Bad_Actor_Germain_3_Automation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# READ DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-07-13 11:04:57.229000\n",
      "Reading permit historical data ...2018-07-13 11:04:57.380000\n",
      "length of df before drop three years ago 804635\n",
      "2018-07-13 11:18:28\n",
      "Subsetting three years ago turned off\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import logging\n",
    "\n",
    "pd.set_option('chained_assignment', None)\n",
    "\n",
    "from datetime import datetime\n",
    "print str(datetime.now())\n",
    "\n",
    "\n",
    "from datetime import date\n",
    "from dateutil.relativedelta import relativedelta\n",
    "from dateutil import parser\n",
    "\n",
    "\n",
    "\n",
    "logging.basicConfig(filename=\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\bad_actor_log.log\",level=logging.DEBUG)\n",
    "#logging.debug('This message should go to the log file')\n",
    "#logging.warning('And this, too')\n",
    "\n",
    "string = \"Reading permit historical data ...\" + str(datetime.now())\n",
    "print string\n",
    "\n",
    "\n",
    "logging.info(str(\"\"))\n",
    "logging.info(str(\"***********************************************\"))\n",
    "logging.info(string)\n",
    "\n",
    "#Job Filings\n",
    "df = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\Permits Issues Historical 2.csv\", low_memory=False)\n",
    "\n",
    "df = df.drop('Permit Type', 1)\n",
    "df = df.drop('Permit Status', 1)\n",
    "df = df.drop('Permit Filing Status', 1)\n",
    "#df = df.drop('Permit Expiration Date', 1)\n",
    "df = df.drop('Pre-File Date', 1)\n",
    "#df = df.drop('Permit Issuance Date', 1)\n",
    "\n",
    "df = df.drop('Count Permits', 1)\n",
    "#df = df.drop('Applicant Business House Number', 1)\n",
    "#df = df.drop('Applicant Business ZIP Code', 1)\n",
    "#df = df.drop('Applicant Business Street Name', 1)\n",
    "\n",
    "#df = df.drop('Owner ZIP Code', 1)\n",
    "\n",
    "#Get dates from one year ago\n",
    "\n",
    "print \"length of df before drop three years ago\", len(df)\n",
    "import datetime\n",
    "import time\n",
    "ts = time.time()\n",
    "st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n",
    "print st\n",
    "\n",
    "print \"Subsetting three years ago turned off\"\n",
    "\n",
    "#df['Permit Issuance Date'] = pd.to_datetime(df['Permit Issuance Date'])\n",
    "#df = df.sort_values(by = 'Permit Issuance Date', ascending=True).reset_index(drop=True)\n",
    "\n",
    "#date = datetime.datetime.fromtimestamp(ts).strftime('%m-%d-%Y')\n",
    "#date = parser.parse(date)\n",
    "#date =  date.date()\n",
    "#three_year_ago = date.today() + relativedelta(years=-3)\n",
    "#print \"Three Years Ago:\", three_year_ago\n",
    "\n",
    "#mask = (df['Permit Issuance Date'] >= three_year_ago)\n",
    "#df = df.loc[mask]\n",
    "#df = df.reset_index(drop=True)\n",
    "#print \"length of df after drop three years ago\", len(df)\n",
    "\n",
    "\n",
    "def CleanData(df):\n",
    "    \n",
    "    #FILING REPS\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].astype(str)\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].map(str.upper)\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].map(str.strip)\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace('[^\\x00-\\x7F]','')\n",
    "    \n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].astype(str)\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].map(str.upper)\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].map(str.strip)\n",
    "    \n",
    "    \n",
    "    for i in range(0, len(df)):\n",
    "        string_replace = df[\"Filing Representative First Name\"][i]\n",
    "        if (\".\" in string_replace and \"/\" not in string_replace):\n",
    "            string_replace = string_replace.replace(\".\", \"/\")\n",
    "            df[\"Filing Representative First Name\"][i] = string_replace\n",
    "        \n",
    "        string_replace = df[\"Filing Representative Last Name\"][i]\n",
    "        if (\".\" in string_replace and \"/\" not in string_replace):\n",
    "            string_replace = string_replace.replace(\".\", \"/\")\n",
    "            df[\"Filing Representative Last Name\"][i] = string_replace\n",
    "    \n",
    "    \n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace(' /', '/')\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace('/ ', '/')\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace(' / ', '/')\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace('//', '/')\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace('.', '')\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace('\\\\', '/')\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace(',', '/')\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace(';', '/')\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace('&', '/')\n",
    "    \n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace('\"RUDY\"', '')\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace('MARY BETH', 'MARYBETH')\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].map(str.strip)\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.replace(' ', '/')\n",
    "    \n",
    "    \n",
    "\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].astype(str)\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].map(str.upper)\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].map(str.strip)\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace('[^\\x00-\\x7F]','')\n",
    "\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace(' /', '/')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace('/ ', '/')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace(' / ', '/')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace('//', '/')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace('.', '')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace('\\\\', '/')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace(',', '/')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace(';', '/')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace('&', '/')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace(' JR', '')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace(' SR', '')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace('/ JR', '')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace('/ SR', '')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace(' III', '')\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].map(str.strip)\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.replace(' ', '/')\n",
    "\n",
    "    #create list of names\n",
    "    df[\"Filing Representative First Name\"] = df[\"Filing Representative First Name\"].str.split(\"/\")\n",
    "    df[\"Filing Representative Last Name\"] = df[\"Filing Representative Last Name\"].str.split(\"/\")\n",
    "    \n",
    "\n",
    "    #APPLICANT\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].astype(str)\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].map(str.upper)\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].map(str.strip)\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace('[^\\x00-\\x7F]','')\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace('.', ' ')\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace(',', ' ')\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace('&', '/')\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace('FR ', '')\n",
    "\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace('   ', ' ')\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace('  ', ' ')\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].map(str.strip)\n",
    "\n",
    "\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].astype(str)\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].map(str.upper)\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].map(str.strip)\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace('[^\\x00-\\x7F]','')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace('.', ' ')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(',', ' ')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace('&', '/')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' JR', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' SR', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' III', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' IV', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' ESQ', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' PE', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' MD', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace('   ', ' ')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace('  ', ' ')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].map(str.strip)\n",
    "\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.split(\"/\")\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.split(\"/\")\n",
    "\n",
    "    #OWNER\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].astype(str)\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].map(str.upper)\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].map(str.strip)\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('[^\\x00-\\x7F]','')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('.', ' ')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace(',', ' ')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('_', ' ')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace(':', ' ')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('&', '/')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('FR ', '')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('REV  ', '')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('PASTOR  ', '')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('DR ', '')\n",
    "\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('   ', ' ')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('  ', ' ')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].map(str.strip)\n",
    "\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].astype(str)\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].map(str.upper)\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].map(str.strip)\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace('[^\\x00-\\x7F]','')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace('.', ' ')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(',', ' ')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace('_', ' ')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(':', ' ')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace('&', '/')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' JR', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' SR', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' III', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' IV', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' ESQ', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' PE', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' MD', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace('   ', ' ')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace('  ', ' ')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].map(str.strip)\n",
    "\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.split(\"/\")\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.split(\"/\")\n",
    "    \n",
    "    #CONTRACTOR\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].astype(str)\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].map(str.upper)\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].map(str.strip)\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].str.replace('[^\\x00-\\x7F]','')\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].str.replace('.', ' ')\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].str.replace(',', ' ')\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].str.replace('_', ' ')\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].str.replace(':', ' ')\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].str.replace('&', '/')\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].str.replace('FR ', '')\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].str.replace('   ', ' ')\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].str.replace('  ', ' ')\n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].map(str.strip)\n",
    "\n",
    "\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].astype(str)\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].map(str.upper)\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].map(str.strip)\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('[^\\x00-\\x7F]','')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('.', ' ')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace(',', ' ')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('_', ' ')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace(':', ' ')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('&', '/')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace(' JR', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace(' SR', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace(' III', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace(' IV', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace(' ESQ', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace(' PE', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace(' MD', '')\n",
    "    \n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('0', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('1', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('2', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('3', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('4', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('5', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('6', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('7', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('8', '')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('9', '')\n",
    "    \n",
    "    \n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('   ', ' ')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.replace('  ', ' ')\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].map(str.strip)\n",
    "    \n",
    "    df[\"Contractor First Name\"] = df[\"Contractor First Name\"].str.split(\"/\")\n",
    "    df[\"Contractor Last Name\"] = df[\"Contractor Last Name\"].str.split(\"/\")\n",
    "    \n",
    "    \n",
    "    #CLEAR FILE REP BUSINESS NAMES\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].astype(str)\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].map(str.upper)\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].map(str.strip)\n",
    "    \n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"] + \" \"\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' P.C ', ' ')\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(',', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('_', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('.', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(';', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('-', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('*', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(':', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('/', ' ')\n",
    "    #df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('\\', ' ')\n",
    "\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('&', ' & ')\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' LLC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PLLC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PLC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PPLC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' INC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' LTD ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' AND ', ' & ')\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' CORP ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' CORPS ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' CO ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' LL ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' LLP ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PLL ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PE ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' LLVC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' XPD ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' P E ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' P C ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PL ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' RA ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' R A ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' R AA ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' RAPC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' AI ', ' ')\n",
    "\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' P A ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PA ', ' ')\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' AIA ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' A I A ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' AI ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' AI A ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' N A ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' N Y ', ' NY ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' ST ', ' STREET ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' AVE ', ' AVENUE ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' BLVD ', ' BOULEVARD ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' BLVE ', ' BOULEVARD ')\n",
    "\n",
    "\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('+', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('%', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('[^\\x00-\\x7F]',' ')\n",
    "    #df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('*', '')\n",
    "    #df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('--', '')\n",
    "\n",
    "\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('   ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('  ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].map(str.strip)\n",
    "    \n",
    "    \n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "df = CleanData(df)\n",
    "\n",
    "#SUBSET THE DATA FOR NAMES TO CREATE NETWORK\n",
    "df = df[[\"BIN Number\", \"Applicant First Name\", \"Applicant Last Name\",\"Applicant License Number\", \n",
    "         \"Owner First Name\", \"Owner Last Name\",\n",
    "         \"Contractor First Name\", \"Contractor Last Name\", \"Contractor License Number\", \n",
    "         \"Filing Representative First Name\", \"Filing Representative Last Name\",\n",
    "        \"Filing Representative Business Name\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\test.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ncount_last = 0\\nfor i in range(0, len(df)):\\n    lastname = df[\"Filing Representative Last Name\"][i]\\n    #lastname = lastname.split()\\n    #print len(lastname)\\n    \\n    #if (len(lastname) > 1 and \"/\" not in df[\"Filing Representative First Name\"][i]):\\n    if \"FOROND\" in lastname:\\n        print df[\"Filing Representative Last Name\"][i]\\n        count_last+=1\\n        \\nprint count_last\\n'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "count_last = 0\n",
    "for i in range(0, len(df)):\n",
    "    lastname = df[\"Filing Representative Last Name\"][i]\n",
    "    #lastname = lastname.split()\n",
    "    #print len(lastname)\n",
    "    \n",
    "    #if (len(lastname) > 1 and \"/\" not in df[\"Filing Representative First Name\"][i]):\n",
    "    if \"FOROND\" in lastname:\n",
    "        print df[\"Filing Representative Last Name\"][i]\n",
    "        count_last+=1\n",
    "        \n",
    "print count_last\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#b.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\New Bad Actor List from Darius 3 s= 0_8 added.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\test.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CREATE LIST OF OWNER, APPLICANTS AND FILING REP NAMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-07-13 13:36:46.333000\n",
      "Creating list of owner, aplicant and file rep names ..2018-07-13 13:36:46.334000\n",
      "\n",
      "Owner data:\n",
      "Length of first or last name 804635\n",
      "Length of first or last name 804635\n",
      "len of df 804635\n",
      "len of own list 804635\n",
      "\n",
      "Applicant data:\n",
      "Length of first or last name 804635\n",
      "Length of first or last name 804635\n",
      "len of df 804635\n",
      "len of df app 804635\n",
      "\n",
      "Contractor data:\n",
      "Length of first or last name 804635\n",
      "Length of first or last name 804635\n",
      "len of df 804635\n",
      "len of df cont 804635\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "print str(datetime.now())\n",
    "from string import digits\n",
    "\n",
    "numbers = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n",
    "\n",
    "string = \"Creating list of owner, aplicant and file rep names ..\" + str(datetime.now())\n",
    "print string\n",
    "\n",
    "#create dictionary for replacing first name permutations\n",
    "first_name_perm = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\first_name_permutations.csv\")\n",
    "first_name_perm[\"Permutation\"] = first_name_perm[\"Permutation\"].map(str.upper)\n",
    "first_name_perm[\"Permutation\"] = first_name_perm[\"Permutation\"].map(str.strip)\n",
    "first_name_perm[\"Name\"] = first_name_perm[\"Name\"].map(str.upper)\n",
    "first_name_perm[\"Name\"] = first_name_perm[\"Name\"].map(str.strip)\n",
    "first_name_perm = first_name_perm.set_index('Permutation')['Name'].to_dict()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def CleanLNames (name):\n",
    "    #print \"Cleaning first and last names ..\"\n",
    "    for i in range(0, len(name)):\n",
    "        for j in range(0, len(name[i])):\n",
    "            name[i][j] = name[i][j].replace('/', '') \n",
    "            name[i][j] = name[i][j].replace('.', '') \n",
    "            name[i][j] = name[i][j].replace('\"', '') \n",
    "            name[i][j] = name[i][j].replace(':', '') \n",
    "            name[i][j] = name[i][j].replace(':', '') \n",
    "            name[i][j] = name[i][j].replace('#', '')\n",
    "            name[i][j] = name[i][j].replace('_', '')\n",
    "            name[i][j] = name[i][j].replace('=-', '')\n",
    "            #namelist[i][j] = namelist[i][j].replace('- -', '')\n",
    "            name[i][j] = name[i][j].strip()  \n",
    "            name[i][j] = name[i][j].strip(\"`\")\n",
    "            name[i][j] = name[i][j].strip(\"-\")\n",
    "            #name[i][j] = name[i][j].strip(\"\")\n",
    "            \n",
    "            #replace three letters\n",
    "            name[i][j] = name[i][j].replace(\"BBB\", \"BB\")\n",
    "            name[i][j] = name[i][j].replace(\"CCC\", \"CC\")\n",
    "            name[i][j] = name[i][j].replace(\"DDD\", \"DD\")\n",
    "            name[i][j] = name[i][j].replace(\"EEEE\", \"EE\")\n",
    "            name[i][j] = name[i][j].replace(\"EEE\", \"EE\")\n",
    "            name[i][j] = name[i][j].replace(\"FFF\", \"FF\")\n",
    "            name[i][j] = name[i][j].replace(\"GGG\", \"GG\")\n",
    "            name[i][j] = name[i][j].replace(\"HH\", \"H\")\n",
    "            name[i][j] = name[i][j].replace(\"IIII\", \"II\")\n",
    "            name[i][j] = name[i][j].replace(\"JJJ\", \"JJ\")\n",
    "            name[i][j] = name[i][j].replace(\"KKK\", \"KK\")\n",
    "            name[i][j] = name[i][j].replace(\"LLL\", \"LL\")\n",
    "            name[i][j] = name[i][j].replace(\"MMM\", \"MM\")\n",
    "            name[i][j] = name[i][j].replace(\"NNN\", \"NN\")\n",
    "            name[i][j] = name[i][j].replace(\"OOO\", \"OO\")\n",
    "            name[i][j] = name[i][j].replace(\"PPP\", \"PP\")\n",
    "            name[i][j] = name[i][j].replace(\"QQ\", \"Q\")\n",
    "            name[i][j] = name[i][j].replace(\"RRR\", \"RR\")\n",
    "            name[i][j] = name[i][j].replace(\"SSS\", \"SS\")\n",
    "            name[i][j] = name[i][j].replace(\"TTT\", \"TT\")\n",
    "            name[i][j] = name[i][j].replace(\"UUU\", \"UU\")\n",
    "            name[i][j] = name[i][j].replace(\"VV\", \"V\")\n",
    "            name[i][j] = name[i][j].replace(\"WW\", \"W\")\n",
    "            name[i][j] = name[i][j].replace(\"YY\", \"Y\")\n",
    "            name[i][j] = name[i][j].replace(\"ZZ\", \"Z\")\n",
    "            \n",
    "            #remove the numbers in nane if appear at the end, ie GUTTMAN  247\n",
    "            s1 = name[i][j]\n",
    "            if len(s1) > 0:\n",
    "                s1 = s1[-1]\n",
    "                if s1 in numbers:\n",
    "                    name[i][j] = name[i][j].translate(None, digits)\n",
    "            \n",
    "            #remove single letter at the end of last name, i.e. JONES L\n",
    "            s1 = name[i][j]\n",
    "            s1 = s1.split()\n",
    "            if len(s1) > 1:\n",
    "                if len(s1[len(s1)-1]) == 1:\n",
    "                    s1.pop(len(s1)-1)\n",
    "                    s1 = ''.join(s1)\n",
    "                    name[i][j] = s1\n",
    "            \n",
    "            if '-' in name[i][j]:\n",
    "                s1 = name[i][j].split('-')\n",
    "                if len(s1) > 3:\n",
    "                    name[i][j] = \"\"\n",
    "            \n",
    "    print \"Length of first or last name\", len(name)\n",
    "    return name\n",
    "\n",
    "\n",
    "def CleanFNames (name):\n",
    "    #print \"Cleaning first and last names ..\"\n",
    "    for i in range(0, len(name)):\n",
    "        for j in range(0, len(name[i])):\n",
    "            name[i][j] = name[i][j].replace('/', '') \n",
    "            name[i][j] = name[i][j].replace('.', '') \n",
    "            name[i][j] = name[i][j].replace('\"', '') \n",
    "            name[i][j] = name[i][j].replace(':', '') \n",
    "            name[i][j] = name[i][j].replace(':', '') \n",
    "            name[i][j] = name[i][j].replace('#', '')\n",
    "            name[i][j] = name[i][j].replace('_', '')\n",
    "            name[i][j] = name[i][j].replace('=-', '')\n",
    "            #namelist[i][j] = namelist[i][j].replace('- -', '')\n",
    "            name[i][j] = name[i][j].strip()  \n",
    "            name[i][j] = name[i][j].strip(\"`\")\n",
    "            name[i][j] = name[i][j].strip(\"-\")\n",
    "            #name[i][j] = name[i][j].strip(\"\")\n",
    "            \n",
    "            #replace three letters\n",
    "            name[i][j] = name[i][j].replace(\"BBB\", \"BB\")\n",
    "            name[i][j] = name[i][j].replace(\"CCC\", \"CC\")\n",
    "            name[i][j] = name[i][j].replace(\"DDD\", \"DD\")\n",
    "            name[i][j] = name[i][j].replace(\"EEEE\", \"EE\")\n",
    "            name[i][j] = name[i][j].replace(\"EEE\", \"EE\")\n",
    "            name[i][j] = name[i][j].replace(\"FFF\", \"FF\")\n",
    "            name[i][j] = name[i][j].replace(\"GGG\", \"GG\")\n",
    "            name[i][j] = name[i][j].replace(\"HH\", \"H\")\n",
    "            name[i][j] = name[i][j].replace(\"IIII\", \"II\")\n",
    "            name[i][j] = name[i][j].replace(\"JJJ\", \"JJ\")\n",
    "            name[i][j] = name[i][j].replace(\"KKK\", \"KK\")\n",
    "            name[i][j] = name[i][j].replace(\"LLL\", \"LL\")\n",
    "            name[i][j] = name[i][j].replace(\"MMM\", \"MM\")\n",
    "            name[i][j] = name[i][j].replace(\"NNN\", \"NN\")\n",
    "            name[i][j] = name[i][j].replace(\"OOO\", \"OO\")\n",
    "            name[i][j] = name[i][j].replace(\"PPP\", \"PP\")\n",
    "            name[i][j] = name[i][j].replace(\"QQ\", \"Q\")\n",
    "            name[i][j] = name[i][j].replace(\"RRR\", \"RR\")\n",
    "            name[i][j] = name[i][j].replace(\"SSS\", \"SS\")\n",
    "            name[i][j] = name[i][j].replace(\"TTT\", \"TT\")\n",
    "            name[i][j] = name[i][j].replace(\"UUU\", \"UU\")\n",
    "            name[i][j] = name[i][j].replace(\"VV\", \"V\")\n",
    "            name[i][j] = name[i][j].replace(\"WW\", \"W\")\n",
    "            name[i][j] = name[i][j].replace(\"YY\", \"Y\")\n",
    "            name[i][j] = name[i][j].replace(\"ZZ\", \"Z\")\n",
    "            \n",
    "            \n",
    "            if '-' in name[i][j]:\n",
    "                s1 = name[i][j].split('-')\n",
    "                if len(s1) > 3:\n",
    "                    name[i][j] = \"\"\n",
    "                    \n",
    "                    \n",
    "            if name[i][j] in first_name_perm:\n",
    "                name[i][j] = first_name_perm[name[i][j]]\n",
    "            \n",
    "    print \"Length of first or last name\", len(name)\n",
    "    return name\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def NameGen (f, l):\n",
    "    \n",
    "    new_name_list = []\n",
    "    \n",
    "    for i in range(0, len(f)):\n",
    "        #combine first and last names\n",
    "        final_name = [x + \" \" + y for x, y in zip(f[i],l[i] )]\n",
    "   \n",
    "        if len(f[i]) > len(l[i]):\n",
    "            for j in range( len(l[i]), len(f[i]) ):\n",
    "                full_name = f[i][j] + \" \" + l[i][len(l[i]) - 1]\n",
    "                final_name.append(full_name)\n",
    "\n",
    "\n",
    "        new_name_list.append(final_name)\n",
    "    \n",
    "    #dflist = pd.DataFrame(new_name_list)\n",
    "    \n",
    "    #strip white space from leading and trailing edge of each name\n",
    "    for i in range(0, len(new_name_list)):\n",
    "        #remove white space from leading and trailing edge\n",
    "        for j in range(0, len(new_name_list[i])):\n",
    "            new_name_list[i][j] = new_name_list[i][j].strip()\n",
    "            \n",
    "            \n",
    "            if  new_name_list[i][j] == \"NAN NAN\":\n",
    "                #print new_name_list[i][j]\n",
    "                new_name_list[i][j] = \"\"\n",
    "                #print new_name_list[i][j]\n",
    "               \n",
    "        \n",
    "    return new_name_list\n",
    "\n",
    "print\n",
    "print \"Owner data:\"\n",
    "f = df[\"Owner First Name\"].tolist()\n",
    "l = df[\"Owner Last Name\"].tolist()\n",
    "f = CleanFNames (f)\n",
    "l = CleanLNames (l)\n",
    "olist = NameGen(f, l)\n",
    "#df2 = NameGen(f, l)\n",
    "#df2 = df2.rename(columns={0: 'Owner Name'})\n",
    "#df[\"Owner Name\"] = df2[\"Owner Name\"]\n",
    "#print \"len of first name list\", len(f)\n",
    "#print \"len of last name list\", len(l)\n",
    "print \"len of df\", len(df)\n",
    "print \"len of own list\", len(olist)\n",
    "\n",
    "print\n",
    "print \"Applicant data:\"\n",
    "f = df[\"Applicant First Name\"].tolist()\n",
    "l = df[\"Applicant Last Name\"].tolist() \n",
    "f = CleanFNames (f)\n",
    "l = CleanLNames (l)\n",
    "alist = NameGen(f, l)\n",
    "#df2 = NameGen(f, l)\n",
    "#df2 = df2.rename(columns={0: 'Applicant Name'})\n",
    "#df[\"Applicant Name\"] = df2[\"Applicant Name\"]   \n",
    "#print \"len of first name list\", len(f)\n",
    "#print \"len of last name list\", len(l)\n",
    "print \"len of df\", len(df)\n",
    "print \"len of df app\", len(alist) \n",
    "\n",
    "print\n",
    "print \"Contractor data:\"\n",
    "f = df[\"Contractor First Name\"].tolist()\n",
    "l = df[\"Contractor Last Name\"].tolist() \n",
    "f = CleanFNames (f)\n",
    "l = CleanLNames (l)\n",
    "clist = NameGen(f, l)\n",
    "#df2 = pd.DataFrame(clist)\n",
    "#df2 = df2.rename(columns={0: 'Contractor Name'})\n",
    "#df[\"Contractor Name\"] = df2[\"Contractor Name\"]   \n",
    "#print \"len of first name list\", len(f)\n",
    "#print \"len of last name list\", len(l)\n",
    "print \"len of df\", len(df)\n",
    "print \"len of df cont\", len(clist)  \n",
    "\n",
    "#print\n",
    "#print \"Filing rep data:\"\n",
    "#f = df[\"Filing Representative First Name\"].tolist()\n",
    "#l = df[\"Filing Representative Last Name\"].tolist()\n",
    "#f = CleanFNames (f)\n",
    "#l = CleanLNames (l)\n",
    "#flist = NameGen(f, l)\n",
    "#df2 = NameGen(f, l)\n",
    "#df2 = df2.rename(columns={0: 'Filing Rep Name'})\n",
    "#df[\"Filing Rep Name\"] = df2[\"Filing Rep Name\"]\n",
    "#print \"len of first name list\", len(f)\n",
    "#print \"len of last name list\", len(l)\n",
    "#print \"len of df\", len(df)\n",
    "#print \"len of df filing rep\", len(flist)\n",
    "\n",
    "#SUBSET DATA\n",
    "#df = df[[\"BIN Number\", \"Owner Name\", \"Applicant Name\", \"Filing Rep Name\"]]\n",
    "\n",
    "print\n",
    "print \"Starting Ethnicity analysis, Chinese names\"\n",
    "#CHINESE SURNAME\n",
    "chinese_name = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\Chinese Names.csv\")\n",
    "\n",
    "chinese_name[\"Name\"] = chinese_name[\"Name\"].map(str.upper)\n",
    "chinese_name[\"Name\"] = chinese_name[\"Name\"].map(str.strip)\n",
    "\n",
    "chinese_name = chinese_name[\"Name\"].tolist()\n",
    "\n",
    "def chinese_names(name_list):\n",
    "    #thenames = []\n",
    "    count=0\n",
    "    for i in range(0, len(name_list)):\n",
    "    #for i in range(0, 1000):    \n",
    "        for j in range(0, len(name_list[i])):\n",
    "            #print olist[i][j]\n",
    "            s1 = name_list[i][j].split()\n",
    "            #print s1\n",
    "\n",
    "            if len(s1) == 3:\n",
    "                if s1[2] in chinese_name:\n",
    "                    name_list[i][j] = s1[0] + s1[1] + \" \" + s1[2]\n",
    "                    #thenames.append(olist[i][j])\n",
    "                    count+=1\n",
    "    print \"Total number of changes\", count                  \n",
    "    return name_list\n",
    "                \n",
    "print \"Owners\"\n",
    "olist = chinese_names(olist)\n",
    "print\n",
    "print \"Applicants\"\n",
    "alist = chinese_names(alist)\n",
    "print\n",
    "print \"Contractors\"\n",
    "clist = chinese_names(clist)\n",
    "print\n",
    "print \"File Reps\"\n",
    "flist = chinese_names(flist)\n",
    "print \"DONE\"\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\test.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REPLACE NAMES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "READ IN NAME DICTIONARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running name replace data ..2018-07-13 13:41:46.728000\n",
      "len before dedup 5898\n",
      "len after drop 5649\n",
      "ADD NAMES REVERSED\n",
      "Final length of name permutations 15803\n",
      "length of name replace dic 15313\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#READ IN NAME REPLACE DATA\n",
    "from datetime import datetime\n",
    "\n",
    "string = \"Running name replace data ..\" + str(datetime.now())\n",
    "print string\n",
    "\n",
    "#bad actor names\n",
    "#b = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Bad Actor Names Only_9_11_17.csv\")\n",
    "#b = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\FILE_REPS_test7_finalfuzzy.csv\")\n",
    "a = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\APPLIC_finalfuzzy3.csv\")\n",
    "a = a[[\"Permutation\", \"Name\"]]\n",
    "o = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\OWNER_finalfuzzy2.csv\")\n",
    "o = o[[\"Permutation\", \"Name\"]]\n",
    "c = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\CONTRACTORS_finalfuzzy3.csv\")\n",
    "c = c[[\"Permutation\", \"Name\"]]\n",
    "\n",
    "e = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\\n",
    "BAD_ACTOR_finalfuzzy.csv\")\n",
    "e = e[[\"Permutation\", \"Name\"]]\n",
    "\n",
    "\n",
    "\n",
    "frames = [a, c, o, e]\n",
    "\n",
    "b = pd.concat(frames)   \n",
    "b = b.reset_index(drop=True)\n",
    "b = b.sort_values(by = 'Name', ascending=True).reset_index(drop=True)\n",
    "\n",
    "#get names not equal to avoid replacing tom jones with tom jones\n",
    "print \"len before dedup\", len(b)\n",
    "b = b[b['Permutation'] != b[\"Name\"]].reset_index(drop=True)\n",
    "b = b.drop_duplicates(subset=['Permutation', 'Name']).reset_index(drop=True)\n",
    "print \"len after drop\", len(b)\n",
    "\n",
    "b[\"Permutation\"] = b[\"Permutation\"].map(str.upper)\n",
    "b[\"Name\"] = b[\"Name\"].map(str.upper)\n",
    "b[\"Permutation\"] = b[\"Permutation\"].map(str.strip)\n",
    "b[\"Name\"] = b[\"Name\"].map(str.strip)\n",
    "\n",
    "#b[\"Permutation\"] = b[\"Permutation\"].str.replace(' JR', '')\n",
    "#b[\"Name\"] = b[\"Name\"].str.replace(' JR', '')\n",
    "\n",
    "#b[\"Permutation\"] = b[\"Permutation\"].str.replace(' SR', '')\n",
    "#b[\"Name\"] = b[\"Name\"].str.replace(' SR', '')\n",
    "\n",
    "#print \"len of name replace\", len(b)\n",
    "\n",
    "\n",
    "print \"ADD NAMES REVERSED\"\n",
    "\n",
    "#PERMUTATIONS\n",
    "name_perm = pd.DataFrame(columns=('Permutation', 'Name'))\n",
    "for i in range(0, len(b)):\n",
    "    name_perm_rev = b[\"Permutation\"][i]\n",
    "    #print name_perm_rev\n",
    "    name_perm_rev = name_perm_rev.split()\n",
    "    #print name_perm_rev[1], name_perm_rev[0]\n",
    "    \n",
    "    if len(name_perm_rev) > 1:\n",
    "        name_perm_rev = name_perm_rev[1] + \" \" + name_perm_rev[0]\n",
    "        name_perm_rev = name_perm_rev.strip()\n",
    "    \n",
    "    #print name_perm_rev, b[\"Name\"][i]\n",
    "    name_perm.loc[i] = [name_perm_rev, b[\"Name\"][i]]\n",
    "\n",
    "\n",
    "#NAMES IN REVERSE, NOT JUST PERMUTATIONS\n",
    "names = pd.DataFrame(columns=('Permutation', 'Name'))\n",
    "\n",
    "name_list = b[\"Name\"].tolist()\n",
    "name_list = set(name_list)\n",
    "name_list = list(name_list)\n",
    "name_list = sorted(name_list)\n",
    "\n",
    "\n",
    "for i in range(0, len(name_list)):\n",
    "    name_rev = name_list[i]\n",
    "    name_rev = name_rev.split()\n",
    "    if len(name_rev) > 1:\n",
    "        name_rev = name_rev[1] + \" \" + name_rev[0]\n",
    "        names.loc[i] = [name_rev, name_list[i]]\n",
    "    \n",
    "    #print name_rev\n",
    "\n",
    "    \n",
    "    \n",
    "frames = [b, name_perm, names]\n",
    "\n",
    "b2 = pd.concat(frames)   \n",
    "b2 = b2.reset_index(drop=True)\n",
    "b2 = b2.sort_values(by = 'Name', ascending=True).reset_index(drop=True)\n",
    "\n",
    "\n",
    "print \"Final length of name permutations\", len(b2)\n",
    "\n",
    "\n",
    "nameDic = b2.set_index('Permutation')['Name'].to_dict()\n",
    "\n",
    "print \"length of name replace dic\", len(nameDic)\n",
    "\n",
    "print \"DONE\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "REPLACE NAMES\n",
    "\n",
    "FIRST DEVELOP LIST FOR REMOVING NAMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\n#FIRST DEVELOP LIST FOR REMOVING NAMES\\n#remove names that are short, one or two letters\\n\\nstring = \"Develop a list for cleaning ...\" + str(datetime.now())\\nprint string\\n\\nall_flat = []\\n\\nfor i in range(0, len(alist)):\\n    #if i%50000 == 0:\\n    #    print i\\n    #for j in range(0, len(flist[i])):\\n    #    all_flat.append(flist[i][j])\\n        \\n    for j in range(0, len(alist[i])):\\n        all_flat.append(alist[i][j])\\n        \\n    for j in range(0, len(olist[i])):\\n        all_flat.append(olist[i][j])\\n        \\n    for j in range(0, len(clist[i])):\\n        all_flat.append(clist[i][j])\\n\\nprint len(all_flat)\\n\\nall_flat = set(all_flat)\\nall_flat = list(all_flat)\\n\\nprint len(all_flat)\\n\\nall_flat= sorted(all_flat)\\n\\n#all_flat = pd.DataFrame(all_flat)\\n\\n\\n\\n#COUNTS THE LENGTH OF NAME\\nall_flat2 = []\\n\\nfor i in range(0, len(all_flat)):\\n    s1 = list(all_flat[i])\\n    s1 = [\"\" if x==\" \" else x for x in s1]\\n    s1 = filter(None, s1)\\n    length = len(s1)\\n    all_flat2.append([all_flat[i],length ])\\n\\n\\n#from operator import itemgetter   \\n#count_char_nodes = sorted(count_char_nodes, key=itemgetter(1), reverse=False)    \\n    \\n#count_char_nodes\\n\\nall_flat = pd.DataFrame(all_flat2)\\n\\n#qn.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\test.csv\", index=False)\\n\\n#mask = (all_flat[1]  < 3 ) & (all_flat[1]  >= 30)\\n\\n#all_flat = all_flat.loc[mask]\\n\\nless_three = all_flat[all_flat[1] < 3].reset_index(drop=True)\\nless_three = less_three[0].tolist()\\nprint \"length of less three\", len(less_three)\\ngreat_thirty = all_flat[all_flat[1] >= 30].reset_index(drop=True)\\ngreat_thirty = great_thirty[0].tolist()\\nprint \"length of greater thirty\", len(great_thirty)\\n\\nremove_list = less_three + great_thirty\\n\\nprint \"length of all\", len(remove_list)\\n\\n#all_flat.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\test.csv\", index=False)\\n\\n\\nprint \"DONE\"\\n\\n'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "\n",
    "#FIRST DEVELOP LIST FOR REMOVING NAMES\n",
    "#remove names that are short, one or two letters\n",
    "\n",
    "string = \"Develop a list for cleaning ...\" + str(datetime.now())\n",
    "print string\n",
    "\n",
    "all_flat = []\n",
    "\n",
    "for i in range(0, len(alist)):\n",
    "    #if i%50000 == 0:\n",
    "    #    print i\n",
    "    #for j in range(0, len(flist[i])):\n",
    "    #    all_flat.append(flist[i][j])\n",
    "        \n",
    "    for j in range(0, len(alist[i])):\n",
