Figure1-Plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.max_rows', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class='alert alert-block alert-info'>City Info</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(366, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_info_df = pd.read_csv('Data/city_info.csv')\n",
    "city_info_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>city_cn</th>\n",
       "      <th>city</th>\n",
       "      <th>city_id</th>\n",
       "      <th>province_cn</th>\n",
       "      <th>province</th>\n",
       "      <th>tier</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>39.910925</td>\n",
       "      <td>116.413384</td>\n",
       "      <td>2153.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>天津市</td>\n",
       "      <td>Tianjin</td>\n",
       "      <td>120000</td>\n",
       "      <td>天津市</td>\n",
       "      <td>Tianjin</td>\n",
       "      <td>new Tier 1</td>\n",
       "      <td>39.093668</td>\n",
       "      <td>117.209523</td>\n",
       "      <td>1561.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>石家庄市</td>\n",
       "      <td>Shijiazhuang</td>\n",
       "      <td>130100</td>\n",
       "      <td>河北省</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>38.048312</td>\n",
       "      <td>114.521532</td>\n",
       "      <td>1039.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>唐山市</td>\n",
       "      <td>Tangshan</td>\n",
       "      <td>130200</td>\n",
       "      <td>河北省</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>39.636584</td>\n",
       "      <td>118.186459</td>\n",
       "      <td>796.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>Qinhuangdao</td>\n",
       "      <td>130300</td>\n",
       "      <td>河北省</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>39.941748</td>\n",
       "      <td>119.608531</td>\n",
       "      <td>314.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_cn          city  city_id province_cn province        tier        lat  \\\n",
       "0     北京市       Beijing   110000         北京市  Beijing      Tier 1  39.910925   \n",
       "1     天津市       Tianjin   120000         天津市  Tianjin  new Tier 1  39.093668   \n",
       "2    石家庄市  Shijiazhuang   130100         河北省    Hebei      Tier 2  38.048312   \n",
       "3     唐山市      Tangshan   130200         河北省    Hebei      Tier 3  39.636584   \n",
       "4    秦皇岛市   Qinhuangdao   130300         河北省    Hebei      Tier 3  39.941748   \n",
       "\n",
       "          lng  population  \n",
       "0  116.413384     2153.60  \n",
       "1  117.209523     1561.83  \n",
       "2  114.521532     1039.42  \n",
       "3  118.186459      796.40  \n",
       "4  119.608531      314.63  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_info_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "366"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "todo_cities = city_info_df['city_cn'].tolist()\n",
    "len(todo_cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class='info-circle alert alert-block alert-info'>Human Mobility Info</div>\n",
    "- human flow from one city to another\n",
    "- aggregated from 2020-01-01 to 2020-01-23 (date format: YYYY-MM-DD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(366, 366)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj_df = pd.read_csv('Data/human_flow_matrix.csv', index_col=0)\n",
    "adj_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>北京市</th>\n",
       "      <th>天津市</th>\n",
       "      <th>石家庄市</th>\n",
       "      <th>唐山市</th>\n",
       "      <th>秦皇岛市</th>\n",
       "      <th>邯郸市</th>\n",
       "      <th>邢台市</th>\n",
       "      <th>保定市</th>\n",
       "      <th>张家口市</th>\n",
       "      <th>承德市</th>\n",
       "      <th>沧州市</th>\n",
       "      <th>廊坊市</th>\n",
       "      <th>衡水市</th>\n",
       "      <th>太原市</th>\n",
       "      <th>大同市</th>\n",
       "      <th>阳泉市</th>\n",
       "      <th>长治市</th>\n",
       "      <th>晋城市</th>\n",
       "      <th>朔州市</th>\n",
       "      <th>晋中市</th>\n",
       "      <th>运城市</th>\n",
       "      <th>忻州市</th>\n",
       "      <th>临汾市</th>\n",
       "      <th>吕梁市</th>\n",
       "      <th>呼和浩特市</th>\n",
       "      <th>包头市</th>\n",
       "      <th>乌海市</th>\n",
       "      <th>赤峰市</th>\n",
       "      <th>通辽市</th>\n",
       "      <th>鄂尔多斯市</th>\n",
       "      <th>呼伦贝尔市</th>\n",
       "      <th>巴彦淖尔市</th>\n",
       "      <th>乌兰察布市</th>\n",
       "      <th>兴安盟</th>\n",
       "      <th>锡林郭勒盟</th>\n",
       "      <th>阿拉善盟</th>\n",
       "      <th>沈阳市</th>\n",
       "      <th>大连市</th>\n",
       "      <th>鞍山市</th>\n",
       "      <th>抚顺市</th>\n",
       "      <th>本溪市</th>\n",
       "      <th>丹东市</th>\n",
       "      <th>锦州市</th>\n",
       "      <th>营口市</th>\n",
       "      <th>阜新市</th>\n",
       "      <th>辽阳市</th>\n",
       "      <th>盘锦市</th>\n",
       "      <th>铁岭市</th>\n",
       "      <th>朝阳市</th>\n",
       "      <th>葫芦岛市</th>\n",
       "      <th>长春市</th>\n",
       "      <th>吉林市</th>\n",
       "      <th>四平市</th>\n",
       "      <th>辽源市</th>\n",
       "      <th>通化市</th>\n",
       "      <th>白山市</th>\n",
       "      <th>松原市</th>\n",
       "      <th>白城市</th>\n",
       "      <th>延边朝鲜族自治州</th>\n",
       "      <th>哈尔滨市</th>\n",
       "      <th>齐齐哈尔市</th>\n",
       "      <th>鸡西市</th>\n",
       "      <th>鹤岗市</th>\n",
       "      <th>双鸭山市</th>\n",
       "      <th>大庆市</th>\n",
       "      <th>伊春市</th>\n",
       "      <th>佳木斯市</th>\n",
       "      <th>七台河市</th>\n",
       "      <th>牡丹江市</th>\n",
       "      <th>黑河市</th>\n",
       "      <th>绥化市</th>\n",
       "      <th>大兴安岭地区</th>\n",
       "      <th>上海市</th>\n",
       "      <th>南京市</th>\n",
       "      <th>无锡市</th>\n",
       "      <th>徐州市</th>\n",
       "      <th>常州市</th>\n",
       "      <th>苏州市</th>\n",
       "      <th>南通市</th>\n",
       "      <th>连云港市</th>\n",
       "      <th>淮安市</th>\n",
       "      <th>盐城市</th>\n",
       "      <th>扬州市</th>\n",
       "      <th>镇江市</th>\n",
       "      <th>泰州市</th>\n",
       "      <th>宿迁市</th>\n",
       "      <th>杭州市</th>\n",
       "      <th>宁波市</th>\n",
       "      <th>温州市</th>\n",
       "      <th>嘉兴市</th>\n",
       "      <th>湖州市</th>\n",
       "      <th>绍兴市</th>\n",
       "      <th>金华市</th>\n",
       "      <th>衢州市</th>\n",
       "      <th>舟山市</th>\n",
       "      <th>台州市</th>\n",
       "      <th>丽水市</th>\n",
       "      <th>合肥市</th>\n",
       "      <th>芜湖市</th>\n",
       "      <th>蚌埠市</th>\n",
       "      <th>淮南市</th>\n",
       "      <th>马鞍山市</th>\n",
       "      <th>淮北市</th>\n",
       "      <th>铜陵市</th>\n",
       "      <th>安庆市</th>\n",
       "      <th>黄山市</th>\n",
       "      <th>滁州市</th>\n",
       "      <th>阜阳市</th>\n",
       "      <th>宿州市</th>\n",
       "      <th>六安市</th>\n",
       "      <th>亳州市</th>\n",
       "      <th>池州市</th>\n",
       "      <th>宣城市</th>\n",
       "      <th>福州市</th>\n",
       "      <th>厦门市</th>\n",
       "      <th>莆田市</th>\n",
       "      <th>三明市</th>\n",
       "      <th>泉州市</th>\n",
       "      <th>漳州市</th>\n",
       "      <th>南平市</th>\n",
       "      <th>龙岩市</th>\n",
       "      <th>宁德市</th>\n",
       "      <th>南昌市</th>\n",
       "      <th>景德镇市</th>\n",
       "      <th>萍乡市</th>\n",
       "      <th>九江市</th>\n",
       "      <th>新余市</th>\n",
       "      <th>鹰潭市</th>\n",
       "      <th>赣州市</th>\n",
       "      <th>吉安市</th>\n",
       "      <th>宜春市</th>\n",
       "      <th>抚州市</th>\n",
       "      <th>上饶市</th>\n",
       "      <th>济南市</th>\n",
       "      <th>青岛市</th>\n",
       "      <th>淄博市</th>\n",
       "      <th>枣庄市</th>\n",
       "      <th>东营市</th>\n",
       "      <th>烟台市</th>\n",
       "      <th>潍坊市</th>\n",
       "      <th>济宁市</th>\n",
       "      <th>泰安市</th>\n",
       "      <th>威海市</th>\n",
       "      <th>日照市</th>\n",
       "      <th>临沂市</th>\n",
       "      <th>德州市</th>\n",
       "      <th>聊城市</th>\n",
       "      <th>滨州市</th>\n",
       "      <th>菏泽市</th>\n",
       "      <th>郑州市</th>\n",
       "      <th>开封市</th>\n",
       "      <th>洛阳市</th>\n",
       "      <th>平顶山市</th>\n",
       "      <th>安阳市</th>\n",
       "      <th>鹤壁市</th>\n",
       "      <th>新乡市</th>\n",
       "      <th>焦作市</th>\n",
       "      <th>濮阳市</th>\n",
       "      <th>许昌市</th>\n",
       "      <th>漯河市</th>\n",
       "      <th>三门峡市</th>\n",
       "      <th>南阳市</th>\n",
       "      <th>商丘市</th>\n",
       "      <th>信阳市</th>\n",
       "      <th>周口市</th>\n",
       "      <th>驻马店市</th>\n",
       "      <th>济源市</th>\n",
       "      <th>武汉市</th>\n",
       "      <th>黄石市</th>\n",
       "      <th>十堰市</th>\n",
       "      <th>宜昌市</th>\n",
       "      <th>襄阳市</th>\n",
       "      <th>鄂州市</th>\n",
       "      <th>荆门市</th>\n",
       "      <th>孝感市</th>\n",
       "      <th>荆州市</th>\n",
       "      <th>黄冈市</th>\n",
       "      <th>咸宁市</th>\n",
       "      <th>随州市</th>\n",
       "      <th>恩施土家族苗族自治州</th>\n",
       "      <th>仙桃市</th>\n",
       "      <th>潜江市</th>\n",
       "      <th>天门市</th>\n",
       "      <th>神农架林区</th>\n",
       "      <th>长沙市</th>\n",
       "      <th>株洲市</th>\n",
       "      <th>湘潭市</th>\n",
       "      <th>衡阳市</th>\n",
       "      <th>邵阳市</th>\n",
       "      <th>岳阳市</th>\n",
       "      <th>常德市</th>\n",
       "      <th>张家界市</th>\n",
       "      <th>益阳市</th>\n",
       "      <th>郴州市</th>\n",
       "      <th>永州市</th>\n",
       "      <th>怀化市</th>\n",
       "      <th>娄底市</th>\n",
       "      <th>湘西土家族苗族自治州</th>\n",
       "      <th>广州市</th>\n",
       "      <th>韶关市</th>\n",
       "      <th>深圳市</th>\n",
       "      <th>珠海市</th>\n",
       "      <th>汕头市</th>\n",
       "      <th>佛山市</th>\n",
       "      <th>江门市</th>\n",
       "      <th>湛江市</th>\n",
       "      <th>茂名市</th>\n",
       "      <th>肇庆市</th>\n",
       "      <th>惠州市</th>\n",
       "      <th>梅州市</th>\n",
       "      <th>汕尾市</th>\n",
       "      <th>河源市</th>\n",
       "      <th>阳江市</th>\n",
       "      <th>清远市</th>\n",
       "      <th>东莞市</th>\n",
       "      <th>中山市</th>\n",
       "      <th>潮州市</th>\n",
       "      <th>揭阳市</th>\n",
       "      <th>云浮市</th>\n",
       "      <th>南宁市</th>\n",
       "      <th>柳州市</th>\n",
       "      <th>桂林市</th>\n",
       "      <th>梧州市</th>\n",
       "      <th>北海市</th>\n",
       "      <th>防城港市</th>\n",
       "      <th>钦州市</th>\n",
       "      <th>贵港市</th>\n",
       "      <th>玉林市</th>\n",
       "      <th>百色市</th>\n",
       "      <th>贺州市</th>\n",
       "      <th>河池市</th>\n",
       "      <th>来宾市</th>\n",
       "      <th>崇左市</th>\n",
       "      <th>海口市</th>\n",
       "      <th>三亚市</th>\n",
       "      <th>三沙市</th>\n",
       "      <th>儋州市</th>\n",
       "      <th>五指山市</th>\n",
       "      <th>琼海市</th>\n",
       "      <th>文昌市</th>\n",
       "      <th>万宁市</th>\n",
       "      <th>东方市</th>\n",
       "      <th>定安县</th>\n",
       "      <th>屯昌县</th>\n",
       "      <th>澄迈县</th>\n",
       "      <th>临高县</th>\n",
       "      <th>白沙黎族自治县</th>\n",
       "      <th>昌江黎族自治县</th>\n",
       "      <th>乐东黎族自治县</th>\n",
       "      <th>陵水黎族自治县</th>\n",
       "      <th>保亭黎族苗族自治县</th>\n",
       "      <th>琼中黎族苗族自治县</th>\n",
       "      <th>重庆市</th>\n",
       "      <th>成都市</th>\n",
       "      <th>自贡市</th>\n",
       "      <th>攀枝花市</th>\n",
       "      <th>泸州市</th>\n",
       "      <th>德阳市</th>\n",
       "      <th>绵阳市</th>\n",
       "      <th>广元市</th>\n",
       "      <th>遂宁市</th>\n",
       "      <th>内江市</th>\n",
       "      <th>乐山市</th>\n",
       "      <th>南充市</th>\n",
       "      <th>眉山市</th>\n",
       "      <th>宜宾市</th>\n",
       "      <th>广安市</th>\n",
       "      <th>达州市</th>\n",
       "      <th>雅安市</th>\n",
       "      <th>巴中市</th>\n",
       "      <th>资阳市</th>\n",
       "      <th>阿坝藏族羌族自治州</th>\n",
       "      <th>甘孜藏族自治州</th>\n",
       "      <th>凉山彝族自治州</th>\n",
       "      <th>贵阳市</th>\n",
       "      <th>六盘水市</th>\n",
       "      <th>遵义市</th>\n",
       "      <th>安顺市</th>\n",
       "      <th>毕节市</th>\n",
       "      <th>铜仁市</th>\n",
       "      <th>黔西南布依族苗族自治州</th>\n",
       "      <th>黔东南苗族侗族自治州</th>\n",
       "      <th>黔南布依族苗族自治州</th>\n",
       "      <th>昆明市</th>\n",
       "      <th>曲靖市</th>\n",
       "      <th>玉溪市</th>\n",
       "      <th>保山市</th>\n",
       "      <th>昭通市</th>\n",
       "      <th>丽江市</th>\n",
       "      <th>普洱市</th>\n",
       "      <th>临沧市</th>\n",
       "      <th>楚雄彝族自治州</th>\n",
       "      <th>红河哈尼族彝族自治州</th>\n",
       "      <th>文山壮族苗族自治州</th>\n",
       "      <th>西双版纳傣族自治州</th>\n",
       "      <th>大理白族自治州</th>\n",
       "      <th>德宏傣族景颇族自治州</th>\n",
       "      <th>怒江傈僳族自治州</th>\n",
       "      <th>迪庆藏族自治州</th>\n",
       "      <th>拉萨市</th>\n",
       "      <th>日喀则市</th>\n",
       "      <th>昌都市</th>\n",
       "      <th>林芝市</th>\n",
       "      <th>山南市</th>\n",
       "      <th>那曲市</th>\n",
       "      <th>阿里地区</th>\n",
       "      <th>西安市</th>\n",
       "      <th>铜川市</th>\n",
       "      <th>宝鸡市</th>\n",
       "      <th>咸阳市</th>\n",
       "      <th>渭南市</th>\n",
       "      <th>延安市</th>\n",
       "      <th>汉中市</th>\n",
       "      <th>榆林市</th>\n",
       "      <th>安康市</th>\n",
       "      <th>商洛市</th>\n",
       "      <th>兰州市</th>\n",
       "      <th>嘉峪关市</th>\n",
       "      <th>金昌市</th>\n",
       "      <th>白银市</th>\n",
       "      <th>天水市</th>\n",
       "      <th>武威市</th>\n",
       "      <th>张掖市</th>\n",
       "      <th>平凉市</th>\n",
       "      <th>酒泉市</th>\n",
       "      <th>庆阳市</th>\n",
       "      <th>定西市</th>\n",
       "      <th>陇南市</th>\n",
       "      <th>临夏回族自治州</th>\n",
       "      <th>甘南藏族自治州</th>\n",
       "      <th>西宁市</th>\n",
       "      <th>海东市</th>\n",
       "      <th>海北藏族自治州</th>\n",
       "      <th>黄南藏族自治州</th>\n",
       "      <th>海南藏族自治州</th>\n",
       "      <th>果洛藏族自治州</th>\n",
       "      <th>玉树藏族自治州</th>\n",
       "      <th>海西蒙古族藏族自治州</th>\n",
       "      <th>银川市</th>\n",
       "      <th>石嘴山市</th>\n",
       "      <th>吴忠市</th>\n",
       "      <th>固原市</th>\n",
       "      <th>中卫市</th>\n",
       "      <th>乌鲁木齐市</th>\n",
       "      <th>克拉玛依市</th>\n",
       "      <th>吐鲁番市</th>\n",
       "      <th>哈密市</th>\n",
       "      <th>昌吉回族自治州</th>\n",
       "      <th>博尔塔拉蒙古自治州</th>\n",
       "      <th>巴音郭楞蒙古自治州</th>\n",
       "      <th>阿克苏地区</th>\n",
       "      <th>克孜勒苏柯尔克孜自治州</th>\n",
       "      <th>喀什地区</th>\n",
       "      <th>和田地区</th>\n",
       "      <th>伊犁哈萨克自治州</th>\n",
       "      <th>塔城地区</th>\n",
       "      <th>阿勒泰地区</th>\n",
       "      <th>石河子市</th>\n",
       "      <th>阿拉尔市</th>\n",
       "      <th>图木舒克市</th>\n",
       "      <th>五家渠市</th>\n",
       "      <th>北屯市</th>\n",
       "      <th>铁门关市</th>\n",
       "      <th>双河市</th>\n",
       "      <th>可克达拉市</th>\n",
       "      <th>昆玉市</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1687.284543</td>\n",
       "      <td>717.929685</td>\n",
       "      <td>578.260399</td>\n",
       "      <td>236.565889</td>\n",
       "      <td>860.003982</td>\n",
       "      <td>498.935097</td>\n",
       "      <td>2435.282618</td>\n",
       "      <td>1186.747992</td>\n",
       "      <td>772.985012</td>\n",
