jupyterlab
jupyterlab项目
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import csv\n",
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"import pymysql\n",
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"\n",
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"def read_data(filename,re_id):\n",
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" detail = {}\n",
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" with open(filename) as f:\n",
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" reader = csv.reader(f)\n",
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" header_row =next(reader)\n",
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" for row in reader:\n",
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" detail.setdefault(row[0],)\n",
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" detail[row[0]].append((re_id,row[0],row[2]))\n",
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" return detail\n",
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"\n",
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"per_id = 1\n",
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"item_id = 1\n",
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"re_date = '2020-07-07'\n",
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"db = pymysql.connect(\"localhost\",\"songyi\",\"yylzs\",\"mydata\" )\n",
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"cursor = db.cursor()\n",
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"filename = '户外跑步数据.csv'\n",
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"sql = \"select id from sports_record where re_date=%s and item_id =%s and person_id =%s\"\n",
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"cursor.execute(sql, (re_date,item_id,per_id))\n",
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"result = cursor.fetchone()\n",
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"if result:\n",
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" print('记录已经存在！')\n",
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"else:\n",
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" sql = 'insert into sports_record (re_date,item_id,person_id) values(%s,%s,%s)'\n",
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" cursor.execute(sql,(re_date,item_id,per_id))\n",
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" db.commit()\n",
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" re_id = cursor.lastrowid;\n",
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" print(re_id)\n",
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" detail = read_data(filename,re_id)\n",
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" sql = \"insert into sports_detail (rec_id_id,target_id_id,value) values(%s,%s,%s)\"\n",
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" try:\n",
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" cursor.executemany(sql,detail)\n",
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" db.commit()\n",
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" print(\"ok!\")\n",
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" except:\n",
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" # 如果发生错误则回滚\n",
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" db.rollback() \n",
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"db.close()\n",
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"\n",
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" "
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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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"metadata": {},
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"outputs": [],
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"source": [
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"choice = input('记录已存在，是否覆盖？（y/n）')\n",
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"if choice.upper() == \"Y\":\n",
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" print('记录已更新')\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import csv\n",
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"import pymysql\n",
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"def read_data(filename,re_id):\n",
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" detail = []\n",
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" with open(filename) as f:\n",
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" reader = csv.reader(f)\n",
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" header_row =next(reader)\n",
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" for row in reader:\n",
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" detail.append((re_id,row[0],row[2]))\n",
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" return detail\n",
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" \n",
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" \n",
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"per_id = 1\n",
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"item_id = 1\n",
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"re_date = '2020-06-28'\n",
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"db = pymysql.connect(\"localhost\",\"songyi\",\"yylzs\",\"mydata\" )\n",
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"cursor = db.cursor()\n",
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"filename = '户外跑步数据.csv'\n",
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"re_id = 19;\n",
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"detail = read_data(filename,re_id)\n",
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"print(detail)\n",
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" \n",
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"db.close()\n"
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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": 32,
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"metadata": {},
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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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"source": [
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"import openpyxl\n",
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"import pymysql\n",
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"import json\n",
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"\n",
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"db = pymysql.connect(\"81.68.135.145\",\"colab\",\"songyi\",\"gaokao\" )\n",
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"cursor = db.cursor()\n",
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"sql = 'select code from college';\n",
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"cursor.execute(sql)\n",
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"results = cursor.fetchall()\n",
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"college = []\n",
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"for result in results:\n",
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" college.append(result[0])\n",
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"wb = openpyxl.load_workbook('./data/2017-2019.xlsx')\n",
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"#sheet = wb.active\n",
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"sheets = wb.sheetnames\n",
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"new_col = []\n",
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"dict1 = {}\n",
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"new_code = []\n",
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"for m in sheets:\n",
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" sheet = wb[m]\n",
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" \n",
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" \n",
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" for n in range(4,sheet.max_row):\n",
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" col_code = sheet.cell(n,1).value\n",
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" \n",
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" if col_code not in college and col_code not in new_code: \n",
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" m_year = []\n",
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" dict2 = {}\n",
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" #dict2.setdefault('nian',[])\n",
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" new_code.append(col_code) \n",
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" dict2['name'] = sheet.cell(n,2).value\n",
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" dict2['nian'] = []\n",
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" #m_year.append(m[0:4])\n",
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" #dict2['nian'][] = (m[0:4])\n",
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" dict1[col_code] = dict2\n",
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" for m_code in dict1.keys():\n",
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" if m[0:4] not in dict1[m_code]['nian']:\n",
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" dict1[m_code]['nian'].append(m[0:4])\n",
