reddit ipython nb

ipython_reddit.py

{
 "metadata": {
  "name": "",
  "signature": "sha256:a8ab547d1c9943ab14fb4b4be340842f1d13a10781f05859c3649d085fdb4212"
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 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "import requests\n",
      "import time\n",
      "\n",
      "pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier\n",
      "matplotlib.rcParams['figure.figsize'] = (10, 4)\n",
      "matplotlib.rcParams['savefig.dpi'] = 2 * matplotlib.rcParams['savefig.dpi']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's find popular subreddits using Reddit public API "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_reddit(path, limit=100, max_entries=100):\n",
      "    headers = {\n",
      "        'User-Agent': 'ipython-reddit/1.0 by FZambia',\n",
      "    }\n",
      "    url = base_url = \"http://www.reddit.com{0}.json?limit={1}\".format(path, limit)\n",
      "    after = None\n",
      "    entries = []\n",
      "    while len(entries) < max_entries:\n",
      "        if after:\n",
      "            url = base_url + \"&after=%s\" % after\n",
      "        print url\n",
      "\n",
      "        try:\n",
      "            response = requests.get(url, headers=headers)\n",
      "        except requests.RequestException as err:\n",
      "            print err\n",
      "            break\n",
      "\n",
      "        if response.status_code != 200:\n",
      "            print \"bad status code - {0}\".format(response.status_code)\n",
      "            break\n",
      "\n",
      "        data = response.json()\n",
      "        entries.extend(data['data']['children'])\n",
      "\n",
      "        after = data[\"data\"].get('after')\n",
      "        if not after:\n",
      "            print \"no more data\"\n",
      "            break\n",
      "\n",
      "        time.sleep(1)\n",
      "\n",
      "    return entries\n",
      "\n",
      "subreddits = get_reddit(\"/subreddits/popular\", limit=100, max_entries=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "http://www.reddit.com/subreddits/popular.json?limit=100\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = [(x['data']['url'], x['data']['subscribers']) for x in subreddits]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(data, columns=[\"subreddit\", \"subscribers\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.sort(\"subscribers\", ascending=False)[:20].plot(kind=\"bar\", x=\"subreddit\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10adbc710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7dsT8+fOxdu1azZolGzZskDhh0xUWFmoeeLOz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\nERERUSf8P0UoB5SfPMcPAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ae01bd0>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "programming_language_subreddits_data = \"\"\"\n",
      "    ada\n",
      "    applescript\n",
      "    Arc\n",
      "    asm\n",
      "    awk\n",
      "    brainfuck\n",
      "    clojure\n",
      "    cobol\n",
      "    common_lisp\n",
      "    coldfusion\n",
      "    cpp\n",
      "    csharp\n",
      "    c_language\n",
      "    d_language\n",
      "    delphi\n",
      "    erlang\n",
      "    fortran\n",
      "    groovy\n",
      "    golang\n",
      "    haskell\n",
      "    haxe\n",
      "    javascript\n",
      "    java\n",
      "    lisp\n",
      "    lua\n",
      "    matlab\n",
      "    modula3\n",
      "    newlisp\n",
      "    objectivec\n",
      "    ocaml\n",
      "    pascal\n",
      "    perl\n",
      "    php\n",
      "    prolog\n",
      "    python\n",
      "    ruby\n",
      "    scala\n",
      "    scheme\n",
      "    smalltalk\n",
      "    swift\n",
      "    visualbasic\n",
      "\"\"\"\n",
      "programming_language_subreddits = [\"/r/{0}/\".format(x.strip()) for x in programming_language_subreddits_data.split()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "programming_language_subreddits"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "['/r/ada/',\n",
        " '/r/applescript/',\n",
        " '/r/Arc/',\n",
        " '/r/asm/',\n",
        " '/r/awk/',\n",
        " '/r/brainfuck/',\n",
        " '/r/clojure/',\n",
        " '/r/cobol/',\n",
        " '/r/common_lisp/',\n",
        " '/r/coldfusion/',\n",
        " '/r/cpp/',\n",
        " '/r/csharp/',\n",
        " '/r/c_language/',\n",
        " '/r/d_language/',\n",
        " '/r/delphi/',\n",
        " '/r/erlang/',\n",
        " '/r/fortran/',\n",
        " '/r/groovy/',\n",
        " '/r/golang/',\n",
        " '/r/haskell/',\n",
        " '/r/haxe/',\n",
        " '/r/javascript/',\n",
        " '/r/java/',\n",
        " '/r/lisp/',\n",
        " '/r/lua/',\n",
        " '/r/matlab/',\n",
        " '/r/modula3/',\n",
        " '/r/newlisp/',\n",
        " '/r/objectivec/',\n",
        " '/r/ocaml/',\n",
        " '/r/pascal/',\n",
        " '/r/perl/',\n",
        " '/r/php/',\n",
        " '/r/prolog/',\n",
        " '/r/python/',\n",
        " '/r/ruby/',\n",
        " '/r/scala/',\n",
        " '/r/scheme/',\n",
        " '/r/smalltalk/',\n",
        " '/r/swift/',\n",
        " '/r/visualbasic/']"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# test on cpp\n",
      "lang = programming_language_subreddits[10]\n",
      "\n",
      "entries = get_reddit(lang + \"new\", limit=100, max_entries=500)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "http://www.reddit.com/r/cpp/new.json?limit=100\n",
        "http://www.reddit.com/r/cpp/new.json?limit=100&after=t3_2bc9qt"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "http://www.reddit.com/r/cpp/new.json?limit=100&after=t3_28o1fs"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "http://www.reddit.com/r/cpp/new.json?limit=100&after=t3_260rc5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "http://www.reddit.com/r/cpp/new.json?limit=100&after=t3_236iqy"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(entries)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "500"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pprint\n",
      "pprint.pprint(entries[0][\"data\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{u'approved_by': None,\n",
        " u'author': u'meetingcpp',\n",
        " u'author_flair_css_class': None,\n",
        " u'author_flair_text': None,\n",
        " u'banned_by': None,\n",
        " u'clicked': False,\n",
        " u'created': 1408122013.0,\n",
        " u'created_utc': 1408093213.0,\n",
        " u'distinguished': None,\n",
        " u'domain': u'thisthread.blogspot.com',\n",
        " u'downs': 0,\n",
        " u'edited': False,\n",
        " u'gilded': 0,\n",
        " u'hidden': False,\n",
        " u'id': u'2dmc03',\n",
        " u'is_self': False,\n",
        " u'likes': None,\n",
        " u'link_flair_css_class': None,\n",
        " u'link_flair_text': None,\n",
        " u'media': None,\n",
        " u'media_embed': {},\n",
        " u'name': u't3_2dmc03',\n",
        " u'num_comments': 0,\n",
        " u'num_reports': None,\n",
        " u'over_18': False,\n",
        " u'permalink': u'/r/cpp/comments/2dmc03/graph_by_adjacency_list/',\n",
        " u'saved': False,\n",
        " u'score': 1,\n",
        " u'secure_media': None,\n",
        " u'secure_media_embed': {},\n",
        " u'selftext': u'',\n",
        " u'selftext_html': None,\n",
        " u'stickied': False,\n",
        " u'subreddit': u'cpp',\n",
        " u'subreddit_id': u't5_2qi27',\n",
        " u'thumbnail': u'',\n",
        " u'title': u'Graph by adjacency list',\n",
        " u'ups': 1,\n",
        " u'url': u'http://thisthread.blogspot.com/2014/08/graph-by-adjacency-list.html',\n",
        " u'visited': False}\n"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "[(u'StdAfx.h for Novices', 2)]"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}