4-空间数据分析/4.2-【ArcGIS Python系列】/4.2.7-处理几何数据代码练习和示例2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 处理几何数据 代码练习\n",
    "> 本notebook是基于[4.2.7-处理几何数据.md](4.2.7-%B4%A6%C0%ED%BC%B8%BA%CE%CA%FD%BE%DD.md)的代码练习，包含了一个示例：通过excel表格制作分年龄的人口普查要素文件。"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1f0ce8bf98d3245a"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import arcpy, os\n",
    "import numpy as np"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:13.026014900Z",
     "start_time": "2023-08-13T01:26:07.047970700Z"
    }
   },
   "id": "70b73bff02213a95"
  },
  {
   "cell_type": "markdown",
   "source": [
    "我们结合游标和几何令牌探索`Point`要素的坐标："
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "32becf90b60ccfd5"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a56c825",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:13.192697400Z",
     "start_time": "2023-08-13T01:26:13.021016400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "肇庆市中心点坐标: 112.46729468100011, 23.054184665000037\n",
      "惠州市中心点坐标: 114.40762969200011, 23.081913476000068\n",
      "梅州市中心点坐标: 116.11274892300003, 24.301738405000037\n",
      "汕尾市中心点坐标: 115.3593122320001, 22.777137241000048\n",
      "河源市中心点坐标: 114.69310608900003, 23.748921188000054\n",
      "阳江市中心点坐标: 111.97005184600005, 21.86197366700003\n",
      "清远市中心点坐标: 113.04575627600002, 23.687492251000037\n",
      "东莞市中心点坐标: 113.74106426800006, 23.048948814000028\n",
      "中山市中心点坐标: 113.37691437000001, 22.523808605000056\n",
      "潮州市中心点坐标: 116.62792106500001, 23.66443661900007\n",
      "揭阳市中心点坐标: 116.35104798300006, 23.546199068000078\n",
      "云浮市中心点坐标: 112.03911767500006, 22.932452674000046\n",
      "南宁市中心点坐标: 108.31601543400006, 22.826785071000074\n",
      "柳州市中心点坐标: 109.40709554500006, 24.31730087300008\n",
      "桂林市中心点坐标: 110.29451952900007, 25.277036941000063\n",
      "梧州市中心点坐标: 111.29245753500004, 23.477370199000063\n",
      "北海市中心点坐标: 109.11496114100004, 21.475728205000053\n",
      "防城港市中心点坐标: 108.34150226600008, 21.61710696600005\n",
      "钦州市中心点坐标: 108.62047458900008, 21.970049999000025\n",
      "贵港市中心点坐标: 109.59795509800006, 23.096508190000065\n",
      "玉林市中心点坐标: 110.14987487600007, 22.63411712800007\n",
      "百色市中心点坐标: 106.61287715800006, 23.900645643000075\n",
      "贺州市中心点坐标: 111.54691900000006, 24.417155120000075\n",
      "河池市中心点坐标: 108.05787239000006, 24.698676444000057\n",
      "来宾市中心点坐标: 109.22547477600006, 23.73640806900005\n",
      "崇左市中心点坐标: 107.34979083300004, 22.40687778100005\n",
      "海口市中心点坐标: 110.32681913300007, 20.03401807700004\n",
      "三亚市中心点坐标: 109.50417354500007, 18.249564659000043\n",
      "三沙市中心点坐标: 112.34388971400006, 16.83265182200006\n",
      "儋州市中心点坐标: 109.57276967300004, 19.519454753000048\n",
      "五指山市中心点坐标: 109.51256653400009, 18.778716191000058\n",
      "琼海市中心点坐标: 110.46241811300007, 19.24763645300004\n",
      "文昌市中心点坐标: 110.74952626700008, 19.61482141700003\n",
      "万宁市中心点坐标: 110.38438466500008, 18.797775287000036\n",
      "东方市中心点坐标: 108.65016718900006, 19.103833466000026\n",
      "定安县中心点坐标: 110.34484290400007, 19.68671263600004\n",
      "屯昌县中心点坐标: 110.09837085300006, 19.364471583000068\n",
      "澄迈县中心点坐标: 110.00289752100008, 19.738989763000063\n",
      "临高县中心点坐标: 109.68353276000005, 19.910406093000063\n",
      "白沙黎族自治县中心点坐标: 109.44831940500006, 19.226178446000063\n",
      "昌江黎族自治县中心点坐标: 109.04924395700004, 19.26255509600003\n",
      "乐东黎族自治县中心点坐标: 109.17134854800008, 18.74924032900003\n",
      "陵水黎族自治县中心点坐标: 110.03294156100009, 18.506632685000056\n",
      "保亭黎族苗族自治县中心点坐标: 109.69832008700007, 18.638156867000077\n",
      "琼中黎族苗族自治县中心点坐标: 109.83592313500009, 19.03741072500003\n",
      "重庆市中心点坐标: 106.50112907900007, 29.535892664000073\n",
      "成都市中心点坐标: 104.06322753600006, 30.661882821000063\n",
      "自贡市中心点坐标: 104.77075025900007, 29.35561149700004\n",
      "攀枝花市中心点坐标: 101.71443552900007, 26.584016699000074\n",
      "泸州市中心点坐标: 105.43975689500007, 28.892164428000058\n",
      "德阳市中心点坐标: 104.39596832000007, 31.130131248000055\n",
      "绵阳市中心点坐标: 104.73894723500007, 31.46621668100005\n",
      "广元市中心点坐标: 105.82602571700005, 32.43604256800006\n",
      "遂宁市中心点坐标: 105.56791848600005, 30.51609656200003\n",
      "内江市中心点坐标: 105.06288708000005, 29.589641658000062\n",
      "乐山市中心点坐标: 103.75905148400005, 29.584767896000074\n",
      "南充市中心点坐标: 106.07896448600007, 30.797588331000043\n",
      "眉山市中心点坐标: 103.82965547700007, 30.05110901300003\n",
      "宜宾市中心点坐标: 104.62847313900005, 28.763629440000045\n",
      "广安市中心点坐标: 106.62957698300005, 30.459174031000032\n",
      "达州市中心点坐标: 107.49771467900007, 31.211728669000024\n",
      "雅安市中心点坐标: 102.99921680300008, 29.99041895700003\n",
      "巴中市中心点坐标: 106.74952080300005, 31.860988560000067\n",
      "资阳市中心点坐标: 104.63947737000007, 30.12503849500007\n",
      "阿坝藏族羌族自治州中心点坐标: 102.21972536300007, 31.90203361600004\n",
      "甘孜藏族自治州中心点坐标: 101.96229201600005, 30.05347989200004\n",
      "凉山彝族自治州中心点坐标: 102.25725463300006, 27.890199231000054\n",
      "贵阳市中心点坐标: 106.70970595900008, 26.58186520600003\n",
      "六盘水市中心点坐标: 104.84422087800004, 26.588291844000025\n",
      "遵义市中心点坐标: 106.93342061500005, 27.710141236000027\n",
      "安顺市中心点坐标: 105.92885423300004, 26.249095388000057\n",
      "毕节市中心点坐标: 105.28178868500004, 27.305066491000048\n",
      "铜仁市中心点坐标: 109.18697966900004, 27.721620035000058\n",
      "黔西南布依族苗族自治州中心点坐标: 104.89551393400006, 25.09122908300003\n",
      "黔东南苗族侗族自治州中心点坐标: 107.97336686700004, 26.58690316900004\n",
      "黔南布依族苗族自治州中心点坐标: 107.51297653100005, 26.261646425000038\n",
      "昆明市中心点坐标: 102.71080946700005, 25.04358171100006\n",
      "曲靖市中心点坐标: 103.79582995000004, 25.504564697000035\n",
      "玉溪市中心点坐标: 102.54264809900008, 24.353519298000037\n",
      "保山市中心点坐标: 99.16622117500003, 25.114669158000027\n",
      "昭通市中心点坐标: 103.71512327000005, 27.340424290000044\n",
      "丽江市中心点坐标: 100.23189804200007, 26.87565561400004\n",
      "普洱市中心点坐标: 100.97114816400006, 22.780313267000054\n",
      "临沧市中心点坐标: 100.08571902100005, 23.88915614900003\n",
      "楚雄彝族自治州中心点坐标: 101.54462403000008, 25.04507084200003\n",
      "红河哈尼族彝族自治州中心点坐标: 103.38214353700005, 23.369279843000072\n",
      "文山壮族苗族自治州中心点坐标: 104.24192302200004, 23.37218646900004\n",
      "西双版纳傣族自治州中心点坐标: 100.79681858700008, 22.004581920000078\n",
      "大理白族自治州中心点坐标: 100.22456576400003, 25.59247028900006\n",
      "德宏傣族景颇族自治州中心点坐标: 98.57820786300005, 24.439861622000024\n",
      "怒江傈僳族自治州中心点坐标: 98.85390882600007, 25.854283057000032\n",
      "迪庆藏族自治州中心点坐标: 99.70536196800003, 27.83034338500005\n",
      "拉萨市中心点坐标: 91.13066869300008, 29.66308143400005\n",
      "日喀则市中心点坐标: 88.88311669900008, 29.270787442000028\n",
      "昌都市中心点坐标: 97.17792859200006, 31.13921513200006\n",
      "林芝市中心点坐标: 94.36118131300003, 29.65738030600005\n",
      "山南市中心点坐标: 91.76530896200006, 29.239102650000063\n",
      "那曲市中心点坐标: 92.05892001100005, 31.47822671800003\n",
      "阿里地区中心点坐标: 80.10328190200005, 32.505668392000075\n",
      "西安市中心点坐标: 108.94335131900004, 34.264736296000024\n",
      "铜川市中心点坐标: 108.97476577900005, 34.91756090800004\n",
      "宝鸡市中心点坐标: 107.14006836400006, 34.37059243600004\n",
      "咸阳市中心点坐标: 108.70045222800007, 34.334885099000076\n",
      "渭南市中心点坐标: 109.49771568700004, 34.50068820600006\n",
      "延安市中心点坐标: 109.48540907400007, 36.596437037000044\n",
      "汉中市中心点坐标: 107.02407707100008, 33.07953280400005\n",
      "榆林市中心点坐标: 109.73560429900004, 38.28964111100004\n",
      "安康市中心点坐标: 109.02440677400006, 32.69236764800007\n",
      "商洛市中心点坐标: 109.93478875300008, 33.86999676200003\n",
      "兰州市中心点坐标: 103.82113137400006, 36.05834678100007\n",
      "嘉峪关市中心点坐标: 98.27672307300008, 39.78570960600007\n",
      "金昌市中心点坐标: 102.18589878700004, 38.51380116400003\n",
      "白银市中心点坐标: 104.17085402700008, 36.54564919000006\n",
      "天水市中心点坐标: 105.72106110400006, 34.579848765000065\n",
      "武威市中心点坐标: 102.63298751900004, 37.92971513600003\n",
      "张掖市中心点坐标: 100.45378365600004, 38.93226664500003\n",
      "平凉市中心点坐标: 106.68040903000008, 35.54321884800004\n",
      "酒泉市中心点坐标: 98.51021724100008, 39.74324827400005\n",
      "庆阳市中心点坐标: 107.63363641100005, 35.73461157700007\n",
      "定西市中心点坐标: 104.62362328500006, 35.58012192100006\n",
      "陇南市中心点坐标: 104.92647887200008, 33.39055027000006\n",
      "临夏回族自治州中心点坐标: 103.20976494700005, 35.59980871300007\n",
      "甘南藏族自治州中心点坐标: 102.90928523200006, 34.987488787000075\n",
      "西宁市中心点坐标: 101.77698435300005, 36.62325694700007\n",
      "海东市中心点坐标: 102.10119945700006, 36.50285113900003\n",
      "海北藏族自治州中心点坐标: 100.89960447800007, 36.95947747500003\n",
      "黄南藏族自治州中心点坐标: 102.01808748200006, 35.518145426000046\n",
      "海南藏族自治州中心点坐标: 100.61826591100004, 36.28075756000004\n",
      "果洛藏族自治州中心点坐标: 100.24080172100008, 34.47507733000003\n",
      "玉树藏族自治州中心点坐标: 97.00808274700006, 33.00618277600006\n",
      "海西蒙古族藏族自治州中心点坐标: 97.36998122800009, 37.374183290000076\n",
      "银川市中心点坐标: 106.27375828800007, 38.465904401000046\n",
      "石嘴山市中心点坐标: 106.37146997300005, 39.01247718500008\n",
      "吴忠市中心点坐标: 106.19501274600009, 37.98562072800007\n",
      "固原市中心点坐标: 106.28100799500004, 36.00472548100004\n",
      "中卫市中心点坐标: 105.18579250700009, 37.514381394000054\n",
      "乌鲁木齐市中心点坐标: 87.61488374000004, 43.791593427000066\n",
      "克拉玛依市中心点坐标: 84.87152733900007, 45.59507255300008\n",
      "吐鲁番市中心点坐标: 89.18122237100005, 42.946051847000035\n",
      "哈密市中心点坐标: 93.51155411100007, 42.83172556900007\n",
      "昌吉回族自治州中心点坐标: 87.30091274700004, 44.013269637000064\n",
      "博尔塔拉蒙古自治州中心点坐标: 82.07148409700005, 44.90222656200007\n",
      "巴音郭楞蒙古自治州中心点坐标: 86.14833061200005, 41.76705688700008\n",
      "阿克苏地区中心点坐标: 80.26289702700007, 41.16972659700008\n",
      "克孜勒苏柯尔克孜自治州中心点坐标: 76.16980108100006, 39.71302697800007\n",
      "喀什地区中心点坐标: 75.98618528000003, 39.467439840000054\n",
      "和田地区中心点坐标: 79.92340984600008, 37.11102672800007\n",
      "伊犁哈萨克自治州中心点坐标: 81.31491671900005, 43.92062324200003\n",
      "塔城地区中心点坐标: 82.98254217100003, 46.74557454200004\n",
      "阿勒泰地区中心点坐标: 88.13627289400006, 47.84708882800004\n",
      "石河子市中心点坐标: 86.03830904200004, 44.30471957300006\n",
      "阿拉尔市中心点坐标: 81.28308676300009, 40.54134559900007\n",
      "图木舒克市中心点坐标: 79.07556437600005, 39.86675565000007\n",
      "五家渠市中心点坐标: 87.52388251700006, 44.166312649000076\n",
      "北屯市中心点坐标: 87.82180134800006, 47.352097815000036\n",
      "铁门关市中心点坐标: 85.49857281600003, 41.82590768000006\n",
      "双河市中心点坐标: 82.35046921700007, 44.839525379000065\n",
      "可克达拉市中心点坐标: 80.63366382400005, 43.68207966500006\n",
      "昆玉市中心点坐标: 79.28515552500005, 37.20809075600005\n",
      "胡杨河市中心点坐标: 84.82512138800007, 44.69206352200007\n",
      "香港特别行政区中心点坐标: 114.16839862400002, 22.32280485900003\n",
      "澳门特别行政区中心点坐标: 113.54400045000011, 22.201901503000045\n"
     ]
    }
   ],
   "source": [
    "fc = \"resource/data2/中国各城市中心/中国各城市中心.shp\"\n",
    "with arcpy.da.SearchCursor(fc, [\"SHAPE@XY\", \"city\"]) as cursor: # 返回几何对象的特定值，此处为xy坐标，元祖格式\n",
    "    for row in cursor:\n",
    "        x, y = row[0] # 解开元祖\n",
    "        city = row[1]\n",
    "        print(f\"{city}中心点坐标: {x}, {y}\") # 打印点坐标"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "我们结合游标和几何令牌探索`Polygon`要素的坐标："
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "adc7fd6df55cd452"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第1个顶点坐标为117.38335014519316, 40.226472314945454\n",
      "第2个顶点坐标为117.38556534536451, 40.22405173030319\n",
      "第3个顶点坐标为117.38697686459068, 40.2201720954659\n",
      "第4个顶点坐标为117.38695820995349, 40.220152554996446\n",
      "第5个顶点坐标为117.38694822028424, 40.22014282972788\n",
      "第6个顶点坐标为117.38694021991523, 40.2201355650044\n",
      "第7个顶点坐标为117.38693159002094, 40.22012824002633\n",
      "第8个顶点坐标为117.38692309502483, 40.22012152478857\n",
      "第9个顶点坐标为117.38691046494603, 40.22011232022743\n",
      "第10个顶点坐标为117.38689809477137, 40.22010405545785\n",
      "第11个顶点坐标为117.38688549526944, 40.220096320388905\n",
      "第12个顶点坐标为117.38546616543124, 40.219262854796966\n",
      "第13个顶点坐标为117.3854561002189, 40.21925705057248\n",
      "第14个顶点坐标为117.38544618969001, 40.21925150984936\n",
      "第15个顶点坐标为117.38543633491895, 40.21924620025197\n",
      "第16个顶点坐标为117.38542637043076, 40.21924112987432\n",
      "第17个顶点坐标为117.38541515498548, 40.21923574473391\n",
      "第18个顶点坐标为117.3854048001914, 40.2192310655613\n",
      "第19个顶点坐标为117.38538968978241, 40.219224684871335\n",
      "第20个顶点坐标为117.38537423493301, 40.21921869538653\n",
      "第21个顶点坐标为117.38535852467623, 40.219213025161025\n",
      "第22个顶点坐标为117.38534255001878, 40.21920764002061\n",
      "第23个顶点坐标为117.38532644495956, 40.219202565146304\n",
      "第24个顶点坐标为117.38531017982098, 40.219197755572\n",
      "第25个顶点坐标为117.38529402529912, 40.21919325986113\n",
      "第26个顶点坐标为117.38528252476874, 40.21919020036751\n",
      "第27个顶点坐标为117.3852709397022, 40.21918724519526\n",
      "第28个顶点坐标为117.3852589904102, 40.21918434038503\n",
      "第29个顶点坐标为117.38524662473196, 40.21918144546737\n",
      "第30个顶点坐标为117.38523370507153, 40.21917853975788\n",
      "第31个顶点坐标为117.38522019005984, 40.21917560976664\n",
      "第32个顶点坐标为117.3852059447986, 40.21917265549371\n",
      "第33个顶点坐标为117.38519090543605, 40.21916962477843\n",
      "第34个顶点坐标为117.38517566012865, 40.21916662014348\n",
      "第35个顶点坐标为117.38515997505283, 40.21916361011256\n",
      "第36个顶点坐标为117.38514596001812, 40.219161000279996\n",
      "第37个顶点坐标为117.38137109979652, 40.21846055521053\n",
      "第38个顶点坐标为117.37996120024923, 40.21825778057217\n",
      "第39个顶点坐标为117.37985882502483, 40.21824452546451\n",
      "第40个顶点坐标为117.37934593986324, 40.21817810513551\n",
      "第41个顶点坐标为117.37783468492285, 40.21825201501855\n",
      "第42个顶点坐标为117.37768978975203, 40.21826360008515\n",
      "第43个顶点坐标为117.37680376508172, 40.21847097025915\n",
      "第44个顶点坐标为117.37091545488443, 40.217179415197904\n",
      "第45个顶点坐标为117.37086222491178, 40.21712997496843\n",
      "第46个顶点坐标为117.3708560501666, 40.2170803503779\n",
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      "第4478个顶点坐标为117.21937586530396, 40.47613123037206\n",
      "第4479个顶点坐标为117.21933213037357, 40.474701234574354\n",
      "第4480个顶点坐标为117.2194000750535, 40.47443476994874\n",
      "第4481个顶点坐标为117.22156479987564, 40.471857134898926\n",
      "第4482个顶点坐标为117.22168592956211, 40.47173175501655\n",
      "第4483个顶点坐标为117.22716947019501, 40.46190279008175\n",
      "第4484个顶点坐标为117.22724528484207, 40.46117474481866\n",
      "第4485个顶点坐标为117.22755370463983, 40.45991083042054\n",
      "第4486个顶点坐标为117.22844073475221, 40.45690771981549\n",
      "第4487个顶点坐标为117.2285161348118, 40.45671483502167\n",
      "第4488个顶点坐标为117.22869718992342, 40.45634837477775\n",
      "第4489个顶点坐标为117.22877636533701, 40.45620160002386\n",
      "第4490个顶点坐标为117.22884369488065, 40.45611847478784\n",
      "第4491个顶点坐标为117.22910485980151, 40.45588372025753\n",
      "第4492个顶点坐标为117.22973801040087, 40.45534115477051\n",
      "第4493个顶点坐标为117.22985974982771, 40.455287424774895\n",
      "第4494个顶点坐标为117.24042283044935, 40.45089120496522\n",
      "第4495个顶点坐标为117.25558526974442, 40.43903890482164\n",
      "第4496个顶点坐标为117.25094088021751, 40.43378020009624\n",
      "第4497个顶点坐标为117.24381735029056, 40.426857979811984\n",
      "第4498个顶点坐标为117.23744426504084, 40.42152427972178\n",
      "第4499个顶点坐标为117.230837400126, 40.41712254976591\n",
      "第4500个顶点坐标为117.22928029504965, 40.41620900494166\n",
      "第4501个顶点坐标为117.22871841032645, 40.41595467486843\n",
      "第4502个顶点坐标为117.23066714497281, 40.399063090189486\n",
      "第4503个顶点坐标为117.23295705002795, 40.392916824751694\n",
      "第4504个顶点坐标为117.23013088463267, 40.3914147303056\n",
      "第4505个顶点坐标为117.22262133510662, 40.385531479694066\n",
      "第4506个顶点坐标为117.22205991982946, 40.3850909701722\n",
      "第4507个顶点坐标为117.22151438478113, 40.38446300506371\n",
      "第4508个顶点坐标为117.2214100103638, 40.38434285473903\n",
      "第4509个顶点坐标为117.22028331542276, 40.382699994808945\n",
      "第4510个顶点坐标为117.22026889479378, 40.38266947541604\n",
      "第4511个顶点坐标为117.21782160478324, 40.375195729699215\n",
      "第4512个顶点坐标为117.2176406701808, 40.37408838457134\n",
      "第4513个顶点坐标为117.21739605998027, 40.37219496002945\n",
      "第4514个顶点坐标为117.21739130526464, 40.37212234966677\n",
      "第4515个顶点坐标为117.2175962400745, 40.37007141516375\n",
      "第4516个顶点坐标为117.21957679992943, 40.36789066532202\n",
      "第4517个顶点坐标为117.21976387959944, 40.36778469460802\n",
      "第4518个顶点坐标为117.222930075063, 40.367716745431494\n",
      "第4519个顶点坐标为117.22294446961166, 40.36771864030305\n",
      "第4520个顶点坐标为117.22320105518463, 40.367789370183345\n",
      "第4521个顶点坐标为117.22510922501078, 40.36853388473105\n",
      "第4522个顶点坐标为117.22527592514393, 40.36862947007404\n",
      "第4523个顶点坐标为117.22635399464116, 40.36891613527149\n",
      "第4524个顶点坐标为117.23070358999871, 40.369205545199804\n",
      "第4525个顶点坐标为117.2348130501822, 40.368985165433116\n",
      "第4526个顶点坐标为117.23501233994762, 40.36896826986981\n",
      "第4527个顶点坐标为117.23536065996655, 40.36880212012181\n",
      "第4528个顶点坐标为117.23638605976896, 40.36828175979542\n",
      "第4529个顶点坐标为117.23662650970573, 40.36808761505142\n",
      "第4530个顶点坐标为117.23845135464637, 40.36452057466482\n",
      "第4531个顶点坐标为117.24407767974117, 40.3571230851623\n",
      "第4532个顶点坐标为117.24767942496555, 40.353601474928155\n",
      "第4533个顶点坐标为117.24794070499956, 40.34990044544924\n",
      "第4534个顶点坐标为117.2482299854255, 40.34882532033231\n",
      "第4535个顶点坐标为117.25038168986316, 40.34134496459848\n",
      "第4536个顶点坐标为117.25340298475999, 40.3349094097415\n",
      "第4537个顶点坐标为117.25352032020669, 40.33473773995479\n",
