Fraud Detection.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:643296dbebd78b169c80a2e07b78bb16daad0d76d97ee27e8700368b648f0ff8"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import seaborn\n",
      "import networkx as nx\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import pandas as pd\n",
      "import os\n",
      "import time\n",
      "STAT_THRESHOLD = 10 # This is the threshold to identify the anomalies from the statistical analysis\n",
      "start = time.time()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv(\"hack_small.csv\", converters={'calldno': str,'callgno': str} )\n",
      "df= df.dropna(subset = [\"callgno\",\"callgno\"])\n",
      "\n",
      "print df.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(999999, 16)\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "df['count_sum'] = 1\n",
      "\n",
      "x = df[[\"callgno\",\"calldno\"]].groupby(\"callgno\")\n",
      "#df_NumberOfUniqueCallees = x.apply(lambda x: len(set(x['calldno'])))\n",
      "df_NumberOfUniqueCallees = x.size()\n",
      "df_NumberOfUniqueCallees = df_NumberOfUniqueCallees.order(ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "temp = df_NumberOfUniqueCallees[0:100]\n",
      "fig, ax = plt.subplots()\n",
      "rects1 = ax.bar(range(0,len(temp)), temp.values,color='r')\n",
      "ax.set_xticklabels(temp.index )\n",
      "#legend([temp.index, temp.values], [\"Unique Phone Numbers\", \"Total Number of Unique Callees\"])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[<matplotlib.text.Text at 0xaaa0c22c>,\n",
        " <matplotlib.text.Text at 0xaa9f3c6c>,\n",
        " <matplotlib.text.Text at 0xaa8ae58c>,\n",
        " <matplotlib.text.Text at 0xaa8aebcc>,\n",
        " <matplotlib.text.Text at 0xaa8b83ac>,\n",
        " <matplotlib.text.Text at 0xaa8b8b6c>]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['Arial'] not found. Falling back to Bitstream Vera Sans\n",
        "  (prop.get_family(), self.defaultFamily[fontext]))\n",
        "/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1246: UserWarning: findfont: Could not match :family=Bitstream Vera Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. Returning /usr/share/matplotlib/mpl-data/fonts/ttf/cmb10.ttf\n",
        "  UserWarning)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xa7db252c>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Cumulative Conversation Duration for each Phone Number"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Second Alert is to look at the conversation duration for specific Called Numbers. The idea here is that fraudsters who leave missed calls or entice those to call them back, would have a higher cumulative call duration than the average."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = df.groupby(\"calldno\").sum()\n",
      "sorted_conv_duration = x.sort(\"cvrsn_dur\",ascending=False)[\"cvrsn_dur\"]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.sort(\"cvrsn_dur\",ascending=False)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>cdate</th>\n",
        "      <th>chour</th>\n",
        "      <th>cust_orgno</th>\n",
        "      <th>supp_orgno</th>\n",
        "      <th>destadmin_orgno</th>\n",
        "      <th>servabbr</th>\n",
        "      <th>callgno</th>\n",
        "      <th>calldno</th>\n",
        "      <th>origpricingdestid</th>\n",
        "      <th>pricingdestid</th>\n",
        "      <th>routingdestcd</th>\n",
        "      <th>causecode</th>\n",
        "      <th>answind</th>\n",
        "      <th>bouncedcallind</th>\n",
        "      <th>attempts</th>\n",
        "      <th>cvrsn_dur</th>\n",
        "      <th>count_sum</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>28689 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6511</td>\n",
        "      <td>  2211</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>    3054332798</td>\n",
        "      <td>      15148319067</td>\n",
        "      <td>  1421</td>\n",
        "      <td>   1212</td>\n",
        "      <td>     101</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5313</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28381 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 16219</td>\n",
        "      <td> 20474</td>\n",
        "      <td> 20474</td>\n",
        "      <td>  VTS800</td>\n",
        "      <td>   19063647238</td>\n",
        "      <td> 0850018559732095</td>\n",
        "      <td>   370</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>     152</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5241</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29470 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6511</td>\n",
        "      <td> 27113</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>   14042395931</td>\n",
        "      <td>     557132434869</td>\n",
        "      <td>  1412</td>\n",
        "      <td>   1226</td>\n",
        "      <td>  128013</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5240</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29029 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>   842</td>\n",
        "      <td> 30213</td>\n",
        "      <td> 30213</td>\n",
        "      <td>  ENT800</td>\n",
        "      <td>    6567255315</td>\n",
        "      <td>     086002075380</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>      33</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5054</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3302  </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1745</td>\n",
        "      <td>   234</td>\n",
        "      <td> 28992</td>\n",
        "      <td>  VTS800</td>\n",
        "      <td>    9024065816</td>\n",
        "      <td>       0862053860</td>\n",
        "      <td>  1212</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>     343</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5036</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7446  </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 20516</td>\n",
        "      <td> 18084</td>\n",
        "      <td> 18084</td>\n",
        "      <td> ENTLOCA</td>\n",
        "      <td>  818037010667</td>\n",
        "      <td>       0345899983</td>\n",
        "      <td>   370</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>      33</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5014</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44923 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 17425</td>\n",
        "      <td>  2211</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>    8764200931</td>\n",
        "      <td>      12073448467</td>\n",
        "      <td> 15918</td>\n",
        "      <td>    370</td>\n",
        "      <td>    2641</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4987</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8284  </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 20516</td>\n",
        "      <td> 18084</td>\n",
        "      <td> 18084</td>\n",
        "      <td> ENTLOCA</td>\n",
        "      <td>   81462703056</td>\n",
        "      <td>       0345899983</td>\n",
        "      <td>   370</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>      33</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4965</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28771 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6511</td>\n",
        "      <td>   282</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>   13107968725</td>\n",
        "      <td>       8224956134</td>\n",
        "      <td>  1513</td>\n",
        "      <td>    322</td>\n",
        "      <td>     746</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4948</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>148562</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 22570</td>\n",
        "      <td> 12428</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>  442034411158</td>\n",
        "      <td>      14504245974</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1212</td>\n",
        "      <td>      95</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4939</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56529 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6511</td>\n",
        "      <td> 18285</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>   15084854054</td>\n",
        "      <td>     553332766262</td>\n",
        "      <td>  1336</td>\n",
        "      <td>  51786</td>\n",
        "      <td>   10194</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4935</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>189740</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 17425</td>\n",
        "      <td>  2211</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>    8763831233</td>\n",
        "      <td>      19545159609</td>\n",
        "      <td> 15918</td>\n",
        "      <td>   1422</td>\n",
        "      <td> 2460171</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4912</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28031 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 16219</td>\n",
        "      <td> 20474</td>\n",
        "      <td> 20474</td>\n",
        "      <td>  VTS800</td>\n",
        "      <td>   19312612564</td>\n",
        "      <td> 0850018557656715</td>\n",
        "      <td>  1426</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>     152</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4869</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54633 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 18084</td>\n",
        "      <td> 15980</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  ENTVTS</td>\n",
        "      <td>   16504793213</td>\n",
        "      <td>       6566799354</td>\n",
        "      <td>  1504</td>\n",
        "      <td>    405</td>\n",
        "      <td>     152</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4855</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>117110</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>    57</td>\n",
        "      <td>  2211</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>              </td>\n",
        "      <td>      15192545495</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   1212</td>\n",
        "      <td>     102</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4850</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27918 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2202</td>\n",
        "      <td>  1112</td>\n",
        "      <td> 20105</td>\n",
        "      <td>  VTS800</td>\n",
        "      <td>  524491492335</td>\n",
        "      <td>     086002050461</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>     286</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4838</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>201960</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>   899</td>\n",
        "      <td>   842</td>\n",
        "      <td> 21441</td>\n",
        "      <td>  VTS800</td>\n",
        "      <td>  886921278851</td>\n",
        "      <td>     086002059167</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>     152</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4834</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>147942</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>   152</td>\n",
        "      <td> 27113</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   41433178414</td>\n",
        "      <td>     555130479187</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1224</td>\n",
        "      <td>  128010</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4831</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47676 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 27222</td>\n",
        "      <td> 18084</td>\n",
        "      <td> 18084</td>\n",
        "      <td> ENTLOCA</td>\n",
        "      <td>   15184616964</td>\n",
        "      <td>      16504793213</td>\n",
        "      <td>   370</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>      33</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4826</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>82226 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 26958</td>\n",
        "      <td> 12428</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>   33146781088</td>\n",
        "      <td>      14389895749</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1212</td>\n",
        "      <td>   14358</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4814</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>147966</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 22434</td>\n",
        "      <td> 11109</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>   19253034975</td>\n",
        "      <td>     523222812386</td>\n",
        "      <td>  1504</td>\n",
        "      <td>  13149</td>\n",
        "      <td>   13100</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4813</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>221550</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 17425</td>\n",
        "      <td>  2211</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>   50934694336</td>\n",
        "      <td>      15145078572</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1212</td>\n",
        "      <td>     101</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4801</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30981 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 14837</td>\n",
        "      <td> 11219</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>   15088862759</td>\n",
        "      <td>      18292325349</td>\n",
        "      <td>  1336</td>\n",
        "      <td>    115</td>\n",
        "      <td>    1322</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4775</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58119 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>   616</td>\n",
        "      <td>   842</td>\n",
        "      <td> 21441</td>\n",
        "      <td>  VTS800</td>\n",
        "      <td>    1800814201</td>\n",
        "      <td>     086002058471</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>     152</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4770</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>118539</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 27222</td>\n",
        "      <td> 18084</td>\n",
        "      <td> 18084</td>\n",
        "      <td> ENTLOCA</td>\n",
        "      <td>   16072620882</td>\n",
        "      <td>      16504793213</td>\n",
        "      <td>   370</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>      33</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4764</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>77503 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 14166</td>\n",
        "      <td>  2211</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>   81367463403</td>\n",
        "      <td>      16505846343</td>\n",
        "      <td> 14811</td>\n",
        "      <td>   1505</td>\n",
        "      <td>      49</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4762</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>205324</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>   706</td>\n",
        "      <td> 18084</td>\n",
        "      <td> 18084</td>\n",
        "      <td>  ENT800</td>\n",
        "      <td>    6495832487</td>\n",
        "      <td>     086002033558</td>\n",
        "      <td>   361</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>      33</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4760</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>123699</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 11219</td>\n",
        "      <td> 12428</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>    6613331290</td>\n",
        "      <td>      14508443499</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1212</td>\n",
        "      <td>      95</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4752</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56297 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6511</td>\n",
        "      <td>   499</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>   14793722267</td>\n",
        "      <td>     919884855225</td>\n",
        "      <td>   370</td>\n",
        "      <td>  13187</td>\n",
        "      <td>  116468</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4743</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>79321 </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2265</td>\n",
        "      <td> 23836</td>\n",
        "      <td> 23836</td>\n",
        "      <td>  ENT800</td>\n",
        "      <td>   61409832769</td>\n",
        "      <td>     086002060285</td>\n",
        "      <td>   370</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>      33</td>\n",
        "      <td>  LD716</td>\n",
        "      <td> Y</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4741</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389688</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25819</td>\n",
        "      <td>  6480</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   19854698828</td>\n",
        "      <td>      16128661494</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1471</td>\n",
