Scrape the Competition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <p><a name=\"sections\"></a></p>\n",
    "\n",
    "\n",
    "# Sections\n",
    "- <a href=\"#basic\">Basic Help and Information</a><br>\n",
    "- <a href=\"#func\">User Defined Function</a><br>\n",
    "- <a href=\"#explore\">Explore Results & Combine</a><br>\n",
    "- <a href=\"#clean\">Clean the Data</a><br>\n",
    "- <a href=\"#stats1\">Descriptive Statistics</a><br>\n",
    "- <a href=\"#filter1\">Filter (subset) the Data</a><br>\n",
    "- <a href=\"#company\">Bucketing Results & Analyzing</a><br>\n",
    "- <a href=\"#timeout\"><font color=green>Timeout for Data Dump </font></a><br>\n",
    "\n",
    "- <a href=\"#viz1\">Salary Histograms</a><br>\n",
    "- <a href=\"#addl\">Additional Analyses</a><br>\n",
    "- <a href=\"#end\">End</a><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from bs4 import BeautifulSoup\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')   #hide sloppy coding :)\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Website to scrape\n",
    "http://h1bdata.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"func\"></a></p>\n",
    "### Function to read table on website"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def collect_tables(target):\n",
    "    # access website, populate form, get results as html\n",
    "    url = \"http://h1bdata.info/index.php?em={}\".format(target)\n",
    "    content = requests.get(url).text\n",
    "    \n",
    "    #process the html code\n",
    "    soup = BeautifulSoup(content, \"html.parser\")\n",
    "    table = soup.find(\"tbody\")  #go to table body\n",
    "    colnames = [tag.text for tag in soup.find(\"thead\").find_all(\"th\")]  #fetch Column headers\n",
    "\n",
    "    #cycle through tags and populate df with contents\n",
    "    df = pd.DataFrame(\n",
    "        ((tag.text for tag in lst.find_all(\"td\")) for lst in table.find_all(\"tr\")),\n",
    "        columns = colnames\n",
    "    )\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Invoke Function to Read and Process URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['S%26P Global',\n",
       " 'Bloomberg',\n",
       " 'Factset',\n",
       " 'Thomson',\n",
       " 'Intercontinental Exchange',\n",
       " 'Intercontinentalexchange',\n",
       " 'NYSE',\n",
       " 'Fidessa',\n",
       " 'Markit']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets = ['S%26P Global', \"Bloomberg\", \"Factset\", \"Thomson\", 'Intercontinental Exchange', 'Intercontinentalexchange', 'NYSE', 'Fidessa','Markit']\n",
    "targets\n",
    "\n",
    "# '%26' = ampersand in URLs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#systematically collect the tablular results for each firm in the list 'targets'\n",
    "#store resuls in a list of dataframes\n",
    "\n",
    "collection = [collect_tables(target) for target in targets]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"explore\"></a></p>\n",
    "### Explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th>JOB TITLE</th>\n",
       "      <th>BASE SALARY</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>SUBMIT DATE</th>\n",
       "      <th>START DATE</th>\n",
       "      <th>CASE STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S&amp;P; GLOBAL MARKET INTELLIGENCE INC</td>\n",
       "      <td>ACCOUNT MANAGER</td>\n",
       "      <td>65,000</td>\n",
       "      <td>SAN FRANCISCO, CA</td>\n",
       "      <td>03/13/2018</td>\n",
       "      <td>09/01/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S&amp;P; GLOBAL MARKET INTELLIGENCE INC</td>\n",
       "      <td>ACCOUNT MANAGER</td>\n",
       "      <td>65,000</td>\n",
       "      <td>SAN FRANCISCO, CA</td>\n",
       "      <td>03/19/2018</td>\n",
       "      <td>09/01/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S&amp;P; GLOBAL MARKET INTELLIGENCE</td>\n",
       "      <td>ANALYST II</td>\n",
       "      <td>57,900</td>\n",
       "      <td>CHARLOTTESVILLE, VA</td>\n",
       "      <td>02/01/2018</td>\n",
       "      <td>03/02/2018</td>\n",
       "      <td>DENIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S&amp;P; GLOBAL MARKET INTELLIGENCE</td>\n",
       "      <td>ANALYST II</td>\n",
       "      <td>57,990</td>\n",
       "      <td>CHARLOTTESVILLE, VA</td>\n",
       "      <td>02/06/2018</td>\n",
       "      <td>03/02/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S&amp;P; GLOBAL INC</td>\n",
       "      <td>ANALYST II, INFORMATION SECURITY</td>\n",
       "      <td>85,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>06/07/2017</td>\n",
       "      <td>10/27/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              EMPLOYER                         JOB TITLE  \\\n",
       "0  S&P; GLOBAL MARKET INTELLIGENCE INC                   ACCOUNT MANAGER   \n",
       "1  S&P; GLOBAL MARKET INTELLIGENCE INC                   ACCOUNT MANAGER   \n",
       "2      S&P; GLOBAL MARKET INTELLIGENCE                        ANALYST II   \n",
       "3      S&P; GLOBAL MARKET INTELLIGENCE                        ANALYST II   \n",
       "4                      S&P; GLOBAL INC  ANALYST II, INFORMATION SECURITY   \n",
       "\n",
       "  BASE SALARY             LOCATION SUBMIT DATE  START DATE CASE STATUS  \n",
       "0      65,000    SAN FRANCISCO, CA  03/13/2018  09/01/2018   CERTIFIED  \n",
       "1      65,000    SAN FRANCISCO, CA  03/19/2018  09/01/2018   CERTIFIED  \n",
       "2      57,900  CHARLOTTESVILLE, VA  02/01/2018  03/02/2018      DENIED  \n",
       "3      57,990  CHARLOTTESVILLE, VA  02/06/2018  03/02/2018   CERTIFIED  \n",
       "4      85,000         NEW YORK, NY  06/07/2017  10/27/2017   CERTIFIED  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2149, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th>JOB TITLE</th>\n",
       "      <th>BASE SALARY</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>SUBMIT DATE</th>\n",
       "      <th>START DATE</th>\n",
       "      <th>CASE STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>ABAP DEVELOPER</td>\n",
       "      <td>135,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>02/18/2014</td>\n",
       "      <td>03/03/2014</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>ABAP DEVELOPER</td>\n",
       "      <td>145,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>02/18/2014</td>\n",
       "      <td>08/01/2014</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>ACCOUNT MANAGER</td>\n",
       "      <td>87,500</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>03/15/2016</td>\n",
       "      <td>09/01/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>ACCOUNT MANAGER</td>\n",
       "      <td>89,500</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>01/30/2018</td>\n",
       "      <td>07/26/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>ACCOUNT MANAGER</td>\n",
       "      <td>100,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>01/30/2018</td>\n",
       "      <td>07/26/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       EMPLOYER        JOB TITLE BASE SALARY      LOCATION SUBMIT DATE  \\\n",
       "0  BLOOMBERG LP   ABAP DEVELOPER     135,000  NEW YORK, NY  02/18/2014   \n",
       "1  BLOOMBERG LP   ABAP DEVELOPER     145,000  NEW YORK, NY  02/18/2014   \n",
       "2  BLOOMBERG LP  ACCOUNT MANAGER      87,500  NEW YORK, NY  03/15/2016   \n",
       "3  BLOOMBERG LP  ACCOUNT MANAGER      89,500  NEW YORK, NY  01/30/2018   \n",
       "4  BLOOMBERG LP  ACCOUNT MANAGER     100,000  NEW YORK, NY  01/30/2018   \n",
       "\n",
       "   START DATE CASE STATUS  \n",
       "0  03/03/2014   CERTIFIED  \n",
       "1  08/01/2014   CERTIFIED  \n",
       "2  09/01/2016   CERTIFIED  \n",
       "3  07/26/2018   CERTIFIED  \n",
       "4  07/26/2018   CERTIFIED  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(collection[1].shape)\n",
    "collection[1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th>JOB TITLE</th>\n",
       "      <th>BASE SALARY</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>SUBMIT DATE</th>\n",
       "      <th>START DATE</th>\n",
       "      <th>CASE STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4664</th>\n",
       "      <td>MARKIT NORTH AMERICA INC D/B/A IHS MARKIT</td>\n",
       "      <td>VP, SQL DEVELOPER</td>\n",
       "      <td>91,229</td>\n",
       "      <td>BOULDER, CO</td>\n",
       "      <td>06/23/2017</td>\n",
       "      <td>09/18/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4665</th>\n",
       "      <td>MARKIT NORTH AMERICA INC D/B/A IHS MARKIT</td>\n",
       "      <td>VP, SQL DEVELOPER</td>\n",
       "      <td>91,229</td>\n",
       "      <td>BOULDER, CO</td>\n",
       "      <td>07/13/2017</td>\n",
       "      <td>07/13/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4666</th>\n",
       "      <td>MARKIT NORTH AMERICA INC</td>\n",
       "      <td>VP- QUANTITATIVE SPECIALIST</td>\n",
       "      <td>66,290</td>\n",
       "      <td>NAPERVILLE, IL</td>\n",
       "      <td>06/22/2015</td>\n",
       "      <td>09/23/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4667</th>\n",
       "      <td>MARKITSERV LLC</td>\n",
       "      <td>VP-IMPLEMENTATION ANALYST</td>\n",
       "      <td>81,266</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>09/23/2015</td>\n",
       "      <td>01/21/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4668</th>\n",
       "      <td>MARKIT ON DEMAND INC</td>\n",
       "      <td>VP-SOFTWARE ENGINEER</td>\n",
       "      <td>81,702</td>\n",
       "      <td>BOULDER, CO</td>\n",
       "      <td>06/24/2015</td>\n",
       "      <td>09/19/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       EMPLOYER                    JOB TITLE  \\\n",
       "4664  MARKIT NORTH AMERICA INC D/B/A IHS MARKIT            VP, SQL DEVELOPER   \n",
       "4665  MARKIT NORTH AMERICA INC D/B/A IHS MARKIT            VP, SQL DEVELOPER   \n",
       "4666                   MARKIT NORTH AMERICA INC  VP- QUANTITATIVE SPECIALIST   \n",
       "4667                             MARKITSERV LLC    VP-IMPLEMENTATION ANALYST   \n",
       "4668                       MARKIT ON DEMAND INC         VP-SOFTWARE ENGINEER   \n",
       "\n",
       "     BASE SALARY        LOCATION SUBMIT DATE  START DATE CASE STATUS  \n",
       "4664      91,229     BOULDER, CO  06/23/2017  09/18/2017   CERTIFIED  \n",
       "4665      91,229     BOULDER, CO  07/13/2017  07/13/2017   CERTIFIED  \n",
       "4666      66,290  NAPERVILLE, IL  06/22/2015  09/23/2015   CERTIFIED  \n",
       "4667      81,266    NEW YORK, NY  09/23/2015  01/21/2016   CERTIFIED  \n",
       "4668      81,702     BOULDER, CO  06/24/2015  09/19/2015   CERTIFIED  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a single dataframe from each list constituent\n",
    "df = pd.concat(collection).reset_index(drop=True)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['EMPLOYER', 'JOB TITLE', 'BASE SALARY', 'LOCATION', 'SUBMIT DATE',\n",
       "       'START DATE', 'CASE STATUS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4669"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMPLOYER       object\n",
       "JOB TITLE      object\n",
       "BASE SALARY    object\n",
       "LOCATION       object\n",
       "SUBMIT DATE    object\n",
       "START DATE     object\n",
       "CASE STATUS    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"clean\"></a></p>\n",
    "### Clean\n",
    "<a href=\"#sections\">Top</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Broadcast at DataFrame level__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMPLOYER               object\n",
       "JOB TITLE              object\n",
       "BASE SALARY             int32\n",
       "LOCATION               object\n",
       "SUBMIT DATE    datetime64[ns]\n",
       "START DATE     datetime64[ns]\n",
       "CASE STATUS            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get a fresh DF\n",
    "df_ = pd.concat(collection).reset_index(drop=True)\n",
    "\n",
    "#convert 'BASE SALARY' type to int\n",
    "df_['BASE SALARY'] = df_['BASE SALARY'].str.replace(',','')\n",
    "df_['BASE SALARY'] = df_['BASE SALARY'].str.replace(',','').astype(int)\n",
    "\n",
    "#convert dates columns to date format\n",
    "df_['SUBMIT DATE'] = pd.to_datetime(df_['SUBMIT DATE'],infer_datetime_format=True)\n",
    "df_['START DATE'] = pd.to_datetime(df_['START DATE'],infer_datetime_format=True)\n",
    "df_.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add a Column for 'State'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th>JOB TITLE</th>\n",
       "      <th>BASE SALARY</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>SUBMIT DATE</th>\n",
       "      <th>START DATE</th>\n",
       "      <th>CASE STATUS</th>\n",
       "      <th>STATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S&amp;P; GLOBAL MARKET INTELLIGENCE INC</td>\n",
       "      <td>ACCOUNT MANAGER</td>\n",
       "      <td>65000</td>\n",
       "      <td>SAN FRANCISCO, CA</td>\n",
       "      <td>2018-03-13</td>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S&amp;P; GLOBAL MARKET INTELLIGENCE INC</td>\n",
       "      <td>ACCOUNT MANAGER</td>\n",
       "      <td>65000</td>\n",
       "      <td>SAN FRANCISCO, CA</td>\n",
       "      <td>2018-03-19</td>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S&amp;P; GLOBAL MARKET INTELLIGENCE</td>\n",
       "      <td>ANALYST II</td>\n",
       "      <td>57900</td>\n",
       "      <td>CHARLOTTESVILLE, VA</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>DENIED</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S&amp;P; GLOBAL MARKET INTELLIGENCE</td>\n",
       "      <td>ANALYST II</td>\n",
       "      <td>57990</td>\n",
       "      <td>CHARLOTTESVILLE, VA</td>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S&amp;P; GLOBAL INC</td>\n",
       "      <td>ANALYST II, INFORMATION SECURITY</td>\n",
       "      <td>85000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>2017-06-07</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              EMPLOYER                         JOB TITLE  \\\n",
       "0  S&P; GLOBAL MARKET INTELLIGENCE INC                   ACCOUNT MANAGER   \n",
       "1  S&P; GLOBAL MARKET INTELLIGENCE INC                   ACCOUNT MANAGER   \n",
       "2      S&P; GLOBAL MARKET INTELLIGENCE                        ANALYST II   \n",
       "3      S&P; GLOBAL MARKET INTELLIGENCE                        ANALYST II   \n",
       "4                      S&P; GLOBAL INC  ANALYST II, INFORMATION SECURITY   \n",
       "\n",
       "   BASE SALARY             LOCATION SUBMIT DATE START DATE CASE STATUS STATE  \n",
       "0        65000    SAN FRANCISCO, CA  2018-03-13 2018-09-01   CERTIFIED    CA  \n",
       "1        65000    SAN FRANCISCO, CA  2018-03-19 2018-09-01   CERTIFIED    CA  \n",
       "2        57900  CHARLOTTESVILLE, VA  2018-02-01 2018-03-02      DENIED    VA  \n",
       "3        57990  CHARLOTTESVILLE, VA  2018-02-06 2018-03-02   CERTIFIED    VA  \n",
       "4        85000         NEW YORK, NY  2017-06-07 2017-10-27   CERTIFIED    NY  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add a column call state\n",
    "newcol = df_['LOCATION'].apply(lambda x: x.split(', ') [-1])\n",
    "df_['STATE'] = newcol\n",
    "df_.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Which Firm has hired the most?__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BLOOMBERG LP                                         2130\n",
       "FACTSET RESEARCH SYSTEMS INC                          653\n",
       "THOMSON REUTERS (MARKETS) LLC                         405\n",
       "INTERCONTINENTAL EXCHANGE HOLDINGS INC                281\n",
       "MARKIT NORTH AMERICA INC D/B/A IHS MARKIT             176\n",
       "MARKIT NORTH AMERICA INC                              172\n",
       "NYSE MARKET (DE) INC                                  153\n",
       "THOMSON REUTERS (TAX & ACCOUNTING) INC                142\n",
       "S&P; GLOBAL MARKET INTELLIGENCE INC                    72\n",
       "FIDESSA CORPORATION                                    67\n",
       "THOMSON REUTERS HOLDINGS INC                           59\n",
       "THOMSON REUTERS (GRC) INC                              39\n",
       "MARKIT ON DEMAND INC                                   37\n",
       "THOMSON REUTERS MARKETS LLC                            32\n",
       "MARKITSERV LLC                                         30\n",
       "INTERCONTINENTALEXCHANGE INC                           27\n",
       "S&P; GLOBAL INC                                        25\n",
       "NYSE MARKET INC                                        24\n",
       "MARKIT WSO CORPORATION                                 20\n",
       "THOMSON REUTERS APPLICATIONS INC                       13\n",
       "THOMSON REUTERS (TAX & ACCOUNTING) SERVICES INC        12\n",
       "NYSE ARCA LLC                                          12\n",
       "THOMSON REUTERS (SCIENTIFIC) LLC                       10\n",
       "BLOOMBERG TRADEBOOK LLC                                 9\n",
       "THOMSON REUTERS (TAX AND ACCOUNTING) SERVICES INC       8\n",
       "THOMSON REUTERS (TAX AND ACCOUNTING) INC                8\n",
       "THOMSON REUTERS (SCIENTIFIC) INC                        7\n",
       "NYSE TECHNOLOGIES CONNECTIVITY INC                      6\n",
       "THOMSON LICENSING LLC                                   6\n",
       "NYSE ARCA INC                                           4\n",
       "BLOOMBERG POLARLAKE USA LLC                             4\n",
       "BLOOMBERG INDEX SERVICES LIMITED                        3\n",
       "NYSE TECHNOLOGIES INC                                   2\n",
       "BLOOMBERG ASSOCIATES LLC                                2\n",
       "THOMSON SCITECH INC                                     2\n",
       "NYSE GOVERNANCE SERVICES INC                            2\n",
       "S&P; GLOBAL MARKET INTELLIGENCE                         2\n",
       "THOMSON REUTERS MANAGEMENT SERVICES LLC                 1\n",
       "THOMSON REUTERS GRC INC                                 1\n",
       "FIDESSA BUYSIDE INC                                     1\n",
       "THOMSON HORSTMANN & BRYANNT INC                         1\n",
       "THOMSON LINEAR LLC                                      1\n",
       "BLOOMBERG STP LLC                                       1\n",
       "THOMSON REUTERS (TAX ACCOUNTING) SERVICES INC           1\n",
       "THOMSONS ONLINE BENEFITS INC                            1\n",
       "FIDESSA BUY-SIDE INC                                    1\n",
       "THOMSON HORSTMANN & BRYANT INC                          1\n",
       "S&P; GLOBAL INC - PLATTS DIVISION                       1\n",
       "THOMSON REUTERS (GRC) LLC                               1\n",
       "THOMSON REUTERS (TAX &AMP; ACCOUNTING) INC              1\n",
       "Name: EMPLOYER, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EMPLOYER'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"stats1\"></a></p>\n",
    "### Summary Stats\n",
    "<a href=\"#sections\">Top</a><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BLOOMBERG LP</th>\n",
       "      <td>2130</td>\n",
       "      <td>142303.22676</td>\n",
       "      <td>45000</td>\n",
       "      <td>585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACTSET RESEARCH SYSTEMS INC</th>\n",
       "      <td>653</td>\n",
       "      <td>92029.93568</td>\n",
       "      <td>30035</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THOMSON REUTERS (MARKETS) LLC</th>\n",
       "      <td>405</td>\n",
       "      <td>106515.14815</td>\n",
       "      <td>55000</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTERCONTINENTAL EXCHANGE HOLDINGS INC</th>\n",
       "      <td>281</td>\n",
       "      <td>83150.27402</td>\n",
       "      <td>40500</td>\n",
       "      <td>137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKIT NORTH AMERICA INC D/B/A IHS MARKIT</th>\n",
       "      <td>176</td>\n",
       "      <td>84029.42045</td>\n",
       "      <td>38771</td>\n",
       "      <td>185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKIT NORTH AMERICA INC</th>\n",
       "      <td>172</td>\n",
       "      <td>90368.17442</td>\n",
       "      <td>38126</td>\n",
       "      <td>225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NYSE MARKET (DE) INC</th>\n",
