_ta.setdefault(user,{}) \n", " re_ta[user]['name'] = dict1[user]['name']\n", " re_ta[user]['sex'] = dict1[user]['sex'] \n", " re_ta[user]['unit'] = dict1[user]['unit']\n", " item_name = item[m_item]['name']\n", " re_ta[user].setdefault(item_name,{}) \n", " score = int(result[4])/item[m_item]['divisor'] \n", " re_ta[user][item_name]['成绩'] = f'{score} {item[m_item][\"unit\"]}'\n", "print(len(re_ta))\n", "filename = 'data/result_北京农行0825.json'\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(re_ta, fl, ensure_ascii=False) \n", "print(len(re_ta))" ] }, { "cell_type": "code", "execution_count": null, "id": "bb01b9a9-418d-4a1b-8ea9-1215af0b8285", "metadata": {}, "outputs": [], "source": [ "import json\n", "import time\n", "import csv\n", "\n", "filename = '../item.json'\n", "item = {}\n", "unit = {}\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for k,v in dict1.items():\n", " item[k] = v\n", "re_ta = {}\n", "dict1 = {}\n", "list1 = []\n", "filename = 'data/北京农行人员名单2308.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "\n", "filename = 'data/places_result_20230825.csv'\n", "with open(filename,'r',newline='') as csv_file:\n", " fl = csv.reader(csv_file,delimiter=',')\n", " header = next(fl) \n", " for line in fl:\n", " #line = re.sub('[\\r\\n\\f ]{1,}', '', line)\n", " list1.append(line)\n", "#print(list1)\n", "for result in list1:\n", " user = str(result[2])\n", " if user in dict1.keys(): \n", " m_item = str(result[3]) \n", " re_ta.setdefault(user,{}) \n", " re_ta[user]['name'] = dict1[user]['name']\n", " re_ta[user]['sex'] = dict1[user]['sex'] \n", " re_ta[user]['unit'] = dict1[user]['unit']\n", " item_name = item[m_item]['name']\n", " re_ta[user].setdefault(item_name,{}) \n", " score = int(result[4])/item[m_item]['divisor'] \n", " re_ta[user][item_name]['成绩'] = f'{score} {item[m_item][\"unit\"]}'\n", "print(len(re_ta))\n", "filename = 'data/result_北京农行0825.json'\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(re_ta, fl, ensure_ascii=False) \n", "print(len(re_ta))" ] }, { "cell_type": "markdown", "id": "4d2a0d63-4124-4063-96a7-a02478b9eabe", "metadata": {}, "source": [ "## 查看项目不足人员" ] }, { "cell_type": "code", "execution_count": null, "id": "b26857af-5fa3-42d0-bf79-bc33d9f5e4a8", "metadata": {}, "outputs": [], "source": [ "import json\n", "import time\n", "import csv\n", "\n", "filename = 'data/result_北京农行0825.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "i= 0\n", "for k, v in dict1.items():\n", " if len(v.keys())< 8:\n", " print(k,v['name'])" ] }, { "cell_type": "markdown", "id": "1966307c-4161-4c5b-b19a-b8a143972223", "metadata": {}, "source": [ "## 导出测试人员信息" ] }, { "cell_type": "code", "execution_count": null, "id": "288e39fb-b7e3-4566-ac13-ba98d7e72edb", "metadata": {}, "outputs": [], "source": [ "import json\n", "import openpyxl\n", "\n", "\n", "filename = 'data/result_北京农行0825.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "list1 =[]\n", "for k, v in dict1.items():\n", " list2 = []\n", " list2 = [k,v['name'],v['sex'],v['unit']]\n", " list1.append(list2)\n", "filename = 'data/北京农行测试人员202308.xlsx'\n", "wb = openpyxl.Workbook()\n", "sheet = wb.active\n", "\n", "for row in list1:\n", " sheet.append(row)\n", " \n", "wb.save(filename)" ] }, { "cell_type": "markdown", "id": "a42cb587-8673-4655-8943-3cbd63f22b73", "metadata": {}, "source": [ "## 检查性别" ] }, { "cell_type": "code", "execution_count": null, "id": "6352b95c-0e19-4943-9e99-218138213060", "metadata": {}, "outputs": [], "source": [ "import json\n", "\n", "\n", "filename = 'data/result_北京农行0825.