7-.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这个教程中，你将会学到如何用高德地图api抓取行政区划\n",
    "\n",
    "<div class=\"alert alert-info\"><h2>提供的基础数据是：</h2><p>\n",
    "    没有，我们的数据无中生有<br><br></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 观察网络连接行为"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们从高德地图抓，先观察一下如果在高德地图输入深圳的某一个行政区划查询，它的网络连接行为是怎样的\n",
    "    \n",
    "谷歌浏览器右键检查，或者点设置里面的开发者工具，再点network选项，可以看到网络的连接行为（其他浏览器也有类似的功能，需要找一找）\n",
    "\n",
    "<a href=\"https://mp.weixin.qq.com/s/97xjbnYBf_y49GF1SUNl-w\">爬虫的原理在这里</a>我们这里用到的是爬虫2.0\n",
    "\n",
    "每个网络访问中，有\n",
    "\n",
    "    Response Headers(响应头)\n",
    "    Request Headers(请求头)\n",
    "    Query String Parameters(查询参数)\n",
    "    \n",
    "其中，请求头和查询参数是我们要关注的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# json数据格式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在网络访问行为中，对方服务器返回给我们的数据是json结构，那么json是什么呢"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先补充一下基础知识，学习一下python的\n",
    "\n",
    "<a href=\"https://www.runoob.com/python/python-lists.html\">list</a>\n",
    "\n",
    "<a href=\"https://www.runoob.com/python/python-dictionary.html\">dict</a>\n",
    "\n",
    "<a href=\"https://www.runoob.com/python/python-tuples.html\">tuple</a>\n",
    "\n",
    "把list,dict,tuple自由组合起来就变成了json\n",
    "\n",
    "<a href=\"https://www.runoob.com/json/json-tutorial.html\">JSON 实例</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "直接从高德地图抓是比较困难的，有防爬机制\n",
    "\n",
    "不过，高德专门为开发者提供了抓数据的接口\n",
    "\n",
    "<a href=\"https://lbs.amap.com/\">高德地图开放平台</a>\n",
    "\n",
    "各位需要注册一下高德开发者申请一个key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在其中，高德已经给我们提供了开发者专用的行政区查询服务，以及相关说明<a href=\"https://lbs.amap.com/api/javascript-api/guide/services/district-search\">行政区查询</a>\n",
    "\n",
    "在其中选择一个行政区查询，然后看看网络连接行为吧"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 开始抓行政区划"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-19T03:44:12.354768Z",
     "start_time": "2020-01-19T03:44:11.803160Z"
    }
   },
   "outputs": [],
   "source": [
    "#导入必要的爬虫包\n",
    "import urllib\n",
    "from urllib import parse\n",
    "from urllib import request\n",
    "\n",
    "import pandas as pd\n",
    "#导入json包，后面解析json数据\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-19T03:44:13.091717Z",
     "start_time": "2020-01-19T03:44:13.086729Z"
    }
   },
   "outputs": [],
   "source": [
    "mykey = '在此输入你的key'\n",
    "#这个输入你开发者key，告诉高德这个数据是你抓的，每天会有限额，你们可以注册成为开发者，这样就有自己的key拉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-19T03:44:16.032917Z",
     "start_time": "2020-01-19T03:44:14.094053Z"
    }
   },
   "outputs": [],
   "source": [
    "keywords = '罗湖区'\n",
    "\n",
    "#查询的接口地址\n",
    "url = 'https://restapi.amap.com/v3/config/district?'\n",
    "\n",
    "#查询的条件\n",
    "dict1 = {\n",
    "'subdistrict':'3',\n",
    "    'showbiz':'false',\n",
    "    'extensions':'all',\n",
    "    'key':mykey,#这个是我的开发者key，告诉高德这个数据是我抓的，每天会有限额，你们可以注册成为开发者，这样就有自己的key拉\n",
    "    's':'rsv3',\n",
    "    'output':'json',\n",
    "    'level':'district',\n",
    "    'keywords':keywords,\n",
    "    'platform':'JS',\n",
    "    'logversion':'2.0',\n",
    "    'sdkversion':'1.4.10'\n",
    "}\n",
    "\n",
    "#把查询条件组合成网页地址\n",
    "url_data = parse.urlencode(dict1)\n",
    "url = url+url_data\n",
    "\n",
    "#创建一个访问器\n",
    "request = urllib.request.Request(url)\n",
    "\n",
    "#访问网页\n",
    "response = urllib.request.urlopen(request)\n",
    "\n",
    "#读取网页内容\n",
    "webpage = response.read()\n",
    "\n",
    "#将内容用json解析\n",
    "result = json.loads(webpage.decode('utf8','ignore'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-19T03:44:17.628606Z",
     "start_time": "2020-01-19T03:44:17.611648Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': '1',\n",
       " 'info': 'OK',\n",
       " 'infocode': '10000',\n",
       " 'count': '1',\n",
       " 'suggestion': {'keywords': [], 'cities': []},\n",
       " 'districts': [{'citycode': '0755',\n",
       "   'adcode': '440303',\n",
       "   'name': '罗湖区',\n",
       "   'polyline': 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       "   'center': '114.123885,22.555341',\n",
       "   'level': 'district',\n",
       "   'districts': [{'citycode': '0755',\n",
       "     'adcode': '440303',\n",
       "     'name': '笋岗街道',\n",
       "     'center': '114.104,22.5621',\n",
       "     'level': 'street',\n",
       "     'districts': []},\n",
       "    {'citycode': '0755',\n",
       "     'adcode': '440303',\n",
       "     'name': '东门街道',\n",
       "     'center': '114.116,22.5428',\n",
       "     'level': 'street',\n",
       "     'districts': []},\n",
       "    {'citycode': '0755',\n",
       "     'adcode': '440303',\n",
       "     'name': '黄贝街道',\n",
       "     'center': '114.157,22.5662',\n",
       "     'level': 'street',\n",
       "     'districts': []},\n",
       "    {'citycode': '0755',\n",
       "     'adcode': '440303',\n",
       "     'name': '桂园街道',\n",
       "     'center': '114.109,22.556',\n",
       "     'level': 'street',\n",
       "     'districts': []},\n",
       "    {'citycode': '0755',\n",
       "     'adcode': '440303',\n",
       "     'name': '清水河街道',\n",
       "     'center': '114.082,22.5699',\n",
       "     'level': 'street',\n",
       "     'districts': []},\n",
       "    {'citycode': '0755',\n",
       "     'adcode': '440303',\n",
       "     'name': '南湖街道',\n",
       "     'center': '114.113,22.5389',\n",
       "     'level': 'street',\n",
       "     'districts': []},\n",
       "    {'citycode': '0755',\n",