       "      <td>153</td>\n",
       "      <td>99755.24837</td>\n",
       "      <td>48000</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THOMSON REUTERS (TAX &amp; ACCOUNTING) INC</th>\n",
       "      <td>142</td>\n",
       "      <td>104193.21831</td>\n",
       "      <td>32960</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S&amp;P; GLOBAL MARKET INTELLIGENCE INC</th>\n",
       "      <td>72</td>\n",
       "      <td>91076.72222</td>\n",
       "      <td>54000</td>\n",
       "      <td>150287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIDESSA CORPORATION</th>\n",
       "      <td>67</td>\n",
       "      <td>87052.65672</td>\n",
       "      <td>45000</td>\n",
       "      <td>190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THOMSON REUTERS HOLDINGS INC</th>\n",
       "      <td>59</td>\n",
       "      <td>114467.66102</td>\n",
       "      <td>77000</td>\n",
       "      <td>191000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THOMSON REUTERS (GRC) INC</th>\n",
       "      <td>39</td>\n",
       "      <td>89498.82051</td>\n",
       "      <td>60000</td>\n",
       "      <td>132610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKIT ON DEMAND INC</th>\n",
       "      <td>37</td>\n",
       "      <td>61931.18919</td>\n",
       "      <td>34424</td>\n",
       "      <td>81702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THOMSON REUTERS MARKETS LLC</th>\n",
       "      <td>32</td>\n",
       "      <td>101017.75000</td>\n",
       "      <td>57670</td>\n",
       "      <td>165000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKITSERV LLC</th>\n",
       "      <td>30</td>\n",
       "      <td>91745.03333</td>\n",
       "      <td>50752</td>\n",
       "      <td>131997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTERCONTINENTALEXCHANGE INC</th>\n",
       "      <td>27</td>\n",
       "      <td>81407.00000</td>\n",
       "      <td>48568</td>\n",
       "      <td>125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S&amp;P; GLOBAL INC</th>\n",
       "      <td>25</td>\n",
       "      <td>108595.20000</td>\n",
       "      <td>56375</td>\n",
       "      <td>190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NYSE MARKET INC</th>\n",
       "      <td>24</td>\n",
       "      <td>95432.25000</td>\n",
       "      <td>60362</td>\n",
       "      <td>153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKIT WSO CORPORATION</th>\n",
       "      <td>20</td>\n",
       "      <td>63188.05000</td>\n",
       "      <td>25750</td>\n",
       "      <td>101000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THOMSON REUTERS APPLICATIONS INC</th>\n",
       "      <td>13</td>\n",
       "      <td>121293.84615</td>\n",
       "      <td>73500</td>\n",
       "      <td>149860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           count          mean    min     max\n",
       "EMPLOYER                                                                     \n",
       "BLOOMBERG LP                                2130  142303.22676  45000  585000\n",
       "FACTSET RESEARCH SYSTEMS INC                 653   92029.93568  30035  210000\n",
       "THOMSON REUTERS (MARKETS) LLC                405  106515.14815  55000  550000\n",
       "INTERCONTINENTAL EXCHANGE HOLDINGS INC       281   83150.27402  40500  137000\n",
       "MARKIT NORTH AMERICA INC D/B/A IHS MARKIT    176   84029.42045  38771  185000\n",
       "MARKIT NORTH AMERICA INC                     172   90368.17442  38126  225000\n",
       "NYSE MARKET (DE) INC                         153   99755.24837  48000  160000\n",
       "THOMSON REUTERS (TAX & ACCOUNTING) INC       142  104193.21831  32960  175000\n",
       "S&P; GLOBAL MARKET INTELLIGENCE INC           72   91076.72222  54000  150287\n",
       "FIDESSA CORPORATION                           67   87052.65672  45000  190000\n",
       "THOMSON REUTERS HOLDINGS INC                  59  114467.66102  77000  191000\n",
       "THOMSON REUTERS (GRC) INC                     39   89498.82051  60000  132610\n",
       "MARKIT ON DEMAND INC                          37   61931.18919  34424   81702\n",
       "THOMSON REUTERS MARKETS LLC                   32  101017.75000  57670  165000\n",
       "MARKITSERV LLC                                30   91745.03333  50752  131997\n",
       "INTERCONTINENTALEXCHANGE INC                  27   81407.00000  48568  125000\n",
       "S&P; GLOBAL INC                               25  108595.20000  56375  190000\n",
       "NYSE MARKET INC                               24   95432.25000  60362  153750\n",
       "MARKIT WSO CORPORATION                        20   63188.05000  25750  101000\n",
       "THOMSON REUTERS APPLICATIONS INC              13  121293.84615  73500  149860"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agglist = ('count','mean', 'min', 'max')\n",
    "\n",
    "pd.set_option('precision',5)\n",
    "\n",
    "temp3 = df_.pivot_table('BASE SALARY', index = [\"EMPLOYER\"], aggfunc = agglist)\n",
    "\n",
    "temp3.sort_values('count', ascending=False).head(n=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"filter1\"></a></p>\n",
    "### Filter by State\n",
    "<a href=\"#sections\">Top</a><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th>JOB TITLE</th>\n",
       "      <th>BASE SALARY</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>SUBMIT DATE</th>\n",
       "      <th>START DATE</th>\n",
       "      <th>CASE STATUS</th>\n",
       "      <th>STATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>REPORTER</td>\n",
       "      <td>80000</td>\n",
       "      <td>SEATTLE, WA</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>2017-07-25</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>REPORTER</td>\n",
       "      <td>88000</td>\n",
       "      <td>SEATTLE, WA</td>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3574</th>\n",
       "      <td>THOMSON REUTERS (TAX AND ACCOUNTING) INC</td>\n",
       "      <td>SR. IMPLEMENTATION CONSULTANT</td>\n",
       "      <td>120000</td>\n",
       "      <td>REDMOND, WA</td>\n",
       "      <td>2014-12-04</td>\n",
       "      <td>2014-12-15</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3576</th>\n",
       "      <td>THOMSON REUTERS (TAX AND ACCOUNTING) SERVICES INC</td>\n",
       "      <td>SR. IMPLEMENTATION CONSULTANT</td>\n",
       "      <td>120000</td>\n",
       "      <td>REDMOND, WA</td>\n",
       "      <td>2014-04-11</td>\n",
       "      <td>2014-05-05</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4511</th>\n",
       "      <td>MARKIT NORTH AMERICA INC D/B/A IHS MARKIT</td>\n",
       "      <td>PRODUCT SERVICES SPECIALIST</td>\n",
       "      <td>56139</td>\n",
       "      <td>BELLEVUE, WA</td>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>2017-08-25</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>WA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               EMPLOYER  \\\n",
       "632                                        BLOOMBERG LP   \n",
       "638                                        BLOOMBERG LP   \n",
       "3574           THOMSON REUTERS (TAX AND ACCOUNTING) INC   \n",
       "3576  THOMSON REUTERS (TAX AND ACCOUNTING) SERVICES INC   \n",
       "4511          MARKIT NORTH AMERICA INC D/B/A IHS MARKIT   \n",
       "\n",
       "                          JOB TITLE  BASE SALARY      LOCATION SUBMIT DATE  \\\n",
       "632                        REPORTER        80000   SEATTLE, WA  2017-02-08   \n",
       "638                        REPORTER        88000   SEATTLE, WA  2018-02-06   \n",
       "3574  SR. IMPLEMENTATION CONSULTANT       120000   REDMOND, WA  2014-12-04   \n",
       "3576  SR. IMPLEMENTATION CONSULTANT       120000   REDMOND, WA  2014-04-11   \n",
       "4511    PRODUCT SERVICES SPECIALIST        56139  BELLEVUE, WA  2017-08-15   \n",
       "\n",
       "     START DATE CASE STATUS STATE  \n",
       "632  2017-07-25   CERTIFIED    WA  \n",
       "638  2018-08-01   CERTIFIED    WA  \n",
       "3574 2014-12-15   CERTIFIED    WA  \n",
       "3576 2014-05-05   CERTIFIED    WA  \n",
       "4511 2017-08-25   CERTIFIED    WA  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter by state\n",
    "state = 'WA'\n",
    "temp4 = df_[df_['STATE']==state]\n",
    "temp4.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BASE SALARY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>101573.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21633.81465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>56139.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>90750.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>106500.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>118187.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>126844.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        BASE SALARY\n",
       "count      10.00000\n",
       "mean   101573.30000\n",
       "std     21633.81465\n",
       "min     56139.00000\n",
       "25%     90750.00000\n",
       "50%    106500.00000\n",
       "75%    118187.50000\n",
       "max    126844.00000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp4.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary Job Titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SENIOR SOFTWARE DEVELOPER    764\n",
       "SOFTWARE ENGINEER            552\n",
       "SOFTWARE DEVELOPER           350\n",
       "TEAM LEADER                  171\n",
       "SENIOR SOFTWARE ENGINEER     155\n",
       "SENIOR DEVELOPER              72\n",
       "DEVELOPER                     60\n",
       "LEAD SOFTWARE ENGINEER        47\n",
       "FINANCIAL ENGINEER            46\n",
       "SENIOR QA ANALYST             40\n",
       "REPORTER                      34\n",
       "PRODUCT MANAGER               32\n",
       "QUANTITATIVE ANALYST          30\n",
       "BUSINESS ANALYST              26\n",
       "SYSTEMS ENGINEER              25\n",
       "SENIOR ANALYST                24\n",
       "FIS REPRESENTATIVE            24\n",
       "MANAGEMENT ASSOCIATE          24\n",
       "QUALITY ASSURANCE ANALYST     22\n",
       "EDITOR                        20\n",
       "Name: JOB TITLE, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show types of jobs\n",
    "df_['JOB TITLE'].value_counts().head(n=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bucketing Similar Jobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4669\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DEVELOPER_                   1523\n",
       "SOFTWARE_ENGINEER             806\n",
       "TEAM LEADER                   171\n",
       "FINANCIAL ENGINEER             46\n",
       "SENIOR QA ANALYST              40\n",
       "REPORTER                       34\n",
       "PRODUCT MANAGER                32\n",
       "QUANTITATIVE ANALYST           30\n",
       "BUSINESS ANALYST               26\n",
       "SYSTEMS ENGINEER               25\n",
       "FIS REPRESENTATIVE             24\n",
       "SENIOR ANALYST                 24\n",
       "MANAGEMENT ASSOCIATE           24\n",
       "QUALITY ASSURANCE ANALYST      22\n",
       "DATA ANALYST                   20\n",
       "EDITOR                         20\n",
       "PRODUCT ANALYST                19\n",
       "QA ANALYST                     16\n",
       "ACCOUNT MANAGER                15\n",
       "TECHNICAL LEAD                 14\n",
       "ENGINEER                       14\n",
       "MACHINE_LEARNING               14\n",
       "TEST AUTOMATION ENGINEER       13\n",
       "RESEARCH ASSOCIATE             12\n",
       "UX DESIGNER                    11\n",
       "Name: JOB TITLE, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#two ways to merge similar titles \n",
    "#method1\n",
    "df_['JOB TITLE'][df_['JOB TITLE'].str.contains('SOFTWARE ENGINEER')] =\"SOFTWARE_ENGINEER\"\n",
    "\n",
    "#method2\n",
    "df_['JOB TITLE']= df_['JOB TITLE'].apply(lambda x: \"MACHINE_LEARNING\" if \"MACHINE LEARNING\" in x else x)\n",
    "\n",
    "##bonus groupings\n",
    "df_['JOB TITLE'][df_['JOB TITLE'].str.contains('DEVELOPER')] =\"DEVELOPER_\"\n",
    "\n",
    "print(len(df_))\n",
    "df_['JOB TITLE'].value_counts().head(n=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"company\"></a></p>\n",
    "### Aggregate and Analyze\n",
    "<a href=\"#sections\">Top</a><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#show types of jobs\n",
    "\n",
    "#[\"Bloomberg\", \"Factset\", \"Thomson\", 'Intercontinental Exchange', 'Intercontinentalexchange', 'NYSE', 'Fidessa', 'Markit']\n",
    "#['S%26P Global', \"Bloomberg\", \"Factset\", \"Thomson\", 'Intercontinental Exchange', 'Intercontinentalexchange', 'NYSE', 'Fidessa','Markit']\n",
    "\n",
    "#Group similar names\n",
    "df_['EMPLOYER'][df_['EMPLOYER'].str.contains('S&P')] =\"S&P GLOBAL_\"\n",
    "df_['EMPLOYER'][df_['EMPLOYER'].str.contains('NYSE')] =\"NYSE_\"\n",
    "df_['EMPLOYER'][df_['EMPLOYER'].str.contains('MARKIT')] =\"MARKIT_\"\n",
    "df_['EMPLOYER'][df_['EMPLOYER'].str.contains('FACTSET')] =\"FACTSET_\"\n",
    "df_['EMPLOYER'][df_['EMPLOYER'].str.contains('INTERC')] =\"ICE_\"\n",
    "df_['EMPLOYER'][df_['EMPLOYER'].str.contains('FIDESSA')] =\"FIDESSA_\"\n",
    "df_['EMPLOYER'][df_['EMPLOYER'].str.contains('THOMSON')] =\"THOMSON_\"\n",
    "df_['EMPLOYER'][df_['EMPLOYER'].str.contains('BLOOMBERG')] =\"BLOOMBERG_\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BLOOMBERG_</th>\n",
       "      <td>2149</td>\n",
       "      <td>142300.54584</td>\n",
       "      <td>45000</td>\n",
       "      <td>585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THOMSON_</th>\n",
       "      <td>752</td>\n",
       "      <td>105208.62367</td>\n",
       "      <td>32960</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACTSET_</th>\n",
       "      <td>653</td>\n",
       "      <td>92029.93568</td>\n",
       "      <td>30035</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKIT_</th>\n",
       "      <td>435</td>\n",
       "      <td>84230.04598</td>\n",
       "      <td>25750</td>\n",
       "      <td>225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICE_</th>\n",
       "      <td>308</td>\n",
       "      <td>82997.45455</td>\n",
       "      <td>40500</td>\n",
       "      <td>137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NYSE_</th>\n",
       "      <td>203</td>\n",
       "      <td>98658.02463</td>\n",
       "      <td>48000</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S&amp;P GLOBAL_</th>\n",
       "      <td>100</td>\n",
       "      <td>94666.89000</td>\n",
       "      <td>54000</td>\n",
       "      <td>190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIDESSA_</th>\n",
       "      <td>69</td>\n",
       "      <td>87283.01449</td>\n",
       "      <td>45000</td>\n",
       "      <td>190000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count          mean    min     max\n",
       "EMPLOYER                                       \n",
       "BLOOMBERG_    2149  142300.54584  45000  585000\n",
       "THOMSON_       752  105208.62367  32960  550000\n",
       "FACTSET_       653   92029.93568  30035  210000\n",
       "MARKIT_        435   84230.04598  25750  225000\n",
       "ICE_           308   82997.45455  40500  137000\n",
       "NYSE_          203   98658.02463  48000  160000\n",
       "S&P GLOBAL_    100   94666.89000  54000  190000\n",
       "FIDESSA_        69   87283.01449  45000  190000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp5 = df_.pivot_table('BASE SALARY', index = [\"EMPLOYER\"], aggfunc = agglist)\n",
    "\n",
    "temp5.sort_values('count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   __Visualize__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13bd34e0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig= plt.figure(figsize=(5,5))\n",
    "df_['EMPLOYER'].value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <p><a name=\"timeout\"></a></p>\n",
    "## <font color=green> __TIME OUT: Write Dataframe Contents To File__ </font> <br>\n",
    " <a href=\"#sections\">Top</a><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_.to_csv('scrape_results.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Further Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SOFTWARE_ENGINEER                        184\n",
       "DEVELOPER_                                60\n",
       "MANAGEMENT ASSOCIATE                      24\n",
       "SENIOR MARKET ANALYST                      9\n",
       "RESEARCH SCIENTIST                         9\n",
       "TECHNICAL LEAD                             9\n",
       "SENIOR QA ENGINEER                         7\n",
       "SENIOR DATABASE ENGINEER                   7\n",
       "SOFTWARE DEVELOPMENT ENGINEER              7\n",
       "DEVELOPMENT MANAGER                        5\n",
       "EVALUATOR                                  5\n",
       "SENIOR IMPLEMENTATION CONSULTANT           5\n",
       "SENIOR BUSINESS ANALYST                    5\n",
       "QUANTITATIVE RESEARCH ANALYST              5\n",
       "FINANCIAL ANALYST                          5\n",
       "TECHNOLOGY ASSOCIATE                       4\n",
       "SENIOR NETWORK ENGINEER                    4\n",
       "MANAGING CONSULTANT                        4\n",
       "ARCHITECT, IDENTITY ACCESS MANAGEMENT      4\n",
       "APPLICATION DEVELOPMENT ANALYST            4\n",
       "Name: JOB TITLE, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupeddf = df_[df_['EMPLOYER']==\"THOMSON_\"]\n",
    "\n",
    "groupeddf['JOB TITLE'].value_counts().head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOB TITLE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SOFTWARE_ENGINEER</th>\n",
       "      <td>184</td>\n",
       "      <td>103762.72283</td>\n",
       "      <td>57000</td>\n",
       "      <td>190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEVELOPER_</th>\n",
       "      <td>60</td>\n",
       "      <td>108482.53333</td>\n",
       "      <td>80000</td>\n",
       "      <td>171663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANAGEMENT ASSOCIATE</th>\n",
       "      <td>24</td>\n",
       "      <td>113948.50000</td>\n",
       "      <td>112000</td>\n",
       "      <td>115000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENIOR MARKET ANALYST</th>\n",
       "      <td>9</td>\n",
       "      <td>70292.77778</td>\n",
       "      <td>59779</td>\n",
       "      <td>75005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESEARCH SCIENTIST</th>\n",
       "      <td>9</td>\n",
       "      <td>111782.77778</td>\n",
       "      <td>91333</td>\n",
       "      <td>125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TECHNICAL LEAD</th>\n",
       "      <td>9</td>\n",
       "      <td>126184.88889</td>\n",
       "      <td>107740</td>\n",
       "      <td>162653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOFTWARE DEVELOPMENT ENGINEER</th>\n",
       "      <td>7</td>\n",
       "      <td>94275.14286</td>\n",
       "      <td>64453</td>\n",
       "      <td>148719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENIOR QA ENGINEER</th>\n",
       "      <td>7</td>\n",
       "      <td>104095.42857</td>\n",
       "      <td>87720</td>\n",
       "      <td>131072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENIOR DATABASE ENGINEER</th>\n",
       "      <td>7</td>\n",
       "      <td>111593.14286</td>\n",
       "      <td>90000</td>\n",
       "      <td>120291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENIOR IMPLEMENTATION CONSULTANT</th>\n",
       "      <td>5</td>\n",
       "      <td>105846.20000</td>\n",
       "      <td>94070</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEVELOPMENT MANAGER</th>\n",
       "      <td>5</td>\n",
       "      <td>145394.80000</td>\n",
       "      <td>112632</td>\n",
       "      <td>168600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EVALUATOR</th>\n",
       "      <td>5</td>\n",
       "      <td>84782.60000</td>\n",
       "      <td>75000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QUANTITATIVE RESEARCH ANALYST</th>\n",
       "      <td>5</td>\n",
       "      <td>108619.80000</td>\n",
       "      <td>92000</td>\n",
       "      <td>123288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENIOR BUSINESS ANALYST</th>\n",