      "第4538个顶点坐标为117.25379606493652, 40.33449779993356\n",
      "第4539个顶点坐标为117.2546849152767, 40.33417369506003\n",
      "第4540个顶点坐标为117.2551535403037, 40.33405300514215\n",
      "第4541个顶点坐标为117.25579699983177, 40.33395822469237\n",
      "第4542个顶点坐标为117.2572975852155, 40.33377414965736\n",
      "第4543个顶点坐标为117.25757797494362, 40.3337735246285\n",
      "第4544个顶点坐标为117.25820562978606, 40.33389070988841\n",
      "第4545个顶点坐标为117.25883353463996, 40.33407565007127\n",
      "第4546个顶点坐标为117.25933476009118, 40.33428767963278\n",
      "第4547个顶点坐标为117.2596169700472, 40.33440705474186\n",
      "第4548个顶点坐标为117.259831259605, 40.33448563480425\n",
      "第4549个顶点坐标为117.26017066464453, 40.33447921994008\n",
      "第4550个顶点坐标为117.26082071980045, 40.33435565039298\n",
      "第4551个顶点坐标为117.26496134958666, 40.33309408502407\n",
      "第4552个顶点坐标为117.2652549296713, 40.33296312035179\n",
      "第4553个顶点坐标为117.26779896964683, 40.33171594503489\n",
      "第4554个顶点坐标为117.26813115043228, 40.331532059756796\n",
      "第4555个顶点坐标为117.26837000497176, 40.33138490998567\n",
      "第4556个顶点坐标为117.2684369747866, 40.33130924462597\n",
      "第4557个顶点坐标为117.26863879524421, 40.330997445176024\n",
      "第4558个顶点坐标为117.26869565038407, 40.33090575479679\n",
      "第4559个顶点坐标为117.26870050492437, 40.33081816532609\n",
      "第4560个顶点坐标为117.26868448979747, 40.33071470012334\n",
      "第4561个顶点坐标为117.26817046519488, 40.32963708478371\n",
      "第4562个顶点坐标为117.26749443042365, 40.3283607948149\n",
      "第4563个顶点坐标为117.26696122508054, 40.327990009731195\n",
      "第4564个顶点坐标为117.26590011499832, 40.32635414472804\n",
      "第4565个顶点坐标为117.26478906466366, 40.32421101532617\n",
      "第4566个顶点坐标为117.26475220505029, 40.32409565479071\n",
      "第4567个顶点坐标为117.26472581534415, 40.32399619516832\n",
      "第4568个顶点坐标为117.26471508463351, 40.323912620271244\n",
      "第4569个顶点坐标为117.26471480494433, 40.32384096498862\n",
      "第4570个顶点坐标为117.26538868044327, 40.31794775978136\n",
      "第4571个顶点坐标为117.26791258999413, 40.307261075438305\n",
      "第4572个顶点坐标为117.2680510001536, 40.307047550304276\n",
      "第4573个顶点坐标为117.2682937253752, 40.306807114756566\n",
      "第4574个顶点坐标为117.2686406100762, 40.306486465078365\n",
      "第4575个顶点坐标为117.26905712478651, 40.30617454961583\n",
      "第4576个顶点坐标为117.26965895469709, 40.3057733746416\n",
      "第4577个顶点坐标为117.26985584956788, 40.305675194754485\n",
      "第4578个顶点坐标为117.27001802521181, 40.305603740020615\n",
      "第4579个顶点坐标为117.27050513490417, 40.30554040976216\n",
      "第4580个顶点坐标为117.2708532597702, 40.30553959497638\n",
      "第4581个顶点坐标为117.27128258981975, 40.305529705131846\n",
      "第4582个顶点坐标为117.27151449529777, 40.30548475971392\n",
      "第4583个顶点坐标为117.27200115982578, 40.30530591963253\n",
      "第4584个顶点坐标为117.28096715511015, 40.30060698532418\n",
      "第4585个顶点坐标为117.28112774524925, 40.30051481470696\n",
      "第4586个顶点坐标为117.28146542988577, 40.30030075987224\n",
      "第4587个顶点坐标为117.28686220033035, 40.295216209760326\n",
      "第4588个顶点坐标为117.28783333504043, 40.291288659943746\n",
      "第4589个顶点坐标为117.28785473530786, 40.29116877042247\n",
      "第4590个顶点坐标为117.28808302011532, 40.28969215017264\n",
      "第4591个顶点坐标为117.28808785037404, 40.289604559802626\n",
      "第4592个顶点坐标为117.28808756528895, 40.289537384942435\n",
      "第4593个顶点坐标为117.28729443519012, 40.286382025410205\n",
      "第4594个顶点坐标为117.28729170484837, 40.2863754495674\n",
      "第4595个顶点坐标为117.2872475796122, 40.28626913531241\n",
      "第4596个顶点坐标为117.28714121499513, 40.286120230064626\n",
      "第4597个顶点坐标为117.28681425027753, 40.28583441472654\n",
      "第4598个顶点坐标为117.2866679854393, 40.28566493029234\n",
      "第4599个顶点坐标为117.2863678546928, 40.28516673465714\n",
      "第4600个顶点坐标为117.28628374469918, 40.28494401485443\n",
      "第4601个顶点坐标为117.28619927497664, 40.284636370272324\n",
      "第4602个顶点坐标为117.28619866973293, 40.28449305970702\n",
      "第4603个顶点坐标为117.2863377247063, 40.282953444645784\n",
      "第4604个顶点坐标为117.28641925544434, 40.28256577029413\n",
      "第4605个顶点坐标为117.28646714524257, 40.28241702962225\n",
      "第4606个顶点坐标为117.28651507461109, 40.282278909943784\n",
      "第4607个顶点坐标为117.2890928553511, 40.27712447981094\n",
      "第4608个顶点坐标为117.2892398198619, 40.27691887500612\n",
      "第4609个顶点坐标为117.28935906456923, 40.276836620313816\n",
      "第4610个顶点坐标为117.28941380989943, 40.27682543994217\n",
      "第4611个顶点坐标为117.29807282520073, 40.27681861498712\n",
      "第4612个顶点坐标为117.3005580701988, 40.27753234034526\n",
      "第4613个顶点坐标为117.30491995029558, 40.27893769471888\n",
      "第4614个顶点坐标为117.31532388508924, 40.28297663996\n",
      "第4615个顶点坐标为117.3207681001677, 40.285760780036014\n",
      "第4616个顶点坐标为117.32141245002458, 40.286465010352\n",
      "第4617个顶点坐标为117.32175354489027, 40.28672433975652\n",
      "第4618个顶点坐标为117.32209395537177, 40.28692759463286\n",
      "第4619个顶点坐标为117.32485698476205, 40.28842624467552\n",
      "第4620个顶点坐标为117.32491515471065, 40.288432440105055\n",
      "第4621个顶点坐标为117.32508149961154, 40.28843196975964\n",
      "第4622个顶点坐标为117.32519922986069, 40.288410410312224\n",
      "第4623个顶点坐标为117.32525452467667, 40.288378409735856\n",
      "第4624个顶点坐标为117.32534432468094, 40.288314459844514\n",
      "第4625个顶点坐标为117.32566070977396, 40.28780401993964\n",
      "第4626个顶点坐标为117.32744001484491, 40.2845187596331\n",
      "第4627个顶点坐标为117.32964339522653, 40.27464034457631\n",
      "第4628个顶点坐标为117.33046764006963, 40.26809529995819\n",
      "第4629个顶点坐标为117.33560951476738, 40.255125670413406\n",
      "第4630个顶点坐标为117.33563981022917, 40.254864414661085\n",
      "第4631个顶点坐标为117.33580001995506, 40.25344002973361\n",
      "第4632个顶点坐标为117.33579412489905, 40.25330047473716\n",
      "第4633个顶点坐标为117.33574282487155, 40.25309991512904\n",
      "第4634个顶点坐标为117.33462521038518, 40.25022485988944\n",
      "第4635个顶点坐标为117.33326657039595, 40.2450245148695\n",
      "第4636个顶点坐标为117.33324780514215, 40.24491024970831\n",
      "第4637个顶点坐标为117.33327428028383, 40.244827760292935\n",
      "第4638个顶点坐标为117.3333155753536, 40.24475718959269\n",
      "第4639个顶点坐标为117.33903784012432, 40.23498537027865\n",
      "第4640个顶点坐标为117.34434302541035, 40.228493935147014\n",
      "第4641个顶点坐标为117.34475955001324, 40.228236194844555\n",
      "第4642个顶点坐标为117.34503624532647, 40.228081169710265\n",
      "第4643个顶点坐标为117.34532739544295, 40.22804307982432\n",
      "第4644个顶点坐标为117.34566034964553, 40.228036770180836\n",
      "第4645个顶点坐标为117.34926092035528, 40.22809243012159\n",
      "第4646个顶点坐标为117.34938101492196, 40.228175809865775\n",
      "第4647个顶点坐标为117.34972766040323, 40.22847384519184\n",
      "第4648个顶点坐标为117.35207591976791, 40.230165514926625\n",
      "第4649个顶点坐标为117.35342064994313, 40.23095632027798\n",
      "第4650个顶点坐标为117.35402166956442, 40.231137920378785\n",
      "第4651个顶点坐标为117.35471621508134, 40.23129397523684\n",
      "第4652个顶点坐标为117.3550373701786, 40.23135280528777\n",
      "第4653个顶点坐标为117.35541904515287, 40.231375565330154\n",
      "第4654个顶点坐标为117.36203614525198, 40.23149596027042\n",
      "第4655个顶点坐标为117.36436122009434, 40.231373020248725\n",
      "第4656个顶点坐标为117.36629533985808, 40.230790009551356\n",
      "第4657个顶点坐标为117.36897839973346, 40.22984568992672\n",
      "第4658个顶点坐标为117.37200452488901, 40.22853998043581\n",
      "第4659个顶点坐标为117.37645423997128, 40.226590184589384\n",
      "第4660个顶点坐标为117.37663209529501, 40.22645136523835\n",
      "第4661个顶点坐标为117.37883096467738, 40.22581145533445\n",
      "第4662个顶点坐标为117.37994431997447, 40.225502075060774\n",
      "第4663个顶点坐标为117.38003617492961, 40.225491140203985\n",
      "第4664个顶点坐标为117.38034323495242, 40.22551140013104\n",
      "第4665个顶点坐标为117.38088980512043, 40.22556542960092\n",
      "第4666个顶点坐标为117.38335014519316, 40.226472314945454\n",
      "总共有4666个顶点\n"
     ]
    }
   ],
   "source": [
    "fc = \"./resource/第七次人口普查数据/shp/分省份的数据/分年龄、性别的人口_省份等级.shp\"\n",
    "\n",
    "with arcpy.da.SearchCursor(fc, [\"省\",\"SHAPE@\"]) as cursor: # 使用字符串 \"OID@\" 引用唯一标识符字段。  \n",
    "    for row in cursor:\n",
    "        if row[0] == \"北京市\":\n",
    "            # 探索北京市的顶点坐标\n",
    "            count = 0\n",
    "            for part in row[1]:\n",
    "                for pnt in part:\n",
    "                    count += 1\n",
    "                    print(f\"第{count}个顶点坐标为{pnt.X}, {pnt.Y}\")\n",
    "                print(f\"总共有{count}个顶点\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:13.653132500Z",
     "start_time": "2023-08-13T01:26:13.193222500Z"
    }
   },
   "id": "70a3e15675a3e473"
  },
  {
   "cell_type": "markdown",
   "id": "68769faa",
   "metadata": {},
   "source": [
    "## 示例2：通过excel表格制作分年龄的人口普查要素文件"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 方法一 通过geopandas 安装方法：conda install geopandas -c esri"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "98c6845a1b209054"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "         地区       0岁 Unnamed: 2      1-4岁 Unnamed: 4      5-9岁 Unnamed: 6  \\\n0       NaN        男          女         男          女         男          女   \n1        合计  6312409    5675648  34656922   31238909  48017458   42226598   \n2       北京市    79388      73342    447165     416355    485161     447792   \n3       市辖区    79388      73342    447165     416355    485161     447792   \n4       东城区     2265       2126     13193      12455     21577      19742   \n...     ...      ...        ...       ...        ...       ...        ...   \n3360   铁门关市      271        266      1648       1542      2497       2290   \n3361    双河市      191        203      1093       1030      1450       1415   \n3362  可克达拉市      199        197      1126       1063      1772       1702   \n3363    昆玉市      187        201      1642       1487      4112       3755   \n3364   胡杨河市       68         58       360        318       525        467   \n\n        10-14岁 Unnamed: 8    15-19岁  ...    65-69岁 Unnamed: 30    70-74岁  \\\n0            男          女         男  ...         男           女         男   \n1     45606790   39649204  39053343  ...  36337923    37667637  24162733   \n2       335714     306590    345715  ...    568385      626286    314838   \n3       335714     306590    345715  ...    568385      626286    314838   \n4        13924      13008      8768  ...     24441       27308     13244   \n...        ...        ...       ...  ...       ...         ...       ...   \n3360      2561       2246      2107  ...      1428        1509       993   \n3361      1613       1341      1626  ...       726         757       407   \n3362      1926       1839      2045  ...      1141        1170       753   \n3363      2516       2260      2203  ...       534         569       377   \n3364       632        539      1139  ...       386         370       234   \n\n     Unnamed: 32    75-79岁 Unnamed: 34   80-84岁 Unnamed: 36   85岁及以上  \\\n0              女         男           女        男           女        男   \n1       25427303  14752433    16486416  9157003    11225875  6100269   \n2         353703    184665      230508   152087      196699   124749   \n3         353703    184665      230508   152087      196699   124749   \n4          14362      7332        8981     6550       10128     6931   \n...          ...       ...         ...      ...         ...      ...   \n3360        1570      1099        1517      990        1074      530   \n3361         769       504         759      560         418      189   \n3362        1275       953        1421      913         760      334   \n3363         339       210         237      138         124       93   \n3364         447       253         377      236         251      156   \n\n     Unnamed: 38  \n0              女  \n1        9317688  \n2         160140  \n3         160140  \n4           9972  \n...          ...  \n3360         595  \n3361         200  \n3362         410  \n3363          87  \n3364         189  \n\n[3365 rows x 39 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>地区</th>\n      <th>0岁</th>\n      <th>Unnamed: 2</th>\n      <th>1-4岁</th>\n      <th>Unnamed: 4</th>\n      <th>5-9岁</th>\n      <th>Unnamed: 6</th>\n      <th>10-14岁</th>\n      <th>Unnamed: 8</th>\n      <th>15-19岁</th>\n      <th>...</th>\n      <th>65-69岁</th>\n      <th>Unnamed: 30</th>\n      <th>70-74岁</th>\n      <th>Unnamed: 32</th>\n      <th>75-79岁</th>\n      <th>Unnamed: 34</th>\n      <th>80-84岁</th>\n      <th>Unnamed: 36</th>\n      <th>85岁及以上</th>\n      <th>Unnamed: 38</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>...</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>合计</td>\n      <td>6312409</td>\n      <td>5675648</td>\n      <td>34656922</td>\n      <td>31238909</td>\n      <td>48017458</td>\n      <td>42226598</td>\n      <td>45606790</td>\n      <td>39649204</td>\n      <td>39053343</td>\n      <td>...</td>\n      <td>36337923</td>\n      <td>37667637</td>\n      <td>24162733</td>\n      <td>25427303</td>\n      <td>14752433</td>\n      <td>16486416</td>\n      <td>9157003</td>\n      <td>11225875</td>\n      <td>6100269</td>\n      <td>9317688</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>北京市</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>306590</td>\n      <td>345715</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>市辖区</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>306590</td>\n      <td>345715</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>东城区</td>\n      <td>2265</td>\n      <td>2126</td>\n      <td>13193</td>\n      <td>12455</td>\n      <td>21577</td>\n      <td>19742</td>\n      <td>13924</td>\n      <td>13008</td>\n      <td>8768</td>\n      <td>...</td>\n      <td>24441</td>\n      <td>27308</td>\n      <td>13244</td>\n      <td>14362</td>\n      <td>7332</td>\n      <td>8981</td>\n      <td>6550</td>\n      <td>10128</td>\n      <td>6931</td>\n      <td>9972</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3360</th>\n      <td>铁门关市</td>\n      <td>271</td>\n      <td>266</td>\n      <td>1648</td>\n      <td>1542</td>\n      <td>2497</td>\n      <td>2290</td>\n      <td>2561</td>\n      <td>2246</td>\n      <td>2107</td>\n      <td>...</td>\n      <td>1428</td>\n      <td>1509</td>\n      <td>993</td>\n      <td>1570</td>\n      <td>1099</td>\n      <td>1517</td>\n      <td>990</td>\n      <td>1074</td>\n      <td>530</td>\n      <td>595</td>\n    </tr>\n    <tr>\n      <th>3361</th>\n      <td>双河市</td>\n      <td>191</td>\n      <td>203</td>\n      <td>1093</td>\n      <td>1030</td>\n      <td>1450</td>\n      <td>1415</td>\n      <td>1613</td>\n      <td>1341</td>\n      <td>1626</td>\n      <td>...</td>\n      <td>726</td>\n      <td>757</td>\n      <td>407</td>\n      <td>769</td>\n      <td>504</td>\n      <td>759</td>\n      <td>560</td>\n      <td>418</td>\n      <td>189</td>\n      <td>200</td>\n    </tr>\n    <tr>\n      <th>3362</th>\n      <td>可克达拉市</td>\n      <td>199</td>\n      <td>197</td>\n      <td>1126</td>\n      <td>1063</td>\n      <td>1772</td>\n      <td>1702</td>\n      <td>1926</td>\n      <td>1839</td>\n      <td>2045</td>\n      <td>...</td>\n      <td>1141</td>\n      <td>1170</td>\n      <td>753</td>\n      <td>1275</td>\n      <td>953</td>\n      <td>1421</td>\n      <td>913</td>\n      <td>760</td>\n      <td>334</td>\n      <td>410</td>\n    </tr>\n    <tr>\n      <th>3363</th>\n      <td>昆玉市</td>\n      <td>187</td>\n      <td>201</td>\n      <td>1642</td>\n      <td>1487</td>\n      <td>4112</td>\n      <td>3755</td>\n      <td>2516</td>\n      <td>2260</td>\n      <td>2203</td>\n      <td>...</td>\n      <td>534</td>\n      <td>569</td>\n      <td>377</td>\n      <td>339</td>\n      <td>210</td>\n      <td>237</td>\n      <td>138</td>\n      <td>124</td>\n      <td>93</td>\n      <td>87</td>\n    </tr>\n    <tr>\n      <th>3364</th>\n      <td>胡杨河市</td>\n      <td>68</td>\n      <td>58</td>\n      <td>360</td>\n      <td>318</td>\n      <td>525</td>\n      <td>467</td>\n      <td>632</td>\n      <td>539</td>\n      <td>1139</td>\n      <td>...</td>\n      <td>386</td>\n      <td>370</td>\n      <td>234</td>\n      <td>447</td>\n      <td>253</td>\n      <td>377</td>\n      <td>236</td>\n      <td>251</td>\n      <td>156</td>\n      <td>189</td>\n    </tr>\n  </tbody>\n</table>\n<p>3365 rows × 39 columns</p>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 准备excel文件\n",
    "import pandas as pd\n",
    "df = pd.read_excel(\"./resource/data2/表2 分年龄、性别的人口(全部数据).xlsx\")\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:16.377800200Z",
     "start_time": "2023-08-13T01:26:13.652131300Z"
    }
   },
   "id": "6c4750827e3d17fb"
  },
  {
   "cell_type": "markdown",
   "source": [
    "原始表格，此表格无港澳台人口信息：\n",
