Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Preliminary Data Mining on [*Pyvideo*](http://www.pyvideo.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to Pyvideo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**__Pyvideo__** is a video index website provides **Python** related videos. It has more than 3000 videos so far. Sadly, this site is going to [*close down at some point this year*](http://bluesock.org/~willkg/blog/pyvideo/status_20160316.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=http://www.pyvideo.org width=800 height=350></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "HTML(\"<iframe src=http://www.pyvideo.org width=800 height=350></iframe>\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Site Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Shu/anaconda/envs/python3/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "from pprint import pprint\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "folders = os.listdir(\"./data/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "folders = folders[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "videopath = []\n",
    "for i in range(len(folders)):\n",
    "    a = \"./data/\" + folders[i] + \"/videos/\" \n",
    "    for j in range(len(os.listdir(path=a))):\n",
    "        videopath.append(a + os.listdir(path=a)[j])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look at the path "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./data/bostonpy/videos/boston-python-meetup--how-to-test-the-hard-stuff.json',\n",
       " './data/bostonpy/videos/boston-python-meetup--testing--where-do-i-start.json',\n",
       " './data/chicago-djangonauts/videos/consuming-tasks-with-celery.json',\n",
       " './data/chicago-djangonauts/videos/django-based-autocompl.json',\n",
       " './data/chicago-djangonauts/videos/django-nonrel-intro-and-state.json']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "videopath[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3587"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many videos are indexed?\n",
    "len(videopath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look at the content of one json file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'category': 'Boston Python Meetup',\n",
      " 'copyright_text': '',\n",
      " 'description': 'Testing, Where Do I Start?\\n'\n",
      "                '\\n'\n",
      "                'Presented by Marshall Weir\\n'\n",
      "                '\\n'\n",
      "                'Marshall will cover basic unit test concepts and quickly move '\n",
      "                'to making your\\n'\n",
      "                'tests motivating, easy to run and adding testing to an '\n",
      "                \"existing project. He's\\n\"\n",
      "                'aiming squarely at developers who have projects they want to '\n",
      "                'use testing for,\\n'\n",
      "                \"but don't know where to get started.\\n\"\n",
      "                '\\n'\n",
      "                \"Have you wanted to become test-infected but couldn't find the \"\n",
      "                'start of the\\n'\n",
      "                'thread to pull? Marshall will help.\\n'\n",
      "                '\\n'\n",
      "                'Boston Python Meetup, May 2010\\n'\n",
      "                '\\n',\n",
      " 'duration': None,\n",
      " 'id': 592,\n",
      " 'language': 'English',\n",
      " 'quality_notes': '',\n",
      " 'recorded': None,\n",
      " 'slug': 'boston-python-meetup--testing--where-do-i-start',\n",
      " 'source_url': 'http://vimeo.com/11963043',\n",
      " 'speakers': ['Marshall Weir'],\n",
      " 'summary': '',\n",
      " 'tags': ['bostonpy', 'session', 'testing', 'unittest'],\n",
      " 'thumbnail_url': None,\n",
      " 'title': 'Boston Python Meetup: Testing: Where do I start?',\n",
      " 'videos': []}\n"
     ]
    }
   ],
   "source": [
    "with open(videopath[1]) as json_data:\n",
    "    d = json.load(json_data)\n",
    "pprint(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the result above, the json file is in a dictionary format. We are only interested in \"title\", \"description\" and \"source_url\". We will extract these keys' value and store into lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bostonpy, session, testing, unittest'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(videopath[1]) as json_data:\n",
    "    d = json.load(json_data)[\"tags\"]\n",
    "\", \".join(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Extract key from JSON file and convert to string\n",
    "def extractkeys(key):\n",
    "    keylist = []\n",
    "    if key in [\"speakers\", \"tags\"]:\n",
    "        for i in range(len(videopath)):\n",
    "            with open(videopath[i]) as  json_data:\n",
    "                keylist.append(\", \".join(json.load(json_data)[key]))\n",
    "    else:\n",
