xccds/scraper.ipynb

## scraper.ipynb
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  "name": "",
  "signature": "sha256:f13344cea9592b1459d1c0f0af172ae0a817ca34196a9289685779df4b4d16f0"
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### \u5c0f\u8bd5python\u7684\u7f51\u9875\u6570\u636e\u6293\u53d6\n",
      "- \u6570\u636e\u6293\u53d6\u4e24\u5927\u9636\u6bb5\uff0c\u7f51\u9875\u83b7\u53d6\u548c\u7f51\u9875\u89e3\u6790\n",
      "- R\u7684\u6570\u636e\u6293\u53d6\u4e3b\u8981\u9760\u4e24\u4e2a\u5305\uff0c\u7f51\u9875\u83b7\u53d6\u662fRCurl\uff0c\u89e3\u6790\u662f\u9760XML\n",
      "- python\u7684\u8bdd\u4e5f\u662f\u5bf9\u5e94\u4e24\u4e2a\u5305\uff0c\u7f51\u78c1\u83b7\u53d6\u662frequests\uff0c\u89e3\u6790\u662fBeautifulSoup\n",
      "- \u5982\u4e0b\u662f\u7ec3\u4e60\u4e09\u79cd\u6293\u53d6\u60c5\u51b5\n",
      "    - \u76f4\u63a5\u62ff\u73b0\u6210\u7684\u8868\u683c\n",
      "    - \u6a21\u62df\u767b\u9646\n",
      "    - \u62ff\u8c46\u74e3250\u7535\u5f71"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**\u76f4\u63a5\u62ff\u8868\u683c**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "url = \"http://zh.wikipedia.org/wiki/%E7%9C%81%E4%BC%9A\"\n",
      "dfs = pd.read_html(url, attrs={'class': 'wikitable'})\n",
      "dfs[0].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>10</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> \u7de8\u865f</td>\n",
        "      <td> \u884c\u653f\u5340</td>\n",
        "      <td> \u7c21\u7a31</td>\n",
        "      <td> \u7701\u6703\u6216\u9996\u5e9c</td>\n",
        "      <td> \u5730\u5340</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  \u7de8\u865f</td>\n",
        "      <td> \u884c\u653f\u5340</td>\n",
        "      <td>        \u7c21\u7a31</td>\n",
        "      <td> \u7701\u6703\u6216\u9996\u5e9c</td>\n",
        "      <td>  \u5730\u5340</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 01</td>\n",
        "      <td> \u6c5f\u8607\u7701</td>\n",
        "      <td>  \u8607</td>\n",
        "      <td>    \u93ae\u6c5f</td>\n",
        "      <td> \u83ef\u4e2d</td>\n",
        "      <td> 20</td>\n",
        "      <td> \u7518\u8085\u7701</td>\n",
        "      <td>   \u96b4</td>\n",
        "      <td>        \u862d\u5dde</td>\n",
        "      <td>    \u83ef\u5317</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 02</td>\n",
        "      <td> \u6d59\u6c5f\u7701</td>\n",
        "      <td>  \u6d59</td>\n",
        "      <td>    \u676d\u5dde</td>\n",
        "      <td> \u83ef\u4e2d</td>\n",
        "      <td> 21</td>\n",
        "      <td> \u5be7\u590f\u7701</td>\n",
        "      <td>   \u5be7</td>\n",
        "      <td>        \u9280\u5ddd</td>\n",
        "      <td>    \u585e\u5317</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 03</td>\n",
        "      <td> \u5b89\u5fbd\u7701</td>\n",
        "      <td>  \u7696</td>\n",
        "      <td>    \u5408\u80a5</td>\n",
        "      <td> \u83ef\u4e2d</td>\n",
        "      <td> 22</td>\n",
        "      <td> \u9752\u6d77\u7701</td>\n",
        "      <td>   \u9752</td>\n",
        "      <td>        \u897f\u5be7</td>\n",
        "      <td>    \u897f\u90e8</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 04</td>\n",
        "      <td> \u6c5f\u897f\u7701</td>\n",
        "      <td>  \u8d1b</td>\n",
        "      <td>    \u5357\u660c</td>\n",
        "      <td> \u83ef\u4e2d</td>\n",
        "      <td> 23</td>\n",
        "      <td> \u7d8f\u9060\u7701</td>\n",
        "      <td>   \u7d8f</td>\n",
