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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Setup\n",
      "import pattern.web as web\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from cs109style import customize_mpl, customize_css\n",
      "customize_mpl()\n",
      "customize_css()\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Setting custom matplotlib visual style\n",
        "Setting custom CSS for the IPython Notebook\n",
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Example 2: extracting reddit titles, upvotes, downvotes, and submission time\n",
      "\n",
      "### We'll operate in two phases:\n",
      "* first, find all the URLs to comment pages on the first few front pages of reddit.\n",
      "* second, extract information from each comments page"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_links_from_front_pages(n):\n",
      "    'find  URLs of comments pages, linked from the n first few pages of reddit'\n",
      "    url = web.URL('http://www.reddit.com/')\n",
      "    comment_pages = []\n",
      "    for page_idx in range(n):\n",
      "        dom = web.DOM(url.download(cached=False))\n",
      "    \n",
      "        for entry in dom('a.comments'):\n",
      "            href = entry.attributes.get('href', '')\n",
      "            if href:\n",
      "                comment_pages.append(href)\n",
      "                \n",
      "        # find the next page link - reddit has 25 links per page\n",
      "        for a in dom('a'):\n",
      "            if ('count=%d' % ((page_idx + 1) * 25)) in a.attributes.get('href', ''):\n",
      "                url = web.URL(a.attributes.get('href'))\n",
      "    # use set() to remove repeated pages\n",
      "    return list(set(comment_pages))\n",
      "\n",
      "            \n",
      "print len(get_links_from_front_pages(6))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "170\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def info_from_comments_pages(links):\n",
      "    'fetch title, upvotes, downvotes, time of submission from a sequence of links'\n",
      "    results = []\n",
      "    for urltext in links:\n",
      "        url = web.URL(urltext)\n",
      "        print \"fetching info for\", url\n",
      "        try:\n",
      "            dom = web.DOM(url.download(cached=False))\n",
      "            title = dom('title')[0].content\n",
      "            upvotes = int(dom.by_class('upvotes')[0].children[0].content.replace(',', ''))\n",
      "            downvotes = int(dom.by_class('downvotes')[0].children[0].content.replace(',', ''))\n",
      "            time = dom.by_class('tagline')[0]('time')[0].attributes.get('datetime')\n",
      "            results.append((title, upvotes, downvotes, pd.to_datetime(time)))\n",
      "        except KeyboardInterrupt:\n",
      "            # allow us to interrupt the kernel but use what we've already fetched\n",
      "            break\n",
      "        except:\n",
      "            pass  # some things that look like comment pages don't have the information above\n",
      "    return results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "comments_pages = get_links_from_front_pages(5)\n",
      "print \"Fetching info for\", len(comments_pages), \"pages\"\n",
      "pages = info_from_comments_pages(comments_pages)\n",
      "titles, upvotes, downvotes, dates = zip(*pages)  # zip(*seq) transposes a sequence of sequences.\n",
      "df = pd.DataFrame({'title' : titles, 'upvotes' : upvotes, 'downvotes' : downvotes, 'date' : dates}, index=dates)\n",
      "print df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Fetching info for 139 pages\n",
        "fetching info for http://www.reddit.com/r/AskReddit/comments/1m8t6m/teachers_of_reddit_what_is_the_worst_case_of/\n",
        "fetching info for"
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        " http://www.reddit.com/r/books/comments/1m8l01/the_price_of_libraries_is_cheap_compared_to_that/\n",
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a8PU6t8Q6d89KhHRanM78Z45qvvLGRvB6jdcVB/j8DdmnYRbIctzqmUr2ETT/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\ne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w8GDBiAzWYjODiYb7/9looVKxIeHs6bb75JcnIy3333HXB7fufp7ffq1YuXXnqJ06dP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\nIGzyZzAYDAXyG7jDu2wmiCqpAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c2b1e90>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}