       "      <td>5</td>\n",
       "      <td>91934.60000</td>\n",
       "      <td>75000</td>\n",
       "      <td>111363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANCIAL ANALYST</th>\n",
       "      <td>5</td>\n",
       "      <td>76183.00000</td>\n",
       "      <td>55000</td>\n",
       "      <td>94931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TECHNOLOGY ASSOCIATE</th>\n",
       "      <td>4</td>\n",
       "      <td>113102.00000</td>\n",
       "      <td>112000</td>\n",
       "      <td>114408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TECHNICAL SPECIALIST</th>\n",
       "      <td>4</td>\n",
       "      <td>112334.25000</td>\n",
       "      <td>97410</td>\n",
       "      <td>127089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENIOR NETWORK ENGINEER</th>\n",
       "      <td>4</td>\n",
       "      <td>109660.25000</td>\n",
       "      <td>83873</td>\n",
       "      <td>154700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANAGING CONSULTANT</th>\n",
       "      <td>4</td>\n",
       "      <td>101076.50000</td>\n",
       "      <td>74984</td>\n",
       "      <td>113322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLICATION DEVELOPMENT ANALYST</th>\n",
       "      <td>4</td>\n",
       "      <td>90432.50000</td>\n",
       "      <td>83230</td>\n",
       "      <td>109240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  count          mean     min     max\n",
       "JOB TITLE                                                            \n",
       "SOFTWARE_ENGINEER                   184  103762.72283   57000  190000\n",
       "DEVELOPER_                           60  108482.53333   80000  171663\n",
       "MANAGEMENT ASSOCIATE                 24  113948.50000  112000  115000\n",
       "SENIOR MARKET ANALYST                 9   70292.77778   59779   75005\n",
       "RESEARCH SCIENTIST                    9  111782.77778   91333  125000\n",
       "TECHNICAL LEAD                        9  126184.88889  107740  162653\n",
       "SOFTWARE DEVELOPMENT ENGINEER         7   94275.14286   64453  148719\n",
       "SENIOR QA ENGINEER                    7  104095.42857   87720  131072\n",
       "SENIOR DATABASE ENGINEER              7  111593.14286   90000  120291\n",
       "SENIOR IMPLEMENTATION CONSULTANT      5  105846.20000   94070  120000\n",
       "DEVELOPMENT MANAGER                   5  145394.80000  112632  168600\n",
       "EVALUATOR                             5   84782.60000   75000  100000\n",
       "QUANTITATIVE RESEARCH ANALYST         5  108619.80000   92000  123288\n",
       "SENIOR BUSINESS ANALYST               5   91934.60000   75000  111363\n",
       "FINANCIAL ANALYST                     5   76183.00000   55000   94931\n",
       "TECHNOLOGY ASSOCIATE                  4  113102.00000  112000  114408\n",
       "TECHNICAL SPECIALIST                  4  112334.25000   97410  127089\n",
       "SENIOR NETWORK ENGINEER               4  109660.25000   83873  154700\n",
       "MANAGING CONSULTANT                   4  101076.50000   74984  113322\n",
       "APPLICATION DEVELOPMENT ANALYST       4   90432.50000   83230  109240"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = groupeddf.pivot_table('BASE SALARY', index='JOB TITLE', aggfunc=agglist)\n",
    "new.sort_values('count', ascending = False).head(n=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Job Title Analysis (all firms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOB TITLE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GLOBAL HEAD OF REAL TIME DATA AND TECHNOLOGY</th>\n",
       "      <td>1</td>\n",
       "      <td>585000.0</td>\n",
       "      <td>585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STRATEGIST - ENTERPRISE SOLUTIONS</th>\n",
       "      <td>1</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENIOR QUANTITATIVE LIBRARY ARCHITECT</th>\n",
       "      <td>1</td>\n",
       "      <td>335000.0</td>\n",
       "      <td>335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRODUCT MANAGEMENT - TEAM LEADER</th>\n",
       "      <td>1</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUSINESS PROCESS ANALYST</th>\n",
       "      <td>2</td>\n",
       "      <td>302500.0</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANCHOR</th>\n",
       "      <td>1</td>\n",
       "      <td>277000.0</td>\n",
       "      <td>277000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLOBAL HEAD OF PLATFORM FRAMEWORK</th>\n",
       "      <td>1</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUSINESS MANAGEMENT - SENIOR MANAGER</th>\n",
       "      <td>2</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENIOR HADOOP INFRASTRUCTURE ENGINEER</th>\n",
       "      <td>1</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>265000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TECHNICAL ARCHITECT - TEAM LEADER</th>\n",
       "      <td>1</td>\n",
       "      <td>260000.0</td>\n",
       "      <td>260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRODUCT DESIGNER- ENTERPRISE SOLUTIONS</th>\n",
       "      <td>1</td>\n",
       "      <td>240000.0</td>\n",
       "      <td>240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GROUP TEAM LEADER RECRUITING</th>\n",
       "      <td>1</td>\n",
       "      <td>240000.0</td>\n",
       "      <td>240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOFTWARE SECURITY ARCHITECT</th>\n",
       "      <td>1</td>\n",
       "      <td>240000.0</td>\n",
       "      <td>240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PORTFOLIO RISK AND ANALYTICS RESEARCHER</th>\n",
       "      <td>3</td>\n",
       "      <td>237000.0</td>\n",
       "      <td>266000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENIOR NATURAL GAS ANALYST</th>\n",
       "      <td>1</td>\n",
       "      <td>235000.0</td>\n",
       "      <td>235000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              count      mean     max\n",
       "JOB TITLE                                                            \n",
       "GLOBAL HEAD OF REAL TIME DATA AND TECHNOLOGY      1  585000.0  585000\n",
       "STRATEGIST - ENTERPRISE SOLUTIONS                 1  350000.0  350000\n",
       "SENIOR QUANTITATIVE LIBRARY ARCHITECT             1  335000.0  335000\n",
       "PRODUCT MANAGEMENT - TEAM LEADER                  1  315000.0  315000\n",
       "BUSINESS PROCESS ANALYST                          2  302500.0  550000\n",
       "ANCHOR                                            1  277000.0  277000\n",
       "GLOBAL HEAD OF PLATFORM FRAMEWORK                 1  275000.0  275000\n",
       "BUSINESS MANAGEMENT - SENIOR MANAGER              2  270000.0  270000\n",
       "SENIOR HADOOP INFRASTRUCTURE ENGINEER             1  265000.0  265000\n",
       "TECHNICAL ARCHITECT - TEAM LEADER                 1  260000.0  260000\n",
       "PRODUCT DESIGNER- ENTERPRISE SOLUTIONS            1  240000.0  240000\n",
       "GROUP TEAM LEADER RECRUITING                      1  240000.0  240000\n",
       "SOFTWARE SECURITY ARCHITECT                       1  240000.0  240000\n",
       "PORTFOLIO RISK AND ANALYTICS RESEARCHER           3  237000.0  266000\n",
       "SENIOR NATURAL GAS ANALYST                        1  235000.0  235000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#import seaborn as sns\n",
    "#sns.set()\n",
    "\n",
    "agglist = ('count', 'mean', 'max')\n",
    "new = df_.pivot_table('BASE SALARY', index='JOB TITLE', aggfunc=agglist)\n",
    "new.sort_values('mean', ascending = False).head(n=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <p><a name=\"viz1\"></a></p>\n",
    " # Visualize\n",
    " <a href=\"#sections\">Top</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x153bdd68>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x153bdeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df_, col=\"EMPLOYER\", margin_titles=True)\n",
    "g.map(plt.hist, \"BASE SALARY\", color=\"steelblue\", bins=5,lw=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17cef2b0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x153bda58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df_['BASE SALARY'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7]), <a list of 8 Text xticklabel objects>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ra9DRaDS2dtOmTRly6J4lJMQCYNAhIiLp1Bp08vPz8dVXX0EQBFy/fv2WbSCGDh0qenGk\nHOnpp5CRkW5rM+wQEZEUag06PXr0QHJy8i3tSgw6VB+VvTmVbQYdIiKSQq1Bp669rJKSkkQphoiI\niKgh1Tq9/GZ5eXnYsGED+vfvj3nz5olZEynQ8OGjamwTERGJ6babeiYnJyM6Ohr79++Hg4MDFi1a\nhCFDhkhRGymIj09HeHv72NpERERSqLVHZ/PmzRg0aBCWLl2Kv/71r9i1axeaN2+OYcOGQa/XS1kj\nKUSbNm3Rpk1bucsgIiIVqbVHJywsDH379sW4cePg5+cHjUZTbco5UX0dPXoEADBx4msyV0JERGpR\na9BJTEzErl27sHz5cuTk5GDQoEGwWCxS1kYKsnfvThQVFdragwY9L3NFRESkBrXeunJ3d8fLL7+M\nhIQEbNy4EQBQVlaGIUOGIDIyUrICSRkSEuJqbBMREYnpjmZdeXt7Y968eThy5AimT5/O6eVERETU\nKNzx9HIA0Ol0ePbZZ1FeXi5WPaRQw4ePrLFNREQkpnoFnUr//ve/G7oOUrhBg56Hs7MLnJ1dOD6H\niIgkc9t1dGoiCEJD10EqwJ4cIiKS2l0FHU4zp7vBnhwiIpJarUGnb9++NQYaQRBQVFQkalFERERE\nDaHWoBMREVHrX7JaraIUQ8q2eXPFMgVcMJCIiKRS62Dk1q1b3/I/nU6H7du3Y/z48VLWSArx/fff\n4fvvv5O7DCIiUpE7mnWVmJiIt956C8888wxSUlIQEhIidl2kMJs3b4TVaoXVarX17BAREYlNI9Qy\nhSo3NxdxcXGIjY2FXq/HwIED8dVXX+Hw4cNS1yiKnJwCuUtQlQkTRtlueTo4OGDLlliZKyIiIqVo\n0cK11sdqHaPz9NNPo1+/fli/fj18fHwAALt27Wr46oiIiIhEUuutq7lz5yIzMxPTpk1DaGgoMjIy\npKyLFKZv3/41tomIiMRU662rSr/99hsSEhKwc+dO3LhxA4GBgRgxYgTc3d2lqlEUvHUlvQkTRgEA\nb1sREVGDquvW1W2DTqWysjIcPHgQCQkJSE5Oxk8//dRgBcqBQUd6YWErAQAzZwbJXAkRESnJXY3R\nueVEnQ5+fn7o168f8vLybnt+eXk55s+fjz///BMajQaLFi2Ck5MT5s6dC41Gg/bt2yMkJAQODg6I\njY1FTEwMdDodJk+ejD59+qC4uBhz5sxBbm4uDAYDVq5cCQ8PD5w4cQJLly6FVquFv78/pk6dCgBY\nv349Dh06BJ1Oh3nz5sHX1/dOL40kUlholrsEIiJSmVrH6OTl5WH69OlITk6GIAiYMmUK+vTpg/79\n+yM/P/+2T3zw4EEAQExMDGbMmIEPP/wQy5cvx4wZMxAVFQVBEHDgwAHk5OQgIiICMTEx2LRpE8LC\nwmCxWBAdHQ0vLy9ERUVh6NChCA8PBwCEhIQgNDQU0dHR+OWXX5Ceno60tDSkpKQgLi4OYWFhWLRo\nUQP956GGkp5+ChkZ6cjISEd6+im5yyEiIpWoNei8//776NixIzp27Ii9e/fi119/RVJSEj766CMs\nWbLktk/cr18/vP/++wCA7OxsuLm5IS0tDd26dQMAPPXUUzh27BhOnjyJzp07w9HREa6urvD09ERG\nRgZSU1Px5JNP2s49fvw4TCYTLBYLPD09odFo4O/vj2PHjiE1NRX+/v7QaDRo1aoVysvL76jXiaST\nkBBbY5uIiEhMtd66+uOPP/Dhhx8CqFgwcODAgTAajejQoQOuXLlyZ0+u0yEoKAjfffcd1q5di6NH\nj9r2zzIYDCgoKIDJZIKr6//urRkMBphMpmrHq55rNBqrnZuVlQUnJ6dqg6Mrz/fw8Ki1tqZNXaDT\nae/oOujeOTrqqrXrup9KRETUUGoNOlU39Pzhhx+q9eLUZ1PPlStXYvbs2Rg1ahRKSkpsx81mM9zc\n3GA0GmE2m6sdd3V1rXa8rnPd3Nyg1+trfI66XLtWeMfXQPduyJDhOHnypK3NweBERNRQ6vryXOut\nq1atWmHPnj2Ij49HUVGR7ZbTjh070L59+9u+6FdffYXPPvsMAODs7AyNRoOOHTsiOTkZQEUvkZ+f\nH3x9fZGamoqSkhIUFBTgzJkz8PLyQpcuXWyrMCcmJqJr164wGo3Q6/XIzMyEIAhISkqCn58funTp\ngqSkJFitVmRnZ8NqtdbZm0PS8/HpiBYtWqJFi5bw8ekodzlERKQStU4vv3jxIhYuXIjc3FzMnDkT\n/v7+WL58OQ4ePIgNGzbg4YcfrvOJCwsLERwcjKtXr6KsrAyvvfYa/vKXv2DBggUoLS1Fu3btsGTJ\nEmi1WsTGxmLr1q0QBAFvvPEGBgwYgKKiIgQFBSEnJwd6vR6hoaFo0aIFTpw4gWXLlqG8vBz+/v4I\nDAwEAKxbtw6JiYmwWq0IDg6Gn59fnfWxR0F6r746DgCwaVOkzJUQEZGSNMg6OgBw/fp1uLq6wsHh\njvYCtWsMOtLau3cnIiO/BACMG/cPDBr0vMwVERGRUtzVraua3HfffYoIOSS9hIS4GttERERiYmoh\nIiIixWLQIUkMHz6yxjYREZGYag06a9assbWPHj1a7bG3335bvIpIkQYNeh4ajQYajYbjc1QkPf0U\nV8ImIlnVGnQqp3YDwAcffFDtsXPnzolXESlSevopCIIAQRD4waciCQmxXAmbiGRVa9CpOhnr5olZ\nVRcTJLoTERFf1Ngm5eL+ZkRkD+5ojA6DDd2rK1cu1dgm5eL+ZkRkD2oNOgw3RERE1NjVutfVr7/+\nisceewxAxa2rqm2GIKovnU5v2+tMp9PLXA1JYfjwUVi27D1bm4hIDrUGnYyMDCnrIIUrLDTX2Cbl\n8vHpCG9vH1ubiEgOtQYdAMjLy4Ner4erqyvOnz+Pb7/9Fo899hh69uwpVX2kEHUNbiflYk8OEcmt\n1jE6R44cwZAhQ/Dbb7/h6tWreOmll5Ceno6PPvoIW7dulbJGUgCtVldjm5TNx6cje3OISFa1fuKs\nW7cOUVFRePjhh7Fx40Z4eXnhgw8+QFFREUaNGoXRo0dLWSc1cjqdFuXlZbY2ERGRFGrt0SkpKcHD\nDz8MAPjhhx/Qt29fAICzszNvPVC9VR2AzMHIREQklToXDBQEAUVFRfjpp5/Qq1cvAEBhYSGKi4sl\nK5CUoaiosMY2ERGRmGq9ddW/f39MnjwZVqsV3t7eaN++PTIyMrB27VoMHDhQyhpJETS1tImIiMRT\na9CZNm0a9uzZg6tXr2Lo0KEAKm5hPfbYY5gyZYpkBZIyWK3lNbaJiIjEVOf0l+eee67anydOnIjz\n589jzZo1mDlzpqiFEREREd2rO9rrymq1Yv/+/Zg0aRIGDhyIs2fPilwWERER0b2rs0fn8uXL2Lp1\nK+Lj46HRaGA2m7F37160bdtWqvpIITQajW22HrcQISIiqdTaozN58mSMHTsWN27cQFhYGA4ePAhX\nV1eGHLorBoOxxjYREZGYag06V65cQcuWLeHu7o6mTZtCo9HwmzjdtdJSS41tIiIiMdV66yo+Ph6n\nT59GQkICXn75Zdx///0wmUzIyclBixYtpKyRFKBy5/Kb20RERGKqczCyl5cX5s6di8TEREydOhVd\nunRBv379MH36dKnqIyIiIrprd7S7ok6nQ79+/dCvXz/k5ubi66+/FrsuIiIiontWZ9A5c+YMXF1d\ncf/992PDhg346aef0LFjR0yaNEmq+oiIiIjuWq1B59NPP0VMTAy0Wi26deuG8+fPo3///khJScHC\nhQuxatUqKeskIiIiqrdag87OnTuxd+9eFBYWol+/fjh27BicnZ0xbty4W1ZMJiIiIrJHtQYdnU4H\nZ2dnODs7o23btnB2dgYAaLVaW5uIiIgoPf0UAMDHp6PMldyq1qDj4PC/CVlarbbaY1xPh6KitiAl\n5fhd//0ZMybf0XnduvVEQMCEu34dIiISX0JCLIBGFnTOnj2LCRMm3NIWBAHnzp2TpjpSDAcHB1it\nVlubiIiUIT39FDIy0m1tews7GqFyA6KbpKSk1PkXu3XrJkpBUsnJKZC7BNV5+eWXAAD/+tc2mSsh\nIqKGsmTJQlvQ8fb2wfz5iyWvoUUL11ofq7VHp7EHGbI/7MlRn717dwIABg16XuZKiEit+MlDkvHw\naAYPj2Zyl0ESSkiIQ0JCnNxlEJGIhg8fVWPbXjDoEJEo9u7diaKiQhQVFdp6dohIeXx8OsLb2wfe\n3j52Nz4HuMMtIIjo3tjz1EuxVO3JSUiI4+0rIgWzx56cSgw6RBKw56mXRET3yp7f23jrikhklVMv\nMzLSbT07ajB8+Mga20REUmLQIRJZZW/OzW2lGzToeTg7u8DZ2YW3rYhINrx1RUSiYU8OkTrY8zhE\nUYJOaWkp5s2bhwsXLsBisWDy5Ml49NFHMXfuXGg0GrRv3x4hISFwcHBAbGwsYmJioNPpMHnyZPTp\n0wfFxcWYM2cOcnNzYTAYsHLlSnh4eODEiRNYunQptFot/P39MXXqVADA+vXrcejQIeh0OsybNw++\nvr5iXBbRXRk+fBSWLXvP1lYT9uQQqYM9j0MUJeh8/fXXcHd3x+rVq5Gfn4+hQ4fC29sbM2bMQPfu\n3bFw4UIcOHAAnTp1QkREBOLj41FSUoKAgAD06tUL0dHR8PLywrRp07B7926Eh4dj/vz5CAkJwbp1\n69C2bVu8/vrrSE9PhyAISElJQVxcHC5evIhp06YhPj5ejMsiuis+Ph3h7OxiaxMRKYm9bwEhyhid\ngQMH4u233wZQsTeWVqtFWlqabbXlp556CseOHcPJkyfRuXNnODo6wtXVFZ6ensjIyEBqaiqefPJJ\n27nHjx+HyWSCxWKBp6cnNBoN/P39cezYMaSmpsLf3x8ajQatWrVCeXk58vLyxLgsoruSnn7Ktp6M\nmgYjAxXXrrZrJlIbex+HKErQMRgMMBqNMJlMmD59OmbMmAFBEGy7nhsMBhQUFMBkMsHV1bXa3zOZ\nTNWOVz3XaDRWO7eu40T2wt7fBMSUkBCrumsmIvsi2mDkixcvYsqUKQgICMDzzz+P1atX2x4zm81w\nc3OD0WiE2WyudtzV1bXa8brOdXNzg16vr/E5bqdpUxfodNqGuFS6Q1ptRa6ua/M1JXJ01FVrq+X6\nf/nlF1t3dnb2/8Pjjz8uc0VEJIZXXpmId955x9a2t/c4UYLO1atX8corr2DhwoXo2bMnAMDHxwfJ\nycno3r07EhMT0aNHD/j6+mLNmjUoKSmBxWLBmTNn4OXlhS5duuDw4cPw9fVFYmIiunbtCqPRCL1e\nj8zMTLRt2xZJSUmYOnUqtFotVq9ejVdffRWXLl2C1WqFh4fHbWu8dq1QjEu/LXsemS628nIrAPXt\nHD9kyHCcPHnS1lbL9X/++eZqbTl2NCaSklrf31u1agdvbx9bW473uLvavfxefPrpp7hx4wbCw8MR\nHh4OAHj33XexZMkShIWFoV27dhgwYAC0Wi3Gjx+PgIAACIKAwMBAODk5YezYsQgKCsLYsWOh1+sR\nGhoKAFi0aBFmz56N8vJy+Pv7274h+vn5YfTo0bBarVi4cKEYl9Rg7HlkOomjch+YyrZa3NzTSqR0\nan5/t+cZpRpBEAS5i5CDHIkzPf2UbZrxvHnvqe6XYcaMyQCANWs+kbkS6anxm96bb/4TJlPF75nR\n6IpPP/1C5oqIxKP293e53+Pq6tHhysgSUvOgVLXz8emouje+ypBzc5tIidT+/m7PEw8YdIgkwGnW\nRKRU9r6fH4OOhKrew7Tn+5nU8Oz5245YHBy0NbaJlEjN7+/23pvFva4kpNZBqWpn76uGisXJyQlF\nRYW2NpGS8f3dfrFHR2LDh49SXdpXO3v/tiOWqht6cnNPUgO1vr/be28We3QkxqRPajFo0POIjPzS\n1iZSOrW+v9t7bxZ7dIhEZu/fdsSyd+/OGttEpDz23JvFHh0ikdn7tx2xJCTEVWuzV4dIuez5vY1B\nh0gC9vpNR0xlZaU1tomIpMSgQyQBe/62I5by8vIa20REUuIYHSIJqHHBQKvVWmObiEhK7NEhkoAa\nN/vT6/UoLS21tUkd5N7ziOhmDDpEIlPSgoFRUVuQknL8js7V6x2rBB1H26aud6Jbt54ICJhwVzWS\nvNQY6umJMu2oAAAgAElEQVR/MyvtcdIBgw4BABYvno+8vFxRX6Py+evzgXc3PDyaYeHCJaK+Rn3c\nvGCgWj4AXFxcUFhotrVJ+ZQU6ql+KmdZMuiQ3crLy0Vebg7cnMV7Df1/R4SVFeaI9ho3ikR7agIQ\nEDChXj0tr7/+DwDAmjWfiFUS2RG1hnq127t3p227l717d9pd2GHQIRs3Z2D2QEe5y7gnH3xjkbuE\nWwwfPgrLlr1na6sJe3LUpbIH7+Y2NS71uUUNAFevXrW1IyO3YN++PXf096S6Rc2gQyQyH5+OcHR0\nsrWJlKqoqLjGNpGcGHSIJGCxlMhdApHoTKaCGtvUuNT3FvXevTtt+9qNGzeBt66Upr5dfGazCQBg\nMBjr9TqchdJ4BQUFVmuvXPmhjNUQiadFixbIzDxna5M6VGzgu8XWtjdcMFBiJSUlKCnht3s1uXAh\nq8Y2kdK8/PI/a2yT8rm4uNjtmDz26Nyj+nbxVU6t5iwUIlIaH5+OcHJS73g0NS+WaK8hB2CPDpHo\nWrduW2ObSGnS00/Zeq3VtuUJUDGlvuoUe7IPDDpEIqs6Jofjc0jJbl5HR00qF0vMyEhXZcizZww6\nRCJ7661Xa2wTkXKoOeTZO47RIQAVs8GKi+1zwb36uF4ENBFMcpdRzY0b12tsEymN0hbHrM+s2vz8\nfFv7jz9+595udoRBh4joNuq7jARwd0tJ2OMHXn2v3cGh4kbBhg0f1+t17PHa68PFxcX2RcaeB+aq\nEYMOAah4M3bSFCliCwidS/3WKCISQ+UyEvVdM6uxEwRB7hIaTH1n1U6cOBYAEB6+SayS6C4w6BAR\n3UZ9P/AA5SwlwSU07hx7cuwTgw6RyDQaBwiC1dYmImVydGzcPeJKxXddIpFVhpyb20REJD4GHSIi\nIlIsBh0ikVW9XcVbV0RE0uK7LpHINJqa20REJD4GHSKRWa3WGttERCQ+zroimxtF4q6MXPTfp3YW\ncWLCjSLAgzM878jixfORl5cr6mtUPn99Vom9Gx4ezbBw4RJRX4OIGicGHQJQ8UEhttLiig89Vxfx\nXsvDRZprUYK8vFxczc0BjHrxXkRb8X9XS/LrPu9emErFe24iavQYdKpQ8zdcKb4Nq3khMbtl1EP7\n8l/lruKelP/rN7lLICI7xqBTRV5eLvJyr8KjiXj3Ppwc/vsV11wo2mvkFYv33ERERI0Jg85NPJq4\nYM2A4XKXcU9m7EuQuwQiIiK7wKBDRKQSSro9D3AQOt0ZBh0iIpWoGICeC43BTbTXELQVg9tzi8Ud\nJC6Yb4j6/KQcogadX375BR988AEiIiJw7tw5zJ07FxqNBu3bt0dISAgcHBwQGxuLmJgY6HQ6TJ48\nGX369EFxcTHmzJmD3NxcGAwGrFy5Eh4eHjhx4gSWLl0KrVYLf39/TJ06FQCwfv16HDp0CDqdDvPm\nzYOvr6+Yl0VE1GhpDG4wBgTJXcY9M0WtlLsEaiRECzobN27E119/DWdnZwDA8uXLMWPGDHTv3h0L\nFy7EgQMH0KlTJ0RERCA+Ph4lJSUICAhAr169EB0dDS8vL0ybNg27d+9GeHg45s+fj5CQEKxbtw5t\n27bF66+/jvT0dAiCgJSUFMTFxeHixYuYNm0a4uPjxbosIsUwm01AcWnjn7VkKoW5zCR3FURkp0QL\nOp6enli3bh3eeecdAEBaWhq6desGAHjqqadw9OhRODg4oHPnznB0dISjoyM8PT2RkZGB1NRUTJo0\nyXZueHg4TCYTLBYLPD09AQD+/v44duwYHB0d4e/vD41Gg1atWqG8vBx5eXnw8PAQ69KIiIgaDSWN\nzbqbcVmiBZ0BAwbg/Pnztj8LggDNfzf6MRgMKCgogMlkgqurq+0cg8EAk8lU7XjVc41GY7Vzs7Ky\n4OTkBHd392rHCwoKbht0mjZ1gU6nrXZMq3VA+d1fsl3Rah3QooXr7U+UkFZbseOIvdUlNXu5fjc3\nNxTpyhSxjo6bi5vd/HetZI//3itrUor6vM/NnDkTV69eFbWeyg/7WbOmiPo6zZs3R1hY2B2ff/36\nNeTl5sLDyf32J98lJ81/l7w3ifcpmleSf1efbZINRnZw+N8vmNlshpubG4xGI8xmc7Xjrq6u1Y7X\nda6bmxv0en2Nz3E7167dutZMebly9iEqL7ciJ6dA7jKqqfzva2911VdU1BakpBy/678/btzLd3Re\nt249ERAw4a5f53bU/O9dym+4d/rzvlv1+YZ748YNCMXFihjfIpiv40Z5kzv+uV++fAW5ubkwGsTr\n7ddqnQAAhWbxPuxN5rx6/3svL7fCw8kdoU+FiFaXFGYlLqr12usKP5IFHR8fHyQnJ6N79+5ITExE\njx494OvrizVr1qCkpAQWiwVnzpyBl5cXunTpgsOHD8PX1xeJiYno2rUrjEYj9Ho9MjMz0bZtWyQl\nJWHq1KnQarVYvXo1Xn31VVy6dAlWq5W3rciuNG/eAlev5tjaJL+K2UdXAYOIG6NpK3qMr4q5gKeI\nC48qkdHggVdHfSR3GfdkU+zbcpfQ6EgWdIKCgrBgwQKEhYWhXbt2GDBgALRaLcaPH4+AgAAIgoDA\nwEA4OTlh7NixCAoKwtixY6HX6xEaGgoAWLRoEWbPno3y8nL4+/vj8ccfBwD4+flh9OjRsFqtWLhw\noVSXRCoVEDCh3j0tL7/8EgBuf2FXDC7Qjx0hdxX3pDS6fhMvDAYjirVOipl1ZWgi4j5tpBiiBp02\nbdogNjYWAPDII4/gX//61y3njBo1CqNGjap2zNnZGWvXrr3l3E6dOtmer6pp06Zh2rRp91yv2WxC\nSXFxo19ZOK+4EE5Qzm0JMU2b9jquXxdxw8mbTJgw6vYn3YP77nPHunUbRH0NatwE8w1Rb10JJUUA\nAI2Ts2ivAfx3HZ0m3MCXbo8LBpKqFRcXwWq14r/j5EUnCOIFUEGouB6i2nh4iB8M8gorFvLzaCLe\nooQAgCbNJLkeavwYdKowGIwwwEEZe12JOfZAQQwGIzQORRg4TO5K7t032wEXZ+PtT6zKJPI6OsX/\nHZTZRFv3effCVAo4iff0SiLFdgmV04t5m5bsBYMOqV5RYUVIEFNxccX/N2ki3msUFQIu9bhbIMm3\ne3PFzCMxp7XCSZprIaLGiUGHVE2qD8hCc8WsKxdn8WZduTjX73rU/O2+YlXo4noP5rU75kKYFbRM\nAJEYGHRI1e7mw76+6+hYLJZqbUdHxzv6e2Kvo0NE6lAx0aYEsxIXyV3KPckrzoeTpv73qRl0iERm\nNpuqtR0duc6T3AwGI4q0DoqYXm5owvF4d8JsNqG4uKTRr0NjMuehrJyD0uqDQYeonuq7js6rr45D\neXnFoFydTmd3t3GISNkMBiMMgrMiVkaGof4TGxh0iERWdasFJW27QNSYGAxG6LQGRayM7NREovUw\nFEJZO7wR2aGystIa20REJD726Nwkr7hQ1JWRzaUVA1MN+jsbkHo38ooL4cF1dIjqZi4Ud9ZVyX8H\noTuJ97sOcyHAMTp3zGTOE3WMTnFJxQbTTZwMor2GyZwHJ64IXS8MOlVIMdW4JK9i5VqDiEHEw+DC\ndUWI6iDNCsEVv+seYgaRJvxdv1NS/HcyF5YAAJya1HPhznpw4orQ9cagU4Wa1xUhUhP+rqsPf+bq\nxaBDREQNpuq6UWQ/8kryRV1Hx1xaCAAw6MXrwcwryYeHsf69WQw6RETUYAoKbshdAt1EmmEZ/x1/\nanQV7TU8jHd3245Bh4iIalWflcAtFgsEQQAAvPXWq3e8CjjAlcDFpPbbdpxeTpIpKChAQUGB3GUQ\nkUiq9uawZ4fsBXt06K7Ud78nACgpqdjCuzL53wllfMvTABCqtIkaj/qsBP7yyy/Z2oIg2OW3e1If\nBp17VN8P/Ly8XAD1+7AHGv8HftWenIKCAri6incf1954ez+GjIx0W5uIiKTDoCMxJydlbMZW3/2e\nqn7TKykpxqZN/xKjLLs0fPgoLFv2nq1NRETSYdC5R/X9wCf18fHpCG9vH1ubSKnc3ZsiP/+arU1k\nDxh0iCTAnhxSA1dXN1vQcXV1k7kaogoMOiSJ++5riuvXr9naasOeHFIDg8FQY7ux4hhMZeD0cpLE\ngw8+WGNbLdLTTyE9/ZTcZRCJqmrPpRp7MZ2cnBQzDlNJ2KNDklD7gNyIiC8AAMuXh8pcyb1R6zfc\nu1lO4W6u3d6uu76UNh6NYzCVgUGHJKG0N8D6SE8/haysc7a2mq5fp1PvW4xav9mr8YsM2TeNULle\nt8rk5HCFXqlV3rpR0wc9AAQHz7IFnbZtH2r0vTr1sWTJQgDA/PmLZa6EiMQk9xYQLVrUvjaber9u\nkeTUFnAqXb58qca20qWnn7ItlKi2niwish8cjEwkstJSS41tpUtIiK2xTUQkJQYdIpFVvTus0jvF\nRESyYdAhIlGofaoxEdkHjtEhElmXLk/gp59+tLXVQs0z7YgaM6Utp8CgQySymTODbJuazpwZJHM1\n0mJPDpE62PNyCpxeTiSBsLCVANQXdIiIpFDX9HIGHSIiImrU6go6HIxMREREisWgQ0RERIrFoENE\nRESKxaBDREREisWgQ0RERIrFoENERESKxaBDREREiqWYlZGtVivee+89/Pbbb3B0dMSSJUvw0EMP\nyV0WERERyUgxPTr79++HxWLB1q1bMWvWLKxYsULukoiIiEhmigk6qampePLJJwEAnTp1wqlTp2Su\niIiIiOSmmFtXJpMJRqPR9metVouysjLodDVfYtOmLtDptFKVR0RERDJQTI+O0WiE2Wy2/dlqtdYa\ncgAw5BAREamAYoJOly5dkJiYCAA4ceIEvLy8ZK6IiIiI5KaY3csrZ12dPn0agiBg2bJl+Mtf/iJ3\nWURERCQjxQQdIiIiopsp5tYVERER0c0YdIiIiEixGHSIiIhIsRh0ZGaxWOQugYiISLEYdGQ2adIk\nuUsgGTDgqg9/5uqj5p/5hQsX5C7BhkFHZmqe9BYSEiJ3CbJRa8Bdv3693CXIRq0/c0C9v+tq/pkH\nBwfLXYINg47MNBqN3CXI5s8//5S7BNmoNeCmpKTIXYJs1PozB9T7u67mn7k9XTuDDpEM1Bpw7enN\nT2pq/ZmrmZp/5vZ07Qw6MlPzGz+pjz29+RGROjDoyKSoqAgA8Oijj8pcCcmBAVd9+DNXHzX/zO3p\n2hl0ZDJ+/HgA6h2kB9jXL0JDu3LlSp2PqzXgKvlnfjtq/ZkD6vu5nzhxAoCyf+bp6el1Pt6jRw+J\nKrk97nUlkxEjRiA+Pl7uMkT3559/4pFHHsGWLVtw48YNaDQaTJw4EQaDAaWlpdDr9XKXKIoJEyZg\ny5Ytcpchuezs7Fofa9WqFS5evIgHH3xQwoqk89lnn+GNN94AABw/fhw9e/YEUPFlZtGiRXKWJrrD\nhw/j6aefBgBcu3YNTZs2BQDExMRgzJgxiv5dr2SxWLBz505ERkbCYrFg165dcpckqsb0HqeTuwC1\nUsNYhdjYWOzcuRMRERH46quvMGrUKPzyyy/YsGEDAgMDFf/Gp0aBgYHQaDS2b/AajQbnzp1DQUEB\nTp06pdiQAwBHjx61BZ1PPvnEFnTUMONo06ZNtqDz9ttv2z4A9+zZgzFjxij6d/38+fOIjIzE3r17\nIQgCPvzwQ3Tp0kXusqgKBh2RhYaG3hJqBEHA5cuXZapIOjt37sSGDRsAAAaDAWPGjMGwYcMwduxY\nBAYGylyduNLS0jBmzJhqxwRBgEajQUxMjExViW/