       "      <td>556.446942</td>\n",
       "      <td>3856.471127</td>\n",
       "      <td>462.640544</td>\n",
       "      <td>284.940949</td>\n",
       "      <td>228.703321</td>\n",
       "      <td>56.626593</td>\n",
       "      <td>98.873823</td>\n",
       "      <td>47.926838</td>\n",
       "      <td>62.636306</td>\n",
       "      <td>112.898331</td>\n",
       "      <td>227.429424</td>\n",
       "      <td>106.305882</td>\n",
       "      <td>239.295704</td>\n",
       "      <td>91.076712</td>\n",
       "      <td>201.793026</td>\n",
       "      <td>103.064098</td>\n",
       "      <td>13.846133</td>\n",
       "      <td>242.268505</td>\n",
       "      <td>92.540991</td>\n",
       "      <td>57.719109</td>\n",
       "      <td>50.519730</td>\n",
       "      <td>36.805937</td>\n",
       "      <td>94.644533</td>\n",
       "      <td>30.024863</td>\n",
       "      <td>64.297822</td>\n",
       "      <td>7.825447</td>\n",
       "      <td>266.754603</td>\n",
       "      <td>161.200736</td>\n",
       "      <td>69.453745</td>\n",
       "      <td>40.222041</td>\n",
       "      <td>25.647730</td>\n",
       "      <td>46.409431</td>\n",
       "      <td>87.709110</td>\n",
       "      <td>49.760114</td>\n",
       "      <td>46.459197</td>\n",
       "      <td>27.530344</td>\n",
       "      <td>39.778371</td>\n",
       "      <td>38.943228</td>\n",
       "      <td>97.503146</td>\n",
       "      <td>111.686034</td>\n",
       "      <td>218.039124</td>\n",
       "      <td>93.016335</td>\n",
       "      <td>58.454078</td>\n",
       "      <td>23.042493</td>\n",
       "      <td>41.921958</td>\n",
       "      <td>19.823024</td>\n",
       "      <td>45.053625</td>\n",
       "      <td>29.092305</td>\n",
       "      <td>31.040163</td>\n",
       "      <td>385.224723</td>\n",
       "      <td>93.436602</td>\n",
       "      <td>27.096234</td>\n",
       "      <td>16.562578</td>\n",
       "      <td>21.907652</td>\n",
       "      <td>61.674696</td>\n",
       "      <td>21.396012</td>\n",
       "      <td>50.098066</td>\n",
       "      <td>10.313575</td>\n",
       "      <td>60.844025</td>\n",
       "      <td>21.568651</td>\n",
       "      <td>82.911343</td>\n",
       "      <td>5.380662</td>\n",
       "      <td>515.850627</td>\n",
       "      <td>200.173029</td>\n",
       "      <td>59.250838</td>\n",
       "      <td>105.094389</td>\n",
       "      <td>48.053062</td>\n",
       "      <td>108.204900</td>\n",
       "      <td>79.333539</td>\n",
       "      <td>39.763866</td>\n",
       "      <td>31.451816</td>\n",
       "      <td>54.874255</td>\n",
       "      <td>56.047014</td>\n",
       "      <td>23.960738</td>\n",
       "      <td>41.695641</td>\n",
       "      <td>33.532751</td>\n",
       "      <td>187.511206</td>\n",
       "      <td>52.422659</td>\n",
       "      <td>55.793729</td>\n",
       "      <td>32.436636</td>\n",
       "      <td>15.542331</td>\n",
       "      <td>23.928753</td>\n",
       "      <td>30.462165</td>\n",
       "      <td>12.244635</td>\n",
       "      <td>8.144937</td>\n",
       "      <td>27.325203</td>\n",
       "      <td>8.138529</td>\n",
       "      <td>168.313427</td>\n",
       "      <td>93.576963</td>\n",
       "      <td>28.626899</td>\n",
       "      <td>21.533005</td>\n",
       "      <td>57.166950</td>\n",
       "      <td>19.697372</td>\n",
       "      <td>20.141506</td>\n",
       "      <td>106.835890</td>\n",
       "      <td>15.345293</td>\n",
       "      <td>24.611607</td>\n",
       "      <td>132.134287</td>\n",
       "      <td>56.456832</td>\n",
       "      <td>37.663818</td>\n",
       "      <td>52.038299</td>\n",
       "      <td>27.573201</td>\n",
       "      <td>13.027200</td>\n",
       "      <td>70.441119</td>\n",
       "      <td>87.009694</td>\n",
       "      <td>36.539951</td>\n",
       "      <td>13.873429</td>\n",
       "      <td>37.222540</td>\n",
       "      <td>13.513474</td>\n",
       "      <td>16.940520</td>\n",
       "      <td>14.929918</td>\n",
       "      <td>13.755336</td>\n",
       "      <td>106.041605</td>\n",
       "      <td>11.748058</td>\n",
       "      <td>10.160599</td>\n",
       "      <td>49.470566</td>\n",
       "      <td>6.475570</td>\n",
       "      <td>7.070942</td>\n",
       "      <td>33.814735</td>\n",
       "      <td>37.271662</td>\n",
       "      <td>28.400235</td>\n",
       "      <td>27.053921</td>\n",
       "      <td>27.949803</td>\n",
       "      <td>291.399994</td>\n",
       "      <td>210.116605</td>\n",
       "      <td>80.254085</td>\n",
       "      <td>63.408408</td>\n",
       "      <td>53.998601</td>\n",
       "      <td>135.588242</td>\n",
       "      <td>143.430690</td>\n",
       "      <td>169.159620</td>\n",
       "      <td>155.531300</td>\n",
       "      <td>59.663518</td>\n",
       "      <td>39.055949</td>\n",
       "      <td>130.041904</td>\n",
       "      <td>293.389870</td>\n",
       "      <td>234.675291</td>\n",
       "      <td>78.703713</td>\n",
       "      <td>289.842198</td>\n",
       "      <td>344.604706</td>\n",
       "      <td>77.401325</td>\n",
       "      <td>111.931285</td>\n",
       "      <td>65.493794</td>\n",
       "      <td>192.160985</td>\n",
       "      <td>40.115797</td>\n",
       "      <td>97.928273</td>\n",
       "      <td>53.591049</td>\n",
       "      <td>171.971205</td>\n",
       "      <td>73.883939</td>\n",
       "      <td>40.110599</td>\n",
       "      <td>34.324435</td>\n",
       "      <td>190.754348</td>\n",
       "      <td>179.836707</td>\n",
       "      <td>314.979774</td>\n",
       "      <td>363.222841</td>\n",
       "      <td>229.763140</td>\n",
       "      <td>9.356711</td>\n",
       "      <td>213.943016</td>\n",
       "      <td>20.768958</td>\n",
       "      <td>38.902806</td>\n",
       "      <td>40.559683</td>\n",
       "      <td>61.228033</td>\n",
       "      <td>6.451960</td>\n",
       "      <td>22.657543</td>\n",
       "      <td>64.462821</td>\n",
       "      <td>41.163964</td>\n",
       "      <td>67.237587</td>\n",
       "      <td>22.510855</td>\n",
       "      <td>40.840956</td>\n",
       "      <td>19.188407</td>\n",
       "      <td>12.178825</td>\n",
       "      <td>6.440978</td>\n",
       "      <td>10.016563</td>\n",
       "      <td>0.238153</td>\n",
       "      <td>164.802627</td>\n",
       "      <td>29.818382</td>\n",
       "      <td>15.259259</td>\n",
       "      <td>28.735884</td>\n",
       "      <td>24.736431</td>\n",
       "      <td>38.541040</td>\n",
       "      <td>36.966508</td>\n",
       "      <td>11.978901</td>\n",
       "      <td>17.626012</td>\n",
       "      <td>14.205404</td>\n",
       "      <td>13.175405</td>\n",
       "      <td>17.090933</td>\n",
       "      <td>20.785599</td>\n",
       "      <td>10.678127</td>\n",
       "      <td>227.220288</td>\n",
       "      <td>4.547294</td>\n",
       "      <td>221.173438</td>\n",
       "      <td>51.746135</td>\n",
       "      <td>11.321235</td>\n",
       "      <td>33.959445</td>\n",
       "      <td>8.093168</td>\n",
       "      <td>16.318285</td>\n",
       "      <td>5.690368</td>\n",
       "      <td>4.968361</td>\n",
       "      <td>18.398222</td>\n",
       "      <td>3.844330</td>\n",
       "      <td>2.344491</td>\n",
       "      <td>2.587624</td>\n",
       "      <td>4.278031</td>\n",
       "      <td>5.300496</td>\n",
       "      <td>29.527148</td>\n",
       "      <td>14.686257</td>\n",
       "      <td>3.456797</td>\n",
       "      <td>4.837953</td>\n",
       "      <td>1.472979</td>\n",
       "      <td>58.675670</td>\n",
       "      <td>14.994231</td>\n",
       "      <td>35.963769</td>\n",
       "      <td>4.611463</td>\n",
       "      <td>20.812586</td>\n",
       "      <td>3.694599</td>\n",
       "      <td>3.266351</td>\n",
       "      <td>5.526584</td>\n",
       "      <td>7.883111</td>\n",
       "      <td>4.165500</td>\n",
       "      <td>3.379215</td>\n",
       "      <td>4.584476</td>\n",
       "      <td>2.595048</td>\n",
       "      <td>2.804544</td>\n",
       "      <td>61.913259</td>\n",
       "      <td>130.706636</td>\n",
       "      <td>0.180892</td>\n",
       "      <td>5.930824</td>\n",
       "      <td>1.914012</td>\n",
       "      <td>16.603570</td>\n",
       "      <td>10.503868</td>\n",
       "      <td>14.613029</td>\n",
       "      <td>5.314056</td>\n",
       "      <td>1.561646</td>\n",
       "      <td>0.559364</td>\n",
       "      <td>9.479283</td>\n",
       "      <td>2.906732</td>\n",
       "      <td>0.778939</td>\n",
       "      <td>0.772270</td>\n",
       "      <td>6.561556</td>\n",
       "      <td>28.629664</td>\n",
       "      <td>3.629045</td>\n",
       "      <td>0.537591</td>\n",
       "      <td>268.574329</td>\n",
       "      <td>304.058920</td>\n",
       "      <td>9.196509</td>\n",
       "      <td>2.742754</td>\n",
       "      <td>15.011374</td>\n",
       "      <td>15.033239</td>\n",
       "      <td>60.746089</td>\n",
       "      <td>58.119615</td>\n",
       "      <td>18.343994</td>\n",
       "      <td>8.697021</td>\n",
       "      <td>14.207139</td>\n",
       "      <td>48.880345</td>\n",
       "      <td>13.359528</td>\n",
       "      <td>19.507023</td>\n",
       "      <td>11.315586</td>\n",
       "      <td>49.193167</td>\n",
       "      <td>3.836843</td>\n",
       "      <td>60.709893</td>\n",
       "      <td>9.485928</td>\n",
       "      <td>2.142850</td>\n",
       "      <td>0.728689</td>\n",
       "      <td>8.724315</td>\n",
       "      <td>64.581025</td>\n",
       "      <td>6.883339</td>\n",
       "      <td>24.715294</td>\n",
       "      <td>7.898251</td>\n",
       "      <td>11.414056</td>\n",
       "      <td>10.018477</td>\n",
       "      <td>7.758112</td>\n",
       "      <td>10.914051</td>\n",
       "      <td>10.196811</td>\n",
       "      <td>112.620495</td>\n",
       "      <td>14.950564</td>\n",
       "      <td>6.138236</td>\n",
       "      <td>8.263112</td>\n",
       "      <td>6.975439</td>\n",
       "      <td>16.392088</td>\n",
       "      <td>3.599774</td>\n",
       "      <td>2.207151</td>\n",
       "      <td>4.055208</td>\n",
       "      <td>6.719594</td>\n",
       "      <td>3.056524</td>\n",
       "      <td>13.873582</td>\n",
       "      <td>18.407455</td>\n",
       "      <td>4.342039</td>\n",
       "      <td>0.658222</td>\n",
       "      <td>0.685889</td>\n",
       "      <td>8.376916</td>\n",
       "      <td>0.729315</td>\n",
       "      <td>0.271823</td>\n",
       "      <td>0.443766</td>\n",
       "      <td>0.810757</td>\n",
       "      <td>0.321076</td>\n",
       "      <td>0.035507</td>\n",
       "      <td>353.726316</td>\n",
       "      <td>5.552809</td>\n",
       "      <td>47.327357</td>\n",
       "      <td>49.642640</td>\n",
       "      <td>67.841973</td>\n",
       "      <td>19.777587</td>\n",
       "      <td>36.342918</td>\n",
       "      <td>45.571592</td>\n",
       "      <td>23.433685</td>\n",
       "      <td>10.987286</td>\n",
       "      <td>77.788307</td>\n",
       "      <td>4.150912</td>\n",
       "      <td>4.907869</td>\n",
       "      <td>16.665266</td>\n",
       "      <td>52.454713</td>\n",
       "      <td>10.644596</td>\n",
       "      <td>9.190037</td>\n",
       "      <td>27.207079</td>\n",
       "      <td>10.941075</td>\n",
       "      <td>44.066290</td>\n",
       "      <td>22.481084</td>\n",
       "      <td>53.270143</td>\n",
       "      <td>5.835290</td>\n",
       "      <td>1.463069</td>\n",
       "      <td>32.826502</td>\n",
       "      <td>5.644061</td>\n",
       "      <td>0.533817</td>\n",
       "      <td>0.360547</td>\n",
       "      <td>0.645230</td>\n",
       "      <td>0.082484</td>\n",
       "      <td>1.008710</td>\n",
       "      <td>3.599359</td>\n",
       "      <td>64.461035</td>\n",
       "      <td>8.162637</td>\n",
       "      <td>9.710034</td>\n",
       "      <td>6.610745</td>\n",
       "      <td>8.696535</td>\n",
       "      <td>65.107392</td>\n",
       "      <td>5.028291</td>\n",
       "      <td>2.071475</td>\n",
       "      <td>5.131276</td>\n",
       "      <td>8.425802</td>\n",
       "      <td>1.492038</td>\n",
       "      <td>8.123240</td>\n",
       "      <td>4.180934</td>\n",
       "      <td>0.599440</td>\n",
       "      <td>3.820052</td>\n",
       "      <td>1.889751</td>\n",
       "      <td>7.443148</td>\n",
       "      <td>3.031521</td>\n",
       "      <td>1.412136</td>\n",
       "      <td>3.627545</td>\n",
       "      <td>0.636727</td>\n",
       "      <td>0.194443</td>\n",
       "      <td>0.819107</td>\n",
       "      <td>0.290680</td>\n",
       "      <td>0.115440</td>\n",
       "      <td>0.187783</td>\n",
       "      <td>0.213991</td>\n",
       "      <td>0.050358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津市</th>\n",
       "      <td>1667.005135</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>217.225708</td>\n",
       "      <td>851.995742</td>\n",
       "      <td>127.646818</td>\n",
       "      <td>383.082977</td>\n",
       "      <td>161.676366</td>\n",
       "      <td>359.872730</td>\n",
       "      <td>137.385146</td>\n",
       "      <td>113.433066</td>\n",
       "      <td>872.315430</td>\n",
       "      <td>917.989155</td>\n",
       "      <td>196.719985</td>\n",
       "      <td>70.992986</td>\n",
       "      <td>70.499883</td>\n",
       "      <td>20.992254</td>\n",
       "      <td>30.181102</td>\n",
       "      <td>12.437540</td>\n",
       "      <td>25.339450</td>\n",
       "      <td>40.687716</td>\n",
       "      <td>51.371505</td>\n",
       "      <td>39.615529</td>\n",
       "      <td>50.980915</td>\n",
       "      <td>28.817128</td>\n",
       "      <td>36.307536</td>\n",
       "      <td>24.327426</td>\n",
       "      <td>3.737073</td>\n",
       "      <td>78.859947</td>\n",
       "      <td>41.671012</td>\n",
       "      <td>11.657209</td>\n",
       "      <td>22.665904</td>\n",
       "      <td>10.322606</td>\n",
       "      <td>24.131154</td>\n",
       "      <td>11.094950</td>\n",
       "      <td>10.668161</td>\n",
       "      <td>1.764454</td>\n",
       "      <td>72.996423</td>\n",
       "      <td>43.008200</td>\n",
       "      <td>19.658618</td>\n",
       "      <td>15.915731</td>\n",
       "      <td>9.101027</td>\n",
       "      <td>12.478476</td>\n",
       "      <td>37.383442</td>\n",
       "      <td>15.720624</td>\n",
       "      <td>17.125008</td>\n",
       "      <td>9.153367</td>\n",
       "      <td>13.094095</td>\n",
       "      <td>18.065435</td>\n",
       "      <td>35.194523</td>\n",
       "      <td>54.225153</td>\n",
       "      <td>66.673166</td>\n",
       "      <td>34.045116</td>\n",
       "      <td>26.355373</td>\n",
       "      <td>12.491587</td>\n",
       "      <td>32.804506</td>\n",
       "      <td>6.793874</td>\n",
       "      <td>19.799583</td>\n",
       "      <td>13.156826</td>\n",
       "      <td>8.897856</td>\n",
       "      <td>128.467928</td>\n",
       "      <td>77.404044</td>\n",
       "      <td>8.089636</td>\n",
       "      <td>5.051685</td>\n",