    "![表2 分年龄、性别的人口(全部数据).xlsx](https://image-1315363329.cos.ap-shanghai.myqcloud.com/lessons/202308122146861.png)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3e39cbb386d49304"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "         地区       0岁 Unnamed: 2      1-4岁 Unnamed: 4      5-9岁 Unnamed: 6  \\\n0       NaN        男          女         男          女         男          女   \n1        合计  6312409    5675648  34656922   31238909  48017458   42226598   \n2       北京市    79388      73342    447165     416355    485161     447792   \n3       市辖区    79388      73342    447165     416355    485161     447792   \n4       东城区     2265       2126     13193      12455     21577      19742   \n...     ...      ...        ...       ...        ...       ...        ...   \n3360   铁门关市      271        266      1648       1542      2497       2290   \n3361    双河市      191        203      1093       1030      1450       1415   \n3362  可克达拉市      199        197      1126       1063      1772       1702   \n3363    昆玉市      187        201      1642       1487      4112       3755   \n3364   胡杨河市       68         58       360        318       525        467   \n\n        10-14岁 Unnamed: 8    15-19岁  ...    65-69岁 Unnamed: 30    70-74岁  \\\n0            男          女         男  ...         男           女         男   \n1     45606790   39649204  39053343  ...  36337923    37667637  24162733   \n2       335714     306590    345715  ...    568385      626286    314838   \n3       335714     306590    345715  ...    568385      626286    314838   \n4        13924      13008      8768  ...     24441       27308     13244   \n...        ...        ...       ...  ...       ...         ...       ...   \n3360      2561       2246      2107  ...      1428        1509       993   \n3361      1613       1341      1626  ...       726         757       407   \n3362      1926       1839      2045  ...      1141        1170       753   \n3363      2516       2260      2203  ...       534         569       377   \n3364       632        539      1139  ...       386         370       234   \n\n     Unnamed: 32    75-79岁 Unnamed: 34   80-84岁 Unnamed: 36   85岁及以上  \\\n0              女         男           女        男           女        男   \n1       25427303  14752433    16486416  9157003    11225875  6100269   \n2         353703    184665      230508   152087      196699   124749   \n3         353703    184665      230508   152087      196699   124749   \n4          14362      7332        8981     6550       10128     6931   \n...          ...       ...         ...      ...         ...      ...   \n3360        1570      1099        1517      990        1074      530   \n3361         769       504         759      560         418      189   \n3362        1275       953        1421      913         760      334   \n3363         339       210         237      138         124       93   \n3364         447       253         377      236         251      156   \n\n     Unnamed: 38  \n0              女  \n1        9317688  \n2         160140  \n3         160140  \n4           9972  \n...          ...  \n3360         595  \n3361         200  \n3362         410  \n3363          87  \n3364         189  \n\n[3365 rows x 39 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>地区</th>\n      <th>0岁</th>\n      <th>Unnamed: 2</th>\n      <th>1-4岁</th>\n      <th>Unnamed: 4</th>\n      <th>5-9岁</th>\n      <th>Unnamed: 6</th>\n      <th>10-14岁</th>\n      <th>Unnamed: 8</th>\n      <th>15-19岁</th>\n      <th>...</th>\n      <th>65-69岁</th>\n      <th>Unnamed: 30</th>\n      <th>70-74岁</th>\n      <th>Unnamed: 32</th>\n      <th>75-79岁</th>\n      <th>Unnamed: 34</th>\n      <th>80-84岁</th>\n      <th>Unnamed: 36</th>\n      <th>85岁及以上</th>\n      <th>Unnamed: 38</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>...</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>合计</td>\n      <td>6312409</td>\n      <td>5675648</td>\n      <td>34656922</td>\n      <td>31238909</td>\n      <td>48017458</td>\n      <td>42226598</td>\n      <td>45606790</td>\n      <td>39649204</td>\n      <td>39053343</td>\n      <td>...</td>\n      <td>36337923</td>\n      <td>37667637</td>\n      <td>24162733</td>\n      <td>25427303</td>\n      <td>14752433</td>\n      <td>16486416</td>\n      <td>9157003</td>\n      <td>11225875</td>\n      <td>6100269</td>\n      <td>9317688</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>北京市</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>306590</td>\n      <td>345715</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>市辖区</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>306590</td>\n      <td>345715</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>东城区</td>\n      <td>2265</td>\n      <td>2126</td>\n      <td>13193</td>\n      <td>12455</td>\n      <td>21577</td>\n      <td>19742</td>\n      <td>13924</td>\n      <td>13008</td>\n      <td>8768</td>\n      <td>...</td>\n      <td>24441</td>\n      <td>27308</td>\n      <td>13244</td>\n      <td>14362</td>\n      <td>7332</td>\n      <td>8981</td>\n      <td>6550</td>\n      <td>10128</td>\n      <td>6931</td>\n      <td>9972</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3360</th>\n      <td>铁门关市</td>\n      <td>271</td>\n      <td>266</td>\n      <td>1648</td>\n      <td>1542</td>\n      <td>2497</td>\n      <td>2290</td>\n      <td>2561</td>\n      <td>2246</td>\n      <td>2107</td>\n      <td>...</td>\n      <td>1428</td>\n      <td>1509</td>\n      <td>993</td>\n      <td>1570</td>\n      <td>1099</td>\n      <td>1517</td>\n      <td>990</td>\n      <td>1074</td>\n      <td>530</td>\n      <td>595</td>\n    </tr>\n    <tr>\n      <th>3361</th>\n      <td>双河市</td>\n      <td>191</td>\n      <td>203</td>\n      <td>1093</td>\n      <td>1030</td>\n      <td>1450</td>\n      <td>1415</td>\n      <td>1613</td>\n      <td>1341</td>\n      <td>1626</td>\n      <td>...</td>\n      <td>726</td>\n      <td>757</td>\n      <td>407</td>\n      <td>769</td>\n      <td>504</td>\n      <td>759</td>\n      <td>560</td>\n      <td>418</td>\n      <td>189</td>\n      <td>200</td>\n    </tr>\n    <tr>\n      <th>3362</th>\n      <td>可克达拉市</td>\n      <td>199</td>\n      <td>197</td>\n      <td>1126</td>\n      <td>1063</td>\n      <td>1772</td>\n      <td>1702</td>\n      <td>1926</td>\n      <td>1839</td>\n      <td>2045</td>\n      <td>...</td>\n      <td>1141</td>\n      <td>1170</td>\n      <td>753</td>\n      <td>1275</td>\n      <td>953</td>\n      <td>1421</td>\n      <td>913</td>\n      <td>760</td>\n      <td>334</td>\n      <td>410</td>\n    </tr>\n    <tr>\n      <th>3363</th>\n      <td>昆玉市</td>\n      <td>187</td>\n      <td>201</td>\n      <td>1642</td>\n      <td>1487</td>\n      <td>4112</td>\n      <td>3755</td>\n      <td>2516</td>\n      <td>2260</td>\n      <td>2203</td>\n      <td>...</td>\n      <td>534</td>\n      <td>569</td>\n      <td>377</td>\n      <td>339</td>\n      <td>210</td>\n      <td>237</td>\n      <td>138</td>\n      <td>124</td>\n      <td>93</td>\n      <td>87</td>\n    </tr>\n    <tr>\n      <th>3364</th>\n      <td>胡杨河市</td>\n      <td>68</td>\n      <td>58</td>\n      <td>360</td>\n      <td>318</td>\n      <td>525</td>\n      <td>467</td>\n      <td>632</td>\n      <td>539</td>\n      <td>1139</td>\n      <td>...</td>\n      <td>386</td>\n      <td>370</td>\n      <td>234</td>\n      <td>447</td>\n      <td>253</td>\n      <td>377</td>\n      <td>236</td>\n      <td>251</td>\n      <td>156</td>\n      <td>189</td>\n    </tr>\n  </tbody>\n</table>\n<p>3365 rows × 39 columns</p>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初步处理此dataframe\n",
    "# 删除空值\n",
    "df = df.dropna(axis=0, how='all') \n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:16.475532300Z",
     "start_time": "2023-08-13T01:26:16.367800800Z"
    }
   },
   "id": "701b11163d5f7256"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "    地区     0岁 Unnamed: 2    1-4岁 Unnamed: 4    5-9岁 Unnamed: 6  10-14岁  \\\n0  NaN      男          女       男          女       男          女       男   \n1  北京市  79388      73342  447165     416355  485161     447792  335714   \n2  市辖区  79388      73342  447165     416355  485161     447792  335714   \n3  东城区   2265       2126   13193      12455   21577      19742   13924   \n4  西城区   3635       3339   20958      19709   35042      31641   22773   \n\n  Unnamed: 8  15-19岁  ...  65-69岁 Unnamed: 30  70-74岁 Unnamed: 32  75-79岁  \\\n0          女       男  ...       男           女       男           女       男   \n1     306590  345715  ...  568385      626286  314838      353703  184665   \n2     306590  345715  ...  568385      626286  314838      353703  184665   \n3      13008    8768  ...   24441       27308   13244       14362    7332   \n4      20815   13800  ...   37033       41923   20204       22432   11531   \n\n  Unnamed: 34  80-84岁 Unnamed: 36  85岁及以上 Unnamed: 38  \n0           女       男           女       男           女  \n1      230508  152087      196699  124749      160140  \n2      230508  152087      196699  124749      160140  \n3        8981    6550       10128    6931        9972  \n4       14168   10271       15502   11907       16061  \n\n[5 rows x 39 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>地区</th>\n      <th>0岁</th>\n      <th>Unnamed: 2</th>\n      <th>1-4岁</th>\n      <th>Unnamed: 4</th>\n      <th>5-9岁</th>\n      <th>Unnamed: 6</th>\n      <th>10-14岁</th>\n      <th>Unnamed: 8</th>\n      <th>15-19岁</th>\n      <th>...</th>\n      <th>65-69岁</th>\n      <th>Unnamed: 30</th>\n      <th>70-74岁</th>\n      <th>Unnamed: 32</th>\n      <th>75-79岁</th>\n      <th>Unnamed: 34</th>\n      <th>80-84岁</th>\n      <th>Unnamed: 36</th>\n      <th>85岁及以上</th>\n      <th>Unnamed: 38</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>...</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>北京市</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>306590</td>\n      <td>345715</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>市辖区</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>306590</td>\n      <td>345715</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>东城区</td>\n      <td>2265</td>\n      <td>2126</td>\n      <td>13193</td>\n      <td>12455</td>\n      <td>21577</td>\n      <td>19742</td>\n      <td>13924</td>\n      <td>13008</td>\n      <td>8768</td>\n      <td>...</td>\n      <td>24441</td>\n      <td>27308</td>\n      <td>13244</td>\n      <td>14362</td>\n      <td>7332</td>\n      <td>8981</td>\n      <td>6550</td>\n      <td>10128</td>\n      <td>6931</td>\n      <td>9972</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>西城区</td>\n      <td>3635</td>\n      <td>3339</td>\n      <td>20958</td>\n      <td>19709</td>\n      <td>35042</td>\n      <td>31641</td>\n      <td>22773</td>\n      <td>20815</td>\n      <td>13800</td>\n      <td>...</td>\n      <td>37033</td>\n      <td>41923</td>\n      <td>20204</td>\n      <td>22432</td>\n      <td>11531</td>\n      <td>14168</td>\n      <td>10271</td>\n      <td>15502</td>\n      <td>11907</td>\n      <td>16061</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 39 columns</p>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去除第一行\n",
    "df = df.drop(1, axis=0).reset_index(drop=True)\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:16.659135800Z",
     "start_time": "2023-08-13T01:26:16.416798500Z"
    }
   },
   "id": "b1ff79eac4634ba6"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "# 重命名列名 此出直接指定列名\n",
    "fields = [\n",
    "    \"Province\",\n",
    "    \"M0\",\n",
    "    \"F0\",\n",
    "    \"M1_4\",\n",
    "    \"F1_4\",\n",
    "    \"M5_9\",\n",
    "    \"F5_9\",\n",
    "    \"M10_14\",\n",
    "    \"F10_14\",\n",
    "    \"M15_19\",\n",
    "    \"F15_19\",\n",
    "    \"M20_24\",\n",
    "    \"F20_24\",\n",
    "    \"M25_29\",\n",
    "    \"F25_29\",\n",
    "    \"M30_34\",\n",
    "    \"F30_34\",\n",
    "    \"M35_39\",\n",
    "    \"F35_39\",\n",
    "    \"M40_44\",\n",
    "    \"F40_44\",\n",
    "    \"M45_49\",\n",
    "    \"F45_49\",\n",
    "    \"M50_54\",\n",
    "    \"F50_54\",\n",
    "    \"M55_59\",\n",
    "    \"F55_59\",\n",
    "    \"M60_64\",\n",
    "    \"F60_64\",\n",
    "    \"M65_69\",\n",
    "    \"F65_69\",\n",
    "    \"M70_74\",\n",
    "    \"F70_74\",\n",
    "    \"M75_79\",\n",
    "    \"F75_79\",\n",
    "    \"M80_84\",\n",
    "    \"F80_84\",\n",
    "    \"M85+\",\n",
    "    \"F85+\"\n",
    "]\n",
    "df.columns = fields"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:16.660134600Z",
     "start_time": "2023-08-13T01:26:16.447989Z"
    }
   },
   "id": "75e6db066ad84ad9"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "  Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n0      NaN      男      女       男       女       男       女       男       女   \n1      北京市  79388  73342  447165  416355  485161  447792  335714  306590   \n2      市辖区  79388  73342  447165  416355  485161  447792  335714  306590   \n3      东城区   2265   2126   13193   12455   21577   19742   13924   13008   \n4      西城区   3635   3339   20958   19709   35042   31641   22773   20815   \n\n   M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  M80_84  \\\n0       男  ...       男       女       男       女       男       女       男   \n1  345715  ...  568385  626286  314838  353703  184665  230508  152087   \n2  345715  ...  568385  626286  314838  353703  184665  230508  152087   \n3    8768  ...   24441   27308   13244   14362    7332    8981    6550   \n4   13800  ...   37033   41923   20204   22432   11531   14168   10271   \n\n   F80_84    M85+    F85+  \n0       女       男       女  \n1  196699  124749  160140  \n2  196699  124749  160140  \n3   10128    6931    9972  \n4   15502   11907   16061  \n\n[5 rows x 39 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Province</th>\n      <th>M0</th>\n      <th>F0</th>\n      <th>M1_4</th>\n      <th>F1_4</th>\n      <th>M5_9</th>\n      <th>F5_9</th>\n      <th>M10_14</th>\n      <th>F10_14</th>\n      <th>M15_19</th>\n      <th>...</th>\n      <th>M65_69</th>\n      <th>F65_69</th>\n      <th>M70_74</th>\n      <th>F70_74</th>\n      <th>M75_79</th>\n      <th>F75_79</th>\n      <th>M80_84</th>\n      <th>F80_84</th>\n      <th>M85+</th>\n      <th>F85+</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>...</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n      <td>男</td>\n      <td>女</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>北京市</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>306590</td>\n      <td>345715</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>市辖区</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>306590</td>\n      <td>345715</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>东城区</td>\n      <td>2265</td>\n      <td>2126</td>\n      <td>13193</td>\n      <td>12455</td>\n      <td>21577</td>\n      <td>19742</td>\n      <td>13924</td>\n      <td>13008</td>\n      <td>8768</td>\n      <td>...</td>\n      <td>24441</td>\n      <td>27308</td>\n      <td>13244</td>\n      <td>14362</td>\n      <td>7332</td>\n      <td>8981</td>\n      <td>6550</td>\n      <td>10128</td>\n      <td>6931</td>\n      <td>9972</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>西城区</td>\n      <td>3635</td>\n      <td>3339</td>\n      <td>20958</td>\n      <td>19709</td>\n      <td>35042</td>\n      <td>31641</td>\n      <td>22773</td>\n      <td>20815</td>\n      <td>13800</td>\n      <td>...</td>\n      <td>37033</td>\n      <td>41923</td>\n      <td>20204</td>\n      <td>22432</td>\n      <td>11531</td>\n      <td>14168</td>\n      <td>10271</td>\n      <td>15502</td>\n      <td>11907</td>\n      <td>16061</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 39 columns</p>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:16.663137700Z",
     "start_time": "2023-08-13T01:26:16.462527500Z"
    }
   },
   "id": "2c3cdbc11b4c8a4c"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "  Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n0      北京市  79388  73342  447165  416355  485161  447792  335714  306590   \n1      市辖区  79388  73342  447165  416355  485161  447792  335714  306590   \n2      东城区   2265   2126   13193   12455   21577   19742   13924   13008   \n3      西城区   3635   3339   20958   19709   35042   31641   22773   20815   \n4      朝阳区  12329  11304   66048   61070   76137   71025   50283   46996   \n\n   M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  M80_84  \\\n0  345715  ...  568385  626286  314838  353703  184665  230508  152087   \n1  345715  ...  568385  626286  314838  353703  184665  230508  152087   \n2    8768  ...   24441   27308   13244   14362    7332    8981    6550   \n3   13800  ...   37033   41923   20204   22432   11531   14168   10271   \n4   41172  ...   89009  101817   50634   59091   32240   40815   29164   \n\n   F80_84    M85+    F85+  \n0  196699  124749  160140  \n1  196699  124749  160140  \n2   10128    6931    9972  \n3   15502   11907   16061  \n4   38203   23785   28017  \n\n[5 rows x 39 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Province</th>\n      <th>M0</th>\n      <th>F0</th>\n      <th>M1_4</th>\n      <th>F1_4</th>\n      <th>M5_9</th>\n      <th>F5_9</th>\n      <th>M10_14</th>\n      <th>F10_14</th>\n      <th>M15_19</th>\n      <th>...</th>\n      <th>M65_69</th>\n      <th>F65_69</th>\n      <th>M70_74</th>\n      <th>F70_74</th>\n      <th>M75_79</th>\n      <th>F75_79</th>\n      <th>M80_84</th>\n      <th>F80_84</th>\n      <th>M85+</th>\n      <th>F85+</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>北京市</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>306590</td>\n      <td>345715</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>市辖区</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>306590</td>\n      <td>345715</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>东城区</td>\n      <td>2265</td>\n      <td>2126</td>\n      <td>13193</td>\n      <td>12455</td>\n      <td>21577</td>\n      <td>19742</td>\n      <td>13924</td>\n      <td>13008</td>\n      <td>8768</td>\n      <td>...</td>\n      <td>24441</td>\n      <td>27308</td>\n      <td>13244</td>\n      <td>14362</td>\n      <td>7332</td>\n      <td>8981</td>\n      <td>6550</td>\n      <td>10128</td>\n      <td>6931</td>\n      <td>9972</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>西城区</td>\n      <td>3635</td>\n      <td>3339</td>\n      <td>20958</td>\n      <td>19709</td>\n      <td>35042</td>\n      <td>31641</td>\n      <td>22773</td>\n      <td>20815</td>\n      <td>13800</td>\n      <td>...</td>\n      <td>37033</td>\n      <td>41923</td>\n      <td>20204</td>\n      <td>22432</td>\n      <td>11531</td>\n      <td>14168</td>\n      <td>10271</td>\n      <td>15502</td>\n      <td>11907</td>\n      <td>16061</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>朝阳区</td>\n      <td>12329</td>\n      <td>11304</td>\n      <td>66048</td>\n      <td>61070</td>\n      <td>76137</td>\n      <td>71025</td>\n      <td>50283</td>\n      <td>46996</td>\n      <td>41172</td>\n      <td>...</td>\n      <td>89009</td>\n      <td>101817</td>\n      <td>50634</td>\n      <td>59091</td>\n      <td>32240</td>\n      <td>40815</td>\n      <td>29164</td>\n      <td>38203</td>\n      <td>23785</td>\n      <td>28017</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 39 columns</p>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(0, axis=0).reset_index(drop=True)\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:16.665135400Z",