r1q22tsViwdq1a2E2m7Fx40YZq5JG1c5xtXWU\n13btSv/v8Oabb8JkMuHFF1/Erl27MGPGDNWEnJ9++gn+/v41PpaUlCRxNXVj0BFZu3btajw+a9Ys\niSuRh7OzMwDgueeeAwA4OTnBaDTKWZIkHn30UYSGhspdhmwyMjIwd+5c9OzZE/Hx8XB0dJS7JNFV\n/UKjhh7bqmq7djX8d9BqtSguLobValXF9Vbq3LkzIiIi5C7jjjDoiGzYsGG3HPv+++8RGRmJoUOH\nylCRdEpKSmy9GGPHjgVQ8Q2vrKxM5srE5+joiNatW8tdhuSsVis+/fRT7Nq1C4sXL4afn5/cJUnm\n8uXL2Lp1q63HtrJ9u4HpSlBUVISzZ8/CarWiuLi4WlvJPv30U1y8eBHx8fEYOXIkCgsLkZiYCH9/\nfzg4cK6PveBgZInk5+cjLi4OsbGx8PT0xEsvvYRBgwbJXZao1q9fj6KiIsyaNQsODg4QBAFr1qyB\ni4uLbSyDUu3YsQMvvvii3GVIbuTIkcjOzsakSZPg4uJS7bHRo0fLVJU06treYurUqRJWIr3KWaQ1\naSzf+u+VIAg4cuQItm3bhpMnT+LQoUNylySqY8eO4e9//7vcZdwRBh2RnTp1CpGRkfjpp58waNAg\npKamquYXv7y8HGFhYdi7dy/c3d2Rn5+PAQMGYPbs2dBqtXKXJ6r4+HiMGDECAPD777+jffv2ACo+\nDJX8oafmD3ug4guNu7s7ACAnJwdarRYeHh4yV0VS+f3336HX6+Hq6opmzZrJXY6oahp/WmnmzJkS\nV3MbAomqQ4cOQlhYmFBSUiIIgiBMmjRJ5oqkV1ZWJuTk5AilpaWCIAhCUVGRzBWJb/z48bdtK9n5\n8+eFlJQUISsrS+5SJJOcnCz07t1byM/PFwRBEI4cOSL07t1b+PHHH2WuTHxZWVnCW2+9JZSWlgop\nKSnC3//+d6Ffv37Czz//LHdpokpKShKefvppwWKxCFFRUcIzzzwjjBw5UoiNjZW7NNElJCTU+j97\nwzE6IouKikJcXByGDBmC/v37o7CwUO6SJHPhwgV88cUXcHNzw2uvvQadTofDhw9jyZIl+O677+Qu\nT1SCSmehmM1mzJo1C/n5+WjdujXOnTsHDw8PhIWFKX4Q+po1axAREYH77rsPAODv74/PP/8c7777\nLqKiomSuTlyLFy/G6NGjodPpsGLFCqxatQqPPvooZs+erege7I8//hhxcXHQ6/XYuHEjvvjiCzz4\n4IMYP348Ro4cKXd5ovLx8cFf//rXW47v2LFDhmrqxqAjMl9fX/j6+qKwsBC7d+/Gv//9b4wcORIv\nvvgiXn75ZbnLE9WsWbMwbNgwZGdnY+3atdDr9fj222+xfPlyuUsTnVpnoYSGhmLgwIHVBtrHxcVh\n1apVWLx4sYyViU+r1aJNmzbVjj3yyCOqGJRaWFiIZ555BteuXcOlS5fQq1cvABWD05VMp9OhRYsW\nyMrKgl6vx0MPPQQAqviZT58+HRs3boSnpyeAiuUkFi1ahP/85z92Nz6RQUciLi4uGDlyJEaOHInT\np08jNjZW7pJEp9FobANQ+/btiyeeeAI7duyAk5OTzJWJLz8/H0ePHoXVasX169eRlJQEQRBw/fp1\nuUsTVUZGBhYuXFjt2MiRI7Ft2zaZKpKOIAiwWq3VPuTKy8tRWloqY1XSqPydPn78uG3pf0EQUFBQ\nIGdZotNoNCgrK8OhQ4dsa8qYzWbFzzYDgKVLl+Ktt97Cpk2bUFhYiBkzZuCJJ56wy991Bh2RlZeX\n48CBA/Dw8IC3tzdWrlyJ0tJSVQzM1On+98/L3d0dK1asUHyPRqUOHTrYloD38fHB7t27bW0lq/oz\nr0rpg88B4Pnnn8fMmTPx5ptvok2bNrh06RI+/vhjxc+uBID27dtj1qxZOHXqFN5//31cuXIFa9eu\ntav9jsQwbNgwPPfccygrK8OXX36J06dPY86cOXXOQlMKPz8/LFiwAK+88gosFgveffdd9O7dW+6y\nasSgI7KgoCAIggCz2YxLly6hb9++ePDBBzFv3rxGs0/I3aoaaoxGo2pCDoBbbs/duHEDDg4Oih+n\n4u7ujv/85z/429/+Zjv2n//8xzZuRclGjx4NV1dXLF++HFeuXEGrVq0wYsQI22KZShYUFITExERM\nnDgRf/vb3/Dbb7/h0UcfVfwH/tChQ9GvXz84OjrC0dEROTk5WL58ueK/0FTq3r075s+fjzVr1qBr\n165yl1MrTi8X2ZgxYxATE4Py8nI899xz2LdvHwBg4sSJ2Lx5s7zFiaxjx462qbZVp90C9rdEeENL\nS0vDu+++i7i4OBw8eBAhISFwc3NDUFAQ+vbtK3d5ojl//jwmT56M7t27o23btjh//jyOHz+OTz75\nBG3btpW7PFGNHj262j5flZS+7Qfwv8071Wj//v3o168fCgoKEB4eDkdHR7zxxhu3rCOlNFX/vWdm\nZsLZ2Rn3338/ANjdv3f26Iiscul7rVaLli1b2o4rfZAeULGGkFqtWrUKK1asgF6vx5o1a7Bx40Y8\n/PDDmDRpkqKDTps2bbBt2zYcOnQIWVlZePzxxxEYGKj4N30ACAsLk7sE2Rw7dgyJiYlYtmxZtS80\nSvfBBx/g3Llz6N27N95//304OzujZcuWeO+997Bq1Sq5yxNVY/r3rvyh4TLLz89HUlISjhw5YhuU\nWtlWuvj4eFv7999/t7XrWlROKaxWK7y9vXH58mUUFRWhY8eOMBqNqpiN8eeff2LAgAGYMGECrl+/\njt27d6si2Ldu3brW/ynd2rVrMXjwYEyYMEHxvbVV/fvf/8a6desAAIcPH8bcuXMxceJEZGVlyVyZ\n+Fq2bIm0tDRkZWXZ/p07OTlh9erVcpd2C/boiKxDhw7VBqKqZVAqULGeQuXqwO+//75tTFJKSoqc\nZUmiclDukSNH0LNnTwBAaWkpzGaznGWJ7osvvsCePXsQHR2NVatWITs7G61atcKyZcswf/58ucsj\nEQ0ePBje3t4YPXo0mjRpYjuu5OBjMBgAACdPnkT79u1tmxirYaZd5Qr3OTk5+OOPP9CmTRu8++67\nmDBhgtyl3YJBR2SVg1ItFguuXr2KZs2aqWJ6NaDeRfMAoGfPnhgzZgwuXbqETz75BJmZmVi8eLHi\nB6Z+8803iImJgUajwa5du/Dtt9/Czc1NteM31GTbtm345JNPMH/+fMVvWFxJp9MhKSkJ27dvx7PP\nPgsA+PHHH+Hm5iZzZeLLzMxEQkICLBYLRowYAb1ejy1btuAvf/mL3KXdgkFHZGVlZVi2bBkSExPR\nvHlzXLlyBb1790ZwcDD0er3c5YlKrYvmAcDrr7+OZ555BkajES1btkRmZiZGjx6N/v37y12aqAwG\nA7RaLdLS0tC2bVvbG74awq2aTZo0CYIgIDIyEg888IDc5Ujm3XffRVhYGJo3b44xY8bgyJEjWL16\nNdasWSN3aaKrnEHq6OgIq9WKzz//3G7HZzHoiGz9+vVo1qwZ9u/fD6BiXZ2PP/4YH330EWbPni1z\ndeJS66J5ALBkyZJqt2o8PT1tK4gqmUajwZ9//ont27fbBl2fPXtWFevoqFmfPn3g7OyMH3744ZbH\nlNy74+npWS3UPPnkk3jyySdlrEgezZo1s9uQAzDoiC45ORnR0dG2P2u1WkyfPh2jRo2SsSppqHXR\nPAA4ffq03CXI4u2338Y777yD5s2bIzAwECkpKZgzZw4++ugjuUsjEV26dKnanwVBQEJCApo0aaLo\noFP1C83XX3+NF154AQAwZcoUfPzxx3KWJro//vgDs2bNgiAItnal0NBQGSu7FYOOyGq7PVXbCrJK\ncvOieRaLBcD/ptwr2eXLl7F169YaH6vcFkOJfH19ERcXZ/tzp06dsH//fsXfplW7qh9ymZmZCAoK\nQu/evTFv3jwZqxJf1S8027ZtswWdGzduyFWSZKr2ZNn7GDzlf9rKzMnJCZmZmdVuW2RmZlablaBU\nGRkZWLNmDZo1a4bBgwcjMDAQABAcHKzob3lAxayLnJwcucuQXHBwcK2PqWEzV7WLjIzEl19+ieDg\nYPTp00fuckRX2yQLNYxD7NatW7U/m0wmbN++HdHR0dizZ49MVdWMQUdks2bNwuTJk/HSSy/ZVoqt\n3M1Z6d577z1MmzYN169fx5QpU7B9+3Z4eHhg0qRJig86rVu3VsV+Zjc7deoUiouL8cILL6Bz584c\nhKwSly9fRnBwMO677z7ExcWpYssPQH2TLGryxx9/4F//+he++eYbPPvss1ixYoXcJd2CQUdk3t7e\n2LJlC3bs2IGjR4+iZcuW+Pzzz6utkqxUer0evXr1AgBs2bIFDz/8MACoYpVcNfx8a7Jz506cPn0a\nX3/9NTZs2IAnnngCL7zwAh566CG5SyMRDR48GI6OjujRowcWL15c7TF7G6/RkLKyshAWFgZBEKq1\nz58/L3dpotu3bx8iIyNRWlqK4cOH488//7zlZ28vGHQk0KxZM7zyyivVjm3evBkTJ06UpyCJVP2G\nU3VcjhpWyf3ggw9w5swZ25oSWVlZKCoqgpeXl8yVic/Ly8s2o/DHH39EaGgoLl26hNjYWJkrI7GE\nh4fLXYIspk+fXmN72rRpcpQjqaCgIEyYMAH//Oc/0bRpU3z77bdyl1QrBh2Z7Nq1S/FBp6ZR+YIg\n4MyZM3KXJrp9+/YhLCwM27Ztg6urK3JychAcHIw5c+agX79+cpcnOpPJhO+++w67du1CUVGRbZAm\nKdPN4zXUYtiwYbBYLEhNTcW1a9fwwAMPoFOnTqrY6mXfvn3Yvn07xo0bBy8vL1y7dk3ukmrF3ctl\nMmLEiGp7QSlRXVs9KP2NcfTo0fjss8+qrS2Rm5uLyZMnK7pnY8+ePdizZw+ys7Px7LPPYsiQIWjT\npo3cZRGJ4tdff8XMmTPRoUMHNGvWDNnZ2Thz5gzWrVtnlysEi+X48eOIjY3FL7/8ggEDBiAoKEju\nkqphj47IKqdUq5FaB+cBFbfqbl5ASw3bf8ycORPt2rWDt7c3Tp8+jQ8//ND2mJLHapA6ffDBB/j4\n44/Rrl0727Hff/8dK1aswMaNG2WsTHw3z7Bs0qQJvLy8EBcXx6CjNgMHDrR94Ktt+uH48ePh6emJ\nv/3tbwD+d/0ajQZPPPGEnKWJTqPRoLi4uNoyAkVFRYrf7K9y41YiNSguLq4WcgCgffv2iv89B2qf\nYV0GqpoAAAzESURBVDlu3DiZK7sVg47Ivv/+e7lLkE18fDx27dqFtLQ09OjRAy+88IJqbmNMmDAB\nr732Gv7xj3+gbdu2uHTpEv7v//7PLt8EGlK3bt1s43MuXLiAVq1a4dlnn7Xti0OkJLVtbaKGCReN\naYYlx+iIrKysDNu3b8fIkSMxa9Ys5OTkQKPR4P3331fF3kdARU/ODz/8gJ07d+Lq1avo27ev3a+k\n2RB+/vlnxMbG4sqVK2jdujWGDx+OTp06yV2WqM6ePYspU6agb9++aNOmDc6ePYtDhw4hPDwcjzzy\niNzlETWoAQMG3DKjVhAEbN68Gd98841MVcnjxx9/REREhF3OsGSPjshWrVplG4GfnZ2NpUuXIjk5\nGeHh4Xa5sJIYNBoNOnfujNzcXHz11VeIi4tTRdDp3LkzOnfubPvz6dOnsXDhQrtda6IhrFy5EqGh\nofD29rYdGzJkCFauXIlPP/1UxsqIGt7zzz9f4wroQ4YMkaEaeTSGGZYMOiLLyMiwjVvQ6XRo164d\n2rVrh23btslcmfhKS0uRmJiIXbt24c8//8QzzzyDefPm3XJPW8nKy8vx7bffIjIyElevXsXIkSPl\nLklUJpOpWsgBKjZ3VcOO9aQ+alz9vNLNMywXLVpkt0MTGHREVvVebdWN79QwZuHvf/877r//fgwe\nPBjDhw+HRqNBdnY2srOz4e/vL3d5osrJycHWrVuxY8cOdOrUCRaLRRVd2bXdCS8vL5e4EiLx1fU+\nlpSUJGEl0mtMMywZdEQmCAJMJhOMRqNtfIbJZFLFHkDPPPMMNBoNsrKykJWVVe0xpQedZ599FhMm\nTMD27dthNBoxadIkuUuSxGOPPYbIyMhqg66joqLQoUMHGasiEsfYsWMxZcoUABX7falp65fGNMOS\ng5FFtnv3bmzbtg1BQUG2TT1XrVqFMWPGoH///nKXJwmLxYKrV6+qYh2ZSnv27MG2bdtw/fp1jBgx\nAvv27cOXX34pd1miKywsxIIFC3D69Gl4enriwoUL8PT0xMqVK+Hs7Cx3eUQNasKECbYP/Kptsi/s\n0RHZ4MGDYTQaERoaivPnz6NVq1YYN24c+vbtK3dpoisrK8OyZcuQmJiI5s2b48qVK+jduzeCg4Oh\n1+vlLk9Uzz33HJ577jmcP38e27ZtQ1ZWFmbMmIEXX3wRffr0kbs80bi4uCA0NBTXrl1DVlYWWrZs\nqapvuaQuVfsJ2Gdgvxh0JPD000/j6aeflrsMya1fvx7NmjXD/v37AVSM0/j444/x0Ucf2TZ9VKrw\n8HC89dZbaNOmDQICAjB9+nQcOnQIcXFxig4669evr/UxNQ/cJGWquvCrGhaBbax464pEM3bsWERH\nR99yfNSoUXa3zkJDU2uXdkxMjK29efPmahvXqmFJAVKXrl27on379raNiyvbGo2m2u8CyYs9OiSa\n2m5P6XTK/2en1i7tqmFm9+7dDDekaF9//bXcJdAdUP4njsysVisOHz4MFxcXdO/eXe5yJOXk5ITM\nzMxqK0BnZmZW2/9Jqdilrd7rJvVo3bq13CXQHWDQEdl7772HgoICFBYWIi0t7ZblwpVs1qxZmDx5\nMl566SXbjLO4uDisWrVK7tJEl5aWhjFjxti6tCvb7NImIpIWx+iILCAgAFFRUSgtLcVrr72GzZs3\ny12SpHJzc7Fjxw5kZWXhgQcewNChQ1UxC+fChQu1Pqbkb4GjR4+GRqPhmAUishvs0RFZ5XgUvV6v\nih1tb+bh4YEOHTqgadOmaNWqFe6//365S5KEksNMXR544AG88847cpdBRGTDoEOiuXr1Kt544w08\n9NBDaNOmDQ4ePIgVK1bgs88+U03gUZtr166pNuQRkX3irSuRdezYEe7u7gCA/Px8WxtQ/l4o/7+9\nuwtp6o3jAP6d5Us1C9KciYTSRSGz11nNhaRRTkuyzV7AlMrKLmrRm714USnCYZFYSBZlppBRxiIk\nKaSMamRCVGSliFTDC19KL8qcU7f/xZ9G+0/h//fv2Ynt+7k6e/bs7Ht2sx/nec7zHDlyBHq9Hmq1\n2tn29OlT3L1712VfFPIeiYmJSEtLG/W9Q4cOeTgNERHv6IiuublZ6giS6ezsdClyACAhIQEXL16U\nKBGJLSgoCNHR0VLHICJyYqHjQb29vWhsbER0dDSMRiMqKiqkjiQqPz8/qSOQh4WGhmLjxo1SxyAi\ncuI/kci+f/+O+vp6/Pz5EyMjI6iqqkJGRgY2bdokdTTRRUREoKGhwaXtyZMnnMPhxZRKpdQRiIhc\ncI6OyLKyshAZGYnu7m7o9XoUFxcjIyMDbW1tOHfunNTxRNXb24v9+/cjODgYc+bMQUdHB759+4ay\nsjLMnDlT6nhEROQDWOiITKfTwWQyIT8/H69evcL169cRHh6OzMxM3LhxQ+p4HtHc3OzcyXrJkiVS\nxyEiIh/CoSuRaTQaaDQa9Pb2YtasWbDZbLBYLBgeHpY6mscolUqkpKQ4i5yjR49KnIiIiHwF7+h4\nwODgIAIDA/HhwwccPHgQQ0NDEAQBy5YtkzqaJDIyMnDnzh2pYxARkQ/gU1ceEBgYCACIiYnB7du3\nMWnSJMjlcolTEREReT8WOiJ7//498vPzUVNTg4aGBpw6dQrTp0/HsWPHkJSUJHU8UY22IKLD4cCP\nHz8kSENERL6IhY7IjEYjBEGAv78/SkpKcOXKFURFRWHXrl1eX+jcv3/f5fXg4CAAYPHixVLEISIi\nH8TJyCKz2+2YP38+urq6MDAwAKVSCblc7hOL6WVnZ6Ovrw9+fn5Yv349zGYzzGYzli9fLnU0IiLy\nEd7/byuxX7uXP3v2zLkdwtDQEPr7+6WM5RFnzpxBdnY2NBoN9u3bh1u3bqG+vh7V1dVSRyMiIh/B\noSuRqdVqbN26FZ2dnSgrK4PFYkFBQQFSU1OljiY6f39/xMfHAwCqqqoQFRUFAJg6daqEqYiIyJew\n0BHZnj17sHr1asjlcigUClgsFmzZsgVr1qyROproZDKZ8zggIMB5bLfbpYhDREQ+iOvokGji4+Oh\nVqvhcDjQ2NjoPH758iXMZrPU8YiIyAew0CHRNDU1jfmery6WSEREnsVCh4iIiLwWn7oiIiIir8VC\nh4iIiLwWn7oiIo/o6OiAVqvF3LlzXdo3b96M8vJy+Pv74+HDh8724eFhrFy5EqtWrYIgCDh+/Dga\nGxsxY8YMAIDNZkNmZia2bdsGk8mEpqYmCILg9r1fv36FIAh48+YNpkyZgrCwMBw+fBgxMTF4+/Yt\ndu/ejdraWigUCgCA1WpFWloaTp48ib6+PgiCgNmzZ7ucs6CgACEhIS7XY7fb0d/fj/T0dBgMhgn9\n7Yho/FjoEJHHhIWF4d69e27t5eXlsFqtaG1txbx58wAAL168cFmiAAAMBgN0Oh2AvwuYlJQULF26\ndMzvs1qtyM7Ohl6vx9mzZyGTyWA2m7Fz505UV1dj4cKF0Ov1KCwsRGlpKQDg/PnzUKlUSExMhMlk\nQlJS0qgFVEdHh9v1dHV1ITk5GevWrXMr6IhIGhy6IqI/wtq1a13u6NTV1SE5OXnM/qGhoYiKisLn\nz5/H7FNXV4eQkBDk5OQ4iyaNRgOdToerV68CAA4cOIC2tjY8evQIHz9+RH19PfLz88d1DT09PXA4\nHJg2bdq4Pk9EE493dIjIY7q7u7FhwwaXNqPRCADQarU4ffo0DAYDbDYbWlpakJWVNeYyBS0tLfj0\n6RNiY2PH7PPu3TvExsa6tcfFxaG4uBgAEBQUhKKiIpw4cQIhISEoLCyEXC539n38+LFL5oCAANTU\n1Lhcz+DgIPr6+hAbG4vS0lKEh4f/h1+FiMTEQoeIPGasoSsAUCgUkMvlaG9vh8VigUajcetz4cIF\nVFZWwm63IygoCAUFBYiMjByz0JHJZBgZGXFrHxoacnmtUqmgUqngcDice9L9MtbQ1e/XY7fbIQgC\nWltbsWLFilH7EpE0WOgQ0R9Dq9XiwYMH+PLlC7Zv346WlhaX93+fo/NvLFiwADdv3nRrf/36tdud\nnoiIiPGFBuDn54e8vDykp6fj2rVryM3NHfe5iGhicY4OEf0xfhU67e3tiImJ+d/nS01NxcDAAC5f\nvoxfa6M+f/4cJpMJOTk5//v8v5s8eTLy8vJw6dIl9PT0TOi5iWj8eEeHiDxmtDk6cXFxzmOFQoHg\n4OBxbRFSW1vrMpk5NzcXe/fuRWVlJYxGI7RaLWQyGSIiIlBRUfGvn4r65xwdANixYwdUKpVb34SE\nBCxatAglJSUoKir6z9dARBOPW0AQERGR1+LQFREREXktFjpERETktVjoEBERkddioUNERERei4UO\nEREReS0WOkREROS1WOgQERGR12KhQ0RERF7rL7K/RXl1ATnmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cb41d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='EMPLOYER', y='BASE SALARY', data=df_)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#sns.swarmplot(x='EMPLOYER', y='BASE SALARY', data=df_, color = \".25\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17ff5198>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SesxMxOvxAQA8Hl+f3g+2R+bBNsks2B6ZBdsjPvHEXFyX0GuvvYZXX30Vy5cvx2WXXYZF\nixZh2rRp2LFjBwBg06ZNmDJlCsaNG4fy8nK43W7YbDYcP34cY8eOxaRJk7Bx40Z538mTJyMvLw8m\nkwlnzpyBJEnYsmULpkyZgkmTJmHLli0IBAKora1FIBBIaF0hfYiOTiFCCCHpI66FRYu5c+fiySef\nxOLFizFy5EhMnz4dBoMB99xzD2bOnAlJkvDQQw/BYrFgxowZmDt3LmbMmAGTyYRnn30WALBw4UI8\n/PDD8Pv9KCsrw/jx4wEAU6ZMwV133YVAIID58+f37JUSQggh5LxFJ53naR7pNq3RnBfk6dd240h1\nGy4ZXoC535/UZ+fB9sg82CaZBdsjs2B7xCdllxAhsaBDiBBCSDqhYCGEEEJIxkPBQrrFee1PJIQQ\nct5AwUJSgklChBBC0gkFCyGEEEIyHgoW0j3O7yQzQggh5wkULIQQQgjJeChYCCGEEJLxULCQbkGH\nECGEkHRAwUJSQsc0IUIIIWmEgoUQQgghGQ8FCyGEEEIyHgoW0i0Yw0IIISQdULAQQgghJOOhYCGE\nEEJIxkPBQroHfUKEEELSAAULSQlmNRNCCEknFCyEEEIIyXgoWEi3kOgTIoQQkgYoWEhK0CNECCEk\nnVCwEEIIISTjoWAh3YMeIUIIIWmAgoWkBtOECCGEpBEKFkIIIYRkPBQspFvQI0QIISQdULCQlKBD\niBBCSDqhYCGEEEJIxkPBQrqFRJ8QIYSQNEDBQlKDPiFCCCFphIKFEEIIIRkPBQshhBBCMh4KFtJN\nGMRCCCGk96FgISmhYxALIYSQNELBQgghhJCMh4KFdAumNRNCCEkHFCwkJbj2ISGEkHRCwUIIIYSQ\njIeChXQLeoQIIYSkAwoWQgghhGQ8xkQ7+P1+zJs3DydPnoROp8PChQthsVjw6KOPQqfTYcyYMViw\nYAH0ej1WrVqFlStXwmg0YtasWbj55pvhcrnwyCOPoLm5Gbm5uVi0aBGKioqwd+9ePPXUUzAYDCgr\nK8ODDz4IAFi6dCk2bNgAo9GIxx9/HOPGjev1m0AIIYSQzCahYFm/fj0AYOXKldixYwd+//vfQ5Ik\nzJkzB9dccw3mz5+PdevWYcKECVi+fDnWrFkDt9uNmTNn4vrrr8eKFSswduxYzJ49G++99x6WLVuG\nefPmYcGCBViyZAmGDx+OBx54AAcPHoQkSdi5cydWr16Nuro6zJ49G2vWrOn1m0C6AX1ChBBC0kBC\nwfLVr34VN910EwCgtrYW+fn52LZtG6ZOnQoAmDZtGrZu3Qq9Xo+JEyfCbDbDbDajtLQUVVVVKC8v\nx3333Sfvu2zZMtjtdng8HpSWlgIAysrKsG3bNpjNZpSVlUGn02HIkCHw+/1oaWlBUVFRL10+SRUm\nCRFCCEknCQULABiNRsydOxeffPIJnnvuOWzduhW6UF5rbm4ubDYb7HY7rFar/J3c3FzY7XbVduW+\neXl5qn2rq6thsVhQUFCg2m6z2eIKlsLCHBiNhq5ddTcpKbEm3ukCx2wJPjpGk77P70dfH59EwzbJ\nLNgemQXbIzWSEiwAsGjRIjz88MO488474Xa75e0OhwP5+fnIy8uDw+FQbbdarart8fbNz8+HyWTS\n/I14tLY6k72EHqGkxIrGRltaj5mJeNw+AIDX6+/T+8H2yDzYJpkF2yOzYHvEJ56YS5gl9Pbbb+PP\nf/4zACA7Oxs6nQ5XXnklduzYAQDYtGkTpkyZgnHjxqG8vBxutxs2mw3Hjx/H2LFjMWnSJGzcuFHe\nd/LkycjLy4PJZMKZM2cgSRK2bNmCKVOmYNKkSdiyZQsCgQBqa2sRCAToDspQdKwcRwghJI0ktLB8\n7Wtfw2OPPYbvf//78Pl8ePzxxzFq1Cg8+eSTWLx4MUaOHInp06fDYDDgnnvuwcyZMyFJEh566CFY\nLBbMmDEDc+fOxYwZM2AymfDss88CABYuXIiHH34Yfr8fZWVlGD9+PABgypQpuOuuuxAIBDB//vze\nvXpCCCGEnBfoJOn8Xg0m3aY1mvOCPPdGBfYea0LpgDz84odT++w82B6ZB9sks2B7ZBZsj/h0yyVE\nCCGEENLXULAQQgghJOOhYCGEEEJIxkPBQrrFeR0ARQgh5LyBgoWkBLOaCSGEpBMKFkIIIYRkPBQs\npFtUN9jRGap6SwghhPQWFCyk2xw+09bXp0AIIeQCh4KFdBvGsxBCCOltKFhIt+G6QoQQQnobChaS\nEkqRQr1CCCGkt6FgId2GgoUQQkhvQ8FCug1dQoQQQnobChaSEroY/yaEEEJ6AwoW0m1oYSGEENLb\nULCQbqOnXiGEENLLULCQ1FCIFFpYCCGE9DYULIQQQgjJeChYSLfR08JCCCGkl6FgISmhyhKiXiGE\nENLLULCQ7kPBQgghpJehYCHdhi4hQgghvQ0FC0kNriVECCEkjVCwkG6jo0+IEEJIL0PBQroNLSyE\nEEJ6GwoW0m1YOI4QQkhvQ8FCUoKLHxJCCEknFCyk29DAQgghpLehYCHdhi4hQgghvQ0FC0kJnU77\n34QQQkhvQMFCCCGEkIyHgoUQQgghGQ8FCyGEEEIyHgoWQnqZlg4XfP5Ar/2+vdMLp8vXa79PCCGZ\nAAULIb2I2+vHw8u24YFnNvTaMX76x8148A+beu33CSEkE6BgISnBVObkoOWDEEJ6BgoWQgghhGQ8\nFCyEEEIIyXgoWEhK0CFECCEknVCwEEIIISTjMcb70Ov14vHHH8fZs2fh8Xgwa9YsjB49Go8++ih0\nOh3GjBmDBQsWQK/XY9WqVVi5ciWMRiNmzZqFm2++GS6XC4888giam5uRm5uLRYsWoaioCHv37sVT\nTz0Fg8GAsrIyPPjggwCApUuXYsOGDTAajXj88ccxbty4tNwEQgghhGQ2cQXLO++8g4KCAjzzzDNo\na2vDrbfeiksvvRRz5szBNddcg/nz52PdunWYMGECli9fjjVr1sDtdmPmzJm4/vrrsWLFCowdOxaz\nZ8/Ge++9h2XLlmHevHlYsGABlixZguHDh+OBBx7AwYMHIUkSdu7cidWrV6Ourg6zZ8/GmjVr0nUf\nSFehT4gQQkgaiStYvv71r2P69OkAAEmSYDAYUFlZialTpwIApk2bhq1bt0Kv12PixIkwm80wm80o\nLS1FVVUVysvLcd9998n7Llu2DHa7HR6PB6WlpQCAsrIybNu2DWazGWVlZdDpdBgyZAj8fj9aWlpQ\nVFQU9wIKC3NgNBq6fSO6QkmJNa3Hy0SyLCb530VFuX16TzK5PfTm8CvW2+eZSfchk86FsD0yDbZH\nasQVLLm5uQAAu92On/70p5gzZw4WLVok1+DIzc2FzWaD3W6H1WpVfc9ut6u2K/fNy8tT7VtdXQ2L\nxYKCggLVdpvNllCwtLY6u3jJ3aOkxIrGRltaj5mJuNxe+d8tLQ5Y+sjikunt0Wpzy//u7fPMlPuQ\n6W0SD5fHh0OnWzF+VDH0+gvDjHg+t8eFCNsjPvHEXMKg27q6Ovz7v/87vvOd7+CWW26BXh/+isPh\nQH5+PvLy8uBwOFTbrVaranu8feP9BiGEpIuX3zuEJWv2Y+v+ur4+FUJIBHEFS1NTE374wx/ikUce\nwe233w4AuPzyy7Fjxw4AwKZNmzBlyhSMGzcO5eXlcLvdsNlsOH78OMaOHYtJkyZh48aN8r6TJ09G\nXl4eTCYTzpw5A0mSsGXLFkyZMgWTJk3Cli1bEAgEUFtbi0AgkNC6QtKHJEnocHrkvy+MuSchaipP\ntQIAzjTY+/hMCCGRxHUJ/elPf0JHRweWLVuGZcuWAQCeeOIJ/PrXv8bixYsxcuRITJ8+HQaDAffc\ncw9mzpwJSZLw0EMPwWKxYMaMGZg7dy5mzJgBk8mEZ599FgCwcOFCPPzww/D7/SgrK8P48eMBAFOm\nTMFdd92FQCCA+fPn9/Klk67wykeHsXFvLX7xg6tROpCWr3Sx/0QzDp1uxR03jeJyCGlAeIEkSerb\nE+khfvj0pwCAlx/9ch+fCSHdJ65gmTdvHubNmxe1/dVXX43aduedd+LOO+9UbcvOzsZzzz0Xte+E\nCROwatWqqO2zZ8/G7NmzE540ST8b99YCAA6faaNgSSMf7zyDylOt+NZ1FyE3y5T4C6RbCFF4gegV\nQi4oWDiOdAl/QPTk8Wf7Z5sc2FXV0PsndIHTEgra5QCaHmQLS9+exnlFXbMD9WlOfiBfTChYSJcI\nJDlyPvniDix7+wACAXb93UGZZUR6n7CFhc9tsixZsx//++aBvj4N8gUgrkuIkEi6KkACkgQ9Q3RT\notPtg8vj7+vT+EKhu4BiWNJ1DU6XFw6XD4GAdMGkgpPMhBYW0iWES4jxn71PC60raUdYWC4Ew6DP\nn56LCEjBfqHNzueV9C4ULCQphD7xXwg9+XlCq83V16fwheNCyhLy+tJjnRP3qrGtMy3HI19cKFhI\nUghT74XQkZ8vtHb03ox17bZTePKlHb32++crF1KWkNcXSMtxxL1qbKPAJr0LY1hIUhj0OvgDUtgl\n1Mfn80WgNwNu39p0AgDgcHmZLq3BhSDMPekSLKGcqqZ2WlhI70ILSwYjSRLqW5wZ0XkKC4s/TX5x\nArSkwSXkSzCoBSQJT72yC29uOt7r55IJ6C+gGJa0CRZaWEiaoGDJYHYfacRjz3+GAydb+vpUYAgJ\nFqYpp490BN0miklye/w4XtuBygx4BtPBhZQllL4YluD/aWH5YhKQJHzw2emkavG0dLjw3vZT8PlT\nE9MULBmMmLF0ODwJ9ux9hIVFrsNCn1Cvk44aLL4EgsUb6lgy4RlMB+FYrT4+kR4gfTEswiVEC8sX\nkep6O1ZvOI5Py8/G3U+SJDy/9iDWbDyBw2faUjoWBUsG4/L4AGRGZo7sEgqkpxO8UHG6fPjtq+U4\ncKI54b6xgm5dHh+efrUcFccT/0YiElnMvN5ge7c7vBeE1UGLQ6db0RDKcLmQCselyyUkHqE2mztt\nIimSC6G9zlfEONXp9sXd7/OqBhypDgoV5UK6XYGCJYNxujNHsBhkwdL353I+s2lfLY7WtGPxqn1x\n93N5fHL7R/JZZT2O1LTjD6vj/4Y/EED54Ua44xSf8ycwzQoLi88fQKf7witi5/H68ftVe/H6J0cA\nKF1C2vs3tXVi7daTKZu004kQm72NEAsSgOaO9FtZ1u+uwY8WrWdaNYA9Rxu77Jo7WdeBn//fNpyp\nt6V0TLc32C8I4aKF1xfAqvXH5L/tTm9Kx6JgyWCEYu1u3Mj2ynM419K9tT7kYEQ5S4g+oVRINAsR\nxHMHJTuLXberBv/71n68+vFh1XblbDSRAPV4wyIl1qxoz9HGbj9fkfj8AbSnoRCZy+uHzy/hXHPw\n/MVzHWsJii376/DW5pM4WtPe6+fWXTxpjmEBgoIu3Sz/OCg29xxt6tXj2Du9WL+7ps+sSIloau/E\nkjX7serTY4l3VvDqx0fQ1O7CGxtSC6z3hISxyxv7eTvTYENLhxuD++cAAGydFCwXHGJGm2gWHI9z\nLU68sPYgHn/+s26dixx0G+qcdh6q79bvfVERgiXLbIi7X1zBkuTzcLreDgA4XK32Fys73ESCRXks\nrTgWp8uHpW/ux2shC0VP8fRru/HQ0q2wp9ixJYuwQjR3uOAPBBSF42LsH7of6RBT3SVtMSyKpSIb\n+zCOxWTs3eHsL+8fwvKPj+CjnWd69TipUhcS3afOdc1SIqyFRkNq9y9sYYktWOqagud2+cVFAJDy\ne03BksGIwc3fDf9sT3X44bTm4MOdLv/4hYZo02xL/BJI8QRLolTkMNrPjbLtNlfUxf0FpVtBS7B0\nun2QJOD42fYezSA7UdsBoPcDj4UVwh+Q0NrhVpTm174WcY3t50EQcjoLx4kJTV9YWASmFAdcIOg+\ndcexEABAbZMDAFDTaE/5OL1JfcjK2dTugsOVfL8fFiypWc3FfYvneq5rDt67S4YXAADsjGHpfapO\nt+KWn/0Dh8+0puV4zh5wCfXUIMIYlp6hM/RS5yQQLCKl2awxaxSzfEOSC81Frvuk7Fg27Dkrd3Ra\nKMWN1iCtnF2JTul8wqMQZI3tLvlexXpvRMz5+SBY0jGpEO7F4oJsAOePhWXTvlpsP3BO/vuJF3Zg\n1rMb4348Ywc1AAAgAElEQVTHYgpaRT1pig3qKvWtYbF4pj55USULlhQtVLJLKE4MixB7Y4b1A0AL\nS1p4a3OwOug/tpxMy/FcPRB021MC40JK9+xLkrawhIIXC62WqM/EzDlRBx2rqSJnkvEWrVPO0rUs\nLMrfOh6yisSjq8GqvR0ppby+xrZOWbDEOk8hZM6HNO901GER/UFBrhlGg75vLSxdGHD/+kEVXnj3\noPx3Q2vi8zaH3LixLDGSJOHAiWY4XcnFqfU0yjooXQmgFYtkdtclFN/C4oQ1x4R+eRbkWIyMYUkH\n6bAylB9uwF8/qIIkSWGXUDeqyx463TPWIHHtPqY1dwtnkoJFWFjiCRajQY+zTYmtGpEB0l0JxlQO\nelpBt8qg3BO18QNR9xxtxAPPbMDeY8kHR/a2PnYrrq+pvVMegGPVpxHu2fPBwpIOl5Bwnen1OhT3\ny+rTTJ1UXRrJcPqcDcdCgdaxBMv+Ey1YvGoflqypSPk4Pn8grqUiHg0tnXJyRNcES+/GsHi8fjS2\ndWJw/1wAQF6OiVlC6UCfhmqv//vWAWzaV4uWDjecoaDbWP70ZHh326keOa90XPsXgbCFJXHQrcVs\n0BQ2YiDS63V48sU4CxjGaKp4M6FYxwJiWVjCnyeysAjL5IY98QtMpRNljE5jm0t+vmMFussxLPbk\nBcvZJodK2KWLdMaZ6XVAcUEWHC5f0plwPU0qXVOyfesv//a5/O9Y78/ZJu0g967w0nuH8MQLO7ps\nHfP5A2hqd2HEECuyzIbUXEJJupgjEc+2xxfQHB/OtTghARhSHBQs1mwT7J2p1XWiYOkCBn3wdqUj\njsPrD8gPUibEjRh0X0zBIkkS9h5tStrMGwhIeP2TIzhZpz14i848UQxLq82NIqtFDgJVkmz1Wbml\nImNYuuCD9yQQLMqBuLbRoTlYNbZ1wuXxyYN8v1xz0sfvbZTWpsa2TnkAi2XVlF1CSQYN1jY58OSL\nO7D473u7eaZdJx11WMSgo9PpUNIvFMfSR1aWWAPg2SYHTsfInIkUH7F+Q7lZWBQOn2lVxWIkHwyv\njSRJOHiqBa02NypPds0y3tTuQkCSMKgwB8MH5KGu2Zm0SBbZqKnGsCgtTlpWltpQbJtIac7LNsEf\nkFKq60TB0gXSGXiqTJvMhAUH+8LCUnmyJSmXR2+y+0gTnltTgWVv709q/33HmvDP8hr86m+7ND8X\nA7olTlqzx+uHvdOr6Q4Cum/q78ps35tk0G1+jgkSECXUbE4PnnxxB97efFL+fr+85AVLb1cwVV5f\nU1un/G7HdAmFttucnqTeBRFXcCTFui0dDo9chberpKMOi7gFOp0OxQVZAPquRH8sb/WTL+7Awr9+\nrvlZ5EQkmcfN7fWjpcOF372+RxXP6OtmP91m98AWcpWUH27o0ndF4PyAohyUDrQiIEmoaUzcdyqr\nZafuEgrfeC13lkhpFhaWvJzg6vD2zq67VSlYukA6B+02hck5nSIhEJCw71hT1Mwj3VlCkiTh2b/v\nje/ySAMNoQHn4KnkZjyJot9Fpxav8F6rPXb8CpC8YJFnvxHbE6Vvqo8VP4ZF/NalFxUCCKcjC2qb\nHPD4AirrjNkY3x2mpLefN3VhPK8cYxQr6Fau6ioFRUsiYg0CPn8AT79ajs8qz0V99tHOM3Ll3TlL\ntuDRP21PeBwtkq3X0x3CFhbIFpa+CrxNRdxGWgSTcRG5QxMKCeHsFyD8rCabvRdJdUPYjbPnaFOX\nAtRFhtDAwmyUDswDAJxOIo7l09018r9NKcYAKd8hrb5FZA8O6S9cQsEJSyqBtxQsXaDPLCxpTM3Z\nVFGLP75RgVc+qlJtNxjS5w4Duhe305M+9K7OOnoibkCsIVRozdL8vLvZH12xsIjrsZgN8HijAwJF\nByUKQkUKFi3rQFeeoZ6KwYqFuD7hphJuq1gxLMpzTybwNlY9jKPVbThS047n1x6M+uzvnx7DP8tr\nVNtSGYzT4xIK/l8HyBaWvkptjuwzjte248mX4k94Ipe/SOY2e7wBeeKhLIPf3eDV6oagwBhQkA2n\n24eqLiRMCEvewMIcXDTQCiBx4G1TWyf294CFxZPQJeREltmAgpBlVbawOL340z8OdMmKTsHSBQzp\ntLAoOsPuVLq9bdrILu1fE1L5kQvrRZbm721SPc6R6jb81+834b3tp3rkPLrq1+2JzAxRLK0ohoWl\ny6bniDiYrsSwiOsp7hccjCLjWIQlrqQgG/3zs3C8tl01uIp0UeXA0JUFNKtSXNU1WYRgEeZqQax7\nrHwuk0ltfv6daEECAF19SlKZKHRXPO88VI8PPjsddx9ZsOh0KO4DC4uyPSLFxlOvlONsArdItEso\nufssxElLh1sRqC3Sg+NYT21uvPL+Qc1JlQiU/XbZxQCAXYcbkzoXAGgQLqHCbAwpzoVBr0soWDbu\nq1XF5afas6tdQmrB4vMHUN/ixJDiXDkeLy87KFg27avFzkMNXbKiU7B0Ab2GhcXh8mLD3rM9nkKo\ntLB0RyQU5GkPevUtTvzw6U+jZsSilkHk9cjusDRZe1K15OwK+X7f234aTpcPB040dysOoquR8/Hi\nBpK9dy222DVYgK6Losgr6FIMS6hj7p8vBIvaYiCKRplNeowamg+b06sqRCcCMJWL4nVFcPV0xsnm\nfbWoPNUi/y2sVUOjBEuMLCGpaxaWWEhdfL5T6V+6a4n70z8qsXrD8bjvjyjLr9MBuVlGZFsMaYth\nOXymFR8qyuSn0jdFPl/J/oQyIUJMMETJh3iWir99WIXV647KNb2UVDfYkWMx4trLByE/x4Q9RxuT\n7vvrWzvRL9eMbIsRRoMeQ0tyUdPoiDk58PkD2LyvFmZT+FxT7SfVFhb1/WwMxYWJgFsgnHCQSmzW\nF0Kw1Lc6Ux5oX3z3IN4PzTLCLqHwQ7B1/zm88uHhHqt3IlDGsPSUS0iSJLz+zyM4cKIZazYFX5hI\n148sWCI6bDHopc0llOJxpNBp63U6LFlTgcWr9iUdf6L5e13cXwzgWn7sZNOJ49VgAZKPTYj12HQl\nhkXMGoW1J3KQFr9lMRkwcnA+AKhM2cLC0qISLD0j7r2+gOp3E9Fmd+MvH1ThjfXhRd5Eew0piRAs\nCYJuge4Jlq4+3qkJlp65z/HWiBHPmF6nk60sje2dvR4s3dDWiUWv71Et2JfKMSNdQsmOE8pnWLiF\nxDZDHAuLCKoV6/4I3B4/6lucGD4gD3q9DpPGlsDm9OJIEinSXl8AzR0uDCzMlreVDrTC6wtEHUdQ\nfrgRHU4vbrhqiLwt1a5d2Z9E9nG1EQG3QFDYAtHWrWS44AXLvmNNeOzPn6W8WN+2A+fwxobjcHl8\nmi4hYQlJtdhPLJSdYU+5YVptbvxzVw0Wr9on/2YgIMGtKKsuZgeR7634s6cES4czfvZDrAEjEQGE\nC1mJegipplkeONmMfV0ocgbEr0Kb9ErNoRiWovyeiWGJKs3fJcESvB4hniIDb1WCZWiw7Pbh02EL\nhrj3yhTGeFlvNQ32qHcp1kD01qYTePTP25MKfgXCbk6ltUe4TSItLInqsADR8TpdoasTqFTER3dc\nQsp7Hm9dmoAyiAVB16HHG5AH5t5gc0WtZiByIk+j1j13RlxbsqLH6wvvJyxKsktIH3tYzc0Wg7X6\nuDVNdkgAhocCZidfMgBAUFgkorEtWPBwQFHYipEojmV9qBbSV6cMk7f1jIVF3be89F7QJSqKxgFA\nTlbQJeRg0G00W/cHF3dL5MtMxNOv7dZ0CYmskJ6aNQp6I605VjzG82sr8cQLO3Cm3hazvLV4mHtC\nPHl9fsx5bgvmvRB7BenULSwhwaIYpFNdxXXx3/d1ecl6ISa01gBKWrDY3DAZ9fJMJPoYyT1rsQbF\nrqyFIp71ovwYMSwKwXLRwDwY9DocDllY7J1eODRmUbHelYZWJ+a/vBNPv7pbtV3rNwBg3/Em+PxS\nzM