plot-success-rate-timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "import requests\n",
    "import asinfo\n",
    "import matplotlib.dates as dates\n",
    "\n",
    "from datetime import datetime as dt\n",
    "import filterbyip as fbi\n",
    "import clusterbycolumn as cbc"
   ]
  },
  {
   "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": {
      "text/html": [
       "<style>body {\n",
       "    margin: 0;\n",
       "    font-family: Helvetica;\n",
       "}\n",
       "table.dataframe {\n",
       "    border-collapse: collapse;\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe tr {\n",
       "    border: none;\n",
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       "}\n",
       "h3 {\n",
       "    color: white;\n",
       "    background-color: black;\n",
       "    padding: 0.5em;\n",
       "}\n",
       "\n",
       ".CodeMirror {\n",
       "    font-family: 'Monaco for Powerline';\n",
       "}\n",
       "\n",
       "div.cell.code_cell {\n",
       "    font-family: 'Monaco for Powerline';\n",
       "}\n",
       "\n",
       "div.output_area pre {\n",
       "    font-family: 'Monaco for Powerline';\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "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": "markdown",
   "metadata": {},
   "source": [
    "### The failures mostly happen due to stall (607) or when the test completes too fast (608)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query = '''select   msmpoint.name as probe\n",
    "                  , dtime\n",
    "                  , video_address\n",
    "                  , video_id\n",
    "                  , successes\n",
    "                  , failures\n",
    "           from youtube, msmpoint\n",
    "           where youtube.unit_id = msmpoint.skunitid\n",
    "           and msmpoint.skunitid <> ''\n",
    "        --   and error_code <> 607       -- stall happened\n",
    "        --   and error_code <> 608       -- test completed too fast\n",
    "           order by dtime DESC\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/html": [
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       "    .dataframe tbody tr th {\n",
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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>video_id</th>\n",
       "      <th>successes</th>\n",
       "      <th>failures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>2017-06-27 23:37:44</td>\n",
       "      <td>2001:b30:ef00:99ca::c</td>\n",
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       "      <td>2017-06-27 23:36:40</td>\n",
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       "      <td>None</td>\n",
       "      <td>qzlGB3eOjlU</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>2017-06-27 23:36:00</td>\n",
       "      <td>2001:1a80:801::c</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27 23:35:09</td>\n",
       "      <td>173.194.20.202</td>\n",
       "      <td>qzlGB3eOjlU</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       probe                dtime          video_address     video_id  \\\n",
       "0  samsbox08  2017-06-27 23:37:44  2001:b30:ef00:99ca::c  qzlGB3eOjlU   \n",
       "1  samsbox08  2017-06-27 23:36:40         195.95.178.204  qzlGB3eOjlU   \n",
       "2  samsbox45  2017-06-27 23:36:12                   None  qzlGB3eOjlU   \n",
       "3  samsbox03  2017-06-27 23:36:00       2001:1a80:801::c  qzlGB3eOjlU   \n",
       "4  samsbox45  2017-06-27 23:35:09         173.194.20.202  qzlGB3eOjlU   \n",
       "\n",
       "   successes  failures  \n",
       "0          1         0  \n",
       "1          1         0  \n",
       "2          0         1  \n",
       "3          1         0  \n",
       "4          1         0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_probes = len(set(df['probe']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['dtime'] = df['dtime'].apply(round_time_to_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <td>173.194.20.202</td>\n",
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      ],
      "text/plain": [
       "       probe       dtime          video_address     video_id  successes  \\\n",
       "0  samsbox08  2017-06-27  2001:b30:ef00:99ca::c  qzlGB3eOjlU          1   \n",
       "1  samsbox08  2017-06-27         195.95.178.204  qzlGB3eOjlU          1   \n",
       "2  samsbox45  2017-06-27                   None  qzlGB3eOjlU          0   \n",
       "3  samsbox03  2017-06-27       2001:1a80:801::c  qzlGB3eOjlU          1   \n",
       "4  samsbox45  2017-06-27         173.194.20.202  qzlGB3eOjlU          1   \n",
       "\n",
       "   failures  \n",
       "0         0  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probe</th>\n",
       "      <th>dtime</th>\n",
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       "      <td>2014-08-20</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "             probe       dtime    video_address     video_id  successes  \\\n",
       "5241629  samsbox24  2014-08-20  173.194.137.147  u5KQQWlIgGc          1   \n",
       "5241630  samsbox24  2014-08-20             None  u5KQQWlIgGc          0   \n",
       "5241631  samsbox24  2014-08-20             None  u5KQQWlIgGc          0   \n",
       "5241632  samsbox24  2014-08-20  173.194.137.147  u5KQQWlIgGc          1   \n",
       "5241633  samsbox24  2014-08-20             None  u5KQQWlIgGc          0   \n",
       "\n",
       "         failures  \n",
       "5241629         0  \n",
       "5241630         1  \n",
       "5241631         1  \n",
       "5241632         0  \n",
       "5241633         1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "probe            5241634\n",
       "dtime            5241634\n",
       "video_address    4727767\n",
       "video_id         5241633\n",
       "successes        5241634\n",
       "failures         5241634\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": [