    "        all_flat.append(alist[i][j])\n",
    "        \n",
    "    for j in range(0, len(olist[i])):\n",
    "        all_flat.append(olist[i][j])\n",
    "        \n",
    "    for j in range(0, len(clist[i])):\n",
    "        all_flat.append(clist[i][j])\n",
    "\n",
    "print len(all_flat)\n",
    "\n",
    "all_flat = set(all_flat)\n",
    "all_flat = list(all_flat)\n",
    "\n",
    "print len(all_flat)\n",
    "\n",
    "all_flat= sorted(all_flat)\n",
    "\n",
    "#all_flat = pd.DataFrame(all_flat)\n",
    "\n",
    "\n",
    "\n",
    "#COUNTS THE LENGTH OF NAME\n",
    "all_flat2 = []\n",
    "\n",
    "for i in range(0, len(all_flat)):\n",
    "    s1 = list(all_flat[i])\n",
    "    s1 = [\"\" if x==\" \" else x for x in s1]\n",
    "    s1 = filter(None, s1)\n",
    "    length = len(s1)\n",
    "    all_flat2.append([all_flat[i],length ])\n",
    "\n",
    "\n",
    "#from operator import itemgetter   \n",
    "#count_char_nodes = sorted(count_char_nodes, key=itemgetter(1), reverse=False)    \n",
    "    \n",
    "#count_char_nodes\n",
    "\n",
    "all_flat = pd.DataFrame(all_flat2)\n",
    "\n",
    "#qn.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\test.csv\", index=False)\n",
    "\n",
    "#mask = (all_flat[1]  < 3 ) & (all_flat[1]  >= 30)\n",
    "\n",
    "#all_flat = all_flat.loc[mask]\n",
    "\n",
    "less_three = all_flat[all_flat[1] < 3].reset_index(drop=True)\n",
    "less_three = less_three[0].tolist()\n",
    "print \"length of less three\", len(less_three)\n",
    "great_thirty = all_flat[all_flat[1] >= 30].reset_index(drop=True)\n",
    "great_thirty = great_thirty[0].tolist()\n",
    "print \"length of greater thirty\", len(great_thirty)\n",
    "\n",
    "remove_list = less_three + great_thirty\n",
    "\n",
    "print \"length of all\", len(remove_list)\n",
    "\n",
    "#all_flat.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\test.csv\", index=False)\n",
    "\n",
    "\n",
    "print \"DONE\"\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "REPLACE THE NAMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replacing names ...2018-07-13 13:42:55.650000\n",
      "len of owner list 804635\n",
      "len of owner list 804635\n",
      "len of app list 804635\n",
      "********\n",
      "Applicants:\n",
      "number of replacements 43458\n",
      "number of names removed 0\n",
      "len of original list 804635\n",
      "len of name replaced list list 804635\n",
      "********\n",
      "Owners:\n",
      "number of replacements 28524\n",
      "number of names removed 0\n",
      "len of original list 804635\n",
      "len of name replaced list list 804635\n",
      "********\n",
      "Conctractor:\n",
      "number of replacements 33876\n",
      "number of names removed 0\n",
      "len of original list 804635\n",
      "len of name replaced list list 804635\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nprint \"********\"\\nprint \"Filing Reps:\"\\nflist = NameReplace(flist)\\ndf2 = pd.DataFrame(flist)\\ndf2 = df2.rename(columns={0: \\'Filing Rep Name\\'})\\ndf[\"Filing Rep Name\"] = df2[\"Filing Rep Name\"] \\n\\n'"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#REPLACEMENT, DO SO USING BAD ACTOR LIST FROM ISABELLA AND FUZZY LOGIC\n",
    "\n",
    "#FILE REP BUSINESS NAMES\n",
    "d = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\\n",
    "BAD_ACTOR_FILE_REP_BUSINESS_finalfuzzy.csv\")\n",
    "d = d[[\"Permutation\", \"Name\"]]\n",
    "\n",
    "fileBusDic = d.set_index('Permutation')['Name'].to_dict()\n",
    "\n",
    "\n",
    "#from datetime import datetime\n",
    "string = \"Replacing names ...\" + str(datetime.now())\n",
    "print string\n",
    "logging.info(string)\n",
    "\n",
    "\n",
    "count = 0\n",
    "countf = 0\n",
    "counto = 0\n",
    "\n",
    "#flist = df[\"Filing Rep Name\"].tolist()\n",
    "#olist = df[\"Owner Name\"].tolist()\n",
    "#alist = df[\"Applicant Name\"].tolist()\n",
    "\n",
    "#print 'len of fil rep list', len(flist)\n",
    "print 'len of owner list', len(olist)\n",
    "print 'len of owner list', len(clist)\n",
    "print 'len of app list', len(alist)\n",
    "\n",
    "\n",
    "\n",
    "#need to do all names again in loop to make sure capture all\n",
    "\n",
    "#import re\n",
    "\n",
    "def NameReplace(namelist):\n",
    "    new_app_list = []\n",
    "    count = 0\n",
    "    count_remove_list = 0\n",
    "    for i in range(0, len(namelist)):\n",
    "        for j in range(0, len(namelist[i])):\n",
    "            \n",
    "            if namelist[i][j] in nameDic:\n",
    "                #print\n",
    "                #print flist[i][j]\n",
    "                namelist[i][j] = nameDic[namelist[i][j]]\n",
    "                #print flist[i][j]\n",
    "                count += 1\n",
    "                \n",
    "            #if namelist[i][j] in remove_list:\n",
    "            #    namelist[i][j] = \"\"\n",
    "            #    count_remove_list+=1\n",
    "                \n",
    "        new_app_list.append([namelist[i]])\n",
    "    \n",
    "       \n",
    "    print \"number of replacements\", count\n",
    "    print \"number of names removed\", count_remove_list\n",
    "    print \"len of original list\", len(namelist)\n",
    "    print \"len of name replaced list list\", len(new_app_list)\n",
    "    \n",
    "    return new_app_list\n",
    "    \n",
    "#dfapp = pd.DataFrame(new_app_list)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print \"********\"\n",
    "print \"Applicants:\"\n",
    "alist = NameReplace(alist)\n",
    "df2 = pd.DataFrame(alist)\n",
    "df2 = df2.rename(columns={0: 'Applicant Name'})\n",
    "df[\"Applicant Name\"] = df2[\"Applicant Name\"] \n",
    "\n",
    "print \"********\"\n",
    "print \"Owners:\"\n",
    "olist = NameReplace(olist)\n",
    "df2 = pd.DataFrame(olist)\n",
    "df2 = df2.rename(columns={0: 'Owner Name'})\n",
    "df[\"Owner Name\"] = df2[\"Owner Name\"] \n",
    "\n",
    "print \"********\"\n",
    "print \"Conctractor:\"\n",
    "clist = NameReplace(clist)\n",
    "df2 = pd.DataFrame(clist)\n",
    "df2 = df2.rename(columns={0: 'Contractor Name'})\n",
    "df[\"Contractor Name\"] = df2[\"Contractor Name\"] \n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "print \"********\"\n",
    "print \"Filing Reps:\"\n",
    "flist = NameReplace(flist)\n",
    "df2 = pd.DataFrame(flist)\n",
    "df2 = df2.rename(columns={0: 'Filing Rep Name'})\n",
    "df[\"Filing Rep Name\"] = df2[\"Filing Rep Name\"] \n",
    "\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CLEANING OF FILE REPRESENTATIVE BUSINESS NAMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len of fblist 804635\n",
      "Inidtial Cleaning ...\n",
      "0\n",
      "80463\n",
      "160926\n",
      "241389\n",
      "321852\n",
      "402315\n",
      "482778\n",
      "563241\n",
      "643704\n",
      "724167\n",
      "804630\n",
      "Number of name permuation replacements 728440\n",
      "\n",
      "Replace exact names ...\n",
      "len of enames 518\n",
      "len of enames 476\n",
      "0\n",
      "80463\n",
      "160926\n",
      "241389\n",
      "321852\n",
      "402315\n",
      "482778\n",
      "563241\n",
      "643704\n",
      "724167\n",
      "804630\n",
      "Number of exact name replacements 63022\n",
      "\n",
      "Replace names from fuzzy logic ...\n",
      "len of fnames 1071\n",
      "len of fnames 855\n",
      "0\n",
      "80463\n",
      "160926\n",
      "241389\n",
      "321852\n",
      "402315\n",
      "482778\n",
      "563241\n",
      "643704\n",
      "724167\n",
      "804630\n",
      "Number of fuzzy name replaceents 16559\n",
      "\n",
      "Replace bad actor names from fuzzy logic\n",
      "len of badnames 65\n",
      "len of badnames 64\n",
      "0\n",
      "80463\n",
      "160926\n",
      "241389\n",
      "321852\n",
      "402315\n",
      "482778\n",
      "563241\n",
      "643704\n",
      "724167\n",
      "804630\n",
      "Number of fuzzy name replaceents 56428\n",
      "length of dataframe 804635\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.split(\" \")\n",
    "fblist = df[\"Filing Representative Business Name\"].tolist()\n",
    "\n",
    "print \"len of fblist\", len(fblist)\n",
    "#print \"length of fblist set\", len(set(fblist))\n",
    "\n",
    "\n",
    "busDic = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\\n",
    "File Rep Business Name Permutations.csv\", low_memory=False)\n",
    "names = sorted(list(set(busDic[\"Name\"].tolist() ) ) )\n",
    "\n",
    "#print names\n",
    "busDic[\"Permutation\"] = busDic[\"Permutation\"].map(str.strip)\n",
    "busDic[\"Name\"] = busDic[\"Name\"].map(str.strip)\n",
    "busDic = busDic.set_index('Permutation')['Name'].to_dict()\n",
    "\n",
    "#df = df.reset_index(drop=True)\n",
    "\n",
    "count_amp = 0\n",
    "\n",
    "in_here = 0\n",
    "\n",
    "name_perm  = 0\n",
    "\n",
    "\n",
    "print \"Inidtial Cleaning ...\"\n",
    "\n",
    "#for i in range(0, len(df)):\n",
    "for i in range(0, len(fblist)):    \n",
    "    if i%(len(fblist)/10) == 0:\n",
    "        print i\n",
    "    \n",
    "    a = fblist[i]\n",
    "    \n",
    "    #pop last element in list \n",
    "    #del a[-1]\n",
    "    \n",
    "    for j in range(0, len(a)):\n",
    "        if a[j] in busDic:\n",
    "            a[j] = busDic[a[j]]\n",
    "            name_perm+=1\n",
    "            \n",
    "    \n",
    "    a2 = a[0:5]\n",
    "    #print a2\n",
    "    \n",
    "    #CHANGE IF & THEN HAVE a = a[0:4], else a = a[0:3]\n",
    "    \n",
    "    #if \"&\" in a[len(a) - 1]:\n",
    "    #if a[len(a) - 1] == \"&\":\n",
    "    \n",
    "    #    count_amp +=1\n",
    "        \n",
    "    #    a2 = a[0:4]\n",
    "        \n",
    "        #a[len(a) - 1] = \"\"\n",
    "        #a = filter(None, a)\n",
    "        \n",
    "    \n",
    "    a2 = \" \".join(a2)\n",
    "    #print a2\n",
    "    a2 = a2.strip()\n",
    "    \n",
    "    \n",
    "    type_list = ['ARCHITECTS', 'CONSTRUCTION',  'CONTRACTORS','ENGINEERING',\n",
    "                 'BUILDING',  'CONSULTING', \n",
    "                  'CORPORATION', 'DESIGN', \n",
    "                  'EXPEDITING', 'INSPECTION', 'SERVICES', \n",
    "                 'SOLUTIONS', 'MANAGMENT', 'ENTERPRISE','ASSOCIATES']\n",
    "    \n",
    "    #remove architects, building, constructoin, etc, or clean data from above key word\n",
    "    \n",
    "    for j in range(0, len(type_list)):\n",
    "        if type_list[j] in a2:\n",
    "            #check if key word at beginning, if so, do not split\n",
    "            location = a2.find(type_list[j])\n",
    "            \n",
    "            #if location == 0:\n",
    "            #    in_here+=1\n",
    "            \n",
    "            if location > 0:\n",
    "                a2 = a2.split(type_list[j])\n",
    "                #strip out white spaces from split\n",
    "                for k in range(0, len(a2)):\n",
    "                    a2[k] = a2[k].strip()\n",
    "                    \n",
    "                a2 = a2[0] + \" \" + type_list[j]\n",
    "            \n",
    "\n",
    "    \n",
    "    #remove ampersand at end of string\n",
    "    if len(a2) > 0:\n",
    "        s2 = a2[-1]\n",
    "        if s2 == \"&\":\n",
    "            a2 = a2.replace(\"&\", \"\")\n",
    "            a2 = a2.strip()\n",
    "            #print a2\n",
    "            \n",
    "        if \"(\" in a2:\n",
    "            a2 = a2.split(\"(\")\n",
    "            a2 = a2[0]\n",
    "            a2 = a2.strip()\n",
    "    \n",
    "    \n",
    "    fblist[i] = a2\n",
    "            \n",
    "    #df[\"Filing Representative Business Name\"][i] = a\n",
    "    \n",
    "\n",
    "print \"Number of name permuation replacements\", name_perm    \n",
    "    \n",
    "print\n",
    "print \"Replace exact names ...\"\n",
    "\n",
    "#INSERT EXACT NAMES\n",
    "enames =  pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\FILE_REP_BUSNAME_exactnames.csv\", low_memory=False)\n",
    "\n",
    "enames[\"Permutation\"] = enames[\"Permutation\"].map(str.strip)\n",
    "enames[\"Name\"] = enames[\"Name\"].map(str.strip)\n",
    "\n",
    "print \"len of enames\", len(enames)\n",
    "enames = enames[enames['Permutation'] != enames[\"Name\"]]\n",
    "enames = enames.drop_duplicates(['Permutation', 'Name']).reset_index(drop=True)\n",
    "enames = enames.reset_index(drop=True)\n",
    "print \"len of enames\", len(enames)\n",
    "\n",
    "enamesDic = enames.set_index('Permutation')['Name'].to_dict()\n",
    "\n",
    "exact_names = 0\n",
    "for i in range(0, len(fblist)):\n",
    "    if i%(len(fblist)/10) == 0:\n",
    "        print i\n",
    "    \n",
    "    if fblist[i] in enamesDic:\n",
    "        fblist[i] = enamesDic[fblist[i]]\n",
    "        exact_names+=1\n",
    "        \n",
    "print \"Number of exact name replacements\", exact_names\n",
    "print\n",
    "print \"Replace names from fuzzy logic ...\"\n",
    "\n",
    "fnames = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\\n",
    "FILE_REP_BUSNAME_finalfuzzy.csv\", low_memory=False)\n",
    "\n",
    "fnames[\"Permutation\"] = fnames[\"Permutation\"].map(str.strip)\n",
    "fnames[\"Name\"] = fnames[\"Name\"].map(str.strip)\n",
    "\n",
    "\n",
    "print \"len of fnames\", len(fnames)\n",
    "fnames = fnames[fnames['Permutation'] != fnames[\"Name\"]]\n",
    "fnames = fnames.drop_duplicates(['Permutation', 'Name']).reset_index(drop=True)\n",
    "fnames = fnames.reset_index(drop=True)\n",
    "print \"len of fnames\", len(fnames)\n",
    "\n",
    "fnamesDic = fnames.set_index('Permutation')['Name'].to_dict()\n",
    "\n",
    "names_fuzzy = 0\n",
    "\n",
    "for i in range(0, len(fblist)):\n",
    "    if i%(len(fblist)/10) == 0:\n",
    "        print i\n",
    "    \n",
    "    if fblist[i] in fnamesDic:\n",
    "        fblist[i] = fnamesDic[fblist[i]]\n",
    "        names_fuzzy+=1\n",
    "           \n",
    "print \"Number of fuzzy name replaceents\", names_fuzzy\n",
    "\n",
    "print\n",
    "print \"Replace bad actor names from fuzzy logic\"\n",
    "badnames = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\\n",
    "BAD_ACTOR_FILE_REP_BUSINESS_finalfuzzy.csv\", low_memory=False)\n",
    "\n",
    "badnames[\"Permutation\"] = badnames[\"Permutation\"].map(str.strip)\n",
    "badnames[\"Name\"] = badnames[\"Name\"].map(str.strip)\n",
    "\n",
    "print \"len of badnames\", len(badnames)\n",
    "badnames = badnames[badnames['Permutation'] != badnames[\"Name\"]]\n",
    "badnames = badnames.drop_duplicates(['Permutation', 'Name']).reset_index(drop=True)\n",
    "badnames = badnames.reset_index(drop=True)\n",
    "print \"len of badnames\", len(badnames)\n",
    "\n",
    "badnamesDic = badnames.set_index('Permutation')['Name'].to_dict()\n",
    "\n",
    "names_fuzzy = 0\n",
    "for i in range(0, len(fblist)):\n",
    "    if i%(len(fblist)/10) == 0:\n",
    "        print i\n",
    "    \n",
    "    if fblist[i] in badnamesDic:\n",
    "        fblist[i] = badnamesDic[fblist[i]]\n",
    "        names_fuzzy+=1\n",
    "           \n",
    "print \"Number of fuzzy name replaceents\", names_fuzzy\n",
    "\n",
    "\n",
    "    \n",
    "#file_business_list = df[\"Filing Representative Business Name\"].tolist()\n",
    "#file_business_list = sorted(list(set(file_business_list)))\n",
    "\n",
    "\n",
    "df2 = pd.DataFrame(fblist)\n",
    "df2 = df2.rename(columns={0: 'Filing Representative Business Name'})\n",
    "\n",
    "df[\"Filing Representative Business Name\"] = df2[\"Filing Representative Business Name\"]\n",
    "\n",
    "#file_business_list = pd.DataFrame(fblist)\n",
    "#file_business_list = file_business_list.rename(columns={0: 'Name'})\n",
    "\n",
    "\n",
    "#file_business_list[\"Value\"] = 1\n",
    "\n",
    "#file_business_list = file_business_list.drop_duplicates([0]).reset_index(drop=True)\n",
    "#file_business_list = file_business_list.groupby(['Name']).sum()\n",
    "#file_business_list = file_business_list.add_suffix('').reset_index()\n",
    "\n",
    "print \"length of dataframe\", len(df)\n",
    "\n",
    "#split to make into a list\n",
    "df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.split(\"/\")\n",
    "\n",
    "\n",
    "print \"DONE\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NETWORK ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BASIC NETWORK ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start creating network ...2018-07-13 13:43:52.610000\n",
      "0\n",
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "len of network 4449576\n",
      "len of nodes 118336\n"
     ]
    }
   ],
   "source": [
    "string = \"Start creating network ...\" + str(datetime.now())\n",
    "print string\n",
    "logging.info(string)\n",
    "\n",
    "import itertools\n",
    "\n",
    "df[\"Total Actors\"] = df[\"Owner Name\"] + df[\"Applicant Name\"] + df[\"Contractor Name\"] + df[\"Filing Representative Business Name\"]\n",
    "\n",
    "nameL = df[\"Total Actors\"].tolist()\n",
    "\n",
    "#remove blank elements in list\n",
    "for i in range(0, len(nameL)):\n",
    "    nameL[i] = list(filter(None, nameL[i]))\n",
    "    if \"NAN\" in nameL[i]:\n",
    "        nameL[i].remove(\"NAN\")\n",
    "        \n",
    "    \n",
    "network_all = []\n",
    "node_all = []\n",
    "\n",
    "#net = pd.DataFrame(columns=('Node1', 'Node2'))\n",
    "\n",
    "for i in range(0, len(nameL)):\n",
    "#for i in range(0, 4):\n",
    "    \n",
    "    if i%100000 == 0:\n",
    "        print i\n",
    "    \n",
    "    network = []\n",
    "    #print nameL[i][0]\n",
    "    #print nameL[i][1]\n",
    "    \n",
    "    #print len(nameL[i])\n",
    "    #print nameL[i]\n",
    "    \n",
    "    a = nameL[i]\n",
    "    \n",
    "    #print i, a\n",
    "\n",
    "    \n",
    "    for j in range(0, len(a)):\n",
    "        for k in range(0, len(a)):\n",
    "            if a[j] != a[k]:\n",
    "                #this code removes duplicates, ie david, tom and tom,david\n",
    "                network_temp = []\n",
    "                network_temp.append(sorted([a[j], a[k]]))\n",
    "                network_temp = sum(network_temp, [])\n",
    "                if network_temp not in network:\n",
    "                    network.append(sorted([a[j], a[k]]))\n",
    "                #create list of nodes\n",
    "                node_all.append(a[j])\n",
    "                node_all.append(a[k])\n",
    "    \n",
    "    for z in range(0, len(network)):\n",
    "        network_all.append([network[z][0],network[z][1]])\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "nodes = list(set(node_all) )   \n",
    "nodes = sorted(nodes)\n",
    "\n",
    "print \"len of network\", len(network_all)   \n",
    "print \"len of nodes\", len(nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#nodes_out = pd.DataFrame(nodes)\n",
    "\n",
    "#nodes_out.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\nodelist_brut_force.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NODE METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create node metrics ...2018-07-13 13:44:35.456000\n",
      "0\n",
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "len of dfGnn 118340\n",
      "len of name_list 118340\n",
      "len of total_tie_list 118340\n",
      "len of bad_tie_list 118340\n",
      "len of total_unique_ties df 804635\n",
      "len of total_unique_bad_ties 118340\n",
      "len of bad_flag_df 118340\n",
      "Assigning rank to bad actors...\n",
      "keep nodes with  total bad ties > 0 118340\n",
      "length after drop 38189\n",
      "\n",
      "total number of bad actors 309\n",
      "Total run time: 199.06799984\n"
     ]
    }
   ],
   "source": [
    "#NODE METRICS NEW\n",
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "string = \"Create node metrics ...\" + str(datetime.now())\n",
    "print string\n",
    "logging.info(string)\n",
    "\n",
    "#badBusiness = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Industry Bad Actor Data Set - Updated 5_4_18 FR BUSINESS.csv\")\n",
    "#badPeople =  pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Industry Bad Actor Data Set - Updated 5_4_18 PEOPLE.csv\")\n",
    "\n",
    "#badBusiness = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Bad Actor Dataset-Top Criteria-6_4_18 FR BUSINESS.csv\")\n",
    "#badPeople =  pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Bad Actor Dataset-Top Criteria-6_4_18 PEOPLE.csv\")\n",
    "\n",
    "badAllRoles = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Bad Actor Dataset-Top Criteria-6_4_18 ALL ROLES.csv\")\n",
    "\n",
    "#badBusiness = badBusiness[\"Name\"].tolist()\n",
    "#badPeople = badPeople[\"Name\"].tolist()\n",
    "\n",
    "#badActor = badBusiness + badPeople\n",
    "\n",
    "\n",
    "badActor = badAllRoles[\"Name\"].tolist()\n",
    "\n",
    "\n",
    "nodes_new_temp = []\n",
    "\n",
    "for i in range(0, len(nameL)):\n",
    "    \n",
    "    tie_list = []\n",
    "    badlist = []\n",
    "    \n",
    "    if i%100000 == 0:\n",
    "        print i\n",
    "    \n",
    "    for j in range(0, len(nameL[i])):\n",
    "        #remove target name from list\n",
    "        tie_list = [x for x in nameL[i] if x not in nameL[i][j] ] \n",
    "        \n",
    "        #create bad actor list, interesection of list with bad actor list\n",
    "        badlist = list(set(badActor).intersection(tie_list))\n",
    "        \n",
    "\n",
    "        #nameL[i][j] = target name\n",
    "        nodes_new_temp.append([ nameL[i][j], tie_list, badlist, len(tie_list),  len(badlist)])\n",
    "        \n",
    "             \n",
    "  \n",
    "\n",
    "dfnn = pd.DataFrame(nodes_new_temp, columns=('Name', 'Total_Ties', 'Bad Tie List','Total Ties', 'Total Bad Ties' ) )\n",
    "\n",
    "dfnn[\"Name\"] = dfnn[\"Name\"].astype(str)\n",
    "\n",
    "\n",
    "nodes_new = (dfnn.groupby('Name').agg({'Total_Ties':'sum', 'Bad Tie List':'sum','Total Ties':'sum',\n",
    "                               'Total Bad Ties':'sum' }).reset_index().rename(columns={'Total_Ties':'Total_Ties_sum'}) )\n",
    "\n",
    "\n",
    "nodes_new = nodes_new.rename(columns={'Total_Ties_sum': 'Total Tie List'})\n",
    "\n",
    "nodes_new = nodes_new[[\"Name\", \"Total Tie List\", \"Bad Tie List\", \"Total Ties\", \"Total Bad Ties\"]]\n",
    "nodes_new[\"Total Bad Ties\"] = nodes_new[\"Total Bad Ties\"].astype(float)\n",
    "nodes_new[\"Total Ties\"] = nodes_new[\"Total Ties\"].astype(float)\n",
    "\n",
    "nodes_new[\"Bad Tie Density\"] = nodes_new[\"Total Bad Ties\"]/nodes_new[\"Total Ties\"]\n",
    "nodes_new[\"Bad Tie Density\"] = nodes_new[\"Bad Tie Density\"].round(3)\n",
    "\n",
    "\n",
    "\n",
    "#COUNT TOTALK AND UNIQE TIES\n",
    "total_tie_list = nodes_new[\"Total Tie List\"].tolist()\n",
    "bad_tie_list = nodes_new[\"Bad Tie List\"].tolist()\n",
    "name_list = nodes_new[\"Name\"].tolist()\n",
    "\n",
    "print \"len of dfGnn\", len(nodes_new)\n",
    "print \"len of name_list\", len(name_list)\n",
    "print \"len of total_tie_list\", len(total_tie_list)\n",
    "print \"len of bad_tie_list\", len(bad_tie_list)\n",
    "\n",
    "total_tie_list_set = []\n",
    "total_bad_tie_list_set = []\n",
    "bad_flag_list = []\n",
    "\n",
    "#total_tie_set = []\n",
    "#bad_tie_set = []\n",
    "\n",
    "count_bad = 0\n",
    "\n",
    "for i in range(0, len(total_tie_list)):\n",
    "    \n",
    "    temp_set = set(total_tie_list[i])\n",
    "    total_tie_list_set.append(len(temp_set) )\n",
    "    \n",
    "    \n",
    "    temp_bad_set = set(bad_tie_list[i])\n",
    "    total_bad_tie_list_set.append(len(temp_bad_set) )\n",
    "    \n",
    "    \n",
    "    if name_list[i] in badActor:\n",
    "        bad_flag = 1\n",
    "        count_bad+=1\n",
    "    else:\n",
    "        bad_flag = 0\n",
    "        \n",
    "    bad_flag_list.append(bad_flag)\n",
    "        \n",
    "    \n",
    "\n",
    "    \n",
    "#Total Unique ties  \n",
    "total_unique_ties = pd.DataFrame(total_tie_list_set)\n",
    "print \"len of total_unique_ties df\", len(df)\n",
    "nodes_new[\"Total Unique Ties\"] = total_unique_ties[0]\n",
    "nodes_new[\"Total Unique Ties\"] = nodes_new[\"Total Unique Ties\"].astype(float)\n",
    "#ties = pd.DataFrame(total_tie_set)\n",
    "#nodes_new[\"Total Tie Set\"] = ties[0]\n",
    "\n",
    "#Total unique bad ties\n",
    "total_unique_bad_ties = pd.DataFrame(total_bad_tie_list_set)\n",
    "print \"len of total_unique_bad_ties\", len(total_unique_bad_ties)\n",
    "nodes_new[\"Total Unique Bad Ties\"] = total_unique_bad_ties[0]\n",
    "nodes_new[\"Total Unique Bad Ties\"] = nodes_new[\"Total Unique Bad Ties\"].astype(float)   \n",
    "nodes_new[\"Bad Unique Density\"] = nodes_new[\"Total Unique Bad Ties\"]/nodes_new[\"Total Unique Ties\"]\n",
    "nodes_new[\"Bad Unique Density\"] = nodes_new[\"Bad Unique Density\"].round(3)\n",
    "#badguys = pd.DataFrame(bad_tie_set)\n",
    "#nodes_new[\"Bad Tie Set\"] = badguys[0]\n",
    "\n",
    "\n",
    "bad_flag_df = pd.DataFrame(bad_flag_list)\n",
    "print \"len of bad_flag_df\", len(bad_flag_df)\n",
    "nodes_new[\"Bad Flag\"] = bad_flag_df[0]\n",
    "\n",
    "nodes_new = nodes_new[[\"Name\", \"Bad Flag\", \"Total Ties\",\"Total Bad Ties\", \n",
    "                  \"Bad Tie Density\", \"Total Unique Ties\", \"Total Unique Bad Ties\", \n",
    "                  \"Bad Unique Density\"]]\n",
    "\n",
    "print \"Assigning rank to bad actors...\"\n",
    "\n",
    "\n",
    "\n",
    "print \"keep nodes with  total bad ties > 0\", len(nodes_new)\n",
    "#ONLY KEEP NODES WHERE TOTAL BAD TIES > 0\n",
    "nodes_new = nodes_new[nodes_new[\"Total Bad Ties\"] > 0].reset_index(drop=True)\n",
    "print \"length after drop\", len(nodes_new)\n",
    "\n",
    "print\n",
    "print \"total number of bad actors\", count_bad\n",
    "\n",
    "end_time = time.time()\n",
    "print \"Total run time:\", end_time - start_time    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # INSERT BAD ACTOR RANK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#badBusiness = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Bad Actor Dataset-Top Criteria-6_4_18 FR BUSINESS.csv\")\n",
    "#badPeople =  pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Bad Actor Dataset-Top Criteria-6_4_18 PEOPLE.csv\")\n",
    "\n",
    "badAllRoles = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Bad Actor Dataset-Top Criteria-6_4_18 ALL ROLES.csv\")\n",
    "\n",
    "\n",
    "#badBusinessDic = badBusiness.set_index('Name')['Rank'].to_dict()\n",
    "#badPeopleDic = badPeople.set_index('Name')['Rank'].to_dict()\n",
    "\n",
    "badAllRolesDic = badAllRoles.set_index('Name')['Rank'].to_dict()\n",
    "\n",
    "nodes_new[\"Bad Rank\"] = 0\n",
    "for i in range(0, len(nodes_new)):\n",
    "    if nodes_new[\"Name\"][i] in badAllRolesDic:\n",
    "        nodes_new[\"Bad Rank\"][i] = badAllRolesDic[nodes_new[\"Name\"][i]]\n",
    "    \n",
    "    \n",
    "    #if nodes_new[\"Name\"][i] in badBusinessDic:\n",
    "    #    nodes_new[\"Bad Rank\"][i] = badBusinessDic[nodes_new[\"Name\"][i]]\n",
    "    #if nodes_new[\"Name\"][i] in badPeopleDic:   \n",
    "    #    nodes_new[\"Bad Rank\"][i] = badPeopleDic[nodes_new[\"Name\"][i]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nodes_new.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\nodes_latest2.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Bad Flag</th>\n",
       "      <th>Total Ties</th>\n",
       "      <th>Total Bad Ties</th>\n",
       "      <th>Bad Tie Density</th>\n",
       "      <th>Total Unique Ties</th>\n",
       "      <th>Total Unique Bad Ties</th>\n",
       "      <th>Bad Unique Density</th>\n",
       "      <th>Bad Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.111</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 RS CORPORATION</td>\n",
       "      <td>0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101 SERVICES</td>\n",
       "      <td>0</td>\n",
       "      <td>4845.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.011</td>\n",
       "      <td>406.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1028 NEILL AVNUE</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.111</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104 14 NORTHER BOULEVARD</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>106 EAST 85 TNT CORP</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11 HANOVER SQUARE CORP</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.412</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1214 GREENE PRO LLC</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>147 PRINCE STREET</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>156 ERASMUS ST LLC</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>160 ERASMUS ST LLC</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1629-1631 ST JOHNS PL LLC</td>\n",
       "      <td>0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1713 WEST 6TH STREET</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1725 ST MARKS AVE LLC</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>177 VERONICA LLC</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>185 VERONICA LLC</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1OMASZ SZYGIEL</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1ST LIGHT ENERGY</td>\n",
       "      <td>0</td>\n",
       "      <td>4092.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2 ALLEN STREET 4H</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2 BY 4 ARCHITECTS</td>\n",
       "      <td>0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.016</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020 GROUP SERVICES</td>\n",
       "      <td>0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.176</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21 ENGINEERING</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.111</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>229 WEST 74 STREET CORP</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3 AXIS DESIGN</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>315 W 46 LLC</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>326013874</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>366 BROADWAY</td>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.025</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3LG ARCHITECTS</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.074</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>43 AVENUE -</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4P PROJECTS</td>\n",
       "      <td>0</td>\n",
       "      <td>888.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.048</td>\n",
       "      <td>143.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.028</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38159</th>\n",
       "      <td>ZOU ZI WANG</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38160</th>\n",
       "      <td>ZOUBIDA MAHRACH</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38161</th>\n",
       "      <td>ZPROEKT ARCHITECTS</td>\n",
       "      <td>0</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>0.290</td>\n",
       "      <td>149.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38162</th>\n",
       "      <td>ZSOLT JEGES</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38163</th>\n",
       "      <td>ZSOLT MARKO</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.417</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38164</th>\n",
       "      <td>ZU HUA WANG</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38165</th>\n",
       "      <td>ZU PING ZHANG</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38166</th>\n",
       "      <td>ZU ZENG YANG</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.444</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38167</th>\n",
       "      <td>ZUBAIR HAYAT</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38168</th>\n",
       "      <td>ZUBAIR SHAIKH</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.606</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38169</th>\n",
       "      <td>ZUBEER SHEIKH</td>\n",
       "      <td>0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.172</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38170</th>\n",
       "      <td>ZULIQUARNAIN CHOUDHRY</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.127</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38171</th>\n",
       "      <td>ZUMRAT KHAIMOVA</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38172</th>\n",
       "      <td>ZUN BI CHEN</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38173</th>\n",
       "      <td>ZUNILDA ESTRELLA</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.444</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38174</th>\n",
       "      <td>ZUNING IN</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38175</th>\n",
       "      <td>ZUNING LIN</td>\n",
       "      <td>0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.140</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38176</th>\n",
       "      <td>ZUO RONG TANG</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38177</th>\n",
       "      <td>ZUO YU GAO</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.105</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38178</th>\n",
       "      <td>ZUOCHAO CHEN</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38179</th>\n",
       "      <td>ZURITA WASHINGTON</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38180</th>\n",
       "      <td>ZUYONG XIE</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38181</th>\n",
       "      <td>ZUZANA ZALEWSKI</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38182</th>\n",
       "      <td>ZVI GOLDBERGER</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38183</th>\n",
       "      <td>ZVI GREENHEIM</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.083</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38184</th>\n",
       "      <td>ZVI LIPSITZ</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.381</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38185</th>\n",
       "      <td>ZVI ZADOK</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.133</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38186</th>\n",
       "      <td>ZYGMUNT MOSCICKI</td>\n",
       "      <td>0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.163</td>\n",
       "      <td>208.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38187</th>\n",
       "      <td>ZYGMUNT PARDA</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38188</th>\n",
       "      <td>\\SHAD AJIM</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.139</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38189 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Name  Bad Flag  Total Ties  Total Bad Ties  \\\n",
       "0                              0         0        18.0             2.0   \n",
       "1               1 RS CORPORATION         0       378.0             3.0   \n",
       "2                   101 SERVICES         0      4845.0            54.0   \n",
       "3               1028 NEILL AVNUE         0        18.0             2.0   \n",
       "4       104 14 NORTHER BOULEVARD         0         6.0             1.0   \n",
       "5           106 EAST 85 TNT CORP         0         6.0             2.0   \n",
       "6         11 HANOVER SQUARE CORP         0        17.0             7.0   \n",
       "7            1214 GREENE PRO LLC         0        48.0            24.0   \n",
       "8              147 PRINCE STREET         0         6.0             1.0   \n",
       "9             156 ERASMUS ST LLC         0        18.0             6.0   \n",
       "10            160 ERASMUS ST LLC         0        18.0             6.0   \n",
       "11     1629-1631 ST JOHNS PL LLC         0       204.0            34.0   \n",
       "12          1713 WEST 6TH STREET         0         6.0             2.0   \n",
       "13         1725 ST MARKS AVE LLC         0        36.0            18.0   \n",
       "14              177 VERONICA LLC         0        21.0             7.0   \n",
       "15              185 VERONICA LLC         0        24.0             8.0   \n",
       "16                1OMASZ SZYGIEL         0        21.0             7.0   \n",
       "17              1ST LIGHT ENERGY         0      4092.0             1.0   \n",
       "18             2 ALLEN STREET 4H         0         6.0             2.0   \n",
       "19             2 BY 4 ARCHITECTS         0       567.0             9.0   \n",
       "20           2020 GROUP SERVICES         0       370.0            65.0   \n",
       "21                21 ENGINEERING         0        18.0             2.0   \n",
       "22       229 WEST 74 STREET CORP         0         3.0             1.0   \n",
       "23                 3 AXIS DESIGN         0        12.0             4.0   \n",
       "24                  315 W 46 LLC         0        12.0             4.0   \n",
       "25                     326013874         0         3.0             1.0   \n",
       "26                  366 BROADWAY         0        81.0             2.0   \n",
       "27                3LG ARCHITECTS         0        27.0             2.0   \n",
       "28                   43 AVENUE -         0         6.0             4.0   \n",
       "29                   4P PROJECTS         0       888.0            43.0   \n",
       "...                          ...       ...         ...             ...   \n",
       "38159                ZOU ZI WANG         0         6.0             2.0   \n",
       "38160            ZOUBIDA MAHRACH         0         6.0             1.0   \n",
       "38161         ZPROEKT ARCHITECTS         0      1377.0           399.0   \n",
       "38162                ZSOLT JEGES         0        20.0             1.0   \n",
       "38163                ZSOLT MARKO         0        12.0             5.0   \n",
       "38164                ZU HUA WANG         0        12.0             2.0   \n",
       "38165              ZU PING ZHANG         0         6.0             2.0   \n",
       "38166               ZU ZENG YANG         0        27.0            12.0   \n",
       "38167               ZUBAIR HAYAT         0        30.0             5.0   \n",
       "38168              ZUBAIR SHAIKH         0        33.0            20.0   \n",
       "38169              ZUBEER SHEIKH         0       274.0            47.0   \n",
       "38170      ZULIQUARNAIN CHOUDHRY         0        71.0             9.0   \n",
       "38171            ZUMRAT KHAIMOVA         0         3.0             1.0   \n",
       "38172                ZUN BI CHEN         0         6.0             1.0   \n",
       "38173           ZUNILDA ESTRELLA         0         9.0             4.0   \n",
       "38174                  ZUNING IN         0         5.0             2.0   \n",
       "38175                 ZUNING LIN         0       507.0            71.0   \n",
       "38176              ZUO RONG TANG         0         3.0             1.0   \n",
       "38177                 ZUO YU GAO         0        19.0             2.0   \n",
       "38178               ZUOCHAO CHEN         0         6.0             2.0   \n",
       "38179          ZURITA WASHINGTON         0         6.0             2.0   \n",
       "38180                 ZUYONG XIE         0         6.0             3.0   \n",
       "38181            ZUZANA ZALEWSKI         0         9.0             6.0   \n",
       "38182             ZVI GOLDBERGER         0         3.0             1.0   \n",
       "38183              ZVI GREENHEIM         0        12.0             1.0   \n",
       "38184                ZVI LIPSITZ         0        21.0             8.0   \n",
       "38185                  ZVI ZADOK         0        60.0             8.0   \n",
       "38186           ZYGMUNT MOSCICKI         0      1611.0           263.0   \n",
       "38187              ZYGMUNT PARDA         0        60.0             6.0   \n",
       "38188                 \\SHAD AJIM         0        36.0             5.0   \n",
       "\n",
       "       Bad Tie Density  Total Unique Ties  Total Unique Bad Ties  \\\n",
       "0                0.111                5.0                    1.0   \n",
       "1                0.008               33.0                    1.0   \n",
       "2                0.011              406.0                    3.0   \n",
       "3                0.111                6.0                    1.0   \n",
       "4                0.167                4.0                    1.0   \n",
       "5                0.333                3.0                    1.0   \n",
       "6                0.412                6.0                    1.0   \n",
       "7                0.500                4.0                    2.0   \n",
       "8                0.167                4.0                    1.0   \n",
       "9                0.333                4.0                    1.0   \n",
       "10               0.333                5.0                    1.0   \n",
       "11               0.167                6.0                    1.0   \n",
       "12               0.333                4.0                    1.0   \n",
       "13               0.500                4.0                    2.0   \n",
       "14               0.333                5.0                    1.0   \n",
       "15               0.333                5.0                    1.0   \n",
       "16               0.333                9.0                    1.0   \n",