8jEYLF3umVO1lv6P8FoZLh4XOUNDtw5T3dfUQ9mJQfbsAnu6pj1uBR0tXBQelq9PoCScWJdEew\nKKsRx5sJK2NYgGAsEwA0tvdeHMuOg9qTz0QiUKtPiawFkmy3o3yGhShPxsKSJw/W6ntaHYpfGT4g\nKFguKS1AbpYR5UcaEl6XCLgdpBAsIlNIq4Dc2UY7jlS34fKLCzFQ8Z1UjWKxYlgCkiT/rbKwhERb\nKs/nBS1YXB6f3El1t5T2mXp7XMHS0zEsyk64t90wYlA+fKZNLhAXiXiYe+JcKkMuGp9fiuljTVWw\nyHUhFIrFbEo+jVaQ6nog4uU1aaTuJlsoqdXmQmGMonFA8s9aLKGbrIXlhbUH8VlocBAuoXiCxWQ0\noHSgFSdr2+H1+WNatmLFsAiL25kGdSer9e52OD0xzd1aeH0BVJ4MW35E3IHoNM1GvZzlItAaKOI9\nl//71gGs+OfRmDV4lGgNDpIkqdZ1UooaZZu/tekEfv6n7Th2Nn5tF183YliUKbZx3wVJPUEQgqWp\nLf2ZQsr7pTU50JrRO92pWViUgqU5ZGFJZk2enNAkJNJqJZ750gFW+TcmjClGm92T0JJX3xJOaRYM\nH5AHHbQtLBv21AIAbp44FEDYuthVEb3naCN+9/ruiMJx4fveoohlKlZYi3NDoi0VLmjBsu9Ys9wh\ndaWUdiKUnZZDtrD0nqjojUBX5YxNZAA1d7gSChKtDtvnl/D0a7uxaV9tUsdWVoR0exKnj3YFeW0T\nxWCfSnUBrcE2mewW2cJiin61Iv3l2t8PoMPp1cwQ+uHTn+KHT38a06IgakLUNTsgSVLMdZ+0BIvT\n5cVf3j8kD+Sdbh+2K2qEFMawsHhCMyhT6HpHDcmHzy/hdL09akG5YaE4kVj3MdZjrpWNc7S6a4XY\njlS3we31y25dEfsiLC0mo0EebAU+v4TN+2rx1PJd8qDtD0g9siCj1nu0vfIc/nvpVvlv5TugFCwf\nfR4MNP3N8nLsORLbZaCcwdqcHqzddippK58ycDqeBUtZOA4IZ5M19aCFxeHy4lycFcUjzwXQXqD2\noaVbcTAUcC2eg0jXVXhiFsCf36nEC2srNftf1aKZQrDIQbfhJ2TnoXr8v+c2y6JGDNaR97S6wQaD\nXqeyRAi30K6q+EXkGhQpzYIssxEDinJwpt4uC5FPdlVj4V8+x7rdNSjIM2PCmGIAwD3TLwHQtXGm\nrtmBP/+jElVn2mAy6jFhdPC3lH1LnaLN9IoJZJbZoOqfu8IFLVhE3IoOahdLqsjpa0oLS+jBizXr\ndXv9WLKmIqngqVh0J605Fm228P0oyg8Ojk3troQFs7SEREOrE0eq2/DXD6qiPktErNl+T7qEUnHX\nacXXxAs+FIRn7IbQd8IdUzKDRZs9fg2WeDS1d2LHwXo88cIOvLvtlGxhibTURGYJNbR1ovJUKzZX\n1GFzSHSKctqCfrlm6HRAe1QMSwBmk17ugEYODQbenjjbHnUPLykNFpdLJmVYSbsj+t09WtO192nf\n8aAVcfIlJQDCgc1ye5n0UYLF7w/gs4P1OH62QxZvAUlSdb6x0JqtKouMaV1pZB+hfG6V/YvS9H9U\nUUG3vsWJT3fXyFZfpWD56wdVeGvTiaTr2tQrxGZkvIUSZeE4ACgu6F55fkmScLbJgU37avHy+4fw\nxAufYfYfNuPx5z+TXW2xxtVEFhYAKA8JPFEL5GhNu2qg/vM7lQCA1euPY8fBeuw42ACXxm8pJx/N\nIXEm3jeDwsLy8nuHYHN65WymHA03byAgoabBgcH9c1Tu6ysuLkK2xYiNe9WZbZGItiopVD+/Fw3M\ng9Ptk2Ns/rH5pFxM7sYJQ+WlZsS7G+u+Ol0+fPDZafkZ9PkDePHdg/D4Aph165X488M34fabRgFQ\n95F1oef9x9+5QvV7Op1O8z4kwwUrWDrdPuw/0YKhxbkoKcxW1TVJ/TeDjdGVGJbPDzVgz9EmPP3a\nbs3PkyFSJNQ1O7C5IjlrRixaFYIlK1QmvrbJEXMwEVu1OuJUXC6CWMHKkdcsSRI+3nlGDg6OReSM\nD0icVaM14xcd7k0hsykAdCbhJpKDbkMWhxbFfU5GsMg1WPLVFpZkBLfHG3Z7rN9zVn4mI9sssg7L\nyTqbvE9NY9A0XRtRzMlk1MOaY9Z0CVkU7T9ySDDw9nhtBxojLCyXhgRLLAEe28ISPWB2ZQIgSRIq\njjUjy2zANZcNBBBuF68vAL1OB6NBL1sH5OM6vfLztnHvWUghn7zRqMeIwda4sVGNbZ148PebVNuU\n6b5a4izSxaV8F4X4kCRJnq0DQQG1q6oBz6zYg8ee/wyvfnwEWyrq0NLhUmVsnAqtpZPsGkjJWFia\n211YF6qUKio3F4aKg2m1WTK8seE4nnxxB/76QVXwOmxu2eqQyAWWzCRHCkhobOuULe72Tq9cVRgI\nrm7/2cFz+Pjz6uBvSpKc6aJE6V5qsbnhU6z9piyFMCKUOXesph1Ha9qwdusp1e+s312D37xaDrfX\nj+Ehd5DAZNTjR9+6DP5AAH9YtS/mKuf1rU4UWi2q9xAIZgoBYbeQsPwU98vCjRPC2UGiq4xlYVlX\nXo3VG47Lfct720/jZJ0N110xEFdfGrQCiTFE+cwJC8uQ/rmIhIIlgmBAXgBXXzoABbnmpNf+iEdz\nRAqlJEkKl5B2J6zlGkiWWIXq5r+0E395vwrHa1NbnwRQd0JCFde3OlUKWTnQiX8myt7paqxQshaW\nvceasPLTY3jihfhFhiQNl1A8d11DWyfu/90GvB1RF0G4M74yaSi+MikYSe90+xLGfwjzrCU0mCn9\nuPEEi9vrR8XxJvkZi0xpTqaAmsfnlzsCp9snX3fksxlpYVEGiYr1R+oiOmmDXod8DcHi8akFS0m/\nLPTLM+NEbbt8LwRjhwfFTKxnKJYPPdLC0un2JVV2XHCuxYmGtk5cMaJInoW2CpeQzy+LSxHwKFj2\n1gG5vEBNowNVp1tR1+TEsJJcGPR6+P0Smto6VeJf8NcPquKmzGpdq1aqq0AI4Q6HBx5fQBa0H39e\njWVvH8Ch060YELJueH1+bAklGwjEOWqt/h1JZLB0ZJyHYMmaCnzwWdByIF43IeKU7shnVuzBy+8d\n0vyNjz+vxpqN4dRkcQ/u/soY/OIHV+N/50yTZ+iJEieSWTvHH5AwNyLD6NPd6tXD15XXQK/T4bor\nBgEIV6BVIiaq+blmSFLQxagVwyKeqdP1Nvz21d1Rz8Q7207hZG0H+udn4borBkYdZ9LYEjx05wQE\nJEmziJ/H60dLh1sVvyIIl+i3w+3xo8PpxRUXF2LRj69T1efSJbCwiBganz+Ak3UdWLv1FAqtFnz/\nX8bK+wjBEmlh0emAgUXR5xYroSARF6xg2RsKJJ00tgT98iyQpORnF7GIrPng8vhlS0CsWXyswEkt\nIgOksi1GGPS6qDos4piRMQKRSJKkaU6PRAykkhSeYQPBtR6EOVgUiHJ7/HGDwGoaEi9rruzMYtUm\nibSwLFmT3OKD8mq7imPEW0VVzBreiZj5CAtLSUE2sizBl/HT3TV48PebUN/ihL3Ti80VtarjtHS4\n0BxKSRY9uMrCEif1fdv+OvxhdYXse48WLIlryXi8AVmwBMuHa6/2HSm6lCvZ1rcGV3lVuoQM+mCN\njX65Jrg8fvXaIR61YNHpdLiktAjNHW6crrerUqpzQxUuY6YMJxnDEqymq72vFvuOBQPvx43qj6KQ\nq012CXkDckbXmGEF+K/vXiV/T7wLpaFB57V/HkVAkjBicD6MBh0CkoTfvrYbc/+0DavXH1MdU8sq\nEa8qq83piVqHSvkuitiomiax8m141tov14xf33cN7vn6JfJvb95XFzXjBoBsc+KBoj4iXiRW0K3S\nai3aWV7RPjR4e7x+HDrdip1V9VGWzEBAwj+2nMQHn52J+qzsqsEoHWiFXq/DoKIcGPQ6nG2yh35b\n+/nZtK82oRUzmTgNm8OL/FwTxo3qD0AdgCwQ7Ts45J5ranfJ44PSZRjPHS1JEuxOL0YOycczP/kS\nrhzZX3O/yy4qRG6WSfOZEm5XZbaPQGlhEVlbJQXZUWOS+FNLREuShJOh/sHj8+P5tQcRkCTc963L\n5PRkILyYq9JiXtfiREm/bM0EhJwUA28vSMHi9QVQcbwZJQVZGFqSG7VOiBY+fwDLPzoc1zceaWFR\ndjAxY1iSLBYGADsPqYOrcoRgiWEhsCWwZqzdegprNkZXVFQiQa2KlbPFOc9twYN/2BzcT3EKv34l\ndhaEspONxWeV4bTEWHEhqQbdiiA6r+KeCYFUdboV72w9qeq0YlndGlo7UZBnhtlkkGel+0+0wB+Q\ncLrehhffPYi/vF+FjXuDrrlDp1tV7Sd+Vzwz/XLN6HTFfhaE0BEitCgihiWZdUU8voCqYJ3oLCMt\nTFqVboUlQZKCs1ylS0ikaebnCnN/+LkLxrCoO6RLQgshur1+jB7aD8NKcnFr2QjodToY9LrYMSwx\nLSzq51wIZvFeJ7KcVoTiV8aN7I+cLCOyzAZ5cPH6/KrFGEWMi5KbJg3FgMJs+Z6MGJyvCNr0wOeX\n8MGOM6rvaA1y72wNB4JGXqtWxpPS8iX6l8j0VyCY7aEM1jx4qgXNHS5cc/mAqN8U4jse9a3JCRZl\n7RoxCOr1Ouh04WevWQ5uDkRZ7aob7Oh0+xCQJE0rlcBo0GNgUQ5qm4LB5J1x+tREFtBk+hVbpwd5\n2WbZMqDVlmIiNyhUwfWjndXyc6p8HuUJjAYdTi/8AQnWnMSrl2eZDSoxVtfswGPPf4aPdwZdV8qA\nW0F+jhmFVktQsISETXFBtLUjXgxLS4dbft837K1FfYsT/zJlOC4LrR0mMOj1MBn1cn9u7/TC5vSq\nKtwqibSwJLPMBXCBCpaqM61wefyYOKYkODMM+VXb4sQBnDpnw/o9Z/HaJ0dimqYjNysFS6xOWPkC\nJSr4FRnclm0xQq/XRXXI4gGLDICM5G2NSPlIvL6AZkyOEkmS4ibpKzvbZCwsSktVMi6hZFOM/YGA\nXHE4X9EJCAvL25tP4O3NJ7Ftfzj7Rautff4AWmwuOQhTCBbRqbbZ3PJgUtPowJ4jjXhmxR6sUsyy\nxe+Kay3Kz4IjhmkdiL7vkRaWZGqNeLx+VY0VIXQjZ6RubyBqvZNTisHxeG27Kt5CFMLqFzIjC+tE\nICDB5w/AEuH2FIIFCFoCfvmja/DtshEAguLH5w9g7bZT+NuHVarsj1iWsMjOTAzeYk2SeS/uwLK3\n9mu6I50uH47WtGPEYKt8/kX5WWjpCAfdJnLbDi3OVfn8Rw7ODy8G6pcwpDg3qawHZU2MSMGilQVz\nQilYQm0o3BPDS8KCRXluAHAkFIh7w3j1diC5WhsiTVYQ69lTDkbKOGSjQS/3h8pJXmR9msMKq+Gi\nBDF+Q4tz0en2o9XmjmtFSVTbI5mwgE63H9YcEwYUBK9Py9VkD9VREQHQ+0+E114T777T5Y1bk+dc\nyIopAoDjkWMxqq57x8F61Lc4ZdeflksICFoHlanRkYHlgCKGRePeKM9frFd0w/jBmsfKMhvk/lzE\nfg3WiF8Boi0syWSBAReoYBF1RSaG0raEvy5efIVQoGfq7aqOIhZn6m2qwKtYna3SRNZqc8Pe6cUr\nH1Zpuqci/ZvZFoOmSyg/N9jYNkd0DYHDZ1rh9QWSTlGLfPm1BMvZRodcO0WLleuOyv8+dc6GeYrF\nrP5nxR7Vw+h0eVW+aC0Ly6sfH1attXEkwuoVy2p1RBHnoaxz4A0VAasOHfeNDcdkEaR8R0X6YHO7\nK1g5UggWs3pW2mb3yOZ2t8eHrQfCAkisSCp+VwyM1hxTVLEoJeK+m016mI16zU4sVql+gcfnV1n6\n3CGB7FO5IiR4vH7ZWiJQPvOfR1j6hIVFdIpiBq6swaJkzPACOfV3QERHatTr0Wpz4+3NJ7Bxby2e\neOEzfBTKoIjlVhXvbbs9+D2twne7Djdi3gufYduBOpUIrTwVtIqNG1UsbyuyWuB0++Dy+ODxBaKC\nZ7Mi2ntw/1xcf9VgGA065FiMGFCYLVtYJASDGSO/k4jI11MrmPykwvUqrvlMgx0WkwFDQyni/fLM\nshBTMrAoR14qQclHO8/gbx9Wxa25EW1hUfcJJ+s68Ozf90asGaQIcvcF5IGuRWFhiBy8lXFZzR3u\nuGtcieutaXTEFyw9YGEBgmI4J8sIa45Jc1Il+pdBGq4YcYi3Np2EvdOLb1xTqpkKL7J7rEkIliyL\nES6PH7uPNMLnD0RZXAdonAcQdguJirklBdHZh1oWFnF9yjYT1uvIvkNgMRnk/lxMYpO1sCRb9faC\nEywBScKeo43IyzZh9LBgkF/YJRTbwqKsHLk+IghLi417ayMsLNGdqCRJ2FUVrpPQanNj5bqj2LC3\nFq98eDhq/8iZTHaESyggSfhgx2nZfB8pwD6vasCi1/dg56F6HNXIomjWWJQsGZfV/Jd3JtxHcLbJ\noXInHKlpVwXcHa/tgITwgxx5/Ha7G5/uPqtamyny5Vy1QR0vIChX1KSIbJvmdhc63T6YjXp0OL1y\nrIiy41729gEAYb+wCNDMighUbLO7w4LFq+48Lg+ZSo9Ut0GSJNnCokP89FB7pxc6AA/dMR6zbr1S\nc8Yu6ibEwuMNqOrrCBeo0qXo9QUXL+gX0eko46EORzw7IohQdM5CgIqOPNIllJNlktfmGRAxozMY\ndGh3eCBJwLVXDITFZMC68mCmSayByObwIiBJeGPDcbyz9ZRmPM/IUP2XF989hD++USHf94pQZsX4\n0eH4ABGw2hIaJM0RPnaLQnzk55qRl21Cfo4Z999yBX7wzcugC7m2BCajPilXi5JkXELK/sAbeoZr\nGx24aJAVpQOtmHPHOPzm/ms1f3/ssH6a8XM2pxcb99aq0pYjifxMeR6nz9mwev0xVJ5sUa3ormW9\n7lSk1AJqURwISFGZXpHBr0qGFgctSmcb7eh0+zG4fw6mTx0etV+iKs7JWmuFiIgU3AIxoA8ozI6y\nVhC8HyUAACAASURBVAYCEk6fs+HTPTUYVJSDW28YKQsuJedCbW7NTuwSElWYl765H+t3n8WpehuG\nleThu9NG4tLSgtgWlpBgEe+sWFFbiWxhCcUpVhxvwpzntuDNTcdVgkVCUNwIy2YkRVYL2mxuHKlu\nS8LCou5Tl7y5Hw6XN2EiyQUnWE7V2dBu92D86P5yAJiYgcRLbRbFf7ItRuw81JAwq+hITZs6hkVD\nsBw81arKZhAWFgBRGRRAfJeQ0+XD0jX7sVqxcFurTS1ARMlqm9MrVyhVolWBU1xhTxTDioVyEBUx\nQleOCA4grojZy0GNWI1Dp1tVkfeb9taivtWJj3eekZV5QJKw+0ijPJAoLQ0+X0D2Q3/9mlIMKMjG\nuvIanG20ay5SKAbvWBaWVptbHqRP1nWoLGNXKHy7B0+3qkzU8dw69k4vcrKMuKS0EONHh4WJUlCN\nDq3VE4uPP6/WNIn7/QFsO1CH9XvOyiKjX258a40ScY8GhTof0dHGsrAAwNjhBQDUJbmBsPjRAbjj\nptG4eJAVze0uuEPZDloEJAnnmp3YEbL8aL2Xk8eW4Fc/morLLy5ExfFmzHtxB7YdqEPFiWb0yzXL\nHTcQrnHT1B5cgyXSJaRcC2aIYoZ49aUD5BgXZa0Na7Y5qWBWJZEGjnMRgiUnQiR7fX5s2lcLCcD1\nVwazV8aNKo6Z9TNiSLR1RYnShaE+Lyk66Db0fDtdXjyzYo9mxlpkGjwQFGFiktQv14yaBodsAalp\ntMPp9mGU4jxXrT8WM3VXDPgn6zoQkCQU98vWrFWUyMKS7BpHYlAWbqHo44TrLf3nt6/Ev30tnDHj\nD0h45aPDkCTg3742FiajHqOHFUT9hhARwmIeD6Ug3lxRC483gGElubjlSxfj5zMnxayue9HAsOsw\n22LUzM5RZgl5fQG8/slR+AMS3tt2OiqV3Jpriun+vP2m0YAuuGCwsA4OLo5lYYm+5tl/2IynXinX\n3F+QlGDZt28f7rnnHgDA6dOnMWPGDMycORMLFixAIBTQuGrVKtx222248847sX79egCAy+XC7Nmz\nMXPmTNx///1oaQlmZOzduxd33HEH7r77bixdulQ+ztKlS3H77bfj7rvvRkVFRTKnFsWeo8FZ9qQx\n4eA5EcMSGXQrSZI80xEv1jevLYXPH8CW/XWqgTaSs40OlYVDK+j2ve2nAAC3hvz3rTa3/GBpzQTa\nIs7PaAjO5OydXvzqlV3Ye6xJJSyUgWpujx8HQhkvHp8fu6oa5OtOBkOcctLJ8qUrB2m6LZRm2GM1\n7dABuGpkcGCPFGkHT0YXSKpusGPMsPBg7Q9IePzPn2Hlp8ew4IVgiuLJ2g602T2aQZM+f1iwjByS\nj7u/OgYBScJrnxyJ6vA7HB5VhhAQnQoatLAE71dksODlF4fjN05FmMDjLSJn7/RqzlzEDPXS0oKk\nBkWt51AC8OK7h7D8o8OyyFBek6ilEAvxbOTnmJBtMcgzcGEd0xIst00biUfunhAlWIT4GTO8AIVW\nCwb3z4WE4IAtrCKRM1YAeHfbqbgZFwaDHsUF2fjZXRNw7zcuhU4XvGab04urRvVXdbKiivC5UKxG\npIVFefzBxdozRKXQzcs2ddklpBRdXp8fje2dch2YYSV5uHnSUNX+Lo8fmytqkW0xYOpl0emvkWi5\ng5RUarxnQDAQNNJN63R5IUkSPtlVE7Nac5OG9ba2yYGWDhd0ACaOLUFAkuQS9EL0fCkkvoD4qa4D\nCrJhNOjlAVS4yyPx+Pw4fc4W071o70wuuFMEwmql5CoxGnSYfEkJrlJk+Bw7246TdR2YetkA2eJ6\n5YiiqO+KOiXJBN0q31cRUxPL0qGkf78s+b6WFGRpWt2UWUKf7KqWSwBAFx2bGWmZVTJ6WD9867qL\n0NTuwpGaduTnmmOW4Y8U5MmScJR64YUXMG/ePLjdwY75t7/9LebMmYPXX38dkiRh3bp1aGxsxPLl\ny7Fy5Uq89NJLWLx4MTweD1asWIGxY8fi9ddfx6233oply5YBABYsWIBnn30WK1aswL59+3Dw4EFU\nVlZi586dWL16NRYvXoyFCxemdEG7jzTCbNTjcsUDkpdtgkGvU6X4+vwBLFmzH/Nf2omAJKHD6UG2\nxYibJg6F2ajH+t1nE7pLlKbpyM70eG07qs604YoRRbIpv8XmlgfoSL+oPxBAm90Ng14ni5rmDjcM\nej2cbh/qW5z4xjWlmKQYkB0un3yO+080y4PVvmPNcLh8cpGsZNAaJLqKTgfMuvXKqO1+RT2QE7Ud\nGFqSi6GhoEHlrFqSpJgVHS9VBHICYcuQWGhPLESnNfj6/OGOcvgAKyaMLsa4Uf1RdaYtquM+2xgu\nKS9cQtGCxaM5SJtNelU8gXLW6fEF4HT5FLEP6rgSR6dXM25FCK1LSwuTGhQTrTMkisYp3R63TRsp\n/1t5reKZEP/X6YIppg2tTgQCUniWaY7uRnKzTFGZBMHfCu479bJgOwlBU9fskAM0szSEmXLBOzk9\nXBXoGT7HaeOH4OpLw8/++FFqV5pYyFHMcCNjWJSzVa2iV8rjAcGgya7HsITbv74laOm5YkQRfnXf\nNZh/7xQMK1HXhNl7tAltdg+uu2KQqu1iESkUlRT3y0JVKNYtEmFdUS7e6fNLaLN75GJqWtwRqnSq\nRLRpvzyzPOEQM28RcHvVqP747g0jMOvWK/HQnRNi/r5er8OQ4hx5UpdtMaraQAiffceasfCvn6sy\nEZVoxehpIVtYFK6WfI33UywoG/kMmYx63PXlMfLfE8cUy+nxAhGGkEwMi9ZkJZnnQKfTyRllWgG3\nQDiGpdXmxtptp5CXbcKs71yBr19TCgAqd1as+BXBt68fId+LwTHiaoBerMNSWlqKJUuWyH9XVlZi\n6tSpAIBp06Zh27ZtqKiowMSJE2E2m2G1WlFaWoqqqiqUl5fjhhtukPfdvn077HY7PB4PSktLodPp\nUFZWhm3btqG8vBxlZWXQ6XQYMmQI/H6/bJFJlnMtTtQ1O3HFiCLVgKLX6ZCfa5YtLAFJwsvvH8Le\nY02obXLA6fKhw+GRFeE1lw9EU7tLtRrr9KnD8dT916gK7ijrkUQG3b6/PVjk51vXXoSC0Iyusa1T\nnr13ODwq82W7PejXn3xJCcrGBaOws0wGWHNNMJv0+PF3rsAdN49GVsRA2RJyC+06HA6UFH7Ha68Y\nmLAjFeed6orGkYwe2k+u7igQNRZO1HbA4wtg9LAC9Mszw6DXqbIIapudUVYmwWUKwXLDOHWUuiRJ\nKD/SCIvJgKtG9o+6lqCFxYa8bJMcFDvjq2NgNOiiXGc1TQ40tnciy2yANdRpKQdxnS4oNiO9Eo9+\nfxL+MLtMte2sQrAcOt0Kf0CSrU0f7azG3qNNwTRNd7CeT16c2gSXlBYk1UHFswoC4Ywb5YCkXF12\nWIm6vgegtiYMKsqBzy+hqb0Ti14PZnZoibdYmIx66HThdVJELFNts0NRGyX6GiTFObd0uFFotagG\n9cjZttKnP3qY2pUmYli2h4KlI89feT+GxAgaNOiVLiGTpsiKh/L5ETPtwUU5GFqcC6NBH1XETlgw\n4llXPIoJVqSL4LsKUTpxTAk83oBmCQcRcFuYr3a3/GPLSVWMkTKY8p7pl+Ab114U9Vs1jQ602tzo\n3y9L7hOES+dIdRuK+2WhuF82brl+BK6+dABGJnBjiTgWIDiAK9tAuCATLgiZ5JpvQkQo04V/8M3L\n8L0bR6r2E/c58n7fMG6wytqs0+kw/96rVfuJviCZGJZsjRgprRXhtRDu0BKN+JXguQX/X3G8GW6P\nH7fdOBI5WSZ8b9oo/OzuCSi7KtzfxrOwAMH7IIRwLOskkHodloRv2fTp01FTUyP/LUmSbFbKzc2F\nzWaD3W6H1Rr2Eefm5sJut6u2K/fNy8tT7VtdXQ2LxYKCggLVdpvNhqKi6FmaksLCHBhDJt3NoQ5o\n2qRhKClRlznuX5CN03UdKC7Ow8trK1UKXG82wt7pRemgfJSUWHHbV8Zic0UdPikPX/ewQf0w7tJB\nclXM4L0I/l+nA6DXycc8fa4De4424ZKLClE2ORgYZjToVUFqEgCfTo+hoe80hzJ+hg3Mx4zpl6C4\nMAffvmGkHMMiOhBrhN82oNejX0FOlE96aEkuplw5BONG12DnwXOIhXh9Lx/RHztC66Xk50ZXNE2G\nrCwTSkqsuGhwfkSwVvDevBRaa+ir11yEgQPyUVyQjTa7W75v20MxCllmg8osnW0x4OqrhuA3P8nC\n5j1n8cB3r0L17zfKLpfaNhcaWjtRNn4Ihg4pwPenX4q/vndQ/r7bF0BjmwvjRhdjwIBgp1hSYsV3\nbxqN1YoMJwBo6nCjqd2FocV58r4FCjE6Ykg/nDjbDk+ERW3QACuGD1X7qUUg5U++Nw4fbD+lyvoA\ngOfWVOD+W6/E1MuDs8P+hTlRz61g5EVFMevxqEiQXusPfV6omG31Lwq/j6NLC+X1aYr6ZaO5w41s\ni0k+r5HDC7G9sh5nW8MLZWqdd6zruPeWK9Bhd2P0xUET+lWWYMd1rtUlP3OxUlNvnDQMn4Qyiv7f\n3ROxXVHNtbBAfQ6jLioCEIz3unh4oWqgyC/IgTXHjE63F1eO6o9bbx6t+m62JdyZXj+5VNP1kKeY\nuAweYEVzkouriuPkKjr+jlBs0yUji+XPi4qiO/vcLCOuHT80pvvWGLI2WnPMUff/B9++Enm5Fky+\ndADa7G58sqsaJ87ZcePVaqFhC9UKGliUo4pl2VxRi3554UnfFSOLUdccbIviIu3n9tjZNvgDEoaU\nWHHFmAHIzTbhTIMdTp8UtABfOTjmc1JcnCcXGhSMHF4gr+lUXJSjeoZLQpawRJW4k+WiYYUoKbEi\nOy/c3w4b3A8jS4tUta0GD+oHg16HEgD3futyud+Z+Y3LUaJhYfjtf12PR57brNo24qKihKK/pH9e\n1LaiwtyY90/JpMsH4ePPq3HpyP6a+wcMiqU1hvbDbV+5RH7mBw7Mx1pFxubgEmtSxwSAMaWFMfdV\nHrMrdNkuo1eoWofDgfz8fOTl5cHhcKi2W61W1fZ4++bn58NkMmn+RiJaFcGrm/eehU4HjBiYh8ZG\nddXYPIsRXl8Az79ZgXe3ncLg/jm4ZHgBNuytxcGjjZAkIMukR2OjDf0sBowckq+yoDidbjQ22mAy\nRJv+jAYdOl0++ZivvR/MivnalGFoClVn7J9vQX1rJ667YhAGFmXj7c0nceh4I3KMwQfj+JmgNcli\n1MHW3olvTh0On6JuR2NjaAkAr9qHfLK6DY3NDnS6/RgzrJ882Fw1sj+amuz4j+lj4woWIDg7nTg6\nLFgK8ywpCRaXyxu8BxFVKz1ePw4ebcC2ilr8//buPDqKMt0f+Le6eu9OOklnA7IvQAgECJiFnYAE\nFEFAQRC8iBvoqAMyMteLc8TB0XsFnIOCmJH5McL4Q0ccFfXgyDYKyshlcGFfBDGyZSd7r/eP7qpU\nV1cvWUhXkudzzpwzku7k7X5reep9n/d5k2KN6GXSoKysFpFGNU5dqsblKzVQKRX45tgVvu2HBTuU\nZiZEoKqyHvHhGtw9Ng1VlfW4c1Qq/vi37wAAe9w3sV6ROpSV1WL0wDicu1SFw6euo9lqxxn38HO8\n++ecREFC2LSRKfjo4EX+hhgXqfV4rUrpenJIjjO6NvYTJSZWVTWgTPQUxD1p9InS4em5Q/H54Z9R\ncaOJLzan0yjx/z87jQj3hVmlgNdxy6msrPc69qTUN1j5m7NUzsfPV1zHh10wilFW3vI3IwQ3Ce42\n7XA4+HaFuT/j/v9tmR5obLB4tDsmJszn50iNMQAxBv7nTqcTOo0SR09fl3w9Jys5ErHuHI8xg3sj\nyazHXsHwPnd+crSCr6qq0jsh9MWHC8AwLaNnwvc63cdv/6QIVFZI1xWyCM5Np90OJsgb5YVLlXhn\nz1moBaNl593Hp0HJeLRj+qhU/PtMGT8tmJUciUqJz8LJcucQjB/ax+v7Ly+vw3h3/QyNQgMlq8A3\nx69iakGSZ/t+cY26hImG7J1OV3u41Y29zTr8+u7B2PjBD8hKMHn8vYw+Jpz7pYbfe82oYVFRUYeU\nOCOOX6zC7kMXAQApsd7XaWF7G0Rt0ApGvmwWGxoEpSGa3LkpVySSf1sjOT4M+Vlx0LMtx4RRp0Jd\noxUN9c2wCR5YGQYex8eYQfGorG6AXqOEwm6X/GxmvQr/79lJuP/3/wDgmta5UR24BoldcN2Pi9Th\nWlUjrBarz+9PKDPeiKfnDkXfxAjJ11cJ8o9mj0v3Oubr6gQ1mRjf1yixMC3r87XNQe4aLtbqeYAB\nAwbgX/9y1dn44osvMHz4cOTk5ODIkSNobm5GbW0tzp8/j759+yI3Nxf//Oc/+dcOGzYMRqMRKpUK\nly5dgtPpxIEDBzB8+HDk5ubiwIEDcDgcuHz5MhwOR8DRFaGaegvOl9YgMyHCo2gYh5t7+/gr1z4I\ny2YP4ZdpcrkGwvm58UM9k964eT7xEk6jTuUxDFZe7doxt3e0wWO1x/ziflg8PRsPTs3ih3uF9Um4\nXA5xhVMx8XRHdV0zjrgv9sLh4kj3E6BOo0TBAM9hZPFwc3ZqlMdUl3jjvWAd+9EddImmLux2Bz4/\n/DOcTqA4P4kfoTOHc2XSXSfEpet1iArXeExLAC2b5gnlpJvx8LQBAFwjWgD4OyzDMFh0exb++IRr\nioZbnSP+3MIAQDhEmRwfhrvHZ3i8VqdRQq1U8AW7/FXmFIsK00KjYjF1RIpHGyYM64O6Riu+co8U\nBEqiC2bawVW5VSG5jBJo2cBTOJwsHM1iWQZDM6NxS/9YPmARJqxy58wJQa5Ra/ePEmIYV24CN1oj\n9R3MGJ2K+ZP6Ii8rFvMmZmLuhEyv1whX9gC+l6Ny9FqlzxU2XJG7qSNSfL5fOB1h1KmCXtb87zNl\nOHjsqkfphMsV9VApFYgSbb44fVQqnlkwjP/vQT5Kt3OUrAKzxqbzOTq+aFQs+iWaUFpW57Uc+Vpl\nIzQq1mvIPinWiDE5LYXo0nubkJNuxqanxnlNh/zn/FyMERStM7s/V4p7Wohbvtw/yXvljD/Cz6VW\neSbdtmcDVqG03uGYLLhGAS3Ti2qVwmMkROoB4s7RaZiUl+T170LCXLWwIBJnAc8cFi6fTyNR8l4K\nwzDonxzpc5dxg85V/XnkoHh+as2XQFNCQr0kRgk5rc354rR6hGXFihV49tlnsW7dOqSlpaG4uBgs\ny2LBggWYN28enE4nli5dCo1Gg7lz52LFihWYO3cuVCoV1q5dCwBYtWoVli9fDrvdjlGjRmHw4MEA\ngOHDh2POnDlwOBz43e9+16p2fXeuHE60FIsTE16gl84eDLNJyx843P4cJkGgM7x/LDYL6odwna0U\nBQwGrQoqpQKXrtWiocmGXd9cgsPpxG0FSR4XeuFyV+6i/+8zrkS6yhtN/G6qgYIF8UlisTnw7dly\nRBjVHstehSeF+EAVJhPnpJsxY3Sax/4+5gAXPF+4G5d4eLO+yYYvv7+CyDCNR1IsdwGqqGmCOVyL\n6tpmZCZGeNU9yUr2DlgAIMsdyFy8Ih3Fi4M7r4BFcMIzjOu7r7zRjIRog0cABwDZKZGwO5x8PlKw\nDFqlz9yTnLRofPzVT/jOPVUYKGDRCJJbn38gD7/b7F0fx2JzQKVSoE+0wWOPIP7ngqXIGjXr2gtI\n0D6nE3h8Vg4A4MVtriWGwlkmbk5fuPLLbGrb8cLpZTbg/C+uoHNQmpkf9ufcMTKV//8Th3vX3gA8\n806AluCuNSvlOANSorB5xXi/+4C1dZWQVEXPq5UNiI/SSy4XFR7Dvvaa8WfzivGIiQnjR3o52alm\nHL9YheMXKjHSnaPgdDpxvboBcZF6j1U2sZE6LCju53Ed6eMnP4FhGI+AmTvPuZVLdY1WmMM1kmXi\nW3iPWJkF10Zx0m2wN+9AxIEv4FoiXlpejzCdGmqVwlUby+H0uZQ4EI2KdVcDdgS1QgjwPAdHDeqF\nUz9VIa2P/5yfYGnVSqx9bGRQOXKBkm6F/N3LWrPHnlBQAUtCQgLeffddAEBqaiq2bdvm9ZrZs2dj\n9uzZHv+m0+mwfv16r9cOGTKE/31Cjz/+OB5//PGgGi521J0g6ytg4U42jYrlk/W4g0VqhEV80+XO\nVXGik1GnRN/ECFy4cgP/OnkNX35/BeZwrd/kuJgIHdQqBS5cucHneqhVCvRNMPm9EADeN+Ezl6pQ\n32TDhNwEjxM4zE/A0mS1Y8GkvnACKHLvRCzceDHQE1ogUgd+s9WOaaNSPE5y7kZXcaMJlbXNcMIV\nLImXvCXESn8nJqMGZpNWsiAeAH7vGrvDCVbBeK2cEH6XCsZVxbQSzZJLNx+6w7VjrDgI4H6/r5Pd\nX/CXHG+EkmX4lQuBAhbhU72v5YJWmwMq1vcIC187Rc3iDw8V4KdrtR6JhcLVay05WoIbg5rlAzsA\neHjagKCW2fojXIkzMC3KK2DxRXguSt08Niwd4/OpMpBAF1TW41xTB510K663ArhKHPiqCMoV6jKb\ntAErHUthGEbyswxMi8K7+4BjgoClus4Ci9WBuEgdv41IVLgGLz1SyL/vmfnDYLU7An6vwnMt2n0O\nCGvD9JMYNRWy2BwQfyPCuiuuZc0tfR5oe4VgSa2WvGtsOqbkJ/PFzrh9jdq6spJhGBi0StTUW4Ja\nIQR4XsPT+5jwoqBPOkIwu3gDwY2wjBgYD5vd0eagxJ+2rS2SmSaLDccvViEhxoBYiU2ghITfIXew\nXOML+PjuDH5KyB3JR7qr+kWGaTAozYyPDl7EO3vPwmpzYHJ+kt/oW8kqsGJeLipvNCPapOXXygfT\nweIRFi5nJbdfjFcxK444abDJYsN4d6DCv0bw3og2PJV6kJjO16pZjB3sOc3GByw1Tahwr54ym7Re\nJw8r8dTDyUiIQEWN7xscy7oCil5mg1efeAYsLdNC/qphikdY/mfJCP4JmSMc/fAX/KmULJLjw/jR\nhWDqKnCMOulT12K1w2TUYGCq2aPIoPDngGsIPTJMw98EFYxr92HhShBu6a343hQfpUfljWYkxhpR\nMCAe7dVbkEuUHBeGcL0KN4Io8DU+tw92uxPjpUYngr0It4XwWNJrlEGPsFzxsWeKr4qgAPD03KEd\n/ln6RBsQGabBsR8rUFNvgcmg5kd/4tz9C3g/uIlXXPkiDEK58zzC6Dreqmqb0c/HdNB9xf3w7bly\nyWux8HzVaZQeKzPFlVPbSiqhWa1iPaaceptdAUttkEukpRh0qlYFLNmpUZh0SyLyB7Tv4aC9ghmx\nfHDqgJv297tFpdtjP1bCZndgSKZ30TAOt8JCPJQLtAxvS+W+cMRTQuF6NZbOGYyZY9KR2iscBq0S\nFqsD4XqV17JbKam9wjGsXwyS48Ng1KmCjkbFJ5TT/Tn6Jpo8onDhzU/8NCS12kT4tNDWJWccqf2B\nxgzu7XVR4Z68Km40ody9vDlaImDxJyPAnCtXKyRRYpRGGPwx7hEWwH9F2jC9yuMY0muVXlNWCTFG\nfnou0BRfpmBlkb+ARfydSG3ZDrhHWJSuZbHPP5DnkUsACOqwiEbquGWTwr5zSIywAC1b2fsaxWkt\n4c0tKlzjt4aIUC+zAbPGpkGnUfocobhZhMeAQsEEPcJSXt0o2c/+2p8Qa2z3tJsYwzAoyu2D+iYb\n1r3zLRqarPyS5rhIPWaOSUNWciSWTPeuqxSMCKMaOo1rp3PhsZuZYALDtEznio0b2ge/vntwwM0k\ndWql57RkuBb9EiP44nttJawM6wt3fAa5XZskrg5JsFNCCobBPRMyvUpGdCZuL61Q6hYjLNxmh7l9\nfe+1wm0gKLx5iw8WfyWSuYOTG3q02h18eXnAFQF/c/I6br0lscMSwKRIDcUOzYwGq1CAFZzkHjks\nopO/MNv7qdgzAbV9h4W4KJ6CYTBxeILX67ibeUVNEyrCXQGL1JSQP2kBytVzpHJPPEZYFAz/uRv9\n7KqscO/+zT2BSi15BVpG7wLlA6UL844kbmSrFuXh5E9VfDA9OS8JdX4q5lpsDn6qJCHG6PU7+f1/\nRCMCWjWL+iYbmgQjLNxnEC8v5UaTAk1fBsts0mLkwHjUN9mgVSvRJ9ooWf5dyu2FKZhSkBzUbskd\nSdzvwSbd2h1ODE43e2yYGRmm4SuidqbbCpJRcaMZ+4/+gj++9z2f4xUXpYPZpMVv5g5t8+9mGAZ3\njU33qlc0d2JfTBiWECB/JTCDTsU/5HB/7+l5Q1FV24zlG79q8+/N7ev7oZcTbEDtDzel6+8hWW7C\nDeqbMs3TGl0+YLHZHfj+fDkiwzRIjvO9DJrbQkB4w9eoWKhVCv4p3N+UEBfMcFNC4kJxtxemwKhT\n8TkhN4vU8cIV4RLOqwuHcoVP+ZFhGtw3uZ/X7xCO3LS1CiFncn4SXxVTrVQgf0Cc5KZbKiWLcIPr\n5s8VkDObtF5TEP6k9gouYJEqyiYMWBi0BGq+yo9zIowtORy+5vK5YDhQPlCGIHFOHBgArkRhYbLw\n7KIMr9eICXM7xNWaLT72/3ElOjejUfD6eyZkIjPB5JWjktc/Fud/qZEMfNuCYRg8IBhG7t3KkZvO\nDlYAiYClFaseekUbkJUcyW/w+dScIa2aDuwoDMNg/q190dBkxTcnr+Oce3o5LsC0erDE086AKwei\nNStNfNFrlF4jxQzDtDlnCXCNIARzQ/ZV/bg1WkZYOr/f20oOwVWXD1jOlta4ihANiPN7sHHDh0rR\nAR2mU6HC2gy1SuE1rDt7fAbe3efaGZgbTeFucuK9KhJjjZg/yTsQ6Gjii7NOw/L71/g6WYvzkpAc\nF4Zzv9SgYECcZH6NSjgl1M5hP+EKm5cfHeF3xMYcrsXP12tRXq1x/7fG46YZSLTEdulSpIIBrxEW\nDTfC4v/vc6MUrILxebOMj9KDQeBpE5NRg7hIHRqabW1edSAmnC5K6xOOPf92XSDrm2z8lI94YQdI\nOQAAFCxJREFUFJBbNtlkaQnW4qP0uL0wRbLNi9s4VRAMYQlzroS/3DSL9gJrzeaH8VF6JMYacfKn\nKjw4NatDntjbSqFg8ODUAWiy2PH9+QroNKysb6L/OT8XVyoaoFGzfPmDIYLyEZ0RvMb5KTkfLO56\nJOfvWqwjAs326vIBC786KMBQHhewiG/qRr0aFTeaJaPH4rxEPmDh3sdNLfhapXGziU/HwRnR/I3O\n1/SEgmEwICXK77Az24FTQkI6jdJv0qw5XIMLV27g4rVamAxqqJRsq4Ydha/1t/eT1KiR0iOHJfjc\nnSEZ0filrB7339bf52smDEvAoLQofs8kf5bcOVByM8y2Eq6YKMyOx7C+MXhv/3nsPlKKn6/XQadR\nel18uBGCQMFaZ0jvY8JjMwYhtVdYm1bGdAbxKJxwhIVbOeZLfJQevaMNKPmNdw2TUFCyCiy5cyBK\nPjqOqHDpDfLkIjMhApnunY/jovR4eckIRIS1HMvtGWEJlkqpwG/uGRJ0/omU7NQoHL9Yydem6Qpa\ns6T5Zun6AcvZcug0SvQLkHzp4AMWzwsEF+FKRY9SJ+5dRZkor2pA8S3S9SBuNmGbJg5LwARBbohU\nDYFgcUm3KqXCZ0LnzcAlEzZb7HxORGsv4rEROlyvbvTYu0csUIDJMAyf+Bio6NjUwhTkD4jz2qBO\nSKVUBBWsAC17fXQU8dJ34WgKwwCPTMv2mhIqHBiPYxcqQ74KgSO167accLk+XEKpcHT2sZmD8O7e\nc5I1VxQMwx9fcghWOBoVy9ff6UrEycidNT0otbFnawxKMwcsBCg3balp1NG6fMBScaPJ5zSHkL8p\nISD4bG29VoUFnTD144vwfJx3a1+Pn2nULH41c5DHEttgsQqFe5TBdUi89EgBfrpWh62fnUZdoxVG\nnQqNzTa/T47CodlgCZNSpRJUg1lindo7HNerG1HvZ5lhoFEjBcNgYGoUHrg9y2ehOo5rODq4YCQU\n1BIBJ5cUOmd8BnLSvS+Uhdnx6J8U2f4l7T0EV3eH2xxROMIyJCMa6b3D8eT6A17vi4nQyipQ6W58\njTKT9jMZQj/a2eUDFgAY4qNYnJDDx5QQF6jIYbgrGIGGPIPJcvdFySr4PI7YSD1iI/V4a5drw8LM\nBBMijBrsO/qL5HtvL0zGrLHeW8wHInxCklq6+d+LRwT8Hfe6A7dpI1N8vibQUmmGcY2yjBwUeEm6\n3EntvD05LxnZKVF+S2/LdfpFjqbkJ8FidaA4zzXS6uu6IsZNZ5Cbox2DzMQHboS2rVu2dKQuH7Ao\nWSaooTWfOSzuERZ/AUsoMvh9ye0bgwEpkZh0E6akRg7q5VH+GmipxcGyCuT2jfEZsLSVrxGW2eMz\nUNtgkbz5ihl1KjwyLVvyZ1yyaaDkNjnP27eWVNVPvVYZsLooCZ5eq8LciZ57Gk0YloDwAMdZ4cCO\nWVlFpHWn81gu8rPioNcoZTGF1eUDluK8pKAKjXEBi3jIkAtUfGVA//m3Re1sYcfSqFgsv6ft9RH8\nua9YaqrLPZXGMj6rUwJtL6Lka4Rlcr7/DcSC9YeHC1BdZwlY2Mti7ZxkU26ZczCBWFsFs6sz6Xj3\niqZoxQxapd9ziLSf1Aj0YzMGYcPffwAAPHFXDlLiw7DstYOd3bQuS6NmMby/PFbrdfmAJdhpCIeP\ngGVYvxj8dLVWtssnQ40bYVEqFFCyCtw5OhUffHmB/3mYXoXaBivq/RQz84cra95ksfOVbztSmF4d\nVH5SZwUsQzKicU9RBgo6qIaJlM5MmibBe/XXY0LdhG5PwTBg4Lk7iDCBOyspMqhN/kjnSu8djvtv\ny8KpS1V+X9flA5Zg+RphMWhVWCA5skCAlv1kuFVEiaJkU5NBg9oGK6prm73eGwyGYWA2afFLWX2H\nlx9vjaZOClgABNx+vr06aiM40jH+e3EhJYN2IoXEsvI+0Qb8Ul5/U0c2Sdv9133DAQSuItyDAhbv\nSrckMH4PJvc0AyP6/iLC1CgtA6rrLW3+G0VD+6C0vP6mblYXiL8aLl1NKCq/Et9i2lmGnrSOQsHA\nqFfhvxYMQ32ja/n5c4tugcXqvcv0E3fl4HplA7bvPReKpvZoJqMaNXWtu2/0mHCzZUqox3zkDsE9\nqXCRr