       "      <td>6.128706</td>\n",
       "      <td>21.865177</td>\n",
       "      <td>8.565340</td>\n",
       "      <td>14.794586</td>\n",
       "      <td>3.355503</td>\n",
       "      <td>17.996596</td>\n",
       "      <td>11.655312</td>\n",
       "      <td>50.394175</td>\n",
       "      <td>1.742145</td>\n",
       "      <td>101.366797</td>\n",
       "      <td>35.635631</td>\n",
       "      <td>17.817994</td>\n",
       "      <td>38.270275</td>\n",
       "      <td>12.864440</td>\n",
       "      <td>30.722190</td>\n",
       "      <td>25.597960</td>\n",
       "      <td>13.630589</td>\n",
       "      <td>9.247920</td>\n",
       "      <td>20.278565</td>\n",
       "      <td>15.017555</td>\n",
       "      <td>6.488047</td>\n",
       "      <td>14.011300</td>\n",
       "      <td>9.490777</td>\n",
       "      <td>35.309355</td>\n",
       "      <td>17.551755</td>\n",
       "      <td>18.482008</td>\n",
       "      <td>6.878563</td>\n",
       "      <td>4.198738</td>\n",
       "      <td>8.743501</td>\n",
       "      <td>10.977308</td>\n",
       "      <td>2.495532</td>\n",
       "      <td>4.275312</td>\n",
       "      <td>10.737401</td>\n",
       "      <td>2.431365</td>\n",
       "      <td>34.070573</td>\n",
       "      <td>22.021565</td>\n",
       "      <td>9.098945</td>\n",
       "      <td>6.316041</td>\n",
       "      <td>6.825536</td>\n",
       "      <td>6.075772</td>\n",
       "      <td>3.442957</td>\n",
       "      <td>41.001768</td>\n",
       "      <td>3.465978</td>\n",
       "      <td>7.989293</td>\n",
       "      <td>89.624035</td>\n",
       "      <td>23.021271</td>\n",
       "      <td>9.815058</td>\n",
       "      <td>27.918331</td>\n",
       "      <td>8.224246</td>\n",
       "      <td>3.616611</td>\n",
       "      <td>15.724294</td>\n",
       "      <td>25.054933</td>\n",
       "      <td>14.715156</td>\n",
       "      <td>4.395529</td>\n",
       "      <td>17.766851</td>\n",
       "      <td>3.092594</td>\n",
       "      <td>3.989655</td>\n",
       "      <td>2.788674</td>\n",
       "      <td>5.312940</td>\n",
       "      <td>17.399302</td>\n",
       "      <td>3.964284</td>\n",
       "      <td>2.291983</td>\n",
       "      <td>11.861103</td>\n",
       "      <td>1.869136</td>\n",
       "      <td>2.758127</td>\n",
       "      <td>7.829918</td>\n",
       "      <td>6.499965</td>\n",
       "      <td>6.745808</td>\n",
       "      <td>7.139372</td>\n",
       "      <td>9.094234</td>\n",
       "      <td>110.524382</td>\n",
       "      <td>57.161011</td>\n",
       "      <td>27.416595</td>\n",
       "      <td>17.512842</td>\n",
       "      <td>24.000924</td>\n",
       "      <td>38.329164</td>\n",
       "      <td>46.512084</td>\n",
       "      <td>66.525819</td>\n",
       "      <td>54.904997</td>\n",
       "      <td>13.616948</td>\n",
       "      <td>10.988737</td>\n",
       "      <td>40.150425</td>\n",
       "      <td>389.196029</td>\n",
       "      <td>94.705632</td>\n",
       "      <td>129.023523</td>\n",
       "      <td>123.385068</td>\n",
       "      <td>61.884064</td>\n",
       "      <td>23.864803</td>\n",
       "      <td>27.851335</td>\n",
       "      <td>16.435691</td>\n",
       "      <td>68.339371</td>\n",
       "      <td>16.335168</td>\n",
       "      <td>33.283225</td>\n",
       "      <td>12.920346</td>\n",
       "      <td>67.794500</td>\n",
       "      <td>15.011232</td>\n",
       "      <td>10.285443</td>\n",
       "      <td>7.698881</td>\n",
       "      <td>61.622666</td>\n",
       "      <td>56.870563</td>\n",
       "      <td>49.427290</td>\n",
       "      <td>92.010497</td>\n",
       "      <td>73.233671</td>\n",
       "      <td>2.224003</td>\n",
       "      <td>34.888987</td>\n",
       "      <td>3.590799</td>\n",
       "      <td>6.974953</td>\n",
       "      <td>5.997745</td>\n",
       "      <td>11.173292</td>\n",
       "      <td>1.430773</td>\n",
       "      <td>5.810861</td>\n",
       "      <td>15.911473</td>\n",
       "      <td>10.735402</td>\n",
       "      <td>43.062947</td>\n",
       "      <td>6.654695</td>\n",
       "      <td>7.130063</td>\n",
       "      <td>5.557726</td>\n",
       "      <td>1.883182</td>\n",
       "      <td>1.459443</td>\n",
       "      <td>4.255251</td>\n",
       "      <td>0.009242</td>\n",
       "      <td>28.477699</td>\n",
       "      <td>5.156337</td>\n",
       "      <td>2.600515</td>\n",
       "      <td>6.967824</td>\n",
       "      <td>5.305496</td>\n",
       "      <td>8.420861</td>\n",
       "      <td>8.148155</td>\n",
       "      <td>2.845275</td>\n",
       "      <td>3.860917</td>\n",
       "      <td>2.522566</td>\n",
       "      <td>2.797889</td>\n",
       "      <td>3.036391</td>\n",
       "      <td>4.999205</td>\n",
       "      <td>1.691076</td>\n",
       "      <td>46.502918</td>\n",
       "      <td>1.150665</td>\n",
       "      <td>30.610847</td>\n",
       "      <td>10.677273</td>\n",
       "      <td>1.898660</td>\n",
       "      <td>7.733657</td>\n",
       "      <td>1.852048</td>\n",
       "      <td>3.451894</td>\n",
       "      <td>1.343324</td>\n",
       "      <td>0.893186</td>\n",
       "      <td>4.012819</td>\n",
       "      <td>0.592020</td>\n",
       "      <td>0.406459</td>\n",
       "      <td>0.487497</td>\n",
       "      <td>0.687413</td>\n",
       "      <td>0.774417</td>\n",
       "      <td>7.034878</td>\n",
       "      <td>3.364091</td>\n",
       "      <td>0.483394</td>\n",
       "      <td>0.841078</td>\n",
       "      <td>0.369584</td>\n",
       "      <td>18.091439</td>\n",
       "      <td>5.998549</td>\n",
       "      <td>14.693566</td>\n",
       "      <td>2.083314</td>\n",
       "      <td>4.624701</td>\n",
       "      <td>1.114419</td>\n",
       "      <td>1.668724</td>\n",
       "      <td>2.341617</td>\n",
       "      <td>3.157605</td>\n",
       "      <td>1.007780</td>\n",
       "      <td>1.460123</td>\n",
       "      <td>1.675252</td>\n",
       "      <td>1.195017</td>\n",
       "      <td>0.911977</td>\n",
       "      <td>13.749019</td>\n",
       "      <td>33.148273</td>\n",
       "      <td>0.009635</td>\n",
       "      <td>1.774579</td>\n",
       "      <td>0.321383</td>\n",
       "      <td>2.668699</td>\n",
       "      <td>1.388344</td>\n",
       "      <td>2.594981</td>\n",
       "      <td>0.980631</td>\n",
       "      <td>0.327808</td>\n",
       "      <td>0.215924</td>\n",
       "      <td>1.739322</td>\n",
       "      <td>0.496222</td>\n",
       "      <td>0.165072</td>\n",
       "      <td>0.233596</td>\n",
       "      <td>1.126130</td>\n",
       "      <td>3.379797</td>\n",
       "      <td>1.077746</td>\n",
       "      <td>0.095482</td>\n",
       "      <td>69.151706</td>\n",
       "      <td>62.892148</td>\n",
       "      <td>2.641923</td>\n",
       "      <td>0.830469</td>\n",
       "      <td>4.761081</td>\n",
       "      <td>5.407440</td>\n",
       "      <td>13.573233</td>\n",
       "      <td>9.535464</td>\n",
       "      <td>4.168142</td>\n",
       "      <td>3.038757</td>\n",
       "      <td>3.801677</td>\n",
       "      <td>11.908913</td>\n",
       "      <td>3.883602</td>\n",
       "      <td>5.030701</td>\n",
       "      <td>5.023374</td>\n",
       "      <td>12.379687</td>\n",
       "      <td>1.164072</td>\n",
       "      <td>7.690211</td>\n",
       "      <td>2.528125</td>\n",
       "      <td>0.751528</td>\n",
       "      <td>0.100926</td>\n",
       "      <td>1.815553</td>\n",
       "      <td>21.745957</td>\n",
       "      <td>5.284952</td>\n",
       "      <td>11.949144</td>\n",
       "      <td>4.276010</td>\n",
       "      <td>6.592233</td>\n",
       "      <td>5.588249</td>\n",
       "      <td>3.787180</td>\n",
       "      <td>4.848299</td>\n",
       "      <td>5.055526</td>\n",
       "      <td>28.853298</td>\n",
       "      <td>5.854992</td>\n",
       "      <td>2.525501</td>\n",
       "      <td>2.115545</td>\n",
       "      <td>2.946172</td>\n",
       "      <td>4.130226</td>\n",
       "      <td>0.990712</td>\n",
       "      <td>0.907917</td>\n",
       "      <td>2.699158</td>\n",
       "      <td>2.368399</td>\n",
       "      <td>0.677069</td>\n",
       "      <td>4.645936</td>\n",
       "      <td>4.655093</td>\n",
       "      <td>1.178043</td>\n",
       "      <td>0.084664</td>\n",
       "      <td>0.164977</td>\n",
       "      <td>3.279510</td>\n",
       "      <td>0.488610</td>\n",
       "      <td>0.207549</td>\n",
       "      <td>0.249024</td>\n",
       "      <td>0.534900</td>\n",
       "      <td>0.238503</td>\n",
       "      <td>0.009689</td>\n",
       "      <td>78.446157</td>\n",
       "      <td>1.091908</td>\n",
       "      <td>10.494694</td>\n",
       "      <td>15.546977</td>\n",
       "      <td>16.837996</td>\n",
       "      <td>4.525594</td>\n",
       "      <td>10.704575</td>\n",
       "      <td>11.077888</td>\n",
       "      <td>12.291888</td>\n",
       "      <td>3.276977</td>\n",
       "      <td>33.804616</td>\n",
       "      <td>1.576985</td>\n",
       "      <td>2.759389</td>\n",
       "      <td>10.894087</td>\n",
       "      <td>25.051234</td>\n",
       "      <td>8.660275</td>\n",
       "      <td>5.028631</td>\n",
       "      <td>17.453015</td>\n",
       "      <td>5.112013</td>\n",
       "      <td>25.316634</td>\n",
       "      <td>11.104039</td>\n",
       "      <td>17.991955</td>\n",
       "      <td>2.157812</td>\n",
       "      <td>1.102300</td>\n",
       "      <td>10.409380</td>\n",
       "      <td>3.861083</td>\n",
       "      <td>0.292775</td>\n",
       "      <td>0.316050</td>\n",
       "      <td>0.227352</td>\n",
       "      <td>0.040331</td>\n",
       "      <td>0.156084</td>\n",
       "      <td>1.430920</td>\n",
       "      <td>16.481458</td>\n",
       "      <td>3.917795</td>\n",
       "      <td>3.964979</td>\n",
       "      <td>3.022296</td>\n",
       "      <td>3.798205</td>\n",
       "      <td>20.774382</td>\n",
       "      <td>1.121560</td>\n",
       "      <td>1.225142</td>\n",
       "      <td>1.728600</td>\n",
       "      <td>3.593349</td>\n",
       "      <td>0.561883</td>\n",
       "      <td>2.406641</td>\n",
       "      <td>1.650262</td>\n",
       "      <td>0.199569</td>\n",
       "      <td>1.540387</td>\n",
       "      <td>0.965426</td>\n",
       "      <td>3.406421</td>\n",
       "      <td>1.396981</td>\n",
       "      <td>0.578351</td>\n",
       "      <td>1.317927</td>\n",
       "      <td>0.142610</td>\n",
       "      <td>0.065685</td>\n",
       "      <td>0.400378</td>\n",
       "      <td>0.094892</td>\n",
       "      <td>0.037286</td>\n",
       "      <td>0.075453</td>\n",
       "      <td>0.088086</td>\n",
       "      <td>0.044267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>石家庄市</th>\n",
       "      <td>500.998208</td>\n",
       "      <td>157.181700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>193.238588</td>\n",
       "      <td>77.230149</td>\n",
       "      <td>607.812191</td>\n",
       "      <td>1134.107468</td>\n",
       "      <td>1014.047514</td>\n",
       "      <td>164.385632</td>\n",
       "      <td>113.772738</td>\n",
       "      <td>331.943378</td>\n",
       "      <td>151.574872</td>\n",
       "      <td>512.571443</td>\n",
       "      <td>81.992708</td>\n",
       "      <td>20.065021</td>\n",
       "      <td>73.101999</td>\n",
       "      <td>12.851726</td>\n",
       "      <td>5.995174</td>\n",
       "      <td>9.319001</td>\n",
       "      <td>68.785837</td>\n",
       "      <td>20.170931</td>\n",
       "      <td>63.327796</td>\n",
       "      <td>15.665252</td>\n",
       "      <td>23.164833</td>\n",
       "      <td>11.797720</td>\n",
       "      <td>6.589008</td>\n",
       "      <td>1.132685</td>\n",
       "      <td>11.199688</td>\n",
       "      <td>3.528738</td>\n",
       "      <td>8.795914</td>\n",
       "      <td>2.199104</td>\n",
       "      <td>2.551074</td>\n",
       "      <td>6.770518</td>\n",
       "      <td>1.567455</td>\n",
       "      <td>4.052377</td>\n",
       "      <td>1.020233</td>\n",
       "      <td>17.703213</td>\n",
       "      <td>8.327628</td>\n",
       "      <td>4.132047</td>\n",
       "      <td>2.197317</td>\n",
       "      <td>1.386189</td>\n",
       "      <td>1.964619</td>\n",
       "      <td>5.049825</td>\n",
       "      <td>3.136399</td>\n",
       "      <td>1.719215</td>\n",
       "      <td>1.970782</td>\n",
       "      <td>2.253990</td>\n",
       "      <td>3.058425</td>\n",
       "      <td>4.294540</td>\n",
       "      <td>6.449666</td>\n",
       "      <td>12.204332</td>\n",
       "      <td>5.153236</td>\n",
       "      <td>3.726798</td>\n",
       "      <td>1.458109</td>\n",
       "      <td>2.898018</td>\n",
       "      <td>0.987140</td>\n",
       "      <td>2.456195</td>\n",
       "      <td>1.393235</td>\n",
       "      <td>1.253397</td>\n",
       "      <td>22.143539</td>\n",
       "      <td>4.562248</td>\n",
       "      <td>0.977034</td>\n",
       "      <td>0.877793</td>\n",
       "      <td>1.057026</td>\n",
       "      <td>3.241875</td>\n",
       "      <td>1.530958</td>\n",
       "      <td>2.494092</td>\n",
       "      <td>0.657520</td>\n",
       "      <td>2.816644</td>\n",
       "      <td>1.162162</td>\n",
       "      <td>4.726304</td>\n",
       "      <td>0.322013</td>\n",
       "      <td>35.950083</td>\n",
       "      <td>20.072416</td>\n",
       "      <td>6.699459</td>\n",
       "      <td>18.959636</td>\n",
       "      <td>4.247160</td>\n",
       "      <td>11.536824</td>\n",
       "      <td>13.085013</td>\n",
       "      <td>4.943847</td>\n",
       "      <td>4.502958</td>\n",
       "      <td>9.195434</td>\n",
       "      <td>5.588011</td>\n",
       "      <td>2.768320</td>\n",
       "      <td>5.109983</td>\n",
       "      <td>4.426938</td>\n",
       "      <td>18.024095</td>\n",
       "      <td>4.878647</td>\n",
       "      <td>11.413157</td>\n",
       "      <td>3.729250</td>\n",
       "      <td>2.209536</td>\n",
       "      <td>3.267091</td>\n",
       "      <td>6.135877</td>\n",
       "      <td>1.066740</td>\n",
       "      <td>0.304979</td>\n",
       "      <td>6.006504</td>\n",
       "      <td>1.100658</td>\n",
       "      <td>12.269058</td>\n",
       "      <td>4.369807</td>\n",
       "      <td>3.053967</td>\n",
       "      <td>2.144119</td>\n",
       "      <td>2.323855</td>\n",
       "      <td>1.975529</td>\n",
       "      <td>1.202360</td>\n",
       "      <td>7.630829</td>\n",
       "      <td>0.588766</td>\n",
       "      <td>3.136522</td>\n",
       "      <td>18.699315</td>\n",
       "      <td>6.986211</td>\n",
       "      <td>2.560606</td>\n",
       "      <td>6.251935</td>\n",
       "      <td>1.414237</td>\n",
       "      <td>1.374479</td>\n",
       "      <td>4.800524</td>\n",
       "      <td>9.291889</td>\n",
       "      <td>3.668928</td>\n",
       "      <td>1.487583</td>\n",
       "      <td>6.546559</td>\n",
       "      <td>1.360663</td>\n",
       "      <td>1.113323</td>\n",
