plot-tcp-connect-time-startup-delay-prebuffering-duration-difference-timeseries.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "import filterbyip as fbi\n",
    "import clusterbycolumn as cbc\n",
    "import requests\n",
    "import asinfo\n",
    "\n",
    "import matplotlib.dates as dates\n",
    "from datetime import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "execution_count": 3,
     "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": 4,
   "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": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dailydb_location = '/data/vbajpai/datasets/youtube-data-collection-python3/youtube.db'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con = sqlite3.connect(dailydb_location)\n",
    "df = pd.read_sql(query, con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "  </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",
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       "      <td>2016-09-05 16:30:23</td>\n",
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       "    <tr>\n",
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       "      <td>samsbox81</td>\n",
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       "      <td>samsbox81</td>\n",
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       "      <td>2001:4600:9:307::f</td>\n",
       "      <td>17781.0</td>\n",
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       "      <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",
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      ],
      "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['dtime'] = df['dtime'].apply(round_time_to_nearest_hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>samsbox81</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samsbox81</td>\n",
       "      <td>2016-09-05 16:00:00</td>\n",
       "      <td>2001:4600:9:307::f</td>\n",
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       "      <td>542534</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>samsbox81</td>\n",
       "      <td>2016-09-05 17:00:00</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 17:00:00</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 18:00:00</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 16:00:00        193.212.4.15   6916.0    8731   \n",
       "1  samsbox81  2016-09-05 16:00:00  2001:4600:9:307::f  17844.0   11176   \n",
       "2  samsbox81  2016-09-05 17:00:00        193.212.4.15   7122.0    8904   \n",
       "3  samsbox81  2016-09-05 17:00:00  2001:4600:9:307::f  17781.0   11010   \n",
       "4  samsbox81  2016-09-05 18:00:00        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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_probes = len(set(df['probe']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfv4, dfv6 = filter_by_ip(df, column = 'video_address')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <th>0</th>\n",
       "      <td>2016-09-05 16:00:00</td>\n",
       "      <td>193.212.4.15</td>\n",
       "      <td>6916.0</td>\n",
       "      <td>8731</td>\n",
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       "      <td>1179709.0</td>\n",
       "      <td>528170</td>\n",
       "      <td>#81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-09-05 17:00:00</td>\n",
       "      <td>193.212.4.15</td>\n",
       "      <td>7122.0</td>\n",
       "      <td>8904</td>\n",
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       "      <td>1050626.0</td>\n",
       "      <td>538893</td>\n",
       "      <td>#81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-09-05 18:00:00</td>\n",
       "      <td>193.212.4.15</td>\n",
       "      <td>8110.0</td>\n",
       "      <td>9015</td>\n",
       "      <td>7859</td>\n",
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       "      <td>#81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-09-05 19:00:00</td>\n",
       "      <td>193.212.4.15</td>\n",
       "      <td>9605.0</td>\n",
       "      <td>9016</td>\n",
       "      <td>7965</td>\n",
       "      <td>1004614.0</td>\n",
       "      <td>555959</td>\n",
       "      <td>#81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-09-05 20:00:00</td>\n",