        "      <td>      55</td>\n",
        "      <td>    NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389689</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td>   477</td>\n",
        "      <td>  2211</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td> 2348050267282</td>\n",
        "      <td>      16477855589</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1212</td>\n",
        "      <td>    8654</td>\n",
        "      <td> NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389690</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25819</td>\n",
        "      <td>  6480</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   19854698828</td>\n",
        "      <td>      19729383435</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1448</td>\n",
        "      <td>      59</td>\n",
        "      <td>    NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389691</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td>   766</td>\n",
        "      <td> 24995</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>   97433733427</td>\n",
        "      <td>         91840912</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  13187</td>\n",
        "      <td>   13950</td>\n",
        "      <td> NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389670</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 20474</td>\n",
        "      <td> 11219</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   18778912941</td>\n",
        "      <td>      19168588581</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   1509</td>\n",
        "      <td>    6616</td>\n",
        "      <td> NNA731</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389668</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 22250</td>\n",
        "      <td>  2310</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>  971555297727</td>\n",
        "      <td>     639152380901</td>\n",
        "      <td>   529</td>\n",
        "      <td> 122852</td>\n",
        "      <td>    3821</td>\n",
        "      <td> NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389645</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 27088</td>\n",
        "      <td>   151</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>    6046706699</td>\n",
        "      <td>      12506521491</td>\n",
        "      <td>  1212</td>\n",
        "      <td>   1212</td>\n",
        "      <td>      81</td>\n",
        "      <td>    FEF</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389667</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25256</td>\n",
        "      <td>   151</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>   17862044363</td>\n",
        "      <td>      17789850390</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1212</td>\n",
        "      <td>    8655</td>\n",
        "      <td> NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389646</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td>    88</td>\n",
        "      <td> 10921</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>              </td>\n",
        "      <td>      41794949039</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  14855</td>\n",
        "      <td>  115019</td>\n",
        "      <td> NNA731</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389647</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 19943</td>\n",
        "      <td> 32996</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td> 9779815487677</td>\n",
        "      <td>     966532860918</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>    310</td>\n",
        "      <td>    4905</td>\n",
        "      <td> NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389648</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 21092</td>\n",
        "      <td>   351</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>  91808901EEEE</td>\n",
        "      <td>     971562076522</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> 140341</td>\n",
        "      <td>  125105</td>\n",
        "      <td> NNA717</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389649</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 24284</td>\n",
        "      <td> 11109</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   17148680125</td>\n",
        "      <td>     528993944095</td>\n",
        "      <td>  1512</td>\n",
        "      <td>  13149</td>\n",
        "      <td>   12957</td>\n",
        "      <td> NEV728</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389650</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25819</td>\n",
        "      <td>  6480</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   19854698828</td>\n",
        "      <td>      19722868122</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1448</td>\n",
        "      <td>      59</td>\n",
        "      <td>    NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389651</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25525</td>\n",
        "      <td> 18576</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>   96895407520</td>\n",
        "      <td>         91755805</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  13187</td>\n",
        "      <td>  118624</td>\n",
        "      <td> NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389652</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25819</td>\n",
        "      <td>  6480</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   19854698828</td>\n",
        "      <td>      14693625773</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1448</td>\n",
        "      <td>      59</td>\n",
        "      <td>    NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389653</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25819</td>\n",
        "      <td>  6480</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   19854698828</td>\n",
        "      <td>      15153824724</td>\n",
        "      <td>   370</td>\n",
        "      <td>    370</td>\n",
        "      <td> 2006325</td>\n",
        "      <td>    NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389654</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25819</td>\n",
        "      <td>  6480</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   19854698828</td>\n",
        "      <td>      18064352478</td>\n",
        "      <td>   370</td>\n",
        "      <td>    370</td>\n",
        "      <td> 2005465</td>\n",
        "      <td>    NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389655</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25819</td>\n",
        "      <td>  6480</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   19854698828</td>\n",
        "      <td>      12145013253</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1447</td>\n",
        "      <td> 2005525</td>\n",
        "      <td>    NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389656</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25819</td>\n",
        "      <td> 30453</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   19896832644</td>\n",
        "      <td>      13604797482</td>\n",
        "      <td>   370</td>\n",
        "      <td>   1496</td>\n",
        "      <td>      51</td>\n",
        "      <td> NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389657</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 24282</td>\n",
        "      <td> 20630</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>              </td>\n",
        "      <td>      50258356973</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  16504</td>\n",
        "      <td>   12013</td>\n",
        "      <td> NNA718</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389658</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25819</td>\n",
        "      <td>  6480</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>    0000000005</td>\n",
        "      <td>      15202949923</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>    370</td>\n",
        "      <td>    8364</td>\n",
        "      <td>    NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389659</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 24234</td>\n",
        "      <td> 26111</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>  923076471278</td>\n",
        "      <td>     966592756712</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> 125064</td>\n",
        "      <td>  115694</td>\n",
        "      <td> NNA718</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389660</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 17684</td>\n",
        "      <td>   151</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   17607058893</td>\n",
        "      <td>      12502152079</td>\n",
        "      <td>  1515</td>\n",
        "      <td>   1212</td>\n",
        "      <td>      81</td>\n",
        "      <td> NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389661</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td>    74</td>\n",
        "      <td> 11219</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td> 0050245935920</td>\n",
        "      <td>      18055612404</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>    370</td>\n",
        "      <td>    8713</td>\n",
        "      <td>    NEC</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389662</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25819</td>\n",
        "      <td>  6480</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>    0000000005</td>\n",
        "      <td>      19287833705</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   1487</td>\n",
        "      <td>      53</td>\n",
        "      <td>    NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389663</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 27088</td>\n",
        "      <td>   151</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>    6046706699</td>\n",
        "      <td>      17783292873</td>\n",
        "      <td>  1212</td>\n",
        "      <td>   1212</td>\n",
        "      <td>    8655</td>\n",
        "      <td> FEV701</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389664</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 25819</td>\n",
        "      <td>  6480</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   13094740868</td>\n",
        "      <td>      18609456012</td>\n",
        "      <td>   370</td>\n",
        "      <td>  14826</td>\n",
        "      <td> 2009201</td>\n",
        "      <td> NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389665</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td>   477</td>\n",
        "      <td> 12428</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>    7146350442</td>\n",
        "      <td>      18194282142</td>\n",
        "      <td>  1513</td>\n",
        "      <td>   1212</td>\n",
        "      <td>  121662</td>\n",
        "      <td> NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389666</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 1</td>\n",
        "      <td>    88</td>\n",
        "      <td>   499</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>   61288850005</td>\n",
        "      <td>     914439901819</td>\n",
        "      <td>   370</td>\n",
        "      <td>    178</td>\n",
        "      <td>     273</td>\n",
        "      <td>    NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999998</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 17684</td>\n",
        "      <td> 18285</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>              </td>\n",
        "      <td>     555536511397</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     40</td>\n",
        "      <td>   14232</td>\n",
        "      <td>    FEC</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td>    0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>999999 rows \u00d7 17 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "           cdate  chour  cust_orgno  supp_orgno  destadmin_orgno servabbr  \\\n",
        "28689   20140627      0        6511        2211              NaN  VTSPREM   \n",
        "28381   20140627      0       16219       20474            20474   VTS800   \n",
        "29470   20140627      0        6511       27113              NaN  VTSPREF   \n",
        "29029   20140627      0         842       30213            30213   ENT800   \n",
        "3302    20140627      0        1745         234            28992   VTS800   \n",
        "7446    20140627      0       20516       18084            18084  ENTLOCA   \n",
        "44923   20140627      0       17425        2211              NaN      VTS   \n",
        "8284    20140627      0       20516       18084            18084  ENTLOCA   \n",
        "28771   20140627      0        6511         282              NaN  VTSPREF   \n",
        "148562  20140627      0       22570       12428              NaN       TP   \n",
        "56529   20140627      0        6511       18285              NaN  VTSPREF   \n",
        "189740  20140627      0       17425        2211              NaN      VTS   \n",
        "28031   20140627      0       16219       20474            20474   VTS800   \n",
        "54633   20140627      0       18084       15980              NaN   ENTVTS   \n",
        "117110  20140627      0          57        2211              NaN       TP   \n",
        "27918   20140627      0        2202        1112            20105   VTS800   \n",
        "201960  20140627      0         899         842            21441   VTS800   \n",
        "147942  20140627      0         152       27113              NaN  VTSPREM   \n",
        "47676   20140627      0       27222       18084            18084  ENTLOCA   \n",
        "82226   20140627      0       26958       12428              NaN      VTS   \n",
        "147966  20140627      0       22434       11109              NaN  VTSPREF   \n",
        "221550  20140627      0       17425        2211              NaN      VTS   \n",
        "30981   20140627      0       14837       11219              NaN      VTS   \n",
        "58119   20140627      0         616         842            21441   VTS800   \n",
        "118539  20140627      0       27222       18084            18084  ENTLOCA   \n",
        "77503   20140627      0       14166        2211              NaN       TP   \n",
        "205324  20140627      0         706       18084            18084   ENT800   \n",
        "123699  20140627      0       11219       12428              NaN      VTS   \n",
        "56297   20140627      0        6511         499              NaN      VTS   \n",
        "79321   20140627      0        2265       23836            23836   ENT800   \n",
        "...          ...    ...         ...         ...              ...      ...   \n",
        "389688  20140627      1       25819        6480              NaN  VTSPREM   \n",
        "389689  20140627      1         477        2211              NaN       TP   \n",
        "389690  20140627      1       25819        6480              NaN  VTSPREM   \n",
        "389691  20140627      1         766       24995              NaN       TP   \n",
        "389670  20140627      1       20474       11219              NaN  VTSPREM   \n",
        "389668  20140627      1       22250        2310              NaN  VTSPREF   \n",
        "389645  20140627      1       27088         151              NaN  VTSPREM   \n",
        "389667  20140627      1       25256         151              NaN  VTSPREF   \n",
        "389646  20140627      1          88       10921              NaN  VTSPREF   \n",
        "389647  20140627      1       19943       32996              NaN  VTSPREF   \n",
        "389648  20140627      1       21092         351              NaN  VTSPREM   \n",
        "389649  20140627      1       24284       11109              NaN  VTSPREM   \n",
        "389650  20140627      1       25819        6480              NaN  VTSPREM   \n",
        "389651  20140627      1       25525       18576              NaN       TP   \n",
        "389652  20140627      1       25819        6480              NaN  VTSPREM   \n",
        "389653  20140627      1       25819        6480              NaN  VTSPREM   \n",
        "389654  20140627      1       25819        6480              NaN  VTSPREM   \n",
        "389655  20140627      1       25819        6480              NaN  VTSPREM   \n",
        "389656  20140627      1       25819       30453              NaN  VTSPREM   \n",
        "389657  20140627      1       24282       20630              NaN      VTS   \n",
        "389658  20140627      1       25819        6480              NaN  VTSPREM   \n",
        "389659  20140627      1       24234       26111              NaN  VTSPREM   \n",
        "389660  20140627      1       17684         151              NaN  VTSPREM   \n",
        "389661  20140627      1          74       11219              NaN       TP   \n",
        "389662  20140627      1       25819        6480              NaN  VTSPREM   \n",
        "389663  20140627      1       27088         151              NaN  VTSPREM   \n",
        "389664  20140627      1       25819        6480              NaN  VTSPREM   \n",
        "389665  20140627      1         477       12428              NaN       TP   \n",