       "      <td>1.565407</td>\n",
       "      <td>1.462646</td>\n",
       "      <td>8.652189</td>\n",
       "      <td>1.089976</td>\n",
       "      <td>0.877087</td>\n",
       "      <td>3.530950</td>\n",
       "      <td>0.370585</td>\n",
       "      <td>1.209572</td>\n",
       "      <td>2.782676</td>\n",
       "      <td>1.270480</td>\n",
       "      <td>1.459275</td>\n",
       "      <td>2.786848</td>\n",
       "      <td>2.020903</td>\n",
       "      <td>43.470221</td>\n",
       "      <td>25.967199</td>\n",
       "      <td>14.585981</td>\n",
       "      <td>6.841634</td>\n",
       "      <td>7.572105</td>\n",
       "      <td>12.510213</td>\n",
       "      <td>24.089889</td>\n",
       "      <td>15.394113</td>\n",
       "      <td>13.715350</td>\n",
       "      <td>5.444265</td>\n",
       "      <td>5.956682</td>\n",
       "      <td>22.521433</td>\n",
       "      <td>47.248229</td>\n",
       "      <td>34.527694</td>\n",
       "      <td>16.270254</td>\n",
       "      <td>19.813276</td>\n",
       "      <td>53.820122</td>\n",
       "      <td>13.288331</td>\n",
       "      <td>10.955080</td>\n",
       "      <td>6.469774</td>\n",
       "      <td>29.833626</td>\n",
       "      <td>5.661142</td>\n",
       "      <td>17.949544</td>\n",
       "      <td>8.054123</td>\n",
       "      <td>12.695249</td>\n",
       "      <td>9.356957</td>\n",
       "      <td>7.024869</td>\n",
       "      <td>3.517122</td>\n",
       "      <td>17.768526</td>\n",
       "      <td>16.075388</td>\n",
       "      <td>12.104998</td>\n",
       "      <td>31.306614</td>\n",
       "      <td>29.004791</td>\n",
       "      <td>1.538264</td>\n",
       "      <td>20.514584</td>\n",
       "      <td>1.093099</td>\n",
       "      <td>4.321936</td>\n",
       "      <td>3.972229</td>\n",
       "      <td>6.396933</td>\n",
       "      <td>0.802006</td>\n",
       "      <td>3.523367</td>\n",
       "      <td>6.254021</td>\n",
       "      <td>6.227039</td>\n",
       "      <td>6.175196</td>\n",
       "      <td>2.383577</td>\n",
       "      <td>2.342947</td>\n",
       "      <td>2.345383</td>\n",
       "      <td>1.602329</td>\n",
       "      <td>0.577201</td>\n",
       "      <td>10.342529</td>\n",
       "      <td>0.078178</td>\n",
       "      <td>14.166528</td>\n",
       "      <td>2.275499</td>\n",
       "      <td>1.153647</td>\n",
       "      <td>3.316504</td>\n",
       "      <td>2.401745</td>\n",
       "      <td>3.641109</td>\n",
       "      <td>3.597378</td>\n",
       "      <td>0.804577</td>\n",
       "      <td>1.152825</td>\n",
       "      <td>1.311936</td>\n",
       "      <td>1.107749</td>\n",
       "      <td>0.459884</td>\n",
       "      <td>1.667006</td>\n",
       "      <td>0.820189</td>\n",
       "      <td>20.437265</td>\n",
       "      <td>0.694574</td>\n",
       "      <td>13.468726</td>\n",
       "      <td>2.951020</td>\n",
       "      <td>1.465629</td>\n",
       "      <td>4.037717</td>\n",
       "      <td>0.855864</td>\n",
       "      <td>2.574813</td>\n",
       "      <td>0.340349</td>\n",
       "      <td>0.128897</td>\n",
       "      <td>0.771588</td>\n",
       "      <td>0.327885</td>\n",
       "      <td>0.439408</td>\n",
       "      <td>0.134502</td>\n",
       "      <td>0.288907</td>\n",
       "      <td>0.543756</td>\n",
       "      <td>1.964245</td>\n",
       "      <td>1.652922</td>\n",
       "      <td>0.308888</td>\n",
       "      <td>1.016401</td>\n",
       "      <td>0.196408</td>\n",
       "      <td>6.394137</td>\n",
       "      <td>2.008092</td>\n",
       "      <td>6.269026</td>\n",
       "      <td>0.518407</td>\n",
       "      <td>6.759485</td>\n",
       "      <td>0.744580</td>\n",
       "      <td>0.247972</td>\n",
       "      <td>0.179674</td>\n",
       "      <td>0.214078</td>\n",
       "      <td>0.053034</td>\n",
       "      <td>0.402608</td>\n",
       "      <td>0.275372</td>\n",
       "      <td>0.284367</td>\n",
       "      <td>0.471893</td>\n",
       "      <td>6.506081</td>\n",
       "      <td>13.773652</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>1.698751</td>\n",
       "      <td>1.113240</td>\n",
       "      <td>2.747159</td>\n",
       "      <td>0.683063</td>\n",
       "      <td>2.852114</td>\n",
       "      <td>1.102843</td>\n",
       "      <td>0.363289</td>\n",
       "      <td>0.163738</td>\n",
       "      <td>1.802861</td>\n",
       "      <td>0.348963</td>\n",
       "      <td>0.097079</td>\n",
       "      <td>0.197254</td>\n",
       "      <td>0.969656</td>\n",
       "      <td>4.779114</td>\n",
       "      <td>0.596765</td>\n",
       "      <td>0.243456</td>\n",
       "      <td>34.059965</td>\n",
       "      <td>26.925784</td>\n",
       "      <td>1.322806</td>\n",
       "      <td>0.172344</td>\n",
       "      <td>3.202147</td>\n",
       "      <td>1.782338</td>\n",
       "      <td>4.703687</td>\n",
       "      <td>2.887126</td>\n",
       "      <td>1.091919</td>\n",
       "      <td>0.735997</td>\n",
       "      <td>1.369630</td>\n",
       "      <td>4.505599</td>\n",
       "      <td>1.375162</td>\n",
       "      <td>1.929448</td>\n",
       "      <td>1.796934</td>\n",
       "      <td>4.854988</td>\n",
       "      <td>0.590282</td>\n",
       "      <td>6.015464</td>\n",
       "      <td>0.601784</td>\n",
       "      <td>0.142103</td>\n",
       "      <td>0.071187</td>\n",
       "      <td>0.563992</td>\n",
       "      <td>6.008739</td>\n",
       "      <td>0.218367</td>\n",
       "      <td>1.489871</td>\n",
       "      <td>0.106313</td>\n",
       "      <td>0.312694</td>\n",
       "      <td>0.274696</td>\n",
       "      <td>0.200534</td>\n",
       "      <td>0.084074</td>\n",
       "      <td>0.909669</td>\n",
       "      <td>14.922246</td>\n",
       "      <td>1.825960</td>\n",
       "      <td>0.688232</td>\n",
       "      <td>0.596767</td>\n",
       "      <td>0.330247</td>\n",
       "      <td>2.888297</td>\n",
       "      <td>0.415777</td>\n",
       "      <td>0.081432</td>\n",
       "      <td>0.503843</td>\n",
       "      <td>0.933709</td>\n",
       "      <td>0.837856</td>\n",
       "      <td>1.488877</td>\n",
       "      <td>2.274485</td>\n",
       "      <td>0.136012</td>\n",
       "      <td>0.080580</td>\n",
       "      <td>0.193709</td>\n",
       "      <td>0.826990</td>\n",
       "      <td>0.090514</td>\n",
       "      <td>0.029219</td>\n",
       "      <td>0.044839</td>\n",
       "      <td>0.062109</td>\n",
       "      <td>0.067595</td>\n",
       "      <td>0.006428</td>\n",
       "      <td>34.751294</td>\n",
       "      <td>0.391223</td>\n",
       "      <td>4.310868</td>\n",
       "      <td>6.633886</td>\n",
       "      <td>8.150525</td>\n",
       "      <td>2.720995</td>\n",
       "      <td>4.680316</td>\n",
       "      <td>36.490064</td>\n",
       "      <td>8.916541</td>\n",
       "      <td>1.497083</td>\n",
       "      <td>6.769463</td>\n",
       "      <td>0.406361</td>\n",
       "      <td>0.568936</td>\n",
       "      <td>2.173716</td>\n",
       "      <td>4.977936</td>\n",
       "      <td>2.427721</td>\n",
       "      <td>1.357517</td>\n",
       "      <td>1.579135</td>\n",
       "      <td>1.304311</td>\n",
       "      <td>2.251524</td>\n",
       "      <td>2.395642</td>\n",
       "      <td>1.184234</td>\n",
       "      <td>0.990007</td>\n",
       "      <td>0.069542</td>\n",
       "      <td>3.424318</td>\n",
       "      <td>1.452065</td>\n",
       "      <td>0.089259</td>\n",
       "      <td>0.031944</td>\n",
       "      <td>0.180031</td>\n",
       "      <td>0.018537</td>\n",
       "      <td>0.059303</td>\n",
       "      <td>0.707946</td>\n",
       "      <td>6.820555</td>\n",
       "      <td>1.054683</td>\n",
       "      <td>1.291438</td>\n",
       "      <td>0.405527</td>\n",
       "      <td>1.537278</td>\n",
       "      <td>4.801978</td>\n",
       "      <td>0.168310</td>\n",
       "      <td>0.385659</td>\n",
       "      <td>0.478369</td>\n",
       "      <td>0.995402</td>\n",
       "      <td>0.104372</td>\n",
       "      <td>1.830633</td>\n",
       "      <td>0.477850</td>\n",
       "      <td>0.024460</td>\n",
       "      <td>0.337656</td>\n",
       "      <td>0.188639</td>\n",
       "      <td>0.831438</td>\n",
       "      <td>0.299756</td>\n",
       "      <td>0.156454</td>\n",
       "      <td>0.230253</td>\n",
       "      <td>0.003160</td>\n",
       "      <td>0.040211</td>\n",
       "      <td>0.153368</td>\n",
       "      <td>0.048382</td>\n",
       "      <td>0.030596</td>\n",
       "      <td>0.029922</td>\n",
       "      <td>0.012028</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>唐山市</th>\n",
       "      <td>507.113329</td>\n",
       "      <td>816.433368</td>\n",
       "      <td>160.452109</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>434.067338</td>\n",
       "      <td>73.805152</td>\n",
       "      <td>57.912206</td>\n",
       "      <td>124.784995</td>\n",
       "      <td>55.473960</td>\n",
       "      <td>217.266393</td>\n",
       "      <td>112.637852</td>\n",
       "      <td>118.496497</td>\n",
       "      <td>52.600801</td>\n",
       "      <td>10.680178</td>\n",
       "      <td>12.069057</td>\n",
       "      <td>2.251319</td>\n",
       "      <td>2.911949</td>\n",
       "      <td>1.186374</td>\n",
       "      <td>2.571971</td>\n",
       "      <td>8.446604</td>\n",
       "      <td>5.584692</td>\n",
       "      <td>4.395690</td>\n",
       "      <td>4.943328</td>\n",
       "      <td>4.215755</td>\n",
       "      <td>5.336319</td>\n",
       "      <td>4.723447</td>\n",
       "      <td>2.391254</td>\n",
       "      <td>36.859048</td>\n",
       "      <td>8.591873</td>\n",
       "      <td>4.498766</td>\n",
       "      <td>3.451938</td>\n",
       "      <td>2.036552</td>\n",
       "      <td>4.804797</td>\n",
       "      <td>2.835139</td>\n",
       "      <td>3.005449</td>\n",
       "      <td>1.298343</td>\n",
       "      <td>28.207381</td>\n",
       "      <td>12.269788</td>\n",
       "      <td>12.222368</td>\n",
       "      <td>3.589933</td>\n",
       "      <td>2.619635</td>\n",
       "      <td>3.571583</td>\n",
       "      <td>15.447723</td>\n",
       "      <td>7.838291</td>\n",
       "      <td>5.001964</td>\n",
       "      <td>3.988721</td>\n",
       "      <td>6.656123</td>\n",
       "      <td>5.325223</td>\n",
       "      <td>23.832952</td>\n",
       "      <td>30.189672</td>\n",
       "      <td>15.087335</td>\n",
       "      <td>7.320084</td>\n",
       "      <td>6.840804</td>\n",
       "      <td>4.630140</td>\n",
       "      <td>5.893529</td>\n",
       "      <td>1.550243</td>\n",
       "      <td>4.555100</td>\n",
       "      <td>3.516721</td>\n",
       "      <td>2.169266</td>\n",
       "      <td>26.272391</td>\n",
       "      <td>11.345498</td>\n",
       "      <td>1.195927</td>\n",
       "      <td>0.797691</td>\n",
       "      <td>1.422321</td>\n",
       "      <td>4.281239</td>\n",
       "      <td>3.298585</td>\n",
       "      <td>2.622191</td>\n",
       "      <td>0.689878</td>\n",
       "      <td>2.960905</td>\n",
       "      <td>2.808741</td>\n",
       "      <td>11.047416</td>\n",
       "      <td>0.545965</td>\n",
       "      <td>15.580032</td>\n",
       "      <td>7.334497</td>\n",
       "      <td>4.309980</td>\n",
       "      <td>7.729680</td>\n",
       "      <td>3.829456</td>\n",
       "      <td>5.410542</td>\n",
       "      <td>5.162689</td>\n",
       "      <td>2.880159</td>\n",
       "      <td>1.588815</td>\n",
       "      <td>4.013364</td>\n",
       "      <td>3.704052</td>\n",
       "      <td>1.489348</td>\n",
       "      <td>2.698896</td>\n",
       "      <td>1.609275</td>\n",
       "      <td>5.825805</td>\n",
       "      <td>0.439698</td>\n",
       "      <td>3.082088</td>\n",
       "      <td>0.278977</td>\n",
       "      <td>0.413134</td>\n",
       "      <td>0.598470</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.094613</td>\n",
       "      <td>1.586598</td>\n",
       "      <td>1.125148</td>\n",
       "      <td>0.225225</td>\n",
       "      <td>3.682325</td>\n",
       "      <td>1.832289</td>\n",
       "      <td>0.597511</td>\n",
       "      <td>0.885828</td>\n",
       "      <td>1.620995</td>\n",
       "      <td>0.773767</td>\n",
       "      <td>0.265651</td>\n",
       "      <td>2.358318</td>\n",
       "      <td>0.220779</td>\n",
       "      <td>0.735509</td>\n",
       "      <td>10.330994</td>\n",
       "      <td>3.863168</td>\n",
       "      <td>0.142954</td>\n",
       "      <td>4.487090</td>\n",
       "      <td>0.462659</td>\n",
       "      <td>0.220988</td>\n",
       "      <td>1.093260</td>\n",
       "      <td>3.705369</td>\n",
       "      <td>1.564053</td>\n",
       "      <td>0.185811</td>\n",
       "      <td>0.580200</td>\n",
       "      <td>0.137273</td>\n",
       "      <td>0.263373</td>\n",
       "      <td>0.114007</td>\n",
       "      <td>0.338048</td>\n",
       "      <td>1.260368</td>\n",
       "      <td>0.422613</td>\n",
       "      <td>0.160091</td>\n",
       "      <td>0.657430</td>\n",
       "      <td>0.018274</td>\n",
       "      <td>0.259671</td>\n",
       "      <td>0.361159</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.284172</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.490181</td>\n",
       "      <td>10.398709</td>\n",
       "      <td>5.628135</td>\n",
       "      <td>2.625961</td>\n",
       "      <td>4.789043</td>\n",
       "      <td>8.026083</td>\n",
       "      <td>10.977404</td>\n",
       "      <td>9.103085</td>\n",
       "      <td>7.663256</td>\n",
       "      <td>4.068008</td>\n",
       "      <td>2.477001</td>\n",
       "      <td>10.052941</td>\n",
       "      <td>19.621361</td>\n",
       "      <td>9.254223</td>\n",
       "      <td>16.547567</td>\n",
       "      <td>11.695463</td>\n",
       "      <td>10.530309</td>\n",
       "      <td>5.147032</td>\n",
       "      <td>4.287620</td>\n",
       "      <td>1.540170</td>\n",
       "      <td>9.585012</td>\n",
       "      <td>2.736873</td>\n",
       "      <td>9.242684</td>\n",
       "      <td>2.014252</td>\n",
       "      <td>7.288162</td>\n",
       "      <td>2.069786</td>\n",
       "      <td>1.165080</td>\n",
       "      <td>0.661580</td>\n",
       "      <td>8.363592</td>\n",
       "      <td>4.949485</td>\n",
       "      <td>6.897287</td>\n",
       "      <td>16.720770</td>\n",
       "      <td>9.340071</td>\n",
       "      <td>0.342053</td>\n",
       "      <td>5.872687</td>\n",
       "      <td>0.032896</td>\n",
       "      <td>1.643546</td>\n",
       "      <td>0.097547</td>\n",
       "      <td>0.459762</td>\n",
       "      <td>0.134918</td>\n",
       "      <td>0.223411</td>\n",
       "      <td>0.401667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.659811</td>\n",
       "      <td>0.235755</td>\n",
       "      <td>0.380807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.082443</td>\n",
       "      <td>0.083649</td>\n",
       "      <td>0.628567</td>\n",
       "      <td>0.027496</td>\n",
