选择器.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyquery import PyQuery as pq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from lxml import etree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from parsel import Selector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试环境\n",
    "1. 硬件环境：\n",
    "    1. 操作系统：Windows7 64\n",
    "    2. 处理器：Inter(R)Xeon(R) CPU E5-2650 v3\n",
    "    3. 内存：8G\n",
    "2. 软件环境：\n",
    "    1. pyquery:1.2.17\n",
    "    2. lxml:3.7.3\n",
    "    3. bs4:4.5.1\n",
    "    4. parsel:1.1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "html = u\"\"\"\n",
    "<html>\n",
    " <head>\n",
    "  <base href='http://example.com/' />\n",
    "  <title>Example website</title>\n",
    " </head>\n",
    " <body>\n",
    "  <div id='images'>\n",
    "   <a href='image1.html'>Name: My image 1 <br /><img src='image1_thumb.jpg' /></a>\n",
    "   <a href='image2.html'>Name: My image 2 <br /><img src='image2_thumb.jpg' /></a>\n",
    "   <a href='image3.html'>Name: My image 3 <br /><img src='image3_thumb.jpg' /></a>\n",
    "   <a href='image4.html'>Name: My image 4 <br /><img src='image4_thumb.jpg' /></a>\n",
    "   <a href='image5.html'>Name: My image 5 <br /><img src='image5_thumb.jpg' /></a>\n",
    "  </div>\n",
    " </body>\n",
    "</html>\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 104 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 10000 pq(html)('title').html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 78.5 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 10000 etree.HTML(html).xpath('//title/text()')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 795 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 10000 BeautifulSoup(html,\"lxml\").title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 1.04 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 10000 BeautifulSoup(html,\"html.parser\").title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 3.62 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 10000 BeautifulSoup(html,\"html5lib\").title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 192 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 10000 Selector(text=html).xpath('//title/text()').extract_first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 322 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 10000 Selector(text=html).css('title::text').extract_first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = Series([104,78.5,795,1040,3620,192,322],index=['pq','xpath','bs4_lxml','bs4_html.parser','bs4_html5lib','parsel_xpath','parsel_css'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s.sort_values(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa11776a0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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5oM6VwOHttb5DM9XyqKoanlEhSZJmyETXadhoklFVPwUe2+E6V9EkDhurcz7N+g+SJGkW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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa115ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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