/hmGOFe6lZT5znCMnZIb6iViqCWcI7PTQjZUvF095b37SkCFWoqpcIjg/+6e+drElqT85Pa\ntNSftE/fxAgMzYxBtEmH5HhXjSNWoZB8IBqSES25zxi5+dY8OgI6jRJLZw8O+j09ZoRlxpg0rHvn\nO9xemBzqpnRJ3O7D4niPKyZULYqU/2Nyf/zHZN+VYOXiV7Ny8M2Jaxg7pHfgF8vMkIxoXK1swB8e\nLgAAHPuxAiU7T2Ds4K73Wbqj2eMD7/tEOt5Tc4a06vU3c7Na4hurUGDD0tbldfWYgGVgqhmbV4yn\nZW+tdPe4dBy7UMnviSOuI1GUm4Cvjl3F/AArJOTKZFDj1hBVLW6vJ+7yrEw6MM2M9U+ODlFrCOma\ntBSwdBk9an6EgpXWm1KQjN/MHcpPBWlUrMdS78gwDdY+NjLgXk6EECJHtGqo6+gxIyykY61/crTX\nfjSEENLVaClg6TIoYCFtIqfqv4QQ0lb04NV19KgpIUIIIUSIRli6DgpYCCGE9Fi0SqjroCkhQggh\nPYpKqYDOPbKiZLl6UW3cEI10GgpYCCGE9Cgblo6BcNGoaz8zW+gaRIJCU0KEEEJ6FCWr8Kh6Tom3\nXQMFLIQQQno0SrztGihgIYQQ0qOF6VU0ytIFUA4LIYSQHm3hbVmob7SGuhkkAApYCCGE9GixETog\nQhfqZpAAaEqIEEIIIbJHAQshhBBCZI8CFkIIIYTIHgUshBBCCJE9ClgIIYQQInsUsBBCCCFE9ihg\nIYQQQojsUcBCCCGEENmjgIUQQgghskcBCyGEEEJkj3E6nc5QN4IQQgghxB8aYSGEEEKI7FHAQggh\nhBDZo4CFEEIIIbJHAQshhBBCZI8CFkIIIYTIHgUshBBCCJE9ClgIIaQboAoVpLujgMUPugDIQ0ND\nA+rr60PdDOJms9no3JCZ6upqlJeXh7oZhNxUFLCI7Nu3DytXrgx1M4jbtm3bsGzZMpw+fTrUTSEA\nNm3ahN///vfYv39/qJtC3P7+97+juLgY27dvD3VTCICtW7diy5YtdM26CShgEfnpp5/wwQcf4MyZ\nM2AYBna7PdRN6pEqKysxZcoUVFRUYM2aNcjNzeV/Rk/3nc9isWD16tWoqanB/fffD4vFwv+M+iM0\njh49igceeADffvstBg4ciFGjRgGg/giVuro6LFmyBCdOnAAAlJSU4MyZMyFuVfdCAYubw+Hg/39x\ncTFefvllAADLsqFqUo8WFRWFzMxMJCcnY+PGjVi5ciXfJwzDhLh1PQ/LsrBYLBg7dizefvttfPPN\nNygpKQFA/REqly5dwiOPPIJVq1Zh9OjROHv2LADqj1Cx2WyIiIjA8uXLsXDhQhgMBpjN5lA3q1vp\n0QHL9u3b8c477wBwPZU0Njbi+PHjWLt2LSoqKrBo0SLs3r07xK3sOYT9YbfbMWrUKLz11ltITk7G\nsmXL8P3332Pjxo0APANMcnMI++PatWsAgG+//Rb9+/fHkiVL8MUXX2DDhg0AqD86y/bt2/H2228D\nAKZNm4a8vDzY7XacPXsWiYmJAKgvOtP27dv5qbgbN24gNTUVr732Gp5//nns2rULJSUl2LJlCwDq\nl47QowOWw4cP44033kBjYyNYlkVTUxOSk5PxwQcfwOl04uTJkxgxYkSom9ljiPsjMzMT8+bNw4wZ\nMxAVFYXnnnsOu3fvhsVigULRow/dTiHsj969e8NgMODzzz9HZmYmoqOjsWrVKuzZswfNzc3UH53k\n8OHDePPNN9HY2AiGYWCxWMCyLFJSUrBr1y4AoL7oRIcPH0ZJSQkaGxuRlJSEefPmwW63o6KiAgcP\nHsSsWbPw5z//GY2NjdQvHaBHfYNlZWX8/z979iyMRiNSU1Oxbt06AK4Iedu2bThy5Ag2b96M7Oxs\n/OlPfwpVc7s9X/2xdu1aAEB2djZmzJiB6upqAEBpaSmKioqgVqtD0t7uzld/rFmzBgBwzz33ICYm\nBqdPn4bdbkdpaSkKCgqg0WhC1eRuz1efvPLKKwBapqwLCgpgMplw/fr1kLSzpwh0D2FZFna7HcXF\nxVCpVKitrcWECRMotaCDMM4ekKF19epVvPrqq6ioqEBRURFGjhyJ8PBwlJWVIS4uDtOmTcOmTZuQ\nmZmJU6dOoX///gBcCbilpaUYOXJkiD9B9xJMf5SUlCA9PR1ff/01PvzwQ1y7dg0KhQIPPfQQCgoK\nQv0RupVg+uONN95ARkYGdu/eja+//hoXL15EY2MjHn30UT7Zk3Sc1pwjAPDDDz/gr3/9KxYsWIDs\n7OwQt777ac05smnTJlRUVODy5ctobGzEwoULMWbMmFB/hG6hRwQsGzduhNVqxcyZM/Hhhx+iqqoK\ny5Ytg8FgAAC89tprOHnyJD8fD7gSqJRKZaia3K21pj9sNhvsdjsOHz5MN8abpDX94XQ6wTAMvvvu\nOwwePDjELe++gumT06dP49VXX+Xfc/jwYdxyyy2hanK3Fkx/nDhxAhs3boTNZkN9fT2OHDmCoqKi\nELe8e2Gfe+6550LdiJthx44d+Mtf/oLTp0+jtLQU9913HxITExEXF4dTp07h0qVLGDJkCAAgLy8P\nL730EpKSkpCWlgaA5oE7Wlv7IyMjA0qlEklJSSH+BN1LW/uDe6KPj48PZfO7pdb2yYsvvuhxzerT\np08om9/ttOeapdVqkZqaGuJP0P10y4BlzZo1+OGHH7Bo0SJ89tln+OSTT6BWqzFy5EjodDqwLIvj\nx49j0KBB0Gq1AICsrCwkJCQgKioqxK3vfqg/5IX6Q36oT+SF+kOeuuWcR21tLebMmYPs7Gzce++9\niI2Nxccff4ypU6ciKysLZrMZzc3N0Ov1/BB3YWFhqJvdbVF/yAv1h/xQn8gL9Yc8dbt5D4fDgUmT\nJiEnJwcA8Omnn2LMmDF49NFH8cILL+DChQv46quvUF1dDYfDQUWWbjLqD3mh/pAf6hN5of6Qr26d\ndFtXV4eFCxfi9ddfR0xMDF5//XXU1NSgvLwcK1asQExMTKib2KNQf8gL9Yf8UJ/IC/WHvHTLKSHO\ntWvXMGLECNTW1mL16tXIzMzEU089BZVKFeqm9UjUH/JC/SE/1CfyQv0hL906YOGqEB4/fhzTp0/H\ntGnTQt2kHo36Q16oP+SH+kReqD/kpVtPCe3YsQNlZWVYtGgRVUeVAeoPeaH+kB/qE3mh/pCXbh2w\ncNnbRB6oP+SF+kN+qE/khfpDXrp1wEIIIYSQ7qHbLWsmhBBCSPdDAQshhBBCZI8CFkIIIYTIHgUs\nhBBCCJG9bl2HhRDSfrt27UJJSQlsNhucTiemT5+OBx98EF9++SXWrFkDALh06RKio6Oh1+uRkJCA\nDRs2AACqqqowZswYLF26FIsWLeJ/529/+1scOnQIJpMJANDY2IiIiAi8+OKLsNlsePrppwEAV65c\ngV6vh8lkglqtxt/+9jePthUVFUGr1UKlUsFqtSI8PBzPPPMMX1YdAGw2G8aNG4fi4mI8++yzfLsW\nLlwIACgvLwcAREdHAwC2bNmCJ554AlevXoVer+d/T3R0NDZv3txh3yshpJWchBDiw9WrV53jxo1z\nVlZWOp1Op7Ours45Y8YM5+7duz1eN3/+fOehQ4e83v/WW285H3/8ceekSZOcDoeD//cVK1Y4d+zY\n4fHa1atXO5988kmPf5N6ndD48eOdP//8M//f+/btc+bl5TkrKir4f9uzZ49z8eLFzsLCQmdDQ4PX\n71i/fr1z/fr1QX0eQkjo0JQQIcSnqqoqWK1WNDU1AQAMBgNeeuklZGRkBPX+999/H/feey/UajUO\nHTrk83UWiwVlZWX8iEtbjRs3Djk5Ofj444892nDrrbciJycHn3zySbt+PyEkdGhKiBDiU//+/TFh\nwgRMnDgRWVlZyM/Pxx133IHk5OSA7z116hTKysowfPhwTJkyBdu3b0dhYSH/8/Xr12PLli2orq6G\nRqPBxIkT8dhjj7W7zZmZmfjxxx8BAJWVlTh48CBeeOEFsCyLrVu34q677grq96xcudJjSmjy5MlY\nsmRJu9tHCGkbGmEhhPi1atUq7N27F3PnzsXly5cxe/Zs/OMf/wj4vh07dmDy5MlgWRa33XYb9uzZ\nw+eLAMATTzyBjz76CFu2bIHVakV+fj6MRmO728swDLRaLQDgo48+QkFBAUwmEyZMmIAzZ87gxIkT\nQf2e1atX48MPP+T/R8EKIaFFAQshxKf9+/fj008/RVxcHGbNmoVXXnkFK1euxHvvvef3fVarFTt3\n7sSuXbtQVFTEJ9zu2LHD67VpaWlYvnw5nnnmGdTW1ra7zadPn0Z6ejoA13TQ0aNHUVRUhGnTpkGh\nUGD79u3t/huEkM5HAQshxCetVou1a9eitLQUgGtvlXPnziErK8vv+/bt24eoqCgcOHAAe/fuxd69\ne/H888/j3XffhVNiN5CpU6ciMTERGzdubFd79+7di5MnT2LKlCk4fvw4rl69iv379/NteOONN7Bz\n507U1dW16+8QQjof5bAQQnwqKCjAr371KyxevBhWqxUAMHr06IC5Ju+//z7mzp3r8W9Tp07FunXr\n8OWXX0q+5+mnn8bChQsxb948JCYmBt3Ghx9+GCqVCgAQGRmJN998E0ajEe+//z5mzpzJTw8BQH5+\nPlJTU7Fz506v9omJc1gAYOvWrQgPDw+6bYSQjkObHxJCCCFE9mhKiBBCCCGyRwELIYQQQmSPAhZC\nCCGEyB4FLIQQQgiRPQpYCCGEECJ7FLAQQgghRPYoYCGEEEKI7P0fUmWuS0kjYA4AAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17ff06d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fix this: crappy time series chart\n",
    "df_[df_['EMPLOYER']=='THOMSON_'].plot(x='START DATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th>JOB TITLE</th>\n",
       "      <th>BASE SALARY</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>SUBMIT DATE</th>\n",
       "      <th>START DATE</th>\n",
       "      <th>CASE STATUS</th>\n",
       "      <th>STATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S&amp;P GLOBAL_</td>\n",
       "      <td>ACCOUNT MANAGER</td>\n",
       "      <td>65000</td>\n",
       "      <td>SAN FRANCISCO, CA</td>\n",
       "      <td>2018-03-13</td>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S&amp;P GLOBAL_</td>\n",
       "      <td>ACCOUNT MANAGER</td>\n",
       "      <td>65000</td>\n",
       "      <td>SAN FRANCISCO, CA</td>\n",
       "      <td>2018-03-19</td>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S&amp;P GLOBAL_</td>\n",
       "      <td>ANALYST II</td>\n",
       "      <td>57900</td>\n",
       "      <td>CHARLOTTESVILLE, VA</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>DENIED</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S&amp;P GLOBAL_</td>\n",
       "      <td>ANALYST II</td>\n",
       "      <td>57990</td>\n",
       "      <td>CHARLOTTESVILLE, VA</td>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S&amp;P GLOBAL_</td>\n",
       "      <td>ANALYST II, INFORMATION SECURITY</td>\n",
       "      <td>85000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>2017-06-07</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      EMPLOYER                         JOB TITLE  BASE SALARY  \\\n",
       "0  S&P GLOBAL_                   ACCOUNT MANAGER        65000   \n",
       "1  S&P GLOBAL_                   ACCOUNT MANAGER        65000   \n",
       "2  S&P GLOBAL_                        ANALYST II        57900   \n",
       "3  S&P GLOBAL_                        ANALYST II        57990   \n",
       "4  S&P GLOBAL_  ANALYST II, INFORMATION SECURITY        85000   \n",
       "\n",
       "              LOCATION SUBMIT DATE START DATE CASE STATUS STATE  \n",
       "0    SAN FRANCISCO, CA  2018-03-13 2018-09-01   CERTIFIED    CA  \n",
       "1    SAN FRANCISCO, CA  2018-03-19 2018-09-01   CERTIFIED    CA  \n",
       "2  CHARLOTTESVILLE, VA  2018-02-01 2018-03-02      DENIED    VA  \n",
       "3  CHARLOTTESVILLE, VA  2018-02-06 2018-03-02   CERTIFIED    VA  \n",
       "4         NEW YORK, NY  2017-06-07 2017-10-27   CERTIFIED    NY  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th>JOB TITLE</th>\n",
       "      <th>BASE SALARY</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>SUBMIT DATE</th>\n",
       "      <th>START DATE</th>\n",
       "      <th>CASE STATUS</th>\n",
       "      <th>STATE</th>\n",
       "      <th>START MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S&amp;P GLOBAL_</td>\n",
       "      <td>ACCOUNT MANAGER</td>\n",
       "      <td>65000</td>\n",
       "      <td>SAN FRANCISCO, CA</td>\n",
       "      <td>2018-03-13</td>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>CA</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S&amp;P GLOBAL_</td>\n",
       "      <td>ACCOUNT MANAGER</td>\n",
       "      <td>65000</td>\n",
       "      <td>SAN FRANCISCO, CA</td>\n",
       "      <td>2018-03-19</td>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>CA</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S&amp;P GLOBAL_</td>\n",
       "      <td>ANALYST II</td>\n",
       "      <td>57900</td>\n",
       "      <td>CHARLOTTESVILLE, VA</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>DENIED</td>\n",
       "      <td>VA</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S&amp;P GLOBAL_</td>\n",
       "      <td>ANALYST II</td>\n",
       "      <td>57990</td>\n",
       "      <td>CHARLOTTESVILLE, VA</td>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>VA</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S&amp;P GLOBAL_</td>\n",
       "      <td>ANALYST II, INFORMATION SECURITY</td>\n",
       "      <td>85000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>2017-06-07</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>NY</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      EMPLOYER                         JOB TITLE  BASE SALARY  \\\n",
       "0  S&P GLOBAL_                   ACCOUNT MANAGER        65000   \n",
       "1  S&P GLOBAL_                   ACCOUNT MANAGER        65000   \n",
       "2  S&P GLOBAL_                        ANALYST II        57900   \n",
       "3  S&P GLOBAL_                        ANALYST II        57990   \n",
       "4  S&P GLOBAL_  ANALYST II, INFORMATION SECURITY        85000   \n",
       "\n",
       "              LOCATION SUBMIT DATE START DATE CASE STATUS STATE  START MONTH  \n",
       "0    SAN FRANCISCO, CA  2018-03-13 2018-09-01   CERTIFIED    CA            9  \n",
       "1    SAN FRANCISCO, CA  2018-03-19 2018-09-01   CERTIFIED    CA            9  \n",
       "2  CHARLOTTESVILLE, VA  2018-02-01 2018-03-02      DENIED    VA            3  \n",
       "3  CHARLOTTESVILLE, VA  2018-02-06 2018-03-02   CERTIFIED    VA            3  \n",
       "4         NEW YORK, NY  2017-06-07 2017-10-27   CERTIFIED    NY           10  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_['START MONTH'] = df_['START DATE'].apply(lambda x: x.month)\n",
    "df_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x180744a8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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JngAwDbnuZU+H4AnA0nz+MVV73LJd0KrR0ujVUCCswQAHjABgKp5K9bLn9gqliQieACwr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\nCSk8hRBCCCGESUjhKYQQQgghTOL/ATVZML2TP+J/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17feb9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chartdf = df_.groupby('START MONTH').agg({'START DATE': 'first', 'BASE SALARY': 'count'})\n",
    "chartdf.set_index('START DATE').plot(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <p><a name=\"addl\"></a></p>\n",
    "# More Analyses\n",
    " <a href=\"#sections\">Top</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BASE SALARY</th>\n",
       "      <th>START MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>46.00000</td>\n",
       "      <td>46.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>95928.73913</td>\n",
       "      <td>8.95652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28954.11838</td>\n",
       "      <td>0.91788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40500.00000</td>\n",
       "      <td>7.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>85000.00000</td>\n",
       "      <td>8.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>90000.00000</td>\n",
       "      <td>9.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>94291.50000</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>230000.00000</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        BASE SALARY  START MONTH\n",
       "count      46.00000     46.00000\n",
       "mean    95928.73913      8.95652\n",
       "std     28954.11838      0.91788\n",
       "min     40500.00000      7.00000\n",
       "25%     85000.00000      8.00000\n",
       "50%     90000.00000      9.00000\n",
       "75%     94291.50000     10.00000\n",
       "max    230000.00000     10.00000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter/sub-set just on a single job description\n",
    "title = 'FINANCIAL ENGINEER'\n",
    "position1 = df_[df_['JOB TITLE'] == title]\n",
    "position1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18114b70>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lJUUhISHq3bu3du7caZnu7e2t999/XydOnJCHh4fWrl173XqVqWj97du3q1OnTgoLC9OH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x155b5278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.xlim(75000,150000)\n",
    "\n",
    "position1['BASE SALARY'].plot(kind='hist', title = title, legend = True,  bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1816ecf8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ly5ZJks6dO6fCwkJFR0fr/ffflyTt3btX7du3V6tWrfTBBx/oq6++UkFBgf773/+qefPm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\nbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x180f5a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#position1['BASE SALARY'].plot(kind='box', title = title, legend=True)\n",
    "sns.boxplot(x=position1['BASE SALARY'], showmeans=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Drilldown to firms that employ specific Job Title__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BLOOMBERG_</th>\n",
       "      <td>8</td>\n",
       "      <td>140277</td>\n",
       "      <td>95722</td>\n",
       "      <td>230000</td>\n",
       "      <td>46166.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACTSET_</th>\n",
       "      <td>36</td>\n",
       "      <td>88611</td>\n",
       "      <td>85000</td>\n",
       "      <td>100000</td>\n",
       "      <td>4722.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICE_</th>\n",
       "      <td>2</td>\n",
       "      <td>50250</td>\n",
       "      <td>40500</td>\n",
       "      <td>60000</td>\n",
       "      <td>13788.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count    mean    min     max       std\n",
       "EMPLOYER                                          \n",
       "BLOOMBERG_      8  140277  95722  230000  46166.12\n",
       "FACTSET_       36   88611  85000  100000   4722.46\n",
       "ICE_            2   50250  40500   60000  13788.58"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agglist = ('count','mean', 'min', 'max', 'std')\n",
    "\n",
    "pd.set_option('precision',2)\n",
    "\n",
    "result1 = position1.pivot_table('BASE SALARY', index = [\"EMPLOYER\"], aggfunc = agglist)\n",
    "\n",
    "result1.sort_values('count', ascending=False)\n",
    "result1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New Fetch: Single Title for all Employers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Quantitative+Analyst'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title = 'Quantitative Analyst'\n",
    "titlelist = title.split(' ')\n",
    "len(titlelist)\n",
    "\n",
    "titlestring = titlelist[0]+ '+'+ titlelist[1]\n",
    "titlestring\n",
    "#titlestring = [titlestring.append(titlelist[x]) for x in len(titlelist)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://h1bdata.info/index.php?em=&job=Quantitative+Analyst&city=&year=All+Years'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseurl = 'http://h1bdata.info/index.php?em=&job='\n",
    "tailurl = '&city=&year=All+Years'\n",
    "\n",
    "url2 = baseurl+titlestring+tailurl\n",
    "url2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "content = requests.get(url2).text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#process the html code\n",
    "soup = BeautifulSoup(content, \"html.parser\")\n",
    "#type(soup)\n",
    "\n",
    "#go to table body\n",
    "table = soup.find(\"tbody\")\n",
    "\n",
    "#fetch Column headers\n",
    "colnames = [tag.text for tag in soup.find(\"thead\").find_all(\"th\")]  \n",
    "\n",
    "#cycle through tags and populate df with contents\n",
    "webtable = pd.DataFrame(\n",
    "        ((tag.text for tag in lst.find_all(\"td\")) for lst in table.find_all(\"tr\")),\n",
    "        columns = colnames\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th>JOB TITLE</th>\n",
       "      <th>BASE SALARY</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>SUBMIT DATE</th>\n",
       "      <th>START DATE</th>\n",
       "      <th>CASE STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1226</td>\n",
       "      <td>1226</td>\n",
       "      <td>1226</td>\n",
       "      <td>1226</td>\n",
       "      <td>1226</td>\n",
       "      <td>1226</td>\n",
       "      <td>1226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>378</td>\n",
       "      <td>1</td>\n",
       "      <td>342</td>\n",
       "      <td>145</td>\n",
       "      <td>560</td>\n",
       "      <td>530</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>GOOGLE INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>150,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>03/05/2015</td>\n",
       "      <td>09/01/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>89</td>\n",
       "      <td>1226</td>\n",
       "      <td>61</td>\n",
       "      <td>454</td>\n",
       "      <td>22</td>\n",
       "      <td>29</td>\n",
       "      <td>1147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          EMPLOYER             JOB TITLE BASE SALARY      LOCATION  \\\n",
       "count         1226                  1226        1226          1226   \n",
       "unique         378                     1         342           145   \n",
       "top     GOOGLE INC  QUANTITATIVE ANALYST     150,000  NEW YORK, NY   \n",
       "freq            89                  1226          61           454   \n",
       "\n",
       "       SUBMIT DATE  START DATE CASE STATUS  \n",
       "count         1226        1226        1226  \n",
       "unique         560         530           3  \n",
       "top     03/05/2015  09/01/2016   CERTIFIED  \n",
       "freq            22          29        1147  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "webtable.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th>JOB TITLE</th>\n",
       "      <th>BASE SALARY</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>SUBMIT DATE</th>\n",
       "      <th>START DATE</th>\n",
       "      <th>CASE STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RISK PREMIUM INVESTMENT MANAGEMENT COMPANY LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>0</td>\n",
       "      <td>,</td>\n",
       "      <td>03/14/2018</td>\n",
       "      <td>09/13/2018</td>\n",
       "      <td>DENIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EDISON SOLAR TECHNOLOGYINC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>40,200</td>\n",
       "      <td>EL MONTE, CA</td>\n",
       "      <td>03/28/2016</td>\n",
       "      <td>09/15/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EDISON SOLAR TECHNOLOGYINC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>44,200</td>\n",
       "      <td>EL MONTE, CA</td>\n",
       "      <td>03/16/2016</td>\n",
       "      <td>09/15/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AIM BIG INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>47,160</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>03/06/2017</td>\n",
       "      <td>09/01/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HI-TECH SOLUTIONS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>47,160</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>03/08/2017</td>\n",
       "      <td>09/07/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HI-TECH SOLUTIONS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>47,160</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>03/08/2017</td>\n",
       "      <td>09/07/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AIM BIG INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>47,160</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>09/06/2016</td>\n",
       "      <td>09/06/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AIM BIG INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>47,160</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>09/06/2016</td>\n",
