plot-tcp-connect-time-startup-delay-difference.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "import filterbyip as fbi\n",
    "import requests\n",
    "import asinfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "h3 {\n",
       "    color: white;\n",
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       "    padding: 0.5em;\n",
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       "\n",
       ".CodeMirror {\n",
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       "\n",
       "div.output_area pre {\n",
       "    font-family: 'Monaco for Powerline';\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "css = open('style-table.css').read() + open('style-notebook.css').read()\n",
    "HTML('<style>{}</style>'.format(css))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def asn_from_probe_name(probe_name):\n",
    "    base = 'http://leone-collector.eecs.jacobs-university.de/msmpoint'\n",
    "    msm_metadata = requests.get('%s/%s'%(base, probe_name))\n",
    "    asn4 = str(msm_metadata.json()['asn4'])\n",
    "    asn6 = str(msm_metadata.json()['asn6'])\n",
    "    return asn4,asn6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dailydb_location = '/data/vbajpai/datasets/youtube-data-collection-python3/youtube.db'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query = '''select   msmpoint.name as probe\n",
    "                  , dtime\n",
    "                  , video_address\n",
    "                  , web_connect_time as w_time\n",
    "                  , audio_connect_time as a_time\n",
    "                  , video_connect_time as v_time\n",
    "                  , startup_delay\n",
    "                  , prebuffering_duration as pd\n",
    "           from youtube, msmpoint\n",
    "           where youtube.unit_id = msmpoint.skunitid\n",
    "           and msmpoint.skunitid <> ''\n",
    "           and successes = 1\n",
    "           and failures = 0;\n",
    "        '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con = sqlite3.connect(dailydb_location)\n",
    "df = pd.read_sql(query, con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "probe            4422465\n",
       "dtime            4422465\n",
       "video_address    4422465\n",
       "w_time           4346299\n",
       "a_time           4422465\n",
       "v_time           4422465\n",
       "startup_delay    4346299\n",
       "pd               4422465\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probe</th>\n",
       "      <th>dtime</th>\n",
       "      <th>video_address</th>\n",
       "      <th>w_time</th>\n",
       "      <th>a_time</th>\n",
       "      <th>v_time</th>\n",
       "      <th>startup_delay</th>\n",
       "      <th>pd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>samsbox81</td>\n",
       "      <td>2016-09-05 16:29:41</td>\n",
       "      <td>193.212.4.15</td>\n",
       "      <td>6916.0</td>\n",
       "      <td>8731</td>\n",
       "      <td>7658</td>\n",
       "      <td>1179709.0</td>\n",
       "      <td>528170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samsbox81</td>\n",
       "      <td>2016-09-05 16:30:23</td>\n",
       "      <td>2001:4600:9:307::f</td>\n",
       "      <td>17844.0</td>\n",
       "      <td>11176</td>\n",
       "      <td>10076</td>\n",
       "      <td>1046380.0</td>\n",
       "      <td>542534</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>samsbox81</td>\n",
       "      <td>2016-09-05 17:26:17</td>\n",
       "      <td>193.212.4.15</td>\n",
       "      <td>7122.0</td>\n",
       "      <td>8904</td>\n",
       "      <td>7760</td>\n",
       "      <td>1050626.0</td>\n",
       "      <td>538893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>samsbox81</td>\n",
       "      <td>2016-09-05 17:26:59</td>\n",
       "      <td>2001:4600:9:307::f</td>\n",
       "      <td>17781.0</td>\n",
       "      <td>11010</td>\n",
       "      <td>9826</td>\n",
       "      <td>1033682.0</td>\n",
       "      <td>544550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>samsbox81</td>\n",
       "      <td>2016-09-05 18:25:53</td>\n",
       "      <td>193.212.4.15</td>\n",
       "      <td>8110.0</td>\n",
       "      <td>9015</td>\n",
       "      <td>7859</td>\n",
       "      <td>1087679.0</td>\n",
       "      <td>558563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       probe                dtime       video_address   w_time  a_time  \\\n",
       "0  samsbox81  2016-09-05 16:29:41        193.212.4.15   6916.0    8731   \n",
       "1  samsbox81  2016-09-05 16:30:23  2001:4600:9:307::f  17844.0   11176   \n",
       "2  samsbox81  2016-09-05 17:26:17        193.212.4.15   7122.0    8904   \n",
       "3  samsbox81  2016-09-05 17:26:59  2001:4600:9:307::f  17781.0   11010   \n",
       "4  samsbox81  2016-09-05 18:25:53        193.212.4.15   8110.0    9015   \n",
       "\n",
       "   v_time  startup_delay      pd  \n",
       "0    7658      1179709.0  528170  \n",
       "1   10076      1046380.0  542534  \n",
       "2    7760      1050626.0  538893  \n",
       "3    9826      1033682.0  544550  \n",
       "4    7859      1087679.0  558563  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def round_time_to_nearest_hour(dtime):\n",
    "    import datetime;    \n",
    "    d = datetime.datetime.strptime(dtime, \"%Y-%m-%d %H:%M:%S\")\n",
    "    d = d.replace(minute=0, second=0)\n",
    "    dtime = d.strftime('%Y-%m-%d %H:%M:%S')\n",
    "    return dtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['dtime'] = df['dtime'].apply(round_time_to_nearest_hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.sort_values('dtime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probe</th>\n",