        "389666  20140627      1          88         499              NaN       TP   \n",
        "999998  20140627      0       17684       18285              NaN  VTSPREM   \n",
        "\n",
        "              callgno           calldno  origpricingdestid  pricingdestid  \\\n",
        "28689      3054332798       15148319067               1421           1212   \n",
        "28381     19063647238  0850018559732095                370            NaN   \n",
        "29470     14042395931      557132434869               1412           1226   \n",
        "29029      6567255315      086002075380                NaN            NaN   \n",
        "3302       9024065816        0862053860               1212            NaN   \n",
        "7446     818037010667        0345899983                370            NaN   \n",
        "44923      8764200931       12073448467              15918            370   \n",
        "8284      81462703056        0345899983                370            NaN   \n",
        "28771     13107968725        8224956134               1513            322   \n",
        "148562   442034411158       14504245974                370           1212   \n",
        "56529     15084854054      553332766262               1336          51786   \n",
        "189740     8763831233       19545159609              15918           1422   \n",
        "28031     19312612564  0850018557656715               1426            NaN   \n",
        "54633     16504793213        6566799354               1504            405   \n",
        "117110                      15192545495                NaN           1212   \n",
        "27918    524491492335      086002050461                NaN            NaN   \n",
        "201960   886921278851      086002059167                NaN            NaN   \n",
        "147942    41433178414      555130479187                370           1224   \n",
        "47676     15184616964       16504793213                370            NaN   \n",
        "82226     33146781088       14389895749                370           1212   \n",
        "147966    19253034975      523222812386               1504          13149   \n",
        "221550    50934694336       15145078572                370           1212   \n",
        "30981     15088862759       18292325349               1336            115   \n",
        "58119      1800814201      086002058471                NaN            NaN   \n",
        "118539    16072620882       16504793213                370            NaN   \n",
        "77503     81367463403       16505846343              14811           1505   \n",
        "205324     6495832487      086002033558                361            NaN   \n",
        "123699     6613331290       14508443499                370           1212   \n",
        "56297     14793722267      919884855225                370          13187   \n",
        "79321     61409832769      086002060285                370            NaN   \n",
        "...               ...               ...                ...            ...   \n",
        "389688    19854698828       16128661494                370           1471   \n",
        "389689  2348050267282       16477855589                370           1212   \n",
        "389690    19854698828       19729383435                370           1448   \n",
        "389691    97433733427          91840912                NaN          13187   \n",
        "389670    18778912941       19168588581                NaN           1509   \n",
        "389668   971555297727      639152380901                529         122852   \n",
        "389645     6046706699       12506521491               1212           1212   \n",
        "389667    17862044363       17789850390                370           1212   \n",
        "389646                      41794949039                NaN          14855   \n",
        "389647  9779815487677      966532860918                NaN            310   \n",
        "389648   91808901EEEE      971562076522                NaN         140341   \n",
        "389649    17148680125      528993944095               1512          13149   \n",
        "389650    19854698828       19722868122                370           1448   \n",
        "389651    96895407520          91755805                NaN          13187   \n",
        "389652    19854698828       14693625773                370           1448   \n",
        "389653    19854698828       15153824724                370            370   \n",
        "389654    19854698828       18064352478                370            370   \n",
        "389655    19854698828       12145013253                370           1447   \n",
        "389656    19896832644       13604797482                370           1496   \n",
        "389657                      50258356973                NaN          16504   \n",
        "389658     0000000005       15202949923                NaN            370   \n",
        "389659   923076471278      966592756712                NaN         125064   \n",
        "389660    17607058893       12502152079               1515           1212   \n",
        "389661  0050245935920       18055612404                NaN            370   \n",
        "389662     0000000005       19287833705                NaN           1487   \n",
        "389663     6046706699       17783292873               1212           1212   \n",
        "389664    13094740868       18609456012                370          14826   \n",
        "389665     7146350442       18194282142               1513           1212   \n",
        "389666    61288850005      914439901819                370            178   \n",
        "999998                     555536511397                NaN             40   \n",
        "\n",
        "        routingdestcd causecode answind bouncedcallind  attempts  cvrsn_dur  \\\n",
        "28689             101     LD716       Y              N         1       5313   \n",
        "28381             152     LD716       Y              N         1       5241   \n",
        "29470          128013     LD716       Y              N         1       5240   \n",
        "29029              33     LD716       Y              N         1       5054   \n",
        "3302              343     LD716       Y              N         1       5036   \n",
        "7446               33     LD716       Y              N         1       5014   \n",
        "44923            2641     LD716       Y              N         1       4987   \n",
        "8284               33     LD716       Y              N         1       4965   \n",
        "28771             746     LD716       Y              N         1       4948   \n",
        "148562             95     LD716       Y              N         1       4939   \n",
        "56529           10194     LD716       Y              N         1       4935   \n",
        "189740        2460171     LD716       Y              N         1       4912   \n",
        "28031             152     LD716       Y              N         1       4869   \n",
        "54633             152     LD716       Y              N         1       4855   \n",
        "117110            102     LD716       Y              N         1       4850   \n",
        "27918             286     LD716       Y              N         1       4838   \n",
        "201960            152     LD716       Y              N         1       4834   \n",
        "147942         128010     LD716       Y              N         1       4831   \n",
        "47676              33     LD716       Y              N         1       4826   \n",
        "82226           14358     LD716       Y              N         1       4814   \n",
        "147966          13100     LD716       Y              N         1       4813   \n",
        "221550            101     LD716       Y              N         1       4801   \n",
        "30981            1322     LD716       Y              N         1       4775   \n",
        "58119             152     LD716       Y              N         1       4770   \n",
        "118539             33     LD716       Y              N         1       4764   \n",
        "77503              49     LD716       Y              N         1       4762   \n",
        "205324             33     LD716       Y              N         1       4760   \n",
        "123699             95     LD716       Y              N         1       4752   \n",
        "56297          116468     LD716       Y              N         1       4743   \n",
        "79321              33     LD716       Y              N         1       4741   \n",
        "...               ...       ...     ...            ...       ...        ...   \n",
        "389688             55       NEF       N              Y         1          0   \n",
        "389689           8654    NNA716       N              N         1          0   \n",
        "389690             59       NEF       N              Y         1          0   \n",
        "389691          13950    NNA716       N              N         1          0   \n",
        "389670           6616    NNA731       N              N         1          0   \n",
        "389668           3821    NNA716       N              N         1          0   \n",
        "389645             81       FEF       N              N         1          0   \n",
        "389667           8655    NNA716       N              N         1          0   \n",
        "389646         115019    NNA731       N              N         1          0   \n",
        "389647           4905    NNA716       N              N         1          0   \n",
        "389648         125105    NNA717       N              N         1          0   \n",
        "389649          12957    NEV728       N              N         1          0   \n",
        "389650             59       NEF       N              Y         1          0   \n",
        "389651         118624    NNA716       N              N         1          0   \n",
        "389652             59       NEF       N              Y         1          0   \n",
        "389653        2006325       NEF       N              Y         1          0   \n",
        "389654        2005465       NEF       N              Y         1          0   \n",
        "389655        2005525       NEF       N              Y         1          0   \n",
        "389656             51    NNA716       N              N         1          0   \n",
        "389657          12013    NNA718       N              N         1          0   \n",
        "389658           8364       NEF       N              Y         1          0   \n",
        "389659         115694    NNA718       N              N         1          0   \n",
        "389660             81    NNA716       N              N         1          0   \n",
        "389661           8713       NEC       N              N         1          0   \n",
        "389662             53       NEF       N              Y         1          0   \n",
        "389663           8655    FEV701       N              N         1          0   \n",
        "389664        2009201    NNA716       N              N         1          0   \n",
        "389665         121662    NNA716       N              N         1          0   \n",
        "389666            273       NEF       N              Y         1          0   \n",
        "999998          14232       FEC       N              Y         1          0   \n",
        "\n",
        "        count_sum  \n",
        "28689           1  \n",
        "28381           1  \n",
        "29470           1  \n",
        "29029           1  \n",
        "3302            1  \n",
        "7446            1  \n",
        "44923           1  \n",
        "8284            1  \n",
        "28771           1  \n",
        "148562          1  \n",
        "56529           1  \n",
        "189740          1  \n",
        "28031           1  \n",
        "54633           1  \n",
        "117110          1  \n",
        "27918           1  \n",
        "201960          1  \n",
        "147942          1  \n",
        "47676           1  \n",
        "82226           1  \n",
        "147966          1  \n",
        "221550          1  \n",
        "30981           1  \n",
        "58119           1  \n",
        "118539          1  \n",
        "77503           1  \n",
        "205324          1  \n",
        "123699          1  \n",
        "56297           1  \n",
        "79321           1  \n",
        "...           ...  \n",
        "389688          1  \n",
        "389689          1  \n",
        "389690          1  \n",
        "389691          1  \n",
        "389670          1  \n",
        "389668          1  \n",
        "389645          1  \n",
        "389667          1  \n",
        "389646          1  \n",
        "389647          1  \n",
        "389648          1  \n",
        "389649          1  \n",
        "389650          1  \n",
        "389651          1  \n",
        "389652          1  \n",
        "389653          1  \n",
        "389654          1  \n",
        "389655          1  \n",
        "389656          1  \n",
        "389657          1  \n",
        "389658          1  \n",
        "389659          1  \n",
        "389660          1  \n",
        "389661          1  \n",
        "389662          1  \n",
        "389663          1  \n",
        "389664          1  \n",
        "389665          1  \n",
        "389666          1  \n",
        "999998          1  \n",
        "\n",
        "[999999 rows x 17 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "rects1 = ax.bar(range(0,len(sorted_conv_duration[0:100])), sorted_conv_duration.values[0:100], log = False,color='r')\n",
      "ax.set_xticklabels( sorted_conv_duration.index[0:100] )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[<matplotlib.text.Text at 0xaa8d4b4c>,\n",
        " <matplotlib.text.Text at 0xaa8444cc>,\n",
        " <matplotlib.text.Text at 0xaa61facc>,\n",
        " <matplotlib.text.Text at 0xaa62412c>,\n",
        " <matplotlib.text.Text at 0xaa62488c>,\n",
        " <matplotlib.text.Text at 0xaa624fec>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ts+yCbttoYun7xfe7LNskTd6Wc7bb41R/PjxAD9eMWotfVVxJcZDF7RQ/TfoT\nigM7zpcDeDfFlUH/mKKZP0gp/aty2SXXqIiI11HsWnkj8N6U0l9FxA3Ap1JKbyjnmaB4M/0x8Fcp\npUPlch+k2OXyAsWBSvu5uLt+HvgNisZ9HPgXbePd2blsSun8anvTL+19o+jVh+gYe2cfKT50Fj0H\nEfESij6eotgd9iHglSzt7URmrXN9H6b4RUCje9vRzw/R0ZOU0nxEfIa2bbqsVW2/i2pR/GRq0TaY\nUvpaxXy/yNLnZ4KlPV7yekspPd5lfDfQ9roZpIb0uOo94lUVte0sfS6WPD/lWG6gIT3upkbvD1C8\nP67Y03L9i5638u+suI0C/6BzLN2W7UtjamrwttzLdlv1Oes1oyRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkprq/wPY0NNl0uUOzQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaa89a7cc>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Now lets move on to the Next Flag - Number of Unique Callers for each International Phone Number"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = df[[\"callgno\",\"calldno\"]].groupby(\"calldno\")\n",
      "df_NumberOfUniqueCallers = x.apply(lambda x: len(set(x['callgno'])))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_NumberOfUniqueCallers.index\n",
      "df[df.calldno==1685.0].dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "cdate                  int64\n",
        "chour                  int64\n",
        "cust_orgno           float64\n",
        "supp_orgno           float64\n",
        "destadmin_orgno      float64\n",
        "servabbr              object\n",
        "callgno               object\n",
        "calldno               object\n",
        "origpricingdestid    float64\n",
        "pricingdestid        float64\n",
        "routingdestcd        float64\n",
        "causecode             object\n",
        "answind               object\n",
        "bouncedcallind        object\n",
        "attempts               int64\n",
        "cvrsn_dur              int64\n",
        "count_sum              int64\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_NumberOfUniqueCallers = df_NumberOfUniqueCallers.order(ascending=False)\n",
      "\n",
      "temp = df_NumberOfUniqueCallers[0:100]\n",
      "fig, ax = plt.subplots()\n",
      "rects1 = ax.bar(range(0,len(temp)), temp.values,color='r')\n",
      "ax.set_xticklabels(temp.index )\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "[<matplotlib.text.Text at 0xaa831e0c>,\n",
        " <matplotlib.text.Text at 0xaa61fcec>,\n",
        " <matplotlib.text.Text at 0xaa50732c>,\n",
        " <matplotlib.text.Text at 0xaa50796c>,\n",
        " <matplotlib.text.Text at 0xaa50b0ec>,\n",
        " <matplotlib.text.Text at 0xaa50b84c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xaa6337ec>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we have all 3 Alerts, we can create a new feature matrix that is based on phone numbers and the 3 different alerts. Note that these alerts are based on a specific given time window (1 day) in this case. For future work, consider making this whole analysis a time series analysis where each phone number has a moving window for these three metrics."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create node matrix\n",
      "df_NumberOfUniqueCallers\n",
      "sorted_conv_duration\n",
      "df_NumberOfUniqueCallees\n",
      "\n",
      "def FindStatisticalAnomalies (df,approach='mean'):\n",
      "    if approach == 'mean':\n",
      "        return df[df.values>df.values.mean()+3*df.values.std()].index\n",
      "    elif approach == 'std':\n",