       "      <td>193.212.4.15</td>\n",
       "      <td>8338.0</td>\n",
       "      <td>8737</td>\n",
       "      <td>7546</td>\n",
       "      <td>1034223.0</td>\n",
       "      <td>506325</td>\n",
       "      <td>#81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dtime video_address  w_time  a_time  v_time  startup_delay  \\\n",
       "0  2016-09-05 16:00:00  193.212.4.15  6916.0    8731    7658      1179709.0   \n",
       "2  2016-09-05 17:00:00  193.212.4.15  7122.0    8904    7760      1050626.0   \n",
       "4  2016-09-05 18:00:00  193.212.4.15  8110.0    9015    7859      1087679.0   \n",
       "6  2016-09-05 19:00:00  193.212.4.15  9605.0    9016    7965      1004614.0   \n",
       "8  2016-09-05 20:00:00  193.212.4.15  8338.0    8737    7546      1034223.0   \n",
       "\n",
       "       pd name  \n",
       "0  528170  #81  \n",
       "2  538893  #81  \n",
       "4  558563  #81  \n",
       "6  555959  #81  \n",
       "8  506325  #81  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-09-05 16:00:00</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",
       "      <td>#81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-09-05 17:00:00</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",
       "      <td>#81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-09-05 18:00:00</td>\n",
       "      <td>2001:4600:9:307::f</td>\n",
       "      <td>18504.0</td>\n",
       "      <td>11124</td>\n",
       "      <td>9995</td>\n",
       "      <td>1063464.0</td>\n",
       "      <td>547832</td>\n",
       "      <td>#81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-09-05 19:00:00</td>\n",
       "      <td>2001:4600:9:307::f</td>\n",
       "      <td>17403.0</td>\n",
       "      <td>10914</td>\n",
       "      <td>9753</td>\n",
       "      <td>1057225.0</td>\n",
       "      <td>549447</td>\n",
       "      <td>#81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-09-05 20:00:00</td>\n",
       "      <td>2001:4600:9:307::f</td>\n",
       "      <td>17382.0</td>\n",
       "      <td>11195</td>\n",
       "      <td>9736</td>\n",
       "      <td>918502.0</td>\n",
       "      <td>443459</td>\n",
       "      <td>#81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dtime       video_address   w_time  a_time  v_time  \\\n",
       "1  2016-09-05 16:00:00  2001:4600:9:307::f  17844.0   11176   10076   \n",
       "3  2016-09-05 17:00:00  2001:4600:9:307::f  17781.0   11010    9826   \n",
       "5  2016-09-05 18:00:00  2001:4600:9:307::f  18504.0   11124    9995   \n",
       "7  2016-09-05 19:00:00  2001:4600:9:307::f  17403.0   10914    9753   \n",
       "9  2016-09-05 20:00:00  2001:4600:9:307::f  17382.0   11195    9736   \n",
       "\n",
       "   startup_delay      pd name  \n",
       "1      1046380.0  542534  #81  \n",
       "3      1033682.0  544550  #81  \n",
       "5      1063464.0  547832  #81  \n",
       "7      1057225.0  549447  #81  \n",
       "9       918502.0  443459  #81  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.merge(dfv4, dfv6, on=[\"name\", \"dtime\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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"
      ]
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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'                 : 'pd_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'                 : 'pd_v6'                                              \n",
    "                  } ,  inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6916.0</td>\n",
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       "      <td>1179709.0</td>\n",
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       "      <td>1046380.0</td>\n",
       "      <td>542534</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-09-05 17:00:00</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",
       "      <td>#81</td>\n",
       "      <td>2001:4600:9:307::f</td>\n",
       "      <td>17781.0</td>\n",
       "      <td>11010</td>\n",
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       "      <td>1033682.0</td>\n",
       "      <td>544550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-09-05 18:00:00</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",