        "      <td> \u6b78\u7d8f\uff08\u4eca\u547c\u548c\u6d69\u7279\uff09</td>\n",
        "      <td>    \u585e\u5317</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "   0    1   2      3   4   5    6    7          8      9    10\n",
        "0  \u7de8\u865f  \u884c\u653f\u5340  \u7c21\u7a31  \u7701\u6703\u6216\u9996\u5e9c  \u5730\u5340 NaN   \u7de8\u865f  \u884c\u653f\u5340         \u7c21\u7a31  \u7701\u6703\u6216\u9996\u5e9c   \u5730\u5340\n",
        "1  01  \u6c5f\u8607\u7701   \u8607     \u93ae\u6c5f  \u83ef\u4e2d  20  \u7518\u8085\u7701    \u96b4         \u862d\u5dde     \u83ef\u5317  NaN\n",
        "2  02  \u6d59\u6c5f\u7701   \u6d59     \u676d\u5dde  \u83ef\u4e2d  21  \u5be7\u590f\u7701    \u5be7         \u9280\u5ddd     \u585e\u5317  NaN\n",
        "3  03  \u5b89\u5fbd\u7701   \u7696     \u5408\u80a5  \u83ef\u4e2d  22  \u9752\u6d77\u7701    \u9752         \u897f\u5be7     \u897f\u90e8  NaN\n",
        "4  04  \u6c5f\u897f\u7701   \u8d1b     \u5357\u660c  \u83ef\u4e2d  23  \u7d8f\u9060\u7701    \u7d8f  \u6b78\u7d8f\uff08\u4eca\u547c\u548c\u6d69\u7279\uff09     \u585e\u5317  NaN"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**\u6a21\u62df\u767b\u9646**\n",
      "- \u767b\u9646zhihu\uff0c\u8981\u914d\u5408chrome\u7684\u5ba1\u67e5\u5de5\u5177\u6765\u89c2\u5bdf\u767b\u9646\u65f6\u7684\u6570\u636e"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "import requests\n",
      "s = requests.session()\n",
      "data = {'email':'***','password':'***'} # \u53c2\u6570\n",
      "url = \"http://www.zhihu.com/login\"\n",
      "s.post(url,data)\n",
      "r = s.get('http://www.zhihu.com/')\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**\u6293\u53d6\u8c46\u74e3250\u7535\u5f71**\n",
      "- \u6293\u53d6250\u90e8\u6700\u4f73\u7535\u5f71\u7684\u8bc4\u5206\u548c\u8bc4\u4ef7\u4eba\u6570\n",
      "- \u7528R\u6765\u753b\u56fe"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=http://movie.douban.com/top250?start=0&filter=&type=  width=600 height=350></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://movie.douban.com/top250?start=0&filter=&type=  width=600 height=350></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<IPython.core.display.HTML at 0x1040052d0>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import requests\n",
      "from bs4 import BeautifulSoup\n",
      "import re\n",
      "url = \"http://movie.douban.com/top250\"\n",
      "r = requests.get(url)\n",
      "soup_packtpage = BeautifulSoup(r.text)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u6839\u636e\u7f51\u9875\u7279\u5f81\u5b9a\u4e49\u6293\u53d6\u51fd\u6570\n",
      "def namefunc(movie):\n",
      "    names = [x.findChild('span',attrs={'class':'title'}).string for x in movie]\n",
      "    return names\n",
      "def scorefunc(movie):\n",
      "    scores = [float(str(x.findChild('em').string)) for x in movie]\n",
      "    return scores\n",
      "def numfunc(movie):\n",
      "    num = [x.findChild('span',attrs=None).string for x in movie]\n",
      "    num = [int(str(re.sub('\\D', '', x))) for x in num]\n",
      "    return num\n",
      "url = \"http://movie.douban.com/top250\"\n",
      "def getinfo(url):\n",
      "    r = requests.get(url)\n",
      "    soup_packtpage = BeautifulSoup(r.text)\n",
      "    movie = soup_packtpage.findAll('div',attrs={'class':'info'})\n",
      "    names = namefunc(movie)\n",
      "    scores = scorefunc(movie)\n",
      "    num = numfunc(movie)\n",
      "    res = {'names': names, 'scores': scores, 'num': num}\n",