      "        return df[df.values>(df.values.std())].index\n",
      "        \n",
      "\n",
      "\n",
      "#Anomalies = FindStatisticalAnomalies(df_NumberOfUniqueCallees, \n",
      "#Anomalies\n",
      "#Anomalies = np.union1d(FindStatisticalAnomalies(df_NumberOfUniqueCallees),FindStatisticalAnomalies(sorted_conv_duration))\n",
      "#plt.scatter(range(0,len(sorted_conv_duration)),sorted_conv_duration.values)\n",
      "Anomalies = np.union1d(df_NumberOfUniqueCallees[0:STAT_THRESHOLD].index,sorted_conv_duration[0:STAT_THRESHOLD].index)\n",
      "Anomalies = np.union1d(Anomalies, df_NumberOfUniqueCallers[0:STAT_THRESHOLD].index)\n",
      "print len(Anomalies)\n",
      "Anomalies"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "27\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "array(['', '0000000005', '0345899983', '0850018446401471', '086002033557',\n",
        "       '086002035540', '086002059167', '086002069945', '0862033647',\n",
        "       '0862061681', '0862064981', '0864024996', '12502603912',\n",
        "       '13094740868', '16473832360', '16504793212', '16504793213',\n",
        "       '19854698828', '221772828147', '2341239', '31206955948',\n",
        "       '381631808839', '441269849069', '61386442953', '73952940409',\n",
        "       '88213800090', '99999904'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Graph Clustering to Detect Anomalies"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "edgelist = df[[\"callgno\",\"calldno\"]]\n",
      "edgelist = edgelist.astype(str)\n",
      "edgelist.to_csv(\"edgelist.csv\",index=False)\n",
      "G = nx.read_edgelist(\"edgelist.csv\",delimiter=',')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#print df[df['calldno']==Anomalies[0]]\n",
      "#df.calldno==\"8291819583\"\n",
      "tmp = nx.Graph()\n",
      "for i in range(0,len(Anomalies)):\n",
      "    tmp = nx.compose(tmp,nx.ego_graph(G,Anomalies[i],radius=3))\n",
      "\n",
      "len(tmp.nodes())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "137465"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pagerank_matrix = nx.pagerank(tmp,max_iter=200)\n",
      "pagerank_matrix_df = pd.DataFrame.from_dict(pagerank_matrix,orient='index')\n",
      "pagerank_matrix_df.column=[\"Page Rank\"]\n",
      "pagerank_matrix_df.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 137465.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>      0.000007</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>      0.000323</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>      0.000002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>      0.000004</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>      0.000004</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>      0.000004</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>      0.080973</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "                   0\n",
        "count  137465.000000\n",
        "mean        0.000007\n",
        "std         0.000323\n",
        "min         0.000002\n",
        "25%         0.000004\n",
        "50%         0.000004\n",
        "75%         0.000004\n",
        "max         0.080973"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trianglecount_matrix = nx.triangles(tmp)\n",
      "trianglecount_matrix_df = pd.DataFrame.from_dict(trianglecount_matrix,orient='index')\n",
      "trianglecount_matrix_df.column=[\"Triangle Count\"]\n",
      "trianglecount_matrix_df.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 137465.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>      0.004277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>      0.457320</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>    132.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                   0\n",
        "count  137465.000000\n",
        "mean        0.004277\n",
        "std         0.457320\n",
        "min         0.000000\n",
        "25%         0.000000\n",
        "50%         0.000000\n",
        "75%         0.000000\n",
        "max       132.000000"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# betweenness_centrality_matrix = nx.betweenness_centrality(tmp)\n",
      "# betweenness_centrality_matrix_df = pd.DataFrame.from_dict(betweenness_centrality_matrix,orient='index')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "body = pd.merge(pagerank_matrix_df,trianglecount_matrix_df,right_index=True,left_index=True)\n",
      "body.head()\n",
      "body = body.reset_index()\n",
      "body.columns = [\"Phone Number\", \"Page Rank\", \"Triangle Count\"]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "body.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "array([[u'', 0.08097253682808253, 61L],\n",
        "       [u'17083716653', 3.647624882142363e-06, 0L],\n",
        "       [u'61242505005', 3.7893191028958237e-06, 0L],\n",
        "       ..., \n",
        "       [u'61262487734', 3.7893191028958237e-06, 0L],\n",
        "       [u'61299047319', 3.923279706242947e-06, 0L],\n",
        "       [u'61749585111', 3.931395238518095e-06, 0L]], dtype=object)"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.decomposition import PCA, KernelPCA\n",
      "import matplotlib.pyplot as plt\n",
      "X=body.values[:,[1,2]]\n",
      "pca = PCA(n_components=2)\n",
      "pca.fit(X)\n",
      "X_lowed = pca.transform(np.real(X))\n",
      "plt.plot(pca.explained_variance_)\n",
      "plt.plot(X_lowed[:,0],'o')\n",
      "NumOfFrauds = len(X_lowed[X_lowed[:,0] > (3*np.std(X_lowed[:,0]))])\n",
      "indices = np.argsort(X_lowed[:,0])[:len(X_lowed)-(NumOfFrauds+1):-1]\n",
      "indices\n",
      "SuspectFraudPhoneNumbers = body.iloc[indices][\"Phone Number\"].values\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x71ffdf2c>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_lowed[X_lowed[:,0] > (2*np.std(X_lowed[:,0]))]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "array([[60.995736068097855, 0.07004272452802476],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.995722531470447, -0.00017898902406120312],\n",
        "       [0.9957225314486299, -0.00017911085907452404],\n",
        "       [0.9957225319173298, -0.0001764934548519111],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.995722531470447, -0.00017898902406120312],\n",
        "       [0.9957225327678347, -0.00017174390239025033],\n",
        "       [0.9957225317531728, -0.00017741017214041813],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.9957225314266533, -0.00017923358502233686],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.9957225313594096, -0.0001796091004929016],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [1.995722515806006, -0.0003560006654224871],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225317989844, -0.0001771543426097432],\n",
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        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225483621746, -8.465900121421994e-05],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225320059057, -0.0001759988131779477],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.9957225312926782, -0.00017998175441672235],\n",
        "       [0.9957225320059057, -0.0001759988131779477],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225313594096, -0.0001796091004929016],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [12.995722391745442, -0.002033691386011366],\n",
        "       [0.995722531470447, -0.00017898902406120312],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [1.9957225440263664, -0.00019840711863688897],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225314266533, -0.00017923358502233686],\n",
        "       [0.9957225339144762, -0.00016534060774207974],\n",
        "       [0.9957225324840695, -0.00017332855900602264],\n",
        "       [0.9957225327998632, -0.0001715650428217577],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.9957225313594096, -0.0001796091004929016],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225313594096, -0.0001796091004929016],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
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        "       [0.9957225311004169, -0.00018105541693819475],\n",
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        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
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        "       [0.9957225311004169, -0.00018105541693819475],\n",
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        "       [0.9957225311870079, -0.00018057185889572855],\n",
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        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
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        "       [0.9957225311870079, -0.00018057185889572855],\n",
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        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225320059057, -0.0001759988131779477],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225314486299, -0.00017911085907452404],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
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        "       [0.9957225313594096, -0.0001796091004929016],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225325893435, -0.0001727406678575771],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.9957225313594096, -0.0001796091004929016],\n",
        "       [0.9957225314486299, -0.00017911085907452404],\n",
        "       [0.9957225313594096, -0.0001796091004929016],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
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        "       [0.9957225311870079, -0.00018057185889572855],\n",
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        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
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        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225327527085, -0.00017182837333326253],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225313594096, -0.0001796091004929016],\n",
        "       [0.9957225314486299, -0.00017911085907452404],\n",
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        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
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        "       [0.9957225311004169, -0.00018105541693819475],\n",
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        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [1.995722516350126, -0.00035296208580145753],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [1.995722518731532, -0.00033966338176071145],\n",
        "       [0.9957225311870079, -0.00018057185889572855],\n",
        "       [0.9957225341671667, -0.00016392948474134686],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.995722532192057, -0.00017495927112398674],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225322241233, -0.00017478020014332387],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225314486299, -0.00017911085907452404],\n",
        "       [0.9957225559471738, -4.2301394225369214e-05],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.995722531213721, -0.00018042268216097925],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225311004169, -0.00018105541693819475],\n",
        "       [0.9957225311870079, -0.00018057185889572855]], dtype=object)"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pr_max= body[\"Page Rank\"].max()\n",
      "pr_min= body[\"Page Rank\"].min()\n",
      "tc_max= body[\"Triangle Count\"].max()\n",
      "tc_min= body[\"Triangle Count\"].min()\n",
      "print pr_max, pr_min, tc_max, tc_min"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0809725368281 2.31029677695e-06 132 0\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Normal_indices = np.argsort(X_lowed[:,0])[0:len(X_lowed)-(NumOfFrauds+1)]\n",
      "X_normal = body.iloc[Normal_indices][[\"Page Rank\",\"Triangle Count\"]].values\n",
      "X_abnormal = body.iloc[indices][[\"Page Rank\",\"Triangle Count\"]].values\n",
      "from sklearn import preprocessing\n",
      "scaler = preprocessing.StandardScaler().fit(X_normal)\n",
      "X_normal = scaler.transform(X_normal)\n",
      "X_abnormal = scaler.transform(X_abnormal)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.font_manager\n",
      "from sklearn import svm\n",
      "\n",
      "#Normal_indices = np.argsort(X_lowed[:,0])[0:len(X_lowed)-(NumOfFrauds+1)]\n",
      "#X_normal = body.iloc[Normal_indices][[\"Page Rank\",\"Triangle Count\"]].values\n",
      "#X_normal[:,0]\n",
      "#X_abnormal = body.iloc[indices][[\"Page Rank\",\"Triangle Count\"]].values\n",
      "\n",
      "xx, yy = np.meshgrid(np.linspace(pr_min, pr_max, 500), np.linspace(tc_min, tc_max, 500))\n",
      "# Generate train data\n",
      "#X = 0.3 * np.random.randn(100, 2)\n",
      "#X_train = np.r_[X + 2, X - 2]\n",
      "X_train = X_normal[0:100000]\n",
      "X_test = X_normal[100000:]\n",
      "#X=X_normal[200000:]\n",
      "# Generate some regular novel observations\n",
      "#X = 0.3 * np.random.randn(20, 2)\n",
      "#X_test = np.r_[X + 2, X - 2]\n",
      "# Generate some abnormal novel observations\n",
      "X_outliers = X_abnormal\n",
      "#X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))\n",
      "\n",
      "# fit the model\n",
      "clf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.1)\n",
      "clf.fit(X_train)\n",
      "y_pred_train = clf.predict(X_train)\n",
      "y_pred_test = clf.predict(X_test)\n",
      "y_pred_outliers = clf.predict(X_outliers)\n",
      "n_error_train = y_pred_train[y_pred_train == -1].size\n",
      "n_error_test = y_pred_test[y_pred_test == -1].size\n",
      "n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size\n",
      "\n",
      "# plot the line, the points, and the nearest vectors to the plane\n",
      "Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
      "Z = Z.reshape(xx.shape)\n",
      "\n",
      "plt.title(\"Novelty Detection\")\n",
      "plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 70), cmap=plt.cm.Blues_r)\n",
      "a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='red')\n",
      "plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='orange')\n",
      "\n",
      "b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white')\n",
      "b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green')\n",
      "c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red')\n",
      "plt.axis('tight')\n",
      "plt.xlim((pr_min, pr_max))\n",
      "plt.ylim((tc_min, tc_max))\n",
      "plt.legend([a.collections[0], b1, b2, c],\n",
      "           [\"learned frontier\", \"training observations\",\n",
      "            \"new regular observations\", \"new abnormal observations\"],\n",
      "           loc=\"upper left\",\n",
      "           prop=matplotlib.font_manager.FontProperties(size=11))\n",
      "plt.xlabel(\n",
      "    \"error train: %d/200 ; errors novel regular: %d/40 ; \"\n",
      "    \"errors novel abnormal: %d/40\"\n",
      "    % (n_error_train, n_error_test, n_error_outliers))\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ACVlERCQBlJBFREQSQAlZREQkAZSQRUREEkAJWUREJAHGdWOQI0k42M3f2h9y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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaa4faeec>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "plt.title(\"Novelty Detection\")\n",
      "plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 700), cmap=plt.cm.Blues_r)\n",
      "a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='red')\n",
      "plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='orange')\n",
      "\n",
      "b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white')\n",
      "b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green')\n",
      "c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red')\n",
      "plt.axis('tight')\n",
      "plt.xlim((pr_min, pr_max))\n",
      "plt.ylim((tc_min, tc_max))\n",
      "plt.legend([a.collections[0], b1, b2, c],\n",
      "           [\"learned frontier\", \"training observations\",\n",
      "            \"new regular observations\", \"new abnormal observations\"],\n",
      "           loc=\"upper left\",\n",
      "           prop=matplotlib.font_manager.FontProperties(size=11))\n",
      "plt.xlabel(\n",
      "    \"error train: %d/200 ; errors novel regular: %d/40 ; \"\n",
      "    \"errors novel abnormal: %d/40\"\n",
      "    % (n_error_train, n_error_test, n_error_outliers))\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hh//FJ59M4dNPPyYjoxH169fniCO6M2bMTbRp044GDdKpUaMmPXv24u9/v5/MzExuvfVO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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8349174c>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "array([[-0.01822054, -0.03740088],\n",
        "       [-0.01817434, -0.03740088],\n",
        "       [-0.01817434, -0.03740088],\n",
        "       ..., \n",
        "       [-0.01140396, -0.03740088],\n",
        "       [-0.01140396, -0.03740088],\n",
        "       [-0.01140396, -0.03740088]])"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "SuspectFraudPhoneNumbers\n",
      "\n",
      "#trianglecount_matrix_df.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array([u'912066357371', u'', u'16504793213', u'6531576447', u'16504793212',\n",