       "      <td>#81</td>\n",
       "      <td>2001:4600:9:307::f</td>\n",
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       "      <td>9995</td>\n",
       "      <td>1063464.0</td>\n",
       "      <td>547832</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-09-05 19:00:00</td>\n",
       "      <td>193.212.4.15</td>\n",
       "      <td>9605.0</td>\n",
       "      <td>9016</td>\n",
       "      <td>7965</td>\n",
       "      <td>1004614.0</td>\n",
       "      <td>555959</td>\n",
       "      <td>#81</td>\n",
       "      <td>2001:4600:9:307::f</td>\n",
       "      <td>17403.0</td>\n",
       "      <td>10914</td>\n",
       "      <td>9753</td>\n",
       "      <td>1057225.0</td>\n",
       "      <td>549447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-09-05 20:00:00</td>\n",
       "      <td>193.212.4.15</td>\n",
       "      <td>8338.0</td>\n",
       "      <td>8737</td>\n",
       "      <td>7546</td>\n",
       "      <td>1034223.0</td>\n",
       "      <td>506325</td>\n",
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       "      <td>17382.0</td>\n",
       "      <td>11195</td>\n",
       "      <td>9736</td>\n",
       "      <td>918502.0</td>\n",
       "      <td>443459</td>\n",
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      ],
      "text/plain": [
       "                 dtime       v_ep_v4    w_v4  a_v4  v_v4       s_v4   pd_v4  \\\n",
       "0  2016-09-05 16:00:00  193.212.4.15  6916.0  8731  7658  1179709.0  528170   \n",
       "1  2016-09-05 17:00:00  193.212.4.15  7122.0  8904  7760  1050626.0  538893   \n",
       "2  2016-09-05 18:00:00  193.212.4.15  8110.0  9015  7859  1087679.0  558563   \n",
       "3  2016-09-05 19:00:00  193.212.4.15  9605.0  9016  7965  1004614.0  555959   \n",
       "4  2016-09-05 20:00:00  193.212.4.15  8338.0  8737  7546  1034223.0  506325   \n",
       "\n",
       "  name             v_ep_v6     w_v6   a_v6   v_v6       s_v6   pd_v6  \n",
       "0  #81  2001:4600:9:307::f  17844.0  11176  10076  1046380.0  542534  \n",
       "1  #81  2001:4600:9:307::f  17781.0  11010   9826  1033682.0  544550  \n",
       "2  #81  2001:4600:9:307::f  18504.0  11124   9995  1063464.0  547832  \n",
       "3  #81  2001:4600:9:307::f  17403.0  10914   9753  1057225.0  549447  \n",
       "4  #81  2001:4600:9:307::f  17382.0  11195   9736   918502.0  443459  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
       "pd_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",
       "pd_v6      6549945\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['v4v6_p']  = df.pd_v4.map(str) + ',' + df.pd_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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <td>17781.0</td>\n",
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       "      <td>193.212.4.15</td>\n",
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       "      <td>11124</td>\n",
       "      <td>9995</td>\n",
       "      <td>1063464.0</td>\n",
       "      <td>547832</td>\n",
       "      <td>558563,547832</td>\n",
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       "      <td>7859,9995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-09-05 19:00:00</td>\n",
       "      <td>193.212.4.15</td>\n",
       "      <td>9605.0</td>\n",
       "      <td>9016</td>\n",
       "      <td>7965</td>\n",
       "      <td>1004614.0</td>\n",
       "      <td>555959</td>\n",
       "      <td>#81</td>\n",
       "      <td>2001:4600:9:307::f</td>\n",
       "      <td>17403.0</td>\n",
       "      <td>10914</td>\n",
       "      <td>9753</td>\n",
       "      <td>1057225.0</td>\n",
       "      <td>549447</td>\n",
       "      <td>555959,549447</td>\n",
       "      <td>1004614.0,1057225.0</td>\n",
       "      <td>9605.0,17403.0</td>\n",
       "      <td>9016,10914</td>\n",
       "      <td>7965,9753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-09-05 20:00:00</td>\n",
       "      <td>193.212.4.15</td>\n",
       "      <td>8338.0</td>\n",
       "      <td>8737</td>\n",
       "      <td>7546</td>\n",
       "      <td>1034223.0</td>\n",
       "      <td>506325</td>\n",
       "      <td>#81</td>\n",