       "      <td>3.413018</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.077197</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.567352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.409216</td>\n",
       "      <td>1.344863</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.126861</td>\n",
       "      <td>0.092094</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012986</td>\n",
       "      <td>0.046675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026298</td>\n",
       "      <td>0.080370</td>\n",
       "      <td>0.062944</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.133091</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.315240</td>\n",
       "      <td>1.358945</td>\n",
       "      <td>0.405634</td>\n",
       "      <td>0.732004</td>\n",
       "      <td>0.654434</td>\n",
       "      <td>0.018046</td>\n",
       "      <td>0.012451</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021059</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.164264</td>\n",
       "      <td>0.104630</td>\n",
       "      <td>1.951955</td>\n",
       "      <td>8.938982</td>\n",
       "      <td>0.012843</td>\n",
       "      <td>0.367751</td>\n",
       "      <td>0.271403</td>\n",
       "      <td>0.844147</td>\n",
       "      <td>0.215665</td>\n",
       "      <td>0.409102</td>\n",
       "      <td>0.089917</td>\n",
       "      <td>0.184031</td>\n",
       "      <td>0.056974</td>\n",
       "      <td>0.394671</td>\n",
       "      <td>0.196393</td>\n",
       "      <td>0.015639</td>\n",
       "      <td>0.063160</td>\n",
       "      <td>0.148444</td>\n",
       "      <td>1.511749</td>\n",
       "      <td>0.128742</td>\n",
       "      <td>0.015034</td>\n",
       "      <td>9.654157</td>\n",
       "      <td>8.215592</td>\n",
       "      <td>0.033315</td>\n",
       "      <td>0.117494</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.087990</td>\n",
       "      <td>0.450529</td>\n",
       "      <td>0.200148</td>\n",
       "      <td>0.142644</td>\n",
       "      <td>0.041512</td>\n",
       "      <td>0.058969</td>\n",
       "      <td>0.121943</td>\n",
       "      <td>0.212383</td>\n",
       "      <td>0.042439</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.786876</td>\n",
       "      <td>4.828380</td>\n",
       "      <td>0.059612</td>\n",
       "      <td>0.061541</td>\n",
       "      <td>0.019031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.169370</td>\n",
       "      <td>0.083868</td>\n",
       "      <td>0.060642</td>\n",
       "      <td>0.210373</td>\n",
       "      <td>0.385574</td>\n",
       "      <td>0.246649</td>\n",
       "      <td>0.054126</td>\n",
       "      <td>0.008931</td>\n",
       "      <td>0.020878</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.915329</td>\n",
       "      <td>0.204711</td>\n",
       "      <td>0.159705</td>\n",
       "      <td>0.017917</td>\n",
       "      <td>0.007569</td>\n",
       "      <td>0.142764</td>\n",
       "      <td>0.002863</td>\n",
       "      <td>0.005725</td>\n",
       "      <td>0.039529</td>\n",
       "      <td>0.006199</td>\n",
       "      <td>0.015639</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.868149</td>\n",
       "      <td>0.093603</td>\n",
       "      <td>1.661309</td>\n",
       "      <td>0.458396</td>\n",
       "      <td>2.434987</td>\n",
       "      <td>0.771709</td>\n",
       "      <td>2.144742</td>\n",
       "      <td>2.190834</td>\n",
       "      <td>3.996228</td>\n",
       "      <td>0.349665</td>\n",
       "      <td>0.170771</td>\n",
       "      <td>0.151693</td>\n",
       "      <td>0.117493</td>\n",
       "      <td>0.255879</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.226799</td>\n",
       "      <td>0.129675</td>\n",
       "      <td>0.307784</td>\n",
       "      <td>0.377415</td>\n",
       "      <td>0.507915</td>\n",
       "      <td>0.145182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030453</td>\n",
       "      <td>0.111316</td>\n",
       "      <td>0.218057</td>\n",
       "      <td>0.028199</td>\n",
       "      <td>0.002922</td>\n",
       "      <td>0.050519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003049</td>\n",
       "      <td>0.132779</td>\n",
       "      <td>1.367282</td>\n",
       "      <td>1.568954</td>\n",
       "      <td>0.231872</td>\n",
       "      <td>0.171810</td>\n",
       "      <td>0.232389</td>\n",
       "      <td>0.265524</td>\n",
       "      <td>0.084713</td>\n",
       "      <td>0.084013</td>\n",
       "      <td>0.280259</td>\n",
       "      <td>0.197851</td>\n",
       "      <td>0.020227</td>\n",
       "      <td>0.235281</td>\n",
       "      <td>0.044822</td>\n",
       "      <td>0.009184</td>\n",
       "      <td>0.078670</td>\n",
       "      <td>0.057041</td>\n",
       "      <td>0.196232</td>\n",
       "      <td>0.011199</td>\n",
       "      <td>0.011141</td>\n",
       "      <td>0.030387</td>\n",
       "      <td>0.005553</td>\n",
       "      <td>0.009447</td>\n",
       "      <td>0.018040</td>\n",
       "      <td>0.012539</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011999</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>0.003219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>秦皇岛市</th>\n",
       "      <td>192.041041</td>\n",
       "      <td>109.433668</td>\n",
       "      <td>80.930937</td>\n",
       "      <td>407.641438</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.492297</td>\n",
       "      <td>28.585493</td>\n",
       "      <td>57.328738</td>\n",
       "      <td>21.700224</td>\n",
       "      <td>51.500020</td>\n",
       "      <td>43.161734</td>\n",
       "      <td>40.103727</td>\n",
       "      <td>33.617664</td>\n",
       "      <td>5.784157</td>\n",
       "      <td>5.321548</td>\n",
       "      <td>0.823287</td>\n",
       "      <td>0.731117</td>\n",
       "      <td>0.540832</td>\n",
       "      <td>1.620169</td>\n",
       "      <td>3.097062</td>\n",
       "      <td>1.430597</td>\n",
       "      <td>1.935768</td>\n",
       "      <td>1.738098</td>\n",
       "      <td>0.830203</td>\n",
       "      <td>2.003089</td>\n",
       "      <td>1.644898</td>\n",
       "      <td>0.712580</td>\n",
       "      <td>8.690178</td>\n",
       "      <td>4.249139</td>\n",
       "      <td>1.526133</td>\n",
       "      <td>4.202603</td>\n",
       "      <td>1.229998</td>\n",
       "      <td>1.406533</td>\n",
       "      <td>1.849073</td>\n",
       "      <td>0.754752</td>\n",
       "      <td>0.606828</td>\n",
       "      <td>27.245621</td>\n",
       "      <td>10.939928</td>\n",
       "      <td>6.002884</td>\n",
       "      <td>2.478890</td>\n",
       "      <td>1.642964</td>\n",
       "      <td>2.450162</td>\n",
       "      <td>15.981716</td>\n",
       "      <td>5.174772</td>\n",
       "      <td>4.171886</td>\n",
       "      <td>3.430650</td>\n",
       "      <td>4.917110</td>\n",
       "      <td>3.732018</td>\n",
       "      <td>12.577639</td>\n",
       "      <td>167.952525</td>\n",
       "      <td>13.469682</td>\n",
       "      <td>6.178861</td>\n",
       "      <td>4.936886</td>\n",
       "      <td>2.152886</td>\n",
       "      <td>3.766575</td>\n",
       "      <td>1.612584</td>\n",
       "      <td>3.181532</td>\n",
       "      <td>2.351955</td>\n",
       "      <td>2.157982</td>\n",
       "      <td>26.512954</td>\n",
       "      <td>11.281163</td>\n",
       "      <td>1.792036</td>\n",
       "      <td>1.520829</td>\n",
       "      <td>1.697817</td>\n",
       "      <td>3.904684</td>\n",
       "      <td>3.160497</td>\n",
       "      <td>3.300582</td>\n",
       "      <td>0.978819</td>\n",
       "      <td>3.669276</td>\n",
       "      <td>2.748050</td>\n",
       "      <td>9.290403</td>\n",
       "      <td>0.983872</td>\n",
       "      <td>9.404085</td>\n",
       "      <td>4.771027</td>\n",
       "      <td>1.531760</td>\n",
       "      <td>3.252622</td>\n",
       "      <td>0.283965</td>\n",
       "      <td>3.594593</td>\n",
       "      <td>2.021675</td>\n",
       "      <td>0.699862</td>\n",
       "      <td>0.402637</td>\n",
       "      <td>0.833291</td>\n",
       "      <td>0.796357</td>\n",
       "      <td>0.109860</td>\n",
       "      <td>0.782050</td>\n",
       "      <td>0.468627</td>\n",
       "      <td>3.771656</td>\n",
       "      <td>1.126777</td>\n",
       "      <td>1.162439</td>\n",
       "      <td>0.118973</td>\n",
       "      <td>0.060492</td>\n",
       "      <td>0.088258</td>\n",
       "      <td>0.369964</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.905442</td>\n",
       "      <td>0.378598</td>\n",
       "      <td>0.110321</td>\n",
       "      <td>2.051935</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.102726</td>\n",
       "      <td>0.101621</td>\n",
       "      <td>0.181008</td>\n",
       "      <td>0.190818</td>\n",
       "      <td>0.034698</td>\n",
       "      <td>0.130074</td>\n",
       "      <td>0.089428</td>\n",
       "      <td>0.035386</td>\n",
       "      <td>3.324109</td>\n",
       "      <td>0.408770</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.210282</td>\n",
       "      <td>0.037665</td>\n",
       "      <td>0.108446</td>\n",
       "      <td>0.647478</td>\n",
       "      <td>0.848433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032964</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090475</td>\n",
       "      <td>0.082738</td>\n",
       "      <td>0.181421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500245</td>\n",
       "      <td>0.036265</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.377998</td>\n",
       "      <td>0.040310</td>\n",
       "      <td>0.064192</td>\n",
       "      <td>0.155683</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034905</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.409639</td>\n",
       "      <td>5.568279</td>\n",
       "      <td>2.050882</td>\n",
       "      <td>0.700654</td>\n",
       "      <td>1.471984</td>\n",
       "      <td>3.519406</td>\n",
       "      <td>6.264308</td>\n",
       "      <td>2.857506</td>\n",
       "      <td>2.726781</td>\n",
       "      <td>2.171262</td>\n",
       "      <td>0.997444</td>\n",
       "      <td>3.442613</td>\n",
       "      <td>6.833784</td>\n",
       "      <td>2.971391</td>\n",
       "      <td>3.707487</td>\n",
       "      <td>6.632969</td>\n",
       "      <td>5.354176</td>\n",
       "      <td>0.936536</td>\n",
       "      <td>0.959851</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.111459</td>\n",
       "      <td>0.450006</td>\n",
       "      <td>1.507789</td>\n",
       "      <td>0.073463</td>\n",
       "      <td>1.044048</td>\n",
       "      <td>0.061450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.113173</td>\n",
       "      <td>1.398484</td>\n",
       "      <td>1.505624</td>\n",
       "      <td>1.397990</td>\n",
       "      <td>3.573004</td>\n",
       "      <td>1.695695</td>\n",
       "      <td>0.012484</td>\n",
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       "      <td>4.311267</td>\n",
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       "      <td>0.270360</td>\n",
       "      <td>1.915006</td>\n",
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       "      <td>0.039641</td>\n",
       "      <td>0.250636</td>\n",
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       "      <td>0.025941</td>\n",
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       "      <td>0.101335</td>\n",
       "      <td>0.280071</td>\n",
       "      <td>0.350580</td>\n",
       "      <td>0.011612</td>\n",
       "      <td>5.150403</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              北京市          天津市        石家庄市         唐山市        秦皇岛市  \\\n",
       "北京市      0.000000  1687.284543  717.929685  578.260399  236.565889   \n",
       "天津市   1667.005135     0.000000  217.225708  851.995742  127.646818   \n",
       "石家庄市   500.998208   157.181700    0.000000  193.238588   77.230149   \n",
       "唐山市    507.113329   816.433368  160.452109    0.000000  434.067338   \n",
       "秦皇岛市   192.041041   109.433668   80.930937  407.641438    0.000000   \n",
       "\n",
       "             邯郸市          邢台市          保定市         张家口市         承德市  \\\n",
       "北京市   860.003982   498.935097  2435.282618  1186.747992  772.985012   \n",
       "天津市   383.082977   161.676366   359.872730   137.385146  113.433066   \n",
       "石家庄市  607.812191  1134.107468  1014.047514   164.385632  113.772738   \n",
       "唐山市    73.805152    57.912206   124.784995    55.473960  217.266393   \n",
       "秦皇岛市   32.492297    28.585493    57.328738    21.700224   51.500020   \n",
       "\n",
       "             沧州市          廊坊市         衡水市         太原市         大同市        阳泉市  \\\n",
       "北京市   556.446942  3856.471127  462.640544  284.940949  228.703321  56.626593   \n",
       "天津市   872.315430   917.989155  196.719985   70.992986   70.499883  20.992254   \n",
       "石家庄市  331.943378   151.574872  512.571443   81.992708   20.065021  73.101999   \n",
       "唐山市   112.637852   118.496497   52.600801   10.680178   12.069057   2.251319   \n",
       "秦皇岛市   43.161734    40.103727   33.617664    5.784157    5.321548   0.823287   \n",
       "\n",
       "            长治市        晋城市        朔州市         晋中市         运城市         忻州市  \\\n",
       "北京市   98.873823  47.926838  62.636306  112.898331  227.429424  106.305882   \n",
       "天津市   30.181102  12.437540  25.339450   40.687716   51.371505   39.615529   \n",
       "石家庄市  12.851726   5.995174   9.319001   68.785837   20.170931   63.327796   \n",
       "唐山市    2.911949   1.186374   2.571971    8.446604    5.584692    4.395690   \n",
       "秦皇岛市   0.731117   0.540832   1.620169    3.097062    1.430597    1.935768   \n",
       "\n",
       "             临汾市        吕梁市       呼和浩特市         包头市        乌海市         赤峰市  \\\n",
       "北京市   239.295704  91.076712  201.793026  103.064098  13.846133  242.268505   \n",
       "天津市    50.980915  28.817128   36.307536   24.327426   3.737073   78.859947   \n",
       "石家庄市   15.665252  23.164833   11.797720    6.589008   1.132685   11.199688   \n",
       "唐山市     4.943328   4.215755    5.336319    4.723447   2.391254   36.859048   \n",
       "秦皇岛市    1.738098   0.830203    2.003089    1.644898   0.712580    8.690178   \n",
       "\n",
       "            通辽市      鄂尔多斯市      呼伦贝尔市      巴彦淖尔市      乌兰察布市        兴安盟  \\\n",
       "北京市   92.540991  57.719109  50.519730  36.805937  94.644533  30.024863   \n",
       "天津市   41.671012  11.657209  22.665904  10.322606  24.131154  11.094950   \n",
       "石家庄市   3.528738   8.795914   2.199104   2.551074   6.770518   1.567455   \n",
       "唐山市    8.591873   4.498766   3.451938   2.036552   4.804797   2.835139   \n",
       "秦皇岛市   4.249139   1.526133   4.202603   1.229998   1.406533   1.849073   \n",
       "\n",
       "          锡林郭勒盟      阿拉善盟         沈阳市         大连市        鞍山市        抚顺市  \\\n",
       "北京市   64.297822  7.825447  266.754603  161.200736  69.453745  40.222041   \n",
       "天津市   10.668161  1.764454   72.996423   43.008200  19.658618  15.915731   \n",