        "       u'13053570815', u'5749055115', u'13023573649', u'18778912941',\n",
        "       u'18002373906', u'0864008860', u'50232952757', u'50232969641',\n",
        "       u'50233081712'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subg = nx.ego_graph(G,\"13053570815\",radius=2)\n",
      "print len(subg.nodes())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "25586\n"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "with d3py.NetworkXFigure(subg, name=\"graph\",width=1000, height=1000) as p:\n",
      "    p += d3py.ForceLayout()\n",
      "    p.css['.node'] = {'fill': 'blue', 'stroke': 'magenta'}\n",
      "    p.show() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "You can find your chart at http://localhost:8000/graph.html\n",
        "Ctrl-C to stop serving the chart and quit!\n",
        "Cleanup after exception: <type 'exceptions.KeyboardInterrupt'>: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Shutting down httpd\n",
        "Shutting down httpd\n"
       ]
      },
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-77-c1b9d70e95df>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mp\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0md3py\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mForceLayout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcss\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'.node'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'fill'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'blue'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'stroke'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'magenta'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/d3py-0.2.3-py2.7.egg/d3py/figure.pyc\u001b[0m in \u001b[0;36mshow\u001b[1;34m(self, interactive)\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mblocking\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_serve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblocking\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    287\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m             \u001b[1;31m# if not blocking, we serve the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/d3py-0.2.3-py2.7.egg/d3py/figure.pyc\u001b[0m in \u001b[0;36m_serve\u001b[1;34m(self, blocking)\u001b[0m\n\u001b[0;32m    316\u001b[0m                 \u001b[1;32mprint\u001b[0m \u001b[1;34m\"Ctrl-C to stop serving the chart and quit!\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    317\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_server_thread\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 318\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhttpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mserve_forever\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    319\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    320\u001b[0m                 \u001b[0mlogging\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'serving asynchronously on port %s'\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mmsgparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/python2.7/SocketServer.pyc\u001b[0m in \u001b[0;36mserve_forever\u001b[1;34m(self, poll_interval)\u001b[0m\n\u001b[0;32m    234\u001b[0m                 \u001b[1;31m# shutdown request and wastes cpu at all other times.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    235\u001b[0m                 r, w, e = _eintr_retry(select.select, [self], [], [],\n\u001b[1;32m--> 236\u001b[1;33m                                        poll_interval)\n\u001b[0m\u001b[0;32m    237\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    238\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle_request_noblock\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/python2.7/SocketServer.pyc\u001b[0m in \u001b[0;36m_eintr_retry\u001b[1;34m(func, *args)\u001b[0m\n\u001b[0;32m    153\u001b[0m     \u001b[1;32mwhile\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mOSError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mselect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0merrno\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEINTR\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import pyplot as plt\n",
      "#ax = scatter(figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\n",
      "#plt.figure.figsize((30,30))\n",
      "subg = nx.ego_graph(G,\"912066357371\",radius=2)\n",
      "nx.draw_networkx(subg,with_labels=False)\n",
      "print len(subg.nodes())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "'912066357371'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-27-26d93d4d814e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m#ax = scatter(figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m#plt.figure.figsize((30,30))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0msubg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mego_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mG\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"912066357371\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mradius\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_networkx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubg\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mwith_labels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/networkx/generators/ego.pyc\u001b[0m in \u001b[0;36mego_graph\u001b[1;34m(G, n, radius, center, undirected, distance)\u001b[0m\n\u001b[0;32m     63\u001b[0m                                            weight=distance)\n\u001b[0;32m     64\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m             \u001b[0msp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msingle_source_shortest_path_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mG\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcutoff\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mradius\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m     \u001b[0mH\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mG\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubgraph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/networkx/algorithms/shortest_paths/unweighted.pyc\u001b[0m in \u001b[0;36msingle_source_shortest_path_length\u001b[1;34m(G, source, cutoff)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mseen\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m                 \u001b[0mseen\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m \u001b[1;31m# set the level of vertex v\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[0mnextlevel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mG\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# add neighbors of v\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcutoff\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mcutoff\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m         \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/networkx/classes/graph.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, n)\u001b[0m\n\u001b[0;32m    317\u001b[0m         \u001b[1;33m{\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    318\u001b[0m         \"\"\"\n\u001b[1;32m--> 319\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madj\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    321\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyError\u001b[0m: '912066357371'"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "REINFORCEMENT LEARNING"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Feed the Anamolies produced from D3 graph visualization into Supervised learning"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numbers = SuspectFraudPhoneNumbers\n",
      "df_new = df\n",
      "df_new['label'] = 0\n",
      "print len(df_new)\n",
      "\n",
      "for num in numbers:\n",
      "    ind = df_new[df_new['callgno'] == num].index\n",
      "    df_new.loc[ind,'label']=1\n",
      "#    ind2 = df_new[df_new['calldno'] == num].index\n",
      "#    df_new.loc[ind2,'label']=1\n",
      "df_new.label.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "999999\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "0    948678\n",
        "1     51321\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_new[df_new.label==1].callgno.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "array(['', '5749055115', '6531576447', '18778912941', '13023573649',\n",
        "       '16504793213', '13053570815', '18002373906', '16504793212',\n",
        "       '912066357371'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numbers"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "array([u'912066357371', u'', u'16504793213', u'6531576447', u'16504793212',\n",
        "       u'13053570815', u'5749055115', u'13023573649', u'18778912941',\n",
        "       u'18002373906', u'0864008860', u'50232952757', u'50232969641',\n",
        "       u'50233081712'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_final = df_new[['answind','origpricingdestid','routingdestcd','supp_orgno','cvrsn_dur','attempts','cust_orgno','pricingdestid']]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ind = df_final[df_final['answind'] == 'N'].index\n",
      "df_final.loc[ind,'answind']=0\n",
      "ind1 = df_final[df_final['answind'] == 'Y'].index\n",
      "df_final.loc[ind1,'answind']=1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py:389: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_index,col_indexer] = value instead\n",
        "  self.obj[item] = s\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_final.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>answind</th>\n",
        "      <th>origpricingdestid</th>\n",
        "      <th>routingdestcd</th>\n",
        "      <th>supp_orgno</th>\n",
        "      <th>cvrsn_dur</th>\n",
        "      <th>attempts</th>\n",
        "      <th>cust_orgno</th>\n",
        "      <th>pricingdestid</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "      <td>  370</td>\n",
        "      <td>     212</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>    36</td>\n",
        "      <td>    99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  119171</td>\n",
        "      <td> 499</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 15309</td>\n",
        "      <td> 13187</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1469</td>\n",
        "      <td>     861</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 22381</td>\n",
        "      <td>    90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>   13948</td>\n",
        "      <td> 499</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 15309</td>\n",
        "      <td> 13187</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1421</td>\n",
        "      <td> 2999995</td>\n",
        "      <td>  57</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 24284</td>\n",
        "      <td>   370</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "  answind  origpricingdestid  routingdestcd  supp_orgno  cvrsn_dur  attempts  \\\n",
        "0       0                370            212         NaN          0         1   \n",
        "1       0                NaN         119171         499          0         1   \n",
        "2       0               1469            861         NaN          0         1   \n",
        "3       0                NaN          13948         499          0         1   \n",
        "4       0               1421        2999995          57          0         1   \n",
        "\n",
        "   cust_orgno  pricingdestid  \n",
        "0          36             99  \n",
        "1       15309          13187  \n",
        "2       22381             90  \n",
        "3       15309          13187  \n",
        "4       24284            370  "
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#df_final = df_final.fillna(method = 'bfill',axis = 0)\n",
      "df_final = df_final.fillna(0)\n",
      "y = df_new['label'].values\n",
      "len(df_final)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "999999"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = np.array(df_final)\n",
      "len(X)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "999999"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "SparK can be used on Big Data Real-time Streaming detection of Anomalies to scale at Peta Bytes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LogisticRegression\n",
      "from sklearn.cross_validation import train_test_split\n",
      "from sklearn.ensemble import RandomForestClassifier\n",
      "from sklearn import cross_validation\n",
      "\n",
      "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
      "\n",
      "print \"Running Logistic Regression ...\"\n",
      "clf  = RandomForestClassifier(n_estimators=3,min_samples_leaf=3)\n",
      "\n",
      "clf = clf.fit(X_train, y_train)\n",
      "scores = cross_validation.cross_val_score(clf, X, y, cv=5)\n",
      "print \"%s -- %s\" % (clf.__class__, np.mean(scores))\n",
      "probas_ = clf.predict_proba(X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running Logistic Regression ...\n",
        "<class 'sklearn.ensemble.forest.RandomForestClassifier'> -- 0.97760097763"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.metrics import roc_curve, auc\n",
      "\n",
      "# Compute ROC curve and area the curve\n",
      "fpr, tpr, thresholds = roc_curve(y_test, probas_[:, 1])\n",
      "roc_auc = auc(fpr, tpr)\n",
      "print(\"Area under the ROC curve : %f\" % roc_auc)\n",
      "\n",
      "# Plot ROC curve\n",
      "plt.clf()\n",
      "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
      "plt.plot([0, 1], [0, 1], 'k--')\n",
      "plt.xlim([0.0, 1.0])\n",
      "plt.ylim([0.0, 1.0])\n",
      "plt.xlabel('False Positive Rate')\n",
      "plt.ylabel('True Positive Rate')\n",
      "plt.title('Receiver operating characteristic example')\n",
      "plt.legend(loc=\"lower right\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Area under the ROC curve : 0.962801\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1246: UserWarning: findfont: Could not match :family=Bitstream Vera Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=11.0. Returning /usr/share/matplotlib/mpl-data/fonts/ttf/cmb10.ttf\n",
        "  UserWarning)\n",
        "/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1246: UserWarning: findfont: Could not match :family=Bitstream Vera Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0. Returning /usr/share/matplotlib/mpl-data/fonts/ttf/cmb10.ttf\n",
        "  UserWarning)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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I1FPTpz/FoEH9Wbt2DaNGjeX99xdqHvZ6LiYr9JxtkQq9uSp0EamfOnbsSNu2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\nJf4Hp/kzqxXj29cAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x818b7b8c>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print time.time() - start\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3806.30259919\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>cdate</th>\n",
        "      <th>chour</th>\n",
        "      <th>cust_orgno</th>\n",
        "      <th>supp_orgno</th>\n",
        "      <th>destadmin_orgno</th>\n",
        "      <th>servabbr</th>\n",
        "      <th>callgno</th>\n",
        "      <th>calldno</th>\n",
        "      <th>origpricingdestid</th>\n",
        "      <th>pricingdestid</th>\n",
        "      <th>routingdestcd</th>\n",
        "      <th>causecode</th>\n",
        "      <th>answind</th>\n",
        "      <th>bouncedcallind</th>\n",
        "      <th>attempts</th>\n",
        "      <th>cvrsn_dur</th>\n",
        "      <th>count_sum</th>\n",
        "      <th>label</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>    36</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>  213660646009</td>\n",
        "      <td> 242800112117</td>\n",
        "      <td>  370</td>\n",
        "      <td>    99</td>\n",
        "      <td>     212</td>\n",
        "      <td> IBLK812</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15309</td>\n",
        "      <td> 499</td>\n",
        "      <td>NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td> 9779867710086</td>\n",
        "      <td> 917525819747</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 13187</td>\n",
        "      <td>  119171</td>\n",
        "      <td>  NNA731</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 22381</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>   12103677877</td>\n",
        "      <td>    573700845</td>\n",
        "      <td> 1469</td>\n",
        "      <td>    90</td>\n",
        "      <td>     861</td>\n",
        "      <td>     NEV</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15309</td>\n",
        "      <td> 499</td>\n",
        "      <td>NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td> 9779847201600</td>\n",
        "      <td> 918483815584</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 13187</td>\n",
        "      <td>   13948</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 24284</td>\n",
        "      <td>  57</td>\n",
        "      <td>NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>   17863451573</td>\n",
        "      <td>  19494117781</td>\n",
        "      <td> 1421</td>\n",
        "      <td>   370</td>\n",
        "      <td> 2999995</td>\n",