       "      <td>2001:4600:9:307::f</td>\n",
       "      <td>17382.0</td>\n",
       "      <td>11195</td>\n",
       "      <td>9736</td>\n",
       "      <td>918502.0</td>\n",
       "      <td>443459</td>\n",
       "      <td>506325,443459</td>\n",
       "      <td>1034223.0,918502.0</td>\n",
       "      <td>8338.0,17382.0</td>\n",
       "      <td>8737,11195</td>\n",
       "      <td>7546,9736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dtime       v_ep_v4    w_v4  a_v4  v_v4       s_v4   pd_v4  \\\n",
       "0  2016-09-05 16:00:00  193.212.4.15  6916.0  8731  7658  1179709.0  528170   \n",
       "1  2016-09-05 17:00:00  193.212.4.15  7122.0  8904  7760  1050626.0  538893   \n",
       "2  2016-09-05 18:00:00  193.212.4.15  8110.0  9015  7859  1087679.0  558563   \n",
       "3  2016-09-05 19:00:00  193.212.4.15  9605.0  9016  7965  1004614.0  555959   \n",
       "4  2016-09-05 20:00:00  193.212.4.15  8338.0  8737  7546  1034223.0  506325   \n",
       "\n",
       "  name             v_ep_v6     w_v6   a_v6   v_v6       s_v6   pd_v6  \\\n",
       "0  #81  2001:4600:9:307::f  17844.0  11176  10076  1046380.0  542534   \n",
       "1  #81  2001:4600:9:307::f  17781.0  11010   9826  1033682.0  544550   \n",
       "2  #81  2001:4600:9:307::f  18504.0  11124   9995  1063464.0  547832   \n",
       "3  #81  2001:4600:9:307::f  17403.0  10914   9753  1057225.0  549447   \n",
       "4  #81  2001:4600:9:307::f  17382.0  11195   9736   918502.0  443459   \n",
       "\n",
       "          v4v6_p               v4v6_s          v4v6_w      v4v6_a      v4v6_v  \n",
       "0  528170,542534  1179709.0,1046380.0  6916.0,17844.0  8731,11176  7658,10076  \n",
       "1  538893,544550  1050626.0,1033682.0  7122.0,17781.0  8904,11010   7760,9826  \n",
       "2  558563,547832  1087679.0,1063464.0  8110.0,18504.0  9015,11124   7859,9995  \n",
       "3  555959,549447  1004614.0,1057225.0  9605.0,17403.0  9016,10914   7965,9753  \n",
       "4  506325,443459   1034223.0,918502.0  8338.0,17382.0  8737,11195   7546,9736  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Absolute Difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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, 1000) # to milliseconds\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": 30,
   "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": 31,
   "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": 32,
   "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",
       "pd_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",
       "pd_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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "del df['w_v4']; del df['a_v4']; del df['v_v4']; \n",
    "del df['w_v6']; del df['a_v6']; del df['v_v6']; \n",
    "del df['s_v4'];    del df['s_v6']\n",
    "del df['pd_v4'];   del df['pd_v6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df['v_ep_v4']; del df['v_ep_v6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>w_v4</th>\n",
       "      <th>a_v4</th>\n",
       "      <th>v_v4</th>\n",
       "      <th>s_v4</th>\n",
       "      <th>pd_v4</th>\n",
       "      <th>name</th>\n",
       "      <th>w_v6</th>\n",
       "      <th>a_v6</th>\n",
       "      <th>v_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>2016-09-05 16:00:00</td>\n",
       "      <td>6916.0</td>\n",
       "      <td>8731</td>\n",
       "      <td>7658</td>\n",
       "      <td>1179709.0</td>\n",
       "      <td>528170</td>\n",
       "      <td>#81</td>\n",
       "      <td>17844.0</td>\n",
       "      <td>11176</td>\n",
       "      <td>10076</td>\n",
       "      <td>...</td>\n",
       "      <td>-14.36</td>\n",
       "      <td>133.33</td>\n",
       "      <td>-10.93</td>\n",
       "      <td>-2.44</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-09-05 17:00:00</td>\n",
       "      <td>7122.0</td>\n",
       "      <td>8904</td>\n",
       "      <td>7760</td>\n",
       "      <td>1050626.0</td>\n",
       "      <td>538893</td>\n",
       "      <td>#81</td>\n",
       "      <td>17781.0</td>\n",
       "      <td>11010</td>\n",
       "      <td>9826</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>16.94</td>\n",