     "start_time": "2023-08-13T01:26:16.493528900Z"
    }
   },
   "id": "e09066fd1cb1015e"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "        省     省级码                                           geometry\n0     北京市  110000  POLYGON ((117.38335 40.22647, 117.38557 40.224...\n1     天津市  120000  POLYGON ((117.56937 40.19153, 117.56744 40.189...\n2     河北省  130000  MULTIPOLYGON (((118.26945 38.98097, 118.26871 ...\n3     山西省  140000  POLYGON ((114.13714 40.73445, 114.13860 40.732...\n4  内蒙古自治区  150000  POLYGON ((121.49813 53.32607, 121.50116 53.321...",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>省</th>\n      <th>省级码</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>北京市</td>\n      <td>110000</td>\n      <td>POLYGON ((117.38335 40.22647, 117.38557 40.224...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>天津市</td>\n      <td>120000</td>\n      <td>POLYGON ((117.56937 40.19153, 117.56744 40.189...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>河北省</td>\n      <td>130000</td>\n      <td>MULTIPOLYGON (((118.26945 38.98097, 118.26871 ...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>山西省</td>\n      <td>140000</td>\n      <td>POLYGON ((114.13714 40.73445, 114.13860 40.732...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>内蒙古自治区</td>\n      <td>150000</td>\n      <td>POLYGON ((121.49813 53.32607, 121.50116 53.321...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要做的就是把人口数据全部通过地区字段和省份地图匹配 \n",
    "import geopandas as gpd\n",
    "gdf = gpd.read_file(\"./resource/data2/中国分省份空地图/中国各省份地图.shp\", encoding=\"utf-8\")\n",
    "gdf.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:17.653248200Z",
     "start_time": "2023-08-13T01:26:16.527959Z"
    }
   },
   "id": "9143bb76f87df9f0"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "        省     省级码                                           geometry      M0  \\\n0     北京市  110000  POLYGON ((117.38335 40.22647, 117.38557 40.224...   79388   \n1     天津市  120000  POLYGON ((117.56937 40.19153, 117.56744 40.189...   43247   \n2     河北省  130000  MULTIPOLYGON (((118.26945 38.98097, 118.26871 ...  316473   \n3     山西省  140000  POLYGON ((114.13714 40.73445, 114.13860 40.732...  147446   \n4  内蒙古自治区  150000  POLYGON ((121.49813 53.32607, 121.50116 53.321...   89403   \n\n       F0     M1_4     F1_4     M5_9     F5_9   M10_14  ...   M65_69   F65_69  \\\n0   73342   447165   416355   485161   447792   335714  ...   568385   626286   \n1   39802   272661   253305   350320   314666   316570  ...   417479   443253   \n2  290961  1869601  1715582  2975262  2614026  2826356  ...  2118493  2215930   \n3  140301   767699   729614  1009444   939984  1023296  ...   914113   909574   \n4   83548   456610   426095   616732   567333   592970  ...   645995   678268   \n\n    M70_74   F70_74  M75_79  F75_79  M80_84  F80_84    M85+    F85+  \n0   314838   353703  184665  230508  152087  196699  124749  160140  \n1   251367   269201  140779  160876   88866  109030   71210   93631  \n2  1369966  1495659  721592  825659  428901  527035  265129  419573  \n3   581441   598387  339271  366699  215588  253613  136350  189638  \n4   376914   409266  220192  261066  154556  184935   94244  113482  \n\n[5 rows x 41 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>省</th>\n      <th>省级码</th>\n      <th>geometry</th>\n      <th>M0</th>\n      <th>F0</th>\n      <th>M1_4</th>\n      <th>F1_4</th>\n      <th>M5_9</th>\n      <th>F5_9</th>\n      <th>M10_14</th>\n      <th>...</th>\n      <th>M65_69</th>\n      <th>F65_69</th>\n      <th>M70_74</th>\n      <th>F70_74</th>\n      <th>M75_79</th>\n      <th>F75_79</th>\n      <th>M80_84</th>\n      <th>F80_84</th>\n      <th>M85+</th>\n      <th>F85+</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>北京市</td>\n      <td>110000</td>\n      <td>POLYGON ((117.38335 40.22647, 117.38557 40.224...</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>天津市</td>\n      <td>120000</td>\n      <td>POLYGON ((117.56937 40.19153, 117.56744 40.189...</td>\n      <td>43247</td>\n      <td>39802</td>\n      <td>272661</td>\n      <td>253305</td>\n      <td>350320</td>\n      <td>314666</td>\n      <td>316570</td>\n      <td>...</td>\n      <td>417479</td>\n      <td>443253</td>\n      <td>251367</td>\n      <td>269201</td>\n      <td>140779</td>\n      <td>160876</td>\n      <td>88866</td>\n      <td>109030</td>\n      <td>71210</td>\n      <td>93631</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>河北省</td>\n      <td>130000</td>\n      <td>MULTIPOLYGON (((118.26945 38.98097, 118.26871 ...</td>\n      <td>316473</td>\n      <td>290961</td>\n      <td>1869601</td>\n      <td>1715582</td>\n      <td>2975262</td>\n      <td>2614026</td>\n      <td>2826356</td>\n      <td>...</td>\n      <td>2118493</td>\n      <td>2215930</td>\n      <td>1369966</td>\n      <td>1495659</td>\n      <td>721592</td>\n      <td>825659</td>\n      <td>428901</td>\n      <td>527035</td>\n      <td>265129</td>\n      <td>419573</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>山西省</td>\n      <td>140000</td>\n      <td>POLYGON ((114.13714 40.73445, 114.13860 40.732...</td>\n      <td>147446</td>\n      <td>140301</td>\n      <td>767699</td>\n      <td>729614</td>\n      <td>1009444</td>\n      <td>939984</td>\n      <td>1023296</td>\n      <td>...</td>\n      <td>914113</td>\n      <td>909574</td>\n      <td>581441</td>\n      <td>598387</td>\n      <td>339271</td>\n      <td>366699</td>\n      <td>215588</td>\n      <td>253613</td>\n      <td>136350</td>\n      <td>189638</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>内蒙古自治区</td>\n      <td>150000</td>\n      <td>POLYGON ((121.49813 53.32607, 121.50116 53.321...</td>\n      <td>89403</td>\n      <td>83548</td>\n      <td>456610</td>\n      <td>426095</td>\n      <td>616732</td>\n      <td>567333</td>\n      <td>592970</td>\n      <td>...</td>\n      <td>645995</td>\n      <td>678268</td>\n      <td>376914</td>\n      <td>409266</td>\n      <td>220192</td>\n      <td>261066</td>\n      <td>154556</td>\n      <td>184935</td>\n      <td>94244</td>\n      <td>113482</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 41 columns</p>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_new = pd.merge(gdf, df, left_on=\"省\", right_on=\"Province\", how=\"left\").drop(\"Province\", axis=1)\n",
    "gdf_new.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:18.133793900Z",
     "start_time": "2023-08-13T01:26:17.647679300Z"
    }
   },
   "id": "e88740d8a658fc3c"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "           省     省级码                                           geometry  \\\n29  新疆维吾尔自治区  650000  POLYGON ((87.79720 49.18060, 87.81916 49.17268...   \n30       江苏省  320000  MULTIPOLYGON (((121.56617 32.22928, 121.56693 ...   \n31       台湾省  710000  MULTIPOLYGON (((123.69793 25.92930, 123.69726 ...   \n32   香港特别行政区  810000  MULTIPOLYGON (((114.22665 22.54375, 114.22661 ...   \n33   澳门特别行政区  820000  MULTIPOLYGON (((113.55346 22.21547, 113.55374 ...   \n\n        M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  ...   M65_69  \\\n29   92597   86127   596218   560675  1306278  1237342   991442  ...   352686   \n30  293718  269494  1702238  1553177  2518917  2193839  2360131  ...  2537937   \n31     NaN     NaN      NaN      NaN      NaN      NaN      NaN  ...      NaN   \n32     NaN     NaN      NaN      NaN      NaN      NaN      NaN  ...      NaN   \n33     NaN     NaN      NaN      NaN      NaN      NaN      NaN  ...      NaN   \n\n     F65_69   M70_74   F70_74   M75_79   F75_79  M80_84  F80_84    M85+  \\\n29   366298   242125   282936   173453   211851  123657  123562   62484   \n30  2571942  1739491  1807185  1102639  1214600  680452  839548  462422   \n31      NaN      NaN      NaN      NaN      NaN     NaN     NaN     NaN   \n32      NaN      NaN      NaN      NaN      NaN     NaN     NaN     NaN   \n33      NaN      NaN      NaN      NaN      NaN     NaN     NaN     NaN   \n\n      F85+  \n29   66833  \n30  770315  \n31     NaN  \n32     NaN  \n33     NaN  \n\n[5 rows x 41 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>省</th>\n      <th>省级码</th>\n      <th>geometry</th>\n      <th>M0</th>\n      <th>F0</th>\n      <th>M1_4</th>\n      <th>F1_4</th>\n      <th>M5_9</th>\n      <th>F5_9</th>\n      <th>M10_14</th>\n      <th>...</th>\n      <th>M65_69</th>\n      <th>F65_69</th>\n      <th>M70_74</th>\n      <th>F70_74</th>\n      <th>M75_79</th>\n      <th>F75_79</th>\n      <th>M80_84</th>\n      <th>F80_84</th>\n      <th>M85+</th>\n      <th>F85+</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>29</th>\n      <td>新疆维吾尔自治区</td>\n      <td>650000</td>\n      <td>POLYGON ((87.79720 49.18060, 87.81916 49.17268...</td>\n      <td>92597</td>\n      <td>86127</td>\n      <td>596218</td>\n      <td>560675</td>\n      <td>1306278</td>\n      <td>1237342</td>\n      <td>991442</td>\n      <td>...</td>\n      <td>352686</td>\n      <td>366298</td>\n      <td>242125</td>\n      <td>282936</td>\n      <td>173453</td>\n      <td>211851</td>\n      <td>123657</td>\n      <td>123562</td>\n      <td>62484</td>\n      <td>66833</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>江苏省</td>\n      <td>320000</td>\n      <td>MULTIPOLYGON (((121.56617 32.22928, 121.56693 ...</td>\n      <td>293718</td>\n      <td>269494</td>\n      <td>1702238</td>\n      <td>1553177</td>\n      <td>2518917</td>\n      <td>2193839</td>\n      <td>2360131</td>\n      <td>...</td>\n      <td>2537937</td>\n      <td>2571942</td>\n      <td>1739491</td>\n      <td>1807185</td>\n      <td>1102639</td>\n      <td>1214600</td>\n      <td>680452</td>\n      <td>839548</td>\n      <td>462422</td>\n      <td>770315</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>台湾省</td>\n      <td>710000</td>\n      <td>MULTIPOLYGON (((123.69793 25.92930, 123.69726 ...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>香港特别行政区</td>\n      <td>810000</td>\n      <td>MULTIPOLYGON (((114.22665 22.54375, 114.22661 ...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>澳门特别行政区</td>\n      <td>820000</td>\n      <td>MULTIPOLYGON (((113.55346 22.21547, 113.55374 ...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 41 columns</p>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_new.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:18.490930Z",
     "start_time": "2023-08-13T01:26:18.135794800Z"
    }
   },
   "id": "289dda6bcaa23579"
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "ea2e281d681e1211"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "省             object\n省级码            int64\ngeometry    geometry\nM0            object\nF0            object\nM1_4          object\nF1_4          object\nM5_9          object\nF5_9          object\nM10_14        object\nF10_14        object\nM15_19        object\nF15_19        object\nM20_24        object\nF20_24        object\nM25_29        object\nF25_29        object\nM30_34        object\nF30_34        object\nM35_39        object\nF35_39        object\nM40_44        object\nF40_44        object\nM45_49        object\nF45_49        object\nM50_54        object\nF50_54        object\nM55_59        object\nF55_59        object\nM60_64        object\nF60_64        object\nM65_69        object\nF65_69        object\nM70_74        object\nF70_74        object\nM75_79        object\nF75_79        object\nM80_84        object\nF80_84        object\nM85+          object\nF85+          object\ndtype: object"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_new.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:18.491928700Z",
     "start_time": "2023-08-13T01:26:18.419603200Z"
    }
   },
   "id": "a0c545e6321c9dc5"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "# 将数据列转化为整数"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:18.541014100Z",
     "start_time": "2023-08-13T01:26:18.436359500Z"
    }
   },
   "id": "7be2ad60ba4e0e30"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "省             object\n省级码            int64\ngeometry    geometry\nM0            object\nF0            object\nM1_4          object\nF1_4          object\nM5_9          object\nF5_9          object\nM10_14        object\nF10_14        object\nM15_19        object\nF15_19        object\nM20_24        object\nF20_24        object\nM25_29        object\nF25_29        object\nM30_34        object\nF30_34        object\nM35_39        object\nF35_39        object\nM40_44        object\nF40_44        object\nM45_49        object\nF45_49        object\nM50_54        object\nF50_54        object\nM55_59        object\nF55_59        object\nM60_64        object\nF60_64        object\nM65_69        object\nF65_69        object\nM70_74        object\nF70_74        object\nM75_79        object\nF75_79        object\nM80_84        object\nF80_84        object\nM85+          object\nF85+          object\ndtype: object"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_convert = gdf_new.columns[3:] \n",
    "\n",
    "gdf_new[cols_to_convert] = gdf_new[cols_to_convert].apply(lambda x : x.astype(int) if not pd.isnull else x)   \n",
    "\n",
    "gdf_new.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:18.542524200Z",
     "start_time": "2023-08-13T01:26:18.451416800Z"
    }
   },
   "id": "2ba3e8ccbc6fcf5f"
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "# 保存为shp文件"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:18.557549200Z",
     "start_time": "2023-08-13T01:26:18.480928400Z"
    }
   },
   "id": "d9834303960c57af"
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "import os\n",
    "if os.path.exists(\"./resource/data2/output\") == False:\n",
    "    os.mkdir(\"./resource/data2/output\")\n",
    "gdf_new.to_file(\"./resource/data2/output/分年龄、性别的人口_省份等级_方法1.shp\", encoding=\"utf-8\", driver=\"ESRI Shapefile\",  engine=\"pyogrio\") # pip安装pyogrio"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:18.845636900Z",
     "start_time": "2023-08-13T01:26:18.496929300Z"
    }
   },
   "id": "948e73dbf0dbc649"
  },
  {
   "cell_type": "markdown",
   "source": [
    "出现了列名编码问题！！老老实实把字段写成英文"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "97f8e4620a6f4ac3"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 使用arcpy 方法二\n",
    "此方法如果只用arcpy的游标更新数据，会很麻烦，试了一下放弃了，用pandas的merge的方法会快一万倍。\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a33e9acf3b22d617"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import arcpy\n",
    "import pandas as pd\n",
    "import os"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T04:20:55.806913500Z",
     "start_time": "2023-08-13T04:20:47.839084300Z"
    }
   },
   "id": "7fbb9a0e21ca1e46"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "arcpy.env.workspace = \"./resource/data2\"\n",
    "arcpy.env.overwriteOutput = True"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T04:21:03.180096900Z",
     "start_time": "2023-08-13T04:21:02.883437600Z"
    }
   },
   "id": "99b187b55d171130"