        "      <td>  FEV701</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "      cdate  chour  cust_orgno  supp_orgno  destadmin_orgno servabbr  \\\n",
        "0  20140627      0          36         NaN              NaN      NaN   \n",
        "1  20140627      0       15309         499              NaN       TP   \n",
        "2  20140627      0       22381         NaN              NaN      NaN   \n",
        "3  20140627      0       15309         499              NaN       TP   \n",
        "4  20140627      0       24284          57              NaN  VTSPREF   \n",
        "\n",
        "         callgno       calldno  origpricingdestid  pricingdestid  \\\n",
        "0   213660646009  242800112117                370             99   \n",
        "1  9779867710086  917525819747                NaN          13187   \n",
        "2    12103677877     573700845               1469             90   \n",
        "3  9779847201600  918483815584                NaN          13187   \n",
        "4    17863451573   19494117781               1421            370   \n",
        "\n",
        "   routingdestcd causecode answind bouncedcallind  attempts  cvrsn_dur  \\\n",
        "0            212   IBLK812       N              N         1          0   \n",
        "1         119171    NNA731       N              N         1          0   \n",
        "2            861       NEV       N              N         1          0   \n",
        "3          13948    NNA716       N              N         1          0   \n",
        "4        2999995    FEV701       N              N         1          0   \n",
        "\n",
        "   count_sum  label  \n",
        "0          1      0  \n",
        "1          1      0  \n",
        "2          1      0  \n",
        "3          1      0  \n",
        "4          1      0  "
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xx = df[0:1000].groupby(\"callgno\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yy = xx.servabbr.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "RandomForestClassifier(bootstrap=True, compute_importances=None,\n",
        "            criterion='gini', max_depth=None, max_features='auto',\n",
        "            min_density=None, min_samples_leaf=3, min_samples_split=2,\n",
        "            n_estimators=3, n_jobs=1, oob_score=False, random_state=None,\n",
        "            verbose=0)"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import cPickle as pickle\n",
      "pickle.dump(clf, open( \"RF_phone_Fraud.pickle\", \"wb\" ) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "classifier_file = \"RF_phone_Fraud.pickle\"\n",
      "Classifier = pickle.load( open( classifier_file, \"rb\" ) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "MLprediction = Classifier.predict_proba(X)\n",
      "sum(MLprediction[:,1]==1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "17159"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nums = df_new[MLprediction[:,1]==1].callgno.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nums"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "array(['', '6531576447', '552179566266', '18778912941', '18002373906',\n",
        "       '13023573649', '16504793212', '1582123142889', '96565007378',\n",
        "       '8613902884787', '16504793213', '59995220497', '912066357371',\n",
        "       '85239104005', '8007367542', '8772246534', '966555133971',\n",
        "       '2100098', '3332085863', '96560029771', '82262527096', '5167',\n",
        "       '966533332539', '12031064809', '593994339308', '96566495815',\n",
        "       '6582647858', '8009672351', '447917340630', '582123172228',\n",
        "       '6556439117', '4740064634', '85363207527', '4915121925005',\n",
        "       '226343391', '8886068399', '821045901949', '96896748662',\n",
        "       '85298860389', '18007826804', '966143920588', '966560851887',\n",
        "       '64223736944', '447978376751', '1711318933', '447739386645',\n",
        "       '584123837624', '584129725875', '85568455113', '96162498',\n",
        "       '8992207568', '08159048474', '642040387974', '97477442975',\n",
        "       '449147614422', '17', '81120315237', '966568005334',\n",
        "       '0014156550007', '6285276517381', '588472194057', '96566977353',\n",
        "       '966503140097', '74805694', '27826181552', '25005', '37525016',\n",
        "       '1116', '5353789168', '234572', '34655877341'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(set.union(set(numbers),set(Anomalies),set(nums)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "101"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_NumberOfUniqueCallers"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "calldno\n",
        "381631808839        275\n",
        "16473832360         143\n",
        "086002069945        127\n",
        "                    104\n",
        "16504793213         103\n",
        "12502603912         102\n",
        "88213800090          99\n",
        "0850018446401471     94\n",
        "086002033557         80\n",
        "73952940409          75\n",
        "73952930062          74\n",
        "60212345683          70\n",
        "33562193262          67\n",
        "0345899983           67\n",
        "0864028215           63\n",
        "...\n",
        "862488577590    1\n",
        "862488577605    1\n",
        "862488577617    1\n",
        "862488577618    1\n",
        "862488577622    1\n",
        "862488577624    1\n",
        "862488577647    1\n",
        "862488577652    1\n",
        "862488577654    1\n",
        "862488577659    1\n",
        "862488577686    1\n",
        "862488577688    1\n",
        "862488577697    1\n",
        "862488577700    1\n",
        "61415023452     1\n",
        "Length: 579303, dtype: int64"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Anomalies"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "array(['', '0000000005', '0345899983', '0850018446401471', '086002033557',\n",
        "       '086002035540', '086002059167', '086002069945', '0862033647',\n",
        "       '0862061681', '0862064981', '0864024996', '12502603912',\n",
        "       '13094740868', '16473832360', '16504793212', '16504793213',\n",
        "       '19854698828', '221772828147', '2341239', '31206955948',\n",
        "       '381631808839', '441269849069', '61386442953', '73952940409',\n",
        "       '88213800090', '99999904'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.font_manager\n",
      "from sklearn import svm\n",
      "\n",
      "xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))\n",
      "# Generate train data\n",
      "X = 0.3 * np.random.randn(100, 2)\n",
      "X_train = np.r_[X + 2, X - 2]\n",
      "# Generate some regular novel observations\n",
      "X = 0.3 * np.random.randn(20, 2)\n",
      "X_test = np.r_[X + 2, X - 2]\n",
      "# Generate some abnormal novel observations\n",
      "X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))\n",
      "\n",
      "# fit the model\n",
      "clf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.1)\n",
      "clf.fit(X_train)\n",
      "y_pred_train = clf.predict(X_train)\n",
      "y_pred_test = clf.predict(X_test)\n",
      "y_pred_outliers = clf.predict(X_outliers)\n",
      "n_error_train = y_pred_train[y_pred_train == -1].size\n",
      "n_error_test = y_pred_test[y_pred_test == -1].size\n",
      "n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size\n",
      "\n",
      "# plot the line, the points, and the nearest vectors to the plane\n",
      "Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
      "Z = Z.reshape(xx.shape)\n",
      "\n",
      "plt.title(\"Novelty Detection\")\n",
      "plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 100), cmap=plt.cm.Blues_r)\n",
      "a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='red')\n",
      "plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='orange')\n",
      "\n",
      "b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white')\n",
      "b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green')\n",
      "c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red')\n",
      "plt.axis('tight')\n",
      "plt.xlim((-5, 5))\n",
      "plt.ylim((-5, 5))\n",
      "plt.legend([a.collections[0], b1, b2, c],\n",
      "           [\"learned frontier\", \"training observations\",\n",
      "            \"new regular observations\", \"new abnormal observations\"],\n",
      "           loc=\"upper left\",\n",
      "           prop=matplotlib.font_manager.FontProperties(size=11))\n",
      "plt.xlabel(\n",
      "    \"error train: %d/200 ; errors novel regular: %d/40 ; \"\n",
      "    \"errors novel abnormal: %d/40\"\n",
      "    % (n_error_train, n_error_test, n_error_outliers))\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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n1LaIUmwBycHtHFnzHv3+D96JKsUZXP5G2ga3lx+PCIigqbQff4zckksRyncq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CAAAg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\nJJrM9ezvP676VZ9rS8AQroJtXdHFet/Ny5aIg15XC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cv8/Xp4BnUj9OM04ItSyrNQruof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\nGTJkyNDn8P8B2ATD62GG75oAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9000078c>"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Z.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "(500, 500)"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>cdate</th>\n",
        "      <th>chour</th>\n",
        "      <th>cust_orgno</th>\n",
        "      <th>supp_orgno</th>\n",
        "      <th>destadmin_orgno</th>\n",
        "      <th>servabbr</th>\n",
        "      <th>callgno</th>\n",
        "      <th>calldno</th>\n",
        "      <th>origpricingdestid</th>\n",
        "      <th>pricingdestid</th>\n",
        "      <th>routingdestcd</th>\n",
        "      <th>causecode</th>\n",
        "      <th>answind</th>\n",
        "      <th>bouncedcallind</th>\n",
        "      <th>attempts</th>\n",
        "      <th>cvrsn_dur</th>\n",
        "      <th>count_sum</th>\n",
        "      <th>label</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0     </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>    36</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>    213660646009</td>\n",
        "      <td>  242800112117</td>\n",
        "      <td>    370</td>\n",
        "      <td>     99</td>\n",
        "      <td>     212</td>\n",
        "      <td> IBLK812</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1     </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15309</td>\n",
        "      <td>   499</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>   9779867710086</td>\n",
        "      <td>  917525819747</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>  13187</td>\n",
        "      <td>  119171</td>\n",
        "      <td>  NNA731</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2     </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 22381</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>     12103677877</td>\n",
        "      <td>     573700845</td>\n",
        "      <td>   1469</td>\n",
        "      <td>     90</td>\n",
        "      <td>     861</td>\n",
        "      <td>     NEV</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3     </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15309</td>\n",
        "      <td>   499</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>   9779847201600</td>\n",
        "      <td>  918483815584</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>  13187</td>\n",
        "      <td>   13948</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4     </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 24284</td>\n",
        "      <td>    57</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>     17863451573</td>\n",
        "      <td>   19494117781</td>\n",
        "      <td>   1421</td>\n",
        "      <td>    370</td>\n",
        "      <td> 2999995</td>\n",
        "      <td>  FEV701</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5     </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15309</td>\n",
        "      <td>   499</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>   9779866909245</td>\n",
        "      <td>  919805607755</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>  13187</td>\n",
        "      <td>  119160</td>\n",
        "      <td>     FEF</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6     </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 28888</td>\n",
        "      <td>   499</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   9779617506990</td>\n",
        "      <td>  918962189710</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>  13187</td>\n",
        "      <td>   13927</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7     </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>   616</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>      7322016230</td>\n",
        "      <td>   19196502662</td>\n",
        "      <td>   1351</td>\n",
        "      <td>   1403</td>\n",
        "      <td> 2004265</td>\n",
        "      <td>  NEU716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8     </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 27445</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        91899813</td>\n",
        "      <td>    8291819583</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>    322</td>\n",
        "      <td>     146</td>\n",
        "      <td>     NEV</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9     </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 32952</td>\n",
        "      <td> 26574</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>     37066185005</td>\n",
        "      <td> 2349096145937</td>\n",
        "      <td>    NaN</td>\n",
        "      <td> 127991</td>\n",
        "      <td>  117605</td>\n",
        "      <td>     FEF</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 28888</td>\n",
        "      <td>   499</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   9779617508669</td>\n",
        "      <td>  917503389039</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>  13187</td>\n",
        "      <td>   13947</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 24234</td>\n",
        "      <td> 25229</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>    923029034615</td>\n",
        "      <td>  966592942149</td>\n",
        "      <td>    NaN</td>\n",
        "      <td> 125064</td>\n",
        "      <td>  115694</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 32786</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>            9900</td>\n",
        "      <td>   25299832061</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>    320</td>\n",
        "      <td>     148</td>\n",
        "      <td> IBLK812</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6511</td>\n",
        "      <td>   499</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>     12105716340</td>\n",
        "      <td>  918861200080</td>\n",
        "      <td>    370</td>\n",
        "      <td>  13187</td>\n",
        "      <td>  119163</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15332</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>      6043189900</td>\n",
        "      <td>   16043181292</td>\n",
        "      <td>   1212</td>\n",
        "      <td>   1212</td>\n",
        "      <td>    2685</td>\n",
        "      <td>  NEC430</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 25819</td>\n",
        "      <td> 25519</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>                </td>\n",
        "      <td>   19096099182</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>   1512</td>\n",
        "      <td> 2007305</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>   443</td>\n",
        "      <td> 17425</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>     13055944450</td>\n",
        "      <td>   18768489327</td>\n",
        "      <td>   1422</td>\n",
        "      <td>  15918</td>\n",
        "      <td>    9477</td>\n",
        "      <td>     NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 24284</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td> 791569921458681</td>\n",
        "      <td>  593931403358</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>    124</td>\n",
        "      <td>     225</td>\n",
        "      <td> IBLK812</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 13518</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>    213662835815</td>\n",
        "      <td>   22396627846</td>\n",
        "      <td>    370</td>\n",
        "      <td>  13047</td>\n",
        "      <td>   11514</td>\n",
        "      <td>    IBLK</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>    36</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>    213660514072</td>\n",
        "      <td>  242800112117</td>\n",
        "      <td>    370</td>\n",
        "      <td>     99</td>\n",
        "      <td>     212</td>\n",
        "      <td> IBLK812</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 25819</td>\n",
        "      <td> 25519</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>     14032851770</td>\n",