       "      <td>-10.66</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-09-05 18:00:00</td>\n",
       "      <td>8110.0</td>\n",
       "      <td>9015</td>\n",
       "      <td>7859</td>\n",
       "      <td>1087679.0</td>\n",
       "      <td>558563</td>\n",
       "      <td>#81</td>\n",
       "      <td>18504.0</td>\n",
       "      <td>11124</td>\n",
       "      <td>9995</td>\n",
       "      <td>...</td>\n",
       "      <td>10.73</td>\n",
       "      <td>24.21</td>\n",
       "      <td>-10.39</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-09-05 19:00:00</td>\n",
       "      <td>9605.0</td>\n",
       "      <td>9016</td>\n",
       "      <td>7965</td>\n",
       "      <td>1004614.0</td>\n",
       "      <td>555959</td>\n",
       "      <td>#81</td>\n",
       "      <td>17403.0</td>\n",
       "      <td>10914</td>\n",
       "      <td>9753</td>\n",
       "      <td>...</td>\n",
       "      <td>6.51</td>\n",
       "      <td>-52.61</td>\n",
       "      <td>-7.80</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-09-05 20:00:00</td>\n",
       "      <td>8338.0</td>\n",
       "      <td>8737</td>\n",
       "      <td>7546</td>\n",
       "      <td>1034223.0</td>\n",
       "      <td>506325</td>\n",
       "      <td>#81</td>\n",
       "      <td>17382.0</td>\n",
       "      <td>11195</td>\n",
       "      <td>9736</td>\n",
       "      <td>...</td>\n",
       "      <td>62.87</td>\n",
       "      <td>115.72</td>\n",
       "      <td>-9.04</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>-2.19</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dtime    w_v4  a_v4  v_v4       s_v4   pd_v4 name     w_v6  \\\n",
       "0  2016-09-05 16:00:00  6916.0  8731  7658  1179709.0  528170  #81  17844.0   \n",
       "1  2016-09-05 17:00:00  7122.0  8904  7760  1050626.0  538893  #81  17781.0   \n",
       "2  2016-09-05 18:00:00  8110.0  9015  7859  1087679.0  558563  #81  18504.0   \n",
       "3  2016-09-05 19:00:00  9605.0  9016  7965  1004614.0  555959  #81  17403.0   \n",
       "4  2016-09-05 20:00:00  8338.0  8737  7546  1034223.0  506325  #81  17382.0   \n",
       "\n",
       "    a_v6   v_v6  ...     p_a     s_a    w_a   a_a   v_a   p_r   s_r   w_r  \\\n",
       "0  11176  10076  ...  -14.36  133.33 -10.93 -2.44 -2.42 -0.03  0.11 -1.58   \n",
       "1  11010   9826  ...   -5.66   16.94 -10.66 -2.11 -2.07 -0.01  0.02 -1.50   \n",
       "2  11124   9995  ...   10.73   24.21 -10.39 -2.11 -2.14  0.02  0.02 -1.28   \n",
       "3  10914   9753  ...    6.51  -52.61  -7.80 -1.90 -1.79  0.01 -0.05 -0.81   \n",
       "4  11195   9736  ...   62.87  115.72  -9.04 -2.46 -2.19  0.12  0.11 -1.08   \n",
       "\n",
       "    a_r   v_r  \n",
       "0 -0.28 -0.32  \n",
       "1 -0.24 -0.27  \n",
       "2 -0.23 -0.27  \n",
       "3 -0.21 -0.22  \n",
       "4 -0.28 -0.29  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def round_time_to_day(dtime):\n",
    "    import datetime;    \n",
    "    d = datetime.datetime.strptime(dtime, \"%Y-%m-%d %H:%M:%S\")\n",
    "    d = d.replace(hour=0, minute=0, second=0)\n",
    "    dtime = d.strftime('%Y-%m-%d')\n",
    "    return dtime\n",
    "\n",
    "df['dtime'] = df['dtime'].apply(round_time_to_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dtime</th>\n",
       "      <th>w_v4</th>\n",
       "      <th>a_v4</th>\n",
       "      <th>v_v4</th>\n",
       "      <th>s_v4</th>\n",
       "      <th>pd_v4</th>\n",
       "      <th>name</th>\n",
       "      <th>w_v6</th>\n",
       "      <th>a_v6</th>\n",
       "      <th>v_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>2016-09-05</td>\n",
       "      <td>6916.0</td>\n",
       "      <td>8731</td>\n",
       "      <td>7658</td>\n",
       "      <td>1179709.0</td>\n",
       "      <td>528170</td>\n",
       "      <td>#81</td>\n",
       "      <td>17844.0</td>\n",
       "      <td>11176</td>\n",
       "      <td>10076</td>\n",
       "      <td>...</td>\n",
       "      <td>-14.36</td>\n",
       "      <td>133.33</td>\n",
       "      <td>-10.93</td>\n",
       "      <td>-2.44</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-09-05</td>\n",
       "      <td>7122.0</td>\n",
       "      <td>8904</td>\n",