       "      <th>dtime</th>\n",
       "      <th>video_address</th>\n",
       "      <th>w_time</th>\n",
       "      <th>a_time</th>\n",
       "      <th>v_time</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3036654</th>\n",
       "      <td>samsbox24</td>\n",
       "      <td>2014-08-20 01:00:00</td>\n",
       "      <td>173.194.137.147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17455</td>\n",
       "      <td>16764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>279739</td>\n",
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       "      <th>3036655</th>\n",
       "      <td>samsbox24</td>\n",
       "      <td>2014-08-20 04:00:00</td>\n",
       "      <td>173.194.137.147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17501</td>\n",
       "      <td>19992</td>\n",
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       "    <tr>\n",
       "      <th>3036656</th>\n",
       "      <td>samsbox24</td>\n",
       "      <td>2014-08-20 06:00:00</td>\n",
       "      <td>173.194.137.147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17105</td>\n",
       "      <td>17226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>282252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3036657</th>\n",
       "      <td>samsbox24</td>\n",
       "      <td>2014-08-20 08:00:00</td>\n",
       "      <td>173.194.137.147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17100</td>\n",
       "      <td>20053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>296452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3036658</th>\n",
       "      <td>samsbox24</td>\n",
       "      <td>2014-08-20 09:00:00</td>\n",
       "      <td>173.194.137.147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19009</td>\n",
       "      <td>19057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>289677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             probe                dtime    video_address  w_time  a_time  \\\n",
       "3036654  samsbox24  2014-08-20 01:00:00  173.194.137.147     NaN   17455   \n",
       "3036655  samsbox24  2014-08-20 04:00:00  173.194.137.147     NaN   17501   \n",
       "3036656  samsbox24  2014-08-20 06:00:00  173.194.137.147     NaN   17105   \n",
       "3036657  samsbox24  2014-08-20 08:00:00  173.194.137.147     NaN   17100   \n",
       "3036658  samsbox24  2014-08-20 09:00:00  173.194.137.147     NaN   19009   \n",
       "\n",
       "         v_time  startup_delay      pd  \n",
       "3036654   16764            NaN  279739  \n",
       "3036655   19992            NaN  301344  \n",
       "3036656   17226            NaN  282252  \n",
       "3036657   20053            NaN  296452  \n",
       "3036658   19057            NaN  289677  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probe</th>\n",
       "      <th>dtime</th>\n",
       "      <th>video_address</th>\n",
       "      <th>w_time</th>\n",
       "      <th>a_time</th>\n",
       "      <th>v_time</th>\n",
       "      <th>startup_delay</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1979966</th>\n",
       "      <td>samsbox60</td>\n",
       "      <td>2017-06-27 23:00:00</td>\n",
       "      <td>173.194.61.40</td>\n",
       "      <td>22594.0</td>\n",
       "      <td>19639</td>\n",
       "      <td>18952</td>\n",
       "      <td>1398930.0</td>\n",
       "      <td>679307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2942273</th>\n",
       "      <td>samsbox93</td>\n",
       "      <td>2017-06-27 23:00:00</td>\n",
       "      <td>2607:f8b0:400a:14::8</td>\n",
       "      <td>28582.0</td>\n",
       "      <td>19854</td>\n",
       "      <td>18706</td>\n",
       "      <td>1289122.0</td>\n",
       "      <td>604367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2409424</th>\n",
       "      <td>samsbox98</td>\n",
       "      <td>2017-06-27 23:00:00</td>\n",
       "      <td>173.194.160.152</td>\n",
       "      <td>23838.0</td>\n",
       "      <td>19514</td>\n",
       "      <td>18347</td>\n",
       "      <td>1637748.0</td>\n",
       "      <td>936900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3481891</th>\n",
       "      <td>samsbox75</td>\n",
       "      <td>2017-06-27 23:00:00</td>\n",
       "      <td>74.125.13.42</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1640</td>\n",
       "      <td>1116</td>\n",
       "      <td>1088307.0</td>\n",
       "      <td>592534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4422464</th>\n",
       "      <td>samsbox91</td>\n",
       "      <td>2017-06-27 23:00:00</td>\n",
       "      <td>62.231.78.13</td>\n",
       "      <td>19917.0</td>\n",
       "      <td>6344</td>\n",
       "      <td>5199</td>\n",
       "      <td>1169666.0</td>\n",
       "      <td>532110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             probe                dtime         video_address   w_time  \\\n",
       "1979966  samsbox60  2017-06-27 23:00:00         173.194.61.40  22594.0   \n",
       "2942273  samsbox93  2017-06-27 23:00:00  2607:f8b0:400a:14::8  28582.0   \n",
       "2409424  samsbox98  2017-06-27 23:00:00       173.194.160.152  23838.0   \n",
       "3481891  samsbox75  2017-06-27 23:00:00          74.125.13.42   1652.0   \n",
       "4422464  samsbox91  2017-06-27 23:00:00          62.231.78.13  19917.0   \n",
       "\n",
       "         a_time  v_time  startup_delay      pd  \n",
       "1979966   19639   18952      1398930.0  679307  \n",
       "2942273   19854   18706      1289122.0  604367  \n",