        "      <td>   14034544264</td>\n",
        "      <td>   1212</td>\n",
        "      <td>   1212</td>\n",
        "      <td>      92</td>\n",
        "      <td>  FEV701</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>   152</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>    447717441985</td>\n",
        "      <td>    8448006682</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>   1214</td>\n",
        "      <td>    5909</td>\n",
        "      <td>     NEV</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15332</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>     17786543244</td>\n",
        "      <td>   13063485870</td>\n",
        "      <td>   1212</td>\n",
        "      <td>   1212</td>\n",
        "      <td>      85</td>\n",
        "      <td>  NEC430</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 16075</td>\n",
        "      <td> 28888</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>     81363278983</td>\n",
        "      <td> 9779844400131</td>\n",
        "      <td>  14811</td>\n",
        "      <td> 132593</td>\n",
        "      <td>  115939</td>\n",
        "      <td>     NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>   443</td>\n",
        "      <td> 17425</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>     13055944450</td>\n",
        "      <td>   18768489327</td>\n",
        "      <td>   1422</td>\n",
        "      <td>  15918</td>\n",
        "      <td>    9477</td>\n",
        "      <td>  NNA718</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 24284</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>     59160497459</td>\n",
        "      <td>    5689597478</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>     77</td>\n",
        "      <td>     208</td>\n",
        "      <td> IBLK812</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 11219</td>\n",
        "      <td>   499</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>        66490664</td>\n",
        "      <td>  919942253597</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>  13187</td>\n",
        "      <td>   13941</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6511</td>\n",
        "      <td> 27113</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>     18123448628</td>\n",
        "      <td>  551149916056</td>\n",
        "      <td>    370</td>\n",
        "      <td>     41</td>\n",
        "      <td>  128014</td>\n",
        "      <td>  NNA717</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 32572</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>      1146127027</td>\n",
        "      <td>    8293815963</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>    322</td>\n",
        "      <td>     146</td>\n",
        "      <td>     NEV</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29    </th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6511</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>     18662434362</td>\n",
        "      <td>  966596528492</td>\n",
        "      <td>    NaN</td>\n",
        "      <td> 125064</td>\n",
        "      <td>  115694</td>\n",
        "      <td>    IBLK</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999969</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  7139</td>\n",
        "      <td> 12428</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>     9477640EEEE</td>\n",
        "      <td>   15149695695</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>   1212</td>\n",
        "      <td>     101</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999970</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 24284</td>\n",
        "      <td>   477</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>                </td>\n",
        "      <td>   59994657722</td>\n",
        "      <td>    NaN</td>\n",
        "      <td> 126054</td>\n",
        "      <td>  117007</td>\n",
        "      <td>  NNA717</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999971</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>    73</td>\n",
        "      <td> 30053</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>    966554087269</td>\n",
        "      <td>  251917471459</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>  13725</td>\n",
        "      <td>   12254</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999972</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 29765</td>\n",
        "      <td>  6469</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>     19804042202</td>\n",
        "      <td>  558183443903</td>\n",
        "      <td>   1398</td>\n",
        "      <td>     45</td>\n",
        "      <td>    3542</td>\n",
        "      <td>  NNA731</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999973</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 29907</td>\n",
        "      <td>   288</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>      4088506466</td>\n",
        "      <td>   50670609674</td>\n",
        "      <td>   1504</td>\n",
        "      <td> 138891</td>\n",
        "      <td>  123805</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999974</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 32014</td>\n",
        "      <td>  6480</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>    447860802143</td>\n",
        "      <td>   19292998436</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>     NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999975</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 11417</td>\n",
        "      <td> 22570</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>    441517929017</td>\n",
        "      <td>  447894845735</td>\n",
        "      <td> 135141</td>\n",
        "      <td>   1163</td>\n",
        "      <td>    9313</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999976</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>    57</td>\n",
        "      <td> 25717</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>     17179753842</td>\n",
        "      <td>      91446699</td>\n",
        "      <td>    370</td>\n",
        "      <td>    178</td>\n",
        "      <td>     273</td>\n",
        "      <td>     NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999977</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 21876</td>\n",
        "      <td> 21092</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>   9779804253221</td>\n",
        "      <td>      91809090</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>  13187</td>\n",
        "      <td>   13928</td>\n",
        "      <td>  NNA720</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999978</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>    57</td>\n",
        "      <td> 21092</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>      TP</td>\n",
        "      <td>     17179753842</td>\n",
        "      <td>      91446699</td>\n",
        "      <td>    370</td>\n",
        "      <td>    178</td>\n",
        "      <td>     273</td>\n",
        "      <td>     FEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999979</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>    57</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>     19095765440</td>\n",
        "      <td>   12042192816</td>\n",
        "      <td>   1514</td>\n",
        "      <td>   1212</td>\n",
        "      <td>      77</td>\n",
        "      <td>     NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999980</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 22327</td>\n",
        "      <td>  2423</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>      5712747663</td>\n",
        "      <td>   14034038186</td>\n",
        "      <td>   1365</td>\n",
        "      <td>   1212</td>\n",
        "      <td>      92</td>\n",
        "      <td>  FEV701</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999981</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>   303</td>\n",
        "      <td> 11219</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>                </td>\n",
        "      <td>   17724109728</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>   1421</td>\n",
        "      <td> 2460185</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999982</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6511</td>\n",
        "      <td> 22509</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>     13105185224</td>\n",
        "      <td>   50472196992</td>\n",
        "      <td>   1513</td>\n",
        "      <td>    167</td>\n",
        "      <td>    3466</td>\n",
        "      <td>     NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999983</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 23240</td>\n",
        "      <td> 12369</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>      5625994605</td>\n",
        "      <td>   12424418592</td>\n",
        "      <td>   1513</td>\n",
        "      <td> 131391</td>\n",
        "      <td>  119305</td>\n",
        "      <td>  FEV701</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999984</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 27072</td>\n",
        "      <td> 19900</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>     85238773843</td>\n",
        "      <td>  861354758174</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>     82</td>\n",
        "      <td>     904</td>\n",
        "      <td>     NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999985</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6517</td>\n",
        "      <td> 11352</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>    118092167039</td>\n",
        "      <td> 5215518109257</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>  16226</td>\n",
        "      <td>   14341</td>\n",
        "      <td>     NEF</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999986</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 25717</td>\n",
        "      <td> 18754</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>     08040570046</td>\n",
        "      <td>      91996827</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>  13187</td>\n",
        "      <td>   13950</td>\n",
        "      <td>  NNA731</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999987</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 25717</td>\n",
        "      <td> 18576</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREF</td>\n",
        "      <td>    923045310187</td>\n",
        "      <td>      91851489</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>  13187</td>\n",
        "      <td>  118620</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999988</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 27088</td>\n",
        "      <td>  2211</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>      6046706699</td>\n",
        "      <td>   17785656495</td>\n",
        "      <td>   1212</td>\n",
        "      <td>   1212</td>\n",
        "      <td>    8655</td>\n",
        "      <td>     FEC</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999989</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6268</td>\n",
        "      <td>   617</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>    258878185984</td>\n",
        "      <td>    2651873227</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>    223</td>\n",
        "      <td>     308</td>\n",
        "      <td>  NNA718</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999990</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 11029</td>\n",
        "      <td>    74</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>                </td>\n",
        "      <td>  543534533283</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>   1181</td>\n",
        "      <td>    8513</td>\n",
        "      <td>  FEV701</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999991</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15865</td>\n",
        "      <td>   152</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     VTS</td>\n",
        "      <td>    923465130434</td>\n",
        "      <td>  447867867785</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>   1166</td>\n",
        "      <td>    9316</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999992</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 25819</td>\n",
        "      <td> 25194</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>     61381993318</td>\n",
        "      <td>   61732605489</td>\n",
        "      <td>   1212</td>\n",
        "      <td>  71793</td>\n",
        "      <td>    8793</td>\n",
        "      <td>  FEV701</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999993</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1028</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>    584262353021</td>\n",
        "      <td> 5214181139134</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>  16223</td>\n",
        "      <td>   14320</td>\n",
        "      <td> IBLK806</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999994</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>   152</td>\n",
        "      <td> 21695</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>   6282819033023</td>\n",
        "      <td>    8521862806</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>    168</td>\n",
        "      <td>     269</td>\n",
        "      <td>  FEV701</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999995</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 21092</td>\n",
        "      <td>   351</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>    91808992EEEE</td>\n",
        "      <td>  971508278961</td>\n",
        "      <td>    NaN</td>\n",
        "      <td> 140341</td>\n",
        "      <td>  125105</td>\n",
        "      <td>  NNA716</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999996</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 29765</td>\n",
        "      <td>   143</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>     17542276381</td>\n",
        "      <td>  556195870799</td>\n",
        "      <td>    370</td>\n",
        "      <td>     45</td>\n",
        "      <td>   12693</td>\n",
        "      <td>     NNA</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999997</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td>  6530</td>\n",
        "      <td> 20474</td>\n",
        "      <td> 20474</td>\n",
        "      <td>  LOCALA</td>\n",
        "      <td>                </td>\n",
        "      <td>   13142604075</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>     152</td>\n",
        "      <td>  NNA719</td>\n",
        "      <td> N</td>\n",
        "      <td> N</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999998</th>\n",
        "      <td> 20140627</td>\n",
        "      <td> 0</td>\n",
        "      <td> 17684</td>\n",
        "      <td> 18285</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> VTSPREM</td>\n",
        "      <td>                </td>\n",
        "      <td>  555536511397</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>     40</td>\n",
        "      <td>   14232</td>\n",
        "      <td>     FEC</td>\n",
        "      <td> N</td>\n",
        "      <td> Y</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>999999 rows \u00d7 18 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "           cdate  chour  cust_orgno  supp_orgno  destadmin_orgno servabbr  \\\n",
        "0       20140627      0          36         NaN              NaN      NaN   \n",
        "1       20140627      0       15309         499              NaN       TP   \n",
        "2       20140627      0       22381         NaN              NaN      NaN   \n",
        "3       20140627      0       15309         499              NaN       TP   \n",
        "4       20140627      0       24284          57              NaN  VTSPREF   \n",
        "5       20140627      0       15309         499              NaN       TP   \n",
        "6       20140627      0       28888         499              NaN  VTSPREM   \n",
        "7       20140627      0         616         NaN              NaN      NaN   \n",
        "8       20140627      0       27445         NaN              NaN      NaN   \n",
        "9       20140627      0       32952       26574              NaN  VTSPREM   \n",
        "10      20140627      0       28888         499              NaN  VTSPREM   \n",
        "11      20140627      0       24234       25229              NaN  VTSPREM   \n",
        "12      20140627      0       32786         NaN              NaN      NaN   \n",
        "13      20140627      0        6511         499              NaN      VTS   \n",
        "14      20140627      0       15332         NaN              NaN      NaN   \n",
        "15      20140627      0       25819       25519              NaN  VTSPREM   \n",
        "16      20140627      0         443       17425              NaN  VTSPREM   \n",
        "17      20140627      0       24284         NaN              NaN      NaN   \n",
        "18      20140627      0       13518         NaN              NaN      NaN   \n",
        "19      20140627      0          36         NaN              NaN      NaN   \n",
        "20      20140627      0       25819       25519              NaN  VTSPREM   \n",