    "        for i in range(len(videopath)):\n",
    "            with open(videopath[i]) as json_data:\n",
    "                keylist.append(json.load(json_data)[key])\n",
    "    return pd.Series(keylist, name = key.capitalize())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "video = pd.concat([extractkeys(\"title\"),extractkeys(\"recorded\"),extractkeys(\"description\"),extractkeys(\"speakers\")\n",
    "                   ,extractkeys(\"tags\"),extractkeys(\"category\"),extractkeys(\"source_url\"),],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert Recorded value to datetime type\n",
    "from datetime import datetime\n",
    "for i in range(len(video)):\n",
    "    if video[\"Recorded\"][i] != None:\n",
    "        video[\"Recorded\"][i] == datetime.strptime(video[\"Recorded\"][i], \"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Recorded</th>\n",
       "      <th>Description</th>\n",
       "      <th>Speakers</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Category</th>\n",
       "      <th>Source_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boston Python Meetup: How to test the hard stuff</td>\n",
       "      <td>None</td>\n",
       "      <td>How to Test the Hard Stuff\\n\\nPresented by Ste...</td>\n",
       "      <td>Stephan Richter</td>\n",
       "      <td>bostonpy, session, testing, unittest</td>\n",
       "      <td>Boston Python Meetup</td>\n",
       "      <td>http://vimeo.com/11980200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Python Meetup: Testing: Where do I start?</td>\n",
       "      <td>None</td>\n",
       "      <td>Testing, Where Do I Start?\\n\\nPresented by Mar...</td>\n",
       "      <td>Marshall Weir</td>\n",
       "      <td>bostonpy, session, testing, unittest</td>\n",
       "      <td>Boston Python Meetup</td>\n",
       "      <td>http://vimeo.com/11963043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Consuming Tasks with Celery</td>\n",
       "      <td>2012-02-02</td>\n",
       "      <td></td>\n",
       "      <td>Joe Jasinski</td>\n",
       "      <td>RabbitMQ Celery</td>\n",
       "      <td>Chicago Djangonauts</td>\n",
       "      <td>http://youtu.be/MCmNGmFh6wE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Django Based Autocompl…</td>\n",
       "      <td>2015-09-03</td>\n",
       "      <td>Django does not do autocomplete out of the box...</td>\n",
       "      <td>Jonathan Bisson</td>\n",
       "      <td>autocomplete, Django</td>\n",
       "      <td>Chicago Djangonauts</td>\n",
       "      <td>http://youtu.be/XMp-wzzMsls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Django-nonrel - intro and state</td>\n",
       "      <td>2012-02-02</td>\n",
       "      <td></td>\n",
       "      <td>Adam Jenkins</td>\n",
       "      <td></td>\n",
       "      <td>Chicago Djangonauts</td>\n",
       "      <td>http://youtu.be/akAtm7SnzWg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Title    Recorded  \\\n",
       "0  Boston Python Meetup: How to test the hard stuff        None   \n",
       "1  Boston Python Meetup: Testing: Where do I start?        None   \n",
       "2                       Consuming Tasks with Celery  2012-02-02   \n",
       "3                           Django Based Autocompl…  2015-09-03   \n",
       "4                   Django-nonrel - intro and state  2012-02-02   \n",
       "\n",
       "                                         Description         Speakers  \\\n",
       "0  How to Test the Hard Stuff\\n\\nPresented by Ste...  Stephan Richter   \n",
       "1  Testing, Where Do I Start?\\n\\nPresented by Mar...    Marshall Weir   \n",
       "2                                                        Joe Jasinski   \n",
       "3  Django does not do autocomplete out of the box...  Jonathan Bisson   \n",
       "4                                                        Adam Jenkins   \n",
       "\n",
       "                                   Tags              Category  \\\n",
       "0  bostonpy, session, testing, unittest  Boston Python Meetup   \n",
       "1  bostonpy, session, testing, unittest  Boston Python Meetup   \n",
       "2                       RabbitMQ Celery   Chicago Djangonauts   \n",
       "3                  autocomplete, Django   Chicago Djangonauts   \n",
       "4                                         Chicago Djangonauts   \n",
       "\n",
       "                    Source_url  \n",
       "0    http://vimeo.com/11980200  \n",
       "1    http://vimeo.com/11963043  \n",
       "2  http://youtu.be/MCmNGmFh6wE  \n",
       "3  http://youtu.be/XMp-wzzMsls  \n",
       "4  http://youtu.be/akAtm7SnzWg  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2008-01-01 2015-10-15\n"
     ]
    }
   ],
   "source": [
    "# Find the range of date of video recorded\n",
    "print (min([date for date in video[\"Recorded\"] if date != None])\n",