       "2409424   19514   18347      1637748.0  936900  \n",
       "3481891    1640    1116      1088307.0  592534  \n",
       "4422464    6344    5199      1169666.0  532110  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_probes = len(set(df['probe']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "probe            4422465\n",
       "dtime            4422465\n",
       "video_address    4422465\n",
       "w_time           4346299\n",
       "a_time           4422465\n",
       "v_time           4422465\n",
       "startup_delay    4346299\n",
       "pd               4422465\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "probe            4346299\n",
       "dtime            4346299\n",
       "video_address    4346299\n",
       "w_time           4346299\n",
       "a_time           4346299\n",
       "v_time           4346299\n",
       "startup_delay    4346299\n",
       "pd               4346299\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filter_by_ip(df, column):\n",
    "  \"\"\"reads a DataFrame, calls fbi.filter_v{4,6} to get DataFrames with rows\n",
    "     containing only v4, v6 endpoints. Returns the two DataFrame as a tuple\"\"\"\n",
    "\n",
    "  dfv6 = fbi.filter_v6(df, column)\n",
    "  dfv4 = fbi.filter_v4(df, column)\n",
    "  return dfv4, dfv6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfv4, dfv6 = filter_by_ip(df, column = 'video_address')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfv4['name'] = dfv4['probe'].apply(lambda name: \"#%02d\"%int(name[7:]))\n",
    "dfv6['name'] = dfv6['probe'].apply(lambda name: \"#%02d\"%int(name[7:]))\n",
    "del dfv4['probe']; del dfv6['probe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dtime</th>\n",
       "      <th>video_address</th>\n",
       "      <th>w_time</th>\n",
       "      <th>a_time</th>\n",
       "      <th>v_time</th>\n",
       "      <th>startup_delay</th>\n",
       "      <th>pd</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3037786</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>74.125.105.178</td>\n",
       "      <td>16265.0</td>\n",
       "      <td>17384</td>\n",
       "      <td>17224</td>\n",
       "      <td>762687.0</td>\n",
       "      <td>341348</td>\n",
       "      <td>#24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12246</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>64.15.112.79</td>\n",
       "      <td>17429.0</td>\n",
       "      <td>16168</td>\n",
       "      <td>16517</td>\n",
       "      <td>812486.0</td>\n",
       "      <td>399912</td>\n",
       "      <td>#02</td>\n",
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       "    <tr>\n",
       "      <th>2117093</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>149.3.177.111</td>\n",
       "      <td>35657.0</td>\n",
       "      <td>38650</td>\n",
       "      <td>37155</td>\n",
       "      <td>1601325.0</td>\n",
       "      <td>860880</td>\n",
       "      <td>#06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4302454</th>\n",
       "      <td>2014-12-10 13:00:00</td>\n",
       "      <td>74.125.15.82</td>\n",
       "      <td>40688.0</td>\n",
       "      <td>36044</td>\n",
       "      <td>33055</td>\n",
       "      <td>1207749.0</td>\n",
       "      <td>522773</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4302453</th>\n",
       "      <td>2014-12-10 13:00:00</td>\n",
       "      <td>83.255.235.15</td>\n",
       "      <td>19168.0</td>\n",
       "      <td>12199</td>\n",
       "      <td>12613</td>\n",
       "      <td>855760.0</td>\n",
       "      <td>408308</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       dtime   video_address   w_time  a_time  v_time  \\\n",
       "3037786  2014-12-10 12:00:00  74.125.105.178  16265.0   17384   17224   \n",
       "12246    2014-12-10 12:00:00    64.15.112.79  17429.0   16168   16517   \n",
       "2117093  2014-12-10 12:00:00   149.3.177.111  35657.0   38650   37155   \n",
       "4302454  2014-12-10 13:00:00    74.125.15.82  40688.0   36044   33055   \n",
       "4302453  2014-12-10 13:00:00   83.255.235.15  19168.0   12199   12613   \n",
       "\n",
       "         startup_delay      pd name  \n",
       "3037786       762687.0  341348  #24  \n",
       "12246         812486.0  399912  #02  \n",
       "2117093      1601325.0  860880  #06  \n",
       "4302454      1207749.0  522773  #11  \n",
       "4302453       855760.0  408308  #11  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dtime</th>\n",
       "      <th>video_address</th>\n",
       "      <th>w_time</th>\n",
       "      <th>a_time</th>\n",
       "      <th>v_time</th>\n",
       "      <th>startup_delay</th>\n",
       "      <th>pd</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2265090</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>2a00:1450:4005:6::12</td>\n",
       "      <td>29924.0</td>\n",
       "      <td>21590</td>\n",
       "      <td>21763</td>\n",
       "      <td>1311590.0</td>\n",
       "      <td>578754</td>\n",
       "      <td>#04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3987059</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>2a00:1450:400a:6::12</td>\n",
       "      <td>5203.0</td>\n",
       "      <td>53338</td>\n",
       "      <td>52224</td>\n",
       "      <td>903131.0</td>\n",
       "      <td>538043</td>\n",
       "      <td>#16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779714</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>2a00:1450:4009:29::12</td>\n",
       "      <td>57187.0</td>\n",
       "      <td>41047</td>\n",
       "      <td>41498</td>\n",
       "      <td>1287188.0</td>\n",