       "17               0.000             1302.0                    1.0   \n",
       "18               0.333                3.0                    1.0   \n",
       "19               0.016               94.0                    1.0   \n",
       "20               0.176               77.0                    2.0   \n",
       "21               0.111                5.0                    1.0   \n",
       "22               0.333                3.0                    1.0   \n",
       "23               0.333                4.0                    1.0   \n",
       "24               0.333                6.0                    1.0   \n",
       "25               0.333                3.0                    1.0   \n",
       "26               0.025                8.0                    2.0   \n",
       "27               0.074                9.0                    1.0   \n",
       "28               0.667                4.0                    2.0   \n",
       "29               0.048              143.0                    4.0   \n",
       "...                ...                ...                    ...   \n",
       "38159            0.333                4.0                    1.0   \n",
       "38160            0.167                6.0                    1.0   \n",
       "38161            0.290              149.0                    3.0   \n",
       "38162            0.050               16.0                    1.0   \n",
       "38163            0.417                4.0                    2.0   \n",
       "38164            0.167                7.0                    1.0   \n",
       "38165            0.333                4.0                    1.0   \n",
       "38166            0.444                6.0                    2.0   \n",
       "38167            0.167               20.0                    3.0   \n",
       "38168            0.606                8.0                    2.0   \n",
       "38169            0.172               59.0                    6.0   \n",
       "38170            0.127               21.0                    2.0   \n",
       "38171            0.333                3.0                    1.0   \n",
       "38172            0.167                5.0                    1.0   \n",
       "38173            0.444                5.0                    2.0   \n",
       "38174            0.400                5.0                    2.0   \n",
       "38175            0.140               65.0                    8.0   \n",
       "38176            0.333                3.0                    1.0   \n",
       "38177            0.105                9.0                    1.0   \n",
       "38178            0.333                4.0                    1.0   \n",
       "38179            0.333                3.0                    1.0   \n",
       "38180            0.500                3.0                    1.0   \n",
       "38181            0.667                4.0                    2.0   \n",
       "38182            0.333                3.0                    1.0   \n",
       "38183            0.083               10.0                    1.0   \n",
       "38184            0.381                9.0                    2.0   \n",
       "38185            0.133                9.0                    1.0   \n",
       "38186            0.163              208.0                    5.0   \n",
       "38187            0.100               14.0                    1.0   \n",
       "38188            0.139               13.0                    3.0   \n",
       "\n",
       "       Bad Unique Density  Bad Rank  \n",
       "0                   0.200         0  \n",
       "1                   0.030         0  \n",
       "2                   0.007         0  \n",
       "3                   0.167         0  \n",
       "4                   0.250         0  \n",
       "5                   0.333         0  \n",
       "6                   0.167         0  \n",
       "7                   0.500         0  \n",
       "8                   0.250         0  \n",
       "9                   0.250         0  \n",
       "10                  0.200         0  \n",
       "11                  0.167         0  \n",
       "12                  0.250         0  \n",
       "13                  0.500         0  \n",
       "14                  0.200         0  \n",
       "15                  0.200         0  \n",
       "16                  0.111         0  \n",
       "17                  0.001         0  \n",
       "18                  0.333         0  \n",
       "19                  0.011         0  \n",
       "20                  0.026         0  \n",
       "21                  0.200         0  \n",
       "22                  0.333         0  \n",
       "23                  0.250         0  \n",
       "24                  0.167         0  \n",
       "25                  0.333         0  \n",
       "26                  0.250         0  \n",
       "27                  0.111         0  \n",
       "28                  0.500         0  \n",
       "29                  0.028         0  \n",
       "...                   ...       ...  \n",
       "38159               0.250         0  \n",
       "38160               0.167         0  \n",
       "38161               0.020         0  \n",
       "38162               0.062         0  \n",
       "38163               0.500         0  \n",
       "38164               0.143         0  \n",
       "38165               0.250         0  \n",
       "38166               0.333         0  \n",
       "38167               0.150         0  \n",
       "38168               0.250         0  \n",
       "38169               0.102         0  \n",
       "38170               0.095         0  \n",
       "38171               0.333         0  \n",
       "38172               0.200         0  \n",
       "38173               0.400         0  \n",
       "38174               0.400         0  \n",
       "38175               0.123         0  \n",
       "38176               0.333         0  \n",
       "38177               0.111         0  \n",
       "38178               0.250         0  \n",
       "38179               0.333         0  \n",
       "38180               0.333         0  \n",
       "38181               0.500         0  \n",
       "38182               0.333         0  \n",
       "38183               0.100         0  \n",
       "38184               0.222         0  \n",
       "38185               0.111         0  \n",
       "38186               0.024         0  \n",
       "38187               0.071         0  \n",
       "38188               0.231         0  \n",
       "\n",
       "[38189 rows x 9 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#for i in range(0, len(df)):\n",
    "#    if \"ALEX NUNEZ\" in df[\"Total Actors\"][i]:\n",
    "#        print df[\"Total Actors\"][i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CREATE NAME TO BIN NUMBER DICTIONARY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create name to BIN dictionary, want to count number of unique BINS per person ...2018-07-13 13:48:34.024000\n",
      "2018-07-13 13:48:34.025000\n",
      "Check len to make sure accurate\n",
      "len of bin list 804635\n",
      "len before drop duplicates 3167189\n",
      "len after drop 869060\n",
      "length of nodes_new 38189\n",
      "Inserting total and unique number BINS..\n",
      "0\n",
      "20000\n",
      "number of total and uniqe matches 38189\n",
      "total time minutes: 39.5010001659\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#FOR COUNTING THE NUMBER OF UNIQUE BINS EACH ACTOR WORKED ON\n",
    "string = \"Create name to BIN dictionary, want to count number of unique BINS per person ...\" + str(datetime.now())\n",
    "print string\n",
    "logging.info(string)\n",
    "\n",
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "\n",
    "\n",
    "print str(datetime.now())\n",
    "df[\"BIN Number\"] = df[\"BIN Number\"].astype(str)\n",
    "\n",
    "print \"Check len to make sure accurate\"\n",
    "binList = df[\"BIN Number\"].tolist()\n",
    "print \"len of bin list\", len(binList)\n",
    "\n",
    "nameLBIN = []\n",
    "\n",
    "#for i in range(0, len(binList)):\n",
    "#    nameL.append([binList[i],totalList[i] ])\n",
    "\n",
    "#newList = []\n",
    "\n",
    "for i in range(0, len(nameL)):\n",
    "    #print nameL[i][0]\n",
    "    #print nameL[i][1]\n",
    "    \n",
    "    for j in range(0, len(nameL[i])):\n",
    "        #append bin number and names\n",
    "        #if alist[i][j] != \"\":\n",
    "        nameLBIN.append([binList[i], nameL[i][j] ] )\n",
    "\n",
    "\n",
    "qb = pd.DataFrame(nameLBIN)\n",
    "\n",
    "\n",
    "qb = qb.rename(columns={0: 'BIN', 1: 'Name'})\n",
    "\n",
    "\n",
    "#NUMBER OF UNIQUE BINS\n",
    "print \"len before drop duplicates\", len(qb)\n",
    "qbUnique = qb.drop_duplicates(subset=['BIN', 'Name']).reset_index(drop=True)\n",
    "print \"len after drop\", len(qbUnique)\n",
    "qbUnique = (qbUnique.groupby('Name').agg({'BIN':'count'}).reset_index().rename(columns={'BIN':'BIN_count'}) )\n",
    "#qbUnique = qbUnique.sort_values(by = 'BIN_count', ascending=False).reset_index(drop=True)\n",
    "qbUnique = qbUnique.set_index('Name')['BIN_count'].to_dict()\n",
    "\n",
    "#TOTAL NUMBER OF BINS\n",
    "qbTotal = qb\n",
    "qbTotal[\"Value\"] = 1\n",
    "qbTotal = (qbTotal.groupby('Name').agg({'Value':'sum'}).reset_index().rename(columns={'Value':'Value_count'}) )\n",
    "qbTotal = qbTotal.set_index('Name')['Value_count'].to_dict()\n",
    "\n",
    "print \"length of nodes_new\", len(nodes_new)\n",
    "\n",
    "#print \"Insert number of BINS per node ...\"\n",
    "\n",
    "#APPEND NUMBER OF UNIQUE BINS ASSOCIATED WITH\n",
    "#nodes_new = name, total ties, total bad ties, bad tie density, total unique ties, total unique bad, bad unique density, #BINS\n",
    "\n",
    "nodes_new[\"Total Number BINs\"] = \"\"\n",
    "nodes_new[\"Unique Number BINs\"] = \"\"\n",
    "\n",
    "print \"Inserting total and unique number BINS..\"\n",
    "countt = 0\n",
    "countu = 0\n",
    "for i in range(0, len(nodes_new)):\n",
    "    \n",
    "    if i%20000 == 0:\n",
    "        print i\n",
    "    \n",
    "    #if nodes_new[\"Name\"][i] in qbTotal:\n",
    "    nodes_new[\"Total Number BINs\"][i] = qbTotal[nodes_new[\"Name\"][i]]\n",
    "    countt+=1   \n",
    "        \n",
    "    #if nodes_new[\"Name\"][i] in qbUnique:\n",
    "    nodes_new[\"Unique Number BINs\"][i] = qbUnique[nodes_new[\"Name\"][i]]\n",
    "    #countu+=1   \n",
    "        \n",
    "print \"number of total and uniqe matches\", countt\n",
    "\n",
    "print \"total time minutes:\", start_time - end_time\n",
    "\n",
    "print \"DONE\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nodes_new.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\nodes_latest2.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#qb.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\test.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SUBSET NETWORK FOR MACHINE LEARNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start of subsetting network ...2018-07-13 13:51:31.368000\n",
      "Length of network 4449576\n",
      "length before group 4449576\n",
      "length after group 874463\n",
      "2018-07-13 13:51:34.265000\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "string = \"Start of subsetting network ...\" + str(datetime.now())\n",
    "print string\n",
    "logging.info(string)\n",
    "print \"Length of network\", len(network_all)\n",
    "\n",
    "#START SUBSET OF NETWORK\n",
    "net = pd.DataFrame(network_all, columns=('Node1', 'Node2')) \n",
    "    \n",
    "\n",
    "net[\"Value\"] = 1\n",
    "\n",
    "print \"length before group\", len(net)\n",
    "\n",
    "#count the number of unique ties\n",
    "net = net.groupby(['Node1', 'Node2']).sum()\n",
    "net = net.add_suffix('_Count').reset_index()\n",
    "\n",
    "print \"length after group\", len(net)\n",
    "\n",
    "net = net.sort_values(by = 'Value_Count', ascending=False).reset_index(drop=True)\n",
    "\n",
    "net = net.rename(columns={'Value_Count': 'Weight'})\n",
    "\n",
    "net[\"Weight\"] = net[\"Weight\"].astype(int)\n",
    "\n",
    "print str(datetime.now())\n",
    "\n",
    "print \"DONE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Network Analysis\\\\network all new.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n",
      "5.08835250891\n",
      "51.0\n",
      "len of network 874463\n"
     ]
    }
   ],
   "source": [
    "netMedian = net[\"Weight\"].median()\n",
    "netAve = net[\"Weight\"].mean()\n",
    "\n",
    "print netMedian\n",
    "print netAve\n",
    "\n",
    "\n",
    "p = np.percentile(net[\"Weight\"], 99)\n",
    "\n",
    "print p\n",
    "\n",
    "print \"len of network\", len(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SUBSET NETWORK FOR SPECIFIC ACTORS FOR GRAPHING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ActorList20 = [\"THE BAC GROUP\", \"ALLSTATE EXPEDITING\", \"IMA\", \"JOEL RUBIN\", \"ROBERT CADOCH\",\n",
    "             \"PROCEED PERMIT SERVICES\", \"SUNG HO SHIN\", \"IZET SABOVIC\", \"LEON NEKTALOV\", \"MOHAMMAD BUTT\", \"DILBER KUKIC\", \n",
    "            \"GINO VITALE\", \"PAUL CULOTTA\", \"MUNICIPAL BUILDING CONSULTING\", \"SCOTT SCHNALL\", \n",
    "             \"JM ZONING\", \"JAM CONSULTING\", \"PAUL BAILEY\", \"FIVE BOROUGH CONSULTING & EXPEDITING\", \"YAAKOV STERN\"]\n",
    "\n",
    "ActorList5 = [\"THE BAC GROUP\", \"ALLSTATE EXPEDITING\", \"IMA\", \"JOEL RUBIN\", \"ROBERT CADOCH\"]\n",
    "\n",
    "ActorList1 = [\"THE BAC GROUP\"]\n",
    "\n",
    "\n",
    "node1net = net[net['Node1'].isin(ActorList1)]\n",
    "node2net = net[net['Node2'].isin(ActorList1)]\n",
    "\n",
    "frames = [node1net, node2net]\n",
    "result2 = pd.concat(frames)\n",
    "result2 = result2.reset_index(drop=True)\n",
    "\n",
    "#SUBSET FOR EDGE WEIGHT > 20\n",
    "result2 = result2[result2[\"Weight\"] > 50].reset_index(drop=True)\n",
    "\n",
    "\n",
    "len(result2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CREATE NODES WITH BAD LABEL FROM THIS NETWORK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "node1 = sorted(list(set(result2[\"Node1\"].tolist() ) ) )\n",
    "node2 = sorted(list(set(result2[\"Node2\"].tolist() ) ) )\n",
    "\n",
    "nodes_subset = node1 + node2\n",
    "nodes_subset = sorted(list(set(nodes_subset) ) )\n",
    "\n",
    "nodes_subset = pd.DataFrame(nodes_subset )\n",
    "nodes_subset = nodes_subset.rename(columns={0: 'Name'})\n",
    "\n",
    "nodes_subset[\"Bad\"] = 0\n",
    "nodes_subset[\"BadLabel\"] = \"\"\n",
    "\n",
    "for i in range(0, len(nodes_subset)):\n",
    "    if nodes_subset[\"Name\"][i] in badActor:\n",
    "        nodes_subset[\"Bad\"][i] = 1\n",
    "        \n",
    "    if nodes_subset[\"Name\"][i] in ActorList1:\n",
    "        nodes_subset[\"BadLabel\"][i] = nodes_subset[\"Name\"][i]\n",
    "        \n",
    "    \n",
    "\n",
    "\n",
    "nodes_subsetBadOnly = nodes_subset[nodes_subset[\"Bad\"] == 1].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Bad</th>\n",
       "      <th>BadLabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GENARO URUETA</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JACOB RUBIN</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JEFFREY KAMEN</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KARL FISCHER</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LIPA FRIEDMAN</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MOSHE SILBERSTEIN</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHILLIP MILLER</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>THE BAC GROUP</td>\n",
       "      <td>1</td>\n",
       "      <td>THE BAC GROUP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Bad       BadLabel\n",
       "0      GENARO URUETA    1               \n",
       "1        JACOB RUBIN    1               \n",
       "2      JEFFREY KAMEN    1               \n",
       "3       KARL FISCHER    1               \n",
       "4      LIPA FRIEDMAN    1               \n",
       "5  MOSHE SILBERSTEIN    1               \n",
       "6     PHILLIP MILLER    1               \n",
       "7      THE BAC GROUP    1  THE BAC GROUP"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes_subsetBadOnly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result2.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Network Analysis\\\\edgesBad20NewWeight50.csv\", index=False)\n",
    "nodes_subset.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Network Analysis\\\\nodesBad20NewWeight50.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13381"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nodes_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Node1</th>\n",
       "      <th>Node2</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIVE BOROUGH CONSULTING &amp; EXPEDITING</td>\n",
       "      <td>SURESH MANCHANDA</td>\n",
       "      <td>2240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>WILLIAM LAFFEY</td>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>RAMY ISSAC</td>\n",
       "      <td>1077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>VJOLCA PILKU</td>\n",
       "      <td>1076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>SPRING SCAFFOLDING AED DESIGN</td>\n",
       "      <td>856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>THOMAS DUFFE</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>PO SHENG HSU</td>\n",
       "      <td>664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>MICHAEL CONARD</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FIVE BOROUGH CONSULTING &amp; EXPEDITING</td>\n",
       "      <td>TAN CHEUNG</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>STEFAN BOHDANOWYCZ</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>TERRENCE LOWENBERG</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>MEIR GAMLIEL</td>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>JOHNATHAN O'DONNELL</td>\n",
       "      <td>397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>MICHAEL ZAMPETTI</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>PAUL GROSMAN</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>JOHNATHAN BOWEN</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>PHILIP SUSI</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>TOMER YOGEV</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>MARK MOSKOWITZ</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>MICHAEL GABBAY</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>KEVIN BYRNE</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>KEVIN BARRETT</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>ROBERT SANNA</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>SURESH MANCHANDA</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>YULISA GARCIA</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>IMA</td>\n",
       "      <td>JOSEPH SULTANA</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>LANCE FRANKLIN</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>MILDRED CLAIRE</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>FIVE BOROUGH CONSULTING &amp; EXPEDITING</td>\n",
       "      <td>JERRY DIFALCO</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>LOUIS ABRAMO</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17021</th>\n",
       "      <td>ELIZABETH MOREAU</td>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17022</th>\n",
       "      <td>ELIZABETH PEPPERMAN</td>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17023</th>\n",
       "      <td>ELMAR BENYAMINOV</td>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17024</th>\n",
       "      <td>ELLIOTT VILKAS</td>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17025</th>\n",
       "      <td>ELLEN MCGEE</td>\n",
       "      <td>MUNICIPAL BUILDING CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17026</th>\n",
       "      <td>ELLIOT INICK</td>\n",
       "      <td>MUNICIPAL BUILDING CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17027</th>\n",
       "      <td>ELLIOT CLARK</td>\n",
       "      <td>MUNICIPAL BUILDING CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17028</th>\n",
       "      <td>ELIAS KEFALIDIS</td>\n",
       "      <td>MUNICIPAL BUILDING CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17029</th>\n",
       "      <td>ELIEZER DUBINSKY</td>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17030</th>\n",
       "      <td>ELI ZAMEK</td>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17031</th>\n",
       "      <td>ELI SOKOL</td>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17032</th>\n",
       "      <td>ELI ZABAR</td>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17033</th>\n",
       "      <td>ELIZABETH CRANE</td>\n",
       "      <td>FIVE BOROUGH CONSULTING &amp; EXPEDITING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17034</th>\n",
       "      <td>ELISAVET BATALIAS</td>\n",
       "      <td>IMA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17035</th>\n",
       "      <td>ELIOT TAWIL</td>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17036</th>\n",
       "      <td>EMMANUEL KATERINIS</td>\n",
       "      <td>THE BAC GROUP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17037</th>\n",
       "      <td>EMILIO SALANDRA</td>\n",
       "      <td>MUNICIPAL BUILDING CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17038</th>\n",
       "      <td>EMILY CLARK</td>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17039</th>\n",
       "      <td>EMMANUEL SKOULOUDIS</td>\n",
       "      <td>MUNICIPAL BUILDING CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17040</th>\n",
       "      <td>EMPIRE ASSOCIATES</td>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17041</th>\n",
       "      <td>ELYSE PIKER</td>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17042</th>\n",
       "      <td>ELZBIETA KIELAR</td>\n",
       "      <td>MUNICIPAL BUILDING CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17043</th>\n",
       "      <td>ELYSA HALPERN</td>\n",
       "      <td>MUNICIPAL BUILDING CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17044</th>\n",
       "      <td>ELVIS PACRAMI</td>\n",
       "      <td>SCOTT SCHNALL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17045</th>\n",
       "      <td>EMANUEL TROISE</td>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17046</th>\n",
       "      <td>EMANUEL TROISE</td>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17047</th>\n",
       "      <td>EMANUEL PRELDAKAJ</td>\n",
       "      <td>MUNICIPAL BUILDING CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17048</th>\n",
       "      <td>EMILE MUNIER</td>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17049</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17050</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>FIVE BOROUGH CONSULTING &amp; EXPEDITING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17051 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Node1  \\\n",
       "0      FIVE BOROUGH CONSULTING & EXPEDITING   \n",
       "1                               PAUL BAILEY   \n",
       "2                                 JM ZONING   \n",
       "3                               PAUL BAILEY   \n",
       "4                               PAUL BAILEY   \n",
       "5                            JAM CONSULTING   \n",
       "6                               PAUL BAILEY   \n",
       "7                                 JM ZONING   \n",
       "8      FIVE BOROUGH CONSULTING & EXPEDITING   \n",
       "9                                 JM ZONING   \n",
       "10                                JM ZONING   \n",
       "11                                JM ZONING   \n",
       "12                           JAM CONSULTING   \n",
       "13                                JM ZONING   \n",
       "14                              PAUL BAILEY   \n",
       "15                           JAM CONSULTING   \n",
       "16                              PAUL BAILEY   \n",
       "17                                JM ZONING   \n",
       "18                           JAM CONSULTING   \n",
       "19                           JAM CONSULTING   \n",
       "20                           JAM CONSULTING   \n",
       "21                           JAM CONSULTING   \n",
       "22                           JAM CONSULTING   \n",
       "23                                JM ZONING   \n",
       "24                                JM ZONING   \n",
       "25                                      IMA   \n",
       "26                           JAM CONSULTING   \n",
       "27                           JAM CONSULTING   \n",
       "28     FIVE BOROUGH CONSULTING & EXPEDITING   \n",
       "29                                JM ZONING   \n",
       "...                                     ...   \n",
       "17021                      ELIZABETH MOREAU   \n",
       "17022                   ELIZABETH PEPPERMAN   \n",
       "17023                      ELMAR BENYAMINOV   \n",
       "17024                        ELLIOTT VILKAS   \n",
       "17025                           ELLEN MCGEE   \n",
       "17026                          ELLIOT INICK   \n",
       "17027                          ELLIOT CLARK   \n",
       "17028                       ELIAS KEFALIDIS   \n",
       "17029                      ELIEZER DUBINSKY   \n",
       "17030                             ELI ZAMEK   \n",
       "17031                             ELI SOKOL   \n",
       "17032                             ELI ZABAR   \n",
       "17033                       ELIZABETH CRANE   \n",
       "17034                     ELISAVET BATALIAS   \n",
       "17035                           ELIOT TAWIL   \n",
       "17036                    EMMANUEL KATERINIS   \n",
       "17037                       EMILIO SALANDRA   \n",
       "17038                           EMILY CLARK   \n",
       "17039                   EMMANUEL SKOULOUDIS   \n",
       "17040                     EMPIRE ASSOCIATES   \n",
       "17041                           ELYSE PIKER   \n",
       "17042                       ELZBIETA KIELAR   \n",
       "17043                         ELYSA HALPERN   \n",
       "17044                         ELVIS PACRAMI   \n",
       "17045                        EMANUEL TROISE   \n",
       "17046                        EMANUEL TROISE   \n",
       "17047                     EMANUEL PRELDAKAJ   \n",
       "17048                          EMILE MUNIER   \n",
       "17049                     EMANUEL PIPERAKIS   \n",
       "17050                     EMANUEL PIPERAKIS   \n",
       "\n",
       "                                      Node2  Weight  \n",
       "0                          SURESH MANCHANDA    2240  \n",
       "1                            WILLIAM LAFFEY    1151  \n",
       "2                                RAMY ISSAC    1077  \n",
       "3                              VJOLCA PILKU    1076  \n",
       "4             SPRING SCAFFOLDING AED DESIGN     856  \n",
       "5                              THOMAS DUFFE     800  \n",
       "6                              PO SHENG HSU     664  \n",
       "7                            MICHAEL CONARD     543  \n",
       "8                                TAN CHEUNG     429  \n",
       "9                        STEFAN BOHDANOWYCZ     422  \n",
       "10                       TERRENCE LOWENBERG     414  \n",
       "11                             MEIR GAMLIEL     399  \n",
       "12                      JOHNATHAN O'DONNELL     397  \n",
       "13                         MICHAEL ZAMPETTI     396  \n",
       "14                             PAUL GROSMAN     386  \n",
       "15                          JOHNATHAN BOWEN     358  \n",
       "16                              PHILIP SUSI     357  \n",
       "17                              TOMER YOGEV     356  \n",
       "18                           MARK MOSKOWITZ     330  \n",
       "19                           MICHAEL GABBAY     318  \n",
       "20                              KEVIN BYRNE     317  \n",
       "21                            KEVIN BARRETT     259  \n",
       "22                             ROBERT SANNA     255  \n",
       "23                         SURESH MANCHANDA     241  \n",
       "24                            YULISA GARCIA     231  \n",
       "25                           JOSEPH SULTANA     229  \n",
       "26                           LANCE FRANKLIN     227  \n",
       "27                           MILDRED CLAIRE     217  \n",
       "28                            JERRY DIFALCO     215  \n",
       "29                             LOUIS ABRAMO     213  \n",
       "...                                     ...     ...  \n",
       "17021                             JM ZONING       1  \n",
       "17022                           PAUL BAILEY       1  \n",
       "17023                           PAUL BAILEY       1  \n",
       "17024                        JAM CONSULTING       1  \n",
       "17025         MUNICIPAL BUILDING CONSULTING       1  \n",
       "17026         MUNICIPAL BUILDING CONSULTING       1  \n",
       "17027         MUNICIPAL BUILDING CONSULTING       1  \n",
       "17028         MUNICIPAL BUILDING CONSULTING       1  \n",
       "17029                        JAM CONSULTING       1  \n",
       "17030                           PAUL BAILEY       1  \n",
       "17031                             JM ZONING       1  \n",
       "17032                           PAUL BAILEY       1  \n",
       "17033  FIVE BOROUGH CONSULTING & EXPEDITING       1  \n",
       "17034                                   IMA       1  \n",
       "17035                        JAM CONSULTING       1  \n",
       "17036                         THE BAC GROUP       1  \n",
       "17037         MUNICIPAL BUILDING CONSULTING       1  \n",
       "17038                           PAUL BAILEY       1  \n",
       "17039         MUNICIPAL BUILDING CONSULTING       1  \n",
       "17040                           PAUL BAILEY       1  \n",
       "17041                             JM ZONING       1  \n",
       "17042         MUNICIPAL BUILDING CONSULTING       1  \n",
       "17043         MUNICIPAL BUILDING CONSULTING       1  \n",
       "17044                         SCOTT SCHNALL       1  \n",
       "17045                             JM ZONING       1  \n",
       "17046                        JAM CONSULTING       1  \n",
       "17047         MUNICIPAL BUILDING CONSULTING       1  \n",
       "17048                        JAM CONSULTING       1  \n",
       "17049                             JM ZONING       1  \n",
       "17050  FIVE BOROUGH CONSULTING & EXPEDITING       1  \n",
       "\n",
       "[17051 rows x 3 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result2.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Network Analysis\\\\NETWORK NEW TOP5.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#net.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\test.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SUBSET NETWORK ALL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subsetting network and creating nodes...2018-07-13 13:52:12.990000\n",
      "Creat network list\n",
      "Lengh of network 874463\n",
      "Length of Node1 list 874463\n",
      "Length of Node2 list 874463\n",
      "Length of weight list 874463\n",
      "Subsetting graph first degree...\n",
      "Creating nodes...\n",
      "Number of bad actors 309\n",
      "Checking network...\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#SUBSET NEW\n",
    "\n",
    "string = \"Subsetting network and creating nodes...\" + str(datetime.now())\n",
    "print string\n",
    "logging.info(string)\n",
    "\n",
    "def SubsetFirstDegree (name, netlist):\n",
    "    netSub = []\n",
    "    for i in range(0, len(netlist)):\n",
    "        if ( netlist[i][0] in name or netlist[i][1] in name ):\n",
    "            netSub.append([netlist[i][0], netlist[i][1],  netlist[i][2] ] )\n",
    "    return netSub  \n",
    "\n",
    "\n",
    "\n",
    "#def EdgeGreatWeight(net, eweight):\n",
    "#    net =  net[net[\"Weight\"] > eweight]\n",
    "#    net = net.reset_index(drop=True)\n",
    "#    return net\n",
    "\n",
    "\n",
    "#LABEL BAD ACTORS IN DYADS, DOES BAD ACTOR EXIST?\n",
    "#def LabelBadEdge(net, b2):\n",
    "#    badActor = b2[\"Name\"].tolist()\n",
    "#    badActor = set(badActor)\n",
    "#    badActor = list(badActor)\n",
    "\n",
    "    #print len(badActor)\n",
    "\n",
    "#    net[\"Bad\"] = 0\n",
    "    \n",
    "\n",
    "#    for i in range(0, len(net)):\n",
    "#        if (net[\"Node1\"][i] in badActor or net[\"Node2\"][i] in badActor):\n",
    "#            net[\"Bad\"][i] = 1\n",
    "\n",
    "\n",
    "#    return net\n",
    "\n",
    "\n",
    "def CreateNodes(netSub, name):\n",
    "   \n",
    "    #nodes = []\n",
    "\n",
    "    #add nodes\n",
    "\n",
    "    nodeList1 = []\n",
    "    nodeList2 = []\n",
    "    \n",
    "    for i in range(0, len(netSub)):\n",
    "        nodeList1.append(netSub[i][0])\n",
    "        nodeList2.append(netSub[i][1])\n",
    "\n",
    "    nodes = nodeList1 + nodeList2\n",
    "\n",
    "    nodes = set(nodes)\n",
    "    nodes = list(nodes)\n",
    "\n",
    "    nodes = filter(None, nodes)\n",
    "    \n",
    "    nodes_w_bad = []\n",
    "    count_bad = 0\n",
    "\n",
    "    for i in range(0, len(nodes)):\n",
    "        if nodes[i] in name:\n",
    "            nodes_w_bad.append([nodes[i], 1])\n",
    "            count_bad+=1\n",
    "        else:\n",
    "            nodes_w_bad.append([nodes[i], 0])\n",
    "\n",
    "    nodes_w_bad\n",
    "\n",
    "    print \"Number of bad actors\", count_bad\n",
    "\n",
    "    return nodes_w_bad\n",
    "\n",
    "\n",
    "def CheckNetwork(netSub, nodes):\n",
    "    #This method checks if nodes and edges match\n",
    "\n",
    "    \n",
    "    nodeList = []\n",
    "    \n",
    "    for i in range(0, len(nodes)):\n",
    "        nodeList.append(nodes[i][0])\n",
    "\n",
    "    for i in range(0, len(netSub)):\n",
    "        if netSub[i][0] not in nodeList:\n",
    "            print \"NODES AND EDGES DON'T MATCH\"\n",
    "            print netSub[i][0]\n",
    "        \n",
    "        if netSub[i][1] not in nodeList:\n",
    "            print \"NODES AND EDGES DON'T MATCH\"\n",
    "            print netSub[i][1]\n",
    "\n",
    "\n",
    "#all bad actors ~ 900\n",
    "#name = b[\"Name\"].tolist()\n",
    "#name = set(name)\n",
    "#name = list(name)\n",
    "\n",
    "#ONLY BAD ACTORS\n",
    "#print \"creating nodes\"\n",
    "#nodes = CreateNodes(net, b2, name)\n",
    "#print \"checking network\"\n",
    "#checknetwork = CheckNetwork(net, nodes)\n",
    "\n",
    "\n",
    "#Create netlist\n",
    "print \"Creat network list\"\n",
    "print \"Lengh of network\", len(net)\n",
    "node1 = net[\"Node1\"].tolist()\n",
    "print \"Length of Node1 list\", len(node1)\n",
    "node2 = net[\"Node2\"].tolist()\n",
    "print \"Length of Node2 list\", len(node2)\n",
    "weight = net[\"Weight\"].tolist()\n",
    "print \"Length of weight list\", len(weight)\n",
    "\n",
    "netlist = []\n",
    "\n",
    "for i in range(0, len(node1)):\n",
    "    netlist.append( [ node1[i], node2[i], weight[i] ])\n",
    "\n",
    "\n",
    "#CREATE EGO NETWORK FOR COMMUNITY ML ANALYSIS\n",
    "print \"Subsetting graph first degree...\"\n",
    "netSub = SubsetFirstDegree(badActor, netlist)\n",
    "print \"Creating nodes...\"\n",
    "nodeSub = CreateNodes(netSub, badActor)\n",
    "print \"Checking network...\"\n",
    "checknetwork = CheckNetwork(netSub, nodeSub)\n",
    "\n",
    "#SELECTED BAD ACTORS, EGO NETWORK, FIRST DEGREE\n",
    "#first degree, generates network based on first degree connections from name list\n",
    "#print \"Subsetting graph first degree...\"\n",
    "#netSub = SubsetFirstDegree(name, netlist)\n",
    "#print \"Creating nodes...\"\n",
    "#nodes = CreateNodes(netSub, name)\n",
    "#print \"Checking network...\"\n",
    "#checknetwork = CheckNetwork(netSub, nodes)\n",
    "\n",
    "\n",
    "print \"DONE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "netSub.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Network Analysis\\\\NETWORK ANALYSIS NEW 1.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STRUCTURAL EQUIVELENCE TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76198"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(netSub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Node1</th>\n",
       "      <th>Node2</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AE DESIGN</td>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>8339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GERALD CALIENDO</td>\n",
       "      <td>GERALD CALIENDO ARCHITECTS</td>\n",
       "      <td>4019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATELIER NEW YORK ARCHITECTS</td>\n",
       "      <td>JONATHAN MILLER</td>\n",
       "      <td>3012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHRISTOPHER DOWNES</td>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>2753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PIROOZ SOLTANIZADEH</td>\n",
       "      <td>ROYAL ENGINEERING</td>\n",
       "      <td>2684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CALVANICO ASSOCIATES</td>\n",
       "      <td>PETER CALVANICO</td>\n",
       "      <td>2257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FIVE BOROUGH CONSULTING &amp; EXPEDITING</td>\n",
       "      <td>SURESH MANCHANDA</td>\n",
       "      <td>2240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ROYAL ENGINEERING</td>\n",
       "      <td>VICTOR SUBEN</td>\n",
       "      <td>1560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JRS CONSTRUCTION</td>\n",
       "      <td>WAYNE CLARKE</td>\n",
       "      <td>1473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CITY BUILDING</td>\n",
       "      <td>YING LI</td>\n",
       "      <td>1420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAVIDSON CHRISTMAS</td>\n",
       "      <td>TBC</td>\n",
       "      <td>1418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ANATOLE PLOTKIN</td>\n",
       "      <td>ATELIER NEW YORK ARCHITECTS</td>\n",
       "      <td>1252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AKM HOQUE</td>\n",
       "      <td>ASHRAF CORPORATION</td>\n",
       "      <td>1237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BLUE PRINTS ENGINEERING</td>\n",
       "      <td>MAQSOOD FARUQI</td>\n",
       "      <td>1228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BASIC GROUP CONSULTING</td>\n",
       "      <td>SUGNAM CHANG</td>\n",
       "      <td>1227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HIGH QUALITY CONSTRUCTION UVWLLC</td>\n",
       "      <td>ZHEN HE</td>\n",
       "      <td>1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>WILLIAM LAFFEY</td>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KENNETH PHILOGENE</td>\n",
       "      <td>KMP DESIGN</td>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LAWRENCE GUTHARTZ</td>\n",
       "      <td>LEWSAN CONSULTING</td>\n",
       "      <td>1144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>DAVIDSON CHRISTMAS</td>\n",
       "      <td>PETER MARTINEZ</td>\n",
       "      <td>1142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PETER MARTINEZ</td>\n",
       "      <td>TBC</td>\n",
       "      <td>1140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>D &amp; D EXPEDITING</td>\n",
       "      <td>NEAL RUDIKOFF</td>\n",
       "      <td>1103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>RAMY ISSAC</td>\n",
       "      <td>1077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>VJOLCA PILKU</td>\n",
       "      <td>1076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ANDREW FORMICHELLA</td>\n",
       "      <td>MUNICIPAL BUILDING CONSULTING</td>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>D &amp; D EXPEDITING</td>\n",
       "      <td>NEAL RUDIKOFFP</td>\n",
       "      <td>1018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>JENNY FLORES EXPEDITING</td>\n",
       "      <td>JOSEPH BAZINI</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>BARIS ACAR</td>\n",
       "      <td>EUROCELT EXPEDITING</td>\n",
       "      <td>991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ATELIER NEW YORK ARCHITECTS</td>\n",
       "      <td>JEFFREY KAYE</td>\n",
       "      <td>966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>LEWSAN CONSULTING</td>\n",
       "      <td>SALVADOR TARTAGLIONE</td>\n",
       "      <td>961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76168</th>\n",
       "      <td>ELVIS PACRAMI</td>\n",
       "      <td>SCOTT SCHNALL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76169</th>\n",
       "      <td>EMANUEL KANARIS</td>\n",
       "      <td>SHMUEL FLAUM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76170</th>\n",
       "      <td>EMANUELE DEPERO</td>\n",
       "      <td>WALTER T GORMAN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76171</th>\n",
       "      <td>EMANUELE DEPERO</td>\n",
       "      <td>WALTER GORMAN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76172</th>\n",
       "      <td>EMANUEL TROISE</td>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76173</th>\n",
       "      <td>EMANUEL TROISE</td>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76174</th>\n",
       "      <td>EMH CONSULTING</td>\n",
       "      <td>FRED TORRICO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76175</th>\n",