       "      <td>7760</td>\n",
       "      <td>1050626.0</td>\n",
       "      <td>538893</td>\n",
       "      <td>#81</td>\n",
       "      <td>17781.0</td>\n",
       "      <td>11010</td>\n",
       "      <td>9826</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>16.94</td>\n",
       "      <td>-10.66</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-09-05</td>\n",
       "      <td>8110.0</td>\n",
       "      <td>9015</td>\n",
       "      <td>7859</td>\n",
       "      <td>1087679.0</td>\n",
       "      <td>558563</td>\n",
       "      <td>#81</td>\n",
       "      <td>18504.0</td>\n",
       "      <td>11124</td>\n",
       "      <td>9995</td>\n",
       "      <td>...</td>\n",
       "      <td>10.73</td>\n",
       "      <td>24.21</td>\n",
       "      <td>-10.39</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-09-05</td>\n",
       "      <td>9605.0</td>\n",
       "      <td>9016</td>\n",
       "      <td>7965</td>\n",
       "      <td>1004614.0</td>\n",
       "      <td>555959</td>\n",
       "      <td>#81</td>\n",
       "      <td>17403.0</td>\n",
       "      <td>10914</td>\n",
       "      <td>9753</td>\n",
       "      <td>...</td>\n",
       "      <td>6.51</td>\n",
       "      <td>-52.61</td>\n",
       "      <td>-7.80</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-09-05</td>\n",
       "      <td>8338.0</td>\n",
       "      <td>8737</td>\n",
       "      <td>7546</td>\n",
       "      <td>1034223.0</td>\n",
       "      <td>506325</td>\n",
       "      <td>#81</td>\n",
       "      <td>17382.0</td>\n",
       "      <td>11195</td>\n",
       "      <td>9736</td>\n",
       "      <td>...</td>\n",
       "      <td>62.87</td>\n",
       "      <td>115.72</td>\n",
       "      <td>-9.04</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>-2.19</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        dtime    w_v4  a_v4  v_v4       s_v4   pd_v4 name     w_v6   a_v6  \\\n",
       "0  2016-09-05  6916.0  8731  7658  1179709.0  528170  #81  17844.0  11176   \n",
       "1  2016-09-05  7122.0  8904  7760  1050626.0  538893  #81  17781.0  11010   \n",
       "2  2016-09-05  8110.0  9015  7859  1087679.0  558563  #81  18504.0  11124   \n",
       "3  2016-09-05  9605.0  9016  7965  1004614.0  555959  #81  17403.0  10914   \n",
       "4  2016-09-05  8338.0  8737  7546  1034223.0  506325  #81  17382.0  11195   \n",
       "\n",
       "    v_v6  ...     p_a     s_a    w_a   a_a   v_a   p_r   s_r   w_r   a_r   v_r  \n",
       "0  10076  ...  -14.36  133.33 -10.93 -2.44 -2.42 -0.03  0.11 -1.58 -0.28 -0.32  \n",
       "1   9826  ...   -5.66   16.94 -10.66 -2.11 -2.07 -0.01  0.02 -1.50 -0.24 -0.27  \n",
       "2   9995  ...   10.73   24.21 -10.39 -2.11 -2.14  0.02  0.02 -1.28 -0.23 -0.27  \n",
       "3   9753  ...    6.51  -52.61  -7.80 -1.90 -1.79  0.01 -0.05 -0.81 -0.21 -0.22  \n",
       "4   9736  ...   62.87  115.72  -9.04 -2.46 -2.19  0.12  0.11 -1.08 -0.28 -0.29  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "targets = ['p_a', 's_a', 'w_a', 'a_a', 'v_a', 'p_r', 's_r', 'w_r', 'a_r', 'v_r',\n",
    "           'w_v4','a_v4', 'v_v4', 'w_v6', 'a_v6', 'v_v6', 's_v4', 's_v6', 'pd_v4','pd_v6']\n",
    "\n",
    "def get_difference_across_probes(df):\n",
    "    dfgroupby  = cbc.group(df, column=['dtime'])\n",
    "    df_agg     = cbc.aggregate(dfgroupby, targets, ['median'])\n",
    "    df_merged  = cbc.merge(df, df_agg, column=['dtime'])\n",
    "    df         = df_merged.drop(targets + ['name'], axis=1)\n",
    "    df         = df.drop_duplicates()\n",
    "    df.columns = ['dtime'] + targets\n",
    "    return df\n",
    "\n",
    "df = get_difference_across_probes(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['dtime'] = df['dtime'].apply(lambda d: dt.strptime(d, '%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.sort_values(by = 'dtime', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def convert_to_ms(df, field):\n",
    "    df[field] = df[field].div(1000) # milliseconds\n",
    "\n",
    "targets = ['w_v4','a_v4', 'v_v4', 'w_v6', 'a_v6', 'v_v6', 's_v4', 's_v6', 'pd_v4','pd_v6']\n",
    "\n",
    "for target in targets:\n",