    "       ,max([date for date in video[\"Recorded\"] if date != None]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Original videos websites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "provider = []\n",
    "for i in range(len(video[\"Source_url\"])):\n",
    "    if video[\"Source_url\"][i] == \"\":\n",
    "        provider.append(None)\n",
    "    elif video[\"Source_url\"][i] == None :\n",
    "        provider.append(None)\n",
    "    else:\n",
    "        provider.append(video[\"Source_url\"][i].split(\"/\")[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'blip.tv',\n",
       " 'archive.org',\n",
       " 'www.youtube.com',\n",
       " 'youtu.be',\n",
       " 'vimeo.com',\n",
       " 'youtube.com',\n",
       " None,\n",
       " 'video.google.com'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(provider)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "original = []\n",
    "for i in range(len(provider)):\n",
    "    if provider[i] in [\"youtu.be\",'www.youtube.com','youtube.com']:\n",
    "        original.append(\"YouTube\")\n",
    "    elif provider[i] == 'vimeo.com':\n",
    "        original.append(\"Vimeo\")\n",
    "    elif provider[i] == 'archive.org':\n",
    "        original.append(\"Internet Archive\")\n",
    "    elif provider[i] == 'blip.tv':\n",
    "        original.append(\"Bilp TV\")\n",
    "    elif provider[i] == 'video.google.com':\n",
    "        original.append(\"Google Video\")\n",
    "    else:\n",
    "        original.append(None)\n",
    "video[\"Original Website\"] = pd.Series(original)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Recorded</th>\n",
       "      <th>Description</th>\n",
       "      <th>Speakers</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Category</th>\n",
       "      <th>Source_url</th>\n",
       "      <th>Original Website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3587</td>\n",
       "      <td>3142</td>\n",
       "      <td>3587</td>\n",
       "      <td>3587</td>\n",
       "      <td>3587</td>\n",
       "      <td>3587</td>\n",
       "      <td>3490</td>\n",
       "      <td>2904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3447</td>\n",
       "      <td>206</td>\n",
       "      <td>1834</td>\n",
       "      <td>1851</td>\n",
       "      <td>862</td>\n",
       "      <td>72</td>\n",
       "      <td>2897</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Lightning Talks</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>EuroPython 2011</td>\n",
       "      <td></td>\n",
       "      <td>YouTube</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>18</td>\n",
       "      <td>120</td>\n",
       "      <td>1641</td>\n",
       "      <td>599</td>\n",
       "      <td>1912</td>\n",
       "      <td>183</td>\n",
       "      <td>586</td>\n",
       "      <td>2826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Title    Recorded Description Speakers  Tags  \\\n",
       "count              3587        3142        3587     3587  3587   \n",
       "unique             3447         206        1834     1851   862   \n",
       "top     Lightning Talks  2010-02-19                              \n",
       "freq                 18         120        1641      599  1912   \n",
       "\n",
       "               Category Source_url Original Website  \n",
       "count              3587       3490             2904  \n",
       "unique               72       2897                5  \n",
       "top     EuroPython 2011                     YouTube  \n",
       "freq                183        586             2826  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Recorded</th>\n",
       "      <th>Description</th>\n",
       "      <th>Speakers</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Category</th>\n",
       "      <th>Source_url</th>\n",
       "      <th>Original Website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boston Python Meetup: How to test the hard stuff</td>\n",
       "      <td>None</td>\n",
       "      <td>How to Test the Hard Stuff\\n\\nPresented by Ste...</td>\n",
       "      <td>Stephan Richter</td>\n",
       "      <td>bostonpy, session, testing, unittest</td>\n",
       "      <td>Boston Python Meetup</td>\n",
       "      <td>http://vimeo.com/11980200</td>\n",
       "      <td>Vimeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Python Meetup: Testing: Where do I start?</td>\n",
       "      <td>None</td>\n",
       "      <td>Testing, Where Do I Start?\\n\\nPresented by Mar...</td>\n",
       "      <td>Marshall Weir</td>\n",
       "      <td>bostonpy, session, testing, unittest</td>\n",
       "      <td>Boston Python Meetup</td>\n",
       "      <td>http://vimeo.com/11963043</td>\n",
       "      <td>Vimeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Consuming Tasks with Celery</td>\n",
       "      <td>2012-02-02</td>\n",
       "      <td></td>\n",