       "      <td>EMERALD CONSULTING</td>\n",
       "      <td>FRANCISCO PEREZ</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76176</th>\n",
       "      <td>EMANUEL PRELDAKAJ</td>\n",
       "      <td>MUNICIPAL BUILDING CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76177</th>\n",
       "      <td>EMIL RADOMCIC</td>\n",
       "      <td>TBC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76178</th>\n",
       "      <td>EMIL MOGA</td>\n",
       "      <td>MW EXPEDITING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76179</th>\n",
       "      <td>EMILE MUNIER</td>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76180</th>\n",
       "      <td>EMIL STEFKOV</td>\n",
       "      <td>TBC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76181</th>\n",
       "      <td>EMIDIO BUONO</td>\n",
       "      <td>KARL FISCHER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76182</th>\n",
       "      <td>EMIDIO BUONO</td>\n",
       "      <td>GREGG ROTHSTEINS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76183</th>\n",
       "      <td>EMIDIO BUONO</td>\n",
       "      <td>KO SHEK WONG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76184</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>WAYNE CLARKE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76185</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76186</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>JEFFERY GEARY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76187</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>I &amp; L CONSULTING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76188</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>KANG CHAN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76189</th>\n",
       "      <td>EMANUEL PANTELOGLOU</td>\n",
       "      <td>JEFFREY KAMEN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76190</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>GARY WAISMAN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76191</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>FIVE BOROUGH CONSULTING &amp; EXPEDITING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76192</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>LAWRENCE GUTHARTZ</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76193</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>LAWRENCE OGUNLADE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76194</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>SHIMING TAM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76195</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>S M TAM ARCHITECTS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76196</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>PROJECTS ENTRY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76197</th>\n",
       "      <td>EMANUEL PIPERAKIS</td>\n",
       "      <td>SIGURD LEE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76198 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Node1  \\\n",
       "0                                 AE DESIGN   \n",
       "1                           GERALD CALIENDO   \n",
       "2               ATELIER NEW YORK ARCHITECTS   \n",
       "3                        CHRISTOPHER DOWNES   \n",
       "4                       PIROOZ SOLTANIZADEH   \n",
       "5                      CALVANICO ASSOCIATES   \n",
       "6      FIVE BOROUGH CONSULTING & EXPEDITING   \n",
       "7                         ROYAL ENGINEERING   \n",
       "8                          JRS CONSTRUCTION   \n",
       "9                             CITY BUILDING   \n",
       "10                       DAVIDSON CHRISTMAS   \n",
       "11                          ANATOLE PLOTKIN   \n",
       "12                                AKM HOQUE   \n",
       "13                  BLUE PRINTS ENGINEERING   \n",
       "14                   BASIC GROUP CONSULTING   \n",
       "15         HIGH QUALITY CONSTRUCTION UVWLLC   \n",
       "16                              PAUL BAILEY   \n",
       "17                        KENNETH PHILOGENE   \n",
       "18                        LAWRENCE GUTHARTZ   \n",
       "19                       DAVIDSON CHRISTMAS   \n",
       "20                           PETER MARTINEZ   \n",
       "21                         D & D EXPEDITING   \n",
       "22                                JM ZONING   \n",
       "23                              PAUL BAILEY   \n",
       "24                       ANDREW FORMICHELLA   \n",
       "25                         D & D EXPEDITING   \n",
       "26                  JENNY FLORES EXPEDITING   \n",
       "27                               BARIS ACAR   \n",
       "28              ATELIER NEW YORK ARCHITECTS   \n",
       "29                        LEWSAN CONSULTING   \n",
       "...                                     ...   \n",
       "76168                         ELVIS PACRAMI   \n",
       "76169                       EMANUEL KANARIS   \n",
       "76170                       EMANUELE DEPERO   \n",
       "76171                       EMANUELE DEPERO   \n",
       "76172                        EMANUEL TROISE   \n",
       "76173                        EMANUEL TROISE   \n",
       "76174                        EMH CONSULTING   \n",
       "76175                    EMERALD CONSULTING   \n",
       "76176                     EMANUEL PRELDAKAJ   \n",
       "76177                         EMIL RADOMCIC   \n",
       "76178                             EMIL MOGA   \n",
       "76179                          EMILE MUNIER   \n",
       "76180                          EMIL STEFKOV   \n",
       "76181                          EMIDIO BUONO   \n",
       "76182                          EMIDIO BUONO   \n",
       "76183                          EMIDIO BUONO   \n",
       "76184                     EMANUEL PIPERAKIS   \n",
       "76185                     EMANUEL PIPERAKIS   \n",
       "76186                     EMANUEL PIPERAKIS   \n",
       "76187                     EMANUEL PIPERAKIS   \n",
       "76188                     EMANUEL PIPERAKIS   \n",
       "76189                   EMANUEL PANTELOGLOU   \n",
       "76190                     EMANUEL PIPERAKIS   \n",
       "76191                     EMANUEL PIPERAKIS   \n",
       "76192                     EMANUEL PIPERAKIS   \n",
       "76193                     EMANUEL PIPERAKIS   \n",
       "76194                     EMANUEL PIPERAKIS   \n",
       "76195                     EMANUEL PIPERAKIS   \n",
       "76196                     EMANUEL PIPERAKIS   \n",
       "76197                     EMANUEL PIPERAKIS   \n",
       "\n",
       "                                      Node2     2  \n",
       "0                               PAUL BAILEY  8339  \n",
       "1                GERALD CALIENDO ARCHITECTS  4019  \n",
       "2                           JONATHAN MILLER  3012  \n",
       "3                               PAUL BAILEY  2753  \n",
       "4                         ROYAL ENGINEERING  2684  \n",
       "5                           PETER CALVANICO  2257  \n",
       "6                          SURESH MANCHANDA  2240  \n",
       "7                              VICTOR SUBEN  1560  \n",
       "8                              WAYNE CLARKE  1473  \n",
       "9                                   YING LI  1420  \n",
       "10                                      TBC  1418  \n",
       "11              ATELIER NEW YORK ARCHITECTS  1252  \n",
       "12                       ASHRAF CORPORATION  1237  \n",
       "13                           MAQSOOD FARUQI  1228  \n",
       "14                             SUGNAM CHANG  1227  \n",
       "15                                  ZHEN HE  1206  \n",
       "16                           WILLIAM LAFFEY  1151  \n",
       "17                               KMP DESIGN  1151  \n",
       "18                        LEWSAN CONSULTING  1144  \n",
       "19                           PETER MARTINEZ  1142  \n",
       "20                                      TBC  1140  \n",
       "21                            NEAL RUDIKOFF  1103  \n",
       "22                               RAMY ISSAC  1077  \n",
       "23                             VJOLCA PILKU  1076  \n",
       "24            MUNICIPAL BUILDING CONSULTING  1056  \n",
       "25                           NEAL RUDIKOFFP  1018  \n",
       "26                            JOSEPH BAZINI  1001  \n",
       "27                      EUROCELT EXPEDITING   991  \n",
       "28                             JEFFREY KAYE   966  \n",
       "29                     SALVADOR TARTAGLIONE   961  \n",
       "...                                     ...   ...  \n",
       "76168                         SCOTT SCHNALL     1  \n",
       "76169                          SHMUEL FLAUM     1  \n",
       "76170                       WALTER T GORMAN     1  \n",
       "76171                         WALTER GORMAN     1  \n",
       "76172                             JM ZONING     1  \n",
       "76173                        JAM CONSULTING     1  \n",
       "76174                          FRED TORRICO     1  \n",
       "76175                       FRANCISCO PEREZ     1  \n",
       "76176         MUNICIPAL BUILDING CONSULTING     1  \n",
       "76177                                   TBC     1  \n",
       "76178                         MW EXPEDITING     1  \n",
       "76179                        JAM CONSULTING     1  \n",
       "76180                                   TBC     1  \n",
       "76181                          KARL FISCHER     1  \n",
       "76182                      GREGG ROTHSTEINS     1  \n",
       "76183                          KO SHEK WONG     1  \n",
       "76184                          WAYNE CLARKE     1  \n",
       "76185                             JM ZONING     1  \n",
       "76186                         JEFFERY GEARY     1  \n",
       "76187                      I & L CONSULTING     1  \n",
       "76188                             KANG CHAN     1  \n",
       "76189                         JEFFREY KAMEN     1  \n",
       "76190                          GARY WAISMAN     1  \n",
       "76191  FIVE BOROUGH CONSULTING & EXPEDITING     1  \n",
       "76192                     LAWRENCE GUTHARTZ     1  \n",
       "76193                     LAWRENCE OGUNLADE     1  \n",
       "76194                           SHIMING TAM     1  \n",
       "76195                    S M TAM ARCHITECTS     1  \n",
       "76196                        PROJECTS ENTRY     1  \n",
       "76197                            SIGURD LEE     1  \n",
       "\n",
       "[76198 rows x 3 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netSub = pd.DataFrame(netSub)\n",
    "netSub = netSub.rename(columns={0: 'Node1', 1: 'Node2'})\n",
    "netSub\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running ...\n",
      "Length of iteration 38227\n",
      "0\n",
      "3822\n",
      "7644\n",
      "11466\n",
      "15288\n",
      "19110\n",
      "22932\n",
      "26754\n",
      "30576\n",
      "34398\n",
      "38220\n",
      "DONE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nselist = [\"ABRAHAM GRUNHUT\", \"ABE GREEN\",\\n\"ABE GREENHUT\", \"ABRAHAM GREENHUT\",\\n\"ABE GRUNHUT\",\\n\"ESTHER GRUNHUT\",\\n\"JACOB GREEN\"\\n]\\n\\nnew_list = []\\n\\n\\n\\nnetNode1 = netSub[netSub[\\'Node1\\'].isin(selist)]\\nnetNode2 = netSub[netSub[\\'Node2\\'].isin(selist)]\\n\\nnet2 = pd.DataFrame()\\n\\nnet2 = net2.append(netNode1,ignore_index=False)\\nnet2 = net2.append(netNode2,ignore_index=False)\\n\\nnet2 = net2.reset_index(drop=True)\\n\\n\\nall_list = []\\nnew_list_append = []\\n\\nfor i in range(0, len(selist)):\\n    new_list = []\\n    for j in range(0, len(net2)):\\n        if selist[i] in net2[\"Node1\"][j]:\\n            new_list.append(net2[\"Node2\"][j])\\n        if selist[i] in net2[\"Node2\"][j]:\\n            new_list.append(net2[\"Node1\"][j])\\n            \\n      \\n    new_list = sorted(list(set(new_list)))\\n    if selist[i] in new_list:\\n        new_list.remove(selist[i])\\n        \\n    for k in range(0, len(selist)):\\n        if selist[k] in new_list:\\n            new_list.remove(selist[k])\\n    \\n    new_list_append.append(new_list)\\n    \\n    x = list(set(new_list_append[0]).intersection(new_list))\\n    \\n    all_list.append([selist[i],len(x), len(new_list), new_list])\\n    \\n    \\n'"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GET LIST OF TIES FOR EACH ACTOR\n",
    "\n",
    "print \"Running ...\"\n",
    "\n",
    "netNode1 = netSub[\"Node1\"].tolist()\n",
    "netNode2 = netSub[\"Node2\"].tolist()\n",
    "\n",
    "nodesAll = netNode1 + netNode2\n",
    "nodesAll = sorted(list(set(nodesAll) ))\n",
    "\n",
    "TieList = []\n",
    "\n",
    "netSub2 = netSub[[\"Node1\", \"Node2\"]]\n",
    "\n",
    "netSubList = netSub2.values.tolist()\n",
    "\n",
    "print \"Length of iteration\", len(nodesAll)\n",
    "\n",
    "for i in range(0, len(nodesAll)):\n",
    "    TieListTmp = []\n",
    "    \n",
    "    if i%(len(nodesAll)/10) == 0:\n",
    "        print i\n",
    "    \n",
    "    for j in range(0, len(netSubList)):\n",
    "        if (nodesAll[i] in netSubList[j][0] or nodesAll[i] in netSubList[j][1]):\n",
    "            #print netSubList[j]\n",
    "            #TieListTmp = netSubList[j]\n",
    "            #TieListTmp.remove(nodesAll[i])\n",
    "            for k in netSubList[j]:\n",
    "                if k not in nodesAll[i]:\n",
    "                    TieListTmp.append(k)\n",
    "                    \n",
    "    TieListTmp = sorted(list(set(TieListTmp)))\n",
    "    TieList.append([nodesAll[i], TieListTmp])\n",
    "    \n",
    "            #TieListTmp.append(netSubList[j])\n",
    "    #print \n",
    "    #print TieListTmp\n",
    "    #TieListTmp = sum(TieListTmp, [])\n",
    "    #print\n",
    "    #print nodesAll[i]\n",
    "    #TieListTmp.remove(nodesAll[i])\n",
    "    #print \n",
    "    #print TieListTmp\n",
    "    \n",
    "    \n",
    "print \"DONE\"\n",
    "    \n",
    "\n",
    "\"\"\"\n",
    "\n",
    "for i in range(0, len(nodesAll)):\n",
    "    if i%(len(fblist)/10) == 0:\n",
    "        print i\n",
    "    \n",
    "    \n",
    "    netSubNode1 = netSub[netSub[\"Node1\"] == nodesAll[i]]\n",
    "    #print netSubNode1\n",
    "    netSubNode2 = netSub[netSub[\"Node2\"] == nodesAll[i]]\n",
    "    #print netSubNode2\n",
    "    \n",
    "    frames = [netSubNode1, netSubNode2]\n",
    "\n",
    "    netSubNodes = pd.concat(frames)\n",
    "    netSubNodes = netSubNodes.reset_index(drop=True)\n",
    "    \n",
    "    #print \n",
    "    #print netSubNodes\n",
    "    \n",
    "    netSubNode1List = netSubNodes[\"Node1\"].tolist()\n",
    "    netSubNode2List = netSubNodes[\"Node2\"].tolist()\n",
    "    \n",
    "    netSubNodeList = netSubNode1List + netSubNode2List\n",
    "    netSubNodeList = sorted(list(set(netSubNodeList)))\n",
    "    netSubNodeList.remove(nodesAll[i])\n",
    "    #print \n",
    "    #print netSubNodeList\n",
    "    \n",
    "    TieList.append([nodesAll[i], netSubNodeList ])\n",
    "    \n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "selist = [\"ABRAHAM GRUNHUT\", \"ABE GREEN\",\n",
    "\"ABE GREENHUT\", \"ABRAHAM GREENHUT\",\n",
    "\"ABE GRUNHUT\",\n",
    "\"ESTHER GRUNHUT\",\n",
    "\"JACOB GREEN\"\n",
    "]\n",
    "\n",
    "new_list = []\n",
    "\n",
    "\n",
    "\n",
    "netNode1 = netSub[netSub['Node1'].isin(selist)]\n",
    "netNode2 = netSub[netSub['Node2'].isin(selist)]\n",
    "\n",
    "net2 = pd.DataFrame()\n",
    "\n",
    "net2 = net2.append(netNode1,ignore_index=False)\n",
    "net2 = net2.append(netNode2,ignore_index=False)\n",
    "\n",
    "net2 = net2.reset_index(drop=True)\n",
    "\n",
    "\n",
    "all_list = []\n",
    "new_list_append = []\n",
    "\n",
    "for i in range(0, len(selist)):\n",
    "    new_list = []\n",
    "    for j in range(0, len(net2)):\n",
    "        if selist[i] in net2[\"Node1\"][j]:\n",
    "            new_list.append(net2[\"Node2\"][j])\n",
    "        if selist[i] in net2[\"Node2\"][j]:\n",
    "            new_list.append(net2[\"Node1\"][j])\n",
    "            \n",
    "      \n",
    "    new_list = sorted(list(set(new_list)))\n",
    "    if selist[i] in new_list:\n",
    "        new_list.remove(selist[i])\n",
    "        \n",
    "    for k in range(0, len(selist)):\n",
    "        if selist[k] in new_list:\n",
    "            new_list.remove(selist[k])\n",
    "    \n",
    "    new_list_append.append(new_list)\n",
    "    \n",
    "    x = list(set(new_list_append[0]).intersection(new_list))\n",
    "    \n",
    "    all_list.append([selist[i],len(x), len(new_list), new_list])\n",
    "    \n",
    "    \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TieListdf = pd.DataFrame(TieList)\n",
    "TieListdf= TieListdf.rename(columns={0: 'Name', 1: 'Ties'})\n",
    "\n",
    "olist[0][0][0]\n",
    "\n",
    "TieListdf[\"Owner\"] = 0\n",
    "\n",
    "for i in range(0, len(TieListdf)):\n",
    "#for i in range(0, 50):\n",
    "    for j in range(0, len(olist[i])):\n",
    "        if TieListdf[\"Name\"][i] in olist[i][j]:\n",
    "            TieListdf[\"Owner\"][i] = 1\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in here\n"
     ]
    }
   ],
   "source": [
    "if  'ROBERT FLINK' in olist[0][0]:\n",
    "    print \"in here\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "strucScore\n"
     ]
    }
   ],
   "source": [
    "#CALCULATE THE JACCARD SIMILARITY\n",
    "#http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture7.pdf\n",
    "from operator import itemgetter\n",
    "\n",
    "StructDic = {}\n",
    "\n",
    "#for i in range(0, len(TieList)):\n",
    "for i in range(1, 2):\n",
    "    #print TieList[i]\n",
    "    \n",
    "    if i%(len(TieList)/10) == 0:\n",
    "        print i\n",
    "    \n",
    "    strucScore = []\n",
    "    targetName = TieList[i][0]\n",
    "    list1 = TieList[i][1]\n",
    "    \n",
    "    for j in range(0, len(TieList)):\n",
    "        #print \n",
    "        #print TieList[i][1]\n",
    "        list2 = TieList[j][1]\n",
    "        \n",
    "        x = list(set(list1).intersection(list2))\n",
    "        \n",
    "        setAll = list1 + list2\n",
    "        setAll = set(setAll)\n",
    "        \n",
    "        if len(setAll) != 0:\n",
    "            ratio = float(len(x))/len(setAll)\n",
    "        else:\n",
    "            ratio = 0\n",
    "        \n",
    "        strucScore.append([TieList[j][0], ratio, x])\n",
    "    \n",
    "    #sort from high to low\n",
    "    strucScore = sorted(strucScore, key=itemgetter(1), reverse=True)\n",
    "    #top10 = [strucScore[z][0] for z in range(10)]\n",
    "    print \"strucScore\"\n",
    "    #print strucScore\n",
    "    #top5 = [strucScore[z][0] for z in range(1:5)]\n",
    "    #StructDic[targetName] = top5\n",
    "        \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1 RS CORPORATION'"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targetName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['1 RS CORPORATION', 1.0, ['MD ALI']],\n",
       " ['ABDUL MALIK', 1.0, ['MD ALI']],\n",
       " ['ABUL AZAD', 1.0, ['MD ALI']],\n",
       " ['AFROZ HUSAIN', 1.0, ['MD ALI']],\n",
       " ['AKLIMA CHOWDHURY', 1.0, ['MD ALI']],\n",
       " ['ANAMUL HAQ', 1.0, ['MD ALI']],\n",
       " ['ANIFTOS HELEN', 1.0, ['MD ALI']],\n",
       " ['ASHIN NAYAKA', 1.0, ['MD ALI']],\n",
       " ['AZAM SHIBLE', 1.0, ['MD ALI']],\n",
       " ['BARNET LIBERMAN', 1.0, ['MD ALI']],\n",
       " ['CHAMAN LALL', 1.0, ['MD ALI']],\n",
       " ['CHARLES WHELAN', 1.0, ['MD ALI']],\n",
       " ['CHOUDHRY ABBAS', 1.0, ['MD ALI']],\n",
       " ['CHRISTINE SALERNO', 1.0, ['MD ALI']],\n",
       " ['FRANCIS PAZDEN', 1.0, ['MD ALI']],\n",
       " ['GEORGE ARYEH', 1.0, ['MD ALI']],\n",
       " ['GRANT FRANKEL', 1.0, ['MD ALI']],\n",
       " ['GREGG BAILEY', 1.0, ['MD ALI']],\n",
       " ['HAFIZ CHOUDHURRY', 1.0, ['MD ALI']],\n",
       " ['HELEN NNIFTOS', 1.0, ['MD ALI']],\n",
       " ['HUMYUN ISLAM', 1.0, ['MD ALI']],\n",
       " ['ISAAC AREYH', 1.0, ['MD ALI']],\n",
       " ['JAVID CHAUDHRY', 1.0, ['MD ALI']],\n",
       " ['JEREMY MCKEON', 1.0, ['MD ALI']],\n",
       " ['JOHNATHAN STEVENS', 1.0, ['MD ALI']],\n",
       " ['JUSTIN EHRLICH', 1.0, ['MD ALI']],\n",
       " ['KAREN SCHULLERE', 1.0, ['MD ALI']],\n",
       " ['KUN LEE UI', 1.0, ['MD ALI']],\n",
       " ['LEYDA SOTO', 1.0, ['MD ALI']],\n",
       " ['LOUIS JAARI', 1.0, ['MD ALI']],\n",
       " ['MANON ERNESTO', 1.0, ['MD ALI']],\n",
       " ['MARION SCHAINBERG', 1.0, ['MD ALI']],\n",
       " ['MAXIMO ZARZOSA', 1.0, ['MD ALI']],\n",
       " ['MEHRBAN ALI', 1.0, ['MD ALI']],\n",
       " ['MOHAMMAD AMR', 1.0, ['MD ALI']],\n",
       " ['MORACE NORVILLE', 1.0, ['MD ALI']],\n",
       " ['MOROCHO RUPERTO', 1.0, ['MD ALI']],\n",
       " ['MR ADAM', 1.0, ['MD ALI']],\n",
       " ['MR SHAIN', 1.0, ['MD ALI']],\n",
       " ['MRH ENGINEERING', 1.0, ['MD ALI']],\n",
       " ['MUHAMMAD SARWAR', 1.0, ['MD ALI']],\n",
       " ['MUNI RAJINDER', 1.0, ['MD ALI']],\n",
       " ['NICHOLAS BIASI', 1.0, ['MD ALI']],\n",
       " ['NISAR AWAIS', 1.0, ['MD ALI']],\n",
       " ['PHILIP LAU', 1.0, ['MD ALI']],\n",
       " ['PISACANO JAMES', 1.0, ['MD ALI']],\n",
       " ['RAJINDER KAUR', 1.0, ['MD ALI']],\n",
       " ['ROCK SCAFFOLDING CORPORATION', 1.0, ['MD ALI']],\n",
       " ['RUEVEN SALMAN', 1.0, ['MD ALI']],\n",
       " ['SALVADOR GARGANO', 1.0, ['MD ALI']],\n",
       " ['SHAFIQUE MOHAMMED', 1.0, ['MD ALI']],\n",
       " ['SHELLEY MAZOR', 1.0, ['MD ALI']],\n",
       " ['STEVEN KAPETANOS', 1.0, ['MD ALI']],\n",
       " ['STEVEN MERMELSTEIN', 1.0, ['MD ALI']],\n",
       " ['TASLIM KHAN', 1.0, ['MD ALI']],\n",
       " ['UDDIN NASHIR', 1.0, ['MD ALI']],\n",
       " ['VIOLET HERNANDEZ', 1.0, ['MD ALI']],\n",
       " ['WILLIAM CURRAN', 1.0, ['MD ALI']],\n",
       " ['ZHENG XIANG', 1.0, ['MD ALI']],\n",
       " ['ABDUL HANNAN', 0.5, ['MD ALI']],\n",
       " ['AGIAIA SOPILIOTIS MERIS', 0.5, ['MD ALI']],\n",
       " ['AHSAN RANJHA', 0.5, ['MD ALI']],\n",
       " ['ALLAN HUESSINGER', 0.5, ['MD ALI']],\n",
       " ['ANTHONY PAGANO', 0.5, ['MD ALI']],\n",
       " ['ANTON POPOVIC', 0.5, ['MD ALI']],\n",
       " ['ANTONIO GUALTIERI', 0.5, ['MD ALI']],\n",
       " ['CHIRAG PATEL', 0.5, ['MD ALI']],\n",
       " ['DANIEL BLANCO', 0.5, ['MD ALI']],\n",
       " ['EMPIRE DESIGN', 0.5, ['MD ALI']],\n",
       " ['GLENN SCHERER', 0.5, ['MD ALI']],\n",
       " ['JUAN TAVAREZ', 0.5, ['MD ALI']],\n",
       " ['KENNETH MAK', 0.5, ['MD ALI']],\n",
       " ['KONSTANDINOS VORILLA', 0.5, ['MD ALI']],\n",
       " ['LAKHI ZORIA', 0.5, ['MD ALI']],\n",
       " ['LEE WALLACH', 0.5, ['MD ALI']],\n",
       " ['MARC GOODMAN', 0.5, ['MD ALI']],\n",
       " ['MATHEW KATZ', 0.5, ['MD ALI']],\n",
       " ['MIRA BAIG', 0.5, ['MD ALI']],\n",
       " ['MOHAMMAD CHAUDHRI', 0.5, ['MD ALI']],\n",
       " ['MUAREM LATA', 0.5, ['MD ALI']],\n",
       " ['PRABHJIT SINGH', 0.5, ['MD ALI']],\n",
       " ['PRAINITO DESIGN', 0.5, ['MD ALI']],\n",
       " ['RAJAN VENKTANATHAN', 0.5, ['MD ALI']],\n",
       " ['SAJJAD SYALVI', 0.5, ['MD ALI']],\n",
       " ['SAMUEL LEIFER', 0.5, ['MD ALI']],\n",
       " ['SYED KAFEEL AHMED', 0.5, ['MD ALI']],\n",
       " ['US CONSULTING', 0.5, ['MD ALI']],\n",
       " ['YASIN MOHAMAD', 0.5, ['MD ALI']],\n",
       " ['ABC PERMIT EXPEDITING', 0.3333333333333333, ['MD ALI']],\n",
       " ['AGIAIA MERIS', 0.3333333333333333, ['MD ALI']],\n",
       " ['AK CONSULTING', 0.3333333333333333, ['MD ALI']],\n",
       " ['BUILDING VIOLATIONS SERVICES', 0.3333333333333333, ['MD ALI']],\n",
       " ['CCM CONSULTING', 0.3333333333333333, ['MD ALI']],\n",
       " ['CHARLES ROMANO', 0.3333333333333333, ['MD ALI']],\n",
       " ['EDWARD OSTAD', 0.3333333333333333, ['MD ALI']],\n",
       " ['JASWANT SINGH', 0.3333333333333333, ['MD ALI']],\n",
       " ['JOSE A SANTOS', 0.3333333333333333, ['MD ALI']],\n",
       " ['MHM ENGINEERING', 0.3333333333333333, ['MD ALI']],\n",
       " ['MORRIS LIEBERMAN', 0.3333333333333333, ['MD ALI']],\n",
       " ['PERVEZ CHAUDHRY', 0.3333333333333333, ['MD ALI']],\n",
       " ['PETER JACOV', 0.3333333333333333, ['MD ALI']],\n",
       " ['S & S BUILDING', 0.3333333333333333, ['MD ALI']],\n",
       " ['SAKANDER RAJA', 0.3333333333333333, ['MD ALI']],\n",
       " ['SHAHZAD AHMAD', 0.3333333333333333, ['MD ALI']],\n",
       " ['SHEIKH EXPEDITING', 0.3333333333333333, ['MD ALI']],\n",
       " ['YASMEEN LATIF', 0.3333333333333333, ['MD ALI']],\n",
       " ['ANTHONY VIGILANTE', 0.25, ['MD ALI']],\n",
       " ['ARCHVISION DESIGN', 0.25, ['MD ALI']],\n",
       " ['CHAD SCHWARTZ', 0.25, ['MD ALI']],\n",
       " ['CHUDHARY MAHMOOD', 0.25, ['MD ALI']],\n",
       " ['DONALD FRIEDMAN', 0.25, ['MD ALI']],\n",
       " ['HARKAMSLJIT KAUR', 0.25, ['MD ALI']],\n",
       " ['HELEN ANIFTOS', 0.25, ['MD ALI']],\n",
       " ['PHILIP SUSI', 0.25, ['MD ALI']],\n",
       " ['ABDUL MOHAMMAD', 0.2, ['MD ALI']],\n",
       " ['DEBORAH JOHNSON', 0.2, ['MD ALI']],\n",
       " ['HERUIN CALDERON', 0.2, ['MD ALI']],\n",
       " ['MOHAMMED HASAN', 0.2, ['MD ALI']],\n",
       " ['ROCK GROUP NY CORPORATION', 0.2, ['MD ALI']],\n",
       " ['SHABBIR BILAL', 0.2, ['MD ALI']],\n",
       " ['SHAUKAT ALI', 0.2, ['MD ALI']],\n",
       " ['SHAWN THOMAS', 0.2, ['MD ALI']],\n",
       " ['SYED AHMED', 0.2, ['MD ALI']],\n",
       " ['ALL CITY EXPEDITING', 0.16666666666666666, ['MD ALI']],\n",
       " ['CHAIM SIMKOWITZ', 0.16666666666666666, ['MD ALI']],\n",
       " ['GM ARCHITECTS', 0.16666666666666666, ['MD ALI']],\n",
       " ['LARRY NASS', 0.16666666666666666, ['MD ALI']],\n",
       " ['LIAQUAT CHEEMA', 0.16666666666666666, ['MD ALI']],\n",
       " ['MANDEEP KAUR', 0.16666666666666666, ['MD ALI']],\n",
       " ['MOHAMMAD BILLAH', 0.16666666666666666, ['MD ALI']],\n",
       " ['PRAVIN BHANDERI', 0.16666666666666666, ['MD ALI']],\n",
       " ['RUSTEM GECAJ', 0.16666666666666666, ['MD ALI']],\n",
       " ['TAHIR BHUTTA', 0.16666666666666666, ['MD ALI']],\n",
       " ['TASLIM KHAM', 0.16666666666666666, ['MD ALI']],\n",
       " ['AMRIK SINGH', 0.14285714285714285, ['MD ALI']],\n",
       " ['ASHRAF ALI PCSES', 0.14285714285714285, ['MD ALI']],\n",
       " ['CONSTRUCTION PERMIT SERVICES', 0.14285714285714285, ['MD ALI']],\n",
       " ['MOHAMMAD ROSUL', 0.14285714285714285, ['MD ALI']],\n",
       " ['MOHAMMED UDDIN', 0.14285714285714285, ['MD ALI']],\n",
       " ['ABDUL CHAUDHARY', 0.125, ['MD ALI']],\n",
       " ['BURNHAM NEW YORK', 0.125, ['MD ALI']],\n",
       " ['MANJINDER SINGH', 0.125, ['MD ALI']],\n",
       " ['MARTHA BRAZOBAN', 0.125, ['MD ALI']],\n",
       " ['MICHAEL DIFONZO', 0.125, ['MD ALI']],\n",
       " ['SIMRANPAL SINGH', 0.125, ['MD ALI']],\n",
       " ['ANISHA ASHRAF', 0.1111111111111111, ['MD ALI']],\n",
       " ['MICHAEL GARCIA', 0.1111111111111111, ['MD ALI']],\n",
       " ['MOHAMMED ALAM', 0.1111111111111111, ['MD ALI']],\n",
       " ['JENNY FLORES EXPEDITING', 0.1, ['MD ALI']],\n",
       " ['LAKHWINDER SINGH', 0.1, ['MD ALI']],\n",
       " ['NS ENGINEERING', 0.1, ['MD ALI']],\n",
       " ['YOUBER DESIGN', 0.1, ['MD ALI']],\n",
       " ['ARCHITECTS DESIGN', 0.09090909090909091, ['MD ALI']],\n",
       " ['ION DESIGN', 0.09090909090909091, ['MD ALI']],\n",
       " ['LOUIS HANDLER', 0.09090909090909091, ['MD ALI']],\n",
       " ['SEAD GANIC', 0.09090909090909091, ['MD ALI']],\n",
       " ['SHA ASH', 0.09090909090909091, ['MD ALI']],\n",
       " ['BRENDA BALLISON', 0.08333333333333333, ['MD ALI']],\n",
       " ['JASPAL SINGH', 0.08333333333333333, ['MD ALI']],\n",
       " ['CITY EXPEDITING', 0.07692307692307693, ['MD ALI']],\n",
       " ['HARJIT SINGH', 0.07142857142857142, ['MD ALI']],\n",
       " ['MOSHEREF HUSSAIN', 0.07142857142857142, ['MD ALI']],\n",
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       " ['AKEEB SHEKONI', 0.0, []],\n",
       " ['AKERMAN DESIGN', 0.0, []],\n",
       " ['AKEXABDER BLAKELY', 0.0, []],\n",
       " ['AKHIL BANSAL', 0.0, []],\n",
       " ['AKHLAK HOUDHARY', 0.0, []],\n",
       " ['AKHTAR KHAN', 0.0, []],\n",
       " ['AKHTAR MAJID', 0.0, []],\n",
       " ['AKHTAR NEWAZ', 0.0, []],\n",
       " ['AKIJA LAJQI', 0.0, []],\n",
       " ['AKIKO KYEI-ABOAGYE', 0.0, []],\n",
       " ['AKIKO SUZUKI', 0.0, []],\n",
       " ['AKIM HOQUE', 0.0, []],\n",
       " ['AKIVA HUFFMAN', 0.0, []],\n",
       " ['AKIVA KOBRE', 0.0, []],\n",
       " ['AKIVA KURLAND', 0.0, []],\n",
       " ['AKIVA LICHTMAN', 0.0, []],\n",
       " ['AKM HOGUE', 0.0, []],\n",
       " ['AKM HOSSAIN', 0.0, []],\n",
       " ['AKMAL MAKHAMATKULOV', 0.0, []],\n",
       " ['AKMAL MUHAMATKULOV', 0.0, []],\n",
       " ['AKRAM GHALIZADEH', 0.0, []],\n",
       " ['AKSHAY SACHDEV', 0.0, []],\n",
       " ['AKWASI ACHAMPONG', 0.0, []],\n",
       " ['AL BOKHOUR', 0.0, []],\n",
       " ['AL DELLAFERA', 0.0, []],\n",
       " ['AL FAELLA', 0.0, []],\n",
       " ['AL FAIELLA', 0.0, []],\n",
       " ['AL GLICK', 0.0, []],\n",
       " ['AL HAAG', 0.0, []],\n",
       " ['AL KALFA', 0.0, []],\n",
       " ['AL LAZAR', 0.0, []],\n",
       " ['AL LAZARTE', 0.0, []],\n",
       " ['AL LIEBERMAN', 0.0, []],\n",
       " ['AL LISCHNER', 0.0, []],\n",
       " ['AL LOPINTO', 0.0, []],\n",
       " ['AL MAZALTOV', 0.0, []],\n",
       " ['AL POERANTZ', 0.0, []],\n",
       " ['AL POMERANTZ', 0.0, []],\n",
       " ['AL REGENSHEIN', 0.0, []],\n",
       " ['AL ROSENSHEIN', 0.0, []],\n",
       " ['AL SALSANO', 0.0, []],\n",
       " ['AL SANFILIPPO', 0.0, []],\n",
       " ['AL SATTAUR', 0.0, []],\n",
       " ['AL SAVDIE', 0.0, []],\n",
       " ['AL SHEHADA', 0.0, []],\n",
       " ['AL SUTTON', 0.0, []],\n",
       " ['AL VOCI', 0.0, []],\n",
       " ['ALAADIN HUSSARI', 0.0, []],\n",
       " ['ALAIN KARAOGLAN', 0.0, []],\n",
       " ['ALAIN PEREZ', 0.0, []],\n",
       " ['ALAINA WILLIAMS', 0.0, []],\n",
       " ['ALAM MOHAMMED', 0.0, []],\n",
       " ...]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strucScore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NETWORKX ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkX Analysis: Calculate communities, combine with nodes ...2018-06-08 15:33:33.271000\n",
      "2018-06-08 15:33:33.271000\n",
      "Running ...\n",
      "Calculating degree centrality\n",
      "2018-06-08 15:33:34.692000\n",
      "Calculating closness centrality\n",
      "2018-06-08 16:15:07.113000\n",
      "Calculating betweeness centrality\n",
      "2018-06-09 00:58:35.730000\n",
      "Calculating communities\n",
      "2018-06-09 00:58:39.388000\n",
      "DONE with Training Analysis...2018-06-09 00:58:39.388000\n"
     ]
    }
   ],
   "source": [
    "string = \"NetworkX Analysis: Calculate communities, combine with nodes ...\" + str(datetime.now())\n",
    "print string\n",
    "logging.info(string)\n",
    "\n",
    "print str(datetime.now())\n",
    "\n",
    "#CREATE NETWORK OBJECT USING NETWORKX\n",
    "\n",
    "print \"Running ...\"\n",
    "\n",
    "\n",
    "#convert netsub list to dataframe\n",
    "netSub = pd.DataFrame(netSub)\n",
    "netSub = netSub.rename(columns={0: 'Node1', 1: 'Node2'})\n",
    "\n",
    "#NETWORK ANALYSIS\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import community\n",
    "\n",
    "G=nx.Graph()\n",
    "\n",
    "net2 = netSub\n",
    "\n",
    "net2 = net2[[\"Node1\", \"Node2\"]]\n",
    "\n",
    "G.add_nodes_from(net2['Node1'])\n",
    "G.add_nodes_from(net2['Node2'])\n",
    "\n",
    "edgeList = [tuple(x) for x in net2.values]\n",
    "G.add_edges_from(edgeList)\n",
    "\n",
    "#CALCULATE DEGREE CENTRALITY CENTRALITY\n",
    "print \"Calculating degree centrality\"\n",
    "dcent = nx.degree(G)\n",
    "print str(datetime.now())\n",
    "\n",
    "#CALCULATE CLOSENESS CENTRALITY\n",
    "print \"Calculating closness centrality\"\n",
    "ccent = nx.closeness_centrality(G)\n",
    "print str(datetime.now())\n",
    "\n",
    "#CALCULATE EIGENVECTOR CENTRALITY\n",
    "#print \"Calculating eigenvector centrality\"\n",
    "#egcent = nx.eigenvector_centrality(G)\n",
    "#print str(datetime.now())\n",
    "\n",
    "#CALCULATE BETWEENESS CENTRALITY\n",
    "print \"Calculating betweeness centrality\"\n",
    "bcent = nx.betweenness_centrality(G)\n",
    "print str(datetime.now())\n",
    "\n",
    "#community detection\n",
    "print \"Calculating communities\"\n",
    "com = community.best_partition(G)\n",
    "print str(datetime.now())\n",
    "\n",
    "\n",
    "\n",
    "string = \"DONE with Training Analysis...\" + str(datetime.now())\n",
    "print string\n",
    "logging.info(string)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "net2 = net2[[\"Node1\", \"Node2\"]]\n",
    "net2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "preds = nx.jaccard_coefficient(G, net2)\n",
    "\n",
    "for u, v, p in preds:\n",
    "    #'(%d, %d) -> %.8f' % (u, v, p)\n",
    "    print (u, v, p)\n",
    "    \n",
    "#preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Insert community number and  centrality into nodes list ...\n",
      "length of community dictionary 38223\n",
      "length of nodes new 38185\n",
      "Inserting dictionary data ...\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "print \"Insert community number and  centrality into nodes list ...\"\n",
    "nodes_new[\"Betweeness Centrality\"] = \"\"\n",
    "nodes_new[\"Closeness Centrality\"] = \"\"\n",
    "nodes_new[\"Degree Centrality\"] = \"\"\n",
    "nodes_new[\"Community Number\"] = \"\"\n",
    "\n",
    "\n",
    "print \"length of community dictionary\", len(com)\n",
    "\n",
    "print \"length of nodes new\", len(nodes_new)\n",
    "\n",
    "for i in range(0, len(nodes_new)):\n",
    "    if nodes_new[\"Name\"][i] in com:\n",
    "        nodes_new[\"Community Number\"][i] = com[nodes_new[\"Name\"][i] ]\n",
    "        \n",
    "    if nodes_new[\"Name\"][i] in bcent:\n",
    "        nodes_new[\"Betweeness Centrality\"][i] = bcent[nodes_new[\"Name\"][i]]\n",
    "        \n",
    "    if nodes_new[\"Name\"][i] in ccent:\n",
    "        nodes_new[\"Closeness Centrality\"][i] = ccent[nodes_new[\"Name\"][i]]\n",
    "        \n",
    "    if nodes_new[\"Name\"][i] in dcent:\n",
    "        nodes_new[\"Degree Centrality\"][i] = dcent[nodes_new[\"Name\"][i]]\n",
    "        \n",
    "    \n",
    "        \n",
    "bad_com = nodes_new[[\"Community Number\", \"Total Ties\", \"Total Bad Ties\", \"Total Unique Ties\", \"Total Unique Bad Ties\"]]\n",
    "bad_com = bad_com[bad_com[\"Total Bad Ties\"] > 0].reset_index(drop=True)\n",
    "bad_com[\"Community Number\"] = bad_com[\"Community Number\"].astype(str)\n",
    "bad_com = bad_com.groupby(['Community Number']).sum()\n",
    "bad_com = bad_com.add_suffix('_sum').reset_index()\n",
    "\n",
    "bad_com[\"Community Bad Density Total\"] = bad_com[\"Total Bad Ties_sum\"]/bad_com[\"Total Ties_sum\"]\n",
    "bad_com[\"Community Bad Density Unique\"] = bad_com[\"Total Unique Bad Ties_sum\"]/bad_com[\"Total Unique Ties_sum\"]\n",
    "bad_com = bad_com.sort_values(by = 'Community Bad Density Total', ascending=False).reset_index(drop=True)\n",
    "\n",
    "#bad_com = bad_com[[\"Community Number\", \"Community Bad Density Total\", \"Community Bad Density Unique\"]]\n",
    "\n",
    "nodes_new[\"Community Number\"] = nodes_new[\"Community Number\"].astype(str)\n",
    "\n",
    "\n",
    "\n",
    "dens_total = bad_com.set_index('Community Number')['Community Bad Density Total'].to_dict()\n",
    "dens_unique = bad_com.set_index('Community Number')['Community Bad Density Unique'].to_dict()\n",
    "\n",
    "\n",
    "#Number of bad actors in community\n",
    "\n",
    "count_badcomm = nodes_new[[\"Community Number\", \"Bad Flag\"]]\n",
    "count_badcomm[\"Value\"] = 1\n",
    "count_badcomm = count_badcomm.groupby(['Community Number']).sum()\n",
    "count_badcomm = count_badcomm.add_suffix('_sum').reset_index()\n",
    "count_badcomm[\"Value_sum\"] = count_badcomm[\"Value_sum\"].astype(float)\n",
    "count_badcomm[\"Bad Count Density\"] = count_badcomm[\"Bad Flag_sum\"]/count_badcomm[\"Value_sum\"]\n",
    "count_badcomm = count_badcomm.sort_values(by = 'Bad Count Density', ascending=False).reset_index(drop=True)\n",
    "\n",
    "num_bad_com = count_badcomm.set_index('Community Number')['Bad Flag_sum'].to_dict()\n",
    "num_total_com = count_badcomm.set_index('Community Number')['Value_sum'].to_dict()\n",
    "\n",
    "nodes_new[\"Community Bad Density Total\"] = \"\"\n",
    "nodes_new[\"Community Bad Density Unique\"] = \"\"\n",
    "nodes_new[\"Number Bad Actors in Community\"] = \"\"\n",
    "nodes_new[\"Number in Community\"] = \"\"\n",
    "\n",
    "print \"Inserting dictionary data ...\"\n",
    "for i in range(0, len(nodes_new)):\n",
    "    if nodes_new[\"Community Number\"][i] in dens_total:\n",
    "        nodes_new[\"Community Bad Density Total\"][i] = dens_total[nodes_new[\"Community Number\"][i]]\n",
    "    if nodes_new[\"Community Number\"][i] in dens_unique:\n",
    "        nodes_new[\"Community Bad Density Unique\"][i] = dens_unique[nodes_new[\"Community Number\"][i]]\n",
    "          \n",
    "    if nodes_new[\"Community Number\"][i] in num_total_com:\n",
    "        nodes_new[\"Number in Community\"][i] = num_total_com[nodes_new[\"Community Number\"][i]]\n",
    "    if nodes_new[\"Community Number\"][i] in num_bad_com:\n",
    "        nodes_new[\"Number Bad Actors in Community\"][i] = num_bad_com[nodes_new[\"Community Number\"][i]]\n",
    "        \n",
    "        \n",
    "    \n",
    "        \n",
    "print \"DONE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\noutput[\"B Centrality Rank\"] = pd.cut(output[\\'Betweeness Centrality\\'],8)\\n\\nlabels = [\"1\", \"2\", \"3a\", \"3b\", \"3c\", \"4a\", \"4b\", \"4c\"]\\n\\noutput[\"D Centrality Rank\"] = pd.cut(output[\\'Degree Centrality\\'],8, labels=labels)\\n\\noutput = output.reset_index(drop=True)\\n\\n\\n\\n\\n\\n\\noutput[\\'D Centrality Rank\\'] = output[\\'D Centrality Rank\\'].astype(str)\\n\\n#output[\\'D Centrality Rank\\'] = output[\\'D Centrality Rank\\'].str.replace(r\"\\\\s+\\\\(.*\\\\)\",\"\")\\noutput[\\'D Centrality Rank\\'] = output[\\'D Centrality Rank\\'].str.replace(\"(\", \"\")\\noutput[\\'D Centrality Rank\\'] = output[\\'D Centrality Rank\\'].str.replace(\"]\", \"\")\\n\\n#recategorize centrality\\noutput[\\'D Centrality Rank\\'] = output[\\'D Centrality Rank\\'].str.replace(\"-1.926, 492.75\", \"1\")\\noutput[\\'D Centrality Rank\\'] = output[\\'D Centrality Rank\\'].str.replace(\"492.75, 983.5\", \"2\")\\n\\noutput[\\'D Centrality Rank\\'] = output[\\'D Centrality Rank\\'].str.replace(\"983.5, 1474.25\", \"3\")\\noutput[\\'D Centrality Rank\\'] = output[\\'D Centrality Rank\\'].str.replace(\"1474.25, 1965\", \"3\")\\noutput[\\'D Centrality Rank\\'] = output[\\'D Centrality Rank\\'].str.replace(\"1965, 2455.75\", \"3\")\\n\\noutput[\\'D Centrality Rank\\'] = output[\\'D Centrality Rank\\'].str.replace(\"2455.75, 2946.5\", \"4\")\\noutput[\\'D Centrality Rank\\'] = output[\\'D Centrality Rank\\'].str.replace(\"2946.5, 3437.25\", \"4\")\\noutput[\\'D Centrality Rank\\'] = output[\\'D Centrality Rank\\'].str.replace(\"3437.25, 3928\", \"4\")\\n\\n'"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RANK BAD ACTORS \n",
    "\n",
    "output = nodes_new[[\"Name\", \"Bad Flag\", \"Total Ties\", \"Total Unique Bad Ties\", \"Bad Unique Density\", \"Bad Rank\", \"Unique Number BINs\", \n",
    "                    \"Degree Centrality\", \"Betweeness Centrality\", \"B Centrality Rank\"]]\n",
    "output = output[output[\"Bad Flag\"] == 1]\n",
    "\n",
    "output[\"Bad Rank\"] = output[\"Bad Rank\"].astype(float)\n",
    "output[\"X Bad Rank\"] = output[\"Bad Rank\"]/output[\"Bad Rank\"].max()\n",
    "output[\"X Bad Rank\"] = 1/output[\"X Bad Rank\"]\n",
    "\n",
    "output[\"Total Unique Bad Ties\"] = output[\"Total Unique Bad Ties\"].astype(float)\n",
    "output[\"X Total Unique Bad Ties\"] = output[\"Total Unique Bad Ties\"]/output[\"Total Unique Bad Ties\"].max()\n",
    "\n",
    "#output[\"Bad Unique Density\"] = output[\"Bad Unique Density\"].astype(float)\n",
    "#output[\"X Bad Unique Density\"] = output[\"Bad Unique Density\"]/output[\"Bad Unique Density\"].max()\n",
    "\n",
    "output[\"Betweeness Centrality\"] = output[\"Betweeness Centrality\"].astype(float)\n",
    "output[\"X Betweeness Centrality\"] = output[\"Betweeness Centrality\"]/output[\"Betweeness Centrality\"].max()\n",
    "\n",
    "\n",
    "#Risk scoring\n",
    "#output[\"Risk Score\"] = output[\"X Bad Rank\"] + output[\"X Bad Unique Density\"] + output[\"X Betweeness Centrality\"]\n",
    "\n",
    "#output[\"Risk Score_density_centrality\"] = output[\"X Bad Unique Density\"] + output[\"X Betweeness Centrality\"]\n",
    "\n",
    "#output[\"Risk Score_density_centrality\"] = output[\"X Bad Unique Density\"] + output[\"X Betweeness Centrality\"]\n",
    "\n",
    "\n",
    "output[\"Risk Score_unique_centrality\"] = output[\"X Total Unique Bad Ties\"] + output[\"X Betweeness Centrality\"] + output[\"X Bad Rank\"] \n",
    "\n",
    "output[\"Risk Score_unique_centrality_rank10\"] =  (output[\"Risk Score_unique_centrality\"]/\n",
    "                                                  output[\"Risk Score_unique_centrality\"].max() )\n",
    "\n",
    "output[\"Risk Score_unique_centrality_rank10\"] = output[\"Risk Score_unique_centrality_rank10\"].round(2)\n",
    "output[\"Risk Score_unique_centrality_rank10\"] = (output[\"Risk Score_unique_centrality_rank10\"] ) * 10\n",
    "\n",