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "  Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n0      北京市  79388  73342  447165  416355  485161  447792  335714  306590   \n1      市辖区  79388  73342  447165  416355  485161  447792  335714  306590   \n2      东城区   2265   2126   13193   12455   21577   19742   13924   13008   \n3      西城区   3635   3339   20958   19709   35042   31641   22773   20815   \n4      朝阳区  12329  11304   66048   61070   76137   71025   50283   46996   \n\n   M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  M80_84  \\\n0  345715  ...  568385  626286  314838  353703  184665  230508  152087   \n1  345715  ...  568385  626286  314838  353703  184665  230508  152087   \n2    8768  ...   24441   27308   13244   14362    7332    8981    6550   \n3   13800  ...   37033   41923   20204   22432   11531   14168   10271   \n4   41172  ...   89009  101817   50634   59091   32240   40815   29164   \n\n   F80_84    M85+    F85+  \n0  196699  124749  160140  \n1  196699  124749  160140  \n2   10128    6931    9972  \n3   15502   11907   16061  \n4   38203   23785   28017  \n\n[5 rows x 39 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Province</th>\n      <th>M0</th>\n      <th>F0</th>\n      <th>M1_4</th>\n      <th>F1_4</th>\n      <th>M5_9</th>\n      <th>F5_9</th>\n      <th>M10_14</th>\n      <th>F10_14</th>\n      <th>M15_19</th>\n      <th>...</th>\n      <th>M65_69</th>\n      <th>F65_69</th>\n      <th>M70_74</th>\n      <th>F70_74</th>\n      <th>M75_79</th>\n      <th>F75_79</th>\n      <th>M80_84</th>\n      <th>F80_84</th>\n      <th>M85+</th>\n      <th>F85+</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>北京市</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>306590</td>\n      <td>345715</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>市辖区</td>\n      <td>79388</td>\n      <td>73342</td>\n      <td>447165</td>\n      <td>416355</td>\n      <td>485161</td>\n      <td>447792</td>\n      <td>335714</td>\n      <td>306590</td>\n      <td>345715</td>\n      <td>...</td>\n      <td>568385</td>\n      <td>626286</td>\n      <td>314838</td>\n      <td>353703</td>\n      <td>184665</td>\n      <td>230508</td>\n      <td>152087</td>\n      <td>196699</td>\n      <td>124749</td>\n      <td>160140</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>东城区</td>\n      <td>2265</td>\n      <td>2126</td>\n      <td>13193</td>\n      <td>12455</td>\n      <td>21577</td>\n      <td>19742</td>\n      <td>13924</td>\n      <td>13008</td>\n      <td>8768</td>\n      <td>...</td>\n      <td>24441</td>\n      <td>27308</td>\n      <td>13244</td>\n      <td>14362</td>\n      <td>7332</td>\n      <td>8981</td>\n      <td>6550</td>\n      <td>10128</td>\n      <td>6931</td>\n      <td>9972</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>西城区</td>\n      <td>3635</td>\n      <td>3339</td>\n      <td>20958</td>\n      <td>19709</td>\n      <td>35042</td>\n      <td>31641</td>\n      <td>22773</td>\n      <td>20815</td>\n      <td>13800</td>\n      <td>...</td>\n      <td>37033</td>\n      <td>41923</td>\n      <td>20204</td>\n      <td>22432</td>\n      <td>11531</td>\n      <td>14168</td>\n      <td>10271</td>\n      <td>15502</td>\n      <td>11907</td>\n      <td>16061</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>朝阳区</td>\n      <td>12329</td>\n      <td>11304</td>\n      <td>66048</td>\n      <td>61070</td>\n      <td>76137</td>\n      <td>71025</td>\n      <td>50283</td>\n      <td>46996</td>\n      <td>41172</td>\n      <td>...</td>\n      <td>89009</td>\n      <td>101817</td>\n      <td>50634</td>\n      <td>59091</td>\n      <td>32240</td>\n      <td>40815</td>\n      <td>29164</td>\n      <td>38203</td>\n      <td>23785</td>\n      <td>28017</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 39 columns</p>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 继续用这个表格 \n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:28.870932Z",
     "start_time": "2023-08-13T01:26:28.856428500Z"
    }
   },
   "id": "c7e77525b425e494"
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "<Result './resource/data2/output.gdb\\\\分年龄、性别的人口_省份等级_方法2'>",
      "text/html": "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"开始时间: 2023年8月13日 9:26:39\",\"运行 成功，结束时间： 2023年8月13日 9:26:40 (历时: 0.48 秒)\"]' data-show='true'><div id = 'default' /></div></div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# 创建一个数据库来操作 避免覆盖\n",
    "if os.path.exists(\"./resource/data2/output.gdb\") == False:\n",
    "    arcpy.management.CreateFileGDB(\"./resource/data2\", \"output.gdb\")\n",
    "# 1.选择空的分省份地图要素\n",
    "fc = \"./resource/data2/中国分省份空地图/中国各省份地图.shp\"\n",
    "# 复制到数据库\n",
    "arcpy.conversion.FeatureClassToFeatureClass(fc, \"./resource/data2/output.gdb\", \"分年龄、性别的人口_省份等级_方法2\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:40.021265500Z",
     "start_time": "2023-08-13T01:26:38.043084300Z"
    }
   },
   "id": "6dd7ed2c2bcc3a47"
  },
  {
   "cell_type": "markdown",
   "source": [
    "空地图：\n",
    "![fc](https://image-1315363329.cos.ap-shanghai.myqcloud.com/lessons/202308122308005.png)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "80bcd24676975666"
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "['M0',\n 'F0',\n 'M1_4',\n 'F1_4',\n 'M5_9',\n 'F5_9',\n 'M10_14',\n 'F10_14',\n 'M15_19',\n 'F15_19',\n 'M20_24',\n 'F20_24',\n 'M25_29',\n 'F25_29',\n 'M30_34',\n 'F30_34',\n 'M35_39',\n 'F35_39',\n 'M40_44',\n 'F40_44',\n 'M45_49',\n 'F45_49',\n 'M50_54',\n 'F50_54',\n 'M55_59',\n 'F55_59',\n 'M60_64',\n 'F60_64',\n 'M65_69',\n 'F65_69',\n 'M70_74',\n 'F70_74',\n 'M75_79',\n 'F75_79',\n 'M80_84',\n 'F80_84',\n 'M85+',\n 'F85+']"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.添加人口字段\n",
    "new_field = df.columns[1:].tolist()\n",
    "new_field"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:46.255138600Z",
     "start_time": "2023-08-13T01:26:46.238710Z"
    }
   },
   "id": "8b5412b55dbe3175"
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "# 使用插入游标添加字段"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:26:49.228473300Z",
     "start_time": "2023-08-13T01:26:49.183391200Z"
    }
   },
   "id": "868299e1657c52ca"
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "for field in new_field:\n",
    "    arcpy.management.AddField(\"output.gdb/分年龄、性别的人口_省份等级_方法2\", field, \"LONG\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:27:02.864313400Z",
     "start_time": "2023-08-13T01:26:49.227470700Z"
    }
   },
   "id": "d5fed1192d0c4803"
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "['省',\n 'M0',\n 'F0',\n 'M1_4',\n 'F1_4',\n 'M5_9',\n 'F5_9',\n 'M10_14',\n 'F10_14',\n 'M15_19',\n 'F15_19',\n 'M20_24',\n 'F20_24',\n 'M25_29',\n 'F25_29',\n 'M30_34',\n 'F30_34',\n 'M35_39',\n 'F35_39',\n 'M40_44',\n 'F40_44',\n 'M45_49',\n 'F45_49',\n 'M50_54',\n 'F50_54',\n 'M55_59',\n 'F55_59',\n 'M60_64',\n 'F60_64',\n 'M65_69',\n 'F65_69',\n 'M70_74',\n 'F70_74',\n 'M75_79',\n 'F75_79',\n 'M80_84',\n 'F80_84',\n 'M85_',\n 'F85_']"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 列出字段 字段名被arcgis规整化了\n",
    "gis_field = [field.name for field in arcpy.ListFields(\"output.gdb/分年龄、性别的人口_省份等级_方法2\")]\n",
    "gis_field = gis_field[2:3] + gis_field[6:]\n",
    "gis_field"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:27:03.093814Z",
     "start_time": "2023-08-13T01:27:02.866314200Z"
    }
   },
   "id": "37d411e81eb445a"
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "匹配\n",
      "  Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "0      北京市  79388  73342  447165  416355  485161  447792  335714  306590   \n",
      "\n",
      "   M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  M80_84  \\\n",
      "0  345715  ...  568385  626286  314838  353703  184665  230508  152087   \n",
      "\n",
      "   F80_84    M85+    F85+  \n",
      "0  196699  124749  160140  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "79388\n",
      "F0\n",
      "73342\n",
      "M1_4\n",
      "447165\n",
      "F1_4\n",
      "416355\n",
      "M5_9\n",
      "485161\n",
      "F5_9\n",
      "447792\n",
      "M10_14\n",
      "335714\n",
      "F10_14\n",
      "306590\n",
      "M15_19\n",
      "345715\n",
      "F15_19\n",
      "287842\n",
      "M20_24\n",
      "715999\n",
      "F20_24\n",
      "634503\n",
      "M25_29\n",
      "995896\n",
      "F25_29\n",
      "908792\n",
      "M30_34\n",
      "1309189\n",
      "F30_34\n",
      "1193840\n",
      "M35_39\n",
      "1108136\n",
      "F35_39\n",
      "1035049\n",
      "M40_44\n",
      "838444\n",
      "F40_44\n",
      "763689\n",
      "M45_49\n",
      "842337\n",
      "F45_49\n",
      "776237\n",
      "M50_54\n",
      "842484\n",
      "F50_54\n",
      "777307\n",
      "M55_59\n",
      "828013\n",
      "F55_59\n",
      "799526\n",
      "M60_64\n",
      "677025\n",
      "F60_64\n",
      "709505\n",
      "M65_69\n",
      "568385\n",
      "F65_69\n",
      "626286\n",
      "M70_74\n",
      "314838\n",
      "F70_74\n",
      "353703\n",
      "M75_79\n",
      "184665\n",
      "F75_79\n",
      "230508\n",
      "M80_84\n",
      "152087\n",
      "F80_84\n",
      "196699\n",
      "M85+\n",
      "124749\n",
      "F85+\n",
      "160140\n",
      "匹配\n",
      "   Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "18      天津市  43247  39802  272661  253305  350320  314666  316570  277485   \n",
      "\n",
      "    M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79 M80_84  \\\n",
      "18  338953  ...  417479  443253  251367  269201  140779  160876  88866   \n",
      "\n",
      "    F80_84   M85+   F85+  \n",
      "18  109030  71210  93631  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "43247\n",
      "F0\n",
      "39802\n",
      "M1_4\n",
      "272661\n",
      "F1_4\n",
      "253305\n",
      "M5_9\n",
      "350320\n",
      "F5_9\n",
      "314666\n",
      "M10_14\n",
      "316570\n",
      "F10_14\n",
      "277485\n",
      "M15_19\n",
      "338953\n",
      "F15_19\n",
      "280814\n",
      "M20_24\n",
      "451443\n",
      "F20_24\n",
      "378753\n",
      "M25_29\n",
      "501735\n",
      "F25_29\n",
      "437116\n",
      "M30_34\n",
      "727170\n",
      "F30_34\n",
      "658138\n",
      "M35_39\n",
      "622573\n",
      "F35_39\n",
      "569127\n",
      "M40_44\n",
      "488901\n",
      "F40_44\n",
      "451945\n",
      "M45_49\n",
      "518140\n",
      "F45_49\n",
      "480648\n",
      "M50_54\n",
      "531414\n",
      "F50_54\n",
      "496525\n",
      "M55_59\n",
      "537786\n",
      "F55_59\n",
      "524084\n",
      "M60_64\n",
      "474335\n",
      "F60_64\n",
      "482661\n",
      "M65_69\n",
      "417479\n",
      "F65_69\n",
      "443253\n",
      "M70_74\n",
      "251367\n",
      "F70_74\n",
      "269201\n",
      "M75_79\n",
      "140779\n",
      "F75_79\n",
      "160876\n",
      "M80_84\n",
      "88866\n",
      "F80_84\n",
      "109030\n",
      "M85+\n",
      "71210\n",
      "F85+\n",
      "93631\n",
      "匹配\n",
      "   Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "36      河北省  316473  290961  1869601  1715582  2975262  2614026  2826356   \n",
      "\n",
      "     F10_14   M15_19  ...   M65_69   F65_69   M70_74   F70_74  M75_79  F75_79  \\\n",
      "36  2480707  2129632  ...  2118493  2215930  1369966  1495659  721592  825659   \n",
      "\n",
      "    M80_84  F80_84    M85+    F85+  \n",
      "36  428901  527035  265129  419573  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "316473\n",
      "F0\n",
      "290961\n",
      "M1_4\n",
      "1869601\n",
      "F1_4\n",
      "1715582\n",
      "M5_9\n",
      "2975262\n",
      "F5_9\n",
      "2614026\n",
      "M10_14\n",
      "2826356\n",
      "F10_14\n",
      "2480707\n",
      "M15_19\n",
      "2129632\n",
      "F15_19\n",
      "1892869\n",
      "M20_24\n",
      "1524951\n",
      "F20_24\n",
      "1415980\n",
      "M25_29\n",
      "2110703\n",
      "F25_29\n",
      "2029345\n",
      "M30_34\n",
      "3455469\n",
      "F30_34\n",
      "3431636\n",
      "M35_39\n",
      "2864982\n",
      "F35_39\n",
      "2807312\n",
      "M40_44\n",
      "2337512\n",
      "F40_44\n",
      "2258378\n",
      "M45_49\n",
      "2522393\n",
      "F45_49\n",
      "2538163\n",
      "M50_54\n",
      "3010800\n",
      "F50_54\n",
      "3037358\n",
      "M55_59\n",
      "2621243\n",
      "F55_59\n",
      "2720493\n",
      "M60_64\n",
      "2209545\n",
      "F60_64\n",
      "2214566\n",
      "M65_69\n",
      "2118493\n",
      "F65_69\n",
      "2215930\n",
      "M70_74\n",
      "1369966\n",
      "F70_74\n",
      "1495659\n",
      "M75_79\n",
      "721592\n",
      "F75_79\n",
      "825659\n",
      "M80_84\n",
      "428901\n",
      "F80_84\n",
      "527035\n",
      "M85+\n",
      "265129\n",
      "F85+\n",
      "419573\n",
      "匹配\n",
      "    Province      M0      F0    M1_4    F1_4     M5_9    F5_9   M10_14  \\\n",
      "238      山西省  147446  140301  767699  729614  1009444  939984  1023296   \n",
      "\n",
      "     F10_14  M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  \\\n",
      "238  952111  896160  ...  914113  909574  581441  598387  339271  366699   \n",
      "\n",
      "     M80_84  F80_84    M85+    F85+  \n",
      "238  215588  253613  136350  189638  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "147446\n",
      "F0\n",
      "140301\n",
      "M1_4\n",
      "767699\n",
      "F1_4\n",
      "729614\n",
      "M5_9\n",
      "1009444\n",
      "F5_9\n",
      "939984\n",
      "M10_14\n",
      "1023296\n",
      "F10_14\n",
      "952111\n",
      "M15_19\n",
      "896160\n",
      "F15_19\n",
      "834978\n",
      "M20_24\n",
      "991053\n",
      "F20_24\n",
      "965405\n",
      "M25_29\n",
      "1311592\n",
      "F25_29\n",
      "1248414\n",
      "M30_34\n",
      "1538222\n",
      "F30_34\n",
      "1452041\n",
      "M35_39\n",
      "1307943\n",
      "F35_39\n",
      "1218725\n",
      "M40_44\n",
      "1162056\n",
      "F40_44\n",
      "1086001\n",
      "M45_49\n",
      "1533529\n",
      "F45_49\n",
      "1471144\n",
      "M50_54\n",
      "1519980\n",
      "F50_54\n",
      "1441816\n",
      "M55_59\n",
      "1333902\n",
      "F55_59\n",
      "1285735\n",
      "M60_64\n",
      "1076063\n",
      "F60_64\n",
      "1026288\n",
      "M65_69\n",
      "914113\n",
      "F65_69\n",
      "909574\n",
      "M70_74\n",
      "581441\n",
      "F70_74\n",
      "598387\n",
      "M75_79\n",
      "339271\n",
      "F75_79\n",
      "366699\n",
      "M80_84\n",
      "215588\n",
      "F80_84\n",
      "253613\n",
      "M85+\n",
      "136350\n",
      "F85+\n",
      "189638\n",
      "匹配\n",
      "    Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "371   内蒙古自治区  89403  83548  456610  426095  616732  567333  592970  544982   \n",
      "\n",
      "     M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  M80_84  \\\n",
      "371  523910  ...  645995  678268  376914  409266  220192  261066  154556   \n",
      "\n",
      "     F80_84   M85+    F85+  \n",
      "371  184935  94244  113482  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "89403\n",
      "F0\n",
      "83548\n",
      "M1_4\n",
      "456610\n",
      "F1_4\n",
      "426095\n",
      "M5_9\n",
      "616732\n",
      "F5_9\n",
      "567333\n",
      "M10_14\n",
      "592970\n",
      "F10_14\n",
      "544982\n",
      "M15_19\n",
      "523910\n",
      "F15_19\n",
      "481357\n",
      "M20_24\n",
      "552501\n",
      "F20_24\n",
      "511241\n",
      "M25_29\n",
      "757769\n",
      "F25_29\n",
      "692586\n",
      "M30_34\n",
      "1054898\n",
      "F30_34\n",
      "987557\n",
      "M35_39\n",
      "1003174\n",
      "F35_39\n",
      "920721\n",
      "M40_44\n",
      "924262\n",
      "F40_44\n",
      "856219\n",
      "M45_49\n",
      "1187721\n",
      "F45_49\n",
      "1132367\n",
      "M50_54\n",
      "1181252\n",
      "F50_54\n",
      "1116147\n",
      "M55_59\n",
      "1030880\n",
      "F55_59\n",
      "999687\n",
      "M60_64\n",
      "811291\n",
      "F60_64\n",
      "807024\n",
      "M65_69\n",
      "645995\n",
      "F65_69\n",
      "678268\n",
      "M70_74\n",
      "376914\n",
      "F70_74\n",
      "409266\n",
      "M75_79\n",
      "220192\n",
      "F75_79\n",
      "261066\n",
      "M80_84\n",
      "154556\n",
      "F80_84\n",
      "184935\n",
      "M85+\n",
      "94244\n",
      "F85+\n",
      "113482\n",
      "匹配\n",
      "    Province      M0      F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "492      辽宁省  113136  106944  627520  589043  862850  797515  858999  781932   \n",
      "\n",
      "     M15_19  ...   M65_69   F65_69  M70_74  F70_74  M75_79  F75_79  M80_84  \\\n",
      "492  887252  ...  1505700  1617973  873679  979152  519826  608963  327298   \n",
      "\n",
      "     F80_84    M85+    F85+  \n",
      "492  419251  238359  327280  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "113136\n",
      "F0\n",
      "106944\n",
      "M1_4\n",
      "627520\n",
      "F1_4\n",
      "589043\n",
      "M5_9\n",
      "862850\n",
      "F5_9\n",
      "797515\n",
      "M10_14\n",
      "858999\n",
      "F10_14\n",
      "781932\n",
      "M15_19\n",
      "887252\n",
      "F15_19\n",
      "798299\n",
      "M20_24\n",
      "963247\n",
      "F20_24\n",
      "885083\n",
      "M25_29\n",
      "1149681\n",
      "F25_29\n",
      "1088311\n",
      "M30_34\n",
      "1736758\n",
      "F30_34\n",
      "1696086\n",
      "M35_39\n",
      "1499850\n",
      "F35_39\n",
      "1465362\n",
      "M40_44\n",
      "1581981\n",
      "F40_44\n",
      "1540555\n",
      "M45_49\n",
      "1797709\n",
      "F45_49\n",
      "1781610\n",
      "M50_54\n",
      "1986758\n",
      "F50_54\n",
      "1997566\n",
      "M55_59\n",
      "2001325\n",
      "F55_59\n",
      "2041568\n",
      "M60_64\n",
      "1731601\n",
      "F60_64\n",
      "1805385\n",
      "M65_69\n",
      "1505700\n",
      "F65_69\n",
      "1617973\n",
      "M70_74\n",
      "873679\n",
      "F70_74\n",
      "979152\n",
      "M75_79\n",
      "519826\n",
      "F75_79\n",
      "608963\n",
      "M80_84\n",
      "327298\n",
      "F80_84\n",
      "419251\n",
      "M85+\n",
      "238359\n",
      "F85+\n",
      "327280\n",
      "匹配\n",
      "    Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "607      吉林省  60272  56167  336270  316430  498408  461879  567346  521951   \n",
      "\n",
      "     M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  M80_84  \\\n",
      "607  546207  ...  777856  846715  457389  526101  243880  300814  157719   \n",
      "\n",
      "     F80_84    M85+    F85+  \n",
      "607  197089  106453  143208  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "60272\n",
      "F0\n",
      "56167\n",
      "M1_4\n",
      "336270\n",
      "F1_4\n",
      "316430\n",