       "      <td>Joe Jasinski</td>\n",
       "      <td>RabbitMQ Celery</td>\n",
       "      <td>Chicago Djangonauts</td>\n",
       "      <td>http://youtu.be/MCmNGmFh6wE</td>\n",
       "      <td>YouTube</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Django Based Autocompl…</td>\n",
       "      <td>2015-09-03</td>\n",
       "      <td>Django does not do autocomplete out of the box...</td>\n",
       "      <td>Jonathan Bisson</td>\n",
       "      <td>autocomplete, Django</td>\n",
       "      <td>Chicago Djangonauts</td>\n",
       "      <td>http://youtu.be/XMp-wzzMsls</td>\n",
       "      <td>YouTube</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Django-nonrel - intro and state</td>\n",
       "      <td>2012-02-02</td>\n",
       "      <td></td>\n",
       "      <td>Adam Jenkins</td>\n",
       "      <td></td>\n",
       "      <td>Chicago Djangonauts</td>\n",
       "      <td>http://youtu.be/akAtm7SnzWg</td>\n",
       "      <td>YouTube</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Title    Recorded  \\\n",
       "0  Boston Python Meetup: How to test the hard stuff        None   \n",
       "1  Boston Python Meetup: Testing: Where do I start?        None   \n",
       "2                       Consuming Tasks with Celery  2012-02-02   \n",
       "3                           Django Based Autocompl…  2015-09-03   \n",
       "4                   Django-nonrel - intro and state  2012-02-02   \n",
       "\n",
       "                                         Description         Speakers  \\\n",
       "0  How to Test the Hard Stuff\\n\\nPresented by Ste...  Stephan Richter   \n",
       "1  Testing, Where Do I Start?\\n\\nPresented by Mar...    Marshall Weir   \n",
       "2                                                        Joe Jasinski   \n",
       "3  Django does not do autocomplete out of the box...  Jonathan Bisson   \n",
       "4                                                        Adam Jenkins   \n",
       "\n",
       "                                   Tags              Category  \\\n",
       "0  bostonpy, session, testing, unittest  Boston Python Meetup   \n",
       "1  bostonpy, session, testing, unittest  Boston Python Meetup   \n",
       "2                       RabbitMQ Celery   Chicago Djangonauts   \n",
       "3                  autocomplete, Django   Chicago Djangonauts   \n",
       "4                                         Chicago Djangonauts   \n",
       "\n",
       "                    Source_url Original Website  \n",
       "0    http://vimeo.com/11980200            Vimeo  \n",
       "1    http://vimeo.com/11963043            Vimeo  \n",
       "2  http://youtu.be/MCmNGmFh6wE          YouTube  \n",
       "3  http://youtu.be/XMp-wzzMsls          YouTube  \n",
       "4  http://youtu.be/akAtm7SnzWg          YouTube  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title                  Developing cutting-edge applications with PyQt\n",
       "Recorded                                                   2011-07-18\n",
       "Description         (Presented with Matteo Bertozzi)\\n\\nPython's h...\n",
       "Speakers                             Lorenzo Mancini, Matteo Bertozzi\n",
       "Tags                                deployment, distributed, pyqt, qt\n",
       "Category                                              EuroPython 2011\n",
       "Source_url                 http://www.youtube.com/watch?v=6ko-tO-3bJ0\n",
       "Original Website                                              YouTube\n",
       "Name: 500, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check a single video information\n",
    "video.iloc[500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wordcloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use **_wordcloud_** package to generate wordcloud for  **Title**, **Description** and **Tags**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import wordcloud as wc\n",
    "from scipy.misc import imread\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MASK**\n",
    "![Image of Mask](http://puu.sh/owRnY/7b464d74bd.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getwordcloud(name, stopwords):\n",
    "    more_stopwords = stopwords\n",
    "    words = []\n",
    "    for i in range(len(video[name])):\n",
    "        words += re.findall(r\"[\\w]+\", video[name][i])\n",
    "    text = \" \".join(words)\n",
    "    mask = imread(\"./image/python.png\")\n",
    "    wordcloud = wc.WordCloud(\n",
    "                      font_path='System/Library/Fonts/MarkerFelt.ttc',\n",
    "                      stopwords=wc.STOPWORDS.union(more_stopwords),\n",
    "                      background_color='white',\n",
    "                      width=1800,\n",
    "                      height=1400,\n",
    "                      mask = mask,\n",
    "                     ).generate(text)\n",
    "    plt.imshow(wordcloud)\n",
    "    plt.axis(\"off\")\n",