    "def get_af_from_endpoint(ep):\n",
    "    if ep is None: return None\n",
    "    elif fbi.if_ipv6(ep): return 'v6'\n",
    "    elif fbi.if_ipv4(ep): return 'v4'\n",
    "    else: print('.'); return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".\n"
     ]
    }
   ],
   "source": [
    "df['af'] = df['video_address'].apply(get_af_from_endpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "probe            5241634\n",
       "dtime            5241634\n",
       "video_address    4727767\n",
       "video_id         5241633\n",
       "successes        5241634\n",
       "failures         5241634\n",
       "af               4727766\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probe</th>\n",
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       "      <th>video_address</th>\n",
       "      <th>video_id</th>\n",
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       "      <td>v6</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>None</td>\n",
       "      <td>qzlGB3eOjlU</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>2001:1a80:801::c</td>\n",
       "      <td>qzlGB3eOjlU</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>v6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>173.194.20.202</td>\n",
       "      <td>qzlGB3eOjlU</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>v4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       probe       dtime          video_address     video_id  successes  \\\n",
       "0  samsbox08  2017-06-27  2001:b30:ef00:99ca::c  qzlGB3eOjlU          1   \n",
       "1  samsbox08  2017-06-27         195.95.178.204  qzlGB3eOjlU          1   \n",
       "2  samsbox45  2017-06-27                   None  qzlGB3eOjlU          0   \n",
       "3  samsbox03  2017-06-27       2001:1a80:801::c  qzlGB3eOjlU          1   \n",
       "4  samsbox45  2017-06-27         173.194.20.202  qzlGB3eOjlU          1   \n",
       "\n",
       "   failures    af  \n",
       "0         0    v6  \n",
       "1         0    v4  \n",
       "2         1  None  \n",
       "3         0    v6  \n",
       "4         0    v4  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\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>probe</th>\n",
       "      <th>dtime</th>\n",
       "      <th>video_address</th>\n",
       "      <th>video_id</th>\n",
       "      <th>successes</th>\n",
       "      <th>failures</th>\n",
       "      <th>af</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>2001:b30:ef00:99ca::c</td>\n",
       "      <td>qzlGB3eOjlU</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>v6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>195.95.178.204</td>\n",
       "      <td>qzlGB3eOjlU</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>v4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>None</td>\n",
       "      <td>qzlGB3eOjlU</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>2001:1a80:801::c</td>\n",
       "      <td>qzlGB3eOjlU</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>v6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>173.194.20.202</td>\n",
       "      <td>qzlGB3eOjlU</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>v4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       probe       dtime          video_address     video_id  successes  \\\n",
       "0  samsbox08  2017-06-27  2001:b30:ef00:99ca::c  qzlGB3eOjlU          1   \n",
       "1  samsbox08  2017-06-27         195.95.178.204  qzlGB3eOjlU          1   \n",
       "2  samsbox45  2017-06-27                   None  qzlGB3eOjlU          0   \n",
       "3  samsbox03  2017-06-27       2001:1a80:801::c  qzlGB3eOjlU          1   \n",
       "4  samsbox45  2017-06-27         173.194.20.202  qzlGB3eOjlU          1   \n",
       "\n",
       "   failures    af  \n",
       "0         0    v6  \n",
       "1         0    v4  \n",
       "2         1  None  \n",
       "3         0    v6  \n",
       "4         0    v4  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfgroupby = cbc.group(df, column=['dtime', 'af', 'probe'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_agg = cbc.aggregate(dfgroupby, ['successes', 'failures'], ['sum', 'count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_merged = cbc.merge(df, df_agg, column=['dtime', 'af', 'probe'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df_merged.drop(['video_address', 'video_id', 'successes', 'failures'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "probe                 117995\n",
       "dtime                 117995\n",
       "af                    117995\n",
       "(successes, sum)      117995\n",
       "(successes, count)    117995\n",
       "(failures, sum)       117995\n",
       "(failures, count)     117995\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.rename( columns={  df.columns[-1]: 'failures_count'\n",
    "                    , df.columns[-2]: 'failures_sum'\n",
    "                    , df.columns[-3]: 'successes_count'\n",
    "                    , df.columns[-4]: 'successes_sum'}\n",
    "                , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\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>probe</th>\n",
       "      <th>dtime</th>\n",
       "      <th>af</th>\n",
       "      <th>successes_sum</th>\n",
       "      <th>successes_count</th>\n",
       "      <th>failures_sum</th>\n",
       "      <th>failures_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>v6</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>v4</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>v6</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>v4</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>v4</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       probe       dtime  af  successes_sum  successes_count  failures_sum  \\\n",
       "0  samsbox08  2017-06-27  v6             24               24             0   \n",