      "M5_9\n",
      "498408\n",
      "F5_9\n",
      "461879\n",
      "M10_14\n",
      "567346\n",
      "F10_14\n",
      "521951\n",
      "M15_19\n",
      "546207\n",
      "F15_19\n",
      "510704\n",
      "M20_24\n",
      "512979\n",
      "F20_24\n",
      "504700\n",
      "M25_29\n",
      "640509\n",
      "F25_29\n",
      "619264\n",
      "M30_34\n",
      "1007169\n",
      "F30_34\n",
      "977292\n",
      "M35_39\n",
      "834784\n",
      "F35_39\n",
      "802981\n",
      "M40_44\n",
      "920927\n",
      "F40_44\n",
      "891651\n",
      "M45_49\n",
      "1105529\n",
      "F45_49\n",
      "1093346\n",
      "M50_54\n",
      "1250768\n",
      "F50_54\n",
      "1239260\n",
      "M55_59\n",
      "1115110\n",
      "F55_59\n",
      "1130385\n",
      "M60_64\n",
      "878744\n",
      "F60_64\n",
      "915197\n",
      "M65_69\n",
      "777856\n",
      "F65_69\n",
      "846715\n",
      "M70_74\n",
      "457389\n",
      "F70_74\n",
      "526101\n",
      "M75_79\n",
      "243880\n",
      "F75_79\n",
      "300814\n",
      "M80_84\n",
      "157719\n",
      "F80_84\n",
      "197089\n",
      "M85+\n",
      "106453\n",
      "F85+\n",
      "143208\n",
      "匹配\n",
      "    Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "686     黑龙江省  61632  58061  366042  342860  562089  525799  708824  661159   \n",
      "\n",
      "     M15_19  ...  M65_69   F65_69  M70_74  F70_74  M75_79  F75_79  M80_84  \\\n",
      "686  720078  ...  996038  1099268  596366  697325  321733  411070  219219   \n",
      "\n",
      "     F80_84    M85+    F85+  \n",
      "686  287117  151466  193266  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "61632\n",
      "F0\n",
      "58061\n",
      "M1_4\n",
      "366042\n",
      "F1_4\n",
      "342860\n",
      "M5_9\n",
      "562089\n",
      "F5_9\n",
      "525799\n",
      "M10_14\n",
      "708824\n",
      "F10_14\n",
      "661159\n",
      "M15_19\n",
      "720078\n",
      "F15_19\n",
      "668733\n",
      "M20_24\n",
      "727651\n",
      "F20_24\n",
      "679740\n",
      "M25_29\n",
      "817187\n",
      "F25_29\n",
      "767702\n",
      "M30_34\n",
      "1248438\n",
      "F30_34\n",
      "1196099\n",
      "M35_39\n",
      "1152822\n",
      "F35_39\n",
      "1107225\n",
      "M40_44\n",
      "1256762\n",
      "F40_44\n",
      "1206819\n",
      "M45_49\n",
      "1662108\n",
      "F45_49\n",
      "1602458\n",
      "M50_54\n",
      "1682679\n",
      "F50_54\n",
      "1637819\n",
      "M55_59\n",
      "1513188\n",
      "F55_59\n",
      "1520424\n",
      "M60_64\n",
      "1188146\n",
      "F60_64\n",
      "1234676\n",
      "M65_69\n",
      "996038\n",
      "F65_69\n",
      "1099268\n",
      "M70_74\n",
      "596366\n",
      "F70_74\n",
      "697325\n",
      "M75_79\n",
      "321733\n",
      "F75_79\n",
      "411070\n",
      "M80_84\n",
      "219219\n",
      "F80_84\n",
      "287117\n",
      "M85+\n",
      "151466\n",
      "F85+\n",
      "193266\n",
      "匹配\n",
      "    Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "826      上海市  64748  59918  381130  351703  480179  433843  348491  316284   \n",
      "\n",
      "     M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  M80_84  \\\n",
      "826  391803  ...  810242  837318  504706  509268  265538  286039  167022   \n",
      "\n",
      "     F80_84    M85+    F85+  \n",
      "826  210751  171160  286968  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "64748\n",
      "F0\n",
      "59918\n",
      "M1_4\n",
      "381130\n",
      "F1_4\n",
      "351703\n",
      "M5_9\n",
      "480179\n",
      "F5_9\n",
      "433843\n",
      "M10_14\n",
      "348491\n",
      "F10_14\n",
      "316284\n",
      "M15_19\n",
      "391803\n",
      "F15_19\n",
      "318381\n",
      "M20_24\n",
      "824363\n",
      "F20_24\n",
      "689793\n",
      "M25_29\n",
      "1179905\n",
      "F25_29\n",
      "1025929\n",
      "M30_34\n",
      "1462804\n",
      "F30_34\n",
      "1278489\n",
      "M35_39\n",
      "1207124\n",
      "F35_39\n",
      "1088686\n",
      "M40_44\n",
      "977109\n",
      "F40_44\n",
      "880403\n",
      "M45_49\n",
      "931765\n",
      "F45_49\n",
      "844508\n",
      "M50_54\n",
      "940004\n",
      "F50_54\n",
      "845689\n",
      "M55_59\n",
      "890810\n",
      "F55_59\n",
      "841572\n",
      "M60_64\n",
      "876308\n",
      "F60_64\n",
      "890142\n",
      "M65_69\n",
      "810242\n",
      "F65_69\n",
      "837318\n",
      "M70_74\n",
      "504706\n",
      "F70_74\n",
      "509268\n",
      "M75_79\n",
      "265538\n",
      "F75_79\n",
      "286039\n",
      "M80_84\n",
      "167022\n",
      "F80_84\n",
      "210751\n",
      "M85+\n",
      "171160\n",
      "F85+\n",
      "286968\n",
      "匹配\n",
      "    Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "965      浙江省  240618  218491  1309773  1182607  1612841  1412166  1446894   \n",
      "\n",
      "      F10_14   M15_19  ...   M65_69   F65_69   M70_74   F70_74  M75_79  \\\n",
      "965  1258391  1519440  ...  1615125  1633890  1125829  1157928  627673   \n",
      "\n",
      "     F75_79  M80_84  F80_84    M85+    F85+  \n",
      "965  652850  435724  484451  350330  482549  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "240618\n",
      "F0\n",
      "218491\n",
      "M1_4\n",
      "1309773\n",
      "F1_4\n",
      "1182607\n",
      "M5_9\n",
      "1612841\n",
      "F5_9\n",
      "1412166\n",
      "M10_14\n",
      "1446894\n",
      "F10_14\n",
      "1258391\n",
      "M15_19\n",
      "1519440\n",
      "F15_19\n",
      "1289710\n",
      "M20_24\n",
      "2023475\n",
      "F20_24\n",
      "1665551\n",
      "M25_29\n",
      "2628336\n",
      "F25_29\n",
      "2214095\n",
      "M30_34\n",
      "3271221\n",
      "F30_34\n",
      "2867368\n",
      "M35_39\n",
      "2689753\n",
      "F35_39\n",
      "2418649\n",
      "M40_44\n",
      "2548248\n",
      "F40_44\n",
      "2338080\n",
      "M45_49\n",
      "2930421\n",
      "F45_49\n",
      "2738717\n",
      "M50_54\n",
      "3006857\n",
      "F50_54\n",
      "2802726\n",
      "M55_59\n",
      "2503105\n",
      "F55_59\n",
      "2357371\n",
      "M60_64\n",
      "1794345\n",
      "F60_64\n",
      "1711990\n",
      "M65_69\n",
      "1615125\n",
      "F65_69\n",
      "1633890\n",
      "M70_74\n",
      "1125829\n",
      "F70_74\n",
      "1157928\n",
      "M75_79\n",
      "627673\n",
      "F75_79\n",
      "652850\n",
      "M80_84\n",
      "435724\n",
      "F80_84\n",
      "484451\n",
      "M85+\n",
      "350330\n",
      "F85+\n",
      "482549\n",
      "匹配\n",
      "     Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "1068      安徽省  305006  269876  1615120  1434212  2246453  1908376  2161728   \n",
      "\n",
      "       F10_14   M15_19  ...   M65_69   F65_69   M70_74   F70_74  M75_79  \\\n",
      "1068  1801911  1777277  ...  1625526  1643717  1203061  1204256  824152   \n",
      "\n",
      "      F75_79  M80_84  F80_84    M85+    F85+  \n",
      "1068  856751  473816  545596  307434  475102  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "305006\n",
      "F0\n",
      "269876\n",
      "M1_4\n",
      "1615120\n",
      "F1_4\n",
      "1434212\n",
      "M5_9\n",
      "2246453\n",
      "F5_9\n",
      "1908376\n",
      "M10_14\n",
      "2161728\n",
      "F10_14\n",
      "1801911\n",
      "M15_19\n",
      "1777277\n",
      "F15_19\n",
      "1466556\n",
      "M20_24\n",
      "1583913\n",
      "F20_24\n",
      "1394602\n",
      "M25_29\n",
      "2108084\n",
      "F25_29\n",
      "1987486\n",
      "M30_34\n",
      "2413901\n",
      "F30_34\n",
      "2460495\n",
      "M35_39\n",
      "1772207\n",
      "F35_39\n",
      "1779787\n",
      "M40_44\n",
      "1913653\n",
      "F40_44\n",
      "1875778\n",
      "M45_49\n",
      "2476430\n",
      "F45_49\n",
      "2437152\n",
      "M50_54\n",
      "2842607\n",
      "F50_54\n",
      "2940759\n",
      "M55_59\n",
      "2265681\n",
      "F55_59\n",
      "2318885\n",
      "M60_64\n",
      "1187345\n",
      "F60_64\n",
      "1122480\n",
      "M65_69\n",
      "1625526\n",
      "F65_69\n",
      "1643717\n",
      "M70_74\n",
      "1203061\n",
      "F70_74\n",
      "1204256\n",
      "M75_79\n",
      "824152\n",
      "F75_79\n",
      "856751\n",
      "M80_84\n",
      "473816\n",
      "F80_84\n",
      "545596\n",
      "M85+\n",
      "307434\n",
      "F85+\n",
      "475102\n",
      "匹配\n",
      "     Province      M0      F0     M1_4    F1_4     M5_9     F5_9   M10_14  \\\n",
      "1205      福建省  206764  174229  1173993  994358  1627920  1365950  1342664   \n",
      "\n",
      "       F10_14   M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  \\\n",
      "1205  1139347  1081131  ...  887915  931304  562879  570740  349835  369567   \n",
      "\n",
      "      M80_84  F80_84    M85+    F85+  \n",
      "1205  232858  284503  160675  259723  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "206764\n",
      "F0\n",
      "174229\n",
      "M1_4\n",
      "1173993\n",
      "F1_4\n",
      "994358\n",
      "M5_9\n",
      "1627920\n",
      "F5_9\n",
      "1365950\n",
      "M10_14\n",
      "1342664\n",
      "F10_14\n",
      "1139347\n",
      "M15_19\n",
      "1081131\n",
      "F15_19\n",
      "930965\n",
      "M20_24\n",
      "1098046\n",
      "F20_24\n",
      "941317\n",
      "M25_29\n",
      "1528514\n",
      "F25_29\n",
      "1335197\n",
      "M30_34\n",
      "2049194\n",
      "F30_34\n",
      "1952671\n",
      "M35_39\n",
      "1704483\n",
      "F35_39\n",
      "1620671\n",
      "M40_44\n",
      "1544982\n",
      "F40_44\n",
      "1466467\n",
      "M45_49\n",
      "1818519\n",
      "F45_49\n",
      "1739015\n",
      "M50_54\n",
      "1711798\n",
      "F50_54\n",
      "1650758\n",
      "M55_59\n",
      "1363368\n",
      "F55_59\n",
      "1339896\n",
      "M60_64\n",
      "1021219\n",
      "F60_64\n",
      "1006651\n",
      "M65_69\n",
      "887915\n",
      "F65_69\n",
      "931304\n",
      "M70_74\n",
      "562879\n",
      "F70_74\n",
      "570740\n",
      "M75_79\n",
      "349835\n",
      "F75_79\n",
      "369567\n",
      "M80_84\n",
      "232858\n",
      "F80_84\n",
      "284503\n",
      "M85+\n",
      "160675\n",
      "F85+\n",
      "259723\n",
      "匹配\n",
      "     Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "1300      江西省  233082  193740  1232031  1047370  1876684  1574331  2068265   \n",
      "\n",
      "       F10_14   M15_19  ...   M65_69   F65_69  M70_74  F70_74  M75_79  F75_79  \\\n",
      "1300  1696861  1719313  ...  1017426  1032980  704795  728468  409203  454900   \n",
      "\n",
      "      M80_84  F80_84    M85+    F85+  \n",
      "1300  267214  337712  164072  254251  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "233082\n",
      "F0\n",
      "193740\n",
      "M1_4\n",
      "1232031\n",
      "F1_4\n",
      "1047370\n",
      "M5_9\n",
      "1876684\n",
      "F5_9\n",
      "1574331\n",
      "M10_14\n",
      "2068265\n",
      "F10_14\n",
      "1696861\n",
      "M15_19\n",
      "1719313\n",
      "F15_19\n",
      "1382865\n",
      "M20_24\n",
      "1369289\n",
      "F20_24\n",
      "1179512\n",
      "M25_29\n",
      "1308333\n",
      "F25_29\n",
      "1190816\n",
      "M30_34\n",
      "1671282\n",
      "F30_34\n",
      "1652638\n",
      "M35_39\n",
      "1350321\n",
      "F35_39\n",
      "1360137\n",
      "M40_44\n",
      "1626712\n",
      "F40_44\n",
      "1611438\n",
      "M45_49\n",
      "1883843\n",
      "F45_49\n",
      "1831936\n",
      "M50_54\n",
      "1813375\n",
      "F50_54\n",
      "1785801\n",
      "M55_59\n",
      "1464299\n",
      "F55_59\n",
      "1439580\n",
      "M60_64\n",
      "1138994\n",
      "F60_64\n",
      "1114766\n",
      "M65_69\n",
      "1017426\n",
      "F65_69\n",
      "1032980\n",
      "M70_74\n",
      "704795\n",
      "F70_74\n",
      "728468\n",
      "M75_79\n",
      "409203\n",
      "F75_79\n",
      "454900\n",
      "M80_84\n",
      "267214\n",
      "F80_84\n",
      "337712\n",
      "M85+\n",
      "164072\n",
      "F85+\n",
      "254251\n",
      "匹配\n",
      "     Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "1412      山东省  458336  409473  2993437  2661070  3516229  2932856  3322812   \n",
      "\n",
      "       F10_14   M15_19  ...   M65_69   F65_69   M70_74   F70_74   M75_79  \\\n",
      "1412  2768425  2712478  ...  2958267  3106501  1915118  2117104  1125752   \n",
      "\n",
      "       F75_79  M80_84  F80_84    M85+    F85+  \n",
      "1412  1310865  679355  876889  458540  815687  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "458336\n",
      "F0\n",
      "409473\n",
      "M1_4\n",
      "2993437\n",
      "F1_4\n",
      "2661070\n",
      "M5_9\n",
      "3516229\n",
      "F5_9\n",
      "2932856\n",
      "M10_14\n",
      "3322812\n",
      "F10_14\n",
      "2768425\n",
      "M15_19\n",
      "2712478\n",
      "F15_19\n",
      "2382761\n",
      "M20_24\n",
      "2381018\n",
      "F20_24\n",
      "2216090\n",
      "M25_29\n",
      "2339651\n",
      "F25_29\n",
      "2137929\n",
      "M30_34\n",
      "4650150\n",
      "F30_34\n",
      "4485349\n",
      "M35_39\n",
      "3500343\n",
      "F35_39\n",
      "3389127\n",
      "M40_44\n",
      "3213370\n",
      "F40_44\n",
      "3150260\n",
      "M45_49\n",
      "3917828\n",
      "F45_49\n",
      "3857379\n",
      "M50_54\n",
      "4570021\n",
      "F50_54\n",
      "4574556\n",
      "M55_59\n",
      "3813562\n",
      "F55_59\n",
      "3952137\n",
      "M60_64\n",
      "2906664\n",
      "F60_64\n",
      "2950064\n",
      "M65_69\n",
      "2958267\n",
      "F65_69\n",
      "3106501\n",
      "M70_74\n",
      "1915118\n",
      "F70_74\n",
      "2117104\n",
      "M75_79\n",
      "1125752\n",
      "F75_79\n",
      "1310865\n",
      "M80_84\n",
      "679355\n",
      "F80_84\n",
      "876889\n",
      "M85+\n",
      "458540\n",
      "F85+\n",
      "815687\n",
      "匹配\n",
      "     Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "1582      河南省  476347  439698  2759721  2539616  4444479  3916946  4569462   \n",
      "\n",
      "       F10_14   M15_19  ...   M65_69   F65_69   M70_74   F70_74   M75_79  \\\n",
      "1582  3842685  3466004  ...  2578866  2714657  1722549  1812528  1014076   \n",
      "\n",
      "       F75_79  M80_84  F80_84    M85+    F85+  \n",
      "1582  1170592  571164  746539  379674  691259  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "476347\n",
      "F0\n",
      "439698\n",
      "M1_4\n",
      "2759721\n",
      "F1_4\n",
      "2539616\n",
      "M5_9\n",
      "4444479\n",
      "F5_9\n",
      "3916946\n",
      "M10_14\n",
      "4569462\n",
      "F10_14\n",
      "3842685\n",
      "M15_19\n",
      "3466004\n",
      "F15_19\n",
      "2857730\n",
      "M20_24\n",
      "2544846\n",
      "F20_24\n",
      "2348883\n",
      "M25_29\n",
      "2723249\n",
      "F25_29\n",
      "2745715\n",
      "M30_34\n",
      "4174801\n",
      "F30_34\n",
      "4401405\n",
      "M35_39\n",
      "2807864\n",
      "F35_39\n",
      "2934795\n",
      "M40_44\n",
      "2778430\n",
      "F40_44\n",
      "2870004\n",
      "M45_49\n",
      "3521070\n",
      "F45_49\n",
      "3565144\n",
      "M50_54\n",
      "3899778\n",
      "F50_54\n",
      "4191597\n",
      "M55_59\n",
      "3149276\n",
      "F55_59\n",
      "3431926\n",
      "M60_64\n",
      "2250693\n",
      "F60_64\n",
      "2311451\n",
      "M65_69\n",
      "2578866\n",
      "F65_69\n",
      "2714657\n",
      "M70_74\n",
      "1722549\n",
      "F70_74\n",
      "1812528\n",
      "M75_79\n",
      "1014076\n",
      "F75_79\n",
      "1170592\n",
      "M80_84\n",
      "571164\n",
      "F80_84\n",
      "746539\n",
      "M85+\n",
      "379674\n",
      "F85+\n",
      "691259\n",
      "匹配\n",
      "     Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "1783      湖北省  254264  222516  1336323  1174547  1812285  1526013  1690411   \n",
      "\n",
      "       F10_14   M15_19  ...   M65_69   F65_69   M70_74   F70_74  M75_79  \\\n",
      "1783  1404118  1399553  ...  1622862  1644516  1130865  1164743  674077   \n",
      "\n",
      "      F75_79  M80_84  F80_84    M85+    F85+  \n",
      "1783  744126  374137  467782  237626  363605  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "254264\n",
      "F0\n",
      "222516\n",
      "M1_4\n",
      "1336323\n",
      "F1_4\n",
      "1174547\n",
      "M5_9\n",
      "1812285\n",
      "F5_9\n",
      "1526013\n",
      "M10_14\n",
      "1690411\n",
      "F10_14\n",
      "1404118\n",
      "M15_19\n",
      "1399553\n",
      "F15_19\n",
      "1169310\n",
      "M20_24\n",
      "1382378\n",
      "F20_24\n",
      "1172754\n",
      "M25_29\n",
      "2049815\n",
      "F25_29\n",
      "1809358\n",
      "M30_34\n",
      "2648622\n",
      "F30_34\n",
      "2522173\n",
      "M35_39\n",
      "1980079\n",
      "F35_39\n",
      "1887287\n",
      "M40_44\n",
      "1883293\n",
      "F40_44\n",
      "1793000\n",
      "M45_49\n",
      "2334627\n",
      "F45_49\n",
      "2257518\n",
      "M50_54\n",
      "2724100\n",
      "F50_54\n",
      "2669509\n",
      "M55_59\n",
      "2456122\n",
      "F55_59\n",