       "石家庄市   4.052377  1.020233   17.703213    8.327628   4.132047   2.197317   \n",
       "唐山市    3.005449  1.298343   28.207381   12.269788  12.222368   3.589933   \n",
       "秦皇岛市   0.754752  0.606828   27.245621   10.939928   6.002884   2.478890   \n",
       "\n",
       "            本溪市        丹东市        锦州市        营口市        阜新市        辽阳市  \\\n",
       "北京市   25.647730  46.409431  87.709110  49.760114  46.459197  27.530344   \n",
       "天津市    9.101027  12.478476  37.383442  15.720624  17.125008   9.153367   \n",
       "石家庄市   1.386189   1.964619   5.049825   3.136399   1.719215   1.970782   \n",
       "唐山市    2.619635   3.571583  15.447723   7.838291   5.001964   3.988721   \n",
       "秦皇岛市   1.642964   2.450162  15.981716   5.174772   4.171886   3.430650   \n",
       "\n",
       "            盘锦市        铁岭市        朝阳市        葫芦岛市         长春市        吉林市  \\\n",
       "北京市   39.778371  38.943228  97.503146  111.686034  218.039124  93.016335   \n",
       "天津市   13.094095  18.065435  35.194523   54.225153   66.673166  34.045116   \n",
       "石家庄市   2.253990   3.058425   4.294540    6.449666   12.204332   5.153236   \n",
       "唐山市    6.656123   5.325223  23.832952   30.189672   15.087335   7.320084   \n",
       "秦皇岛市   4.917110   3.732018  12.577639  167.952525   13.469682   6.178861   \n",
       "\n",
       "            四平市        辽源市        通化市        白山市        松原市        白城市  \\\n",
       "北京市   58.454078  23.042493  41.921958  19.823024  45.053625  29.092305   \n",
       "天津市   26.355373  12.491587  32.804506   6.793874  19.799583  13.156826   \n",
       "石家庄市   3.726798   1.458109   2.898018   0.987140   2.456195   1.393235   \n",
       "唐山市    6.840804   4.630140   5.893529   1.550243   4.555100   3.516721   \n",
       "秦皇岛市   4.936886   2.152886   3.766575   1.612584   3.181532   2.351955   \n",
       "\n",
       "       延边朝鲜族自治州        哈尔滨市      齐齐哈尔市        鸡西市        鹤岗市       双鸭山市  \\\n",
       "北京市   31.040163  385.224723  93.436602  27.096234  16.562578  21.907652   \n",
       "天津市    8.897856  128.467928  77.404044   8.089636   5.051685   6.128706   \n",
       "石家庄市   1.253397   22.143539   4.562248   0.977034   0.877793   1.057026   \n",
       "唐山市    2.169266   26.272391  11.345498   1.195927   0.797691   1.422321   \n",
       "秦皇岛市   2.157982   26.512954  11.281163   1.792036   1.520829   1.697817   \n",
       "\n",
       "            大庆市        伊春市       佳木斯市       七台河市       牡丹江市        黑河市  \\\n",
       "北京市   61.674696  21.396012  50.098066  10.313575  60.844025  21.568651   \n",
       "天津市   21.865177   8.565340  14.794586   3.355503  17.996596  11.655312   \n",
       "石家庄市   3.241875   1.530958   2.494092   0.657520   2.816644   1.162162   \n",
       "唐山市    4.281239   3.298585   2.622191   0.689878   2.960905   2.808741   \n",
       "秦皇岛市   3.904684   3.160497   3.300582   0.978819   3.669276   2.748050   \n",
       "\n",
       "            绥化市    大兴安岭地区         上海市         南京市        无锡市         徐州市  \\\n",
       "北京市   82.911343  5.380662  515.850627  200.173029  59.250838  105.094389   \n",
       "天津市   50.394175  1.742145  101.366797   35.635631  17.817994   38.270275   \n",
       "石家庄市   4.726304  0.322013   35.950083   20.072416   6.699459   18.959636   \n",
       "唐山市   11.047416  0.545965   15.580032    7.334497   4.309980    7.729680   \n",
       "秦皇岛市   9.290403  0.983872    9.404085    4.771027   1.531760    3.252622   \n",
       "\n",
       "            常州市         苏州市        南通市       连云港市        淮安市        盐城市  \\\n",
       "北京市   48.053062  108.204900  79.333539  39.763866  31.451816  54.874255   \n",
       "天津市   12.864440   30.722190  25.597960  13.630589   9.247920  20.278565   \n",
       "石家庄市   4.247160   11.536824  13.085013   4.943847   4.502958   9.195434   \n",
       "唐山市    3.829456    5.410542   5.162689   2.880159   1.588815   4.013364   \n",
       "秦皇岛市   0.283965    3.594593   2.021675   0.699862   0.402637   0.833291   \n",
       "\n",
       "            扬州市        镇江市        泰州市        宿迁市         杭州市        宁波市  \\\n",
       "北京市   56.047014  23.960738  41.695641  33.532751  187.511206  52.422659   \n",
       "天津市   15.017555   6.488047  14.011300   9.490777   35.309355  17.551755   \n",
       "石家庄市   5.588011   2.768320   5.109983   4.426938   18.024095   4.878647   \n",
       "唐山市    3.704052   1.489348   2.698896   1.609275    5.825805   0.439698   \n",
       "秦皇岛市   0.796357   0.109860   0.782050   0.468627    3.771656   1.126777   \n",
       "\n",
       "            温州市        嘉兴市        湖州市        绍兴市        金华市        衢州市  \\\n",
       "北京市   55.793729  32.436636  15.542331  23.928753  30.462165  12.244635   \n",
       "天津市   18.482008   6.878563   4.198738   8.743501  10.977308   2.495532   \n",
       "石家庄市  11.413157   3.729250   2.209536   3.267091   6.135877   1.066740   \n",
       "唐山市    3.082088   0.278977   0.413134   0.598470   0.000000   0.094613   \n",
       "秦皇岛市   1.162439   0.118973   0.060492   0.088258   0.369964   0.000000   \n",
       "\n",
       "           舟山市        台州市       丽水市         合肥市        芜湖市        蚌埠市  \\\n",
       "北京市   8.144937  27.325203  8.138529  168.313427  93.576963  28.626899   \n",
       "天津市   4.275312  10.737401  2.431365   34.070573  22.021565   9.098945   \n",
       "石家庄市  0.304979   6.006504  1.100658   12.269058   4.369807   3.053967   \n",
       "唐山市   1.586598   1.125148  0.225225    3.682325   1.832289   0.597511   \n",
       "秦皇岛市  0.905442   0.378598  0.110321    2.051935   0.000000   0.102726   \n",
       "\n",
       "            淮南市       马鞍山市        淮北市        铜陵市         安庆市        黄山市  \\\n",
       "北京市   21.533005  57.166950  19.697372  20.141506  106.835890  15.345293   \n",
       "天津市    6.316041   6.825536   6.075772   3.442957   41.001768   3.465978   \n",
       "石家庄市   2.144119   2.323855   1.975529   1.202360    7.630829   0.588766   \n",
       "唐山市    0.885828   1.620995   0.773767   0.265651    2.358318   0.220779   \n",
       "秦皇岛市   0.101621   0.181008   0.190818   0.034698    0.130074   0.089428   \n",
       "\n",
       "            滁州市         阜阳市        宿州市        六安市        亳州市        池州市  \\\n",
       "北京市   24.611607  132.134287  56.456832  37.663818  52.038299  27.573201   \n",
       "天津市    7.989293   89.624035  23.021271   9.815058  27.918331   8.224246   \n",
       "石家庄市   3.136522   18.699315   6.986211   2.560606   6.251935   1.414237   \n",
       "唐山市    0.735509   10.330994   3.863168   0.142954   4.487090   0.462659   \n",
       "秦皇岛市   0.035386    3.324109   0.408770   0.000000   0.210282   0.037665   \n",
       "\n",
       "            宣城市        福州市        厦门市        莆田市        三明市        泉州市  \\\n",
       "北京市   13.027200  70.441119  87.009694  36.539951  13.873429  37.222540   \n",
       "天津市    3.616611  15.724294  25.054933  14.715156   4.395529  17.766851   \n",
       "石家庄市   1.374479   4.800524   9.291889   3.668928   1.487583   6.546559   \n",
       "唐山市    0.220988   1.093260   3.705369   1.564053   0.185811   0.580200   \n",
       "秦皇岛市   0.108446   0.647478   0.848433   0.000000   0.032964   0.000000   \n",
       "\n",
       "            漳州市        南平市        龙岩市        宁德市         南昌市       景德镇市  \\\n",
       "北京市   13.513474  16.940520  14.929918  13.755336  106.041605  11.748058   \n",
       "天津市    3.092594   3.989655   2.788674   5.312940   17.399302   3.964284   \n",
       "石家庄市   1.360663   1.113323   1.565407   1.462646    8.652189   1.089976   \n",
       "唐山市    0.137273   0.263373   0.114007   0.338048    1.260368   0.422613   \n",
       "秦皇岛市   0.090475   0.082738   0.181421   0.000000    0.500245   0.036265   \n",
       "\n",
       "            萍乡市        九江市       新余市       鹰潭市        赣州市        吉安市  \\\n",
       "北京市   10.160599  49.470566  6.475570  7.070942  33.814735  37.271662   \n",
       "天津市    2.291983  11.861103  1.869136  2.758127   7.829918   6.499965   \n",
       "石家庄市   0.877087   3.530950  0.370585  1.209572   2.782676   1.270480   \n",
       "唐山市    0.160091   0.657430  0.018274  0.259671   0.361159   0.000000   \n",
       "秦皇岛市   0.000000   0.377998  0.040310  0.064192   0.155683   0.000000   \n",
       "\n",
       "            宜春市        抚州市        上饶市         济南市         青岛市        淄博市  \\\n",
       "北京市   28.400235  27.053921  27.949803  291.399994  210.116605  80.254085   \n",
       "天津市    6.745808   7.139372   9.094234  110.524382   57.161011  27.416595   \n",
       "石家庄市   1.459275   2.786848   2.020903   43.470221   25.967199  14.585981   \n",
       "唐山市    0.000000   0.284172   0.000000   15.490181   10.398709   5.628135   \n",
       "秦皇岛市   0.034905   0.000000   0.000000    6.409639    5.568279   2.050882   \n",
       "\n",
       "            枣庄市        东营市         烟台市         潍坊市         济宁市         泰安市  \\\n",
       "北京市   63.408408  53.998601  135.588242  143.430690  169.159620  155.531300   \n",
       "天津市   17.512842  24.000924   38.329164   46.512084   66.525819   54.904997   \n",
       "石家庄市   6.841634   7.572105   12.510213   24.089889   15.394113   13.715350   \n",
       "唐山市    2.625961   4.789043    8.026083   10.977404    9.103085    7.663256   \n",
       "秦皇岛市   0.700654   1.471984    3.519406    6.264308    2.857506    2.726781   \n",
       "\n",
       "            威海市        日照市         临沂市         德州市         聊城市         滨州市  \\\n",
       "北京市   59.663518  39.055949  130.041904  293.389870  234.675291   78.703713   \n",
       "天津市   13.616948  10.988737   40.150425  389.196029   94.705632  129.023523   \n",
       "石家庄市   5.444265   5.956682   22.521433   47.248229   34.527694   16.270254   \n",
       "唐山市    4.068008   2.477001   10.052941   19.621361    9.254223   16.547567   \n",
       "秦皇岛市   2.171262   0.997444    3.442613    6.833784    2.971391    3.707487   \n",
       "\n",
       "             菏泽市         郑州市        开封市         洛阳市       平顶山市         安阳市  \\\n",
       "北京市   289.842198  344.604706  77.401325  111.931285  65.493794  192.160985   \n",
       "天津市   123.385068   61.884064  23.864803   27.851335  16.435691   68.339371   \n",
       "石家庄市   19.813276   53.820122  13.288331   10.955080   6.469774   29.833626   \n",
       "唐山市    11.695463   10.530309   5.147032    4.287620   1.540170    9.585012   \n",
       "秦皇岛市    6.632969    5.354176   0.936536    0.959851   0.000000    2.111459   \n",
       "\n",
       "            鹤壁市        新乡市        焦作市         濮阳市        许昌市        漯河市  \\\n",
       "北京市   40.115797  97.928273  53.591049  171.971205  73.883939  40.110599   \n",
       "天津市   16.335168  33.283225  12.920346   67.794500  15.011232  10.285443   \n",
       "石家庄市   5.661142  17.949544   8.054123   12.695249   9.356957   7.024869   \n",
       "唐山市    2.736873   9.242684   2.014252    7.288162   2.069786   1.165080   \n",
       "秦皇岛市   0.450006   1.507789   0.073463    1.044048   0.061450   0.000000   \n",
       "\n",
       "           三门峡市         南阳市         商丘市         信阳市         周口市        驻马店市  \\\n",
       "北京市   34.324435  190.754348  179.836707  314.979774  363.222841  229.763140   \n",
       "天津市    7.698881   61.622666   56.870563   49.427290   92.010497   73.233671   \n",
       "石家庄市   3.517122   17.768526   16.075388   12.104998   31.306614   29.004791   \n",
       "唐山市    0.661580    8.363592    4.949485    6.897287   16.720770    9.340071   \n",
       "秦皇岛市   0.113173    1.398484    1.505624    1.397990    3.573004    1.695695   \n",
       "\n",
       "           济源市         武汉市        黄石市        十堰市        宜昌市        襄阳市  \\\n",
       "北京市   9.356711  213.943016  20.768958  38.902806  40.559683  61.228033   \n",
       "天津市   2.224003   34.888987   3.590799   6.974953   5.997745  11.173292   \n",
       "石家庄市  1.538264   20.514584   1.093099   4.321936   3.972229   6.396933   \n",
       "唐山市   0.342053    5.872687   0.032896   1.643546   0.097547   0.459762   \n",
       "秦皇岛市  0.012484    3.371267   0.000000   0.000000   0.000000   0.109551   \n",
       "\n",
       "           鄂州市        荆门市        孝感市        荆州市        黄冈市        咸宁市  \\\n",
       "北京市   6.451960  22.657543  64.462821  41.163964  67.237587  22.510855   \n",
       "天津市   1.430773   5.810861  15.911473  10.735402  43.062947   6.654695   \n",
       "石家庄市  0.802006   3.523367   6.254021   6.227039   6.175196   2.383577   \n",
       "唐山市   0.134918   0.223411   0.401667   0.000000   0.659811   0.235755   \n",
       "秦皇岛市  0.014334   0.000000   0.000000   0.081053   3.222323   0.000000   \n",
       "\n",
       "            随州市  恩施土家族苗族自治州        仙桃市       潜江市        天门市     神农架林区  \\\n",
       "北京市   40.840956   19.188407  12.178825  6.440978  10.016563  0.238153   \n",
       "天津市    7.130063    5.557726   1.883182  1.459443   4.255251  0.009242   \n",
       "石家庄市   2.342947    2.345383   1.602329  0.577201  10.342529  0.078178   \n",
       "唐山市    0.380807    0.000000   0.082443  0.083649   0.628567  0.027496   \n",
       "秦皇岛市   0.000000    0.000000   0.000000  0.020274   0.000000  0.031347   \n",
       "\n",
       "             长沙市        株洲市        湘潭市        衡阳市        邵阳市        岳阳市  \\\n",
       "北京市   164.802627  29.818382  15.259259  28.735884  24.736431  38.541040   \n",
       "天津市    28.477699   5.156337   2.600515   6.967824   5.305496   8.420861   \n",
       "石家庄市   14.166528   2.275499   1.153647   3.316504   2.401745   3.641109   \n",
       "唐山市     3.413018   0.000000   0.075585   0.000000   0.000000   0.000000   \n",
       "秦皇岛市    2.156648   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "\n",
       "            常德市       张家界市        益阳市        郴州市        永州市        怀化市  \\\n",
       "北京市   36.966508  11.978901  17.626012  14.205404  13.175405  17.090933   \n",
       "天津市    8.148155   2.845275   3.860917   2.522566   2.797889   3.036391   \n",
       "石家庄市   3.597378   0.804577   1.152825   1.311936   1.107749   0.459884   \n",
       "唐山市    0.000000   0.000000   0.077197   0.000000   0.026748   0.000000   \n",
       "秦皇岛市   0.000000   0.031775   0.000000   0.000000   0.000000   0.000000   \n",
       "\n",
       "            娄底市  湘西土家族苗族自治州         广州市       韶关市         深圳市        珠海市  \\\n",
       "北京市   20.785599   10.678127  227.220288  4.547294  221.173438  51.746135   \n",
       "天津市    4.999205    1.691076   46.502918  1.150665   30.610847  10.677273   \n",
       "石家庄市   1.667006    0.820189   20.437265  0.694574   13.468726   2.951020   \n",
       "唐山市    0.000000    0.000000    5.567352  0.000000    3.409216   1.344863   \n",
       "秦皇岛市   0.000000    0.000000    4.311267  0.000000    2.454688   0.000000   \n",
       "\n",
       "            汕头市        佛山市       江门市        湛江市       茂名市       肇庆市  \\\n",
       "北京市   11.321235  33.959445  8.093168  16.318285  5.690368  4.968361   \n",
       "天津市    1.898660   7.733657  1.852048   3.451894  1.343324  0.893186   \n",
       "石家庄市   1.465629   4.037717  0.855864   2.574813  0.340349  0.128897   \n",
       "唐山市    0.000000   0.126861  0.092094   0.000000  0.012986  0.046675   \n",
       "秦皇岛市   0.000000   0.112720  0.000000   0.000000  0.000000  0.000000   \n",
       "\n",
       "            惠州市       梅州市       汕尾市       河源市       阳江市       清远市        东莞市  \\\n",
       "北京市   18.398222  3.844330  2.344491  2.587624  4.278031  5.300496  29.527148   \n",
       "天津市    4.012819  0.592020  0.406459  0.487497  0.687413  0.774417   7.034878   \n",