       "      <td>421167</td>\n",
       "      <td>#07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779713</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>2a00:1450:4003:15::12</td>\n",
       "      <td>25108.0</td>\n",
       "      <td>61707</td>\n",
       "      <td>61746</td>\n",
       "      <td>999274.0</td>\n",
       "      <td>466046</td>\n",
       "      <td>#07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3844417</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>2001:1a80:100:1c::c</td>\n",
       "      <td>15060.0</td>\n",
       "      <td>13701</td>\n",
       "      <td>13842</td>\n",
       "      <td>875990.0</td>\n",
       "      <td>414562</td>\n",
       "      <td>#03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       dtime          video_address   w_time  a_time  v_time  \\\n",
       "2265090  2014-12-10 12:00:00   2a00:1450:4005:6::12  29924.0   21590   21763   \n",
       "3987059  2014-12-10 12:00:00   2a00:1450:400a:6::12   5203.0   53338   52224   \n",
       "1779714  2014-12-10 12:00:00  2a00:1450:4009:29::12  57187.0   41047   41498   \n",
       "1779713  2014-12-10 12:00:00  2a00:1450:4003:15::12  25108.0   61707   61746   \n",
       "3844417  2014-12-10 12:00:00    2001:1a80:100:1c::c  15060.0   13701   13842   \n",
       "\n",
       "         startup_delay      pd name  \n",
       "2265090      1311590.0  578754  #04  \n",
       "3987059       903131.0  538043  #16  \n",
       "1779714      1287188.0  421167  #07  \n",
       "1779713       999274.0  466046  #07  \n",
       "3844417       875990.0  414562  #03  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.merge(dfv4, dfv6, on=[\"name\", \"dtime\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtime              6549945\n",
       "video_address_x    6549945\n",
       "w_time_x           6549945\n",
       "a_time_x           6549945\n",
       "v_time_x           6549945\n",
       "startup_delay_x    6549945\n",
       "pd_x               6549945\n",
       "name               6549945\n",
       "video_address_y    6549945\n",
       "w_time_y           6549945\n",
       "a_time_y           6549945\n",
       "v_time_y           6549945\n",
       "startup_delay_y    6549945\n",
       "pd_y               6549945\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.rename(columns={  'video_address_x'      : 'v_ep_v4'\n",
    "                   , 'w_time_x'             : 'w_v4'\n",
    "                   , 'a_time_x'             : 'a_v4'\n",
    "                   , 'v_time_x'             : 'v_v4'                   \n",
    "                   , 'startup_delay_x'      : 's_v4'             \n",
    "                   , 'pd_x'                 : 'p_v4'                   \n",
    "                   , 'video_address_y'      : 'v_ep_v6'\n",
    "                   , 'w_time_y'             : 'w_v6'\n",
    "                   , 'a_time_y'             : 'a_v6'\n",
    "                   , 'v_time_y'             : 'v_v6'                   \n",
    "                   , 'startup_delay_y'      : 's_v6'             \n",
    "                   , 'pd_y'                 : 'p_v6'                                      \n",
    "                  } ,  inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dtime</th>\n",
       "      <th>v_ep_v4</th>\n",
       "      <th>w_v4</th>\n",
       "      <th>a_v4</th>\n",
       "      <th>v_v4</th>\n",
       "      <th>s_v4</th>\n",
       "      <th>p_v4</th>\n",
       "      <th>name</th>\n",
       "      <th>v_ep_v6</th>\n",
       "      <th>w_v6</th>\n",
       "      <th>a_v6</th>\n",
       "      <th>v_v6</th>\n",
       "      <th>s_v6</th>\n",
       "      <th>p_v6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>64.15.112.79</td>\n",
       "      <td>17429.0</td>\n",
       "      <td>16168</td>\n",
       "      <td>16517</td>\n",
       "      <td>812486.0</td>\n",
       "      <td>399912</td>\n",
       "      <td>#02</td>\n",
       "      <td>2a00:1450:4005:6::12</td>\n",
       "      <td>16136.0</td>\n",
       "      <td>13244</td>\n",
       "      <td>13598</td>\n",
       "      <td>823774.0</td>\n",
       "      <td>390896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>149.3.177.111</td>\n",
       "      <td>35657.0</td>\n",
       "      <td>38650</td>\n",
       "      <td>37155</td>\n",
       "      <td>1601325.0</td>\n",
       "      <td>860880</td>\n",
       "      <td>#06</td>\n",
       "      <td>2a00:1450:4002:9::12</td>\n",
       "      <td>36757.0</td>\n",
       "      <td>35595</td>\n",
       "      <td>34203</td>\n",
       "      <td>1712994.0</td>\n",
       "      <td>742766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-12-10 13:00:00</td>\n",
       "      <td>149.3.177.111</td>\n",
       "      <td>34160.0</td>\n",
       "      <td>39748</td>\n",
       "      <td>37298</td>\n",
       "      <td>1402994.0</td>\n",
       "      <td>709893</td>\n",
       "      <td>#06</td>\n",
       "      <td>2a00:1450:4002:9::12</td>\n",
       "      <td>40201.0</td>\n",
       "      <td>36146</td>\n",
       "      <td>35134</td>\n",
       "      <td>1642471.0</td>\n",
       "      <td>798319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-12-10 13:00:00</td>\n",
       "      <td>202.224.62.79</td>\n",
       "      <td>22511.0</td>\n",
       "      <td>21983</td>\n",
       "      <td>22085</td>\n",
       "      <td>1227717.0</td>\n",
       "      <td>582028</td>\n",
       "      <td>#12</td>\n",
       "      <td>2404:6800:400a:8::12</td>\n",
       "      <td>47443.0</td>\n",
       "      <td>38578</td>\n",