       "1  samsbox08  2017-06-27  v4             24               24             0   \n",
       "3  samsbox03  2017-06-27  v6             24               24             0   \n",
       "4  samsbox45  2017-06-27  v4             24               24             0   \n",
       "5  samsbox03  2017-06-27  v4             24               24             0   \n",
       "\n",
       "   failures_count  \n",
       "0              24  \n",
       "1              24  \n",
       "3              24  \n",
       "4              24  \n",
       "5              24  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfv6 = df[df.af == 'v6']\n",
    "dfv4 = df[df.af == 'v4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\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>probe</th>\n",
       "      <th>dtime</th>\n",
       "      <th>af</th>\n",
       "      <th>successes_sum</th>\n",
       "      <th>successes_count</th>\n",
       "      <th>failures_sum</th>\n",
       "      <th>failures_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>v6</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>v6</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>samsbox97</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>v6</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>samsbox51</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>v6</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>samsbox33</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>v6</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       probe       dtime  af  successes_sum  successes_count  failures_sum  \\\n",
       "0  samsbox08  2017-06-27  v6             24               24             0   \n",
       "3  samsbox03  2017-06-27  v6             24               24             0   \n",
       "6  samsbox97  2017-06-27  v6             18               18             0   \n",
       "8  samsbox51  2017-06-27  v6             24               24             0   \n",
       "9  samsbox33  2017-06-27  v6             24               24             0   \n",
       "\n",
       "   failures_count  \n",
       "0              24  \n",
       "3              24  \n",
       "6              18  \n",
       "8              24  \n",
       "9              24  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cal_success_rate(x):\n",
    "    success = x['successes_sum']\n",
    "    total   = x['successes_count']    \n",
    "    \n",
    "    rate = (success / total) * 100\n",
    "    return rate\n",
    "\n",
    "dfv6['success_rate'] = dfv6.apply(cal_success_rate, axis = 1)\n",
    "dfv4['success_rate'] = dfv4.apply(cal_success_rate, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del dfv4['successes_sum']; del dfv4['successes_count']\n",
    "del dfv6['successes_sum']; del dfv6['successes_count']\n",
    "\n",
    "del dfv4['failures_sum']; del dfv4['failures_count']\n",
    "del dfv6['failures_sum']; del dfv6['failures_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del dfv4['af']; del dfv6['af']"
   ]
  },
  {
   "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",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\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>probe</th>\n",
       "      <th>dtime</th>\n",
       "      <th>success_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>samsbox97</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>samsbox51</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        probe       dtime  success_rate\n",
       "1   samsbox08  2017-06-27         100.0\n",
       "4   samsbox45  2017-06-27         100.0\n",
       "5   samsbox03  2017-06-27         100.0\n",
       "7   samsbox97  2017-06-27         100.0\n",
       "12  samsbox51  2017-06-27         100.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
       "        text-align: left;\n",
       "    }\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>probe</th>\n",
       "      <th>dtime</th>\n",
       "      <th>success_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>samsbox97</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>samsbox51</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>samsbox33</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       probe       dtime  success_rate\n",
       "0  samsbox08  2017-06-27         100.0\n",
       "3  samsbox03  2017-06-27         100.0\n",
       "6  samsbox97  2017-06-27         100.0\n",
       "8  samsbox51  2017-06-27         100.0\n",
       "9  samsbox33  2017-06-27         100.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_success_rate_across_probes(df):\n",
    "    dfgroupby  = cbc.group(df, column=['dtime'])\n",
    "    df_agg     = cbc.aggregate(dfgroupby, ['success_rate'], ['median'])\n",
    "    df_merged  = cbc.merge(df, df_agg, column=['dtime'])\n",
    "    df         = df_merged.drop(['probe', 'success_rate'], axis=1)\n",
    "    df         = df.drop_duplicates()\n",
    "    df.columns = ['dtime', 'success_rate']\n",
    "    return df\n",
    "\n",
    "dfv4 = get_success_rate_across_probes(dfv4)\n",
    "dfv6 = get_success_rate_across_probes(dfv6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\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>success_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3427</th>\n",
       "      <td>2017-06-26</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10241</th>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20543</th>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34667</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            dtime  success_rate\n",
       "0      2017-06-27         100.0\n",
       "3427   2017-06-26         100.0\n",
       "10241  2017-06-25         100.0\n",
       "20543  2017-06-24         100.0\n",