    "output = output.sort_values(by = 'Risk Score_unique_centrality_rank10', ascending=False).reset_index(drop=True)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "output[\"B Centrality Rank\"] = pd.cut(output['Betweeness Centrality'],8)\n",
    "\n",
    "labels = [\"1\", \"2\", \"3a\", \"3b\", \"3c\", \"4a\", \"4b\", \"4c\"]\n",
    "\n",
    "output[\"D Centrality Rank\"] = pd.cut(output['Degree Centrality'],8, labels=labels)\n",
    "\n",
    "output = output.reset_index(drop=True)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "output['D Centrality Rank'] = output['D Centrality Rank'].astype(str)\n",
    "\n",
    "#output['D Centrality Rank'] = output['D Centrality Rank'].str.replace(r\"\\s+\\(.*\\)\",\"\")\n",
    "output['D Centrality Rank'] = output['D Centrality Rank'].str.replace(\"(\", \"\")\n",
    "output['D Centrality Rank'] = output['D Centrality Rank'].str.replace(\"]\", \"\")\n",
    "\n",
    "#recategorize centrality\n",
    "output['D Centrality Rank'] = output['D Centrality Rank'].str.replace(\"-1.926, 492.75\", \"1\")\n",
    "output['D Centrality Rank'] = output['D Centrality Rank'].str.replace(\"492.75, 983.5\", \"2\")\n",
    "\n",
    "output['D Centrality Rank'] = output['D Centrality Rank'].str.replace(\"983.5, 1474.25\", \"3\")\n",
    "output['D Centrality Rank'] = output['D Centrality Rank'].str.replace(\"1474.25, 1965\", \"3\")\n",
    "output['D Centrality Rank'] = output['D Centrality Rank'].str.replace(\"1965, 2455.75\", \"3\")\n",
    "\n",
    "output['D Centrality Rank'] = output['D Centrality Rank'].str.replace(\"2455.75, 2946.5\", \"4\")\n",
    "output['D Centrality Rank'] = output['D Centrality Rank'].str.replace(\"2946.5, 3437.25\", \"4\")\n",
    "output['D Centrality Rank'] = output['D Centrality Rank'].str.replace(\"3437.25, 3928\", \"4\")\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "#CutG = output[[\"D Centrality Rank\"]]\n",
    "#CutG = CutG.groupby(['D Centrality Rank']).sum()\n",
    "#CutG\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Bad Flag</th>\n",
       "      <th>Total Ties</th>\n",
       "      <th>Total Unique Bad Ties</th>\n",
       "      <th>Bad Unique Density</th>\n",
       "      <th>Bad Rank</th>\n",
       "      <th>Unique Number BINs</th>\n",
       "      <th>Degree Centrality</th>\n",
       "      <th>Betweeness Centrality</th>\n",
       "      <th>B Centrality Rank</th>\n",
       "      <th>X Bad Rank</th>\n",
       "      <th>X Total Unique Bad Ties</th>\n",
       "      <th>X Betweeness Centrality</th>\n",
       "      <th>Risk Score_unique_centrality</th>\n",
       "      <th>Risk Score_unique_centrality_rank10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>THE BAC GROUP</td>\n",
       "      <td>1</td>\n",
       "      <td>9203.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.058</td>\n",
       "      <td>1.0</td>\n",
       "      <td>317</td>\n",
       "      <td>549</td>\n",
       "      <td>1.097393e-02</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>7.701472e-02</td>\n",
       "      <td>5.077015</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALLSTATE EXPEDITING</td>\n",
       "      <td>1</td>\n",
       "      <td>3828.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.068</td>\n",
       "      <td>1.0</td>\n",
       "      <td>180</td>\n",
       "      <td>339</td>\n",
       "      <td>5.009844e-03</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.71875</td>\n",
       "      <td>3.515893e-02</td>\n",
       "      <td>4.753909</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IMA</td>\n",
       "      <td>1</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>213</td>\n",
       "      <td>411</td>\n",
       "      <td>1.220447e-02</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.18750</td>\n",
       "      <td>8.565060e-02</td>\n",
       "      <td>4.273151</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JOEL RUBIN</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.312</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>1.522659e-05</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>1.068598e-04</td>\n",
       "      <td>4.156357</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ROBERT CADOCH</td>\n",
       "      <td>1</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.273</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>8.641133e-06</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>6.064322e-05</td>\n",
       "      <td>4.093811</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LEON NEKTALOV</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>5.604143e-05</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.06250</td>\n",
       "      <td>3.932971e-04</td>\n",
       "      <td>4.062893</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IZET SABOVIC</td>\n",
       "      <td>1</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19</td>\n",
       "      <td>41</td>\n",
       "      <td>9.880283e-04</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>6.933953e-03</td>\n",
       "      <td>4.038184</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SUNG HO SHIN</td>\n",
       "      <td>1</td>\n",
       "      <td>402.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>72</td>\n",
       "      <td>1.536217e-03</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>1.078113e-02</td>\n",
       "      <td>4.042031</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PROCEED PERMIT SERVICES</td>\n",
       "      <td>1</td>\n",
       "      <td>476.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45</td>\n",
       "      <td>86</td>\n",
       "      <td>2.065318e-03</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>1.449434e-02</td>\n",
       "      <td>4.045744</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MOHAMMAD BUTT</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.048</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>3.399983e-04</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>2.386098e-03</td>\n",
       "      <td>4.033636</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GINO VITALE</td>\n",
       "      <td>1</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>1.225276e-04</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>8.598953e-04</td>\n",
       "      <td>4.032110</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PAUL CULOTTA</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.066504e-04</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>7.484697e-04</td>\n",
       "      <td>4.031998</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DILBER KUKIC</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.071</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>2.141026e-04</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>1.502566e-03</td>\n",
       "      <td>4.032753</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MUNICIPAL BUILDING CONSULTING</td>\n",
       "      <td>1</td>\n",
       "      <td>25315.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2141</td>\n",
       "      <td>3928</td>\n",
       "      <td>1.424913e-01</td>\n",
       "      <td>(0.125, 0.142]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.53125</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.531250</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SCOTT SCHNALL</td>\n",
       "      <td>1</td>\n",
       "      <td>14542.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.016</td>\n",
       "      <td>2.0</td>\n",
       "      <td>933</td>\n",
       "      <td>2033</td>\n",
       "      <td>7.668630e-02</td>\n",
       "      <td>(0.0712, 0.0891]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>5.381822e-01</td>\n",
       "      <td>3.538182</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>JM ZONING</td>\n",
       "      <td>1</td>\n",
       "      <td>31868.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.015</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1107</td>\n",
       "      <td>2076</td>\n",
       "      <td>6.205197e-02</td>\n",
       "      <td>(0.0534, 0.0712]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.96875</td>\n",
       "      <td>4.354789e-01</td>\n",
       "      <td>3.404229</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>JAM CONSULTING</td>\n",
       "      <td>1</td>\n",
       "      <td>32635.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2.0</td>\n",
       "      <td>939</td>\n",
       "      <td>3046</td>\n",
       "      <td>1.020565e-01</td>\n",
       "      <td>(0.0891, 0.107]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.43750</td>\n",
       "      <td>7.162298e-01</td>\n",
       "      <td>3.153730</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PAUL BAILEY</td>\n",
       "      <td>1</td>\n",
       "      <td>30309.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4346</td>\n",
       "      <td>2598</td>\n",
       "      <td>9.470845e-02</td>\n",
       "      <td>(0.0891, 0.107]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.46875</td>\n",
       "      <td>6.646611e-01</td>\n",
       "      <td>3.133411</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FIVE BOROUGH CONSULTING &amp; EXPEDITING</td>\n",
       "      <td>1</td>\n",
       "      <td>12009.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.019</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1102</td>\n",
       "      <td>1385</td>\n",
       "      <td>3.751037e-02</td>\n",
       "      <td>(0.0356, 0.0534]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.81250</td>\n",
       "      <td>2.632467e-01</td>\n",
       "      <td>3.075747</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>YAAKOV STERN</td>\n",
       "      <td>1</td>\n",
       "      <td>2686.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.078</td>\n",
       "      <td>2.0</td>\n",
       "      <td>234</td>\n",
       "      <td>384</td>\n",
       "      <td>6.739749e-03</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.93750</td>\n",
       "      <td>4.729936e-02</td>\n",
       "      <td>2.984799</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BUILDING SOLUTIONS SERVICES</td>\n",
       "      <td>1</td>\n",
       "      <td>4509.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.061</td>\n",
       "      <td>2.0</td>\n",
       "      <td>198</td>\n",
       "      <td>460</td>\n",
       "      <td>9.231389e-03</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.87500</td>\n",
       "      <td>6.478561e-02</td>\n",
       "      <td>2.939786</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GERALD CALIENDO ARCHITECTS</td>\n",
       "      <td>1</td>\n",
       "      <td>11442.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.017</td>\n",
       "      <td>2.0</td>\n",
       "      <td>796</td>\n",
       "      <td>1259</td>\n",
       "      <td>2.505034e-02</td>\n",
       "      <td>(0.0178, 0.0356]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.68750</td>\n",
       "      <td>1.758026e-01</td>\n",
       "      <td>2.863303</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>DANIEL GERDES</td>\n",
       "      <td>1</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.073</td>\n",
       "      <td>2.0</td>\n",
       "      <td>179</td>\n",
       "      <td>342</td>\n",
       "      <td>7.511939e-03</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.78125</td>\n",
       "      <td>5.271857e-02</td>\n",
       "      <td>2.833969</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>THOMAS TUNG</td>\n",
       "      <td>1</td>\n",
       "      <td>4613.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.034</td>\n",
       "      <td>2.0</td>\n",
       "      <td>423</td>\n",
       "      <td>619</td>\n",
       "      <td>1.606149e-02</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.65625</td>\n",
       "      <td>1.127191e-01</td>\n",
       "      <td>2.768969</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>A &amp; A BUILDING CONSULTING</td>\n",
       "      <td>1</td>\n",
       "      <td>4791.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.018</td>\n",
       "      <td>2.0</td>\n",
       "      <td>595</td>\n",
       "      <td>849</td>\n",
       "      <td>3.015694e-02</td>\n",
       "      <td>(0.0178, 0.0356]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.46875</td>\n",
       "      <td>2.116405e-01</td>\n",
       "      <td>2.680390</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>GERALD CALIENDO</td>\n",
       "      <td>1</td>\n",
       "      <td>14209.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2.0</td>\n",
       "      <td>762</td>\n",
       "      <td>1139</td>\n",
       "      <td>2.248391e-02</td>\n",
       "      <td>(0.0178, 0.0356]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.40625</td>\n",
       "      <td>1.577914e-01</td>\n",
       "      <td>2.564041</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>GREENLINE DEVELOPMENT</td>\n",
       "      <td>1</td>\n",
       "      <td>3407.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.042</td>\n",
       "      <td>2.0</td>\n",
       "      <td>273</td>\n",
       "      <td>402</td>\n",
       "      <td>9.892733e-03</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.53125</td>\n",
       "      <td>6.942691e-02</td>\n",
       "      <td>2.600677</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>EXPEDITING 4 U</td>\n",
       "      <td>1</td>\n",
       "      <td>3606.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.077</td>\n",
       "      <td>2.0</td>\n",
       "      <td>158</td>\n",
       "      <td>233</td>\n",
       "      <td>3.298818e-03</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.56250</td>\n",
       "      <td>2.315101e-02</td>\n",
       "      <td>2.585651</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ZIAD EKWANEEN</td>\n",
       "      <td>1</td>\n",
       "      <td>3932.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.021</td>\n",
       "      <td>2.0</td>\n",
       "      <td>448</td>\n",
       "      <td>666</td>\n",
       "      <td>1.940120e-02</td>\n",
       "      <td>(0.0178, 0.0356]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.43750</td>\n",
       "      <td>1.361570e-01</td>\n",
       "      <td>2.573657</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>I &amp; L CONSULTING</td>\n",
       "      <td>1</td>\n",
       "      <td>5999.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>2.0</td>\n",
       "      <td>517</td>\n",
       "      <td>1212</td>\n",
       "      <td>3.469576e-02</td>\n",
       "      <td>(0.0178, 0.0356]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.31250</td>\n",
       "      <td>2.434938e-01</td>\n",
       "      <td>2.555994</td>\n",
       "      <td>5.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>YOEL KOHN</td>\n",
       "      <td>1</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.091</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>2.643005e-04</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.06250</td>\n",
       "      <td>1.854853e-03</td>\n",
       "      <td>1.064355</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>MOSHE SILBER</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4.124708e-06</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.06250</td>\n",
       "      <td>2.894708e-05</td>\n",
       "      <td>1.062529</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>YOSSEL LICHTMAN</td>\n",
       "      <td>1</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.095</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>3.315114e-04</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.06250</td>\n",
       "      <td>2.326537e-03</td>\n",
       "      <td>1.064827</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>SOLOMON HERMAN</td>\n",
       "      <td>1</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.118</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>1.033608e-05</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.06250</td>\n",
       "      <td>7.253827e-05</td>\n",
       "      <td>1.062573</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>ISAAC JACOBS</td>\n",
       "      <td>1</td>\n",
       "      <td>228.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>5.696508e-06</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.06250</td>\n",
       "      <td>3.997792e-05</td>\n",
       "      <td>1.062540</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>BORRIS DRUCKER</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.400</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7.960574e-07</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.06250</td>\n",
       "      <td>5.586707e-06</td>\n",
       "      <td>1.062506</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>CHAIM HALBERSTAM</td>\n",
       "      <td>1</td>\n",
       "      <td>189.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.037</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>3.476063e-05</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>2.439491e-04</td>\n",
       "      <td>1.031494</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>CHAIM LEFKOWITZ</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>5.554132e-05</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>3.897873e-04</td>\n",
       "      <td>1.031640</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>NECHEMIA PERLSTEIN</td>\n",
       "      <td>1</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4.819194e-07</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>3.382096e-06</td>\n",
       "      <td>1.031253</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>CECIL BRAMBLE</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.031250</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>ABE HERMAN</td>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5.855241e-07</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>4.109191e-06</td>\n",
       "      <td>1.031254</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>WOLFE LANDAU</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7.582322e-09</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>5.321251e-08</td>\n",
       "      <td>1.031250</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>CHESKEL LANDAU</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.067</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>7.280695e-06</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>5.109570e-05</td>\n",
       "      <td>1.031301</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>SHLOMO KUBITSHUK</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1.440605e-06</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>1.011012e-05</td>\n",
       "      <td>1.031260</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>DANIEL COHEN</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>6.170129e-05</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>4.330178e-04</td>\n",
       "      <td>1.031683</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>BORUCH REISS</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4.461978e-07</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>3.131403e-06</td>\n",
       "      <td>1.031253</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>LEOPLOD WEINSTOCK</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.500444e-09</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>2.456602e-08</td>\n",
       "      <td>1.031250</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>DAVID WINBERGER</td>\n",
       "      <td>1</td>\n",
       "      <td>139.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.018</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "      <td>56</td>\n",
       "      <td>9.969029e-04</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>6.996235e-03</td>\n",
       "      <td>1.038246</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>BOJIN ZHU</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.038</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>6.855416e-04</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>4.811110e-03</td>\n",
       "      <td>1.036061</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>ELEIZER SCHWIMER</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5.332805e-05</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>3.742547e-04</td>\n",
       "      <td>1.031624</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>JOSEPH MARTIN</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.328773e-08</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>9.325290e-08</td>\n",
       "      <td>1.031250</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>JOSEPH KAUFMAN</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.056</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>6.041385e-05</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>4.239826e-04</td>\n",
       "      <td>1.031674</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>JOEL BERKOWITZ</td>\n",
       "      <td>1</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.026</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17</td>\n",
       "      <td>38</td>\n",
       "      <td>1.598755e-04</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>1.122002e-03</td>\n",
       "      <td>1.032372</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>JI JUAN LIN</td>\n",
       "      <td>1</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.062</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>6.316773e-05</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>4.433093e-04</td>\n",
       "      <td>1.031693</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>JENNY TSANG</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.888117e-07</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>2.728669e-06</td>\n",
       "      <td>1.031253</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>HERSHEL BODANSKY</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.071431e-06</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>7.519274e-06</td>\n",
       "      <td>1.031258</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>HUI ZHEN KUANG</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1.298178e-06</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>9.110572e-06</td>\n",
       "      <td>1.031259</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>ALEXANDER ASHKENAZI</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.368418e-06</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>9.603516e-06</td>\n",
       "      <td>1.031260</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>SOLOMON EISENBERG</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.641307e-07</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>1.151865e-06</td>\n",
       "      <td>1.031251</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>DOROTHEA DONELAN</td>\n",
       "      <td>1</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.059</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>5.964882e-05</td>\n",
       "      <td>(-0.000142, 0.0178]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>4.186137e-04</td>\n",
       "      <td>1.031669</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>273 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Name  Bad Flag  Total Ties  \\\n",
       "0                           THE BAC GROUP         1      9203.0   \n",
       "1                     ALLSTATE EXPEDITING         1      3828.0   \n",
       "2                                     IMA         1      2280.0   \n",
       "3                              JOEL RUBIN         1        32.0   \n",
       "4                           ROBERT CADOCH         1       114.0   \n",
       "5                           LEON NEKTALOV         1        75.0   \n",
       "6                            IZET SABOVIC         1       205.0   \n",
       "7                            SUNG HO SHIN         1       402.0   \n",
       "8                 PROCEED PERMIT SERVICES         1       476.0   \n",
       "9                           MOHAMMAD BUTT         1        47.0   \n",
       "10                            GINO VITALE         1       173.0   \n",
       "11                           PAUL CULOTTA         1        48.0   \n",
       "12                           DILBER KUKIC         1        25.0   \n",
       "13          MUNICIPAL BUILDING CONSULTING         1     25315.0   \n",
       "14                          SCOTT SCHNALL         1     14542.0   \n",
       "15                              JM ZONING         1     31868.0   \n",
       "16                         JAM CONSULTING         1     32635.0   \n",
       "17                            PAUL BAILEY         1     30309.0   \n",
       "18   FIVE BOROUGH CONSULTING & EXPEDITING         1     12009.0   \n",
       "19                           YAAKOV STERN         1      2686.0   \n",
       "20            BUILDING SOLUTIONS SERVICES         1      4509.0   \n",
       "21             GERALD CALIENDO ARCHITECTS         1     11442.0   \n",
       "22                          DANIEL GERDES         1      1913.0   \n",
       "23                            THOMAS TUNG         1      4613.0   \n",
       "24              A & A BUILDING CONSULTING         1      4791.0   \n",
       "25                        GERALD CALIENDO         1     14209.0   \n",
       "26                  GREENLINE DEVELOPMENT         1      3407.0   \n",
       "27                         EXPEDITING 4 U         1      3606.0   \n",
       "28                          ZIAD EKWANEEN         1      3932.0   \n",
       "29                       I & L CONSULTING         1      5999.0   \n",
       "..                                    ...       ...         ...   \n",
       "243                             YOEL KOHN         1        78.0   \n",
       "244                          MOSHE SILBER         1        15.0   \n",
       "245                       YOSSEL LICHTMAN         1        63.0   \n",
       "246                        SOLOMON HERMAN         1       164.0   \n",
       "247                          ISAAC JACOBS         1       228.0   \n",
       "248                        BORRIS DRUCKER         1        12.0   \n",
       "249                      CHAIM HALBERSTAM         1       189.0   \n",
       "250                       CHAIM LEFKOWITZ         1        37.0   \n",
       "251                    NECHEMIA PERLSTEIN         1        87.0   \n",
       "252                         CECIL BRAMBLE         1         2.0   \n",
       "253                            ABE HERMAN         1        65.0   \n",
       "254                          WOLFE LANDAU         1         3.0   \n",
       "255                        CHESKEL LANDAU         1        60.0   \n",
       "256                      SHLOMO KUBITSHUK         1        18.0   \n",
       "257                          DANIEL COHEN         1        51.0   \n",
       "258                          BORUCH REISS         1        39.0   \n",
       "259                     LEOPLOD WEINSTOCK         1         3.0   \n",
       "260                       DAVID WINBERGER         1       139.0   \n",
       "261                             BOJIN ZHU         1        75.0   \n",
       "262                      ELEIZER SCHWIMER         1         6.0   \n",
       "263                         JOSEPH MARTIN         1         6.0   \n",
       "264                        JOSEPH KAUFMAN         1        34.0   \n",
       "265                        JOEL BERKOWITZ         1       366.0   \n",
       "266                           JI JUAN LIN         1        87.0   \n",
       "267                           JENNY TSANG         1         3.0   \n",
       "268                      HERSHEL BODANSKY         1        24.0   \n",
       "269                        HUI ZHEN KUANG         1        60.0   \n",
       "270                   ALEXANDER ASHKENAZI         1        14.0   \n",
       "271                     SOLOMON EISENBERG         1         6.0   \n",
       "272                      DOROTHEA DONELAN         1       114.0   \n",
       "\n",
       "     Total Unique Bad Ties  Bad Unique Density  Bad Rank Unique Number BINs  \\\n",
       "0                     32.0               0.058       1.0                317   \n",
       "1                     23.0               0.068       1.0                180   \n",
       "2                      6.0               0.015       1.0                213   \n",
       "3                      5.0               0.312       1.0                  3   \n",
       "4                      3.0               0.273       1.0                  5   \n",
       "5                      2.0               0.182       1.0                  2   \n",
       "6                      1.0               0.024       1.0                 19   \n",
       "7                      1.0               0.014       1.0                 25   \n",
       "8                      1.0               0.012       1.0                 45   \n",
       "9                      1.0               0.048       1.0                 11   \n",
       "10                     1.0               0.050       1.0                  7   \n",
       "11                     1.0               0.100       1.0                  5   \n",
       "12                     1.0               0.071       1.0                  6   \n",
       "13                    17.0               0.004       2.0               2141   \n",
       "14                    32.0               0.016       2.0                933   \n",
       "15                    31.0               0.015       2.0               1107   \n",
       "16                    14.0               0.005       2.0                939   \n",
       "17                    15.0               0.006       2.0               4346   \n",
       "18                    26.0               0.019       2.0               1102   \n",
       "19                    30.0               0.078       2.0                234   \n",
       "20                    28.0               0.061       2.0                198   \n",
       "21                    22.0               0.017       2.0                796   \n",
       "22                    25.0               0.073       2.0                179   \n",
       "23                    21.0               0.034       2.0                423   \n",
       "24                    15.0               0.018       2.0                595   \n",
       "25                    13.0               0.011       2.0                762   \n",
       "26                    17.0               0.042       2.0                273   \n",
       "27                    18.0               0.077       2.0                158   \n",
       "28                    14.0               0.021       2.0                448   \n",
       "29                    10.0               0.008       2.0                517   \n",
       "..                     ...                 ...       ...                ...   \n",
       "243                    2.0               0.091       4.0                  5   \n",
       "244                    2.0               0.250       4.0                  4   \n",
       "245                    2.0               0.095       4.0                  9   \n",
       "246                    2.0               0.118       4.0                 12   \n",
       "247                    2.0               0.143       4.0                  2   \n",
       "248                    2.0               0.400       4.0                  1   \n",
       "249                    1.0               0.037       4.0                  8   \n",
       "250                    1.0               0.100       4.0                  4   \n",
       "251                    1.0               0.167       4.0                  2   \n",
       "252                    1.0               0.500       4.0                  1   \n",
       "253                    1.0               0.143       4.0                  1   \n",
       "254                    1.0               0.333       4.0                  1   \n",
       "255                    1.0               0.067       4.0                  6   \n",
       "256                    1.0               0.143       4.0                  2   \n",
       "257                    1.0               0.100       4.0                  3   \n",
       "258                    1.0               0.200       4.0                  1   \n",
       "259                    1.0               0.333       4.0                  1   \n",
       "260                    1.0               0.018       4.0                  7   \n",
       "261                    1.0               0.038       4.0                 13   \n",
       "262                    1.0               0.167       4.0                  2   \n",
       "263                    1.0               0.333       4.0                  1   \n",
       "264                    1.0               0.056       4.0                  5   \n",
       "265                    1.0               0.026       4.0                 17   \n",
       "266                    1.0               0.062       4.0                  4   \n",
       "267                    1.0               0.333       4.0                  1   \n",
       "268                    1.0               0.167       4.0                  1   \n",
       "269                    1.0               0.200       4.0                  1   \n",
       "270                    1.0               0.167       4.0                  1   \n",
       "271                    1.0               0.250       4.0                  1   \n",
       "272                    1.0               0.059       4.0                  5   \n",
       "\n",
       "    Degree Centrality  Betweeness Centrality    B Centrality Rank  X Bad Rank  \\\n",
       "0                 549           1.097393e-02  (-0.000142, 0.0178]         4.0   \n",
       "1                 339           5.009844e-03  (-0.000142, 0.0178]         4.0   \n",
       "2                 411           1.220447e-02  (-0.000142, 0.0178]         4.0   \n",
       "3                  16           1.522659e-05  (-0.000142, 0.0178]         4.0   \n",
       "4                  11           8.641133e-06  (-0.000142, 0.0178]         4.0   \n",
       "5                  11           5.604143e-05  (-0.000142, 0.0178]         4.0   \n",
       "6                  41           9.880283e-04  (-0.000142, 0.0178]         4.0   \n",
       "7                  72           1.536217e-03  (-0.000142, 0.0178]         4.0   \n",
       "8                  86           2.065318e-03  (-0.000142, 0.0178]         4.0   \n",
       "9                  21           3.399983e-04  (-0.000142, 0.0178]         4.0   \n",
       "10                 20           1.225276e-04  (-0.000142, 0.0178]         4.0   \n",
       "11                 10           1.066504e-04  (-0.000142, 0.0178]         4.0   \n",
       "12                 14           2.141026e-04  (-0.000142, 0.0178]         4.0   \n",
       "13               3928           1.424913e-01       (0.125, 0.142]         2.0   \n",
       "14               2033           7.668630e-02     (0.0712, 0.0891]         2.0   \n",
       "15               2076           6.205197e-02     (0.0534, 0.0712]         2.0   \n",
       "16               3046           1.020565e-01      (0.0891, 0.107]         2.0   \n",
       "17               2598           9.470845e-02      (0.0891, 0.107]         2.0   \n",
       "18               1385           3.751037e-02     (0.0356, 0.0534]         2.0   \n",
       "19                384           6.739749e-03  (-0.000142, 0.0178]         2.0   \n",
       "20                460           9.231389e-03  (-0.000142, 0.0178]         2.0   \n",
       "21               1259           2.505034e-02     (0.0178, 0.0356]         2.0   \n",
       "22                342           7.511939e-03  (-0.000142, 0.0178]         2.0   \n",
       "23                619           1.606149e-02  (-0.000142, 0.0178]         2.0   \n",
       "24                849           3.015694e-02     (0.0178, 0.0356]         2.0   \n",
       "25               1139           2.248391e-02     (0.0178, 0.0356]         2.0   \n",
       "26                402           9.892733e-03  (-0.000142, 0.0178]         2.0   \n",
       "27                233           3.298818e-03  (-0.000142, 0.0178]         2.0   \n",
       "28                666           1.940120e-02     (0.0178, 0.0356]         2.0   \n",
       "29               1212           3.469576e-02     (0.0178, 0.0356]         2.0   \n",
       "..                ...                    ...                  ...         ...   \n",
       "243                22           2.643005e-04  (-0.000142, 0.0178]         1.0   \n",
       "244                 8           4.124708e-06  (-0.000142, 0.0178]         1.0   \n",
       "245                21           3.315114e-04  (-0.000142, 0.0178]         1.0   \n",
       "246                17           1.033608e-05  (-0.000142, 0.0178]         1.0   \n",
       "247                14           5.696508e-06  (-0.000142, 0.0178]         1.0   \n",
       "248                 5           7.960574e-07  (-0.000142, 0.0178]         1.0   \n",
       "249                27           3.476063e-05  (-0.000142, 0.0178]         1.0   \n",
       "250                10           5.554132e-05  (-0.000142, 0.0178]         1.0   \n",
       "251                 6           4.819194e-07  (-0.000142, 0.0178]         1.0   \n",
       "252                 2           0.000000e+00  (-0.000142, 0.0178]         1.0   \n",
       "253                 7           5.855241e-07  (-0.000142, 0.0178]         1.0   \n",
       "254                 3           7.582322e-09  (-0.000142, 0.0178]         1.0   \n",
       "255                15           7.280695e-06  (-0.000142, 0.0178]         1.0   \n",
       "256                 7           1.440605e-06  (-0.000142, 0.0178]         1.0   \n",
       "257                10           6.170129e-05  (-0.000142, 0.0178]         1.0   \n",
       "258                 5           4.461978e-07  (-0.000142, 0.0178]         1.0   \n",
       "259                 3           3.500444e-09  (-0.000142, 0.0178]         1.0   \n",
       "260                56           9.969029e-04  (-0.000142, 0.0178]         1.0   \n",
       "261                26           6.855416e-04  (-0.000142, 0.0178]         1.0   \n",
       "262                 6           5.332805e-05  (-0.000142, 0.0178]         1.0   \n",
       "263                 3           1.328773e-08  (-0.000142, 0.0178]         1.0   \n",
       "264                18           6.041385e-05  (-0.000142, 0.0178]         1.0   \n",
       "265                38           1.598755e-04  (-0.000142, 0.0178]         1.0   \n",
       "266                16           6.316773e-05  (-0.000142, 0.0178]         1.0   \n",
       "267                 3           3.888117e-07  (-0.000142, 0.0178]         1.0   \n",
       "268                 6           1.071431e-06  (-0.000142, 0.0178]         1.0   \n",
       "269                 5           1.298178e-06  (-0.000142, 0.0178]         1.0   \n",
       "270                 6           1.368418e-06  (-0.000142, 0.0178]         1.0   \n",
       "271                 4           1.641307e-07  (-0.000142, 0.0178]         1.0   \n",
       "272                17           5.964882e-05  (-0.000142, 0.0178]         1.0   \n",
       "\n",
       "     X Total Unique Bad Ties  X Betweeness Centrality  \\\n",
       "0                    1.00000             7.701472e-02   \n",
       "1                    0.71875             3.515893e-02   \n",
       "2                    0.18750             8.565060e-02   \n",
       "3                    0.15625             1.068598e-04   \n",
       "4                    0.09375             6.064322e-05   \n",
       "5                    0.06250             3.932971e-04   \n",
       "6                    0.03125             6.933953e-03   \n",
       "7                    0.03125             1.078113e-02   \n",
       "8                    0.03125             1.449434e-02   \n",
       "9                    0.03125             2.386098e-03   \n",
       "10                   0.03125             8.598953e-04   \n",
       "11                   0.03125             7.484697e-04   \n",
       "12                   0.03125             1.502566e-03   \n",
       "13                   0.53125             1.000000e+00   \n",
       "14                   1.00000             5.381822e-01   \n",
       "15                   0.96875             4.354789e-01   \n",
       "16                   0.43750             7.162298e-01   \n",
       "17                   0.46875             6.646611e-01   \n",
       "18                   0.81250             2.632467e-01   \n",
       "19                   0.93750             4.729936e-02   \n",
       "20                   0.87500             6.478561e-02   \n",
       "21                   0.68750             1.758026e-01   \n",
       "22                   0.78125             5.271857e-02   \n",
       "23                   0.65625             1.127191e-01   \n",
       "24                   0.46875             2.116405e-01   \n",
       "25                   0.40625             1.577914e-01   \n",
       "26                   0.53125             6.942691e-02   \n",
       "27                   0.56250             2.315101e-02   \n",
       "28                   0.43750             1.361570e-01   \n",
       "29                   0.31250             2.434938e-01   \n",
       "..                       ...                      ...   \n",