       "      <td>38122</td>\n",
       "      <td>1563074.0</td>\n",
       "      <td>657032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-12-10 13:00:00</td>\n",
       "      <td>202.224.62.79</td>\n",
       "      <td>22511.0</td>\n",
       "      <td>21983</td>\n",
       "      <td>22085</td>\n",
       "      <td>1227717.0</td>\n",
       "      <td>582028</td>\n",
       "      <td>#12</td>\n",
       "      <td>2a00:1450:4009:29::12</td>\n",
       "      <td>46896.0</td>\n",
       "      <td>265018</td>\n",
       "      <td>267132</td>\n",
       "      <td>3307632.0</td>\n",
       "      <td>2440394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dtime        v_ep_v4     w_v4   a_v4   v_v4       s_v4  \\\n",
       "0  2014-12-10 12:00:00   64.15.112.79  17429.0  16168  16517   812486.0   \n",
       "1  2014-12-10 12:00:00  149.3.177.111  35657.0  38650  37155  1601325.0   \n",
       "2  2014-12-10 13:00:00  149.3.177.111  34160.0  39748  37298  1402994.0   \n",
       "3  2014-12-10 13:00:00  202.224.62.79  22511.0  21983  22085  1227717.0   \n",
       "4  2014-12-10 13:00:00  202.224.62.79  22511.0  21983  22085  1227717.0   \n",
       "\n",
       "     p_v4 name                v_ep_v6     w_v6    a_v6    v_v6       s_v6  \\\n",
       "0  399912  #02   2a00:1450:4005:6::12  16136.0   13244   13598   823774.0   \n",
       "1  860880  #06   2a00:1450:4002:9::12  36757.0   35595   34203  1712994.0   \n",
       "2  709893  #06   2a00:1450:4002:9::12  40201.0   36146   35134  1642471.0   \n",
       "3  582028  #12   2404:6800:400a:8::12  47443.0   38578   38122  1563074.0   \n",
       "4  582028  #12  2a00:1450:4009:29::12  46896.0  265018  267132  3307632.0   \n",
       "\n",
       "      p_v6  \n",
       "0   390896  \n",
       "1   742766  \n",
       "2   798319  \n",
       "3   657032  \n",
       "4  2440394  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtime      6549945\n",
       "v_ep_v4    6549945\n",
       "w_v4       6549945\n",
       "a_v4       6549945\n",
       "v_v4       6549945\n",
       "s_v4       6549945\n",
       "p_v4       6549945\n",
       "name       6549945\n",
       "v_ep_v6    6549945\n",
       "w_v6       6549945\n",
       "a_v6       6549945\n",
       "v_v6       6549945\n",
       "s_v6       6549945\n",
       "p_v6       6549945\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['v4v6_p']  = df.p_v4.map(str) + ',' + df.p_v6.map(str)\n",
    "df['v4v6_s']  = df.s_v4.map(str) + ',' + df.s_v6.map(str)\n",
    "df['v4v6_w']  = df.w_v4.map(str) + ',' + df.w_v6.map(str)\n",
    "df['v4v6_a']  = df.a_v4.map(str) + ',' + df.a_v6.map(str)\n",
    "df['v4v6_v']  = df.v_v4.map(str) + ',' + df.v_v6.map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dtime</th>\n",
       "      <th>v_ep_v4</th>\n",
       "      <th>w_v4</th>\n",
       "      <th>a_v4</th>\n",
       "      <th>v_v4</th>\n",
       "      <th>s_v4</th>\n",
       "      <th>p_v4</th>\n",
       "      <th>name</th>\n",
       "      <th>v_ep_v6</th>\n",
       "      <th>w_v6</th>\n",
       "      <th>a_v6</th>\n",
       "      <th>v_v6</th>\n",
       "      <th>s_v6</th>\n",
       "      <th>p_v6</th>\n",
       "      <th>v4v6_p</th>\n",
       "      <th>v4v6_s</th>\n",
       "      <th>v4v6_w</th>\n",
       "      <th>v4v6_a</th>\n",
       "      <th>v4v6_v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>64.15.112.79</td>\n",
       "      <td>17429.0</td>\n",
       "      <td>16168</td>\n",
       "      <td>16517</td>\n",
       "      <td>812486.0</td>\n",
       "      <td>399912</td>\n",
       "      <td>#02</td>\n",
       "      <td>2a00:1450:4005:6::12</td>\n",
       "      <td>16136.0</td>\n",
       "      <td>13244</td>\n",
       "      <td>13598</td>\n",
       "      <td>823774.0</td>\n",
       "      <td>390896</td>\n",
       "      <td>399912,390896</td>\n",
       "      <td>812486.0,823774.0</td>\n",
       "      <td>17429.0,16136.0</td>\n",
       "      <td>16168,13244</td>\n",
       "      <td>16517,13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>149.3.177.111</td>\n",
       "      <td>35657.0</td>\n",
       "      <td>38650</td>\n",
       "      <td>37155</td>\n",
       "      <td>1601325.0</td>\n",
       "      <td>860880</td>\n",
       "      <td>#06</td>\n",
       "      <td>2a00:1450:4002:9::12</td>\n",
       "      <td>36757.0</td>\n",
       "      <td>35595</td>\n",
       "      <td>34203</td>\n",
       "      <td>1712994.0</td>\n",
       "      <td>742766</td>\n",
       "      <td>860880,742766</td>\n",
       "      <td>1601325.0,1712994.0</td>\n",
       "      <td>35657.0,36757.0</td>\n",
       "      <td>38650,35595</td>\n",
       "      <td>37155,34203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-12-10 13:00:00</td>\n",
       "      <td>149.3.177.111</td>\n",
       "      <td>34160.0</td>\n",
       "      <td>39748</td>\n",
       "      <td>37298</td>\n",
       "      <td>1402994.0</td>\n",
       "      <td>709893</td>\n",
       "      <td>#06</td>\n",
       "      <td>2a00:1450:4002:9::12</td>\n",
       "      <td>40201.0</td>\n",
       "      <td>36146</td>\n",
       "      <td>35134</td>\n",
       "      <td>1642471.0</td>\n",
       "      <td>798319</td>\n",
       "      <td>709893,798319</td>\n",
       "      <td>1402994.0,1642471.0</td>\n",
       "      <td>34160.0,40201.0</td>\n",
       "      <td>39748,36146</td>\n",
       "      <td>37298,35134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-12-10 13:00:00</td>\n",
       "      <td>202.224.62.79</td>\n",
       "      <td>22511.0</td>\n",
       "      <td>21983</td>\n",