    "    convert_to_ms(df, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtime    929\n",
       "p_a      929\n",
       "s_a      929\n",
       "w_a      929\n",
       "a_a      929\n",
       "v_a      929\n",
       "p_r      929\n",
       "s_r      929\n",
       "w_r      929\n",
       "a_r      929\n",
       "v_r      929\n",
       "w_v4     929\n",
       "a_v4     929\n",
       "v_v4     929\n",
       "w_v6     929\n",
       "a_v6     929\n",
       "v_v6     929\n",
       "s_v4     929\n",
       "s_v6     929\n",
       "pd_v4    929\n",
       "pd_v6    929\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hardcoded dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from dateutil import rrule\n",
    "a=dt.strptime(str('2014-08-01'), '%Y-%m-%d')\n",
    "b=dt.strptime(str('2017-07-01'), '%Y-%m-%d')\n",
    "dateranges = rrule.rrule(rrule.DAILY, dtstart=a).between(a, b, inc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_ = pd.DataFrame(dateranges, columns=['dtime']).sort_values(by = 'dtime').reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.merge(df_, df, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def draw_inset(ax):\n",
    "    \n",
    "    df.plot(   x='dtime'\n",
    "             , y=['w_a', 'a_a', 'v_a']\n",
    "             , color=['cyan', 'green', 'wheat']\n",
    "             , kind='line'\n",
    "             , linewidth = 1\n",
    "             , ax = ax\n",
    "            )\n",
    "\n",
    "    ax.grid(False)\n",
    "    ax.set_xlabel('', fontsize=12)\n",
    "    ax.legend(['Web', 'Audio', 'Video'], fontsize=12, loc='best', ncol = 3)\n",
    "    ax.legend_.remove()\n",
    "    ax.set_ylim([-0.4, 0])\n",
    "    yticks = np.arange(-0.4,0.1,0.2)\n",
    "    ax.set_yticks(yticks)\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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.gridspec as gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9++IN2tuzZ0/S0tL4yU9+AhihVVZWxpAhQ7jhhhu46667yN+6FVVlyZIlfPHF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\nIZRWEARBUBj+P5IoYpBxmGaLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde1eea6a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 6))\n",
    "plt.rcParams['font.size'] = 12\n",
    "\n",
    "ax1 = fig.add_subplot(411)\n",
    "ax2 = fig.add_subplot(412, sharex = ax1)\n",
    "ax3 = fig.add_subplot(413, sharex = ax1)\n",
    "ax4 = fig.add_subplot(414, sharex = ax1)\n",
    "\n",
    "sl = df.plot(  x='dtime'\n",
    "             , y=['w_a']\n",
    "             , color='cyan'\n",
    "             , kind='line'\n",
    "             , linewidth = 1\n",
    "             , ax = ax1\n",
    "            )\n",
    "\n",
    "ax1.grid(False)\n",
    "ax1.set_xlabel('', fontsize=22)\n",
    "ax1.set_ylabel('', fontsize=22)\n",
    "ax1.set_yscale('linear')\n",
    "\n",
    "ax1.spines['right'].set_color('none')\n",
    "ax1.spines['top'].set_color('none')\n",
    "ax1.yaxis.set_ticks_position('left')\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "ax1.spines['bottom'].set_position(('axes', -0.05))\n",
    "ax1.spines['left'].set_position(('axes', -0.05))\n",
    "\n",
    "ax1.xaxis.set_major_locator(dates.YearLocator(1, month=1, day=1))\n",
    "ax1.xaxis.set_major_formatter(dates.DateFormatter('\\n%Y'))\n",
    "\n",
    "ax1.set_xticklabels('')\n",
    "\n",
    "\n",
    "sl = df.plot(  x='dtime'\n",
    "             , y=['a_a', 'v_a']\n",
    "             , color=['green', 'coral']\n",
    "             , kind='line'\n",
    "             , linewidth = 1\n",
    "             , ax = ax2\n",
    "            )\n",
    "\n",
    "ax2.grid(False)\n",
    "ax2.set_xlabel('', fontsize=22)\n",
    "ax2.set_ylabel('', fontsize=22)\n",
    "ax2.set_yscale('linear')\n",
    "ax2.set_ylim([-0.4, 0])\n",
    "yticks = np.arange(-0.4,0.1,0.1)\n",
    "ax2.set_yticks(yticks)\n",
    "\n",
    "ax2.spines['right'].set_color('none')\n",
    "ax2.spines['top'].set_color('none')\n",
    "ax2.yaxis.set_ticks_position('left')\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "ax2.spines['bottom'].set_position(('axes', -0.05))\n",
    "ax2.spines['left'].set_position(('axes', -0.05))\n",
    "\n",
    "ax2.xaxis.set_major_locator(dates.YearLocator(1, month=1, day=1))\n",