       "243                  0.06250             1.854853e-03   \n",
       "244                  0.06250             2.894708e-05   \n",
       "245                  0.06250             2.326537e-03   \n",
       "246                  0.06250             7.253827e-05   \n",
       "247                  0.06250             3.997792e-05   \n",
       "248                  0.06250             5.586707e-06   \n",
       "249                  0.03125             2.439491e-04   \n",
       "250                  0.03125             3.897873e-04   \n",
       "251                  0.03125             3.382096e-06   \n",
       "252                  0.03125             0.000000e+00   \n",
       "253                  0.03125             4.109191e-06   \n",
       "254                  0.03125             5.321251e-08   \n",
       "255                  0.03125             5.109570e-05   \n",
       "256                  0.03125             1.011012e-05   \n",
       "257                  0.03125             4.330178e-04   \n",
       "258                  0.03125             3.131403e-06   \n",
       "259                  0.03125             2.456602e-08   \n",
       "260                  0.03125             6.996235e-03   \n",
       "261                  0.03125             4.811110e-03   \n",
       "262                  0.03125             3.742547e-04   \n",
       "263                  0.03125             9.325290e-08   \n",
       "264                  0.03125             4.239826e-04   \n",
       "265                  0.03125             1.122002e-03   \n",
       "266                  0.03125             4.433093e-04   \n",
       "267                  0.03125             2.728669e-06   \n",
       "268                  0.03125             7.519274e-06   \n",
       "269                  0.03125             9.110572e-06   \n",
       "270                  0.03125             9.603516e-06   \n",
       "271                  0.03125             1.151865e-06   \n",
       "272                  0.03125             4.186137e-04   \n",
       "\n",
       "     Risk Score_unique_centrality  Risk Score_unique_centrality_rank10  \n",
       "0                        5.077015                                 10.0  \n",
       "1                        4.753909                                  9.4  \n",
       "2                        4.273151                                  8.4  \n",
       "3                        4.156357                                  8.2  \n",
       "4                        4.093811                                  8.1  \n",
       "5                        4.062893                                  8.0  \n",
       "6                        4.038184                                  8.0  \n",
       "7                        4.042031                                  8.0  \n",
       "8                        4.045744                                  8.0  \n",
       "9                        4.033636                                  7.9  \n",
       "10                       4.032110                                  7.9  \n",
       "11                       4.031998                                  7.9  \n",
       "12                       4.032753                                  7.9  \n",
       "13                       3.531250                                  7.0  \n",
       "14                       3.538182                                  7.0  \n",
       "15                       3.404229                                  6.7  \n",
       "16                       3.153730                                  6.2  \n",
       "17                       3.133411                                  6.2  \n",
       "18                       3.075747                                  6.1  \n",
       "19                       2.984799                                  5.9  \n",
       "20                       2.939786                                  5.8  \n",
       "21                       2.863303                                  5.6  \n",
       "22                       2.833969                                  5.6  \n",
       "23                       2.768969                                  5.5  \n",
       "24                       2.680390                                  5.3  \n",
       "25                       2.564041                                  5.1  \n",
       "26                       2.600677                                  5.1  \n",
       "27                       2.585651                                  5.1  \n",
       "28                       2.573657                                  5.1  \n",
       "29                       2.555994                                  5.0  \n",
       "..                            ...                                  ...  \n",
       "243                      1.064355                                  2.1  \n",
       "244                      1.062529                                  2.1  \n",
       "245                      1.064827                                  2.1  \n",
       "246                      1.062573                                  2.1  \n",
       "247                      1.062540                                  2.1  \n",
       "248                      1.062506                                  2.1  \n",
       "249                      1.031494                                  2.0  \n",
       "250                      1.031640                                  2.0  \n",
       "251                      1.031253                                  2.0  \n",
       "252                      1.031250                                  2.0  \n",
       "253                      1.031254                                  2.0  \n",
       "254                      1.031250                                  2.0  \n",
       "255                      1.031301                                  2.0  \n",
       "256                      1.031260                                  2.0  \n",
       "257                      1.031683                                  2.0  \n",
       "258                      1.031253                                  2.0  \n",
       "259                      1.031250                                  2.0  \n",
       "260                      1.038246                                  2.0  \n",
       "261                      1.036061                                  2.0  \n",
       "262                      1.031624                                  2.0  \n",
       "263                      1.031250                                  2.0  \n",
       "264                      1.031674                                  2.0  \n",
       "265                      1.032372                                  2.0  \n",
       "266                      1.031693                                  2.0  \n",
       "267                      1.031253                                  2.0  \n",
       "268                      1.031258                                  2.0  \n",
       "269                      1.031259                                  2.0  \n",
       "270                      1.031260                                  2.0  \n",
       "271                      1.031251                                  2.0  \n",
       "272                      1.031669                                  2.0  \n",
       "\n",
       "[273 rows x 15 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "output.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\nodes_latest2_bad_only.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\BAD ACTOR RANKING PRELIM RESULTS.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
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       "    <tr>\n",
       "      <th>D Centrality Rank</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(-1.926, 63.344]</th>\n",
       "      <td>96.0</td>\n",
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       "    <tr>\n",
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       "      <td>30.0</td>\n",
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       "      <td>40.0</td>\n",
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       "      <td>12.0</td>\n",
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       "      <td>15.0</td>\n",
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       "    <tr>\n",
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       "      <td>5.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>(983.5, 1044.844]</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>(3314.562, 3375.906]</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>(3375.906, 3437.25]</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>(3437.25, 3498.594]</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>(3498.594, 3559.938]</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>(3559.938, 3621.281]</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>(3621.281, 3682.625]</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(3866.656, 3928]</th>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Value\n",
       "D Centrality Rank           \n",
       "(-1.926, 63.344]        96.0\n",
       "(63.344, 124.688]       30.0\n",
       "(124.688, 186.0312]     40.0\n",
       "(186.0312, 247.375]     18.0\n",
       "(247.375, 308.719]      12.0\n",
       "(308.719, 370.0625]     12.0\n",
       "(370.0625, 431.406]     15.0\n",
       "(431.406, 492.75]       11.0\n",
       "(492.75, 554.0938]       5.0\n",
       "(554.0938, 615.438]      5.0\n",
       "(615.438, 676.781]       5.0\n",
       "(676.781, 738.125]       1.0\n",
       "(738.125, 799.469]       2.0\n",
       "(799.469, 860.812]       3.0\n",
       "(860.812, 922.156]       1.0\n",
       "(922.156, 983.5]         1.0\n",
       "(983.5, 1044.844]        2.0\n",
       "(1044.844, 1106.188]     1.0\n",
       "(1106.188, 1167.531]     2.0\n",
       "(1167.531, 1228.875]     2.0\n",
       "(1228.875, 1290.219]     1.0\n",
       "(1290.219, 1351.562]     NaN\n",
       "(1351.562, 1412.906]     1.0\n",
       "(1412.906, 1474.25]      NaN\n",
       "(1474.25, 1535.594]      1.0\n",
       "(1535.594, 1596.938]     1.0\n",
       "(1596.938, 1658.281]     NaN\n",
       "(1658.281, 1719.625]     NaN\n",
       "(1719.625, 1780.969]     NaN\n",
       "(1780.969, 1842.312]     NaN\n",
       "...                      ...\n",
       "(2087.688, 2149.0312]    NaN\n",
       "(2149.0312, 2210.375]    NaN\n",
       "(2210.375, 2271.719]     NaN\n",
       "(2271.719, 2333.0625]    NaN\n",
       "(2333.0625, 2394.406]    NaN\n",
       "(2394.406, 2455.75]      NaN\n",
       "(2455.75, 2517.0938]     NaN\n",
       "(2517.0938, 2578.438]    NaN\n",
       "(2578.438, 2639.781]     1.0\n",
       "(2639.781, 2701.125]     NaN\n",
       "(2701.125, 2762.469]     NaN\n",
       "(2762.469, 2823.812]     NaN\n",
       "(2823.812, 2885.156]     NaN\n",
       "(2885.156, 2946.5]       NaN\n",
       "(2946.5, 3007.844]       NaN\n",
       "(3007.844, 3069.188]     1.0\n",
       "(3069.188, 3130.531]     NaN\n",
       "(3130.531, 3191.875]     NaN\n",
       "(3191.875, 3253.219]     NaN\n",
       "(3253.219, 3314.562]     NaN\n",
       "(3314.562, 3375.906]     NaN\n",
       "(3375.906, 3437.25]      NaN\n",
       "(3437.25, 3498.594]      NaN\n",
       "(3498.594, 3559.938]     NaN\n",
       "(3559.938, 3621.281]     NaN\n",
       "(3621.281, 3682.625]     NaN\n",
       "(3682.625, 3743.969]     NaN\n",
       "(3743.969, 3805.312]     NaN\n",
       "(3805.312, 3866.656]     NaN\n",
       "(3866.656, 3928]         1.0\n",
       "\n",
       "[64 rows x 1 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CutG = nodes_new[nodes_new[\"Bad Flag\"] == 1]\n",
    "CutG = CutG[[\"Degree Centrality\"]]\n",
    "CutG[\"D Centrality Rank\"] = pd.cut(CutG['Degree Centrality'],64)\n",
    "\n",
    "CutG[\"Value\"] = 1\n",
    "CutG = CutG.groupby(['D Centrality Rank']).sum()\n",
    "CutG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "\n",
    "#INSERT REALLY BAD ACTORS\n",
    "vb = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Top Bad Actor Data_9_7_2017.csv\")\n",
    "\n",
    "vb[\"Name\"] = vb[\"Name\"].map(str.upper)\n",
    "vb[\"Name\"] = vb[\"Name\"].map(str.strip)\n",
    "\n",
    "vb[\"Name\"] = vb[\"Name\"].str.replace(' JR', '')\n",
    "vb[\"Name\"] = vb[\"Name\"].str.replace(' SR', '')\n",
    "\n",
    "vbname = vb[\"Name\"].tolist()\n",
    "\n",
    "print \"len of vbad actors\", len(vb)\n",
    "\n",
    "\n",
    "nodes_new[\"Top Bad Flag\"] = 0\n",
    "\n",
    "count = 0\n",
    "\n",
    "for i in range(0, len(nodes_new)):\n",
    "    if nodes_new[\"Name\"][i] in vbname:\n",
    "        nodes_new[\"Top Bad Flag\"][i] = 1\n",
    "        count+=1\n",
    "\n",
    "print \"total top bad\", count\n",
    "print \"DONE\"\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#FINAL RISK ANALYSIS\n",
    "\n",
    "nodes_new[\"Risk1\"] = (nodes_new[\"Bad Tie Density\"] + nodes_new[\"Bad Unique Density\"] \n",
    "                      + nodes_new[\"Community Bad Density Total\"] + nodes_new[\"Community Bad Density Unique\"])\n",
    "nodes_new[\"Risk1\"] = nodes_new[\"Risk1\"]/4.0\n",
    "\n",
    "#nodes_new[\"Risk2\"] = (nodes_new[\"Total Bad Ties\"] + nodes_new[\"Total Unique Bad Ties\"] + \n",
    "#                      nodes_new[\"Number Bad Actors in Community\"] )\n",
    "\n",
    "nodes_new[\"Risk2\"] = (nodes_new[\"Total Unique Bad Ties\"] + nodes_new[\"Number Bad Actors in Community\"] )\n",
    "maxrisk2 = float(nodes_new[\"Risk2\"].max() )\n",
    "nodes_new[\"Risk2\"] = nodes_new[\"Risk2\"]/maxrisk2\n",
    "\n",
    "\n",
    "print maxrisk2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NORMALIZE BY BIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nodes_saved = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\BAD ACTOR RANKING PRELIM RESULTS.csv\")\n",
    "\n",
    "nodesBins = nodes_new[[\"Name\", \"Unique Number BINs\"]]\n",
    "nodes_saved = pd.merge(nodes_saved, nodesBins, how = \"left\")\n",
    "nodes_saved[\"Risk Score\"] = nodes_saved[\"Risk Score\"]/ nodes_saved[\"Unique Number BINs\"]\n",
    "nodes_saved[\"Risk Score\"] = nodes_saved[\"Risk Score\"]/nodes_saved[\"Risk Score\"].max()\n",
    "nodes_saved[\"Risk Score\"] = nodes_saved[\"Risk Score\"]*10\n",
    "\n",
    "nodes_saved = nodes_saved.sort_values(by = 'Risk Score', ascending=False).reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nodes_saved.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\BAD ACTOR RANKING PRELIM RESULTS normalized_number_bins.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SWEEPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "sw = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Sweeps\\\\SEP Bad Actor Audit - 06.02.18 - 06.07.18.csv\")\n",
    "\n",
    "def CleanDataSweeps(df):\n",
    "    \n",
    "    #APPLICANT\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].astype(str)\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].map(str.upper)\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].map(str.strip)\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace('[^\\x00-\\x7F]','')\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace('.', ' ')\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace(',', ' ')\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace('&', '/')\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace('FR ', '')\n",
    "\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace('   ', ' ')\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.replace('  ', ' ')\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].map(str.strip)\n",
    "\n",
    "\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].astype(str)\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].map(str.upper)\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].map(str.strip)\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace('[^\\x00-\\x7F]','')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace('.', ' ')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(',', ' ')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace('&', '/')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' JR', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' SR', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' III', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' IV', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' ESQ', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' PE', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace(' MD', '')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace('   ', ' ')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.replace('  ', ' ')\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].map(str.strip)\n",
    "\n",
    "    df[\"Applicant First Name\"] = df[\"Applicant First Name\"].str.split(\"/\")\n",
    "    df[\"Applicant Last Name\"] = df[\"Applicant Last Name\"].str.split(\"/\")\n",
    "\n",
    "    #OWNER\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].astype(str)\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].map(str.upper)\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].map(str.strip)\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('[^\\x00-\\x7F]','')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('.', ' ')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace(',', ' ')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('_', ' ')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace(':', ' ')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('&', '/')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('FR ', '')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('REV  ', '')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('PASTOR  ', '')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('DR ', '')\n",
    "\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('   ', ' ')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.replace('  ', ' ')\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].map(str.strip)\n",
    "\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].astype(str)\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].map(str.upper)\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].map(str.strip)\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace('[^\\x00-\\x7F]','')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace('.', ' ')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(',', ' ')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace('_', ' ')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(':', ' ')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace('&', '/')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' JR', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' SR', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' III', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' IV', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' ESQ', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' PE', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace(' MD', '')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace('   ', ' ')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.replace('  ', ' ')\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].map(str.strip)\n",
    "\n",
    "    df[\"Owner First Name\"] = df[\"Owner First Name\"].str.split(\"/\")\n",
    "    df[\"Owner Last Name\"] = df[\"Owner Last Name\"].str.split(\"/\")\n",
    "   \n",
    "    \n",
    "    \n",
    "    #CLEAR FILE REP BUSINESS NAMES\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].astype(str)\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].map(str.upper)\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].map(str.strip)\n",
    "    \n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"] + \" \"\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' P.C ', ' ')\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(',', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('_', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('.', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(';', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('-', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('*', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(':', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('/', ' ')\n",
    "    #df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('\\', ' ')\n",
    "\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('&', ' & ')\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' LLC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PLLC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PLC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PPLC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' INC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' LTD ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' AND ', ' & ')\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' CORP ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' CORPS ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' CO ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' LL ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' LLP ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PLL ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PE ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' LLVC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' XPD ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' P E ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' P C ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PL ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' RA ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' R A ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' R AA ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' RAPC ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' AI ', ' ')\n",
    "\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' P A ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' PA ', ' ')\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' AIA ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' A I A ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' AI ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' AI A ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' N A ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' N Y ', ' NY ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' ST ', ' STREET ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' AVE ', ' AVENUE ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' BLVD ', ' BOULEVARD ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace(' BLVE ', ' BOULEVARD ')\n",
    "\n",
    "\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('+', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('%', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('[^\\x00-\\x7F]',' ')\n",
    "    #df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('*', '')\n",
    "    #df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('--', '')\n",
    "\n",
    "\n",
    "\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('   ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].str.replace('  ', ' ')\n",
    "    df[\"Filing Representative Business Name\"] = df[\"Filing Representative Business Name\"].map(str.strip)\n",
    "    \n",
    "    \n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "\n",
    "sw = CleanDataSweeps(sw)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CREATE LIST OF NAMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Owner data:\n",
      "Length of first or last name 252\n",
      "Length of first or last name 252\n",
      "len of df 804635\n",
      "len of own list 804635\n",
      "\n",
      "Applicant data:\n",
      "Length of first or last name 252\n",
      "Length of first or last name 252\n",
      "len of df 804635\n",
      "len of df app 804635\n"
     ]
    }
   ],
   "source": [
    "print\n",
    "print \"Owner data:\"\n",
    "f = sw[\"Owner First Name\"].tolist()\n",
    "l = sw[\"Owner Last Name\"].tolist()\n",
    "f = CleanFNames (f)\n",
    "l = CleanLNames (l)\n",
    "sw_olist = NameGen(f, l)\n",
    "#df2 = NameGen(f, l)\n",
    "#df2 = df2.rename(columns={0: 'Owner Name'})\n",
    "#df[\"Owner Name\"] = df2[\"Owner Name\"]\n",
    "#print \"len of first name list\", len(f)\n",
    "#print \"len of last name list\", len(l)\n",
    "print \"len of df\", len(df)\n",
    "print \"len of own list\", len(olist)\n",
    "\n",
    "print\n",
    "print \"Applicant data:\"\n",
    "f = sw[\"Applicant First Name\"].tolist()\n",
    "l = sw[\"Applicant Last Name\"].tolist() \n",
    "f = CleanFNames (f)\n",
    "l = CleanLNames (l)\n",
    "sw_alist = NameGen(f, l)\n",
    "#df2 = NameGen(f, l)\n",
    "#df2 = df2.rename(columns={0: 'Applicant Name'})\n",
    "#df[\"Applicant Name\"] = df2[\"Applicant Name\"]   \n",
    "#print \"len of first name list\", len(f)\n",
    "#print \"len of last name list\", len(l)\n",
    "print \"len of df\", len(df)\n",
    "print \"len of df app\", len(alist) \n",
    "\n",
    "print\n",
    "print \"Starting Ethnicity analysis, Chinese names\"\n",
    "#CHINESE SURNAME\n",
    "chinese_name = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\Chinese Names.csv\")\n",
    "\n",
    "chinese_name[\"Name\"] = chinese_name[\"Name\"].map(str.upper)\n",
    "chinese_name[\"Name\"] = chinese_name[\"Name\"].map(str.strip)\n",
    "\n",
    "chinese_name = chinese_name[\"Name\"].tolist()\n",
    "\n",
    "def chinese_names(name_list):\n",
    "    #thenames = []\n",
    "    count=0\n",
    "    for i in range(0, len(name_list)):\n",
    "    #for i in range(0, 1000):    \n",
    "        for j in range(0, len(name_list[i])):\n",
    "            #print olist[i][j]\n",
    "            s1 = name_list[i][j].split()\n",
    "            #print s1\n",
    "\n",
    "            if len(s1) == 3:\n",
    "                if s1[2] in chinese_name:\n",
    "                    name_list[i][j] = s1[0] + s1[1] + \" \" + s1[2]\n",
    "                    #thenames.append(olist[i][j])\n",
    "                    count+=1\n",
    "    print \"Total number of changes\", count                  \n",
    "    return name_list\n",
    "                \n",
    "print \"Owners\"\n",
    "sw_olist = chinese_names(sw_olist)\n",
    "print\n",
    "print \"Applicants\"\n",
    "sw_alist = chinese_names(sw_alist)\n",
    "#print\n",
    "#print \"Contractors\"\n",
    "#clist = chinese_names(clist)\n",
    "#print\n",
    "#print \"File Reps\"\n",
    "#flist = chinese_names(flist)\n",
    "\n",
    "\n",
    "print \"DONE\"\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "REPLACE NAMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********\n",
      "Applicants:\n",
      "number of replacements 4\n",
      "number of names removed 0\n",
      "len of original list 252\n",
      "len of name replaced list list 252\n",
      "********\n",
      "Owners:\n",
      "number of replacements 1\n",
      "number of names removed 0\n",
      "len of original list 252\n",
      "len of name replaced list list 252\n"
     ]
    }
   ],
   "source": [
    "print \"********\"\n",
    "print \"Applicants:\"\n",
    "sw_alist = NameReplace(sw_alist)\n",
    "df2 = pd.DataFrame(sw_alist)\n",
    "df2 = df2.rename(columns={0: 'Applicant Name'})\n",
    "sw[\"Applicant Name\"] = df2[\"Applicant Name\"] \n",
    "\n",
    "print \"********\"\n",
    "print \"Owners:\"\n",
    "sw_olist = NameReplace(sw_olist)\n",
    "df2 = pd.DataFrame(sw_olist)\n",
    "df2 = df2.rename(columns={0: 'Owner Name'})\n",
    "sw[\"Owner Name\"] = df2[\"Owner Name\"] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CLEAN FILE REP BUSINESS NAMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len of fblist 252\n",
      "Inidtial Cleaning ...\n",
      "0\n",
      "25\n",
      "50\n",
      "75\n",
      "100\n",
      "125\n",
      "150\n",
      "175\n",
      "200\n",
      "225\n",
      "250\n",
      "Number of name permuation replacements 221\n",
      "\n",
      "Replace exact names ...\n",
      "len of enames 518\n",
      "len of enames 476\n",
      "0\n",
      "25\n",
      "50\n",
      "75\n",
      "100\n",
      "125\n",
      "150\n",
      "175\n",
      "200\n",
      "225\n",
      "250\n",
      "Number of exact name replacements 8\n",
      "\n",
      "Replace names from fuzzy logic ...\n",
      "len of fnames 1071\n",
      "len of fnames 855\n",
      "0\n",
      "25\n",
      "50\n",
      "75\n",
      "100\n",
      "125\n",
      "150\n",
      "175\n",
      "200\n",
      "225\n",
      "250\n",
      "Number of fuzzy name replaceents 5\n",
      "\n",
      "Replace bad actor names from fuzzy logic\n",
      "len of badnames 65\n",
      "len of badnames 64\n",
      "0\n",
      "25\n",
      "50\n",
      "75\n",
      "100\n",
      "125\n",
      "150\n",
      "175\n",
      "200\n",
      "225\n",
      "250\n",
      "Number of fuzzy name replaceents 35\n",
      "length of dataframe 252\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "sw[\"Filing Representative Business Name\"] = sw[\"Filing Representative Business Name\"].str.split(\" \")\n",
    "fblist = sw[\"Filing Representative Business Name\"].tolist()\n",
    "\n",
    "print \"len of fblist\", len(fblist)\n",
    "#print \"length of fblist set\", len(set(fblist))\n",
    "\n",
    "\n",
    "busDic = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\\n",
    "File Rep Business Name Permutations.csv\", low_memory=False)\n",
    "names = sorted(list(set(busDic[\"Name\"].tolist() ) ) )\n",
    "\n",
    "#print names\n",
    "busDic[\"Permutation\"] = busDic[\"Permutation\"].map(str.strip)\n",
    "busDic[\"Name\"] = busDic[\"Name\"].map(str.strip)\n",
    "busDic = busDic.set_index('Permutation')['Name'].to_dict()\n",
    "\n",
    "#sw = sw.reset_index(drop=True)\n",
    "\n",
    "count_amp = 0\n",
    "\n",
    "in_here = 0\n",
    "\n",
    "name_perm  = 0\n",
    "\n",
    "\n",
    "print \"Inidtial Cleaning ...\"\n",
    "\n",
    "#for i in range(0, len(sw)):\n",
    "for i in range(0, len(fblist)):    \n",
    "    if i%(len(fblist)/10) == 0:\n",
    "        print i\n",
    "    \n",
    "    a = fblist[i]\n",
    "    \n",
    "    #pop last element in list \n",
    "    #del a[-1]\n",
    "    \n",
    "    for j in range(0, len(a)):\n",
    "        if a[j] in busDic:\n",
    "            a[j] = busDic[a[j]]\n",
    "            name_perm+=1\n",
    "            \n",
    "    \n",
    "    a2 = a[0:5]\n",
    "    #print a2\n",
    "    \n",
    "    #CHANGE IF & THEN HAVE a = a[0:4], else a = a[0:3]\n",
    "    \n",
    "    #if \"&\" in a[len(a) - 1]:\n",
    "    #if a[len(a) - 1] == \"&\":\n",
    "    \n",
    "    #    count_amp +=1\n",
    "        \n",
    "    #    a2 = a[0:4]\n",
    "        \n",
    "        #a[len(a) - 1] = \"\"\n",
    "        #a = filter(None, a)\n",
    "        \n",
    "    \n",
    "    a2 = \" \".join(a2)\n",
    "    #print a2\n",
    "    a2 = a2.strip()\n",
    "    \n",
    "    \n",
    "    type_list = ['ARCHITECTS', 'CONSTRUCTION',  'CONTRACTORS','ENGINEERING',\n",
    "                 'BUILDING',  'CONSULTING', \n",
    "                  'CORPORATION', 'DESIGN', \n",
    "                  'EXPEDITING', 'INSPECTION', 'SERVICES', \n",
    "                 'SOLUTIONS', 'MANAGMENT', 'ENTERPRISE','ASSOCIATES']\n",
    "    \n",
    "    #remove architects, building, constructoin, etc, or clean data from above key word\n",
    "    \n",
    "    for j in range(0, len(type_list)):\n",
    "        if type_list[j] in a2:\n",
    "            #check if key word at beginning, if so, do not split\n",
    "            location = a2.find(type_list[j])\n",
    "            \n",
    "            #if location == 0:\n",
    "            #    in_here+=1\n",
    "            \n",
    "            if location > 0:\n",
    "                a2 = a2.split(type_list[j])\n",
    "                #strip out white spaces from split\n",
    "                for k in range(0, len(a2)):\n",
    "                    a2[k] = a2[k].strip()\n",
    "                    \n",
    "                a2 = a2[0] + \" \" + type_list[j]\n",
    "            \n",
    "\n",
    "    \n",
    "    #remove ampersand at end of string\n",
    "    if len(a2) > 0:\n",
    "        s2 = a2[-1]\n",
    "        if s2 == \"&\":\n",
    "            a2 = a2.replace(\"&\", \"\")\n",
    "            a2 = a2.strip()\n",
    "            #print a2\n",
    "            \n",
    "        if \"(\" in a2:\n",
    "            a2 = a2.split(\"(\")\n",
    "            a2 = a2[0]\n",
    "            a2 = a2.strip()\n",
    "    \n",
    "    \n",
    "    fblist[i] = a2\n",
    "            \n",
    "    #sw[\"Filing Representative Business Name\"][i] = a\n",
    "    \n",
    "\n",
    "print \"Number of name permuation replacements\", name_perm    \n",
    "    \n",
    "print\n",
    "print \"Replace exact names ...\"\n",
    "\n",
    "#INSERT EXACT NAMES\n",
    "enames =  pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\FILE_REP_BUSNAME_exactnames.csv\", low_memory=False)\n",
    "\n",
    "enames[\"Permutation\"] = enames[\"Permutation\"].map(str.strip)\n",
    "enames[\"Name\"] = enames[\"Name\"].map(str.strip)\n",
    "\n",
    "print \"len of enames\", len(enames)\n",
    "enames = enames[enames['Permutation'] != enames[\"Name\"]]\n",
    "enames = enames.drop_duplicates(['Permutation', 'Name']).reset_index(drop=True)\n",
    "enames = enames.reset_index(drop=True)\n",
    "print \"len of enames\", len(enames)\n",
    "\n",
    "enamesDic = enames.set_index('Permutation')['Name'].to_dict()\n",
    "\n",
    "exact_names = 0\n",
    "for i in range(0, len(fblist)):\n",
    "    if i%(len(fblist)/10) == 0:\n",
    "        print i\n",
    "    \n",
    "    if fblist[i] in enamesDic:\n",
    "        fblist[i] = enamesDic[fblist[i]]\n",
    "        exact_names+=1\n",
    "        \n",
    "print \"Number of exact name replacements\", exact_names\n",
    "print\n",
    "print \"Replace names from fuzzy logic ...\"\n",
    "\n",
    "fnames = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\\n",
    "FILE_REP_BUSNAME_finalfuzzy.csv\", low_memory=False)\n",
    "\n",
    "fnames[\"Permutation\"] = fnames[\"Permutation\"].map(str.strip)\n",
    "fnames[\"Name\"] = fnames[\"Name\"].map(str.strip)\n",
    "\n",
    "\n",
    "print \"len of fnames\", len(fnames)\n",
    "fnames = fnames[fnames['Permutation'] != fnames[\"Name\"]]\n",
    "fnames = fnames.drop_duplicates(['Permutation', 'Name']).reset_index(drop=True)\n",
    "fnames = fnames.reset_index(drop=True)\n",
    "print \"len of fnames\", len(fnames)\n",
    "\n",
    "fnamesDic = fnames.set_index('Permutation')['Name'].to_dict()\n",
    "\n",
    "names_fuzzy = 0\n",
    "\n",
    "for i in range(0, len(fblist)):\n",
    "    if i%(len(fblist)/10) == 0:\n",
    "        print i\n",
    "    \n",
    "    if fblist[i] in fnamesDic:\n",
    "        fblist[i] = fnamesDic[fblist[i]]\n",
    "        names_fuzzy+=1\n",
    "           \n",
    "print \"Number of fuzzy name replaceents\", names_fuzzy\n",
    "\n",
    "print\n",
    "print \"Replace bad actor names from fuzzy logic\"\n",
    "badnames = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Name Dictionaries\\\\\n",
    "BAD_ACTOR_FILE_REP_BUSINESS_finalfuzzy.csv\", low_memory=False)\n",
    "\n",
    "badnames[\"Permutation\"] = badnames[\"Permutation\"].map(str.strip)\n",
    "badnames[\"Name\"] = badnames[\"Name\"].map(str.strip)\n",
    "\n",
    "print \"len of badnames\", len(badnames)\n",
    "badnames = badnames[badnames['Permutation'] != badnames[\"Name\"]]\n",
    "badnames = badnames.drop_duplicates(['Permutation', 'Name']).reset_index(drop=True)\n",
    "badnames = badnames.reset_index(drop=True)\n",
    "print \"len of badnames\", len(badnames)\n",
    "\n",
    "badnamesDic = badnames.set_index('Permutation')['Name'].to_dict()\n",
    "\n",
    "names_fuzzy = 0\n",
    "for i in range(0, len(fblist)):\n",
    "    if i%(len(fblist)/10) == 0:\n",
    "        print i\n",
    "    \n",
    "    if fblist[i] in badnamesDic:\n",
    "        fblist[i] = badnamesDic[fblist[i]]\n",
    "        names_fuzzy+=1\n",
    "           \n",
    "print \"Number of fuzzy name replaceents\", names_fuzzy\n",
    "\n",
    "\n",
    "    \n",
    "#file_business_list = sw[\"Filing Representative Business Name\"].tolist()\n",
    "#file_business_list = sorted(list(set(file_business_list)))\n",
    "\n",
    "\n",
    "sw2 = pd.DataFrame(fblist)\n",
    "sw2 = sw2.rename(columns={0: 'Filing Representative Business Name'})\n",
    "\n",
    "sw[\"Filing Representative Business Name\"] = sw2[\"Filing Representative Business Name\"]\n",
    "\n",
    "#file_business_list = pd.DataFrame(fblist)\n",
    "#file_business_list = file_business_list.rename(columns={0: 'Name'})\n",
    "\n",
    "\n",
    "#file_business_list[\"Value\"] = 1\n",
    "\n",
    "#file_business_list = file_business_list.drop_duplicates([0]).reset_index(drop=True)\n",
    "#file_business_list = file_business_list.groupby(['Name']).sum()\n",
    "#file_business_list = file_business_list.add_suffix('').reset_index()\n",
    "\n",
    "print \"length of dataframe\", len(sw)\n",
    "\n",
    "#split to make into a list\n",
    "sw[\"Filing Representative Business Name\"] = sw[\"Filing Representative Business Name\"].str.split(\"/\")\n",
    "\n",
    "\n",
    "print \"DONE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create node metrics ...2018-07-09 09:19:50.399000\n"
     ]
    }
   ],
   "source": [
    "#NODE METRICS NEW\n",
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "string = \"Create node metrics ...\" + str(datetime.now())\n",
    "print string\n",
    "logging.info(string)\n",
    "\n",
    "badAllRoles = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Bad Actor Dataset-Top Criteria-6_4_18 ALL ROLES.csv\")\n",
    "\n",
    "badActor = badAllRoles[\"Name\"].tolist()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def FindBad(df):\n",
    "    df[\"Number Bad\"] = 0\n",
    "    df[\"Bad List\"] = \"\"\n",
    "    \n",
    "    \n",
    "    for i in range(0, len(df)):\n",
    "        badList = []\n",
    "        for j in range(0, len(df[\"Filing Representative Business Name\"][i])):\n",
    "            if df[\"Filing Representative Business Name\"][i][j] in badActor:\n",
    "                badList.append(df[\"Filing Representative Business Name\"][i][j])\n",
    "        #print badList\n",
    "        for j in range(0, len(df[\"Applicant Name\"][i])):\n",
    "            if df[\"Applicant Name\"][i][j] in badActor:\n",
    "                badList.append(df[\"Applicant Name\"][i][j])\n",
    "        \n",
    "        for j in range(0, len(df[\"Owner Name\"][i])):\n",
    "            if df[\"Owner Name\"][i][j] in badActor:\n",
    "                badList.append(df[\"Owner Name\"][i][j])\n",
    "        #print badList\n",
    "        \n",
    "        df[\"Number Bad\"][i] = len(badList)\n",
    "        df[\"Bad List\"][i] = badList\n",
    "        \n",
    "        \n",
    "FindBad(sw)\n",
    "\n",
    "\n",
    "sw = sw.sort_values(by = 'Number Bad', ascending=False).reset_index(drop=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "INSERT RANK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rank = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\BAD ACTOR RANKING PRELIM RESULTS normalized_number_bins.csv\")\n",
    "rank = rank[[\"Name\", \"Risk Score\"]]\n",
    "\n",
    "rank = rank.set_index('Name')['Risk Score'].to_dict()\n",
    "\n",
    "risk_score = []\n",
    "\n",
    "#sw['Risk Score'] = 0.0\n",
    "\n",
    "sw['Risk Score 2'] = 0.0\n",
    "\n",
    "for i in range(0, len(sw)):\n",
    "    risk_score = []\n",
    "    for j in range(0, len(sw[\"Bad List\"][i]) ):\n",
    "        if sw[\"Bad List\"][i][j] in rank:\n",
    "            risk_score.append(rank[sw[\"Bad List\"][i][j]])\n",
    "    #print risk_score, sum(risk_score)\n",
    "    sw['Risk Score 2'][i] = sum(risk_score)\n",
    "    \n",
    " \n",
    "for i in range(0, len(sw)):\n",
    "    if (sw[\"Risk Score 2\"][i] == 0 and sw[\"Number Bad\"][i] > 0):\n",
    "        sw[\"Risk Score 2\"][i] = 1\n",
    "\n",
    "sw = sw.sort_values(by = 'Risk Score 2', ascending=False).reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>JobNumber</th>\n",
       "      <th>Job Document Number</th>\n",
       "      <th>Job Type</th>\n",
       "      <th>Job Description</th>\n",
       "      <th>Estimated Job Cost</th>\n",
       "      <th>Hub Self Service Application Filed</th>\n",
       "      <th>Current Job Status</th>\n",
       "      <th>Job Status Date</th>\n",
       "      <th>Plan Approval Date</th>\n",
       "      <th>First Permit Date</th>\n",
       "      <th>...</th>\n",
       "      <th>Filing Representative Last Name</th>\n",
       "      <th>Filing Representative Business Name</th>\n",
       "      <th>Owner First Name</th>\n",
       "      <th>Owner Last Name</th>\n",
       "      <th>Owner Business Name</th>\n",
       "      <th>Applicant Name</th>\n",
       "      <th>Owner Name</th>\n",
       "      <th>Number Bad</th>\n",
       "      <th>Bad List</th>\n",
       "      <th>Risk Score 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>421642333</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>RELATED INTERIOR PARTITION WORK AS PER PLANS. ...</td>\n",
       "      <td>55700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>6/15/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>RUG/PIN/TOR/COR/TER</td>\n",