       "      <td>22085</td>\n",
       "      <td>1227717.0</td>\n",
       "      <td>582028</td>\n",
       "      <td>#12</td>\n",
       "      <td>2404:6800:400a:8::12</td>\n",
       "      <td>47443.0</td>\n",
       "      <td>38578</td>\n",
       "      <td>38122</td>\n",
       "      <td>1563074.0</td>\n",
       "      <td>657032</td>\n",
       "      <td>582028,657032</td>\n",
       "      <td>1227717.0,1563074.0</td>\n",
       "      <td>22511.0,47443.0</td>\n",
       "      <td>21983,38578</td>\n",
       "      <td>22085,38122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-12-10 13:00:00</td>\n",
       "      <td>202.224.62.79</td>\n",
       "      <td>22511.0</td>\n",
       "      <td>21983</td>\n",
       "      <td>22085</td>\n",
       "      <td>1227717.0</td>\n",
       "      <td>582028</td>\n",
       "      <td>#12</td>\n",
       "      <td>2a00:1450:4009:29::12</td>\n",
       "      <td>46896.0</td>\n",
       "      <td>265018</td>\n",
       "      <td>267132</td>\n",
       "      <td>3307632.0</td>\n",
       "      <td>2440394</td>\n",
       "      <td>582028,2440394</td>\n",
       "      <td>1227717.0,3307632.0</td>\n",
       "      <td>22511.0,46896.0</td>\n",
       "      <td>21983,265018</td>\n",
       "      <td>22085,267132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dtime        v_ep_v4     w_v4   a_v4   v_v4       s_v4  \\\n",
       "0  2014-12-10 12:00:00   64.15.112.79  17429.0  16168  16517   812486.0   \n",
       "1  2014-12-10 12:00:00  149.3.177.111  35657.0  38650  37155  1601325.0   \n",
       "2  2014-12-10 13:00:00  149.3.177.111  34160.0  39748  37298  1402994.0   \n",
       "3  2014-12-10 13:00:00  202.224.62.79  22511.0  21983  22085  1227717.0   \n",
       "4  2014-12-10 13:00:00  202.224.62.79  22511.0  21983  22085  1227717.0   \n",
       "\n",
       "     p_v4 name                v_ep_v6     w_v6    a_v6    v_v6       s_v6  \\\n",
       "0  399912  #02   2a00:1450:4005:6::12  16136.0   13244   13598   823774.0   \n",
       "1  860880  #06   2a00:1450:4002:9::12  36757.0   35595   34203  1712994.0   \n",
       "2  709893  #06   2a00:1450:4002:9::12  40201.0   36146   35134  1642471.0   \n",
       "3  582028  #12   2404:6800:400a:8::12  47443.0   38578   38122  1563074.0   \n",
       "4  582028  #12  2a00:1450:4009:29::12  46896.0  265018  267132  3307632.0   \n",
       "\n",
       "      p_v6          v4v6_p               v4v6_s           v4v6_w  \\\n",
       "0   390896   399912,390896    812486.0,823774.0  17429.0,16136.0   \n",
       "1   742766   860880,742766  1601325.0,1712994.0  35657.0,36757.0   \n",
       "2   798319   709893,798319  1402994.0,1642471.0  34160.0,40201.0   \n",
       "3   657032   582028,657032  1227717.0,1563074.0  22511.0,47443.0   \n",
       "4  2440394  582028,2440394  1227717.0,3307632.0  22511.0,46896.0   \n",
       "\n",
       "         v4v6_a        v4v6_v  \n",
       "0   16168,13244   16517,13598  \n",
       "1   38650,35595   37155,34203  \n",
       "2   39748,36146   37298,35134  \n",
       "3   21983,38578   22085,38122  \n",
       "4  21983,265018  22085,267132  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Absolute Difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_difference(v4v6_delay, divider):\n",
    "    \n",
    "    v4, v6 = v4v6_delay.split(',')\n",
    "    try: v4 = float(v4); v6 = float(v6)\n",
    "    except Exception as e: print(e, v4v6_delay)\n",
    "    \n",
    "    difference = (v4 - v6)\n",
    "    difference = difference / divider              \n",
    "    return float('%0.2f'%difference)\n",
    "\n",
    "def get_s_delay_difference(v4v6_s_delay):\n",
    "    return get_difference(v4v6_s_delay, 1000000) # to seconds\n",
    "\n",
    "def get_t_delay_difference(v4v6_t_delay):\n",
    "    return get_difference(v4v6_t_delay, 1000) # to milliseconds\n",
    "\n",
    "df['p_a'] = df['v4v6_p'].apply(get_s_delay_difference)\n",
    "df['s_a'] = df['v4v6_s'].apply(get_s_delay_difference)\n",
    "\n",
    "df['w_a'] = df['v4v6_w'].apply(get_t_delay_difference)\n",
    "df['a_a'] = df['v4v6_a'].apply(get_t_delay_difference)\n",
    "df['v_a'] = df['v4v6_v'].apply(get_t_delay_difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Relative Difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_slowness(v4v6_s_delay):\n",
    "    \n",
    "    v4, v6 = v4v6_s_delay.split(',')\n",
    "    try: v4 = float(v4); v6 = float(v6)\n",
    "    except Exception as e: print(e, v4v6_s_delay)\n",
    "    \n",
    "    try: slowness = (v4 - v6) / v4\n",
    "    except Exception as e: return 0\n",
    "    return float('%0.2f'%slowness)\n",
    "\n",
    "df['p_r'] = df['v4v6_p'].apply(get_slowness)\n",
    "df['s_r'] = df['v4v6_s'].apply(get_slowness)\n",
    "df['w_r'] = df['v4v6_w'].apply(get_slowness)\n",
    "df['a_r'] = df['v4v6_a'].apply(get_slowness)\n",
    "df['v_r'] = df['v4v6_v'].apply(get_slowness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df['v4v6_p']\n",
    "del df['v4v6_s']\n",
    "del df['v4v6_w']\n",
    "del df['v4v6_a']\n",
    "del df['v4v6_v']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtime      6549945\n",
       "v_ep_v4    6549945\n",
       "w_v4       6549945\n",
       "a_v4       6549945\n",
       "v_v4       6549945\n",
       "s_v4       6549945\n",
       "p_v4       6549945\n",
       "name       6549945\n",
       "v_ep_v6    6549945\n",
       "w_v6       6549945\n",
       "a_v6       6549945\n",
       "v_v6       6549945\n",
       "s_v6       6549945\n",
       "p_v6       6549945\n",
       "p_a        6549945\n",
       "s_a        6549945\n",