       "34667  2017-06-23         100.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "strptime() argument 1 must be str, not Timestamp",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-534dffa48c51>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdfv4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dtime'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdfv4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dtime'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'%Y-%m-%d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdfv6\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dtime'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdfv6\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dtime'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'%Y-%m-%d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdfv4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'dtime'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdfv6\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'dtime'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/youtube-data-analysis-python3/lib/python3.5/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   2353\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2354\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2355\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2357\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/src/inference.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer (pandas/_libs/lib.c:66645)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<ipython-input-39-534dffa48c51>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(d)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdfv4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dtime'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdfv4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dtime'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'%Y-%m-%d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdfv6\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dtime'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdfv6\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dtime'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'%Y-%m-%d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdfv4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'dtime'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdfv6\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'dtime'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: strptime() argument 1 must be str, not Timestamp"
     ]
    }
   ],
   "source": [
    "dfv4['dtime'] = dfv4['dtime'].apply(lambda d: dt.strptime(d, '%Y-%m-%d'))\n",
    "dfv6['dtime'] = dfv6['dtime'].apply(lambda d: dt.strptime(d, '%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfv4.sort_values(by = 'dtime', inplace = True)\n",
    "dfv6.sort_values(by = 'dtime', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_4 = dfv4['dtime'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_6 = dfv6['dtime'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hardcoded dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_ = pd.DataFrame(dateranges, columns=['dtime']).sort_values(by = 'dtime').reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfv4 = pd.merge(df_, dfv4, how='outer')\n",
    "dfv6 = pd.merge(df_, dfv6, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C1bjnNU3AeG/fr4AJ3u8VwAigATgviD0+bX7HZ7hZYYQpQlxLwkgzTNvjOF8J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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd876270fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 3))\n",
    "plt.rcParams['font.size'] = 12\n",
    "\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax2 = fig.add_subplot(212, sharex=ax1)\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",
    "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",
    "sl = dfv6.plot(  x='dtime'\n",
    "               , y=['success_rate']\n",
    "               , color='red'\n",
    "               , kind='line'\n",
    "               , linewidth = 1\n",
    "               , ax = ax1\n",
    "              )\n",
    "\n",
    "st = dfv4.plot(  x='dtime'\n",
    "               , y=['success_rate']\n",
    "               , color='blue'\n",
    "               , kind='line'\n",
    "               , linewidth = 1\n",
    "               , ax = ax2\n",
    "              )\n",
    "\n",
    "ax1.grid(False)\n",
    "ax1.legend(['IPv6'], fontsize=12, loc='best')\n",
    "ax1.set_yscale('linear')\n",
    "ax1.set_ylim([70, 101])\n",
    "\n",
    "ax2.grid(False)\n",
    "ax2.legend(['IPv4'], fontsize=12, loc='best')\n",
    "ax2.set_yscale('linear')\n",
    "ax2.set_ylim([70, 101])\n",
    "\n",
    "yticks = np.arange(70,110,10)\n",
    "ax1.set_yticks(yticks)\n",
    "ax2.set_yticks(yticks)\n",
    "\n",
    "def to_percent(y, position): return str(int(y)) + '%'\n",
    "ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))\n",
    "ax2.yaxis.set_major_formatter(FuncFormatter(to_percent))\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",
    "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",
    "ax1.set_xlabel('', fontsize=22)\n",
    "ax1.set_ylabel('', fontsize=22)\n",
    "ax2.set_xlabel('', fontsize=22)\n",
    "ax2.set_ylabel('', fontsize=22)\n",
    "\n",
    "fig.subplots_adjust(hspace = 0.6)\n",
    "\n",
    "ax1.set_title('Success Rate', fontsize=12, y=1.05)\n",
    "plt.savefig('youtube-success-rate-timeseries.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfv4 = dfv4.dropna()\n",
    "dfv6 = dfv6.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import Cdf\n",
    "import Pmf\n",
    "samplev4 = dfv4['success_rate'].values\n",
    "samplev6 = dfv6['success_rate'].values\n",
    "\n",
    "c4 = Cdf.MakeCdfFromList(samplev4)\n",
    "c6 = Cdf.MakeCdfFromList(samplev6)\n",
    "\n",
    "cc4 = [1 - p for x, p in c4.Items()]\n",
    "cc6 = [1 - p for x, p in c6.Items()]"
   ]
  }
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