       "石家庄市   0.771588  0.327885  0.439408  0.134502  0.288907  0.543756   1.964245   \n",
       "唐山市    0.000000  0.000000  0.026298  0.080370  0.062944  0.000000   0.000000   \n",
       "秦皇岛市   0.000000  0.017296  0.000000  0.000000  0.038292  0.000000   0.000000   \n",
       "\n",
       "            中山市       潮州市       揭阳市       云浮市        南宁市        柳州市  \\\n",
       "北京市   14.686257  3.456797  4.837953  1.472979  58.675670  14.994231   \n",
       "天津市    3.364091  0.483394  0.841078  0.369584  18.091439   5.998549   \n",
       "石家庄市   1.652922  0.308888  1.016401  0.196408   6.394137   2.008092   \n",
       "唐山市    0.000000  0.133091  0.000000  0.000000   0.000000   0.315240   \n",
       "秦皇岛市   0.063919  0.000000  0.000000  0.000000   0.471758   0.000000   \n",
       "\n",
       "            桂林市       梧州市        北海市      防城港市       钦州市       贵港市       玉林市  \\\n",
       "北京市   35.963769  4.611463  20.812586  3.694599  3.266351  5.526584  7.883111   \n",
       "天津市   14.693566  2.083314   4.624701  1.114419  1.668724  2.341617  3.157605   \n",
       "石家庄市   6.269026  0.518407   6.759485  0.744580  0.247972  0.179674  0.214078   \n",
       "唐山市    1.358945  0.405634   0.732004  0.654434  0.018046  0.012451  0.000000   \n",
       "秦皇岛市   0.379461  0.000000   0.376147  0.034768  0.000000  0.000000  0.084486   \n",
       "\n",
       "           百色市       贺州市       河池市       来宾市       崇左市        海口市         三亚市  \\\n",
       "北京市   4.165500  3.379215  4.584476  2.595048  2.804544  61.913259  130.706636   \n",
       "天津市   1.007780  1.460123  1.675252  1.195017  0.911977  13.749019   33.148273   \n",
       "石家庄市  0.053034  0.402608  0.275372  0.284367  0.471893   6.506081   13.773652   \n",
       "唐山市   0.000000  0.021059  0.000000  0.164264  0.104630   1.951955    8.938982   \n",
       "秦皇岛市  0.000000  0.033281  0.000000  0.000000  0.000000   0.270360    1.915006   \n",
       "\n",
       "           三沙市       儋州市      五指山市        琼海市        文昌市        万宁市       东方市  \\\n",
       "北京市   0.180892  5.930824  1.914012  16.603570  10.503868  14.613029  5.314056   \n",
       "天津市   0.009635  1.774579  0.321383   2.668699   1.388344   2.594981  0.980631   \n",
       "石家庄市  0.003234  1.698751  1.113240   2.747159   0.683063   2.852114  1.102843   \n",
       "唐山市   0.012843  0.367751  0.271403   0.844147   0.215665   0.409102  0.089917   \n",
       "秦皇岛市  0.000000  0.000000  0.039641   0.250636   0.054086   0.109086  0.137555   \n",
       "\n",
       "           定安县       屯昌县       澄迈县       临高县   白沙黎族自治县   昌江黎族自治县   乐东黎族自治县  \\\n",
       "北京市   1.561646  0.559364  9.479283  2.906732  0.778939  0.772270  6.561556   \n",
       "天津市   0.327808  0.215924  1.739322  0.496222  0.165072  0.233596  1.126130   \n",
       "石家庄市  0.363289  0.163738  1.802861  0.348963  0.097079  0.197254  0.969656   \n",
       "唐山市   0.184031  0.056974  0.394671  0.196393  0.015639  0.063160  0.148444   \n",
       "秦皇岛市  0.025941  0.013749  0.010948  0.037975  0.046749  0.019749  0.101335   \n",
       "\n",
       "        陵水黎族自治县  保亭黎族苗族自治县  琼中黎族苗族自治县         重庆市         成都市       自贡市  \\\n",
       "北京市   28.629664   3.629045   0.537591  268.574329  304.058920  9.196509   \n",
       "天津市    3.379797   1.077746   0.095482   69.151706   62.892148  2.641923   \n",
       "石家庄市   4.779114   0.596765   0.243456   34.059965   26.925784  1.322806   \n",
       "唐山市    1.511749   0.128742   0.015034    9.654157    8.215592  0.033315   \n",
       "秦皇岛市   0.280071   0.350580   0.011612    5.150403    6.299697  0.000000   \n",
       "\n",
       "          攀枝花市        泸州市        德阳市        绵阳市        广元市        遂宁市  \\\n",
       "北京市   2.742754  15.011374  15.033239  60.746089  58.119615  18.343994   \n",
       "天津市   0.830469   4.761081   5.407440  13.573233   9.535464   4.168142   \n",
       "石家庄市  0.172344   3.202147   1.782338   4.703687   2.887126   1.091919   \n",
       "唐山市   0.117494   0.000000   0.087990   0.450529   0.200148   0.142644   \n",
       "秦皇岛市  0.005605   0.000000   0.000000   0.057405   0.000000   0.000000   \n",
       "\n",
       "           内江市        乐山市        南充市        眉山市        宜宾市        广安市  \\\n",
       "北京市   8.697021  14.207139  48.880345  13.359528  19.507023  11.315586   \n",
       "天津市   3.038757   3.801677  11.908913   3.883602   5.030701   5.023374   \n",
       "石家庄市  0.735997   1.369630   4.505599   1.375162   1.929448   1.796934   \n",
       "唐山市   0.041512   0.058969   0.121943   0.212383   0.042439   0.000000   \n",
       "秦皇岛市  0.000000   0.080393   0.000000   0.000000   0.000000   0.000000   \n",
       "\n",
       "            达州市       雅安市        巴中市       资阳市  阿坝藏族羌族自治州   甘孜藏族自治州   凉山彝族自治州  \\\n",
       "北京市   49.193167  3.836843  60.709893  9.485928   2.142850  0.728689  8.724315   \n",
       "天津市   12.379687  1.164072   7.690211  2.528125   0.751528  0.100926  1.815553   \n",
       "石家庄市   4.854988  0.590282   6.015464  0.601784   0.142103  0.071187  0.563992   \n",
       "唐山市    0.000000  1.786876   4.828380  0.059612   0.061541  0.019031  0.000000   \n",
       "秦皇岛市   0.000000  0.000000   0.041677  0.000000   0.077588  0.000000  0.000000   \n",
       "\n",
       "            贵阳市      六盘水市        遵义市       安顺市        毕节市        铜仁市  \\\n",
       "北京市   64.581025  6.883339  24.715294  7.898251  11.414056  10.018477   \n",
       "天津市   21.745957  5.284952  11.949144  4.276010   6.592233   5.588249   \n",
       "石家庄市   6.008739  0.218367   1.489871  0.106313   0.312694   0.274696   \n",
       "唐山市    0.000000  0.000000   0.000000  0.000000   0.000000   0.000000   \n",
       "秦皇岛市   0.224470  0.000000   0.000000  0.000000   0.000000   0.000000   \n",
       "\n",
       "      黔西南布依族苗族自治州  黔东南苗族侗族自治州  黔南布依族苗族自治州         昆明市        曲靖市       玉溪市  \\\n",
       "北京市      7.758112   10.914051   10.196811  112.620495  14.950564  6.138236   \n",
       "天津市      3.787180    4.848299    5.055526   28.853298   5.854992  2.525501   \n",
       "石家庄市     0.200534    0.084074    0.909669   14.922246   1.825960  0.688232   \n",
       "唐山市      0.000000    0.000000    0.000000    2.169370   0.083868  0.060642   \n",
       "秦皇岛市     0.000000    0.000000    0.000000    2.038769   0.000000  0.019483   \n",
       "\n",
       "           保山市       昭通市        丽江市       普洱市       临沧市   楚雄彝族自治州  红河哈尼族彝族自治州  \\\n",
       "北京市   8.263112  6.975439  16.392088  3.599774  2.207151  4.055208    6.719594   \n",
       "天津市   2.115545  2.946172   4.130226  0.990712  0.907917  2.699158    2.368399   \n",
       "石家庄市  0.596767  0.330247   2.888297  0.415777  0.081432  0.503843    0.933709   \n",
       "唐山市   0.210373  0.385574   0.246649  0.054126  0.008931  0.020878    0.000000   \n",
       "秦皇岛市  0.047756  0.000000   0.000000  0.000000  0.000000  0.054623    0.000000   \n",
       "\n",
       "      文山壮族苗族自治州  西双版纳傣族自治州    大理白族自治州  德宏傣族景颇族自治州  怒江傈僳族自治州   迪庆藏族自治州  \\\n",
       "北京市    3.056524  13.873582  18.407455    4.342039  0.658222  0.685889   \n",
       "天津市    0.677069   4.645936   4.655093    1.178043  0.084664  0.164977   \n",
       "石家庄市   0.837856   1.488877   2.274485    0.136012  0.080580  0.193709   \n",
       "唐山市    0.000000   0.915329   0.204711    0.159705  0.017917  0.007569   \n",
       "秦皇岛市   0.000000   0.189839   0.037605    0.030820  0.022731  0.007538   \n",
       "\n",
       "           拉萨市      日喀则市       昌都市       林芝市       山南市       那曲市      阿里地区  \\\n",
       "北京市   8.376916  0.729315  0.271823  0.443766  0.810757  0.321076  0.035507   \n",
       "天津市   3.279510  0.488610  0.207549  0.249024  0.534900  0.238503  0.009689   \n",
       "石家庄市  0.826990  0.090514  0.029219  0.044839  0.062109  0.067595  0.006428   \n",
       "唐山市   0.142764  0.002863  0.005725  0.039529  0.006199  0.015639  0.000000   \n",
       "秦皇岛市  0.000000  0.000000  0.000000  0.000000  0.002492  0.000000  0.000000   \n",
       "\n",
       "             西安市       铜川市        宝鸡市        咸阳市        渭南市        延安市  \\\n",
       "北京市   353.726316  5.552809  47.327357  49.642640  67.841973  19.777587   \n",
       "天津市    78.446157  1.091908  10.494694  15.546977  16.837996   4.525594   \n",
       "石家庄市   34.751294  0.391223   4.310868   6.633886   8.150525   2.720995   \n",
       "唐山市    11.868149  0.093603   1.661309   0.458396   2.434987   0.771709   \n",
       "秦皇岛市    6.116924  0.088270   0.344978   0.142018   0.358073   0.180872   \n",
       "\n",
       "            汉中市        榆林市        安康市        商洛市        兰州市      嘉峪关市  \\\n",
       "北京市   36.342918  45.571592  23.433685  10.987286  77.788307  4.150912   \n",
       "天津市   10.704575  11.077888  12.291888   3.276977  33.804616  1.576985   \n",
       "石家庄市   4.680316  36.490064   8.916541   1.497083   6.769463  0.406361   \n",
       "唐山市    2.144742   2.190834   3.996228   0.349665   0.170771  0.151693   \n",
       "秦皇岛市   0.283820   0.658515   0.000000   0.000000   0.067843  0.143953   \n",
       "\n",
       "           金昌市        白银市        天水市        武威市       张掖市        平凉市  \\\n",
       "北京市   4.907869  16.665266  52.454713  10.644596  9.190037  27.207079   \n",
       "天津市   2.759389  10.894087  25.051234   8.660275  5.028631  17.453015   \n",
       "石家庄市  0.568936   2.173716   4.977936   2.427721  1.357517   1.579135   \n",
       "唐山市   0.117493   0.255879   0.000000   0.226799  0.129675   0.307784   \n",
       "秦皇岛市  0.034157   0.058151   0.000000   0.018244  0.000000   0.000000   \n",
       "\n",
       "            酒泉市        庆阳市        定西市        陇南市   临夏回族自治州   甘南藏族自治州  \\\n",
       "北京市   10.941075  44.066290  22.481084  53.270143  5.835290  1.463069   \n",
       "天津市    5.112013  25.316634  11.104039  17.991955  2.157812  1.102300   \n",
       "石家庄市   1.304311   2.251524   2.395642   1.184234  0.990007  0.069542   \n",
       "唐山市    0.377415   0.507915   0.145182   0.000000  0.000000  0.030453   \n",
       "秦皇岛市   0.000000   0.012163   0.000000   0.000000  0.024878  0.000000   \n",
       "\n",
       "            西宁市       海东市   海北藏族自治州   黄南藏族自治州   海南藏族自治州   果洛藏族自治州   玉树藏族自治州  \\\n",
       "北京市   32.826502  5.644061  0.533817  0.360547  0.645230  0.082484  1.008710   \n",
       "天津市   10.409380  3.861083  0.292775  0.316050  0.227352  0.040331  0.156084   \n",
       "石家庄市   3.424318  1.452065  0.089259  0.031944  0.180031  0.018537  0.059303   \n",
       "唐山市    0.111316  0.218057  0.028199  0.002922  0.050519  0.000000  0.003049   \n",
       "秦皇岛市   0.362624  0.043817  0.026267  0.005773  0.002798  0.000000  0.021488   \n",
       "\n",
       "      海西蒙古族藏族自治州        银川市      石嘴山市       吴忠市       固原市       中卫市  \\\n",
       "北京市     3.599359  64.461035  8.162637  9.710034  6.610745  8.696535   \n",
       "天津市     1.430920  16.481458  3.917795  3.964979  3.022296  3.798205   \n",
       "石家庄市    0.707946   6.820555  1.054683  1.291438  0.405527  1.537278   \n",
       "唐山市     0.132779   1.367282  1.568954  0.231872  0.171810  0.232389   \n",
       "秦皇岛市    0.012527   0.800714  0.546087  0.088490  0.086594  0.047681   \n",
       "\n",
       "          乌鲁木齐市     克拉玛依市      吐鲁番市       哈密市   昌吉回族自治州  博尔塔拉蒙古自治州  巴音郭楞蒙古自治州  \\\n",
       "北京市   65.107392  5.028291  2.071475  5.131276  8.425802   1.492038   8.123240   \n",
       "天津市   20.774382  1.121560  1.225142  1.728600  3.593349   0.561883   2.406641   \n",
       "石家庄市   4.801978  0.168310  0.385659  0.478369  0.995402   0.104372   1.830633   \n",
       "唐山市    0.265524  0.084713  0.084013  0.280259  0.197851   0.020227   0.235281   \n",
       "秦皇岛市   0.071719  0.029523  0.020968  0.050873  0.032447   0.013942   0.080846   \n",
       "\n",
       "         阿克苏地区  克孜勒苏柯尔克孜自治州      喀什地区      和田地区  伊犁哈萨克自治州      塔城地区     阿勒泰地区  \\\n",
       "北京市   4.180934     0.599440  3.820052  1.889751  7.443148  3.031521  1.412136   \n",
       "天津市   1.650262     0.199569  1.540387  0.965426  3.406421  1.396981  0.578351   \n",
       "石家庄市  0.477850     0.024460  0.337656  0.188639  0.831438  0.299756  0.156454   \n",
       "唐山市   0.044822     0.009184  0.078670  0.057041  0.196232  0.011199  0.011141   \n",
       "秦皇岛市  0.000000     0.005720  0.000000  0.005879  0.013540  0.060156  0.012764   \n",
       "\n",
       "          石河子市      阿拉尔市     图木舒克市      五家渠市       北屯市      铁门关市       双河市  \\\n",
       "北京市   3.627545  0.636727  0.194443  0.819107  0.290680  0.115440  0.187783   \n",
       "天津市   1.317927  0.142610  0.065685  0.400378  0.094892  0.037286  0.075453   \n",
       "石家庄市  0.230253  0.003160  0.040211  0.153368  0.048382  0.030596  0.029922   \n",
       "唐山市   0.030387  0.005553  0.009447  0.018040  0.012539  0.000000  0.011999   \n",
       "秦皇岛市  0.059737  0.009479  0.006459  0.005537  0.000000  0.006135  0.008810   \n",
       "\n",
       "         可克达拉市       昆玉市  \n",
       "北京市   0.213991  0.050358  \n",
       "天津市   0.088086  0.044267  \n",
       "石家庄市  0.012028  0.000000  \n",
       "唐山市   0.003187  0.003219  \n",
       "秦皇岛市  0.017165  0.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class=\"alert alert-block alert-success\"> Mobility Network Plot</div>\n",
    "* mobility heatmap between cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cities in the same province are located in the same block\n",
    "\n",
    "todo_df = city_info_df[city_info_df['city_cn'].notnull()]\n",
    "todo_df.shape\n",
    "\n",
    "todo_df['number'] = range(len(todo_df))\n",
    "\n",
    "province_label_loc_df = todo_df.groupby(['province'])['number'].agg(list).reset_index()\n",
    "province_label_loc_df['cnt'] = province_label_loc_df['number'].map(len)\n",
    "province_label_loc_df['loc'] = province_label_loc_df['number'].map(np.mean)\n",
    "\n",
    "province_label_loc_df['tick_locs'] = province_label_loc_df['number'].map(lambda x: [np.min(x), np.max(x)])\n",
    "province_label_loc_df.sort_values(['loc'], ascending=True, inplace=True)\n",
    "province_label_loc_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "province_label_loc_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>number</th>\n",
       "      <th>cnt</th>\n",
       "      <th>loc</th>\n",
       "      <th>tick_locs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>[0]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tianjin</td>\n",
       "      <td>[1]</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hebei</td>\n",
       "      <td>[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]</td>\n",
       "      <td>11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[2, 12]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shanxi</td>\n",
       "      <td>[13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]</td>\n",
       "      <td>11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>[13, 23]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neimenggu</td>\n",
       "      <td>[24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]</td>\n",
       "      <td>12</td>\n",
       "      <td>29.5</td>\n",
       "      <td>[24, 35]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    province                                            number  cnt   loc  \\\n",
       "0    Beijing                                               [0]    1   0.0   \n",
       "1    Tianjin                                               [1]    1   1.0   \n",
       "2      Hebei              [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]   11   7.0   \n",