       "      <td>[CONSTRUCTION PERMIT SERVICES]</td>\n",
       "      <td>[NOEMI]</td>\n",
       "      <td>[ROSARIO]</td>\n",
       "      <td>43-01 22ND STREET OWNER, LLC</td>\n",
       "      <td>[ISRAEL WERENTHEIL]</td>\n",
       "      <td>[NOEMI ROSARIO]</td>\n",
       "      <td>1</td>\n",
       "      <td>[ISRAEL WERENTHEIL]</td>\n",
       "      <td>1.281250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>321768921</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INTERIOR RENOVATION OF PARTIAL 2ND FLOOR, APT ...</td>\n",
       "      <td>53000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/13/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>BE/BO/TO/DA</td>\n",
       "      <td>[JM ZONING]</td>\n",
       "      <td>[EVIAN]</td>\n",
       "      <td>[ARCHER]</td>\n",
       "      <td>III NEW BEDFORD MANAGEMENT CORP</td>\n",
       "      <td>[DAVID OBUCHOWSKI]</td>\n",
       "      <td>[EVIAN ARCHER]</td>\n",
       "      <td>2</td>\n",
       "      <td>[JM ZONING, DAVID OBUCHOWSKI]</td>\n",
       "      <td>0.348464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>123203167</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INTERIOR RENOVATION OF 4 EXISTING APARTMENTS. ...</td>\n",
       "      <td>180000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>HAN</td>\n",
       "      <td>[VICTOR K HAN ARCHITECTS]</td>\n",
       "      <td>[PARAG]</td>\n",
       "      <td>[SAWHNEY]</td>\n",
       "      <td>116 ST MARKS LLC</td>\n",
       "      <td>[VICTOR HAN]</td>\n",
       "      <td>[PARAG SAWHNEY]</td>\n",
       "      <td>2</td>\n",
       "      <td>[VICTOR K HAN ARCHITECTS, VICTOR HAN]</td>\n",
       "      <td>0.287698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>321687144</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INTERIOR RESTORATION AND RENOVATION OF EXISTIN...</td>\n",
       "      <td>320350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/13/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>HAN</td>\n",
       "      <td>[VICTOR K HAN ARCHITECTS]</td>\n",
       "      <td>[ABE]</td>\n",
       "      <td>[COHEN]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[VICTOR HAN]</td>\n",
       "      <td>[ABE COHEN]</td>\n",
       "      <td>2</td>\n",
       "      <td>[VICTOR K HAN ARCHITECTS, VICTOR HAN]</td>\n",
       "      <td>0.287698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>421594297</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>TO REPLACE HOT WATER HEATER. NO CHANGE IN USE,...</td>\n",
       "      <td>3000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>1/18/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>PARK/CHOI/CHON</td>\n",
       "      <td>[CHON ENGINEERING]</td>\n",
       "      <td>[THOMAS]</td>\n",
       "      <td>[CAVALLARO]</td>\n",
       "      <td>TENNIS INDOORS OF NEW YORK</td>\n",
       "      <td>[STEVEN CHON]</td>\n",
       "      <td>[THOMAS CAVALLARO]</td>\n",
       "      <td>1</td>\n",
       "      <td>[CHON ENGINEERING]</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>123237933</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>AC REPLACEMENT AS PER PLANS.  NO CHANGE TO USE...</td>\n",
       "      <td>70000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>DEL,AT,HER,AGU</td>\n",
       "      <td>[BROOKBRIDGE CONSULTING]</td>\n",
       "      <td>[JOHNATHAN]</td>\n",
       "      <td>[SCHRENKER]</td>\n",
       "      <td>600 PARTNERS CO, LP</td>\n",
       "      <td>[SPENCER GOROFF]</td>\n",
       "      <td>[JOHNATHAN SCHRENKER]</td>\n",
       "      <td>1</td>\n",
       "      <td>[SPENCER GOROFF]</td>\n",
       "      <td>0.092949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>123448260</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>MINOR PARTITION, PLUMBING AND MECHANICAL WORK ...</td>\n",
       "      <td>47500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7/9/2018 0:00</td>\n",
       "      <td>3/6/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>LIB/VEL/OTA</td>\n",
       "      <td>[LUKE LICALZI]</td>\n",
       "      <td>[STEVEN]</td>\n",
       "      <td>[DENARDO]</td>\n",
       "      <td>HUDBAR ASSOCIATES</td>\n",
       "      <td>[MARK LICALZI]</td>\n",
       "      <td>[STEVEN DENARDO]</td>\n",
       "      <td>1</td>\n",
       "      <td>[MARK LICALZI]</td>\n",
       "      <td>0.075540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>321767904</td>\n",
       "      <td>1</td>\n",
       "      <td>A3</td>\n",
       "      <td>INSTALL OF TEMP PLATFORM, TRUSS AND TENT, NO A...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>H/G/M/D/L/E/G</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING]</td>\n",
       "      <td>[RORY]</td>\n",
       "      <td>[MELVIN]</td>\n",
       "      <td>NYCEDC</td>\n",
       "      <td>[ANDREW FORMICHELLA]</td>\n",
       "      <td>[RORY MELVIN]</td>\n",
       "      <td>2</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...</td>\n",
       "      <td>0.046848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>123238479</td>\n",
       "      <td>1</td>\n",
       "      <td>A3</td>\n",
       "      <td>INSTALL OF TEMPORARY PLATFORMS AND SCENIC ELEM...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>H/G/M/D/L/E/G</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING]</td>\n",
       "      <td>[COSTANZA]</td>\n",
       "      <td>[MUSUMECI]</td>\n",
       "      <td>THOR &amp; MASON, LLC DBA INDUSTRIA</td>\n",
       "      <td>[ANDREW FORMICHELLA]</td>\n",
       "      <td>[COSTANZA MUSUMECI]</td>\n",
       "      <td>2</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...</td>\n",
       "      <td>0.046848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123588411</td>\n",
       "      <td>1</td>\n",
       "      <td>A3</td>\n",
       "      <td>INSTALL OF TEMP TENTS &amp;amp; PLATFORMS FOR 72ND...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>H/G/M/D/L/E/G</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING]</td>\n",
       "      <td>[INES]</td>\n",
       "      <td>[FONTAO]</td>\n",
       "      <td>FONTAO MANAGEMENT INC</td>\n",
       "      <td>[ANDREW FORMICHELLA]</td>\n",
       "      <td>[INES FONTAO]</td>\n",
       "      <td>2</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...</td>\n",
       "      <td>0.046848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>123238371</td>\n",
       "      <td>1</td>\n",
       "      <td>A3</td>\n",
       "      <td>INSTALL OF TEMPORARY PLATFORMS AND PRODUCTION ...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>H/G/M/D/L/E/G</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING]</td>\n",
       "      <td>[RORY]</td>\n",
       "      <td>[MELVIN]</td>\n",
       "      <td>NYCEDC</td>\n",
       "      <td>[ANDREW FORMICHELLA]</td>\n",
       "      <td>[RORY MELVIN]</td>\n",
       "      <td>2</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...</td>\n",
       "      <td>0.046848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>123237461</td>\n",
       "      <td>1</td>\n",
       "      <td>A3</td>\n",
       "      <td>INSTALL TEMP TENTS FOR OCEANS 8 PREMIERE, NO A...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>H/G/M/D/L/E/G</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING]</td>\n",
       "      <td>[PETER]</td>\n",
       "      <td>[FLAMM]</td>\n",
       "      <td>LINCOLN CENTER OF THE PRFMNG ART</td>\n",
       "      <td>[ANDREW FORMICHELLA]</td>\n",
       "      <td>[PETER FLAMM]</td>\n",
       "      <td>2</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...</td>\n",
       "      <td>0.046848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>123237470</td>\n",
       "      <td>1</td>\n",
       "      <td>A3</td>\n",
       "      <td>INSTALL OF TEMP TENTS, NO ALTERATION OF EXISTI...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>H/G/M/D/L/E/G</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING]</td>\n",
       "      <td>[SAMIN]</td>\n",
       "      <td>[SHARMA]</td>\n",
       "      <td>MOUNT SINAI HEALTH SYSTEM</td>\n",
       "      <td>[ANDREW FORMICHELLA]</td>\n",
       "      <td>[SAMIN SHARMA]</td>\n",
       "      <td>2</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...</td>\n",
       "      <td>0.046848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>123587635</td>\n",
       "      <td>1</td>\n",
       "      <td>A3</td>\n",
       "      <td>INSTALL OF TEMP INFLATABLE MOVIE SCREEN, BALLA...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>H/G/M/D/L/E/G</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING]</td>\n",
       "      <td>[CRAIG]</td>\n",
       "      <td>[LACARUBA]</td>\n",
       "      <td>BRYANT PARK CORPORATION</td>\n",
       "      <td>[ANDREW FORMICHELLA]</td>\n",
       "      <td>[CRAIG LACARUBA]</td>\n",
       "      <td>2</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...</td>\n",
       "      <td>0.046848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>123588402</td>\n",
       "      <td>1</td>\n",
       "      <td>A3</td>\n",
       "      <td>INSTALL OF TEMP PLATFORM, NO ALTERATIONS TO EX...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>H/G/M/D/L/E/G</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING]</td>\n",
       "      <td>[JOSEPH]</td>\n",
       "      <td>[LEVY]</td>\n",
       "      <td>APOLLO THEATER</td>\n",
       "      <td>[ANDREW FORMICHELLA]</td>\n",
       "      <td>[JOSEPH LEVY]</td>\n",
       "      <td>2</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...</td>\n",
       "      <td>0.046848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>123237452</td>\n",
       "      <td>1</td>\n",
       "      <td>A3</td>\n",
       "      <td>INSTALL OF TEMP TENTS, SCAFFOLD FLOOR AND PLAT...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>H/G/M/D/L/E/G</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING]</td>\n",
       "      <td>[ANTHONY]</td>\n",
       "      <td>[KHANI]</td>\n",
       "      <td>GENERAL THEOLOGICAL SOCIETY</td>\n",
       "      <td>[ANDREW FORMICHELLA]</td>\n",
       "      <td>[ANTHONY KHANI]</td>\n",
       "      <td>2</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...</td>\n",
       "      <td>0.046848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>123238148</td>\n",
       "      <td>1</td>\n",
       "      <td>A3</td>\n",
       "      <td>INSTALL TEMP SCAFFOLD STRUCTURE ,NO ALTERATION...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>H/G/M/D/L/E/G</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING]</td>\n",
       "      <td>[THOMAS]</td>\n",
       "      <td>[BERNADETTE ARTUS (LP]</td>\n",
       "      <td>TEAM P R OMOTION LLC</td>\n",
       "      <td>[ANDREW FORMICHELLA]</td>\n",
       "      <td>[THOMAS BERNADETTE ARTUS (LP]</td>\n",
       "      <td>2</td>\n",
       "      <td>[MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...</td>\n",
       "      <td>0.046848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>220653157</td>\n",
       "      <td>1</td>\n",
       "      <td>A3</td>\n",
       "      <td>EARTHWORK ONLY. NO FOUNDATION WORK SHALL NOT B...</td>\n",
       "      <td>20000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>ANG/MAR</td>\n",
       "      <td>[GINO O LONGO ARCHITECTS]</td>\n",
       "      <td>[JAMES]</td>\n",
       "      <td>[SULLIVAN]</td>\n",
       "      <td>RIVERDALE CUSTOM HOMES L.L.C.</td>\n",
       "      <td>[GINO LONGO]</td>\n",
       "      <td>[JAMES SULLIVAN]</td>\n",
       "      <td>1</td>\n",
       "      <td>[GINO LONGO]</td>\n",
       "      <td>0.043307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>123455082</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>REMOVAL AND INSTALLATION OF NON LOAD BEARING P...</td>\n",
       "      <td>174000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>6/14/2018 0:00</td>\n",
       "      <td>5/10/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>GORMAN/MORAN</td>\n",
       "      <td>[WALTER T GORMAN]</td>\n",
       "      <td>[ERICA]</td>\n",
       "      <td>[HINTZE]</td>\n",
       "      <td>RCPI LANDMARK PROPERTIES, LLC</td>\n",
       "      <td>[KAREN SOLOMON]</td>\n",
       "      <td>[ERICA HINTZE]</td>\n",
       "      <td>1</td>\n",
       "      <td>[WALTER T GORMAN]</td>\n",
       "      <td>0.040293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>123433971</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INSTALLATION OF TEMPORARY STRUCTURES ASSOCIATE...</td>\n",
       "      <td>200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>5/31/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>GORMAN/MORAN</td>\n",
       "      <td>[WALTER T GORMAN]</td>\n",
       "      <td>[ROBERT]</td>\n",
       "      <td>[LEMIEUX]</td>\n",
       "      <td>THE CITY COLLEGE OF NEW YORK</td>\n",
       "      <td>[MACIEJ FIUTAK]</td>\n",
       "      <td>[ROBERT LEMIEUX]</td>\n",
       "      <td>1</td>\n",
       "      <td>[WALTER T GORMAN]</td>\n",
       "      <td>0.040293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>123568497</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INSTALLATION OF NEW LOUVER (FARYL ROBIN).  NO ...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>MORAN/GORMAN</td>\n",
       "      <td>[WALTER T GORMAN]</td>\n",
       "      <td>[JAY]</td>\n",
       "      <td>[KREISBERG]</td>\n",
       "      <td>200 PARK SOUTH ASSOC LLC C/O ABS</td>\n",
       "      <td>[NICHOLAS TUCCI]</td>\n",
       "      <td>[JAY KREISBERG]</td>\n",
       "      <td>1</td>\n",
       "      <td>[WALTER T GORMAN]</td>\n",
       "      <td>0.040293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>123206128</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>PROPOSED TO REPLACE EXISTING DUCTS AND INSTALL...</td>\n",
       "      <td>30000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>5/4/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>GORMAN/MORAN</td>\n",
       "      <td>[WALTER T GORMAN]</td>\n",
       "      <td>[CALEIGH]</td>\n",
       "      <td>[TOYE]</td>\n",
       "      <td>95 MORTON LLC C/ORFR REALTY, LLC</td>\n",
       "      <td>[CHRISTOPHER BURGESS]</td>\n",
       "      <td>[CALEIGH TOYE]</td>\n",
       "      <td>1</td>\n",
       "      <td>[WALTER T GORMAN]</td>\n",
       "      <td>0.040293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>123365134</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FILING TO RENOVATE EXISTING RESTAURANT.  PLUMB...</td>\n",
       "      <td>34500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>5/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>FALLINS/NORTHERN</td>\n",
       "      <td>[TBC]</td>\n",
       "      <td>[JULIO]</td>\n",
       "      <td>[ALVAREZ]</td>\n",
       "      <td>CARINO RESTAURANT</td>\n",
       "      <td>[DAVIDSON CHRISTMAS]</td>\n",
       "      <td>[JULIO ALVAREZ]</td>\n",
       "      <td>2</td>\n",
       "      <td>[TBC, DAVIDSON CHRISTMAS]</td>\n",
       "      <td>0.021264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>123564311</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FILING TO INSTALL NEW FIRE SUPPRESSION SYSTEM ...</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>5/31/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>FALLINS/NORTHERN</td>\n",
       "      <td>[TBC]</td>\n",
       "      <td>[MYINHO]</td>\n",
       "      <td>[HAHN]</td>\n",
       "      <td>MAIDEN FARM</td>\n",
       "      <td>[DAVIDSON CHRISTMAS]</td>\n",
       "      <td>[MYINHO HAHN]</td>\n",
       "      <td>2</td>\n",
       "      <td>[TBC, DAVIDSON CHRISTMAS]</td>\n",
       "      <td>0.021264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>123564320</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INSTALLING NEW FIRE SUPPRESSION SYSTEM AS PER ...</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>5/29/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>FALLINS/NORTHERN</td>\n",
       "      <td>[TBC]</td>\n",
       "      <td>[BASSAM]</td>\n",
       "      <td>[MUSAID]</td>\n",
       "      <td>STRAIGHT DELI</td>\n",
       "      <td>[DAVIDSON CHRISTMAS]</td>\n",
       "      <td>[BASSAM MUSAID]</td>\n",
       "      <td>2</td>\n",
       "      <td>[TBC, DAVIDSON CHRISTMAS]</td>\n",
       "      <td>0.021264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>123420431</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INTERIOR RENOVATION OF CORRIDORS ON FLOORS 002...</td>\n",
       "      <td>400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>5/25/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>JAFFA/AKERM/GUZ/CUMM</td>\n",
       "      <td>[JACK JAFFA &amp; ASSOCIATES]</td>\n",
       "      <td>[CALYNNE]</td>\n",
       "      <td>[OYOLOKOR]</td>\n",
       "      <td>AKELIUS REAL ESTATE MANAGEMENT</td>\n",
       "      <td>[DANA KOENITZER]</td>\n",
       "      <td>[CALYNNE OYOLOKOR]</td>\n",
       "      <td>1</td>\n",
       "      <td>[JACK JAFFA &amp; ASSOCIATES]</td>\n",
       "      <td>0.020392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>123448251</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>REGISTER LAUNDRY ROOM &amp;amp; EQUIPMENT IN CELLA...</td>\n",
       "      <td>23000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>3/23/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>MURDISON</td>\n",
       "      <td>[LEWSAN CONSULTING]</td>\n",
       "      <td>[ANTHONY]</td>\n",
       "      <td>[HUANG]</td>\n",
       "      <td>FIRM ASSETS INC.</td>\n",
       "      <td>[SALVADOR TARTAGLIONE]</td>\n",
       "      <td>[ANTHONY HUANG]</td>\n",
       "      <td>1</td>\n",
       "      <td>[LEWSAN CONSULTING]</td>\n",
       "      <td>0.018788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>123448251</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>LEGALIZE LAUNDRY ROOM IN CELLAR FOR TENANT'S U...</td>\n",
       "      <td>23000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>3/23/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>MURDISON</td>\n",
       "      <td>[LEWSAN CONSULTING]</td>\n",
       "      <td>[ANTHONY]</td>\n",
       "      <td>[HUANG]</td>\n",
       "      <td>FIRM ASSETS INC.</td>\n",
       "      <td>[SALVADOR TARTAGLIONE]</td>\n",
       "      <td>[ANTHONY HUANG]</td>\n",
       "      <td>1</td>\n",
       "      <td>[LEWSAN CONSULTING]</td>\n",
       "      <td>0.018788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>220652513</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>JIMMY JAZZ: GENERAL CONSTRUCTION INCLUSIVE OF ...</td>\n",
       "      <td>295000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>7/3/2018 0:00</td>\n",
       "      <td>6/1/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>BYRNES</td>\n",
       "      <td>[JAM CONSULTING]</td>\n",
       "      <td>[ROBERT]</td>\n",
       "      <td>[FRASTAI]</td>\n",
       "      <td>VIP BOULEVARD REAL ESTATE CORP.</td>\n",
       "      <td>[JIMMY POWERS]</td>\n",
       "      <td>[ROBERT FRASTAI]</td>\n",
       "      <td>1</td>\n",
       "      <td>[JAM CONSULTING]</td>\n",
       "      <td>0.016507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>123566373</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>MECHANICAL AND SPRINKLER WORK BEING FILED ON T...</td>\n",
       "      <td>49900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/1/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>BE/BO/TO/JIM</td>\n",
       "      <td>[JM ZONING]</td>\n",
       "      <td>[MAURICE]</td>\n",
       "      <td>[RODRIGUES]</td>\n",
       "      <td>AAF BROADWAY 1384, LLC</td>\n",
       "      <td>[JACK GREEN]</td>\n",
       "      <td>[MAURICE RODRIGUES]</td>\n",
       "      <td>1</td>\n",
       "      <td>[JM ZONING]</td>\n",
       "      <td>0.015131</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>123363671</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>REMOVAL OF INTERIOR PARTITIONS TO INCLUDE SLAB...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>4/11/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>RI/TO/BE/CH</td>\n",
       "      <td>[KM ASSOCIATES]</td>\n",
       "      <td>[JOSHUA]</td>\n",
       "      <td>[LANDAU]</td>\n",
       "      <td>FOUR WINDS REAL ESTATE</td>\n",
       "      <td>[ROBERT BRAUN]</td>\n",
       "      <td>[JOSHUA LANDAU]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>123236685</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>TEMPORARY DRY STANDPIPE SYSTEM AS PER PLANS FI...</td>\n",
       "      <td>18000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>5/31/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>HEU/RIS/PLOU/OM/M</td>\n",
       "      <td>[RH CONSULTING]</td>\n",
       "      <td>[SCOTT]</td>\n",
       "      <td>[ALLEN]</td>\n",
       "      <td>CHAOWAI 51ST ST LLC</td>\n",
       "      <td>[MARINA SOLOVCHUK]</td>\n",
       "      <td>[SCOTT ALLEN]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>123236792</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>GENERAL CONSTRUCTION ASSOCIATED WITH HOTEL AME...</td>\n",
       "      <td>1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>5/31/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>DIAZ/RUSSO/LEO</td>\n",
       "      <td>[EAST COAST EXPEDITING]</td>\n",
       "      <td>[JODI]</td>\n",
       "      <td>[STEIN]</td>\n",
       "      <td>LSG 105 WEST 28TH LLC</td>\n",
       "      <td>[NEILL PARKER]</td>\n",
       "      <td>[JODI STEIN]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>321755775</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INTERIOR RENOVATION. REPLACEMENT OF INDOOR AND...</td>\n",
       "      <td>20000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>MARQ/ALEXAN/YOUNG/CH</td>\n",
       "      <td>[RPO ARCHITECTS]</td>\n",
       "      <td>[ALYSSA]</td>\n",
       "      <td>[MALIN]</td>\n",
       "      <td>A&amp;E REAL ESTATE, LLC</td>\n",
       "      <td>[ANANTH SAMPATHKUMAR]</td>\n",
       "      <td>[ALYSSA MALIN]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>321755481</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>REPLACE GAS LINES AND ADD DRYERS IN THE BUILDING</td>\n",
       "      <td>68000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>POT/CON/NIEVES/DUKE</td>\n",
       "      <td>[J CALLAHAN CONSULTING]</td>\n",
       "      <td>[RICHARD]</td>\n",
       "      <td>[MOORE]</td>\n",
       "      <td>219 CLINTON STREET HOUSING CORP</td>\n",
       "      <td>[BRENDAN COBURN]</td>\n",
       "      <td>[RICHARD MOORE]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>321754151</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INSTALLATION OF NEW AIR CONDITIONER SUPPORT FR...</td>\n",
       "      <td>10000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>5/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[NAN]</td>\n",
       "      <td>[ISAAC]</td>\n",
       "      <td>[DAHAN]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[ALEX ROMAO]</td>\n",
       "      <td>[ISAAC DAHAN]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>520328583</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>NEW WORKSTATION IN STATEN ISLAND MALL FOOD COU...</td>\n",
       "      <td>56000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/14/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>KUANG/ZHU/HU/CH</td>\n",
       "      <td>[JRK CREATIVE]</td>\n",
       "      <td>[ROBERT]</td>\n",
       "      <td>[SABAS]</td>\n",
       "      <td>HOSPITALITY INTERNATIONAL GROUP</td>\n",
       "      <td>[DAVID HIATT]</td>\n",
       "      <td>[ROBERT SABAS]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>421643074</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FILING FOR EXTERIOR FACADE REPAIRS AS SHOWN ON...</td>\n",
       "      <td>102310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>7/10/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>CA/BE/ZE/BE/AL</td>\n",
       "      <td>[KM ASSOCIATES]</td>\n",
       "      <td>[E GORDON]</td>\n",
       "      <td>[YOUNG]</td>\n",
       "      <td>THIRD HOUSING COMPANY, INC.</td>\n",
       "      <td>[MASSIMILIANO VOTO]</td>\n",
       "      <td>[E GORDON YOUNG]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>421635065</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INSTALLATION OF 1.5 TON DUCTLESS SPLIT SYSTEM ...</td>\n",
       "      <td>20000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>DAVIDOVITCH</td>\n",
       "      <td>[NEWTEC ENGINEERING]</td>\n",
       "      <td>[RICHARD]</td>\n",
       "      <td>[CANNATA]</td>\n",
       "      <td>MOUNT SINAI QUEENS</td>\n",
       "      <td>[OBAID MIAN]</td>\n",
       "      <td>[RICHARD CANNATA]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>421643083</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...</td>\n",
       "      <td>102310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>7/10/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>CA/BE/ZE/BE/AL</td>\n",
       "      <td>[KM ASSOCIATES]</td>\n",
       "      <td>[E GORDON]</td>\n",
       "      <td>[YOUNG]</td>\n",
       "      <td>THIRD HOUSING COMPANY, INC.</td>\n",
       "      <td>[MASSIMILIANO VOTO]</td>\n",
       "      <td>[E GORDON YOUNG]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>421643092</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...</td>\n",
       "      <td>102310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>7/9/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>CA/BE/ZE/BE/AL</td>\n",
       "      <td>[KM ASSOCIATES]</td>\n",
       "      <td>[E GORDON]</td>\n",
       "      <td>[YOUNG]</td>\n",
       "      <td>THIRD HOUSING COMPANY, INC.</td>\n",
       "      <td>[MASSIMILIANO VOTO]</td>\n",
       "      <td>[E GORDON YOUNG]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>421643127</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...</td>\n",
       "      <td>102310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>7/9/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>CA/BE/ZE/BE/AL</td>\n",
       "      <td>[KM ASSOCIATES]</td>\n",
       "      <td>[E GORDON]</td>\n",
       "      <td>[YOUNG]</td>\n",
       "      <td>THIRD HOUSING COMPANY, INC.</td>\n",
       "      <td>[MASSIMILIANO VOTO]</td>\n",
       "      <td>[E GORDON YOUNG]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>421643136</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...</td>\n",
       "      <td>102310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>7/6/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>CA/BE/ZE/BE/AL</td>\n",
       "      <td>[KM ASSOCIATES]</td>\n",
       "      <td>[E GORDON]</td>\n",
       "      <td>[YOUNG]</td>\n",
       "      <td>THIRD HOUSING COMPANY, INC.</td>\n",
       "      <td>[MASSIMILIANO VOTO]</td>\n",
       "      <td>[E GORDON YOUNG]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>421643145</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...</td>\n",
       "      <td>102310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>7/6/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>CA/BE/ZE/BE/AL</td>\n",
       "      <td>[KM ASSOCIATES]</td>\n",
       "      <td>[E GORDON]</td>\n",
       "      <td>[YOUNG]</td>\n",
       "      <td>THIRD HOUSING COMPANY, INC.</td>\n",
       "      <td>[MASSIMILIANO VOTO]</td>\n",
       "      <td>[E GORDON YOUNG]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>421643154</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...</td>\n",
       "      <td>102310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>7/6/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>CA/BE/ZE/BE/AL</td>\n",
       "      <td>[KM ASSOCIATES]</td>\n",
       "      <td>[E GORDON]</td>\n",
       "      <td>[YOUNG]</td>\n",
       "      <td>THIRD HOUSING COMPANY, INC.</td>\n",
       "      <td>[MASSIMILIANO VOTO]</td>\n",
       "      <td>[E GORDON YOUNG]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>421643163</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...</td>\n",
       "      <td>102310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>7/6/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>CA/BE/ZE/BE/AL</td>\n",
       "      <td>[KM ASSOCIATES]</td>\n",
       "      <td>[E GORDON]</td>\n",
       "      <td>[YOUNG]</td>\n",
       "      <td>THIRD HOUSING COMPANY, INC.</td>\n",
       "      <td>[MASSIMILIANO VOTO]</td>\n",
       "      <td>[E GORDON YOUNG]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>520306552</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INTERNAL WATER MAIN INCLUDING BACKFLOW PREVENT...</td>\n",
       "      <td>64215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X</td>\n",
       "      <td>7/9/2018 0:00</td>\n",
       "      <td>3/30/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>TRANG</td>\n",
       "      <td>[WOHL &amp; O'MARA]</td>\n",
       "      <td>[BRUNO]</td>\n",
       "      <td>[SAVO]</td>\n",
       "      <td>BMN LLC</td>\n",
       "      <td>[ROCCO DEFELIPPIS]</td>\n",
       "      <td>[BRUNO SAVO]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>520328976</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>PROPOSING TO INSTALL TUBULAR SKYLIGHTS. NO CHA...</td>\n",
       "      <td>22200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>5/30/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>HACOHEN</td>\n",
       "      <td>[SANDI LIPSHITZ HACOHEN ARCHITECTS]</td>\n",
       "      <td>[KOSTA]</td>\n",
       "      <td>[TROPAITIS]</td>\n",
       "      <td>FOUR SEASONS AUTO</td>\n",
       "      <td>[SANDRA HACOHEN]</td>\n",
       "      <td>[KOSTA TROPAITIS]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>123568166</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INTERIOR RENOVATION TO APARTMENT #31 ON THE 3R...</td>\n",
       "      <td>56500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>SINGARES</td>\n",
       "      <td>[GRS CONSULTING]</td>\n",
       "      <td>[MARK]</td>\n",
       "      <td>[SCHARFMAN]</td>\n",
       "      <td>C/O BEACH LANE MANAGEMENT</td>\n",
       "      <td>[JAMES MOORE]</td>\n",
       "      <td>[MARK SCHARFMAN]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>520332765</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FILLING TO RELOCATE GAS LINE AT BASEMENT.  NO ...</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>5/25/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>LACHM/DESP/LAGARES</td>\n",
       "      <td>[DESIGN 2147]</td>\n",
       "      <td>[CAROLINE]</td>\n",
       "      <td>[HARRISON]</td>\n",
       "      <td>ADVANCE PUBLICATIONS, INC.</td>\n",
       "      <td>[WILLIAM DASHIELL]</td>\n",
       "      <td>[CAROLINE HARRISON]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>520336182</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FC#20 (MELT SHOP). INTERIOR BUILDOUT INCLUDING...</td>\n",
       "      <td>332000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>BARRIONUEVO/DIAZ</td>\n",
       "      <td>[MIILROSE CONSULTING]</td>\n",
       "      <td>[JAMES]</td>\n",
       "      <td>[EASLEY]</td>\n",
       "      <td>GGP STATEN ISLAND MALL, LLC</td>\n",
       "      <td>[ERIC STRYER]</td>\n",
       "      <td>[JAMES EASLEY]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>421636527</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>INTERIOR RENOVATION TO INCLUDE:  REMOVAL OF SI...</td>\n",
       "      <td>9500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/8/2018 0:00</td>\n",
       "      <td>5/25/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>ALEMAN/BOYD/CONCE</td>\n",
       "      <td>[JCA &amp; ASSOCIATES]</td>\n",
       "      <td>[JOHNATHAN]</td>\n",
       "      <td>[KNOETGEN]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[ROSATO PIETRO]</td>\n",
       "      <td>[JOHNATHAN KNOETGEN]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>421632497</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FACADE REPAIR INCLUDE POINTING, BRICK REPLACEM...</td>\n",
       "      <td>131250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>SOMAIR/MARQ/ALEXAN/Y</td>\n",
       "      <td>[RPO ARCHITECTS]</td>\n",
       "      <td>[LESLIE]</td>\n",
       "      <td>[SIEGEL]</td>\n",
       "      <td>GROVER CLEVELAND TENANTS CORP.</td>\n",
       "      <td>[THOMAS CATALDO]</td>\n",
       "      <td>[LESLIE SIEGEL]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>321740595</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FACADE REPAIRS AS PER PLANS FILED HEREWITH. NO...</td>\n",
       "      <td>37515</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>4/16/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>PATROSZ</td>\n",
       "      <td>[BLUE HORSESHOE ADVISORS]</td>\n",
       "      <td>[JUAN]</td>\n",
       "      <td>[RAMOS]</td>\n",
       "      <td>SOUTHSIDE UNITED HDFC -LOS SURES</td>\n",
       "      <td>[ERIC COWLEY]</td>\n",
       "      <td>[JUAN RAMOS]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>321766852</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>FILING FOR THE INTERIOR OFFICE RENOVATION TO I...</td>\n",
       "      <td>410450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>CA/BE/ZE/BE/AL</td>\n",
       "      <td>[KM ASSOCIATES]</td>\n",
       "      <td>[JACOB]</td>\n",
       "      <td>[EISENSTEIN]</td>\n",
       "      <td>813-25 QUENTIN RD ASSOCIATES C/O</td>\n",
       "      <td>[SALVADOR FERRARA]</td>\n",
       "      <td>[JACOB EISENSTEIN]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>321741790</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>GENERAL CONSTRUCTION TO RENOVATE APT 3R AT 3RD...</td>\n",
       "      <td>60000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/11/2018 0:00</td>\n",
       "      <td>4/12/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>SA/PE/GR/AK/BE</td>\n",
       "      <td>[YOUBER DESIGN]</td>\n",
       "      <td>[ROBERT]</td>\n",
       "      <td>[KAYDANIAN]</td>\n",
       "      <td>INK 206 NASSAU LLC</td>\n",
       "      <td>[KENNETH MAK]</td>\n",
       "      <td>[ROBERT KAYDANIAN]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>321742218</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>MODIFICATIONS TO EXISTING SPRINKLER SYSTEM AS ...</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>5/9/2018 0:00</td>\n",
       "      <td>6/5/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>IS/YS/TR/SA/LA/GI</td>\n",
       "      <td>[OUTSOURCE CONSULTING]</td>\n",
       "      <td>[BERT]</td>\n",
       "      <td>[NISONOFF]</td>\n",
       "      <td>GRA DEB REALTY CO</td>\n",
       "      <td>[JOSEPH VUTO]</td>\n",
       "      <td>[BERT NISONOFF]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>321743404</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>REMOVE FIVE BOILERS FROM THE BATHROOMS IN THE ...</td>\n",
       "      <td>8000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>6/4/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>ELZON/BOGOMOLNY</td>\n",
       "      <td>[SEL CONSULTING]</td>\n",
       "      <td>[EUGINE]</td>\n",
       "      <td>[KRASNITSKY]</td>\n",
       "      <td>275 NEPTUNE AVENUE LLC</td>\n",
       "      <td>[ZARINA KINDO]</td>\n",
       "      <td>[EUGINE KRASNITSKY]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>321747106</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>GENERAL CONSTRUCTION TO RENOVATION APT 3R AT 3...</td>\n",
       "      <td>20000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/29/2018 0:00</td>\n",
       "      <td>4/24/2018 0:00</td>\n",
       "      <td>6/7/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>SA/PE/GR/AK/BE</td>\n",
       "      <td>[YOUBER DESIGN]</td>\n",
       "      <td>[ABI]</td>\n",
       "      <td>[GOLDENBERG]</td>\n",
       "      <td>896 MADISON REALTY LLC</td>\n",
       "      <td>[KENNETH MAK]</td>\n",
       "      <td>[ABI GOLDENBERG]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>520337421</td>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>TO REMOVE AND INSTALL PLUMBING FIXTURES. APPLI...</td>\n",
       "      <td>8600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>5/23/2018 0:00</td>\n",
       "      <td>6/6/2018 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>BAEK/CHOI/KANG</td>\n",
       "      <td>[CHOI DESIGN]</td>\n",
       "      <td>[MICHAEL]</td>\n",
       "      <td>[STONE]</td>\n",
       "      <td>MICHAEL E. STONE AIA, CDP DEVELO</td>\n",
       "      <td>[ILJOONG KIM]</td>\n",
       "      <td>[MICHAEL STONE]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     JobNumber  Job Document Number Job Type  \\\n",
       "0    421642333                    1       A2   \n",
       "1    321768921                    1       A2   \n",
       "2    123203167                    1       A2   \n",
       "3    321687144                    1       A2   \n",
       "4    421594297                    1       A2   \n",
       "5    123237933                    1       A2   \n",
       "6    123448260                    1       A2   \n",
       "7    321767904                    1       A3   \n",
       "8    123238479                    1       A3   \n",
       "9    123588411                    1       A3   \n",
       "10   123238371                    1       A3   \n",
       "11   123237461                    1       A3   \n",
       "12   123237470                    1       A3   \n",
       "13   123587635                    1       A3   \n",
       "14   123588402                    1       A3   \n",
       "15   123237452                    1       A3   \n",
       "16   123238148                    1       A3   \n",
       "17   220653157                    1       A3   \n",
       "18   123455082                    1       A2   \n",
       "19   123433971                    1       A2   \n",
       "20   123568497                    1       A2   \n",
       "21   123206128                    1       A2   \n",
       "22   123365134                    1       A2   \n",
       "23   123564311                    1       A2   \n",
       "24   123564320                    1       A2   \n",
       "25   123420431                    1       A2   \n",
       "26   123448251                    1       A2   \n",
       "27   123448251                    1       A2   \n",
       "28   220652513                    1       A2   \n",
       "29   123566373                    1       A2   \n",
       "..         ...                  ...      ...   \n",
       "222  123363671                    1       A2   \n",
       "223  123236685                    1       A2   \n",
       "224  123236792                    1       A2   \n",
       "225  321755775                    1       A2   \n",
       "226  321755481                    1       A2   \n",
       "227  321754151                    1       A2   \n",
       "228  520328583                    1       A2   \n",
       "229  421643074                    1       A2   \n",
       "230  421635065                    1       A2   \n",
       "231  421643083                    1       A2   \n",
       "232  421643092                    1       A2   \n",
       "233  421643127                    1       A2   \n",
       "234  421643136                    1       A2   \n",
       "235  421643145                    1       A2   \n",
       "236  421643154                    1       A2   \n",
       "237  421643163                    1       A2   \n",
       "238  520306552                    1       A2   \n",
       "239  520328976                    1       A2   \n",
       "240  123568166                    1       A2   \n",
       "241  520332765                    1       A2   \n",
       "242  520336182                    1       A2   \n",
       "243  421636527                    1       A2   \n",
       "244  421632497                    1       A2   \n",
       "245  321740595                    1       A2   \n",
       "246  321766852                    1       A2   \n",
       "247  321741790                    1       A2   \n",
       "248  321742218                    1       A2   \n",
       "249  321743404                    1       A2   \n",
       "250  321747106                    1       A2   \n",
       "251  520337421                    1       A2   \n",
       "\n",
       "                                       Job Description  Estimated Job Cost  \\\n",
       "0    RELATED INTERIOR PARTITION WORK AS PER PLANS. ...               55700   \n",
       "1    INTERIOR RENOVATION OF PARTIAL 2ND FLOOR, APT ...               53000   \n",
       "2    INTERIOR RENOVATION OF 4 EXISTING APARTMENTS. ...              180000   \n",
       "3    INTERIOR RESTORATION AND RENOVATION OF EXISTIN...              320350   \n",
       "4    TO REPLACE HOT WATER HEATER. NO CHANGE IN USE,...                3000   \n",
       "5    AC REPLACEMENT AS PER PLANS.  NO CHANGE TO USE...               70000   \n",
       "6    MINOR PARTITION, PLUMBING AND MECHANICAL WORK ...               47500   \n",
       "7    INSTALL OF TEMP PLATFORM, TRUSS AND TENT, NO A...                5000   \n",
       "8    INSTALL OF TEMPORARY PLATFORMS AND SCENIC ELEM...                5000   \n",
       "9    INSTALL OF TEMP TENTS &amp; PLATFORMS FOR 72ND...                5000   \n",
       "10   INSTALL OF TEMPORARY PLATFORMS AND PRODUCTION ...                5000   \n",
       "11   INSTALL TEMP TENTS FOR OCEANS 8 PREMIERE, NO A...                5000   \n",
       "12   INSTALL OF TEMP TENTS, NO ALTERATION OF EXISTI...                5000   \n",
       "13   INSTALL OF TEMP INFLATABLE MOVIE SCREEN, BALLA...                5000   \n",