      "2397587\n",
      "M60_64\n",
      "1703279\n",
      "F60_64\n",
      "1667377\n",
      "M65_69\n",
      "1622862\n",
      "F65_69\n",
      "1644516\n",
      "M70_74\n",
      "1130865\n",
      "F70_74\n",
      "1164743\n",
      "M75_79\n",
      "674077\n",
      "F75_79\n",
      "744126\n",
      "M80_84\n",
      "374137\n",
      "F80_84\n",
      "467782\n",
      "M85+\n",
      "237626\n",
      "F85+\n",
      "363605\n",
      "匹配\n",
      "     Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "1903      湖南省  301447  264070  1664289  1471282  2483847  2165043  2474466   \n",
      "\n",
      "       F10_14   M15_19  ...   M65_69   F65_69   M70_74   F70_74  M75_79  \\\n",
      "1903  2145078  2085474  ...  1830422  1842656  1314119  1328220  776559   \n",
      "\n",
      "      F75_79  M80_84  F80_84    M85+    F85+  \n",
      "1903  816064  526151  621345  317667  468864  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "301447\n",
      "F0\n",
      "264070\n",
      "M1_4\n",
      "1664289\n",
      "F1_4\n",
      "1471282\n",
      "M5_9\n",
      "2483847\n",
      "F5_9\n",
      "2165043\n",
      "M10_14\n",
      "2474466\n",
      "F10_14\n",
      "2145078\n",
      "M15_19\n",
      "2085474\n",
      "F15_19\n",
      "1757227\n",
      "M20_24\n",
      "1616275\n",
      "F20_24\n",
      "1462282\n",
      "M25_29\n",
      "1664755\n",
      "F25_29\n",
      "1556700\n",
      "M30_34\n",
      "2752660\n",
      "F30_34\n",
      "2695450\n",
      "M35_39\n",
      "2177206\n",
      "F35_39\n",
      "2124403\n",
      "M40_44\n",
      "1981518\n",
      "F40_44\n",
      "1916379\n",
      "M45_49\n",
      "2690595\n",
      "F45_49\n",
      "2549623\n",
      "M50_54\n",
      "3054992\n",
      "F50_54\n",
      "3053521\n",
      "M55_59\n",
      "2564334\n",
      "F55_59\n",
      "2560667\n",
      "M60_64\n",
      "1718897\n",
      "F60_64\n",
      "1650317\n",
      "M65_69\n",
      "1830422\n",
      "F65_69\n",
      "1842656\n",
      "M70_74\n",
      "1314119\n",
      "F70_74\n",
      "1328220\n",
      "M75_79\n",
      "776559\n",
      "F75_79\n",
      "816064\n",
      "M80_84\n",
      "526151\n",
      "F80_84\n",
      "621345\n",
      "M85+\n",
      "317667\n",
      "F85+\n",
      "468864\n",
      "匹配\n",
      "     Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "2055      广东省  691738  599010  3575143  3126704  4597464  3964896  3884271   \n",
      "\n",
      "       F10_14   M15_19  ...   M65_69   F65_69   M70_74   F70_74  M75_79  \\\n",
      "2055  3310656  3556607  ...  2028352  2116493  1313194  1340246  803478   \n",
      "\n",
      "      F75_79  M80_84  F80_84    M85+    F85+  \n",
      "2055  884950  550340  687928  404466  683553  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "691738\n",
      "F0\n",
      "599010\n",
      "M1_4\n",
      "3575143\n",
      "F1_4\n",
      "3126704\n",
      "M5_9\n",
      "4597464\n",
      "F5_9\n",
      "3964896\n",
      "M10_14\n",
      "3884271\n",
      "F10_14\n",
      "3310656\n",
      "M15_19\n",
      "3556607\n",
      "F15_19\n",
      "2778628\n",
      "M20_24\n",
      "5260488\n",
      "F20_24\n",
      "4139074\n",
      "M25_29\n",
      "6549206\n",
      "F25_29\n",
      "5352699\n",
      "M30_34\n",
      "7173902\n",
      "F30_34\n",
      "6009716\n",
      "M35_39\n",
      "5950175\n",
      "F35_39\n",
      "5129044\n",
      "M40_44\n",
      "4785408\n",
      "F40_44\n",
      "4237650\n",
      "M45_49\n",
      "5096764\n",
      "F45_49\n",
      "4723442\n",
      "M50_54\n",
      "4653924\n",
      "F50_54\n",
      "4312663\n",
      "M55_59\n",
      "3605405\n",
      "F55_59\n",
      "3382767\n",
      "M60_64\n",
      "2393321\n",
      "F60_64\n",
      "2358745\n",
      "M65_69\n",
      "2028352\n",
      "F65_69\n",
      "2116493\n",
      "M70_74\n",
      "1313194\n",
      "F70_74\n",
      "1340246\n",
      "M75_79\n",
      "803478\n",
      "F75_79\n",
      "884950\n",
      "M80_84\n",
      "550340\n",
      "F80_84\n",
      "687928\n",
      "M85+\n",
      "404466\n",
      "F85+\n",
      "683553\n",
      "匹配\n",
      "     Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "2201  广西壮族自治区  302822  264556  1602251  1414539  2318348  2017273  2097576   \n",
      "\n",
      "       F10_14   M15_19  ...   M65_69   F65_69  M70_74  F70_74  M75_79  F75_79  \\\n",
      "2201  1825136  1680169  ...  1083074  1114262  736337  777138  490366  588381   \n",
      "\n",
      "      M80_84  F80_84    M85+    F85+  \n",
      "2201  315122  419726  214843  374868  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "302822\n",
      "F0\n",
      "264556\n",
      "M1_4\n",
      "1602251\n",
      "F1_4\n",
      "1414539\n",
      "M5_9\n",
      "2318348\n",
      "F5_9\n",
      "2017273\n",
      "M10_14\n",
      "2097576\n",
      "F10_14\n",
      "1825136\n",
      "M15_19\n",
      "1680169\n",
      "F15_19\n",
      "1511272\n",
      "M20_24\n",
      "1287023\n",
      "F20_24\n",
      "1177172\n",
      "M25_29\n",
      "1432926\n",
      "F25_29\n",
      "1280347\n",
      "M30_34\n",
      "2011388\n",
      "F30_34\n",
      "1853647\n",
      "M35_39\n",
      "1936562\n",
      "F35_39\n",
      "1758325\n",
      "M40_44\n",
      "1784416\n",
      "F40_44\n",
      "1617067\n",
      "M45_49\n",
      "1904383\n",
      "F45_49\n",
      "1724495\n",
      "M50_54\n",
      "1936905\n",
      "F50_54\n",
      "1790927\n",
      "M55_59\n",
      "1650453\n",
      "F55_59\n",
      "1583047\n",
      "M60_64\n",
      "1131205\n",
      "F60_64\n",
      "1118457\n",
      "M65_69\n",
      "1083074\n",
      "F65_69\n",
      "1114262\n",
      "M70_74\n",
      "736337\n",
      "F70_74\n",
      "777138\n",
      "M75_79\n",
      "490366\n",
      "F75_79\n",
      "588381\n",
      "M80_84\n",
      "315122\n",
      "F80_84\n",
      "419726\n",
      "M85+\n",
      "214843\n",
      "F85+\n",
      "374868\n",
      "匹配\n",
      "     Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "2327      海南省  57250  46780  284862  240159  392089  325884  366361  300340   \n",
      "\n",
      "      M15_19  ...  M65_69  F65_69  M70_74  F70_74 M75_79 F75_79 M80_84 F80_84  \\\n",
      "2327  306260  ...  186885  189566  124563  124546  72360  85565  59265  79041   \n",
      "\n",
      "       M85+   F85+  \n",
      "2327  48664  81045  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "57250\n",
      "F0\n",
      "46780\n",
      "M1_4\n",
      "284862\n",
      "F1_4\n",
      "240159\n",
      "M5_9\n",
      "392089\n",
      "F5_9\n",
      "325884\n",
      "M10_14\n",
      "366361\n",
      "F10_14\n",
      "300340\n",
      "M15_19\n",
      "306260\n",
      "F15_19\n",
      "244525\n",
      "M20_24\n",
      "332069\n",
      "F20_24\n",
      "263934\n",
      "M25_29\n",
      "446327\n",
      "F25_29\n",
      "378239\n",
      "M30_34\n",
      "494216\n",
      "F30_34\n",
      "427942\n",
      "M35_39\n",
      "454656\n",
      "F35_39\n",
      "396401\n",
      "M40_44\n",
      "374755\n",
      "F40_44\n",
      "328518\n",
      "M45_49\n",
      "392037\n",
      "F45_49\n",
      "346363\n",
      "M50_54\n",
      "393118\n",
      "F50_54\n",
      "353144\n",
      "M55_59\n",
      "342351\n",
      "F55_59\n",
      "316053\n",
      "M60_64\n",
      "216993\n",
      "F60_64\n",
      "208106\n",
      "M65_69\n",
      "186885\n",
      "F65_69\n",
      "189566\n",
      "M70_74\n",
      "124563\n",
      "F70_74\n",
      "124546\n",
      "M75_79\n",
      "72360\n",
      "F75_79\n",
      "85565\n",
      "M80_84\n",
      "59265\n",
      "F80_84\n",
      "79041\n",
      "M85+\n",
      "48664\n",
      "F85+\n",
      "81045\n",
      "匹配\n",
      "     Province      M0      F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "2360      重庆市  124283  115143  667693  618498  884840  810398  984260  893248   \n",
      "\n",
      "      M15_19  ...   M65_69   F65_69  M70_74  F70_74  M75_79  F75_79  M80_84  \\\n",
      "2360  939652  ...  1029177  1042698  710033  705170  483690  498916  270922   \n",
      "\n",
      "      F80_84    M85+    F85+  \n",
      "2360  310926  174997  247076  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "124283\n",
      "F0\n",
      "115143\n",
      "M1_4\n",
      "667693\n",
      "F1_4\n",
      "618498\n",
      "M5_9\n",
      "884840\n",
      "F5_9\n",
      "810398\n",
      "M10_14\n",
      "984260\n",
      "F10_14\n",
      "893248\n",
      "M15_19\n",
      "939652\n",
      "F15_19\n",
      "827430\n",
      "M20_24\n",
      "963154\n",
      "F20_24\n",
      "917067\n",
      "M25_29\n",
      "1088538\n",
      "F25_29\n",
      "1041519\n",
      "M30_34\n",
      "1231145\n",
      "F30_34\n",
      "1195006\n",
      "M35_39\n",
      "899256\n",
      "F35_39\n",
      "873196\n",
      "M40_44\n",
      "774008\n",
      "F40_44\n",
      "786345\n",
      "M45_49\n",
      "1537647\n",
      "F45_49\n",
      "1534361\n",
      "M50_54\n",
      "1476616\n",
      "F50_54\n",
      "1501644\n",
      "M55_59\n",
      "1167368\n",
      "F55_59\n",
      "1191404\n",
      "M60_64\n",
      "794854\n",
      "F60_64\n",
      "741981\n",
      "M65_69\n",
      "1029177\n",
      "F65_69\n",
      "1042698\n",
      "M70_74\n",
      "710033\n",
      "F70_74\n",
      "705170\n",
      "M75_79\n",
      "483690\n",
      "F75_79\n",
      "498916\n",
      "M80_84\n",
      "270922\n",
      "F80_84\n",
      "310926\n",
      "M85+\n",
      "174997\n",
      "F85+\n",
      "247076\n",
      "匹配\n",
      "     Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "2401      四川省  329691  304871  1777510  1651085  2387321  2217053  2497084   \n",
      "\n",
      "       F10_14   M15_19  ...   M65_69   F65_69   M70_74   F70_74   M75_79  \\\n",
      "2401  2306497  2255617  ...  2611050  2698318  1829916  1841712  1239306   \n",
      "\n",
      "       F75_79  M80_84  F80_84    M85+    F85+  \n",
      "2401  1309624  718779  821176  468036  629683  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "329691\n",
      "F0\n",
      "304871\n",
      "M1_4\n",
      "1777510\n",
      "F1_4\n",
      "1651085\n",
      "M5_9\n",
      "2387321\n",
      "F5_9\n",
      "2217053\n",
      "M10_14\n",
      "2497084\n",
      "F10_14\n",
      "2306497\n",
      "M15_19\n",
      "2255617\n",
      "F15_19\n",
      "2063785\n",
      "M20_24\n",
      "2433508\n",
      "F20_24\n",
      "2307878\n",
      "M25_29\n",
      "2767100\n",
      "F25_29\n",
      "2623789\n",
      "M30_34\n",
      "3262890\n",
      "F30_34\n",
      "3090398\n",
      "M35_39\n",
      "2307155\n",
      "F35_39\n",
      "2220977\n",
      "M40_44\n",
      "2380790\n",
      "F40_44\n",
      "2364598\n",
      "M45_49\n",
      "4023685\n",
      "F45_49\n",
      "3985170\n",
      "M50_54\n",
      "3963543\n",
      "F50_54\n",
      "3984310\n",
      "M55_59\n",
      "2993893\n",
      "F55_59\n",
      "3010864\n",
      "M60_64\n",
      "2042844\n",
      "F60_64\n",
      "1953360\n",
      "M65_69\n",
      "2611050\n",
      "F65_69\n",
      "2698318\n",
      "M70_74\n",
      "1829916\n",
      "F70_74\n",
      "1841712\n",
      "M75_79\n",
      "1239306\n",
      "F75_79\n",
      "1309624\n",
      "M80_84\n",
      "718779\n",
      "F80_84\n",
      "821176\n",
      "M85+\n",
      "468036\n",
      "F85+\n",
      "629683\n",
      "匹配\n",
      "     Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "2609      贵州省  278841  246539  1318228  1161954  1717039  1488437  1618227   \n",
      "\n",
      "       F10_14   M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  \\\n",
      "2609  1412773  1339162  ...  792295  838922  567595  615697  379584  442739   \n",
      "\n",
      "      M80_84  F80_84    M85+    F85+  \n",
      "2609  227533  291865  121701  178524  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "278841\n",
      "F0\n",
      "246539\n",
      "M1_4\n",
      "1318228\n",
      "F1_4\n",
      "1161954\n",
      "M5_9\n",
      "1717039\n",
      "F5_9\n",
      "1488437\n",
      "M10_14\n",
      "1618227\n",
      "F10_14\n",
      "1412773\n",
      "M15_19\n",
      "1339162\n",
      "F15_19\n",
      "1222201\n",
      "M20_24\n",
      "1267001\n",
      "F20_24\n",
      "1284036\n",
      "M25_29\n",
      "1260744\n",
      "F25_29\n",
      "1232781\n",
      "M30_34\n",
      "1410885\n",
      "F30_34\n",
      "1346946\n",
      "M35_39\n",
      "1193618\n",
      "F35_39\n",
      "1102972\n",
      "M40_44\n",
      "1292129\n",
      "F40_44\n",
      "1205823\n",
      "M45_49\n",
      "1586586\n",
      "F45_49\n",
      "1475372\n",
      "M50_54\n",
      "1477676\n",
      "F50_54\n",
      "1444612\n",
      "M55_59\n",
      "1120672\n",
      "F55_59\n",
      "1125537\n",
      "M60_64\n",
      "735777\n",
      "F60_64\n",
      "739125\n",
      "M65_69\n",
      "792295\n",
      "F65_69\n",
      "838922\n",
      "M70_74\n",
      "567595\n",
      "F70_74\n",
      "615697\n",
      "M75_79\n",
      "379584\n",
      "F75_79\n",
      "442739\n",
      "M80_84\n",
      "227533\n",
      "F80_84\n",
      "291865\n",
      "M85+\n",
      "121701\n",
      "F85+\n",
      "178524\n",
      "匹配\n",
      "     Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "2707      云南省  266844  248334  1326888  1223499  1616275  1484862  1609766   \n",
      "\n",
      "       F10_14   M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  \\\n",
      "2707  1461006  1562570  ...  935570  984374  626130  675233  412955  483049   \n",
      "\n",
      "      M80_84  F80_84    M85+    F85+  \n",
      "2707  256159  321811  153036  224942  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "266844\n",
      "F0\n",
      "248334\n",
      "M1_4\n",
      "1326888\n",
      "F1_4\n",
      "1223499\n",
      "M5_9\n",
      "1616275\n",
      "F5_9\n",
      "1484862\n",
      "M10_14\n",
      "1609766\n",
      "F10_14\n",
      "1461006\n",
      "M15_19\n",
      "1562570\n",
      "F15_19\n",
      "1430302\n",
      "M20_24\n",
      "1606057\n",
      "F20_24\n",
      "1481012\n",
      "M25_29\n",
      "1682482\n",
      "F25_29\n",
      "1477417\n",
      "M30_34\n",
      "1997876\n",
      "F30_34\n",
      "1756415\n",
      "M35_39\n",
      "1745094\n",
      "F35_39\n",
      "1518032\n",
      "M40_44\n",
      "1902700\n",
      "F40_44\n",
      "1682606\n",
      "M45_49\n",
      "2149986\n",
      "F45_49\n",
      "1929317\n",
      "M50_54\n",
      "1991175\n",
      "F50_54\n",
      "1865073\n",
      "M55_59\n",
      "1601019\n",
      "F55_59\n",
      "1554640\n",
      "M60_64\n",
      "978342\n",
      "F60_64\n",
      "986429\n",
      "M65_69\n",
      "935570\n",
      "F65_69\n",
      "984374\n",
      "M70_74\n",
      "626130\n",
      "F70_74\n",
      "675233\n",
      "M75_79\n",
      "412955\n",
      "F75_79\n",
      "483049\n",
      "M80_84\n",
      "256159\n",
      "F80_84\n",
      "321811\n",
      "M85+\n",
      "153036\n",
      "F85+\n",
      "224942\n",
      "匹配\n",
      "     Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "2853    西藏自治区  25855  24533  129722  124248  160538  154815  140080  135074   \n",
      "\n",
      "      M15_19  ... M65_69 F65_69 M70_74 F70_74 M75_79 F75_79 M80_84 F80_84  \\\n",
      "2853  121834  ...  37648  41970  25731  32017  15665  22335   7587  12192   \n",
      "\n",
      "      M85+  F85+  \n",
      "2853  4019  7799  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "25855\n",
      "F0\n",
      "24533\n",
      "M1_4\n",
      "129722\n",
      "F1_4\n",
      "124248\n",
      "M5_9\n",
      "160538\n",
      "F5_9\n",
      "154815\n",
      "M10_14\n",
      "140080\n",
      "F10_14\n",
      "135074\n",
      "M15_19\n",
      "121834\n",
      "F15_19\n",
      "112668\n",
      "M20_24\n",
      "137997\n",
      "F20_24\n",
      "118512\n",
      "M25_29\n",
      "184231\n",
      "F25_29\n",
      "155888\n",
      "M30_34\n",
      "202750\n",
      "F30_34\n",
      "168866\n",
      "M35_39\n",
      "170418\n",
      "F35_39\n",
      "146903\n",
      "M40_44\n",
      "139126\n",
      "F40_44\n",
      "119123\n",
      "M45_49\n",
      "143881\n",
      "F45_49\n",
      "118277\n",
      "M50_54\n",
      "125030\n",
      "F50_54\n",
      "104334\n",
      "M55_59\n",
      "90787\n",
      "F55_59\n",
      "81626\n",
      "M60_64\n",
      "50689\n",
      "F60_64\n",
      "53332\n",
      "M65_69\n",
      "37648\n",
      "F65_69\n",
      "41970\n",
      "M70_74\n",
      "25731\n",
      "F70_74\n",
      "32017\n",
      "M75_79\n",
      "15665\n",
      "F75_79\n",
      "22335\n",
      "M80_84\n",
      "7587\n",
      "F80_84\n",
      "12192\n",
      "M85+\n",
      "4019\n",
      "F85+\n",
      "7799\n",
      "匹配\n",
      "     Province      M0      F0    M1_4    F1_4     M5_9     F5_9   M10_14  \\\n",
      "2938      陕西省  183540  169453  989713  915592  1299247  1175209  1121153   \n",
      "\n",
      "      F10_14  M15_19  ...   M65_69   F65_69  M70_74  F70_74  M75_79  F75_79  \\\n",
      "2938  998298  962535  ...  1026429  1043688  671797  693560  437524  486511   \n",
      "\n",
      "      M80_84  F80_84    M85+    F85+  \n",