       "     'adcode': '440303',\n",
       "     'name': '东晓街道',\n",
       "     'center': '114.123,22.589',\n",
       "     'level': 'street',\n",
       "     'districts': []},\n",
       "    {'citycode': '0755',\n",
       "     'adcode': '440303',\n",
       "     'name': '翠竹街道',\n",
       "     'center': '114.128,22.574',\n",
       "     'level': 'street',\n",
       "     'districts': []},\n",
       "    {'citycode': '0755',\n",
       "     'adcode': '440303',\n",
       "     'name': '东湖街道',\n",
       "     'center': '114.204,22.5699',\n",
       "     'level': 'street',\n",
       "     'districts': []},\n",
       "    {'citycode': '0755',\n",
       "     'adcode': '440303',\n",
       "     'name': '莲塘街道',\n",
       "     'center': '114.205,22.565',\n",
       "     'level': 'street',\n",
       "     'districts': []}]}]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据中保存了polyline的线形\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 行政区划边界线型数据提取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-11T10:07:06.479605Z",
     "start_time": "2019-09-11T10:07:06.355734Z"
    },
    "scrolled": true
   },
   "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>lon_lst</th>\n",
       "      <th>lat_lst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114.105177</td>\n",
       "      <td>22.531626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114.104808</td>\n",
       "      <td>22.532512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114.104774</td>\n",
       "      <td>22.535038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>114.104757</td>\n",
       "      <td>22.535105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>114.104772</td>\n",
       "      <td>22.535200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lon_lst    lat_lst\n",
       "0  114.105177  22.531626\n",
       "1  114.104808  22.532512\n",
       "2  114.104774  22.535038\n",
       "3  114.104757  22.535105\n",
       "4  114.104772  22.535200"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将polyline的线型从result中提取\n",
    "polyline = result['districts'][0]['polyline']\n",
    "\n",
    "###############################你需要在下面写代码##################################\n",
    "#想办法把polyline变成一个dataframe，两列，第一列是lon，第二列是lat\n",
    "#提示：可以用for循环遍历\n",
    "#或者可以用re包，一次指定多个分隔符分割字符串，然后用numpy的reshape方法，再变成dataframe\n",
    "\n",
    "\n",
    "###################################################################################\n",
    "\n",
    "polyline.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 火星坐标转换"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从高德抓下来的坐标系是GCJ02,俗称<a href=\"https://blog.csdn.net/huangli0/article/details/81840937\">火星坐标系统</a>，而我们平时用的是WGS84坐标系，因此，需要进行坐标转换，转换的方法有大神开源出来了\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[geo_convert](https://github.com/gaussic/geo_convert)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "好的，我们这里对每一行应用一下这个算法转换，建议使用geopandas的apply函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-11T10:11:06.230683Z",
     "start_time": "2019-09-11T10:11:06.179076Z"
    },
    "scrolled": true
   },
   "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>lon_lst</th>\n",
       "      <th>lat_lst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114.100060</td>\n",
       "      <td>22.534309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114.099691</td>\n",
       "      <td>22.535195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114.099657</td>\n",
       "      <td>22.537721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>114.099640</td>\n",
       "      <td>22.537788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>114.099655</td>\n",
       "      <td>22.537883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lon_lst    lat_lst\n",
       "0  114.100060  22.534309\n",
       "1  114.099691  22.535195\n",
       "2  114.099657  22.537721\n",
       "3  114.099640  22.537788\n",
       "4  114.099655  22.537883"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###############################你需要在下面写代码##################################\n",
    "#坐标转换\n",
    "\n",
    "###################################################################################\n",
    "polyline.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用线型生成geodataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个~前面教程有哦，写一写巩固一下"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里你要做的操作，就是将polyline变成shapely里面的Polygon，然后再转换为geodataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-11T10:12:00.167002Z",
     "start_time": "2019-09-11T10:11:58.202594Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ee804cd30>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas\n",
    "from shapely.geometry import Point,Polygon,shape\n",
    "\n",
    "\n",
    "###############################你需要在下面写代码##################################\n",
    "#线型生成geodataframe\n",
    "#提示Polygon(polyline.values)\n",
    "###################################################################################\n",
    "dataline.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作业"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 抓全深圳的行政区划"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以把上面的抓取过程写成一个函数，输入是行政区划的名字，输出是坐标点或shapely的polygon或geodataframe\n",
    "\n",
    "这样只要循环一下，就可以生成全深圳的行政区划了"
   ]
  },
  {
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   "source": [
    "然后不要忘了把geopandas保存成shp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特别注意：在一个行政区划由多个面组成时，坐标串中间以'|'分隔"
   ]
  }
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