      "    return res"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u5f97\u5230\u4e0d\u540c\u7f51\u5740\n",
      "urls = []\n",
      "index = range(0,250,25)\n",
      "for x in index:\n",
      "    urls.append('http://movie.douban.com/top250?start='+str(x)+'&filter=&type=')\n",
      "urls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "['http://movie.douban.com/top250?start=0&filter=&type=',\n",
        " 'http://movie.douban.com/top250?start=25&filter=&type=',\n",
        " 'http://movie.douban.com/top250?start=50&filter=&type=',\n",
        " 'http://movie.douban.com/top250?start=75&filter=&type=',\n",
        " 'http://movie.douban.com/top250?start=100&filter=&type=',\n",
        " 'http://movie.douban.com/top250?start=125&filter=&type=',\n",
        " 'http://movie.douban.com/top250?start=150&filter=&type=',\n",
        " 'http://movie.douban.com/top250?start=175&filter=&type=',\n",
        " 'http://movie.douban.com/top250?start=200&filter=&type=',\n",
        " 'http://movie.douban.com/top250?start=225&filter=&type=']"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u5bf9\u6bcf\u4e2a\u7f51\u5740\u8fdb\u884c\u6293\u53d6\n",
      "res = {'names': [], 'scores': [], 'num': []}\n",
      "for url in urls:\n",
      "    new = getinfo(url)\n",
      "    res['names'].extend(new['names'])\n",
      "    res['scores'].extend(new['scores'])\n",
      "    res['num'].extend(new['num'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "df = pd.DataFrame(res)\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>names</th>\n",
        "      <th>num</th>\n",
        "      <th>scores</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  \u8096\u7533\u514b\u7684\u6551\u8d4e</td>\n",
        "      <td> 573416</td>\n",
        "      <td> 9.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> \u8fd9\u4e2a\u6740\u624b\u4e0d\u592a\u51b7</td>\n",
        "      <td> 543434</td>\n",
        "      <td> 9.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>    \u963f\u7518\u6b63\u4f20</td>\n",
        "      <td> 484866</td>\n",
        "      <td> 9.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>    \u9738\u738b\u522b\u59ec</td>\n",
        "      <td> 389376</td>\n",
        "      <td> 9.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>    \u7f8e\u4e3d\u4eba\u751f</td>\n",
        "      <td> 265644</td>\n",
        "      <td> 9.4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "     names     num  scores\n",
        "0   \u8096\u7533\u514b\u7684\u6551\u8d4e  573416     9.6\n",
        "1  \u8fd9\u4e2a\u6740\u624b\u4e0d\u592a\u51b7  543434     9.4\n",
        "2     \u963f\u7518\u6b63\u4f20  484866     9.4\n",
        "3     \u9738\u738b\u522b\u59ec  389376     9.4\n",
        "4     \u7f8e\u4e3d\u4eba\u751f  265644     9.4"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**\u53ef\u89c6\u5316**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext rpy2.ipython"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i df -w 500 -h 300 \n",
      "library(ggplot2)\n",
      "p = ggplot(df,aes(x = num, y = scores)) + geom_point(size=4,alpha=0.5) + stat_smooth()\n",
      "print(p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "geom_smooth: method=\"auto\" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PvTvYaeQ/+d335KarPpHWLQNPtxIKA5qkto8FNjzoLmH2ip5Uzc6hM9fpC50T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      }
     ],
     "prompt_number": 38
    }
   ],
   "metadata": {}
  }
 ]
}