       "w_a        6549945\n",
       "a_a        6549945\n",
       "v_a        6549945\n",
       "p_r        6549945\n",
       "s_r        6549945\n",
       "w_r        6549945\n",
       "a_r        6549945\n",
       "v_r        6549945\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dtime</th>\n",
       "      <th>v_ep_v4</th>\n",
       "      <th>w_v4</th>\n",
       "      <th>a_v4</th>\n",
       "      <th>v_v4</th>\n",
       "      <th>s_v4</th>\n",
       "      <th>p_v4</th>\n",
       "      <th>name</th>\n",
       "      <th>v_ep_v6</th>\n",
       "      <th>w_v6</th>\n",
       "      <th>...</th>\n",
       "      <th>p_a</th>\n",
       "      <th>s_a</th>\n",
       "      <th>w_a</th>\n",
       "      <th>a_a</th>\n",
       "      <th>v_a</th>\n",
       "      <th>p_r</th>\n",
       "      <th>s_r</th>\n",
       "      <th>w_r</th>\n",
       "      <th>a_r</th>\n",
       "      <th>v_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>64.15.112.79</td>\n",
       "      <td>17429.0</td>\n",
       "      <td>16168</td>\n",
       "      <td>16517</td>\n",
       "      <td>812486.0</td>\n",
       "      <td>399912</td>\n",
       "      <td>#02</td>\n",
       "      <td>2a00:1450:4005:6::12</td>\n",
       "      <td>16136.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.29</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>149.3.177.111</td>\n",
       "      <td>35657.0</td>\n",
       "      <td>38650</td>\n",
       "      <td>37155</td>\n",
       "      <td>1601325.0</td>\n",
       "      <td>860880</td>\n",
       "      <td>#06</td>\n",
       "      <td>2a00:1450:4002:9::12</td>\n",
       "      <td>36757.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>3.06</td>\n",
       "      <td>2.95</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-12-10 13:00:00</td>\n",
       "      <td>149.3.177.111</td>\n",
       "      <td>34160.0</td>\n",
       "      <td>39748</td>\n",
       "      <td>37298</td>\n",
       "      <td>1402994.0</td>\n",
       "      <td>709893</td>\n",
       "      <td>#06</td>\n",
       "      <td>2a00:1450:4002:9::12</td>\n",
       "      <td>40201.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-6.04</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.16</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-12-10 13:00:00</td>\n",
       "      <td>202.224.62.79</td>\n",
       "      <td>22511.0</td>\n",
       "      <td>21983</td>\n",
       "      <td>22085</td>\n",
       "      <td>1227717.0</td>\n",
       "      <td>582028</td>\n",
       "      <td>#12</td>\n",
       "      <td>2404:6800:400a:8::12</td>\n",
       "      <td>47443.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-24.93</td>\n",
       "      <td>-16.59</td>\n",
       "      <td>-16.04</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-12-10 13:00:00</td>\n",
       "      <td>202.224.62.79</td>\n",
       "      <td>22511.0</td>\n",
       "      <td>21983</td>\n",
       "      <td>22085</td>\n",
       "      <td>1227717.0</td>\n",
       "      <td>582028</td>\n",
       "      <td>#12</td>\n",
       "      <td>2a00:1450:4009:29::12</td>\n",
       "      <td>46896.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-24.39</td>\n",
       "      <td>-243.03</td>\n",
       "      <td>-245.05</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-11.06</td>\n",
       "      <td>-11.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dtime        v_ep_v4     w_v4   a_v4   v_v4       s_v4  \\\n",
       "0  2014-12-10 12:00:00   64.15.112.79  17429.0  16168  16517   812486.0   \n",
       "1  2014-12-10 12:00:00  149.3.177.111  35657.0  38650  37155  1601325.0   \n",
       "2  2014-12-10 13:00:00  149.3.177.111  34160.0  39748  37298  1402994.0   \n",
       "3  2014-12-10 13:00:00  202.224.62.79  22511.0  21983  22085  1227717.0   \n",
       "4  2014-12-10 13:00:00  202.224.62.79  22511.0  21983  22085  1227717.0   \n",
       "\n",
       "     p_v4 name                v_ep_v6     w_v6  ...     p_a   s_a    w_a  \\\n",
       "0  399912  #02   2a00:1450:4005:6::12  16136.0  ...    0.01 -0.01   1.29   \n",
       "1  860880  #06   2a00:1450:4002:9::12  36757.0  ...    0.12 -0.11  -1.10   \n",
       "2  709893  #06   2a00:1450:4002:9::12  40201.0  ...   -0.09 -0.24  -6.04   \n",
       "3  582028  #12   2404:6800:400a:8::12  47443.0  ...   -0.08 -0.34 -24.93   \n",
       "4  582028  #12  2a00:1450:4009:29::12  46896.0  ...   -1.86 -2.08 -24.39   \n",
       "\n",
       "      a_a     v_a   p_r   s_r   w_r    a_r    v_r  \n",
       "0    2.92    2.92  0.02 -0.01  0.07   0.18   0.18  \n",
       "1    3.06    2.95  0.14 -0.07 -0.03   0.08   0.08  \n",
       "2    3.60    2.16 -0.12 -0.17 -0.18   0.09   0.06  \n",
       "3  -16.59  -16.04 -0.13 -0.27 -1.11  -0.75  -0.73  \n",
       "4 -243.03 -245.05 -3.19 -1.69 -1.08 -11.06 -11.10  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import Cdf\n",
    "import Pmf\n",
    "\n",
    "sample = df['p_a'].values\n",
    "pd_c     = Cdf.MakeCdfFromList(sample)\n",
    "pd_cc    = [1 - p for x, p in pd_c.Items()]\n",
    "\n",
    "sample = df['s_a'].values\n",
    "s_c      = Cdf.MakeCdfFromList(sample)\n",
    "s_cc     = [1 - p for x, p in s_c.Items()]\n",
    "\n",
    "sample = df['w_a'].values\n",
    "w_c      = Cdf.MakeCdfFromList(sample)\n",
    "w_cc     = [1 - p for x, p in w_c.Items()]\n",
    "\n",
    "sample = df['a_a'].values\n",
    "a_c      = Cdf.MakeCdfFromList(sample)\n",
    "a_cc     = [1 - p for x, p in a_c.Items()]\n",
    "\n",
    "sample = df['v_a'].values\n",
    "v_c      = Cdf.MakeCdfFromList(sample)\n",