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "for k, v in dict1.items():\n", " if v['sex'] == '男' and '一分钟仰卧起坐' in v.keys():\n", " print(k,'应为女性！')\n", " if v['sex'] == '女' and '俯卧撑' in v.keys():\n", " print(k,'应为男性！')" ] }, { "cell_type": "markdown", "id": "fddd68cd-b20c-4cf9-b503-5d59f52e3c1b", "metadata": {}, "source": [ "## 手工数据生成SQL" ] }, { "cell_type": "code", "execution_count": null, "id": "372294de-5370-41c0-9883-b18de9d8ef7e", "metadata": { "tags": [] }, "outputs": [], "source": [ "import openpyxl\n", "import json\n", "\n", "\n", "wb = openpyxl.load_workbook('data/北京农行测试人员202308.xlsx')\n", "sheet = wb.active\n", "# sheets = wb.sheetnames\n", "dict1 = {}\n", "\n", "data1 =list(sheet.values)\n", "del data1[0]\n", "list1 = []\n", "for item in data1:\n", " code = int(item[0])\n", " list1.append((309118,code,5,item[4],'2023-08-23','2023-08-23 22:00:00'))\n", "print(list1)" ] }, { "cell_type": "markdown", "id": "c32bdb5c-d987-4b0f-98e3-9ce4e3ef3671", "metadata": {}, "source": [ "## 核对问卷人员信息" ] }, { "cell_type": "code", "execution_count": null, "id": "c1b187e0-f566-43fa-88d8-090deb9ead19", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "import csv\n", "\n", "\n", "filename = 'data/北京农行人员名单2308.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", " \n", "filename = 'data/Survey_20230913.csv'\n", "with open(filename,'r',newline='') as csv_file:\n", " fl = csv.reader(csv_file,delimiter=',')\n", " header = next(fl) \n", " for line in fl:\n", " code = str(line[4])\n", " name = line[3]\n", " \n", " if code not in dict1.keys() or name!= dict1[code]['name']:\n", " print(code,name)\n", " \n", " " ] }, { "cell_type": "markdown", "id": "39cd284f-99fe-4ba0-928c-9da01042e394", "metadata": {}, "source": [ "## 合并人员信息" ] }, { "cell_type": "code", "execution_count": null, "id": "7ef51041-ad78-4c6a-af83-ed3e0473b199", "metadata": {}, "outputs": [], "source": [ "import json\n", "\n", "filename = 'data/北京农行人员名单2308.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "\n", "filename = 'data/北京农行人员名单.json'\n", "with open(filename,'r') as fl:\n", " dict2 = json.load(fl) \n", "for k, v in dict2.items():\n", " dict1[k] = v\n", "filename = 'data/北京农行人员名单all.json'\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(dict1, fl, ensure_ascii=False) \n", "print(len(re_ta))" ] }, { "cell_type": "markdown", "id": "3877fc60-171c-434a-aa27-8f1fe9936dd2", "metadata": {}, "source": [ "## 报告按部门分类" ] }, { "cell_type": "code", "execution_count": null, "id": "80a85fcd-0597-4b58-898f-de420239919f", "metadata": { "tags": [] }, "outputs": [], "source": [ "import os,sys,shutil\n", "import json\n", "import glob\n", "from pathlib import Path\n", "\n", "fi_path = '/home/songyi/python/mycrm/flask/pdf/files/309118'\n", "new_path = 'file/309118'\n", "old = []\n", "dict2 = {}\n", "\n", "filename = 'data/北京农行人员名单all.