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" \n",
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"filename = './data/2020年未招生学校.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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" \n",
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" \n",
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" \n",
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"# new_col.append((col_code,sheet.cell(n,2).value,m[0:4]))\n",
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" # print(col_code)\n",
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"\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": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"### 导入2020年投档录取信息\n",
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"import pymysql\n",
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"import json\n",
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"db = pymysql.connect(\"81.68.135.145\",\"colab\",\"songyi\",\"gaokao\" )\n",
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"cursor = db.cursor()\n",
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"sql = \"select * from digao_2020\"\n",
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"cursor.execute(sql)\n",
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"results = cursor.fetchall()\n",
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"college = []\n",
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"m_adm = []\n",
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"for result in results:\n",
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" bm_col = result[1][0:4]\n",
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" bm_adm = result[2][0:2]\n",
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" name_adm = result[2][2:]\n",
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" if bm_col not in college:\n",
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" college.append(bm_col) \n",
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" \n",
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" m_adm.append((bm_col,bm_adm,result[3],result[4],result[5],result[6],result[7],'2020')) \n",
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"sql = \"insert into admission (college,speciality,plan,plan_dispense,num_dispense,num_min,rank_min,nian) values(%s,%s,%s,%s,%s,%s,%s,%s)\"\n",
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"try:\n",
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" cursor.executemany(sql,m_adm)\n",
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" db.commit()\n",
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" print(\"ok!\")\n",
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"except:\n",
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" # 如果发生错误则回滚\n",
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" print(\"error!\")\n",
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" db.rollback() \n",
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"\n",
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"db.close()"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"### 导入2017-2019年投档录取信息\n",
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"import pymysql\n",
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"import json\n",
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"db = pymysql.connect(\"81.68.135.145\",\"colab\",\"songyi\",\"gaokao\" )\n",
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"cursor = db.cursor()\n",
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"sql = \"select * from digao_1719\"\n",
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"cursor.execute(sql)\n",
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"results = cursor.fetchall()\n",
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"college = []\n",
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"m_adm = []\n",
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"for result in results:\n",
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" bm_col = result[1][0:4]\n",
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" bm_adm = result[2][0:2]\n",
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" name_adm = result[2][2:]\n",
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" if bm_col not in college:\n",
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" college.append(bm_col) \n",
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" \n",
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" m_adm.append((bm_col,bm_adm,result[3],result[4],result[5],result[6],result[7],'2020')) \n",
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"sql = \"insert into admission (college,speciality,plan,plan_dispense,num_dispense,num_min,rank_min,nian) values(%s,%s,%s,%s,%s,%s,%s,%s)\"\n",
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"try:\n",
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" cursor.executemany(sql,m_adm)\n",
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" db.commit()\n",
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" print(\"ok!\")\n",
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"except:\n",
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" # 如果发生错误则回滚\n",
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" print(\"error!\")\n",
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" db.rollback() \n",
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"\n",
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"db.close()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 合并excel文件"
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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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"metadata": {},
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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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"for i in range(1,23):\n",
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" fl_name = '识别结果'\n",
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"wb = openpyxl.load_workbook('./data/2017-2019.xlsx')\n",
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"#sheet = wb.active\n",
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"sheets = wb.sheetnames\n",
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"new_col = []\n",
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"dict1 = {}\n",
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"new_code = []\n",
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"for m in sheets:\n",
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" sheet = wb[m]\n",
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" \n",
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" \n",
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" for n in range(4,sheet.max_row):\n",
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" col_code = sheet.cell(n,1).value\n",
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" \n",
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" if col_code not in college and col_code not in new_code: \n",
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" m_year = []\n",
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" dict2 = {}\n",
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" #dict2.setdefault('nian',[])\n",
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" new_code.append(col_code) \n",
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" dict2['name'] = sheet.cell(n,2).value\n",
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" dict2['nian'] = []\n",
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" #m_year.append(m[0:4])\n",
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" #dict2['nian'][] = (m[0:4])\n",
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" dict1[col_code] = dict2\n",
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" for m_code in dict1.keys():\n",
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" if m[0:4] not in dict1[m_code]['nian']:\n",
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" dict1[m_code]['nian'].append(m[0:4])\n",
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" \n",
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"filename = './data/2020年未招生学校.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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" \n",
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" \n",
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" \n",
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"# new_col.append((col_code,sheet.cell(n,2).value,m[0:4]))\n",
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" # print(col_code)\n",
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"\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": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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},
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"toc-autonumbering": false,
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"toc-showmarkdowntxt": false,
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"toc-showtags": false
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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