    "    plt.savefig('./image/wordcloud/%s_wordcloud_stopwords(%s).png' % (name,\",\".join(stopwords)), dpi=500)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wyd64RlCfz2vebfR4Rbg06bEaMta0J7KdqYrGggQVSmwmi4SIJ1zGM6WxNWvU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\n7pCPMBScGOcpGpL/I9n63od7/y3C+uAuzim9L+58oh3JZWWPsjiQOH4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1ziC2u1pmnFu/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Qy4MZeJzxXy2JvFdB5rQZJkymzKlPCQfyrTwE6XxKrfktlrMrDXZGDEyFC++Z9/5mS3pTmYxhhY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\nH6mbC84aKtR6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c0a1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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b1XI0W5f3MFbz81F/9XGhvSUX2eVlUmt1Zy4zTkkKkSMgKUiSHDMmlAzjp/zt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\nD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BZYhK9m48/e7y+ENs/Z/2+BoQmSEH9fVyvkoBJX3m+AvVlB8vVct3B71HZVVcFweVFR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\nkom8XQIiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11406d400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Twh7fVhN9mUJzhILJNa69IY99KkaeSvlT77awty72+JRCW6AQliSXohDxsLJs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\njaJRD1zatjVtTiVdXkgakhR3A0nNyEXxkhK/kJT4hafgjqL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102def98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2s9cyskZ1XvgmBVO88nAdzXFcWiiRkKp8nD6vTM8r9bzzcGXUrtYg4PPK7Nrg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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115089fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What's the hottest topic?\n",
    "getwordcloud(\"Title\",{})\n",
    "getwordcloud(\"Title\",{\"Django\",\"django\",\"Python\",\"python\"})\n",
    "getwordcloud(\"Category\", {})\n",
    "getwordcloud(\"Tags\", {})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top speakers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "py.sign_in('shuwan', \"l21vebo6ki\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "speakers = []\n",
    "for i in range(len(video[\"Speakers\"])):\n",
    "    if video[\"Speakers\"][i] == \"\":\n",
    "        continue\n",
    "    else:\n",
    "        speakers += video[\"Speakers\"][i].split(\", \")\n",
    "topspeaker = pd.DataFrame(Counter(speakers).most_common(10),columns=[\"Speaker\",\"Count\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Speaker</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brandon Rhodes</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Raymond Hettinger</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alex Gaynor</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David Beazley</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jessica McKellar</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mike Müller</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Richard Jones</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fernando Pérez</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Armin Rigo</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kenneth Reitz</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Speaker  Count\n",
       "0     Brandon Rhodes     23\n",
       "1  Raymond Hettinger     20\n",
       "2        Alex Gaynor     19\n",
       "3      David Beazley     15\n",
       "4   Jessica McKellar     14\n",
       "5        Mike Müller     13\n",
       "6      Richard Jones     13\n",
       "7     Fernando Pérez     13\n",
       "8         Armin Rigo     12\n",
       "9      Kenneth Reitz     11"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topspeaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~shuwan/89.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "    go.Bar(\n",
    "        x=topspeaker[\"Speaker\"],\n",
    "        y=topspeaker[\"Count\"]\n",
    "    )\n",
    "]\n",
    "py.iplot(data, filename='top speaker')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tags = []\n",
    "for i in range(len(video[\"Tags\"])):\n",
    "    if video[\"Tags\"][i] == \"\":\n",
    "        continue\n",
    "    else:\n",
    "        tags += video[\"Tags\"][i].split(\", \")    \n",
    "toptags = pd.DataFrame(Counter(tags).most_common(10),columns=[\"Tags\",\"Count\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tags</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pycon</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>talk</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tech</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pycon2010</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tutorial</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>djangocon</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pycon2011</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pycon2009</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>web</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>django</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Tags  Count\n",