       "      <td>09/06/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AIM BIG INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>47,700</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>03/11/2016</td>\n",
       "      <td>09/01/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AIM BIG INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>47,700</td>\n",
       "      <td>METTAWA, IL</td>\n",
       "      <td>03/17/2016</td>\n",
       "      <td>09/01/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LFK GROUP LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>48,460</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>02/12/2014</td>\n",
       "      <td>02/12/2014</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FOUR MARBLES LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>49,000</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>03/13/2014</td>\n",
       "      <td>09/12/2014</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ZACKS INVESTMENT MANAGEMENT INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>50,640</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>03/21/2017</td>\n",
       "      <td>09/20/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AIM BIG INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>50,820</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>03/11/2016</td>\n",
       "      <td>09/01/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HI-TECH SOLUTIONS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>51,340</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>02/27/2018</td>\n",
       "      <td>08/08/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LIGHT HORSE MARKET SOLUTIONS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>52,080</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>03/13/2015</td>\n",
       "      <td>09/10/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DIACONIA SOLUTIONS LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>52,180</td>\n",
       "      <td>WHITE PLAINS, NY</td>\n",
       "      <td>03/19/2018</td>\n",
       "      <td>09/17/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SCORELOGIX LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>56,000</td>\n",
       "      <td>NEWARK, DE</td>\n",
       "      <td>03/13/2015</td>\n",
       "      <td>09/11/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ANGEL OAK CAPITAL ADVISORS LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>56,560</td>\n",
       "      <td>ATLANTA, GA</td>\n",
       "      <td>05/09/2016</td>\n",
       "      <td>05/09/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ANGEL OAK CONSULTING GROUP LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>56,560</td>\n",
       "      <td>ATLANTA, GA</td>\n",
       "      <td>05/09/2016</td>\n",
       "      <td>05/09/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LIGHT HORSE MARKET SOLUTIONS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>63,480</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>03/09/2015</td>\n",
       "      <td>09/08/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ARESVI INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>64,000</td>\n",
       "      <td>NORTHBROOK, IL</td>\n",
       "      <td>03/09/2018</td>\n",
       "      <td>09/08/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>GROUP ONE TRADING LP</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>72,000</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>02/26/2018</td>\n",
       "      <td>03/05/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>GROUP ONE TRADING LP</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>72,000</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>03/07/2018</td>\n",
       "      <td>03/07/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>STARPOINT SOLUTIONS LLC A YOH SERVICES LLC COM...</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>72,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>03/06/2018</td>\n",
       "      <td>08/27/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>XI CAPITAL LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>80,000</td>\n",
       "      <td>PASADENA, CA</td>\n",
       "      <td>03/08/2018</td>\n",
       "      <td>09/07/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>POPULUS GROUP LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>113,420</td>\n",
       "      <td>JERSEY CITY, NJ</td>\n",
       "      <td>11/24/2017</td>\n",
       "      <td>12/04/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>POPULUS GROUP LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>113,420</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>11/16/2016</td>\n",
       "      <td>11/28/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PINE RIVER CAPITAL MANAGEMENT LP</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>10,000</td>\n",
       "      <td>MINNETONKA, MN</td>\n",
       "      <td>03/10/2015</td>\n",
       "      <td>08/03/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SHELTON COMMUNICATIONS GROUP INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>40,000</td>\n",
       "      <td>KNOXVILLE, TN</td>\n",
       "      <td>03/16/2017</td>\n",
       "      <td>09/15/2017</td>\n",
       "      <td>CERTIFIED</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>182,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>12/11/2014</td>\n",
       "      <td>04/30/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>HBK NEW YORK LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>185,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>09/04/2015</td>\n",
       "      <td>09/14/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>TWO SIGMA INVESTMENTS LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>185,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>06/25/2014</td>\n",
       "      <td>08/25/2014</td>\n",
       "      <td>WITHDRAWN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>TWO SIGMA INVESTMENTS LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>185,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>06/26/2014</td>\n",
       "      <td>07/21/2014</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>190,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>01/29/2018</td>\n",
       "      <td>07/24/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>IMPERATIVE EXECUTION INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>200,000</td>\n",
       "      <td>STAMFORD, CT</td>\n",
       "      <td>02/02/2018</td>\n",
       "      <td>02/19/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>CITIGROUP GLOBAL MARKETS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>200,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>03/12/2018</td>\n",
       "      <td>09/01/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>CITIGROUP GLOBAL MARKETS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>200,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>06/21/2017</td>\n",
       "      <td>09/10/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>CITIGROUP GLOBAL MARKETS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>200,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>08/01/2017</td>\n",
       "      <td>12/02/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>FOREX CAPITAL MARKETS LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>200,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>07/13/2016</td>\n",
       "      <td>08/07/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>CITIGROUP GLOBAL MARKETS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>200,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>03/16/2016</td>\n",
       "      <td>09/01/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>CITIGROUP GLOBAL MARKETS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>200,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>06/06/2016</td>\n",
       "      <td>09/01/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>FOREX CAPITAL MARKETS LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>200,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>10/23/2014</td>\n",
       "      <td>11/10/2014</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1209</th>\n",
       "      <td>CITIGROUP GLOBAL MARKETS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>200,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>03/05/2015</td>\n",
       "      <td>07/01/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>KCG AMERICAS LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>200,000</td>\n",
       "      <td>JERSEY CITY, NJ</td>\n",
       "      <td>07/03/2014</td>\n",
       "      <td>07/16/2014</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1211</th>\n",
       "      <td>CITIGROUP GLOBAL MARKETS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>200,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>08/12/2014</td>\n",
       "      <td>10/01/2014</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1212</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>202,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>01/02/2018</td>\n",
       "      <td>01/08/2018</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1213</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>219,500</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>04/10/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1214</th>\n",
       "      <td>BLOOMBERG TRADEBOOK LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>219,500</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>03/22/2017</td>\n",
       "      <td>04/10/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>AIG ASSET MANAGEMENT (US) LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>220,000</td>\n",
       "      <td>WILTON, CT</td>\n",
       "      <td>04/18/2016</td>\n",
       "      <td>07/10/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216</th>\n",
       "      <td>CITADEL LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>220,000</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>06/12/2014</td>\n",
       "      <td>08/04/2014</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1217</th>\n",
       "      <td>CITADEL LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>225,000</td>\n",
       "      <td>CHICAGO, IL</td>\n",
       "      <td>04/14/2016</td>\n",
       "      <td>10/01/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1218</th>\n",
       "      <td>ELLIOTT MANAGEMENT CORPORATION</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>230,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>11/08/2016</td>\n",
       "      <td>11/15/2016</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1219</th>\n",
       "      <td>JPMORGAN CHASE &amp; CO</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>235,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>03/24/2014</td>\n",
       "      <td>09/23/2014</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>CITIGROUP GLOBAL MARKETS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>250,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>04/17/2017</td>\n",
       "      <td>05/01/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>CITIGROUP GLOBAL MARKETS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>250,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>06/15/2017</td>\n",
       "      <td>10/01/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>HBK NEW YORK LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>250,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>11/03/2015</td>\n",
       "      <td>11/16/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>DW PARTNERS LP</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>250,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>06/17/2015</td>\n",
       "      <td>09/18/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>252,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>10/31/2016</td>\n",
       "      <td>04/30/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>CITIGROUP GLOBAL MARKETS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>300,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>04/05/2017</td>\n",
       "      <td>07/25/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1226 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               EMPLOYER             JOB TITLE  \\\n",
       "0        RISK PREMIUM INVESTMENT MANAGEMENT COMPANY LLC  QUANTITATIVE ANALYST   \n",
       "1                            EDISON SOLAR TECHNOLOGYINC  QUANTITATIVE ANALYST   \n",
       "2                            EDISON SOLAR TECHNOLOGYINC  QUANTITATIVE ANALYST   \n",
       "3                                           AIM BIG INC  QUANTITATIVE ANALYST   \n",
       "4                                 HI-TECH SOLUTIONS INC  QUANTITATIVE ANALYST   \n",
       "5                                 HI-TECH SOLUTIONS INC  QUANTITATIVE ANALYST   \n",
       "6                                           AIM BIG INC  QUANTITATIVE ANALYST   \n",
       "7                                           AIM BIG INC  QUANTITATIVE ANALYST   \n",
       "8                                           AIM BIG INC  QUANTITATIVE ANALYST   \n",
       "9                                           AIM BIG INC  QUANTITATIVE ANALYST   \n",
       "10                                        LFK GROUP LLC  QUANTITATIVE ANALYST   \n",
       "11                                     FOUR MARBLES LLC  QUANTITATIVE ANALYST   \n",
       "12                      ZACKS INVESTMENT MANAGEMENT INC  QUANTITATIVE ANALYST   \n",
       "13                                          AIM BIG INC  QUANTITATIVE ANALYST   \n",
       "14                                HI-TECH SOLUTIONS INC  QUANTITATIVE ANALYST   \n",
       "15                     LIGHT HORSE MARKET SOLUTIONS INC  QUANTITATIVE ANALYST   \n",
       "16                               DIACONIA SOLUTIONS LLC  QUANTITATIVE ANALYST   \n",
       "17                                       SCORELOGIX LLC  QUANTITATIVE ANALYST   \n",
       "18                       ANGEL OAK CAPITAL ADVISORS LLC  QUANTITATIVE ANALYST   \n",
       "19                       ANGEL OAK CONSULTING GROUP LLC  QUANTITATIVE ANALYST   \n",
       "20                     LIGHT HORSE MARKET SOLUTIONS INC  QUANTITATIVE ANALYST   \n",
       "21                                           ARESVI INC  QUANTITATIVE ANALYST   \n",
       "22                                 GROUP ONE TRADING LP  QUANTITATIVE ANALYST   \n",
       "23                                 GROUP ONE TRADING LP  QUANTITATIVE ANALYST   \n",
       "24    STARPOINT SOLUTIONS LLC A YOH SERVICES LLC COM...  QUANTITATIVE ANALYST   \n",
       "25                                       XI CAPITAL LLC  QUANTITATIVE ANALYST   \n",
       "26                                    POPULUS GROUP LLC  QUANTITATIVE ANALYST   \n",
       "27                                    POPULUS GROUP LLC  QUANTITATIVE ANALYST   \n",
       "28                     PINE RIVER CAPITAL MANAGEMENT LP  QUANTITATIVE ANALYST   \n",
       "29                     SHELTON COMMUNICATIONS GROUP INC  QUANTITATIVE ANALYST   \n",
       "...                                                 ...                   ...   \n",
       "1196                                       BLOOMBERG LP  QUANTITATIVE ANALYST   \n",
       "1197                                   HBK NEW YORK LLC  QUANTITATIVE ANALYST   \n",
       "1198                          TWO SIGMA INVESTMENTS LLC  QUANTITATIVE ANALYST   \n",
       "1199                          TWO SIGMA INVESTMENTS LLC  QUANTITATIVE ANALYST   \n",
       "1200                                       BLOOMBERG LP  QUANTITATIVE ANALYST   \n",
       "1201                           IMPERATIVE EXECUTION INC  QUANTITATIVE ANALYST   \n",
       "1202                       CITIGROUP GLOBAL MARKETS INC  QUANTITATIVE ANALYST   \n",
       "1203                       CITIGROUP GLOBAL MARKETS INC  QUANTITATIVE ANALYST   \n",
       "1204                       CITIGROUP GLOBAL MARKETS INC  QUANTITATIVE ANALYST   \n",
       "1205                          FOREX CAPITAL MARKETS LLC  QUANTITATIVE ANALYST   \n",
       "1206                       CITIGROUP GLOBAL MARKETS INC  QUANTITATIVE ANALYST   \n",
       "1207                       CITIGROUP GLOBAL MARKETS INC  QUANTITATIVE ANALYST   \n",
       "1208                          FOREX CAPITAL MARKETS LLC  QUANTITATIVE ANALYST   \n",
       "1209                       CITIGROUP GLOBAL MARKETS INC  QUANTITATIVE ANALYST   \n",
       "1210                                   KCG AMERICAS LLC  QUANTITATIVE ANALYST   \n",
       "1211                       CITIGROUP GLOBAL MARKETS INC  QUANTITATIVE ANALYST   \n",
       "1212                                       BLOOMBERG LP  QUANTITATIVE ANALYST   \n",
       "1213                                       BLOOMBERG LP  QUANTITATIVE ANALYST   \n",
       "1214                            BLOOMBERG TRADEBOOK LLC  QUANTITATIVE ANALYST   \n",
       "1215                      AIG ASSET MANAGEMENT (US) LLC  QUANTITATIVE ANALYST   \n",
       "1216                                        CITADEL LLC  QUANTITATIVE ANALYST   \n",
       "1217                                        CITADEL LLC  QUANTITATIVE ANALYST   \n",
       "1218                     ELLIOTT MANAGEMENT CORPORATION  QUANTITATIVE ANALYST   \n",
       "1219                                JPMORGAN CHASE & CO  QUANTITATIVE ANALYST   \n",
       "1220                       CITIGROUP GLOBAL MARKETS INC  QUANTITATIVE ANALYST   \n",
       "1221                       CITIGROUP GLOBAL MARKETS INC  QUANTITATIVE ANALYST   \n",
       "1222                                   HBK NEW YORK LLC  QUANTITATIVE ANALYST   \n",
       "1223                                     DW PARTNERS LP  QUANTITATIVE ANALYST   \n",
       "1224                                       BLOOMBERG LP  QUANTITATIVE ANALYST   \n",
       "1225                       CITIGROUP GLOBAL MARKETS INC  QUANTITATIVE ANALYST   \n",
       "\n",
       "     BASE SALARY          LOCATION SUBMIT DATE  START DATE CASE STATUS  \n",
       "0              0                ,   03/14/2018  09/13/2018      DENIED  \n",
       "1         40,200      EL MONTE, CA  03/28/2016  09/15/2016   CERTIFIED  \n",
       "2         44,200      EL MONTE, CA  03/16/2016  09/15/2016   CERTIFIED  \n",
       "3         47,160       CHICAGO, IL  03/06/2017  09/01/2017   CERTIFIED  \n",
       "4         47,160       CHICAGO, IL  03/08/2017  09/07/2017   CERTIFIED  \n",
       "5         47,160       CHICAGO, IL  03/08/2017  09/07/2017   CERTIFIED  \n",
       "6         47,160       CHICAGO, IL  09/06/2016  09/06/2016   CERTIFIED  \n",
       "7         47,160       CHICAGO, IL  09/06/2016  09/06/2016   CERTIFIED  \n",
       "8         47,700       CHICAGO, IL  03/11/2016  09/01/2016   CERTIFIED  \n",
       "9         47,700       METTAWA, IL  03/17/2016  09/01/2016   CERTIFIED  \n",
       "10        48,460       CHICAGO, IL  02/12/2014  02/12/2014   CERTIFIED  \n",
       "11        49,000       CHICAGO, IL  03/13/2014  09/12/2014   CERTIFIED  \n",
       "12        50,640       CHICAGO, IL  03/21/2017  09/20/2017   CERTIFIED  \n",
       "13        50,820      NEW YORK, NY  03/11/2016  09/01/2016   CERTIFIED  \n",
       "14        51,340       CHICAGO, IL  02/27/2018  08/08/2018   CERTIFIED  \n",
       "15        52,080      NEW YORK, NY  03/13/2015  09/10/2015   CERTIFIED  \n",
       "16        52,180  WHITE PLAINS, NY  03/19/2018  09/17/2018   CERTIFIED  \n",
       "17        56,000        NEWARK, DE  03/13/2015  09/11/2015   CERTIFIED  \n",
       "18        56,560       ATLANTA, GA  05/09/2016  05/09/2016   CERTIFIED  \n",
       "19        56,560       ATLANTA, GA  05/09/2016  05/09/2016   CERTIFIED  \n",
       "20        63,480      NEW YORK, NY  03/09/2015  09/08/2015   CERTIFIED  \n",
       "21        64,000    NORTHBROOK, IL  03/09/2018  09/08/2018   CERTIFIED  \n",
       "22        72,000       CHICAGO, IL  02/26/2018  03/05/2018   CERTIFIED  \n",
       "23        72,000       CHICAGO, IL  03/07/2018  03/07/2018   CERTIFIED  \n",
       "24        72,000      NEW YORK, NY  03/06/2018  08/27/2018   CERTIFIED  \n",
       "25        80,000      PASADENA, CA  03/08/2018  09/07/2018   CERTIFIED  \n",
       "26       113,420   JERSEY CITY, NJ  11/24/2017  12/04/2017   CERTIFIED  \n",