    "v_cc     = [1 - p for x, p in v_c.Items()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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55ptvWLp0aX79kydPAkbAR44cyeOPP+6q3549ezJz5ky/fucBAwbk+9ozMzMZOnQod911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\nEhgr4haLxZLAWBG3WCyWBOb/AXvCOj9/NwfDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f96e4b9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (5,1.5))\n",
    "plt.rcParams['font.size'] = 12\n",
    "ax = plt.gca()\n",
    "yticks = np.arange(0,1.1,0.2)\n",
    "\n",
    "plt.plot(w_c.xs, w_c.ps, marker='s', linewidth=0.3, markersize=5, fillstyle='none', color = 'Cyan')\n",
    "plt.plot(a_c.xs, a_c.ps, marker='o', linewidth=0.3, markersize=5, fillstyle='none', color = 'Green')\n",
    "plt.plot(v_c.xs, v_c.ps, marker='x', linewidth=0.3, markersize=5, fillstyle='none', color = 'Coral')\n",
    "\n",
    "ax = fig.axes[0]\n",
    "ax.grid(False)\n",
    "ax.set_yticks(yticks)\n",
    "ax.set_xlim([-100,50])\n",
    "ax.set_ylim([0,1.05])\n",
    "ax.set_xscale('linear')\n",
    "ax.set_xlabel('∆t (ms)', fontsize=12)\n",
    "ax.set_ylabel('CDF', fontsize=12)\n",
    "\n",
    "plt.legend([\"Web (6.5M)\", \"Audio (6.5M)\", \"Video (6.5M)\"], fontsize=12, loc='upper left')\n",
    "\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.spines['bottom'].set_position(('axes', -0.05))\n",
    "ax.spines['left'].set_position(('axes', -0.05))\n",
    "\n",
    "ax3 = ax.twinx()\n",
    "ax3.spines['right'].set_color('none')\n",
    "ax3.spines['top'].set_color('none')\n",
    "ax3.spines['left'].set_color('none')\n",
    "ax3.spines['bottom'].set_color('none')\n",
    "ax3.yaxis.set_ticks_position('none')\n",
    "ax3.set_ylabel('[\\'14 - \\'17]')\n",
    "plt.setp(ax3.get_yticklabels(), visible=False)\n",
    "\n",
    "plt.title('TCP connect times', fontsize=12, y=1.05)\n",
    "plt.savefig('yt-syn-time-absolute-difference.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CDF of absolute difference of TCP connect times between IPv4 and IPv6 over the entire duration. 40% of the audio and video streams are faster over IPv6 with 8% of them being atleast 10 ms faster and 2% being atleast 100 ms faster. On the contrary, 35% of the audio and video streams are atleast 1 ms slower over IPv6 with 15% of them being atleast 10 ms slower. Worse, 4% of the samples are atleast 50 ms slower and 3% being atleast 100 ms slower over IPv6. The web connect times favour IPv6 even less where only 27% of the samples are faster over IPv6 with 4% being atleast 10ms faster and 1% being atleast 100 ms faster. Half of the web connections are atleast 1 ms slower over IPv6 with 25% of them being atleast 10 ms slower.  Worse, 7% of the samples are atleast 50 ms slower and 3% being atleast 100 ms slower over IPv6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('syn-times-video.csv', 'w') as f: \n",
    "    f.write('values; percentile\\n')\n",
    "    for x, y in zip(v_c.xs, v_c.ps): f.write('%.2f; %.2f\\n'%(x,y))\n",
    "        \n",
    "with open('syn-times-audio.csv', 'w') as f: \n",
    "    f.write('values; percentile\\n')\n",
    "    for x, y in zip(a_c.xs, a_c.ps): f.write('%.2f; %.2f\\n'%(x,y))        \n",
    "        \n",
    "with open('syn-times-web.csv', 'w') as f: \n",
    "    f.write('values; percentile\\n')\n",
    "    for x, y in zip(w_c.xs, w_c.ps): f.write('%.2f; %.2f\\n'%(x,y))                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GulvoUoqWMFQyw8zm5bS7AbhC0s3AnwizWn5iZv4MvHP17Lgu3/t4eM2UTOJm9uvunsTM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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9fc9e9fbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (5,1.5))\n",
    "plt.rcParams['font.size'] = 12\n",
    "ax = plt.gca()\n",
    "yticks = np.arange(0,1.1,0.2)\n",
    "\n",
    "plt.plot(s_c.xs, s_c.ps, marker='s', linewidth=0.3, markersize=5, fillstyle='none', color = 'Magenta')\n",
    "\n",
    "ax = fig.axes[0]\n",
    "ax.grid(False)\n",
    "ax.set_yticks(yticks)\n",
    "ax.set_xlim([-5,1.5])\n",
    "ax.set_ylim([0,1.05])\n",
    "ax.set_xscale('linear')\n",
    "ax.set_xlabel('∆s (s)', fontsize=12)\n",
    "ax.set_ylabel('CDF', fontsize=12)\n",
    "\n",
    "plt.legend([\"6.5M\"], fontsize=12, loc='upper left')\n",
    "\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.spines['bottom'].set_position(('axes', -0.05))\n",
    "ax.spines['left'].set_position(('axes', -0.05))\n",
    "\n",
    "ax3 = ax.twinx()\n",
    "ax3.spines['right'].set_color('none')\n",
    "ax3.spines['top'].set_color('none')\n",
    "ax3.spines['left'].set_color('none')\n",
    "ax3.spines['bottom'].set_color('none')\n",
    "ax3.yaxis.set_ticks_position('none')\n",
    "ax3.set_ylabel('[\\'14 - \\'17]')\n",
    "plt.setp(ax3.get_yticklabels(), visible=False)\n",
    "\n",
    "plt.title('Startup Delay', fontsize=12, y=1.05)\n",
    "plt.savefig('yt-startup-delay-absolute-difference.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CDF of absolute difference of startup delay between IPv4 and IPv6 over the entire duration. 20% of the samples are faster over IPv6 with 10% of them being atleast 100 ms faster and 2% being atleast 1s faster. On the contrary, half of the samples are atleast 100 ms slower over IPv6 with 25% of them being atleast 300 ms slower. Worse 8% of the samples are atleast 1s slower and 4% being atleast 2s slower over IPv6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('startup-delay.csv', 'w') as f: \n",
    "    f.write('values; percentile\\n')\n",
    "    for x, y in zip(s_c.xs, s_c.ps): f.write('%.2f; %.2f\\n'%(x,y))"
   ]
  }
 ],
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