    "ax2.xaxis.set_major_formatter(dates.DateFormatter('\\n%Y'))\n",
    "\n",
    "ax2.set_xticklabels('')\n",
    "\n",
    "sl = df.plot(  x='dtime'\n",
    "             , y=['p_a']\n",
    "             , color='green'\n",
    "             , kind='line'\n",
    "             , linewidth = 1\n",
    "             , ax = ax3\n",
    "            )\n",
    "\n",
    "ax3.grid(False)\n",
    "ax3.set_xlabel('', fontsize=25)\n",
    "ax3.set_ylabel('', fontsize=25)\n",
    "ax3.set_yscale('linear')\n",
    "ax3.set_ylim([-120, 41]); yticks = np.arange(-120,40,40)\n",
    "ax3.set_yticks(yticks)\n",
    "\n",
    "ax3.spines['right'].set_color('none')\n",
    "ax3.spines['top'].set_color('none')\n",
    "ax3.yaxis.set_ticks_position('left')\n",
    "ax3.xaxis.set_ticks_position('bottom')\n",
    "ax3.spines['bottom'].set_position(('axes', -0.05))\n",
    "ax3.spines['left'].set_position(('axes', -0.05))\n",
    "\n",
    "ax3.set_xticklabels('')\n",
    "     \n",
    "sl = df.plot(  x='dtime'\n",
    "             , y=['s_a']\n",
    "             , color='magenta'\n",
    "             , kind='line'\n",
    "             , linewidth = 1\n",
    "             , ax = ax4\n",
    "            )\n",
    "\n",
    "ax4.grid(False)\n",
    "ax4.set_xlabel('', fontsize=25)\n",
    "ax4.set_ylabel('', fontsize=25)\n",
    "ax4.set_yscale('linear')\n",
    "ax4.set_ylim([-400, 0])\n",
    "yticks = np.arange(-400,10,100)\n",
    "ax4.set_yticks(yticks)\n",
    "\n",
    "ax4.spines['right'].set_color('none')\n",
    "ax4.spines['top'].set_color('none')\n",
    "ax4.yaxis.set_ticks_position('left')\n",
    "ax4.xaxis.set_ticks_position('bottom')\n",
    "ax4.spines['bottom'].set_position(('axes', -0.05))\n",
    "ax4.spines['left'].set_position(('axes', -0.05))\n",
    "\n",
    "ax1.legend(['Web'], fontsize=12, loc='lower left')   \n",
    "ax2.legend(['Audio', 'Video'], fontsize=12, loc='best')   \n",
    "ax3.legend_.remove()\n",
    "ax4.legend_.remove()\n",
    "\n",
    "ax1.set_title('TCP Connect Times', fontsize=12, y=1.05)\n",
    "ax2.set_title('TCP Connect Times', fontsize=12, y=1.05)\n",
    "ax3.set_title('Prebuffering Duration', fontsize=12, y=1.05)\n",
    "ax4.set_title('Startup Delay', fontsize=12, y=1.05)\n",
    "\n",
    "ax1.set_ylabel('∆t (ms)', fontsize=12)\n",
    "ax2.set_ylabel('∆t (ms)', fontsize=12)\n",
    "ax3.set_ylabel('∆p (ms)', fontsize=12)\n",
    "ax4.set_ylabel('∆s (ms)', fontsize=12)\n",
    "\n",
    "from datetime import datetime as dt\n",
    "ax1.axvline(x=dt.strptime('2014-08-21', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "ax1.axvline(x=dt.strptime('2014-12-10', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "ax1.axvline(x=dt.strptime('2015-01-22', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "ax1.axvline(x=dt.strptime('2016-09-05', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "\n",
    "ax2.axvline(x=dt.strptime('2014-08-21', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "ax2.axvline(x=dt.strptime('2014-12-10', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "ax2.axvline(x=dt.strptime('2015-01-22', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "ax2.axvline(x=dt.strptime('2016-09-05', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "\n",
    "ax3.axvline(x=dt.strptime('2014-08-21', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "ax3.axvline(x=dt.strptime('2014-12-10', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "ax3.axvline(x=dt.strptime('2015-01-22', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "ax3.axvline(x=dt.strptime('2016-09-05', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "\n",
    "ax4.axvline(x=dt.strptime('2014-08-21', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "ax4.axvline(x=dt.strptime('2014-12-10', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "ax4.axvline(x=dt.strptime('2015-01-22', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "ax4.axvline(x=dt.strptime('2016-09-05', \"%Y-%m-%d\"), linewidth=1.0, ymax = 0.95, ymin = 0, color='black', ls = 'dotted')\n",
    "\n",
    "fig.subplots_adjust(hspace = 0.6)\n",
    "\n",
    "plt.savefig('youtube-delay-timeseries-separate.pdf', format='pdf', bbox_inches='tight')"
   ]
  }
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