        "21      20140627      0         152         NaN              NaN      NaN   \n",
        "22      20140627      0       15332         NaN              NaN      NaN   \n",
        "23      20140627      0       16075       28888              NaN  VTSPREF   \n",
        "24      20140627      0         443       17425              NaN  VTSPREM   \n",
        "25      20140627      0       24284         NaN              NaN      NaN   \n",
        "26      20140627      0       11219         499              NaN      VTS   \n",
        "27      20140627      0        6511       27113              NaN  VTSPREF   \n",
        "28      20140627      0       32572         NaN              NaN      NaN   \n",
        "29      20140627      0        6511         NaN              NaN      NaN   \n",
        "...          ...    ...         ...         ...              ...      ...   \n",
        "999969  20140627      0        7139       12428              NaN  VTSPREM   \n",
        "999970  20140627      0       24284         477              NaN  VTSPREF   \n",
        "999971  20140627      0          73       30053              NaN      VTS   \n",
        "999972  20140627      0       29765        6469              NaN  VTSPREM   \n",
        "999973  20140627      0       29907         288              NaN  VTSPREM   \n",
        "999974  20140627      0       32014        6480              NaN      NaN   \n",
        "999975  20140627      0       11417       22570              NaN      VTS   \n",
        "999976  20140627      0          57       25717              NaN       TP   \n",
        "999977  20140627      0       21876       21092              NaN       TP   \n",
        "999978  20140627      0          57       21092              NaN       TP   \n",
        "999979  20140627      0          57         NaN              NaN      NaN   \n",
        "999980  20140627      0       22327        2423              NaN  VTSPREM   \n",
        "999981  20140627      0         303       11219              NaN  VTSPREF   \n",
        "999982  20140627      0        6511       22509              NaN  VTSPREF   \n",
        "999983  20140627      0       23240       12369              NaN      VTS   \n",
        "999984  20140627      0       27072       19900              NaN  VTSPREF   \n",
        "999985  20140627      0        6517       11352              NaN  VTSPREF   \n",
        "999986  20140627      0       25717       18754              NaN      VTS   \n",
        "999987  20140627      0       25717       18576              NaN  VTSPREF   \n",
        "999988  20140627      0       27088        2211              NaN  VTSPREM   \n",
        "999989  20140627      0        6268         617              NaN  VTSPREM   \n",
        "999990  20140627      0       11029          74              NaN  VTSPREM   \n",
        "999991  20140627      0       15865         152              NaN      VTS   \n",
        "999992  20140627      0       25819       25194              NaN  VTSPREM   \n",
        "999993  20140627      0        1028         NaN              NaN      NaN   \n",
        "999994  20140627      0         152       21695              NaN  VTSPREM   \n",
        "999995  20140627      0       21092         351              NaN  VTSPREM   \n",
        "999996  20140627      0       29765         143              NaN  VTSPREM   \n",
        "999997  20140627      0        6530       20474            20474   LOCALA   \n",
        "999998  20140627      0       17684       18285              NaN  VTSPREM   \n",
        "\n",
        "                callgno        calldno  origpricingdestid  pricingdestid  \\\n",
        "0          213660646009   242800112117                370             99   \n",
        "1         9779867710086   917525819747                NaN          13187   \n",
        "2           12103677877      573700845               1469             90   \n",
        "3         9779847201600   918483815584                NaN          13187   \n",
        "4           17863451573    19494117781               1421            370   \n",
        "5         9779866909245   919805607755                NaN          13187   \n",
        "6         9779617506990   918962189710                NaN          13187   \n",
        "7            7322016230    19196502662               1351           1403   \n",
        "8              91899813     8291819583                NaN            322   \n",
        "9           37066185005  2349096145937                NaN         127991   \n",
        "10        9779617508669   917503389039                NaN          13187   \n",
        "11         923029034615   966592942149                NaN         125064   \n",
        "12                 9900    25299832061                NaN            320   \n",
        "13          12105716340   918861200080                370          13187   \n",
        "14           6043189900    16043181292               1212           1212   \n",
        "15                         19096099182                NaN           1512   \n",
        "16          13055944450    18768489327               1422          15918   \n",
        "17      791569921458681   593931403358                NaN            124   \n",
        "18         213662835815    22396627846                370          13047   \n",
        "19         213660514072   242800112117                370             99   \n",
        "20          14032851770    14034544264               1212           1212   \n",
        "21         447717441985     8448006682                NaN           1214   \n",
        "22          17786543244    13063485870               1212           1212   \n",
        "23          81363278983  9779844400131              14811         132593   \n",
        "24          13055944450    18768489327               1422          15918   \n",
        "25          59160497459     5689597478                NaN             77   \n",
        "26             66490664   919942253597                NaN          13187   \n",
        "27          18123448628   551149916056                370             41   \n",
        "28           1146127027     8293815963                NaN            322   \n",
        "29          18662434362   966596528492                NaN         125064   \n",
        "...                 ...            ...                ...            ...   \n",
        "999969      9477640EEEE    15149695695                NaN           1212   \n",
        "999970                     59994657722                NaN         126054   \n",
        "999971     966554087269   251917471459                NaN          13725   \n",
        "999972      19804042202   558183443903               1398             45   \n",
        "999973       4088506466    50670609674               1504         138891   \n",
        "999974     447860802143    19292998436                NaN            NaN   \n",
        "999975     441517929017   447894845735             135141           1163   \n",
        "999976      17179753842       91446699                370            178   \n",
        "999977    9779804253221       91809090                NaN          13187   \n",
        "999978      17179753842       91446699                370            178   \n",
        "999979      19095765440    12042192816               1514           1212   \n",
        "999980       5712747663    14034038186               1365           1212   \n",
        "999981                     17724109728                NaN           1421   \n",
        "999982      13105185224    50472196992               1513            167   \n",
        "999983       5625994605    12424418592               1513         131391   \n",
        "999984      85238773843   861354758174                NaN             82   \n",
        "999985     118092167039  5215518109257                NaN          16226   \n",
        "999986      08040570046       91996827                NaN          13187   \n",
        "999987     923045310187       91851489                NaN          13187   \n",
        "999988       6046706699    17785656495               1212           1212   \n",
        "999989     258878185984     2651873227                NaN            223   \n",
        "999990                    543534533283                NaN           1181   \n",
        "999991     923465130434   447867867785                NaN           1166   \n",
        "999992      61381993318    61732605489               1212          71793   \n",
        "999993     584262353021  5214181139134                NaN          16223   \n",
        "999994    6282819033023     8521862806                NaN            168   \n",
        "999995     91808992EEEE   971508278961                NaN         140341   \n",
        "999996      17542276381   556195870799                370             45   \n",
        "999997                     13142604075                NaN            NaN   \n",
        "999998                    555536511397                NaN             40   \n",
        "\n",
        "        routingdestcd causecode answind bouncedcallind  attempts  cvrsn_dur  \\\n",
        "0                 212   IBLK812       N              N         1          0   \n",
        "1              119171    NNA731       N              N         1          0   \n",
        "2                 861       NEV       N              N         1          0   \n",
        "3               13948    NNA716       N              N         1          0   \n",
        "4             2999995    FEV701       N              N         1          0   \n",
        "5              119160       FEF       N              N         1          0   \n",
        "6               13927    NNA716       N              N         1          0   \n",
        "7             2004265    NEU716       N              N         1          0   \n",
        "8                 146       NEV       N              N         1          0   \n",
        "9              117605       FEF       N              N         1          0   \n",
        "10              13947    NNA716       N              N         1          0   \n",
        "11             115694    NNA716       N              N         1          0   \n",
        "12                148   IBLK812       N              N         1          0   \n",
        "13             119163    NNA716       N              N         1          0   \n",
        "14               2685    NEC430       N              N         1          0   \n",
        "15            2007305    NNA716       N              N         1          0   \n",
        "16               9477       NEF       N              N         1          0   \n",
        "17                225   IBLK812       N              N         1          0   \n",
        "18              11514      IBLK       N              N         1          0   \n",
        "19                212   IBLK812       N              N         1          0   \n",
        "20                 92    FEV701       N              N         1          0   \n",
        "21               5909       NEV       N              N         1          0   \n",
        "22                 85    NEC430       N              N         1          0   \n",
        "23             115939       NEF       N              Y         1          0   \n",
        "24               9477    NNA718       N              N         1          0   \n",
        "25                208   IBLK812       N              N         1          0   \n",
        "26              13941    NNA716       N              N         1          0   \n",
        "27             128014    NNA717       N              N         1          0   \n",
        "28                146       NEV       N              N         1          0   \n",
        "29             115694      IBLK       N              N         1          0   \n",
        "...               ...       ...     ...            ...       ...        ...   \n",
        "999969            101    NNA716       N              N         1          0   \n",
        "999970         117007    NNA717       N              N         1          0   \n",
        "999971          12254    NNA716       N              N         1          0   \n",
        "999972           3542    NNA731       N              N         1          0   \n",
        "999973         123805    NNA716       N              N         1          0   \n",
        "999974            NaN       NEF       N              Y         1          0   \n",
        "999975           9313    NNA716       N              N         1          0   \n",
        "999976            273       NEF       N              Y         1          0   \n",
        "999977          13928    NNA720       N              N         1          0   \n",
        "999978            273       FEF       N              Y         1          0   \n",
        "999979             77       NEF       N              N         1          0   \n",
        "999980             92    FEV701       N              N         1          0   \n",
        "999981        2460185    NNA716       N              N         1          0   \n",
        "999982           3466       NEF       N              N         1          0   \n",
        "999983         119305    FEV701       N              N         1          0   \n",
        "999984            904       NEF       N              Y         1          0   \n",
        "999985          14341       NEF       N              Y         1          0   \n",
        "999986          13950    NNA731       N              N         1          0   \n",
        "999987         118620    NNA716       N              N         1          0   \n",
        "999988           8655       FEC       N              Y         1          0   \n",
        "999989            308    NNA718       N              N         1          0   \n",
        "999990           8513    FEV701       N              N         1          0   \n",
        "999991           9316    NNA716       N              N         1          0   \n",
        "999992           8793    FEV701       N              N         1          0   \n",
        "999993          14320   IBLK806       N              N         1          0   \n",
        "999994            269    FEV701       N              N         1          0   \n",
        "999995         125105    NNA716       N              N         1          0   \n",
        "999996          12693       NNA       N              Y         1          0   \n",
        "999997            152    NNA719       N              N         1          0   \n",
        "999998          14232       FEC       N              Y         1          0   \n",
        "\n",
        "        count_sum  label  \n",
        "0               1      0  \n",
        "1               1      0  \n",
        "2               1      0  \n",
        "3               1      0  \n",
        "4               1      0  \n",
        "5               1      0  \n",
        "6               1      0  \n",
        "7               1      0  \n",
        "8               1      0  \n",
        "9               1      0  \n",
        "10              1      0  \n",
        "11              1      0  \n",
        "12              1      0  \n",
        "13              1      0  \n",
        "14              1      0  \n",
        "15              1      1  \n",
        "16              1      0  \n",
        "17              1      0  \n",
        "18              1      0  \n",
        "19              1      0  \n",
        "20              1      0  \n",
        "21              1      0  \n",
        "22              1      0  \n",
        "23              1      0  \n",
        "24              1      0  \n",
        "25              1      0  \n",
        "26              1      0  \n",
        "27              1      0  \n",
        "28              1      0  \n",
        "29              1      0  \n",
        "...           ...    ...  \n",
        "999969          1      0  \n",
        "999970          1      1  \n",
        "999971          1      0  \n",
        "999972          1      0  \n",
        "999973          1      0  \n",
        "999974          1      0  \n",
        "999975          1      0  \n",
        "999976          1      0  \n",
        "999977          1      0  \n",
        "999978          1      0  \n",
        "999979          1      0  \n",
        "999980          1      0  \n",
        "999981          1      1  \n",
        "999982          1      0  \n",
        "999983          1      0  \n",
        "999984          1      0  \n",
        "999985          1      0  \n",
        "999986          1      0  \n",
        "999987          1      0  \n",
        "999988          1      0  \n",
        "999989          1      0  \n",
        "999990          1      1  \n",
        "999991          1      0  \n",
        "999992          1      0  \n",
        "999993          1      0  \n",
        "999994          1      0  \n",
        "999995          1      0  \n",
        "999996          1      0  \n",
        "999997          1      1  \n",
        "999998          1      1  \n",
        "\n",
        "[999999 rows x 18 columns]"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    }
   ],
   "metadata": {}
  }
 ]
}