      "2938  259140  314035  146263  187650  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "183540\n",
      "F0\n",
      "169453\n",
      "M1_4\n",
      "989713\n",
      "F1_4\n",
      "915592\n",
      "M5_9\n",
      "1299247\n",
      "F5_9\n",
      "1175209\n",
      "M10_14\n",
      "1121153\n",
      "F10_14\n",
      "998298\n",
      "M15_19\n",
      "962535\n",
      "F15_19\n",
      "823328\n",
      "M20_24\n",
      "1071427\n",
      "F20_24\n",
      "960358\n",
      "M25_29\n",
      "1509945\n",
      "F25_29\n",
      "1402055\n",
      "M30_34\n",
      "1933488\n",
      "F30_34\n",
      "1857990\n",
      "M35_39\n",
      "1476230\n",
      "F35_39\n",
      "1387361\n",
      "M40_44\n",
      "1252007\n",
      "F40_44\n",
      "1188517\n",
      "M45_49\n",
      "1605272\n",
      "F45_49\n",
      "1550719\n",
      "M50_54\n",
      "1658050\n",
      "F50_54\n",
      "1607636\n",
      "M55_59\n",
      "1441456\n",
      "F55_59\n",
      "1397218\n",
      "M60_64\n",
      "1181274\n",
      "F60_64\n",
      "1143331\n",
      "M65_69\n",
      "1026429\n",
      "F65_69\n",
      "1043688\n",
      "M70_74\n",
      "671797\n",
      "F70_74\n",
      "693560\n",
      "M75_79\n",
      "437524\n",
      "F75_79\n",
      "486511\n",
      "M80_84\n",
      "259140\n",
      "F80_84\n",
      "314035\n",
      "M85+\n",
      "146263\n",
      "F85+\n",
      "187650\n",
      "匹配\n",
      "     Province      M0      F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "3056      甘肃省  136254  126757  699511  649497  876391  803261  819837  742035   \n",
      "\n",
      "      M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  M80_84  \\\n",
      "3056  692101  ...  594888  594341  420476  443199  282403  309884  156335   \n",
      "\n",
      "      F80_84   M85+   F85+  \n",
      "3056  182392  73313  90586  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "136254\n",
      "F0\n",
      "126757\n",
      "M1_4\n",
      "699511\n",
      "F1_4\n",
      "649497\n",
      "M5_9\n",
      "876391\n",
      "F5_9\n",
      "803261\n",
      "M10_14\n",
      "819837\n",
      "F10_14\n",
      "742035\n",
      "M15_19\n",
      "692101\n",
      "F15_19\n",
      "631311\n",
      "M20_24\n",
      "663988\n",
      "F20_24\n",
      "644469\n",
      "M25_29\n",
      "854915\n",
      "F25_29\n",
      "851656\n",
      "M30_34\n",
      "1000155\n",
      "F30_34\n",
      "992309\n",
      "M35_39\n",
      "784233\n",
      "F35_39\n",
      "758762\n",
      "M40_44\n",
      "740749\n",
      "F40_44\n",
      "711543\n",
      "M45_49\n",
      "1126475\n",
      "F45_49\n",
      "1102101\n",
      "M50_54\n",
      "1254882\n",
      "F50_54\n",
      "1228549\n",
      "M55_59\n",
      "949220\n",
      "F55_59\n",
      "917879\n",
      "M60_64\n",
      "574822\n",
      "F60_64\n",
      "538352\n",
      "M65_69\n",
      "594888\n",
      "F65_69\n",
      "594341\n",
      "M70_74\n",
      "420476\n",
      "F70_74\n",
      "443199\n",
      "M75_79\n",
      "282403\n",
      "F75_79\n",
      "309884\n",
      "M80_84\n",
      "156335\n",
      "F80_84\n",
      "182392\n",
      "M85+\n",
      "73313\n",
      "F85+\n",
      "90586\n",
      "匹配\n",
      "     Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "3159      青海省  34706  32548  167337  157100  223488  210951  209675  197151   \n",
      "\n",
      "      M15_19  ... M65_69  F65_69 M70_74 F70_74 M75_79 F75_79 M80_84 F80_84  \\\n",
      "3159  183962  ...  95089  100091  62885  72817  42377  54237  25414  31800   \n",
      "\n",
      "       M85+   F85+  \n",
      "3159  12864  16519  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "34706\n",
      "F0\n",
      "32548\n",
      "M1_4\n",
      "167337\n",
      "F1_4\n",
      "157100\n",
      "M5_9\n",
      "223488\n",
      "F5_9\n",
      "210951\n",
      "M10_14\n",
      "209675\n",
      "F10_14\n",
      "197151\n",
      "M15_19\n",
      "183962\n",
      "F15_19\n",
      "172720\n",
      "M20_24\n",
      "188187\n",
      "F20_24\n",
      "176862\n",
      "M25_29\n",
      "234383\n",
      "F25_29\n",
      "219731\n",
      "M30_34\n",
      "256276\n",
      "F30_34\n",
      "238742\n",
      "M35_39\n",
      "212330\n",
      "F35_39\n",
      "197584\n",
      "M40_44\n",
      "237257\n",
      "F40_44\n",
      "220841\n",
      "M45_49\n",
      "278338\n",
      "F45_49\n",
      "255332\n",
      "M50_54\n",
      "269394\n",
      "F50_54\n",
      "250501\n",
      "M55_59\n",
      "195057\n",
      "F55_59\n",
      "184102\n",
      "M60_64\n",
      "104827\n",
      "F60_64\n",
      "100482\n",
      "M65_69\n",
      "95089\n",
      "F65_69\n",
      "100091\n",
      "M70_74\n",
      "62885\n",
      "F70_74\n",
      "72817\n",
      "M75_79\n",
      "42377\n",
      "F75_79\n",
      "54237\n",
      "M80_84\n",
      "25414\n",
      "F80_84\n",
      "31800\n",
      "M85+\n",
      "12864\n",
      "F85+\n",
      "16519\n",
      "匹配\n",
      "     Province     M0     F0    M1_4    F1_4    M5_9    F5_9  M10_14  F10_14  \\\n",
      "3213  宁夏回族自治区  42661  40366  210223  196133  258040  237860  251659  231062   \n",
      "\n",
      "      M15_19  ...  M65_69  F65_69 M70_74 F70_74 M75_79 F75_79 M80_84 F80_84  \\\n",
      "3213  224272  ...  136133  139868  86579  93798  57830  66365  34623  39536   \n",
      "\n",
      "       M85+   F85+  \n",
      "3213  18023  20069  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "42661\n",
      "F0\n",
      "40366\n",
      "M1_4\n",
      "210223\n",
      "F1_4\n",
      "196133\n",
      "M5_9\n",
      "258040\n",
      "F5_9\n",
      "237860\n",
      "M10_14\n",
      "251659\n",
      "F10_14\n",
      "231062\n",
      "M15_19\n",
      "224272\n",
      "F15_19\n",
      "208822\n",
      "M20_24\n",
      "224475\n",
      "F20_24\n",
      "220035\n",
      "M25_29\n",
      "278487\n",
      "F25_29\n",
      "269370\n",
      "M30_34\n",
      "323478\n",
      "F30_34\n",
      "311536\n",
      "M35_39\n",
      "278748\n",
      "F35_39\n",
      "262917\n",
      "M40_44\n",
      "280284\n",
      "F40_44\n",
      "266732\n",
      "M45_49\n",
      "305580\n",
      "F45_49\n",
      "289820\n",
      "M50_54\n",
      "291504\n",
      "F50_54\n",
      "285126\n",
      "M55_59\n",
      "223885\n",
      "F55_59\n",
      "215437\n",
      "M60_64\n",
      "142454\n",
      "F60_64\n",
      "138864\n",
      "M65_69\n",
      "136133\n",
      "F65_69\n",
      "139868\n",
      "M70_74\n",
      "86579\n",
      "F70_74\n",
      "93798\n",
      "M75_79\n",
      "57830\n",
      "F75_79\n",
      "66365\n",
      "M80_84\n",
      "34623\n",
      "F80_84\n",
      "39536\n",
      "M85+\n",
      "18023\n",
      "F85+\n",
      "20069\n",
      "匹配\n",
      "      Province     M0     F0    M1_4    F1_4     M5_9     F5_9  M10_14  \\\n",
      "3241  新疆维吾尔自治区  92597  86127  596218  560675  1306278  1237342  991442   \n",
      "\n",
      "      F10_14  M15_19  ...  M65_69  F65_69  M70_74  F70_74  M75_79  F75_79  \\\n",
      "3241  935477  746906  ...  352686  366298  242125  282936  173453  211851   \n",
      "\n",
      "      M80_84  F80_84   M85+   F85+  \n",
      "3241  123657  123562  62484  66833  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "92597\n",
      "F0\n",
      "86127\n",
      "M1_4\n",
      "596218\n",
      "F1_4\n",
      "560675\n",
      "M5_9\n",
      "1306278\n",
      "F5_9\n",
      "1237342\n",
      "M10_14\n",
      "991442\n",
      "F10_14\n",
      "935477\n",
      "M15_19\n",
      "746906\n",
      "F15_19\n",
      "686562\n",
      "M20_24\n",
      "820427\n",
      "F20_24\n",
      "725342\n",
      "M25_29\n",
      "1060667\n",
      "F25_29\n",
      "949903\n",
      "M30_34\n",
      "1287326\n",
      "F30_34\n",
      "1172979\n",
      "M35_39\n",
      "1022206\n",
      "F35_39\n",
      "934031\n",
      "M40_44\n",
      "946806\n",
      "F40_44\n",
      "884809\n",
      "M45_49\n",
      "1170692\n",
      "F45_49\n",
      "1083045\n",
      "M50_54\n",
      "1117226\n",
      "F50_54\n",
      "1038389\n",
      "M55_59\n",
      "774644\n",
      "F55_59\n",
      "707220\n",
      "M60_64\n",
      "466540\n",
      "F60_64\n",
      "444584\n",
      "M65_69\n",
      "352686\n",
      "F65_69\n",
      "366298\n",
      "M70_74\n",
      "242125\n",
      "F70_74\n",
      "282936\n",
      "M75_79\n",
      "173453\n",
      "F75_79\n",
      "211851\n",
      "M80_84\n",
      "123657\n",
      "F80_84\n",
      "123562\n",
      "M85+\n",
      "62484\n",
      "F85+\n",
      "66833\n",
      "匹配\n",
      "    Province      M0      F0     M1_4     F1_4     M5_9     F5_9   M10_14  \\\n",
      "844      江苏省  293718  269494  1702238  1553177  2518917  2193839  2360131   \n",
      "\n",
      "      F10_14   M15_19  ...   M65_69   F65_69   M70_74   F70_74   M75_79  \\\n",
      "844  2000434  1941316  ...  2537937  2571942  1739491  1807185  1102639   \n",
      "\n",
      "      F75_79  M80_84  F80_84    M85+    F85+  \n",
      "844  1214600  680452  839548  462422  770315  \n",
      "\n",
      "[1 rows x 39 columns]\n",
      "M0\n",
      "293718\n",
      "F0\n",
      "269494\n",
      "M1_4\n",
      "1702238\n",
      "F1_4\n",
      "1553177\n",
      "M5_9\n",
      "2518917\n",
      "F5_9\n",
      "2193839\n",
      "M10_14\n",
      "2360131\n",
      "F10_14\n",
      "2000434\n",
      "M15_19\n",
      "1941316\n",
      "F15_19\n",
      "1606142\n",
      "M20_24\n",
      "2156767\n",
      "F20_24\n",
      "1803740\n",
      "M25_29\n",
      "2996605\n",
      "F25_29\n",
      "2654913\n",
      "M30_34\n",
      "4114085\n",
      "F30_34\n",
      "3942163\n",
      "M35_39\n",
      "2917712\n",
      "F35_39\n",
      "2864346\n",
      "M40_44\n",
      "2764099\n",
      "F40_44\n",
      "2751398\n",
      "M45_49\n",
      "3195796\n",
      "F45_49\n",
      "3218422\n",
      "M50_54\n",
      "3926760\n",
      "F50_54\n",
      "4033204\n",
      "M55_59\n",
      "3207812\n",
      "F55_59\n",
      "3255443\n",
      "M60_64\n",
      "2412689\n",
      "F60_64\n",
      "2366125\n",
      "M65_69\n",
      "2537937\n",
      "F65_69\n",
      "2571942\n",
      "M70_74\n",
      "1739491\n",
      "F70_74\n",
      "1807185\n",
      "M75_79\n",
      "1102639\n",
      "F75_79\n",
      "1214600\n",
      "M80_84\n",
      "680452\n",
      "F80_84\n",
      "839548\n",
      "M85+\n",
      "462422\n",
      "F85+\n",
      "770315\n",
      "不匹配\n",
      "不匹配\n",
      "不匹配\n"
     ]
    }
   ],
   "source": [
    "# 使用游标添加数据 通过省份字段匹配\n",
    "# search_field = [\"省\"] + gis_field\n",
    "with arcpy.da.UpdateCursor(\"output.gdb/分年龄、性别的人口_省份等级_方法2\", gis_field\n",
    ") as cursor:\n",
    "    for i, row in enumerate(cursor):\n",
    "        # 通过游标所在行的\"省\"字段匹配df中\"Province\"的字段\n",
    "        filtered_df = df[df['Province'] == row[0]]\n",
    "        if filtered_df.empty:\n",
    "            print(\"不匹配\")\n",
    "        else:\n",
    "            print(\"匹配\")\n",
    "            print(filtered_df)\n",
    "            # 赋值\n",
    "            \n",
    "            for j, field in enumerate(gis_field[1:]):\n",
    "                # 因为gis把字段规整化了 有两个例外我们替换一下\n",
    "                if field == \"F85_\":\n",
    "                    field = \"F85+\"\n",
    "                elif field == \"M85_\":\n",
    "                    field = \"M85+\"\n",
    "                \n",
    "                print(field)\n",
    "                print(filtered_df.loc[:, field].values[0])\n",
    "                \n",
    "                row[j+1] = filtered_df.loc[:, field].values[0] # 这里的j+1是因为gis_field中去掉了\"省\"字段\n",
    "            cursor.updateRow(row)\n",
    "            # break # 测试用"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:41:59.577304300Z",
     "start_time": "2023-08-13T01:41:59.314224300Z"
    }
   },
   "id": "fb862e32bfbfc190"
  },
  {
   "cell_type": "markdown",
   "source": [
    "运行完整之后和原始df对比一下：\n",
    "![](https://image-1315363329.cos.ap-shanghai.myqcloud.com/lessons/202308130941323.png)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "feb6e12ac2e1874a"
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "Province        北京市\nM0            79388\nF0            73342\nM1_4         447165\nF1_4         416355\nM5_9         485161\nF5_9         447792\nM10_14       335714\nF10_14       306590\nM15_19       345715\nF15_19       287842\nM20_24       715999\nF20_24       634503\nM25_29       995896\nF25_29       908792\nM30_34      1309189\nF30_34      1193840\nM35_39      1108136\nF35_39      1035049\nM40_44       838444\nF40_44       763689\nM45_49       842337\nF45_49       776237\nM50_54       842484\nF50_54       777307\nM55_59       828013\nF55_59       799526\nM60_64       677025\nF60_64       709505\nM65_69       568385\nF65_69       626286\nM70_74       314838\nF70_74       353703\nM75_79       184665\nF75_79       230508\nM80_84       152087\nF80_84       196699\nM85+         124749\nF85+         160140\nName: 0, dtype: object"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0, :]"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ef11afca0c0afc10"
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M0\n",
      "男性0岁人口\n",
      "F0\n",
      "女性0岁人口\n",
      "M1_4\n",
      "男性1-4岁人口\n",
      "F1_4\n",
      "女性1-4岁人口\n",
      "M5_9\n",
      "男性5-9岁人口\n",
      "F5_9\n",
      "女性5-9岁人口\n",
      "M10_14\n",
      "男性10-14岁人口\n",
      "F10_14\n",
      "女性10-14岁人口\n",
      "M15_19\n",
      "男性15-19岁人口\n",
      "F15_19\n",
      "女性15-19岁人口\n",
      "M20_24\n",
      "男性20-24岁人口\n",
      "F20_24\n",
      "女性20-24岁人口\n",
      "M25_29\n",
      "男性25-29岁人口\n",
      "F25_29\n",
      "女性25-29岁人口\n",
      "M30_34\n",
      "男性30-34岁人口\n",
      "F30_34\n",
      "女性30-34岁人口\n",
      "M35_39\n",
      "男性35-39岁人口\n",
      "F35_39\n",
      "女性35-39岁人口\n",
      "M40_44\n",
      "男性40-44岁人口\n",
      "F40_44\n",
      "女性40-44岁人口\n",
      "M45_49\n",
      "男性45-49岁人口\n",
      "F45_49\n",
      "女性45-49岁人口\n",
      "M50_54\n",
      "男性50-54岁人口\n",
      "F50_54\n",
      "女性50-54岁人口\n",
      "M55_59\n",
      "男性55-59岁人口\n",
      "F55_59\n",
      "女性55-59岁人口\n",
      "M60_64\n",
      "男性60-64岁人口\n",
      "F60_64\n",
      "女性60-64岁人口\n",
      "M65_69\n",
      "男性65-69岁人口\n",
      "F65_69\n",
      "女性65-69岁人口\n",
      "M70_74\n",
      "男性70-74岁人口\n",
      "F70_74\n",
      "女性70-74岁人口\n",
      "M75_79\n",
      "男性75-79岁人口\n",
      "F75_79\n",
      "女性75-79岁人口\n",
      "M80_84\n",
      "男性80-84岁人口\n",
      "F80_84\n",
      "女性80-84岁人口\n",
      "M85_\n",
      "男性85岁以上人口\n",
      "F85_\n",
      "女性85岁以上人口\n"
     ]
    }
   ],
   "source": [
    "# 更改别名\n",
    "alias = [\n",
    "    \"男性0岁人口\",\n",
    "    \"女性0岁人口\",\n",
    "    \"男性1-4岁人口\",\n",
    "    \"女性1-4岁人口\",\n",
    "    \"男性5-9岁人口\",\n",
    "    \"女性5-9岁人口\",\n",
    "    \"男性10-14岁人口\",\n",
    "    \"女性10-14岁人口\",\n",
    "    \"男性15-19岁人口\",\n",
    "    \"女性15-19岁人口\",\n",
    "    \"男性20-24岁人口\",\n",
    "    \"女性20-24岁人口\",\n",
    "    \"男性25-29岁人口\",\n",
    "    \"女性25-29岁人口\",\n",
    "    \"男性30-34岁人口\",\n",
    "    \"女性30-34岁人口\",\n",
    "    \"男性35-39岁人口\",\n",
    "    \"女性35-39岁人口\",\n",
    "    \"男性40-44岁人口\",\n",
    "    \"女性40-44岁人口\",\n",
    "    \"男性45-49岁人口\",\n",
    "    \"女性45-49岁人口\",\n",
    "    \"男性50-54岁人口\",\n",
    "    \"女性50-54岁人口\",\n",
    "    \"男性55-59岁人口\",\n",
    "    \"女性55-59岁人口\",\n",
    "    \"男性60-64岁人口\",\n",
    "    \"女性60-64岁人口\",\n",
    "    \"男性65-69岁人口\",\n",
    "    \"女性65-69岁人口\",\n",
    "    \"男性70-74岁人口\",\n",
    "    \"女性70-74岁人口\",\n",
    "    \"男性75-79岁人口\",\n",
    "    \"女性75-79岁人口\",\n",
    "    \"男性80-84岁人口\",\n",
    "    \"女性80-84岁人口\",\n",
    "    \"男性85岁以上人口\",\n",
    "    \"女性85岁以上人口\"\n",
    "]\n",
    "\n",
    "for i, field in enumerate(gis_field[1:]): \n",
    "    print(field)\n",
    "    print(alias[i])\n",
    "    arcpy.management.AlterField(\"output.gdb/分年龄、性别的人口_省份等级_方法2\", field, \"\" , alias[i])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T01:50:23.596164600Z",
     "start_time": "2023-08-13T01:50:18.739623Z"
    }
   },
   "id": "3bbf92a292cfa556"
  },
  {
   "cell_type": "markdown",
   "source": [
    "更改别名后的属性表：![更改别名后的属性表](https://image-1315363329.cos.ap-shanghai.myqcloud.com/lessons/202308130951676.png)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a05f965b60e1fb4b"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Result './resource/data2\\\\output\\\\分年龄、性别的人口_省份等级_方法2_USE_ALIAS.shp'>",
      "text/html": "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"开始时间: 2023年8月13日 12:23:24\",\"运行 成功，结束时间： 2023年8月13日 12:23:24 (历时: 0.38 秒)\"]' data-show='true'><div id = 'default' /></div></div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### 5.导出为shp文件\n",
    "in_features = \"output.gdb/分年龄、性别的人口_省份等级_方法2\"\n",
    "out_features = \"output/分年龄、性别的人口_省份等级_方法2.shp\"\n",
    "\n",
    "arcpy.conversion.ExportFeatures(in_features, out_features)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T04:23:24.552050400Z",
     "start_time": "2023-08-13T04:23:23.849563800Z"
    }
   },
   "id": "8a378030bd34dd32"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "a371e342520d40b5"
  }
 ],
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