       "3     Shanxi      [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]   11  18.0   \n",
       "4  Neimenggu  [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]   12  29.5   \n",
       "\n",
       "  tick_locs  \n",
       "0    [0, 0]  \n",
       "1    [1, 1]  \n",
       "2   [2, 12]  \n",
       "3  [13, 23]  \n",
       "4  [24, 35]  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "province_label_loc_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_df = adj_df.copy()\n",
    "\n",
    "# log transformation\n",
    "for col in plot_df.columns:\n",
    "    plot_df[col] = plot_df[col].map(lambda x: np.log(x+1), na_action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x730.8 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Define two rows for subplots\n",
    "fig, (ax, cax) = plt.subplots(nrows=2, figsize=(10, 10.15),  \n",
    "                              gridspec_kw={\"height_ratios\":[10, 0.15]})\n",
    "\n",
    "# horizontal space between axes\n",
    "# fig.subplots_adjust(hspace=5)\n",
    "\n",
    "sns.heatmap(plot_df, \n",
    "                 cmap=\"Purples\",\n",
    "                 cbar=False,\n",
    "                 ax=ax,\n",
    "                )\n",
    "\n",
    "ax.set_xlabel('')\n",
    "# ax.set_xticklabels([])\n",
    "ax.set_ylabel('')\n",
    "\n",
    "ax.xaxis.set_ticks_position('top') # the rest is the same\n",
    "\n",
    "text_color = '#46ac88'\n",
    "\n",
    "# xaxis\n",
    "for i in range(len(province_label_loc_df)):\n",
    "    row = province_label_loc_df.iloc[i]\n",
    "    \n",
    "    if row['cnt'] == 1: # annotate municipalities: 直轄市\n",
    "        if row['province'] == 'Beijing':\n",
    "            continue\n",
    "\n",
    "        ann = ax.annotate('', xy=(row['tick_locs'][0]+0.5, .5), xycoords='data',\n",
    "                      xytext=(row['tick_locs'][1]+0.5, -6.5), textcoords='data',\n",
    "                      clip_on=False,\n",
    "                      arrowprops=dict(arrowstyle=\"-\", lw=.7, ec=\"k\",)\n",
    "                         )\n",
    "        \n",
    "    else: # annotate province: 省\n",
    "    \n",
    "        frac = 5./(row['cnt']-1)\n",
    "\n",
    "        ann = ax.annotate('', xy=(row['tick_locs'][0]+0.5, 0.5), xycoords='data',\n",
    "                      xytext=(row['tick_locs'][1]+0.5, 0.5), textcoords='data',\n",
    "                      clip_on=False,\n",
    "                      arrowprops=dict(arrowstyle=\"-\", lw=.5,\n",
    "                                      connectionstyle=f\"bar,fraction={frac}\",\n",
    "    #                                   connectionstyle=f\"bar,armA=10\",\n",
    "                                      ec=\"k\",\n",
    "                        ))\n",
    "\n",
    "    ann = ax.text(x=row['loc']+0.5, y=-8, s=row['province'], horizontalalignment='center', verticalalignment='bottom', \n",
    "        fontdict={'color':text_color, 'size':11,},\n",
    "        zorder=10,\n",
    "        rotation='90'\n",
    "       )\n",
    "\n",
    "    \n",
    "\n",
    "# yaxis\n",
    "for i in range(len(province_label_loc_df)):\n",
    "    row = province_label_loc_df.iloc[i]\n",
    "    \n",
    "    if row['cnt'] == 1: # annotate municipalities: 直轄市\n",
    "        if row['province'] == 'Beijing':\n",
    "            continue\n",
    "\n",
    "        ann = ax.annotate('', xy=(.5, row['tick_locs'][0]+0.5), xycoords='data',\n",
    "                      xytext=(-6, row['tick_locs'][1]+0.5), textcoords='data',\n",
    "                      clip_on=False,\n",
    "                      arrowprops=dict(arrowstyle=\"-\", lw=.7, ec=\"k\",)\n",
    "                         )\n",
    "        \n",
    "    else: # annotate province: 省\n",
    "    \n",
    "        frac = -5./(row['cnt']-1)\n",
    "\n",
    "        ann = ax.annotate('', xy=(0.5, row['tick_locs'][0]+0.5), xycoords='data',\n",
    "                      xytext=(0.5, row['tick_locs'][1]+0.5), textcoords='data',\n",
    "                      clip_on=False,\n",
    "                      arrowprops=dict(arrowstyle=\"-\", lw=.5,\n",
    "                                      connectionstyle=f\"bar,fraction={frac}\",\n",
    "                                      # connectionstyle=f\"bar,armA=10\",\n",
    "                                      ec=\"k\",\n",
    "                        ))\n",
    "\n",
    "    ann = ax.text(x=-8, y=row['loc']+0.5, s=row['province'], horizontalalignment='right', verticalalignment='center', \n",
    "        fontdict={'color':text_color, 'size':11,},\n",
    "        zorder=10,\n",
    "        rotation='0'\n",
    "       )\n",
    "    \n",
    "    \n",
    "    \n",
    "# Beijing: special\n",
    "ann = ax.annotate('', xy=(0.5, 0.5), xycoords='data',\n",
    "              xytext=(-6, -6), textcoords='data',\n",
    "              clip_on=False,\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              lw=.7, ec=\"k\",\n",
    "                ))\n",
    "\n",
    "ax.text(x=-5, y=-8, s='Beijing', horizontalalignment='center', verticalalignment='bottom', \n",
    "        fontdict={'color':text_color, 'size':11,},\n",
    "        zorder=10,\n",
    "        rotation='90'\n",
    "       )\n",
    "\n",
    "\n",
    "ax.text(x=-8, y=-5.5, s='Beijing', horizontalalignment='right', verticalalignment='center', \n",
    "        fontdict={'color':text_color, 'size':11,},\n",
    "        zorder=10,\n",
    "        rotation='0'\n",
    "       )\n",
    "\n",
    "ax.set_axis_off()\n",
    "\n",
    "\n",
    "# colorbar\n",
    "fig.colorbar(ax.get_children()[0], cax=cax, orientation=\"horizontal\")\n",
    "cax.tick_params(axis='both', which='major', top=False, right=False, length=6, width=.8, labelsize=13, pad=4, direction='out')\n",
    "# cax.set_xlabel('Migration index (log)', fontsize=13)\n",
    "cax.text(x=-0.1, y=-5, s='Migration\\nindex\\n(log)', horizontalalignment='right', verticalalignment='center', \n",
    "        fontdict={'color':'#444444', 'size':11,})\n",
    "\n",
    "\n",
    "plt.tight_layout(pad=0.1)\n",
    "\n",
    "plt.subplots_adjust(hspace = 0.05)\n",
    "\n",
    "out_dir = 'Outputs'\n",
    "# plt.savefig(os.path.join(out_dir, 'fig1-mobility_heatmap.pdf'), pad_inches=0, bbox_inches='tight')\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class=\"alert alert-block alert-success\"> Wuhan Outlofw Plot</div>\n",
    "* Outflow index of Wuhan in January 2020 and the corresponding period in 2019, aligned by the Lunar New Year\n",
    "* The Lunar New Year is 2020-01-25 in 2020 and 2019-02-05 in 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(166, 2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = os.path.join('Data/outflow_data', 'outflow_Wuhan.csv')\n",
    "city_flow_df = pd.read_csv(file_path)\n",
    "\n",
    "city_flow_df['date'] = city_flow_df['date'].astype(str)\n",
    "city_flow_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>date</th>\n",
       "      <th>migration_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20190112</td>\n",
       "      <td>5.052067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20190113</td>\n",
       "      <td>4.058813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20190114</td>\n",
       "      <td>4.004834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20190115</td>\n",
       "      <td>3.724672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20190116</td>\n",
       "      <td>3.990384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  migration_index\n",
       "0  20190112         5.052067\n",
       "1  20190113         4.058813\n",
       "2  20190114         4.004834\n",
       "3  20190115         3.724672\n",
       "4  20190116         3.990384"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_flow_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "date2index = city_flow_df.set_index('date')['migration_index'].to_dict()\n",
    "# date2index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map 2020 date to 2019 date in Lunar calendar\n",
    "\n",
    "date_dic = {}\n",
    "for i, d20 in enumerate(pd.date_range('20200101', '20200315')):\n",
    "    # print(i, d20.date())\n",
    "    d19 = pd.to_datetime('20190112')+pd.Timedelta(days=i)\n",
    "    d19 = str(d19.date()).replace('-', '')\n",
    "    d20 = str(d20.date()).replace('-', '')\n",
    "    # print(d20, ' -> ', d19)\n",
    "    \n",
    "    date_dic[d20] = d19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20190205'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_dic['20200125']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>date2020</th>\n",
       "      <th>date2019</th>\n",
       "      <th>migration_index_2020</th>\n",
       "      <th>migration_index_2019</th>\n",
       "      <th>date2020_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20200101</td>\n",
       "      <td>20190112</td>\n",
       "      <td>3.455298</td>\n",
       "      <td>5.052067</td>\n",
       "      <td>Jan 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20200102</td>\n",
       "      <td>20190113</td>\n",
       "      <td>3.524602</td>\n",
       "      <td>4.058813</td>\n",
       "      <td>Jan 02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20200103</td>\n",
       "      <td>20190114</td>\n",
       "      <td>5.524427</td>\n",
       "      <td>4.004834</td>\n",
       "      <td>Jan 03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20200104</td>\n",
       "      <td>20190115</td>\n",
       "      <td>6.099818</td>\n",
       "      <td>3.724672</td>\n",
       "      <td>Jan 04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20200105</td>\n",
       "      <td>20190116</td>\n",
       "      <td>5.322478</td>\n",
       "      <td>3.990384</td>\n",
       "      <td>Jan 05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date2020  date2019  migration_index_2020  migration_index_2019 date2020_str\n",
       "0  20200101  20190112              3.455298              5.052067       Jan 01\n",
       "1  20200102  20190113              3.524602              4.058813       Jan 02\n",
       "2  20200103  20190114              5.524427              4.004834       Jan 03\n",
       "3  20200104  20190115              6.099818              3.724672       Jan 04\n",
       "4  20200105  20190116              5.322478              3.990384       Jan 05"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow_df = pd.DataFrame(list(date_dic.items()), columns=['date2020', 'date2019'])\n",
    "flow_df['migration_index_2020'] = flow_df['date2020'].map(date2index)\n",
    "flow_df['migration_index_2019'] = flow_df['date2019'].map(date2index)\n",
    "\n",
    "# pd.to_datetime('20200101').strftime('%b %d')\n",
    "flow_df['date2020_str'] = flow_df['date2020'].map(lambda x: pd.to_datetime(x).strftime('%b %d'))\n",
    "\n",
    "flow_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot_df = flow_df.copy()\n",
    "plot_df = plot_df[plot_df['date2020'].str.contains('202001')]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "# ax.patch.set_alpha(0.015)\n",
    "# ax.set_facecolor('#2b3679')\n",
    "\n",
    "\n",
    "text_color = '#46ac88'\n",
    "\n",
    "\n",
    "# 2020\n",
    "# ---------------------\n",
    "x = plot_df['date2020']\n",
    "y = plot_df['migration_index_2020']\n",
    "\n",
    "ax.plot(x, y, '-o', \n",
    "        color=text_color, \n",
    "        linewidth=2, \n",
    "        markersize=8,\n",
    "        clip_on=False, \n",
    "        markeredgecolor=text_color, \n",
    "        markeredgewidth=1,\n",
    "        markerfacecolor='w',\n",
    "        label='2020', \n",
    "#         alpha=0.9,\n",
    "        zorder=100,\n",
    "       )\n",
    "\n",
    "\n",
    "# 2019\n",
    "# ---------------------\n",
    "x = plot_df['date2020']\n",
    "y = plot_df['migration_index_2019']\n",
    "\n",
    "ax.plot(x, y, '-o', \n",
    "        color='#666666', \n",
    "        linewidth=1.75, \n",
    "        markersize=8,\n",
    "        clip_on=False, \n",
    "        # markeredgecolor='slategrey', \n",
    "        markeredgecolor='#666666', \n",
    "        markeredgewidth=1,\n",
    "        markerfacecolor='w',\n",
    "        label='2019 (in lunar calendar)', \n",
    "        alpha=0.9,\n",
    "        zorder=90,\n",
    "       )\n",
    "\n",
    "\n",
    "\n",
    "line_style = (0, (7.5, 5))\n",
    "ax.vlines(x=list(x).index('20200123'), ymin=-5, ymax=50, linestyles=line_style, linewidth=1.25, color='dodgerblue')\n",
    "ax.text(x=list(x).index('20200123')-0.25, y=2, s='Wuhan\\nlockdown', fontsize=15, ha='right', color='dodgerblue')\n",
    "\n",
    "line_style = (0, (1, 2))\n",
    "ax.vlines(x=list(x).index('20200125'), ymin=-5, ymax=50, linestyles=line_style, linewidth=1.5, color='dodgerblue')\n",
    "ax.text(x=list(x).index('20200125')+0.4, y=9, s='Lunar\\nNew Year', fontsize=15, ha='left', color='dodgerblue')\n",
    "\n",
    "\n",
    "N = len(x)\n",
    "\n",
    "every_nth = 1\n",
    "locs = range(0, N, every_nth)\n",
    "ax.xaxis.set_minor_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "every_nth = 5\n",
    "locs = range(0, N, every_nth)\n",
    "ax.xaxis.set_major_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "\n",
    "locs = range(0, 200, 25)\n",
    "locs = [i/10.0 for i in locs]\n",
    "ax.yaxis.set_major_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "\n",
    "ticks = list(x)\n",
    "ticks = list(x.str[-4:])\n",
    "# ticks.insert(0, '')\n",
    "ticks.append('0316')\n",
    "ticks = [t[:2]+'-'+t[-2:] for t in ticks]\n",
    "# string date\n",
    "# ticks = list(plot_df['date2020_str'])\n",
    "# ticks.append(pd.to_datetime('20200316').strftime('%b %d'))\n",
    "plt.xticks(list(range(0, N+1, every_nth)), ticks[::every_nth], \n",
    "           rotation='0', \n",
    "           horizontalalignment='center',\n",
    "           # va = 'center',\n",
    "           position=(0, 0)\n",
    "           # rotation_mode=\"anchor\"\n",
    "          )\n",
    "\n",
    "\n",
    "real_legend = plt.legend(numpoints=1, fontsize=15, frameon=False, loc='best')\n",
    "\n",
    "\n",
    "# ax.set_xlabel('Date', fontsize=14)\n",
    "ax.set_ylabel('Outflow index of Wuhan', fontsize=16)\n",
    "\n",
    "border_width = 1.6\n",
    "ax.spines['bottom'].set_linewidth(border_width)\n",
    "ax.spines['left'].set_linewidth(border_width)\n",
    "ax.spines['top'].set_linewidth(border_width)\n",
    "ax.spines['right'].set_linewidth(border_width)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "\n",
    "# # custom ticks\n",
    "ax.tick_params(axis='x', which='major', top=False, right=False, pad=6, labelsize=15, length=6, width=1.5, direction='in')\n",
    "ax.tick_params(axis='y', which='major', top=False, right=False, pad=3, labelsize=15, length=6, width=1.5, direction='in')\n",
    "ax.tick_params(axis='both', which='minor', top=False, right=False, length=3.5,  width=1.2, direction='in')\n",
    "\n",
    "\n",
    "plt.xlim(-1, list(x).index('20200131')+1)\n",
    "plt.ylim(-.5, 13)\n",
    "\n",
    "\n",
    "# plt.tight_layout(pad=0.1)\n",
    "out_dir = 'Outputs'\n",
    "# plt.savefig(os.path.join(out_dir, 'fig1-Wuhan_outflow.pdf'), pad_inches=0, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}