       "0      pycon    316\n",
       "1       talk    264\n",
       "2       Tech    127\n",
       "3  pycon2010    118\n",
       "4   tutorial    108\n",
       "5  djangocon    102\n",
       "6  pycon2011     99\n",
       "7  pycon2009     97\n",
       "8        web     87\n",
       "9     django     72"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toptags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~shuwan/91.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "    go.Bar(\n",
    "        x=toptags[\"Tags\"],\n",
    "        y=toptags[\"Count\"]\n",
    "    )\n",
    "]\n",
    "py.iplot(data, filename='top tags')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "category = []\n",
    "for i in range(len(video[\"Category\"])):\n",
    "    if video[\"Category\"][i] in [\"\", None]:\n",
    "        continue\n",
    "    else:\n",
    "        category += video[\"Category\"][i].split(maxsplit = 0)    \n",
    "topcategory = pd.DataFrame(Counter(category).most_common(10),columns=[\"Category\",\"Count\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EuroPython 2011</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PyCon US 2013</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PyCon US 2012</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PyCon US 2014</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SciPy 2013</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PyCon US 2015</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SciPy 2014</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EuroPython 2014</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PyCon US 2010</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PyCon US 2009</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Category  Count\n",
       "0  EuroPython 2011    183\n",
       "1    PyCon US 2013    182\n",
       "2    PyCon US 2012    161\n",
       "3    PyCon US 2014    140\n",
       "4       SciPy 2013    139\n",
       "5    PyCon US 2015    134\n",
       "6       SciPy 2014    123\n",
       "7  EuroPython 2014    120\n",
       "8    PyCon US 2010    120\n",
       "9    PyCon US 2009    100"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topcategory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~shuwan/95.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "    go.Bar(\n",
    "        x=topcategory.Category,\n",
    "        y=topcategory.Count\n",
    "    )\n",
    "]\n",
    "py.iplot(data, filename='top category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Videos distribution over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "timetable = pd.DataFrame.from_dict(Counter(video[\"Recorded\"]), orient='index').reset_index()\n",
    "timetable.columns = [\"Time\", \"Count\"]\n",
    "timetable = timetable[timetable.Count != None]\n",
    "timetable = timetable.sort_values(\"Time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~shuwan/93.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "    go.Scatter(\n",
    "        x=timetable.Time,\n",
    "        y=timetable.Count\n",
    "    )\n",
    "]\n",
    "py.iplot(data, filename='Pyvideo distribution over time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Simple Recommendation System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 8 attributes in our video dataframe. When we do recommendations we only consider the content of the videos. Hence, attributes like *__Source_url__* and *__Original Website__* are irrelevant attributes in our recommendation system. Here *__Recorded__* is not excluded is because when other conditions are the same, we are more interested in the latest video. \n",
    "\n",
    "The rest attributes are all descriptive attributes. Some of the attributes are strongly related. For example, *__Tags__* and *__Category__* are strongly related.\n",
    "\n",
    "Since all attributes are descriptive, we need to extract the key words of each attributes. First, we need to filter some meaningless words, like *__a, you, and, for, with__* and in our case *__python__*.\n",
    "\n",
    "We will use *__stopwords list__* from *__NLTK__* package to filter meaningless words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "keyword = len(video)*[1]\n",
    "for i in range(len(video)):\n",
    "    key = []\n",
    "    for j in range(4):\n",
    "        key += re.findall(r\"[\\w]+\", video.iloc[i][[\"Title\",\"Description\",\"Tags\",\"Category\",\"Speakers\",\"Recorded\"]][j])\n",
    "    keyword[i] = list(set([word for word in key if word not in stopwords.words(\"english\")]))\n",
    "keyword = pd.Series(keyword,name=\"Keyword\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extractkey(key):\n",
    "    keylist = []\n",
    "    for i in range(len(videopath)):\n",