       "14   INSTALL OF TEMP PLATFORM, NO ALTERATIONS TO EX...                5000   \n",
       "15   INSTALL OF TEMP TENTS, SCAFFOLD FLOOR AND PLAT...                5000   \n",
       "16   INSTALL TEMP SCAFFOLD STRUCTURE ,NO ALTERATION...                5000   \n",
       "17   EARTHWORK ONLY. NO FOUNDATION WORK SHALL NOT B...               20000   \n",
       "18   REMOVAL AND INSTALLATION OF NON LOAD BEARING P...              174000   \n",
       "19   INSTALLATION OF TEMPORARY STRUCTURES ASSOCIATE...              200000   \n",
       "20   INSTALLATION OF NEW LOUVER (FARYL ROBIN).  NO ...                5000   \n",
       "21   PROPOSED TO REPLACE EXISTING DUCTS AND INSTALL...               30000   \n",
       "22   FILING TO RENOVATE EXISTING RESTAURANT.  PLUMB...               34500   \n",
       "23   FILING TO INSTALL NEW FIRE SUPPRESSION SYSTEM ...                2000   \n",
       "24   INSTALLING NEW FIRE SUPPRESSION SYSTEM AS PER ...                2000   \n",
       "25   INTERIOR RENOVATION OF CORRIDORS ON FLOORS 002...              400000   \n",
       "26   REGISTER LAUNDRY ROOM &amp; EQUIPMENT IN CELLA...               23000   \n",
       "27   LEGALIZE LAUNDRY ROOM IN CELLAR FOR TENANT'S U...               23000   \n",
       "28   JIMMY JAZZ: GENERAL CONSTRUCTION INCLUSIVE OF ...              295000   \n",
       "29   MECHANICAL AND SPRINKLER WORK BEING FILED ON T...               49900   \n",
       "..                                                 ...                 ...   \n",
       "222  REMOVAL OF INTERIOR PARTITIONS TO INCLUDE SLAB...                5000   \n",
       "223  TEMPORARY DRY STANDPIPE SYSTEM AS PER PLANS FI...               18000   \n",
       "224  GENERAL CONSTRUCTION ASSOCIATED WITH HOTEL AME...                1000   \n",
       "225  INTERIOR RENOVATION. REPLACEMENT OF INDOOR AND...               20000   \n",
       "226   REPLACE GAS LINES AND ADD DRYERS IN THE BUILDING               68000   \n",
       "227  INSTALLATION OF NEW AIR CONDITIONER SUPPORT FR...               10000   \n",
       "228  NEW WORKSTATION IN STATEN ISLAND MALL FOOD COU...               56000   \n",
       "229  FILING FOR EXTERIOR FACADE REPAIRS AS SHOWN ON...              102310   \n",
       "230  INSTALLATION OF 1.5 TON DUCTLESS SPLIT SYSTEM ...               20000   \n",
       "231  FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...              102310   \n",
       "232  FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...              102310   \n",
       "233  FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...              102310   \n",
       "234  FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...              102310   \n",
       "235  FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...              102310   \n",
       "236  FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...              102310   \n",
       "237  FILING FOR EXTERIOR FACADE REPAIRS  AS SHOWN O...              102310   \n",
       "238  INTERNAL WATER MAIN INCLUDING BACKFLOW PREVENT...               64215   \n",
       "239  PROPOSING TO INSTALL TUBULAR SKYLIGHTS. NO CHA...               22200   \n",
       "240  INTERIOR RENOVATION TO APARTMENT #31 ON THE 3R...               56500   \n",
       "241  FILLING TO RELOCATE GAS LINE AT BASEMENT.  NO ...                5000   \n",
       "242  FC#20 (MELT SHOP). INTERIOR BUILDOUT INCLUDING...              332000   \n",
       "243  INTERIOR RENOVATION TO INCLUDE:  REMOVAL OF SI...                9500   \n",
       "244  FACADE REPAIR INCLUDE POINTING, BRICK REPLACEM...              131250   \n",
       "245  FACADE REPAIRS AS PER PLANS FILED HEREWITH. NO...               37515   \n",
       "246  FILING FOR THE INTERIOR OFFICE RENOVATION TO I...              410450   \n",
       "247  GENERAL CONSTRUCTION TO RENOVATE APT 3R AT 3RD...               60000   \n",
       "248  MODIFICATIONS TO EXISTING SPRINKLER SYSTEM AS ...               15000   \n",
       "249  REMOVE FIVE BOILERS FROM THE BATHROOMS IN THE ...                8000   \n",
       "250  GENERAL CONSTRUCTION TO RENOVATION APT 3R AT 3...               20000   \n",
       "251  TO REMOVE AND INSTALL PLUMBING FIXTURES. APPLI...                8600   \n",
       "\n",
       "     Hub Self Service Application Filed Current Job Status Job Status Date  \\\n",
       "0                                   NaN                  Q  6/15/2018 0:00   \n",
       "1                                   NaN                  R  6/13/2018 0:00   \n",
       "2                                   NaN                  Q   6/4/2018 0:00   \n",
       "3                                   NaN                  R  6/13/2018 0:00   \n",
       "4                                   NaN                  R   6/4/2018 0:00   \n",
       "5                                   NaN                  R   6/4/2018 0:00   \n",
       "6                                   NaN                  Q   7/9/2018 0:00   \n",
       "7                                   NaN                  R   6/7/2018 0:00   \n",
       "8                                   NaN                  R   6/6/2018 0:00   \n",
       "9                                   NaN                  R   6/6/2018 0:00   \n",
       "10                                  NaN                  R   6/6/2018 0:00   \n",
       "11                                  NaN                  R   6/4/2018 0:00   \n",
       "12                                  NaN                  R   6/4/2018 0:00   \n",
       "13                                  NaN                  R   6/5/2018 0:00   \n",
       "14                                  NaN                  R   6/6/2018 0:00   \n",
       "15                                  NaN                  R   6/5/2018 0:00   \n",
       "16                                  NaN                  R   6/7/2018 0:00   \n",
       "17                                  NaN                  R   6/7/2018 0:00   \n",
       "18                                  NaN                  Q  6/14/2018 0:00   \n",
       "19                                  NaN                  R   6/6/2018 0:00   \n",
       "20                                  NaN                  R   6/5/2018 0:00   \n",
       "21                                  NaN                  R   6/5/2018 0:00   \n",
       "22                                  NaN                  Q   6/7/2018 0:00   \n",
       "23                                  NaN                  R   6/4/2018 0:00   \n",
       "24                                  NaN                  R   6/4/2018 0:00   \n",
       "25                                  NaN                  R   6/6/2018 0:00   \n",
       "26                                  NaN                  R   6/7/2018 0:00   \n",
       "27                                  NaN                  R   6/7/2018 0:00   \n",
       "28                                  NaN                  R   7/3/2018 0:00   \n",
       "29                                  NaN                  Q   6/4/2018 0:00   \n",
       "..                                  ...                ...             ...   \n",
       "222                                 NaN                  R   6/6/2018 0:00   \n",
       "223                                 NaN                  R   6/7/2018 0:00   \n",
       "224                                 NaN                  R   6/5/2018 0:00   \n",
       "225                                 NaN                  R   6/5/2018 0:00   \n",
       "226                                 NaN                  P   6/6/2018 0:00   \n",
       "227                                 NaN                  Q   6/7/2018 0:00   \n",
       "228                                 NaN                  R  6/14/2018 0:00   \n",
       "229                                 NaN                  R  7/10/2018 0:00   \n",
       "230                                 NaN                  R   6/7/2018 0:00   \n",
       "231                                 NaN                  R  7/10/2018 0:00   \n",
       "232                                 NaN                  R   7/9/2018 0:00   \n",
       "233                                 NaN                  R   7/9/2018 0:00   \n",
       "234                                 NaN                  R   7/6/2018 0:00   \n",
       "235                                 NaN                  R   7/6/2018 0:00   \n",
       "236                                 NaN                  R   7/6/2018 0:00   \n",
       "237                                 NaN                  R   7/6/2018 0:00   \n",
       "238                                 NaN                  X   7/9/2018 0:00   \n",
       "239                                 NaN                  R   6/5/2018 0:00   \n",
       "240                                 NaN                  Q   6/7/2018 0:00   \n",
       "241                                 NaN                  R   6/5/2018 0:00   \n",
       "242                                 NaN                  R   6/6/2018 0:00   \n",
       "243                                 NaN                  R   6/8/2018 0:00   \n",
       "244                                 NaN                  R   6/6/2018 0:00   \n",
       "245                                 NaN                  R   6/5/2018 0:00   \n",
       "246                                 NaN                  Q   6/5/2018 0:00   \n",
       "247                                 NaN                  R  6/11/2018 0:00   \n",
       "248                                 NaN                  R   6/5/2018 0:00   \n",
       "249                                 NaN                  R   6/4/2018 0:00   \n",
       "250                                 NaN                  R  6/29/2018 0:00   \n",
       "251                                 NaN                  R   6/6/2018 0:00   \n",
       "\n",
       "    Plan Approval Date First Permit Date     ...       \\\n",
       "0        6/5/2018 0:00     6/5/2018 0:00     ...        \n",
       "1        6/5/2018 0:00     6/6/2018 0:00     ...        \n",
       "2        6/4/2018 0:00     6/4/2018 0:00     ...        \n",
       "3        6/5/2018 0:00     6/5/2018 0:00     ...        \n",
       "4       1/18/2018 0:00     6/4/2018 0:00     ...        \n",
       "5        6/4/2018 0:00     6/4/2018 0:00     ...        \n",
       "6        3/6/2018 0:00     6/5/2018 0:00     ...        \n",
       "7        6/4/2018 0:00     6/7/2018 0:00     ...        \n",
       "8        6/5/2018 0:00     6/6/2018 0:00     ...        \n",
       "9        6/6/2018 0:00     6/6/2018 0:00     ...        \n",
       "10       6/5/2018 0:00     6/6/2018 0:00     ...        \n",
       "11       6/4/2018 0:00     6/4/2018 0:00     ...        \n",
       "12       6/4/2018 0:00     6/4/2018 0:00     ...        \n",
       "13       6/5/2018 0:00     6/5/2018 0:00     ...        \n",
       "14       6/6/2018 0:00     6/6/2018 0:00     ...        \n",
       "15       6/4/2018 0:00     6/5/2018 0:00     ...        \n",
       "16       6/5/2018 0:00     6/7/2018 0:00     ...        \n",
       "17       6/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "18      5/10/2018 0:00     6/7/2018 0:00     ...        \n",
       "19      5/31/2018 0:00     6/6/2018 0:00     ...        \n",
       "20       6/5/2018 0:00     6/5/2018 0:00     ...        \n",
       "21       5/4/2018 0:00     6/5/2018 0:00     ...        \n",
       "22       5/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "23      5/31/2018 0:00     6/4/2018 0:00     ...        \n",
       "24      5/29/2018 0:00     6/4/2018 0:00     ...        \n",
       "25      5/25/2018 0:00     6/6/2018 0:00     ...        \n",
       "26      3/23/2018 0:00     6/4/2018 0:00     ...        \n",
       "27      3/23/2018 0:00     6/4/2018 0:00     ...        \n",
       "28       6/1/2018 0:00     6/7/2018 0:00     ...        \n",
       "29       6/1/2018 0:00     6/4/2018 0:00     ...        \n",
       "..                 ...               ...     ...        \n",
       "222     4/11/2018 0:00     6/6/2018 0:00     ...        \n",
       "223     5/31/2018 0:00     6/7/2018 0:00     ...        \n",
       "224     5/31/2018 0:00     6/5/2018 0:00     ...        \n",
       "225      6/5/2018 0:00     6/5/2018 0:00     ...        \n",
       "226      6/6/2018 0:00               NaN     ...        \n",
       "227      5/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "228      6/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "229      6/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "230      6/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "231      6/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "232      6/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "233      6/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "234      6/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "235      6/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "236      6/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "237      6/7/2018 0:00     6/7/2018 0:00     ...        \n",
       "238     3/30/2018 0:00     6/6/2018 0:00     ...        \n",
       "239     5/30/2018 0:00     6/5/2018 0:00     ...        \n",
       "240      6/5/2018 0:00     6/7/2018 0:00     ...        \n",
       "241     5/25/2018 0:00     6/5/2018 0:00     ...        \n",
       "242      6/4/2018 0:00     6/5/2018 0:00     ...        \n",
       "243     5/25/2018 0:00     6/7/2018 0:00     ...        \n",
       "244      6/6/2018 0:00     6/6/2018 0:00     ...        \n",
       "245     4/16/2018 0:00     6/5/2018 0:00     ...        \n",
       "246      6/5/2018 0:00     6/5/2018 0:00     ...        \n",
       "247     4/12/2018 0:00     6/5/2018 0:00     ...        \n",
       "248      5/9/2018 0:00     6/5/2018 0:00     ...        \n",
       "249      6/4/2018 0:00     6/4/2018 0:00     ...        \n",
       "250     4/24/2018 0:00     6/7/2018 0:00     ...        \n",
       "251     5/23/2018 0:00     6/6/2018 0:00     ...        \n",
       "\n",
       "    Filing Representative Last Name  Filing Representative Business Name  \\\n",
       "0               RUG/PIN/TOR/COR/TER       [CONSTRUCTION PERMIT SERVICES]   \n",
       "1                       BE/BO/TO/DA                          [JM ZONING]   \n",
       "2                               HAN            [VICTOR K HAN ARCHITECTS]   \n",
       "3                               HAN            [VICTOR K HAN ARCHITECTS]   \n",
       "4                    PARK/CHOI/CHON                   [CHON ENGINEERING]   \n",
       "5                    DEL,AT,HER,AGU             [BROOKBRIDGE CONSULTING]   \n",
       "6                       LIB/VEL/OTA                       [LUKE LICALZI]   \n",
       "7                     H/G/M/D/L/E/G      [MUNICIPAL BUILDING CONSULTING]   \n",
       "8                     H/G/M/D/L/E/G      [MUNICIPAL BUILDING CONSULTING]   \n",
       "9                     H/G/M/D/L/E/G      [MUNICIPAL BUILDING CONSULTING]   \n",
       "10                    H/G/M/D/L/E/G      [MUNICIPAL BUILDING CONSULTING]   \n",
       "11                    H/G/M/D/L/E/G      [MUNICIPAL BUILDING CONSULTING]   \n",
       "12                    H/G/M/D/L/E/G      [MUNICIPAL BUILDING CONSULTING]   \n",
       "13                    H/G/M/D/L/E/G      [MUNICIPAL BUILDING CONSULTING]   \n",
       "14                    H/G/M/D/L/E/G      [MUNICIPAL BUILDING CONSULTING]   \n",
       "15                    H/G/M/D/L/E/G      [MUNICIPAL BUILDING CONSULTING]   \n",
       "16                    H/G/M/D/L/E/G      [MUNICIPAL BUILDING CONSULTING]   \n",
       "17                          ANG/MAR            [GINO O LONGO ARCHITECTS]   \n",
       "18                     GORMAN/MORAN                    [WALTER T GORMAN]   \n",
       "19                     GORMAN/MORAN                    [WALTER T GORMAN]   \n",
       "20                     MORAN/GORMAN                    [WALTER T GORMAN]   \n",
       "21                     GORMAN/MORAN                    [WALTER T GORMAN]   \n",
       "22                 FALLINS/NORTHERN                                [TBC]   \n",
       "23                 FALLINS/NORTHERN                                [TBC]   \n",
       "24                 FALLINS/NORTHERN                                [TBC]   \n",
       "25             JAFFA/AKERM/GUZ/CUMM            [JACK JAFFA & ASSOCIATES]   \n",
       "26                         MURDISON                  [LEWSAN CONSULTING]   \n",
       "27                         MURDISON                  [LEWSAN CONSULTING]   \n",
       "28                           BYRNES                     [JAM CONSULTING]   \n",
       "29                     BE/BO/TO/JIM                          [JM ZONING]   \n",
       "..                              ...                                  ...   \n",
       "222                     RI/TO/BE/CH                      [KM ASSOCIATES]   \n",
       "223               HEU/RIS/PLOU/OM/M                      [RH CONSULTING]   \n",
       "224                  DIAZ/RUSSO/LEO              [EAST COAST EXPEDITING]   \n",
       "225            MARQ/ALEXAN/YOUNG/CH                     [RPO ARCHITECTS]   \n",
       "226             POT/CON/NIEVES/DUKE              [J CALLAHAN CONSULTING]   \n",
       "227                             NaN                                [NAN]   \n",
       "228                 KUANG/ZHU/HU/CH                       [JRK CREATIVE]   \n",
       "229                  CA/BE/ZE/BE/AL                      [KM ASSOCIATES]   \n",
       "230                     DAVIDOVITCH                 [NEWTEC ENGINEERING]   \n",
       "231                  CA/BE/ZE/BE/AL                      [KM ASSOCIATES]   \n",
       "232                  CA/BE/ZE/BE/AL                      [KM ASSOCIATES]   \n",
       "233                  CA/BE/ZE/BE/AL                      [KM ASSOCIATES]   \n",
       "234                  CA/BE/ZE/BE/AL                      [KM ASSOCIATES]   \n",
       "235                  CA/BE/ZE/BE/AL                      [KM ASSOCIATES]   \n",
       "236                  CA/BE/ZE/BE/AL                      [KM ASSOCIATES]   \n",
       "237                  CA/BE/ZE/BE/AL                      [KM ASSOCIATES]   \n",
       "238                           TRANG                      [WOHL & O'MARA]   \n",
       "239                         HACOHEN  [SANDI LIPSHITZ HACOHEN ARCHITECTS]   \n",
       "240                        SINGARES                     [GRS CONSULTING]   \n",
       "241              LACHM/DESP/LAGARES                        [DESIGN 2147]   \n",
       "242                BARRIONUEVO/DIAZ                [MIILROSE CONSULTING]   \n",
       "243               ALEMAN/BOYD/CONCE                   [JCA & ASSOCIATES]   \n",
       "244            SOMAIR/MARQ/ALEXAN/Y                     [RPO ARCHITECTS]   \n",
       "245                         PATROSZ            [BLUE HORSESHOE ADVISORS]   \n",
       "246                  CA/BE/ZE/BE/AL                      [KM ASSOCIATES]   \n",
       "247                  SA/PE/GR/AK/BE                      [YOUBER DESIGN]   \n",
       "248               IS/YS/TR/SA/LA/GI               [OUTSOURCE CONSULTING]   \n",
       "249                 ELZON/BOGOMOLNY                     [SEL CONSULTING]   \n",
       "250                  SA/PE/GR/AK/BE                      [YOUBER DESIGN]   \n",
       "251                  BAEK/CHOI/KANG                        [CHOI DESIGN]   \n",
       "\n",
       "    Owner First Name         Owner Last Name  \\\n",
       "0            [NOEMI]               [ROSARIO]   \n",
       "1            [EVIAN]                [ARCHER]   \n",
       "2            [PARAG]               [SAWHNEY]   \n",
       "3              [ABE]                 [COHEN]   \n",
       "4           [THOMAS]             [CAVALLARO]   \n",
       "5        [JOHNATHAN]             [SCHRENKER]   \n",
       "6           [STEVEN]               [DENARDO]   \n",
       "7             [RORY]                [MELVIN]   \n",
       "8         [COSTANZA]              [MUSUMECI]   \n",
       "9             [INES]                [FONTAO]   \n",
       "10            [RORY]                [MELVIN]   \n",
       "11           [PETER]                 [FLAMM]   \n",
       "12           [SAMIN]                [SHARMA]   \n",
       "13           [CRAIG]              [LACARUBA]   \n",
       "14          [JOSEPH]                  [LEVY]   \n",
       "15         [ANTHONY]                 [KHANI]   \n",
       "16          [THOMAS]  [BERNADETTE ARTUS (LP]   \n",
       "17           [JAMES]              [SULLIVAN]   \n",
       "18           [ERICA]                [HINTZE]   \n",
       "19          [ROBERT]               [LEMIEUX]   \n",
       "20             [JAY]             [KREISBERG]   \n",
       "21         [CALEIGH]                  [TOYE]   \n",
       "22           [JULIO]               [ALVAREZ]   \n",
       "23          [MYINHO]                  [HAHN]   \n",
       "24          [BASSAM]                [MUSAID]   \n",
       "25         [CALYNNE]              [OYOLOKOR]   \n",
       "26         [ANTHONY]                 [HUANG]   \n",
       "27         [ANTHONY]                 [HUANG]   \n",
       "28          [ROBERT]               [FRASTAI]   \n",
       "29         [MAURICE]             [RODRIGUES]   \n",
       "..               ...                     ...   \n",
       "222         [JOSHUA]                [LANDAU]   \n",
       "223          [SCOTT]                 [ALLEN]   \n",
       "224           [JODI]                 [STEIN]   \n",
       "225         [ALYSSA]                 [MALIN]   \n",
       "226        [RICHARD]                 [MOORE]   \n",
       "227          [ISAAC]                 [DAHAN]   \n",
       "228         [ROBERT]                 [SABAS]   \n",
       "229       [E GORDON]                 [YOUNG]   \n",
       "230        [RICHARD]               [CANNATA]   \n",
       "231       [E GORDON]                 [YOUNG]   \n",
       "232       [E GORDON]                 [YOUNG]   \n",
       "233       [E GORDON]                 [YOUNG]   \n",
       "234       [E GORDON]                 [YOUNG]   \n",
       "235       [E GORDON]                 [YOUNG]   \n",
       "236       [E GORDON]                 [YOUNG]   \n",
       "237       [E GORDON]                 [YOUNG]   \n",
       "238          [BRUNO]                  [SAVO]   \n",
       "239          [KOSTA]             [TROPAITIS]   \n",
       "240           [MARK]             [SCHARFMAN]   \n",
       "241       [CAROLINE]              [HARRISON]   \n",
       "242          [JAMES]                [EASLEY]   \n",
       "243      [JOHNATHAN]              [KNOETGEN]   \n",
       "244         [LESLIE]                [SIEGEL]   \n",
       "245           [JUAN]                 [RAMOS]   \n",
       "246          [JACOB]            [EISENSTEIN]   \n",
       "247         [ROBERT]             [KAYDANIAN]   \n",
       "248           [BERT]              [NISONOFF]   \n",
       "249         [EUGINE]            [KRASNITSKY]   \n",
       "250            [ABI]            [GOLDENBERG]   \n",
       "251        [MICHAEL]                 [STONE]   \n",
       "\n",
       "                  Owner Business Name          Applicant Name  \\\n",
       "0        43-01 22ND STREET OWNER, LLC     [ISRAEL WERENTHEIL]   \n",
       "1     III NEW BEDFORD MANAGEMENT CORP      [DAVID OBUCHOWSKI]   \n",
       "2                    116 ST MARKS LLC            [VICTOR HAN]   \n",
       "3                                 NaN            [VICTOR HAN]   \n",
       "4          TENNIS INDOORS OF NEW YORK           [STEVEN CHON]   \n",
       "5                 600 PARTNERS CO, LP        [SPENCER GOROFF]   \n",
       "6                   HUDBAR ASSOCIATES          [MARK LICALZI]   \n",
       "7                              NYCEDC    [ANDREW FORMICHELLA]   \n",
       "8     THOR & MASON, LLC DBA INDUSTRIA    [ANDREW FORMICHELLA]   \n",
       "9               FONTAO MANAGEMENT INC    [ANDREW FORMICHELLA]   \n",
       "10                             NYCEDC    [ANDREW FORMICHELLA]   \n",
       "11   LINCOLN CENTER OF THE PRFMNG ART    [ANDREW FORMICHELLA]   \n",
       "12          MOUNT SINAI HEALTH SYSTEM    [ANDREW FORMICHELLA]   \n",
       "13            BRYANT PARK CORPORATION    [ANDREW FORMICHELLA]   \n",
       "14                     APOLLO THEATER    [ANDREW FORMICHELLA]   \n",
       "15        GENERAL THEOLOGICAL SOCIETY    [ANDREW FORMICHELLA]   \n",
       "16               TEAM P R OMOTION LLC    [ANDREW FORMICHELLA]   \n",
       "17      RIVERDALE CUSTOM HOMES L.L.C.            [GINO LONGO]   \n",
       "18      RCPI LANDMARK PROPERTIES, LLC         [KAREN SOLOMON]   \n",
       "19       THE CITY COLLEGE OF NEW YORK         [MACIEJ FIUTAK]   \n",
       "20   200 PARK SOUTH ASSOC LLC C/O ABS        [NICHOLAS TUCCI]   \n",
       "21   95 MORTON LLC C/ORFR REALTY, LLC   [CHRISTOPHER BURGESS]   \n",
       "22                  CARINO RESTAURANT    [DAVIDSON CHRISTMAS]   \n",
       "23                        MAIDEN FARM    [DAVIDSON CHRISTMAS]   \n",
       "24                      STRAIGHT DELI    [DAVIDSON CHRISTMAS]   \n",
       "25     AKELIUS REAL ESTATE MANAGEMENT        [DANA KOENITZER]   \n",
       "26                   FIRM ASSETS INC.  [SALVADOR TARTAGLIONE]   \n",
       "27                   FIRM ASSETS INC.  [SALVADOR TARTAGLIONE]   \n",
       "28    VIP BOULEVARD REAL ESTATE CORP.          [JIMMY POWERS]   \n",
       "29             AAF BROADWAY 1384, LLC            [JACK GREEN]   \n",
       "..                                ...                     ...   \n",
       "222            FOUR WINDS REAL ESTATE          [ROBERT BRAUN]   \n",
       "223               CHAOWAI 51ST ST LLC      [MARINA SOLOVCHUK]   \n",
       "224             LSG 105 WEST 28TH LLC          [NEILL PARKER]   \n",
       "225              A&E REAL ESTATE, LLC   [ANANTH SAMPATHKUMAR]   \n",
       "226   219 CLINTON STREET HOUSING CORP        [BRENDAN COBURN]   \n",
       "227                               NaN            [ALEX ROMAO]   \n",
       "228   HOSPITALITY INTERNATIONAL GROUP           [DAVID HIATT]   \n",
       "229       THIRD HOUSING COMPANY, INC.     [MASSIMILIANO VOTO]   \n",
       "230                MOUNT SINAI QUEENS            [OBAID MIAN]   \n",
       "231       THIRD HOUSING COMPANY, INC.     [MASSIMILIANO VOTO]   \n",
       "232       THIRD HOUSING COMPANY, INC.     [MASSIMILIANO VOTO]   \n",
       "233       THIRD HOUSING COMPANY, INC.     [MASSIMILIANO VOTO]   \n",
       "234       THIRD HOUSING COMPANY, INC.     [MASSIMILIANO VOTO]   \n",
       "235       THIRD HOUSING COMPANY, INC.     [MASSIMILIANO VOTO]   \n",
       "236       THIRD HOUSING COMPANY, INC.     [MASSIMILIANO VOTO]   \n",
       "237       THIRD HOUSING COMPANY, INC.     [MASSIMILIANO VOTO]   \n",
       "238                           BMN LLC      [ROCCO DEFELIPPIS]   \n",
       "239                 FOUR SEASONS AUTO        [SANDRA HACOHEN]   \n",
       "240         C/O BEACH LANE MANAGEMENT           [JAMES MOORE]   \n",
       "241        ADVANCE PUBLICATIONS, INC.      [WILLIAM DASHIELL]   \n",
       "242       GGP STATEN ISLAND MALL, LLC           [ERIC STRYER]   \n",
       "243                               NaN         [ROSATO PIETRO]   \n",
       "244    GROVER CLEVELAND TENANTS CORP.        [THOMAS CATALDO]   \n",
       "245  SOUTHSIDE UNITED HDFC -LOS SURES           [ERIC COWLEY]   \n",
       "246  813-25 QUENTIN RD ASSOCIATES C/O      [SALVADOR FERRARA]   \n",
       "247                INK 206 NASSAU LLC           [KENNETH MAK]   \n",
       "248                 GRA DEB REALTY CO           [JOSEPH VUTO]   \n",
       "249            275 NEPTUNE AVENUE LLC          [ZARINA KINDO]   \n",
       "250            896 MADISON REALTY LLC           [KENNETH MAK]   \n",
       "251  MICHAEL E. STONE AIA, CDP DEVELO           [ILJOONG KIM]   \n",
       "\n",
       "                        Owner Name  Number Bad  \\\n",
       "0                  [NOEMI ROSARIO]           1   \n",
       "1                   [EVIAN ARCHER]           2   \n",
       "2                  [PARAG SAWHNEY]           2   \n",
       "3                      [ABE COHEN]           2   \n",
       "4               [THOMAS CAVALLARO]           1   \n",
       "5            [JOHNATHAN SCHRENKER]           1   \n",
       "6                 [STEVEN DENARDO]           1   \n",
       "7                    [RORY MELVIN]           2   \n",
       "8              [COSTANZA MUSUMECI]           2   \n",
       "9                    [INES FONTAO]           2   \n",
       "10                   [RORY MELVIN]           2   \n",
       "11                   [PETER FLAMM]           2   \n",
       "12                  [SAMIN SHARMA]           2   \n",
       "13                [CRAIG LACARUBA]           2   \n",
       "14                   [JOSEPH LEVY]           2   \n",
       "15                 [ANTHONY KHANI]           2   \n",
       "16   [THOMAS BERNADETTE ARTUS (LP]           2   \n",
       "17                [JAMES SULLIVAN]           1   \n",
       "18                  [ERICA HINTZE]           1   \n",
       "19                [ROBERT LEMIEUX]           1   \n",
       "20                 [JAY KREISBERG]           1   \n",
       "21                  [CALEIGH TOYE]           1   \n",
       "22                 [JULIO ALVAREZ]           2   \n",
       "23                   [MYINHO HAHN]           2   \n",
       "24                 [BASSAM MUSAID]           2   \n",
       "25              [CALYNNE OYOLOKOR]           1   \n",
       "26                 [ANTHONY HUANG]           1   \n",
       "27                 [ANTHONY HUANG]           1   \n",
       "28                [ROBERT FRASTAI]           1   \n",
       "29             [MAURICE RODRIGUES]           1   \n",
       "..                             ...         ...   \n",
       "222                [JOSHUA LANDAU]           0   \n",
       "223                  [SCOTT ALLEN]           0   \n",
       "224                   [JODI STEIN]           0   \n",
       "225                 [ALYSSA MALIN]           0   \n",
       "226                [RICHARD MOORE]           0   \n",
       "227                  [ISAAC DAHAN]           0   \n",
       "228                 [ROBERT SABAS]           0   \n",
       "229               [E GORDON YOUNG]           0   \n",
       "230              [RICHARD CANNATA]           0   \n",
       "231               [E GORDON YOUNG]           0   \n",
       "232               [E GORDON YOUNG]           0   \n",
       "233               [E GORDON YOUNG]           0   \n",
       "234               [E GORDON YOUNG]           0   \n",
       "235               [E GORDON YOUNG]           0   \n",
       "236               [E GORDON YOUNG]           0   \n",
       "237               [E GORDON YOUNG]           0   \n",
       "238                   [BRUNO SAVO]           0   \n",
       "239              [KOSTA TROPAITIS]           0   \n",
       "240               [MARK SCHARFMAN]           0   \n",
       "241            [CAROLINE HARRISON]           0   \n",
       "242                 [JAMES EASLEY]           0   \n",
       "243           [JOHNATHAN KNOETGEN]           0   \n",
       "244                [LESLIE SIEGEL]           0   \n",
       "245                   [JUAN RAMOS]           0   \n",
       "246             [JACOB EISENSTEIN]           0   \n",
       "247             [ROBERT KAYDANIAN]           0   \n",
       "248                [BERT NISONOFF]           0   \n",
       "249            [EUGINE KRASNITSKY]           0   \n",
       "250               [ABI GOLDENBERG]           0   \n",
       "251                [MICHAEL STONE]           0   \n",
       "\n",
       "                                              Bad List Risk Score 2  \n",
       "0                                  [ISRAEL WERENTHEIL]     1.281250  \n",
       "1                        [JM ZONING, DAVID OBUCHOWSKI]     0.348464  \n",
       "2                [VICTOR K HAN ARCHITECTS, VICTOR HAN]     0.287698  \n",
       "3                [VICTOR K HAN ARCHITECTS, VICTOR HAN]     0.287698  \n",
       "4                                   [CHON ENGINEERING]     0.125000  \n",
       "5                                     [SPENCER GOROFF]     0.092949  \n",
       "6                                       [MARK LICALZI]     0.075540  \n",
       "7    [MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...     0.046848  \n",
       "8    [MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...     0.046848  \n",
       "9    [MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...     0.046848  \n",
       "10   [MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...     0.046848  \n",
       "11   [MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...     0.046848  \n",
       "12   [MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...     0.046848  \n",
       "13   [MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...     0.046848  \n",
       "14   [MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...     0.046848  \n",
       "15   [MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...     0.046848  \n",
       "16   [MUNICIPAL BUILDING CONSULTING, ANDREW FORMICH...     0.046848  \n",
       "17                                        [GINO LONGO]     0.043307  \n",
       "18                                   [WALTER T GORMAN]     0.040293  \n",
       "19                                   [WALTER T GORMAN]     0.040293  \n",
       "20                                   [WALTER T GORMAN]     0.040293  \n",
       "21                                   [WALTER T GORMAN]     0.040293  \n",
       "22                           [TBC, DAVIDSON CHRISTMAS]     0.021264  \n",
       "23                           [TBC, DAVIDSON CHRISTMAS]     0.021264  \n",
       "24                           [TBC, DAVIDSON CHRISTMAS]     0.021264  \n",
       "25                           [JACK JAFFA & ASSOCIATES]     0.020392  \n",
       "26                                 [LEWSAN CONSULTING]     0.018788  \n",
       "27                                 [LEWSAN CONSULTING]     0.018788  \n",
       "28                                    [JAM CONSULTING]     0.016507  \n",
       "29                                         [JM ZONING]     0.015131  \n",
       "..                                                 ...          ...  \n",
       "222                                                 []     0.000000  \n",
       "223                                                 []     0.000000  \n",
       "224                                                 []     0.000000  \n",
       "225                                                 []     0.000000  \n",
       "226                                                 []     0.000000  \n",
       "227                                                 []     0.000000  \n",
       "228                                                 []     0.000000  \n",
       "229                                                 []     0.000000  \n",
       "230                                                 []     0.000000  \n",
       "231                                                 []     0.000000  \n",
       "232                                                 []     0.000000  \n",
       "233                                                 []     0.000000  \n",
       "234                                                 []     0.000000  \n",
       "235                                                 []     0.000000  \n",
       "236                                                 []     0.000000  \n",
       "237                                                 []     0.000000  \n",
       "238                                                 []     0.000000  \n",
       "239                                                 []     0.000000  \n",
       "240                                                 []     0.000000  \n",
       "241                                                 []     0.000000  \n",
       "242                                                 []     0.000000  \n",
       "243                                                 []     0.000000  \n",
       "244                                                 []     0.000000  \n",
       "245                                                 []     0.000000  \n",
       "246                                                 []     0.000000  \n",
       "247                                                 []     0.000000  \n",
       "248                                                 []     0.000000  \n",
       "249                                                 []     0.000000  \n",
       "250                                                 []     0.000000  \n",
       "251                                                 []     0.000000  \n",
       "\n",
       "[252 rows x 50 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sw.to_csv(\"C:\\Users\\dmehri\\Documents\\DATA\\Bad Actor Germain\\Sweeps\\SWEEPS 06_02_18 - 06_07_18.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# APPEND BAD COMMUNITY TO NODES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "#nodes_new = name, total ties, total bad ties, bad tie density, total unique ties, total unique bad, bad unique density, #BINS\n",
    "\n",
    "\n",
    "#nodes_final = pd.DataFrame(nodes_new)\n",
    "#nodes_final = nodes_final.rename(columns={0: 'Name', 1: 'Total Ties', 2:'Total Bad Ties',\n",
    "#                                         3:'Bad Tie Density', 4:'Total Unique Ties',\n",
    "#                                         5:'Total Unique Bad Ties', 6:'Bad Unique Density'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#nodes_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#nodes_final.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\nodes_final_brut_force.csv\", index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\test.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#netSub.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\test.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#len(alist[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#qbG.to_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Automation\\\\test.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANALYSIS ON JOB FILINGS NOT AUDITED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "\n",
    "df = pd.read_csv(\"C:\\\\Users\\\\dmehri\\\\Documents\\\\DATA\\\\Bad Actor Germain\\\\Sweeps\\\\\n",
    "SWEEPS Active Permits.csv\", low_memory=False)\n",
    "\n",
    "#df = df.rename(columns={'Applicant Last Name2': 'Contractor Last Name', 'Applicant First Name3': 'Contractor First Name'})\n",
    "\n",
    "df = df.drop('Permit Type', 1)\n",
    "df = df.drop('Permit Status', 1)\n",
    "df = df.drop('Permit Filing Status', 1)\n",
    "df = df.drop('Pre-File Date', 1)\n",
    "df = df.drop('Permit Issuance Date', 1)\n",
    "\n",
    "df = df.drop('Count Permits', 1)\n",
    "df = df.drop('Applicant Business House Number', 1)\n",
    "df = df.drop('Applicant Business ZIP Code', 1)\n",
    "df = df.drop('Applicant Business Street Name', 1)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OLD CODE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "###OLD OLD\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "string = \"Create node metrics ...\" + str(datetime.now())\n",
    "print string\n",
    "logging.info(string)\n",
    "\n",
    "#nodes definition\n",
    "#name, total ties, total unique ties\n",
    "\n",
    "print str(datetime.now())\n",
    "print \"length of nodes\", len(nodes)\n",
    "\n",
    "badActor = b[\"Name\"].tolist()\n",
    "badActor = set(badActor)\n",
    "badActor = list(badActor)\n",
    "\n",
    "nodes_new = []\n",
    "tie_list = []\n",
    "\n",
    "#nameL = list of ties from total ties field in df\n",
    "\n",
    "#for i in range(0, len(nodes)):\n",
    "for i in range(0, len(nodes)):    \n",
    "    if i%3000 == 0:\n",
    "        print i\n",
    "    \n",
    "    tie_list = []\n",
    "    badlist = []\n",
    "    name2 = nodes[i]\n",
    "    \n",
    "    \n",
    "    \n",
    "    #get list of ties associated with name2\n",
    "    for j in range(0, len(nameL)):\n",
    "        if name2 in nameL[j]:\n",
    "            tie_list.append(nameL[j])\n",
    "            #print nameL[i]\n",
    "    \n",
    "    #get list of ties associated with name2\n",
    "    #tie_list = [k for k in nameL if name2 in k]\n",
    "    \n",
    "    \n",
    "    #total ties, and total unique ties\n",
    "    #flaten the list\n",
    "    tie_list = sum(tie_list, [])\n",
    "    #remove name2\n",
    "    tie_list = [x for x in tie_list if x not in name2 ]  \n",
    "    \n",
    "    tie_set = set(tie_list)\n",
    "    tie_set = list(tie_set)\n",
    "    \n",
    "    #bad ties\n",
    "    for j in range(0, len(tie_list)):\n",
    "        if tie_list[j] in badActor:\n",
    "            badlist.append(tie_list[j])\n",
    "            \n",
    "            #print \"bad in here\"\n",
    "    \n",
    "    \n",
    "    \n",
    "    badset = list(set(badlist))\n",
    "    bad_tie_density =  round( float(len(badlist))/float(len(tie_list)), 2)\n",
    "    \n",
    "    bad_unique_density = round ( float(len(badset))/float(len(tie_set)), 2)\n",
    "    \n",
    "    #name, total ties, total bad ties, bad tie density, total unique ties, total unique bad, bad unique density \n",
    "    nodes_new.append([name2, len(tie_list), len(badlist), bad_tie_density, len(tie_set), len(badset), bad_unique_density  ])\n",
    "   \n",
    "\n",
    "str(datetime.now())\n",
    "\n",
    "\"\"\""
   ]
  }
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