       "27       113,420      NEW YORK, NY  11/16/2016  11/28/2016   CERTIFIED  \n",
       "28        10,000    MINNETONKA, MN  03/10/2015  08/03/2015   CERTIFIED  \n",
       "29        40,000     KNOXVILLE, TN  03/16/2017  09/15/2017   CERTIFIED  \n",
       "...          ...               ...         ...         ...         ...  \n",
       "1196     182,000      NEW YORK, NY  12/11/2014  04/30/2015   CERTIFIED  \n",
       "1197     185,000      NEW YORK, NY  09/04/2015  09/14/2015   CERTIFIED  \n",
       "1198     185,000      NEW YORK, NY  06/25/2014  08/25/2014   WITHDRAWN  \n",
       "1199     185,000      NEW YORK, NY  06/26/2014  07/21/2014   CERTIFIED  \n",
       "1200     190,000      NEW YORK, NY  01/29/2018  07/24/2018   CERTIFIED  \n",
       "1201     200,000      STAMFORD, CT  02/02/2018  02/19/2018   CERTIFIED  \n",
       "1202     200,000      NEW YORK, NY  03/12/2018  09/01/2018   CERTIFIED  \n",
       "1203     200,000      NEW YORK, NY  06/21/2017  09/10/2017   CERTIFIED  \n",
       "1204     200,000      NEW YORK, NY  08/01/2017  12/02/2017   CERTIFIED  \n",
       "1205     200,000      NEW YORK, NY  07/13/2016  08/07/2016   CERTIFIED  \n",
       "1206     200,000      NEW YORK, NY  03/16/2016  09/01/2016   CERTIFIED  \n",
       "1207     200,000      NEW YORK, NY  06/06/2016  09/01/2016   CERTIFIED  \n",
       "1208     200,000      NEW YORK, NY  10/23/2014  11/10/2014   CERTIFIED  \n",
       "1209     200,000      NEW YORK, NY  03/05/2015  07/01/2015   CERTIFIED  \n",
       "1210     200,000   JERSEY CITY, NJ  07/03/2014  07/16/2014   CERTIFIED  \n",
       "1211     200,000      NEW YORK, NY  08/12/2014  10/01/2014   CERTIFIED  \n",
       "1212     202,000      NEW YORK, NY  01/02/2018  01/08/2018   CERTIFIED  \n",
       "1213     219,500      NEW YORK, NY  03/18/2017  04/10/2017   CERTIFIED  \n",
       "1214     219,500      NEW YORK, NY  03/22/2017  04/10/2017   CERTIFIED  \n",
       "1215     220,000        WILTON, CT  04/18/2016  07/10/2016   CERTIFIED  \n",
       "1216     220,000       CHICAGO, IL  06/12/2014  08/04/2014   CERTIFIED  \n",
       "1217     225,000       CHICAGO, IL  04/14/2016  10/01/2016   CERTIFIED  \n",
       "1218     230,000      NEW YORK, NY  11/08/2016  11/15/2016   CERTIFIED  \n",
       "1219     235,000      NEW YORK, NY  03/24/2014  09/23/2014   CERTIFIED  \n",
       "1220     250,000      NEW YORK, NY  04/17/2017  05/01/2017   CERTIFIED  \n",
       "1221     250,000      NEW YORK, NY  06/15/2017  10/01/2017   CERTIFIED  \n",
       "1222     250,000      NEW YORK, NY  11/03/2015  11/16/2015   CERTIFIED  \n",
       "1223     250,000      NEW YORK, NY  06/17/2015  09/18/2015   CERTIFIED  \n",
       "1224     252,000      NEW YORK, NY  10/31/2016  04/30/2017   CERTIFIED  \n",
       "1225     300,000      NEW YORK, NY  04/05/2017  07/25/2017   CERTIFIED  \n",
       "\n",
       "[1226 rows x 7 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "webtable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMPLOYER</th>\n",
       "      <th>JOB TITLE</th>\n",
       "      <th>BASE SALARY</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>SUBMIT DATE</th>\n",
       "      <th>START DATE</th>\n",
       "      <th>CASE STATUS</th>\n",
       "      <th>STATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>CITIGROUP GLOBAL MARKETS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>250,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>06/15/2017</td>\n",
       "      <td>10/01/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>HBK NEW YORK LLC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>250,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>11/03/2015</td>\n",
       "      <td>11/16/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>DW PARTNERS LP</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>250,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>06/17/2015</td>\n",
       "      <td>09/18/2015</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>BLOOMBERG LP</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>252,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>10/31/2016</td>\n",
       "      <td>04/30/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>CITIGROUP GLOBAL MARKETS INC</td>\n",
       "      <td>QUANTITATIVE ANALYST</td>\n",
       "      <td>300,000</td>\n",
       "      <td>NEW YORK, NY</td>\n",
       "      <td>04/05/2017</td>\n",
       "      <td>07/25/2017</td>\n",
       "      <td>CERTIFIED</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          EMPLOYER             JOB TITLE BASE SALARY  \\\n",
       "1221  CITIGROUP GLOBAL MARKETS INC  QUANTITATIVE ANALYST     250,000   \n",
       "1222              HBK NEW YORK LLC  QUANTITATIVE ANALYST     250,000   \n",
       "1223                DW PARTNERS LP  QUANTITATIVE ANALYST     250,000   \n",
       "1224                  BLOOMBERG LP  QUANTITATIVE ANALYST     252,000   \n",
       "1225  CITIGROUP GLOBAL MARKETS INC  QUANTITATIVE ANALYST     300,000   \n",
       "\n",
       "          LOCATION SUBMIT DATE  START DATE CASE STATUS STATE  \n",
       "1221  NEW YORK, NY  06/15/2017  10/01/2017   CERTIFIED    NY  \n",
       "1222  NEW YORK, NY  11/03/2015  11/16/2015   CERTIFIED    NY  \n",
       "1223  NEW YORK, NY  06/17/2015  09/18/2015   CERTIFIED    NY  \n",
       "1224  NEW YORK, NY  10/31/2016  04/30/2017   CERTIFIED    NY  \n",
       "1225  NEW YORK, NY  04/05/2017  07/25/2017   CERTIFIED    NY  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add a column call state\n",
    "newcol = webtable['LOCATION'].apply(lambda x: x.split(', ') [-1])\n",
    "webtable['STATE'] = newcol\n",
    "webtable.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "webtable['BASE SALARY'] = webtable['BASE SALARY'].str.replace(',','').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "agglist = ('count','mean', 'min', 'max')\n",
    "\n",
    "pd.set_option('precision',4)\n",
    "\n",
    "#temp3 = df_.pivot_table('BASE SALARY', index = [\"STATE\", \"EMPLOYER\"], aggfunc = agglist)\n",
    "result2 = webtable.pivot_table('BASE SALARY',index = ['STATE'], aggfunc = agglist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>2</td>\n",
       "      <td>94690.0000</td>\n",
       "      <td>89380</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>155</td>\n",
       "      <td>112111.4194</td>\n",
       "      <td>40200</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>2</td>\n",
       "      <td>96477.0000</td>\n",
       "      <td>67954</td>\n",
       "      <td>125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>20</td>\n",
       "      <td>119048.5500</td>\n",
       "      <td>50000</td>\n",
       "      <td>220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>3</td>\n",
       "      <td>77621.3333</td>\n",
       "      <td>58864</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>3</td>\n",
       "      <td>49946.6667</td>\n",
       "      <td>46000</td>\n",
       "      <td>56000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>19</td>\n",
       "      <td>75819.4737</td>\n",
       "      <td>47010</td>\n",
       "      <td>145000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>23</td>\n",
       "      <td>65192.0435</td>\n",
       "      <td>44000</td>\n",
       "      <td>125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IA</th>\n",
       "      <td>3</td>\n",
       "      <td>80640.0000</td>\n",
       "      <td>75200</td>\n",
       "      <td>91520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <td>172</td>\n",
       "      <td>77536.2209</td>\n",
       "      <td>45510</td>\n",
       "      <td>225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>2</td>\n",
       "      <td>50005.5000</td>\n",
       "      <td>45011</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KS</th>\n",
       "      <td>3</td>\n",
       "      <td>67333.3333</td>\n",
       "      <td>60000</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>146</td>\n",
       "      <td>89545.5068</td>\n",
       "      <td>41662</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <td>13</td>\n",
       "      <td>92346.0000</td>\n",
       "      <td>46051</td>\n",
       "      <td>130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>2</td>\n",
       "      <td>60000.0000</td>\n",
       "      <td>50000</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN</th>\n",
       "      <td>3</td>\n",
       "      <td>64963.3333</td>\n",
       "      <td>10000</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>1</td>\n",
       "      <td>101185.0000</td>\n",
       "      <td>101185</td>\n",
       "      <td>101185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC</th>\n",
       "      <td>33</td>\n",
       "      <td>81508.0909</td>\n",
       "      <td>59509</td>\n",
       "      <td>151000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>32</td>\n",
       "      <td>92499.4688</td>\n",
       "      <td>51000</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>505</td>\n",
       "      <td>117628.5248</td>\n",
       "      <td>47590</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>1</td>\n",
       "      <td>59010.0000</td>\n",
       "      <td>59010</td>\n",
       "      <td>59010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <td>3</td>\n",
       "      <td>70222.3333</td>\n",
       "      <td>60000</td>\n",
       "      <td>78667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>12</td>\n",
       "      <td>80666.6667</td>\n",
       "      <td>55000</td>\n",
       "      <td>124000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PR</th>\n",
       "      <td>10</td>\n",
       "      <td>62100.0000</td>\n",
       "      <td>45000</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RI</th>\n",
       "      <td>1</td>\n",
       "      <td>84115.0000</td>\n",
       "      <td>84115</td>\n",
       "      <td>84115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC</th>\n",
       "      <td>1</td>\n",
       "      <td>60000.0000</td>\n",
       "      <td>60000</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN</th>\n",
       "      <td>5</td>\n",
       "      <td>68280.0000</td>\n",
       "      <td>40000</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>29</td>\n",
       "      <td>88893.2414</td>\n",
       "      <td>43000</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UT</th>\n",
       "      <td>1</td>\n",
       "      <td>100000.0000</td>\n",
       "      <td>100000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <td>10</td>\n",
       "      <td>80850.0000</td>\n",
       "      <td>64500</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VT</th>\n",
       "      <td>4</td>\n",
       "      <td>108750.0000</td>\n",
       "      <td>105000</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <td>5</td>\n",
       "      <td>106396.4000</td>\n",
       "      <td>60000</td>\n",
       "      <td>170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <td>1</td>\n",
       "      <td>62500.0000</td>\n",
       "      <td>62500</td>\n",
       "      <td>62500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count         mean     min     max\n",
       "STATE                                    \n",
       "           1       0.0000       0       0\n",
       "AZ         2   94690.0000   89380  100000\n",
       "CA       155  112111.4194   40200  180000\n",
       "CO         2   96477.0000   67954  125000\n",
       "CT        20  119048.5500   50000  220000\n",
       "DC         3   77621.3333   58864   90000\n",
       "DE         3   49946.6667   46000   56000\n",
       "FL        19   75819.4737   47010  145000\n",
       "GA        23   65192.0435   44000  125000\n",
       "IA         3   80640.0000   75200   91520\n",
       "IL       172   77536.2209   45510  225000\n",
       "IN         2   50005.5000   45011   55000\n",
       "KS         3   67333.3333   60000   80000\n",
       "MA       146   89545.5068   41662  175000\n",
       "MD        13   92346.0000   46051  130000\n",
       "MI         2   60000.0000   50000   70000\n",
       "MN         3   64963.3333   10000  120000\n",
       "MO         1  101185.0000  101185  101185\n",
       "NC        33   81508.0909   59509  151000\n",
       "NJ        32   92499.4688   51000  200000\n",
       "NY       505  117628.5248   47590  300000\n",
       "OH         1   59010.0000   59010   59010\n",
       "OR         3   70222.3333   60000   78667\n",
       "PA        12   80666.6667   55000  124000\n",
       "PR        10   62100.0000   45000   85000\n",
       "RI         1   84115.0000   84115   84115\n",
       "SC         1   60000.0000   60000   60000\n",
       "TN         5   68280.0000   40000   85000\n",
       "TX        29   88893.2414   43000  175000\n",
       "UT         1  100000.0000  100000  100000\n",
       "VA        10   80850.0000   64500   90000\n",
       "VT         4  108750.0000  105000  120000\n",
       "WA         5  106396.4000   60000  170000\n",
       "WI         1   62500.0000   62500   62500"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2.reindex()\n",
    "result2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>2</td>\n",
       "      <td>94690.0000</td>\n",
       "      <td>89380</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>155</td>\n",
       "      <td>112111.4194</td>\n",
       "      <td>40200</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>2</td>\n",
       "      <td>96477.0000</td>\n",
       "      <td>67954</td>\n",
       "      <td>125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>20</td>\n",
       "      <td>119048.5500</td>\n",
       "      <td>50000</td>\n",
       "      <td>220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>3</td>\n",
       "      <td>77621.3333</td>\n",
       "      <td>58864</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>3</td>\n",
       "      <td>49946.6667</td>\n",
       "      <td>46000</td>\n",
       "      <td>56000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>19</td>\n",
       "      <td>75819.4737</td>\n",
       "      <td>47010</td>\n",
       "      <td>145000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>23</td>\n",
       "      <td>65192.0435</td>\n",
       "      <td>44000</td>\n",
       "      <td>125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IA</th>\n",
       "      <td>3</td>\n",
       "      <td>80640.0000</td>\n",
       "      <td>75200</td>\n",
       "      <td>91520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <td>172</td>\n",
       "      <td>77536.2209</td>\n",
       "      <td>45510</td>\n",
       "      <td>225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>2</td>\n",
       "      <td>50005.5000</td>\n",
       "      <td>45011</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KS</th>\n",
       "      <td>3</td>\n",
       "      <td>67333.3333</td>\n",
       "      <td>60000</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>146</td>\n",
       "      <td>89545.5068</td>\n",
       "      <td>41662</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <td>13</td>\n",
       "      <td>92346.0000</td>\n",
       "      <td>46051</td>\n",
       "      <td>130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>2</td>\n",
       "      <td>60000.0000</td>\n",
       "      <td>50000</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN</th>\n",
       "      <td>3</td>\n",
       "      <td>64963.3333</td>\n",
       "      <td>10000</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>1</td>\n",
       "      <td>101185.0000</td>\n",
       "      <td>101185</td>\n",
       "      <td>101185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC</th>\n",
       "      <td>33</td>\n",
       "      <td>81508.0909</td>\n",
       "      <td>59509</td>\n",
       "      <td>151000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>32</td>\n",
       "      <td>92499.4688</td>\n",
       "      <td>51000</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>505</td>\n",
       "      <td>117628.5248</td>\n",
       "      <td>47590</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>1</td>\n",
       "      <td>59010.0000</td>\n",
       "      <td>59010</td>\n",
       "      <td>59010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <td>3</td>\n",
       "      <td>70222.3333</td>\n",
       "      <td>60000</td>\n",
       "      <td>78667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>12</td>\n",
       "      <td>80666.6667</td>\n",
       "      <td>55000</td>\n",
       "      <td>124000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PR</th>\n",
       "      <td>10</td>\n",
       "      <td>62100.0000</td>\n",
       "      <td>45000</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RI</th>\n",
       "      <td>1</td>\n",
       "      <td>84115.0000</td>\n",
       "      <td>84115</td>\n",
       "      <td>84115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC</th>\n",
       "      <td>1</td>\n",
       "      <td>60000.0000</td>\n",
       "      <td>60000</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN</th>\n",
       "      <td>5</td>\n",
       "      <td>68280.0000</td>\n",
       "      <td>40000</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>29</td>\n",
       "      <td>88893.2414</td>\n",
       "      <td>43000</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UT</th>\n",
       "      <td>1</td>\n",
       "      <td>100000.0000</td>\n",
       "      <td>100000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <td>10</td>\n",
       "      <td>80850.0000</td>\n",
       "      <td>64500</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VT</th>\n",
       "      <td>4</td>\n",
       "      <td>108750.0000</td>\n",
       "      <td>105000</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <td>5</td>\n",
       "      <td>106396.4000</td>\n",
       "      <td>60000</td>\n",
       "      <td>170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <td>1</td>\n",
       "      <td>62500.0000</td>\n",
       "      <td>62500</td>\n",
       "      <td>62500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count         mean     min     max\n",
       "STATE                                    \n",
       "           1       0.0000       0       0\n",
       "AZ         2   94690.0000   89380  100000\n",
       "CA       155  112111.4194   40200  180000\n",
       "CO         2   96477.0000   67954  125000\n",
       "CT        20  119048.5500   50000  220000\n",
       "DC         3   77621.3333   58864   90000\n",
       "DE         3   49946.6667   46000   56000\n",
       "FL        19   75819.4737   47010  145000\n",
       "GA        23   65192.0435   44000  125000\n",
       "IA         3   80640.0000   75200   91520\n",
       "IL       172   77536.2209   45510  225000\n",
       "IN         2   50005.5000   45011   55000\n",
       "KS         3   67333.3333   60000   80000\n",
       "MA       146   89545.5068   41662  175000\n",
       "MD        13   92346.0000   46051  130000\n",
       "MI         2   60000.0000   50000   70000\n",
       "MN         3   64963.3333   10000  120000\n",
       "MO         1  101185.0000  101185  101185\n",
       "NC        33   81508.0909   59509  151000\n",
       "NJ        32   92499.4688   51000  200000\n",
       "NY       505  117628.5248   47590  300000\n",
       "OH         1   59010.0000   59010   59010\n",
       "OR         3   70222.3333   60000   78667\n",
       "PA        12   80666.6667   55000  124000\n",
       "PR        10   62100.0000   45000   85000\n",
       "RI         1   84115.0000   84115   84115\n",
       "SC         1   60000.0000   60000   60000\n",
       "TN         5   68280.0000   40000   85000\n",
       "TX        29   88893.2414   43000  175000\n",
       "UT         1  100000.0000  100000  100000\n",
       "VA        10   80850.0000   64500   90000\n",
       "VT         4  108750.0000  105000  120000\n",
       "WA         5  106396.4000   60000  170000\n",
       "WI         1   62500.0000   62500   62500"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2.sort_values('count', ascending=False)\n",
    "result2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<a href=\"#sections\">Top</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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