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "fls = glob.glob(f'{fi_path}/*.pdf')\n", "for fn in fls:\n", " fi_name =Path(fn).stem.split('-')[0]\n", " code = int(fi_name)\n", " unit_path = Path(new_path,dict1[str(code)]['unit'])\n", " unit_path.mkdir(parents = True, exist_ok = True)\n", " n_name = Path(unit_path,Path(fn).stem+'.pdf')\n", " if not os.path.exists(n_name):\n", " shutil.copyfile(fn,n_name)\n", " print(n_name)" ] }, { "cell_type": "markdown", "id": "3022dcdd-8700-468f-86ab-6ce43b118e1c", "metadata": {}, "source": [ "## 生成体测报告打印明细表" ] }, { "cell_type": "code", "execution_count": null, "id": "24433f75-6397-4b12-870e-611b77db8ded", "metadata": { "tags": [] }, "outputs": [], "source": [ "import os,sys,shutil\n", "import json\n", "import glob\n", "from pathlib import Path\n", "import openpyxl\n", "\n", "fi_path = '/home/songyi/python/mycrm/flask/pdf/files/309118'\n", "new_path = 'file/309118'\n", "list1 = []\n", "filename = 'data/北京农行人员名单all.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "fls = glob.glob(f'{fi_path}/*.pdf')\n", "for fn in fls:\n", " list2 = []\n", " fi_name =Path(fn).stem.split('-')[0] \n", " code = int(fi_name)\n", " unit = dict1[str(code)]['unit']\n", " list2 = [fi_name,Path(fn).stem.split('-')[1],unit]\n", " list1.append(list2)\n", "filename = 'data/北京农行体测报告打印明细表.xlsx'\n", "wb = openpyxl.Workbook()\n", "sheet = wb.active\n", "sheet.append(title)\n", "for row in list1:\n", " sheet.append(row)\n", " \n", "wb.save(filename) \n", "print('ok')" ] }, { "cell_type": "markdown", "id": "d71a24d1-ab29-4852-9bfc-384deb8f57a5", "metadata": {}, "source": [ "# 数据分析" ] }, { "cell_type": "markdown", "id": "d70ab6c9-4fb7-4f0d-864d-96e0ce05fb64", "metadata": {}, "source": [ "## 获取清理后数据" ] }, { "cell_type": "code", "execution_count": null, "id": "ac3b256e-7470-4cfa-b2ee-eb1110c9097c", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "import time\n", "import csv\n", "\n", "filename = '../item.json'\n", "item = {}\n", "unit = {}\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for k,v in dict1.items():\n", " item[k] = v\n", "filename = 'data/北京农行人员名单all.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "\n", "re_ta = {}\n", "list1 = []\n", "#print(\"\\n运动项目信息：\")\n", "filename = 'data/places_result_20230922-1695356859947.csv'\n", "with open(filename,'r',newline='') as csv_file:\n", " fl = csv.reader(csv_file,delimiter=',')\n", " header = next(fl) \n", " for line in fl:\n", " #line = re.sub('[\\r\\n\\f ]{1,}', '', line)\n", " list1.append(line)\n", "#print(list1)\n", "for result in list1:\n", " user = str(result[2])\n", " if user in dict1.keys(): \n", " m_item = str(result[3]) \n", " re_ta.setdefault(user,{}) \n", " re_ta[user]['name'] = dict1[user]['name']\n", " \n", " re_ta[user]['sex'] = dict1[user]['sex']\n", " re_ta[user]['birth'] = dict1[user]['birth'].replace('-','/')\n", " item_name = item[m_item]['name']\n", " re_ta[user].setdefault(item_name,{}) \n", " score = int(result[4])/item[m_item]['divisor'] \n", " re_ta[user][item_name]['成绩'] = f'{score} {item[m_item][\"unit\"]}'\n", " re_ta[user][item_name]['得分'] =result[5]\n", "\n", "print(len(re_ta))\n", "liwai = []\n", "for k, v in re_ta.items():\n", " if (len(v)<8 and '身高' in v and '体重' in v) or (len(v)<7 and '身高' not in v and '体重' not in v) :\n", " liwai.append(k)\n", "print(liwai)\n", "for k in liwai:\n", " del re_ta[k]\n", "print(len(re_ta))\n", "filename = 'data/result_北京农行all.json'\n", "with open(filename,'w') as fl:\n", " json.dump(re_ta, fl, ensure_ascii=False) \n", "print('ok')\n", "print(len(re_ta))" ] }, { "cell_type": "markdown", "id": "43c0dad1-9b96-4480-ae3d-d7fb4a2a8df5", "metadata": {}, "source": [ "## 计算人员年龄" ] }, { "cell_type": "code", "execution_count": 49, "id": "567f512c-2f8a-4441-99c1-f091b40dfd3b", "metadata": { "execution": { "iopub.execute_input": "2023-09-23T09:11:03.405894Z", "iopub.status.busy": "2023-09-23T09:11:03.405430Z", "iopub.status.idle": "2023-09-23T09:11:03.475250Z", "shell.execute_reply": "2023-09-23T09:11:03.474127Z", "shell.execute_reply.started": "2023-09-23T09:11:03.405856Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ok\n" ] } ], "source": [ "import json\n", "import datetime\n", "\n", "filename = 'data/result_北京农行all.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "for k, v in dict1.items():\n", " #print(k,v)\n", " if '.' in v['birth']:\n", " birth = v['birth'].split()[0].split('.')\n", " else:\n", " birth = v['birth'].split()[0].split('/')\n", " \n", " nian = int(birth[0].strip())\n", " yue = int(birth[1].strip())\n", " ri = int(birth[2].strip())\n", " #print(k,nian,yue,ri)\n", " if int(k) < 199:\n", " days = (datetime.date(2023, 5, 11)-datetime.date(nian,yue,ri)).days\n", " else:\n", " days = (datetime.date(2023, 8, 24)-datetime.date(nian,yue,ri)).days\n", " v['age'] = int(days/365)\n", "filename = 'data/result_北京农行all.json'\n", "with open(filename,'w') as fl:\n", " json.dump(dict1, fl, ensure_ascii=False) \n", "print('ok') " ] }, { "cell_type": "markdown", "id": "7b2974f0-8b5e-40b9-a872-b0df27097096", "metadata": {}, "source": [ "## 汇总人员信息及成绩" ] }, { "cell_type": "code", "execution_count": null, "id": "576f9b57-237f-4376-b795-122a4cd2222a", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "\n", "\n", "items = ['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", "\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for k,v in dict1.items():\n", " print(k)\n", " score = 0\n", " i = 0 \n", " for k1,v1 in v.items(): \n", " if k1 in items:\n", " score = score + int(v1['得分'])\n", " i+=1\n", " dict1[k]['score'] = round(score/i,2) \n", "\n", "with open(filename,'w') as fl:\n", " json.dump(dict1, fl, ensure_ascii=False) \n", "print('ok')" ] }, { "cell_type": "markdown", "id": "0fd37ce2-f965-4a46-8207-c5ce9a7b426a", "metadata": {}, "source": [ "## 计算测试等级" ] }, { "cell_type": "code", "execution_count": 40, "id": "ec2773cb-16ff-4957-aa51-627bfe0caf95", "metadata": { "execution": { "iopub.execute_input": "2023-09-23T08:20:11.144070Z", "iopub.status.busy": "2023-09-23T08:20:11.143557Z", "iopub.status.idle": "2023-09-23T08:20:11.211460Z", "shell.execute_reply": "2023-09-23T08:20:11.210353Z", "shell.execute_reply.started": "2023-09-23T08:20:11.144030Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0~255分人数：107人,男性：60人,女性:47人\n", "256~332分人数：229人,男性：83人,女性:146人\n", "333~367分人数：128人,男性：35人,女性:93人\n", "368~500分人数：58人,男性：16人,女性:42人\n", "ok\n" ] } ], "source": [ "import json\n", "\n", "\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "dict2 = {}\n", "dict2['不合格'] = [0,255]\n", "dict2['合格'] = [256,332]\n", "dict2['良好'] = [333,367]\n", "dict2['优秀'] = [368,500]\n", "\n", "for k1, v1 in dict2.items():\n", " di = v1[0]\n", " gao = v1[1]\n", " i = 0 \n", " m = 0\n", " f = 0\n", " for k,v in dict1.items():\n", " if int(v['score']*100) in range(di,gao+1):\n", " dict1[k]['level'] = k1\n", " i+=1\n", " if v['sex'] == '男':\n", " m = m +1\n", " else:\n", " f = f+1\n", " print(f'{di}~{gao}分人数：{i}人,男性：{m}人,女性:{f}人')\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(dict1, fl) \n", "print('ok')" ] }, { "cell_type": "markdown", "id": "058f29fc-6de5-42b6-ba07-95d39e014418", "metadata": {}, "source": [ "## 根据年龄汇总人员信息及成绩" ] }, { "cell_type": "code", "execution_count": 50, "id": "c605482a-3aa5-46ff-8648-2675408470a8", "metadata": { "execution": { "iopub.execute_input": "2023-09-23T09:11:19.016454Z", "iopub.status.busy": "2023-09-23T09:11:19.015975Z", "iopub.status.idle": "2023-09-23T09:11:19.033986Z", "shell.execute_reply": "2023-09-23T09:11:19.033423Z", "shell.execute_reply.started": "2023-09-23T09:11:19.016418Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20~24岁平均成绩：2.74分，人数：40人,男性：16人\n", "25~29岁平均成绩：2.98分，人数：102人,男性：48人\n", "30~34岁平均成绩：2.98分，人数：107人,男性：38人\n", "35~39岁平均成绩：3.1分，人数：95人,男性：27人\n", "40~44岁平均成绩：3.19分，人数：37人,男性：14人\n", "45~49岁平均成绩：3.13分，人数：66人,男性：23人\n", "50~54岁平均成绩：3.07分，人数：60人,男性：13人\n", "55~69岁平均成绩：2.85分，人数：15人,男性：15人\n" ] } ], "source": [ "import json\n", "\n", "nld = [[20,24],[25,29],[30,34],[35,39],[40,44],[45,49],[50,54],[55,69]]\n", "\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for item in nld:\n", " di = item[0]\n", " gao = item[1]\n", " i = 0\n", " score = 0\n", " m = 0\n", " f = 0\n", " for k,v in dict1.items():\n", " if v['age'] in range(di,gao+1):\n", " score = score+v['score']\n", " i+=1\n", " if v['sex'] == '男':\n", " m = m +1\n", " \n", " print(f'{di}~{gao}岁平均成绩：{round(score/i,2)}分，人数：{i}人,男性：{m}人')" ] }, { "cell_type": "markdown", "id": "8a15ed82-93bb-4e25-a59e-2358c4d47ca5", "metadata": {}, "source": [ "### 根据年龄汇总人员信息及成绩（男）" ] }, { "cell_type": "code", "execution_count": 51, "id": "a75f1748-444a-4b0f-a38c-79d814a16ae0", "metadata": { "execution": { "iopub.execute_input": "2023-09-23T09:11:22.695232Z", "iopub.status.busy": "2023-09-23T09:11:22.694925Z", "iopub.status.idle": "2023-09-23T09:11:22.717627Z", "shell.execute_reply": "2023-09-23T09:11:22.716976Z", "shell.execute_reply.started": "2023-09-23T09:11:22.695205Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20~24岁平均成绩：2.75分，人数：16人\n", "25~29岁平均成绩：2.85分，人数：48人\n", "30~34岁平均成绩：2.81分，人数：38人\n", "35~39岁平均成绩：3.01分，人数：27人\n", "40~44岁平均成绩：2.99分，人数：14人\n", "45~49岁平均成绩：2.88分，人数：23人\n", "50~54岁平均成绩：2.87分，人数：13人\n", "55~80岁平均成绩：2.85分，人数：15人\n" ] } ], "source": [ "import json\n", "\n", "nld = [[20,24],[25,29],[30,34],[35,39],[40,44],[45,49],[50,54],[55,80]]\n", "\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for item in nld:\n", " di = item[0]\n", " gao = item[1]\n", " i = 0\n", " score = 0\n", " m = 0\n", " f = 0\n", " for k,v in dict1.items():\n", " if v['age'] in range(di,gao+1) and v['sex'] == '男':\n", " score = score+v['score']\n", " i+=1\n", " if i >0:\n", " print(f'{di}~{gao}岁平均成绩：{round(score/i,2)}分，人数：{i}人')\n", " else:\n", " print(f'{di}~{gao}岁平均成绩：0分，人数：0人') " ] }, { "cell_type": "markdown", "id": "9840c5a8-2a06-4d7b-91f5-d3c2d43d1d02", "metadata": {}, "source": [ "### 根据年龄汇总人员信息及成绩（女）" ] }, { "cell_type": "code", "execution_count": 52, "id": "d735d1e2-02d3-4768-a5eb-22792791ca23", "metadata": { "execution": { "iopub.execute_input": "2023-09-23T09:11:26.628891Z", "iopub.status.busy": "2023-09-23T09:11:26.628391Z", "iopub.status.idle": "2023-09-23T09:11:26.652254Z", "shell.execute_reply": "2023-09-23T09:11:26.651303Z", "shell.execute_reply.started": "2023-09-23T09:11:26.628854Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20~24岁平均成绩：2.73分，人数：24人\n", "25~29岁平均成绩：3.1分，人数：54人\n", "30~34岁平均成绩：3.07分，人数：69人\n", "35~39岁平均成绩：3.14分，人数：68人\n", "40~44岁平均成绩：3.31分，人数：23人\n", "45~49岁平均成绩：3.27分，人数：43人\n", "50~54岁平均成绩：3.13分，人数：47人\n", "55~80岁平均成绩：0分，人数：0人\n" ] } ], "source": [ "import json\n", "\n", "nld = [[20,24],[25,29],[30,34],[35,39],[40,44],[45,49],[50,54],[55,80]]\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for item in nld:\n", " di = item[0]\n", " gao = item[1]\n", " i = 0\n", " score = 0\n", " m = 0\n", " f = 0\n", " for k,v in dict1.items():\n", " if v['age'] in range(di,gao+1) and v['sex'] == '女':\n", " score = score+v['score']\n", " i+=1\n", " if i >0:\n", " print(f'{di}~{gao}岁平均成绩：{round(score/i,2)}分，人数：{i}人')\n", " else:\n", " print(f'{di}~{gao}岁平均成绩：0分，人数：0人') " ] }, { "cell_type": "markdown", "id": "f4a02b1d-2468-4430-8f55-3ffaf07efd34", "metadata": {}, "source": [ "## 计算平均成绩" ] }, { "cell_type": "code", "execution_count": 45, "id": "fc897a81-7727-4f37-8ab3-8e9de72855d6", "metadata": { "execution": { "iopub.execute_input": "2023-09-23T08:23:38.968301Z", "iopub.status.busy": "2023-09-23T08:23:38.968098Z", "iopub.status.idle": "2023-09-23T08:23:38.980306Z", "shell.execute_reply": "2023-09-23T08:23:38.979851Z", "shell.execute_reply.started": "2023-09-23T08:23:38.968286Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "平均成绩：2.8683分，男性：194人\n", "平均成绩：3.1148分，女性：328人\n", "平均成绩：3.0232分，总体：522人\n" ] } ], "source": [ "import json\n", "\n", "nld = [[20,24],[25,29],[30,34],[35,39],[40,44],[45,49],[50,54],[55,80]]\n", "#filename = 'data/result_石家庄.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "i = 1\n", "m = 0\n", "f = 0\n", "score = 0\n", "t_score = 0\n", "for k,v in dict1.items():\n", " if v['sex'] == '男':\n", " m = m +1\n", " score = score+v['score']\n", "print(f'平均成绩：{round(score/m,4)}分，男性：{m}人')\n", "t_score = t_score + score\n", "score = 0\n", "for k,v in dict1.items():\n", " if v['sex'] == '女':\n", " f = f +1\n", " score = score+v['score']\n", "print(f'平均成绩：{round(score/f,4)}分，女性：{f}人')\n", "t_score = t_score + score\n", "print(f'平均成绩：{round(t_score/522,4)}分，总体：522人')" ] }, { "cell_type": "markdown", "id": "f4911bde-1a29-4fdf-a7f7-4ebef0905936", "metadata": {}, "source": [ "## 计算各年龄段测试等级(女）" ] }, { "cell_type": "code", "execution_count": 53, "id": "43c8fd61-4bdb-4d8e-8061-f4f186bd1845", "metadata": { "execution": { "iopub.execute_input": "2023-09-23T09:11:32.394933Z", "iopub.status.busy": "2023-09-23T09:11:32.394468Z", "iopub.status.idle": "2023-09-23T09:11:32.420499Z", "shell.execute_reply": "2023-09-23T09:11:32.419180Z", "shell.execute_reply.started": "2023-09-23T09:11:32.394896Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20-24 {'良好': 1, '合格': 15, '不合格': 7, '优秀': 1}\n", "25-29 {'合格': 25, '良好': 19, '不合格': 7, '优秀': 3}\n", "30-34 {'合格': 31, '不合格': 11, '优秀': 8, '良好': 19}\n", "35-39 {'合格': 31, '不合格': 10, '良好': 15, '优秀': 12}\n", "40-44 {'良好': 10, '合格': 7, '优秀': 4, '不合格': 2}\n", "45-49 {'优秀': 6, '合格': 18, '良好': 16, '不合格': 3}\n", "50-54 {'不合格': 7, '合格': 19, '优秀': 8, '良好': 13}\n", "55-80 {'良好': 0, '合格': 0, '不合格': 0, '优秀': 0}\n" ] } ], "source": [ "nld = [[20,24],[25,29],[30,34],[35,39],[40,44],[45,49],[50,54],[55,80]]\n", "\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "dict2 = {}\n", "dict2['不合格'] = [0,255]\n", "dict2['合格'] = [256,332]\n", "dict2['良好'] = [333,367]\n", "dict2['优秀'] = [368,500]\n", "dict3 = {}\n", "for item in nld:\n", " di = item[0]\n", " gao = item[1]\n", " age = f'{di}-{gao}'\n", " dict3.setdefault(age,{})\n", " i = 0 \n", " m = 0\n", " f = 0\n", " for k,v in dict1.items():\n", " if v['age'] in range(di,gao+1): \n", " dict3[age].setdefault(v['level'],0)\n", " if v['sex'] == '女':\n", " dict3[age][v['level']] = dict3[age][v['level']]+1\n", " \n", "for k, v in dict3.items():\n", " print(k,v)" ] }, { "cell_type": "markdown", "id": "6b5d0882-6c64-4e39-813b-a4c3ebb98006", "metadata": {}, "source": [ "## 计算各年龄段测试等级(男）" ] }, { "cell_type": "code", "execution_count": 55, "id": "aefe62b7-8fcf-43e0-a525-a8584b064167", "metadata": { "execution": { "iopub.execute_input": "2023-09-23T09:20:16.478192Z", "iopub.status.busy": "2023-09-23T09:20:16.477735Z", "iopub.status.idle": "2023-09-23T09:20:16.503089Z", "shell.execute_reply": "2023-09-23T09:20:16.501855Z", "shell.execute_reply.started": "2023-09-23T09:20:16.478154Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20-24 {'良好': 3, '合格': 7, '不合格': 6, '优秀': 0}\n", "25-29 {'合格': 22, '良好': 8, '不合格': 15, '优秀': 3}\n", "30-34 {'合格': 16, '不合格': 12, '优秀': 3, '良好': 7}\n", "35-39 {'合格': 12, '不合格': 7, '良好': 4, '优秀': 4}\n", "40-44 {'良好': 3, '合格': 5, '优秀': 2, '不合格': 4}\n", "45-49 {'优秀': 1, '合格': 10, '良好': 5, '不合格': 7}\n", "50-54 {'不合格': 4, '合格': 5, '优秀': 2, '良好': 2}\n", "55-80 {'良好': 3, '合格': 6, '不合格': 5, '优秀': 1}\n" ] } ], "source": [ "nld = [[20,24],[25,29],[30,34],[35,39],[40,44],[45,49],[50,54],[55,80]]\n", "\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "dict2 = {}\n", "dict2['不合格'] = [0,255]\n", "dict2['合格'] = [256,332]\n", "dict2['良好'] = [333,367]\n", "dict2['优秀'] = [368,500]\n", "dict3 = {}\n", "for item in nld:\n", " di = item[0]\n", " gao = item[1]\n", " age = f'{di}-{gao}'\n", " dict3.setdefault(age,{})\n", " i = 0 \n", " m = 0\n", " f = 0\n", " for k,v in dict1.items():\n", " if v['age'] in range(di,gao+1): \n", " dict3[age].setdefault(v['level'],0)\n", " if v['sex'] == '男':\n", " dict3[age][v['level']] = dict3[age][v['level']]+1\n", " \n", "for k, v in dict3.items():\n", " print(k,v)" ] }, { "cell_type": "markdown", "id": "446217a9-4805-4627-a904-ca734e348433", "metadata": {}, "source": [ "## 计算各项目成绩" ] }, { "cell_type": "code", "execution_count": 48, "id": "23b7518c-7434-467d-8d38-642f78bde7d9", "metadata": { "execution": { "iopub.execute_input": "2023-09-23T08:25:08.604892Z", "iopub.status.busy": "2023-09-23T08:25:08.604367Z", "iopub.status.idle": "2023-09-23T08:25:08.629527Z", "shell.execute_reply": "2023-09-23T08:25:08.628314Z", "shell.execute_reply.started": "2023-09-23T08:25:08.604852Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "体重 3.8 517\n", "肺活量 3.73 518\n", "握力 1.88 519\n", "坐位体前屈 2.93 503\n", "纵跳 2.54 505\n", "俯卧撑 3.64 184\n", "一分钟仰卧起坐 4.29 303\n", "单脚站立 2.57 514\n", "选择反应时 3.19 488\n", "台阶指数 2.57 486\n" ] } ], "source": [ "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "items = ['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", "for item in items:\n", " score = 0\n", " n = 0\n", " for k, v in dict1.items():\n", " if item in v.keys():\n", " n = n + 1\n", " score =score + int(v[item]['得分'])\n", " print(item,round(score/n,2),n)" ] }, { "cell_type": "code", "execution_count": null, "id": "88b726b3-16db-4df5-af9d-24409934593f", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 }