    "        with open(videopath[i]) as json_data:\n",
    "            keylist.append(json.load(json_data)[key])\n",
    "    return pd.Series(keylist, name = key.capitalize())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "videoRecommend = pd.concat([keyword, extractkey(\"speakers\"),extractkey(\"recorded\")],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(len(videoRecommend)):\n",
    "    for j in range(2):\n",
    "        if videoRecommend.iloc[i][j] == None:\n",
    "            videoRecommend.iloc[i][j] == []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Keyword</th>\n",
       "      <th>Speakers</th>\n",
       "      <th>Recorded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[projects, doctest, may, sort, 2010, well, Tes...</td>\n",
       "      <td>[Stephan Richter]</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[projects, He, 2010, start, unittest, move, kn...</td>\n",
       "      <td>[Marshall Weir]</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Tasks, Celery, Chicago, RabbitMQ, Consuming, ...</td>\n",
       "      <td>[Joe Jasinski]</td>\n",
       "      <td>2012-02-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Keyword           Speakers  \\\n",
       "0  [projects, doctest, may, sort, 2010, well, Tes...  [Stephan Richter]   \n",
       "1  [projects, He, 2010, start, unittest, move, kn...    [Marshall Weir]   \n",
       "2  [Tasks, Celery, Chicago, RabbitMQ, Consuming, ...     [Joe Jasinski]   \n",
       "\n",
       "     Recorded  \n",
       "0        None  \n",
       "1        None  \n",
       "2  2012-02-02  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "videoRecommend.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getRecommend(idx, number):\n",
    "    \n",
    "    keywordScore = []\n",
    "    speakerScore = []\n",
    "    totalScore = []\n",
    "    \n",
    "    candidate = videoRecommend\n",
    "    candidate = candidate.drop(idx)\n",
    "    \n",
    "    for i in candidate.index:\n",
    "        keywordScore.append(len(set(videoRecommend[\"Keyword\"][idx]) & set(candidate[\"Keyword\"][i])))\n",
    "    for i in candidate.index:\n",
    "        speakerScore.append(len(set(videoRecommend[\"Speakers\"][idx]) & set(candidate[\"Speakers\"][i])))\n",
    "    totalScore = keywordScore + speakerScore\n",
    "    top = sorted(totalScore, reverse=True)[:number]\n",
    "    \n",
    "    totalScore = pd.Series(totalScore, name = \"Total Score\")\n",
    "    Recommend = pd.concat([pd.Series(candidate.index, name = \"Index\"),totalScore,candidate[\"Recorded\"]],axis =1)\n",
    "    Recommend = Recommend.sort_values([\"Total Score\",\"Recorded\"], ascending = False)\n",
    "    return Recommend[:number]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Total Score</th>\n",
       "      <th>Recorded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>230.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2012-09-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>1235.0</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>1292.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-08-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>949.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2014-09-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>1190.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2014-08-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Index  Total Score    Recorded\n",
       "230    230.0           10  2012-09-06\n",
       "1234  1235.0           10         NaN\n",
       "1291  1292.0            9  2015-08-04\n",
       "949    949.0            9  2014-09-13\n",
       "1190  1190.0            9  2014-08-11"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getRecommend(1234, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title                               About Django from the Pyramid Guy\n",
       "Recorded                                                   2012-09-06\n",
       "Description         # About Django from the Pyramid Guy\\n\\n## Who ...\n",
       "Speakers                                              Chris McDonough\n",
       "Tags                                                           django\n",
       "Category                                               DjangoCon 2012\n",
       "Source_url                 http://www.youtube.com/watch?v=eN7h6ZbzMy0\n",
       "Original Website                                              YouTube\n",
       "Name: 230, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video.iloc[230]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improvements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*__NTLK__* package currently does not support Chinese. But from the database, I found several videos recoreded from the *__Pycon TW__*, which are using *__Traditional Chinses__*. "
   ]
  }
 ],
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