003_Chicago Employees_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "First, let's import libraries and run notebook \"magic\" to have plots appear inline\n",
    "--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import scipy.stats\n",
    "import seaborn as sns\n",
    "\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we load our .csv file as a pandas dataframe into a variable called df. csv stands for \"comma separated values\"\n",
    "--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/Employees.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Position Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Employee Annual Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0    </th>\n",
       "      <td>          AARON,  ELVIA J</td>\n",
       "      <td>                              WATER RATE TAKER</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td>  $90744.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1    </th>\n",
       "      <td>        AARON,  JEFFERY M</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2    </th>\n",
       "      <td>           AARON,  KARINA</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3    </th>\n",
       "      <td>      AARON,  KIMBERLEI R</td>\n",
       "      <td>                      CHIEF CONTRACT EXPEDITER</td>\n",
       "      <td> GENERAL SERVICES</td>\n",
       "      <td>  $89880.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4    </th>\n",
       "      <td>      ABAD JR,  VICENTE M</td>\n",
       "      <td>                             CIVIL ENGINEER IV</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td> $106836.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5    </th>\n",
       "      <td>          ABARCA,  ANABEL</td>\n",
       "      <td>                          ASST TO THE ALDERMAN</td>\n",
       "      <td>     CITY COUNCIL</td>\n",
       "      <td>  $70764.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6    </th>\n",
       "      <td>        ABARCA,  EMMANUEL</td>\n",
       "      <td>                         GENERAL LABORER - DSS</td>\n",
       "      <td>    STREETS &amp; SAN</td>\n",
       "      <td>  $41849.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7    </th>\n",
       "      <td>        ABASCAL,  REECE E</td>\n",
       "      <td>                   TRAFFIC CONTROL AIDE-HOURLY</td>\n",
       "      <td>             OEMC</td>\n",
       "      <td>  $20051.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8    </th>\n",
       "      <td>     ABBASI,  CHRISTOPHER</td>\n",
       "      <td>                    STAFF ASST TO THE ALDERMAN</td>\n",
       "      <td>     CITY COUNCIL</td>\n",
       "      <td>  $49452.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9    </th>\n",
       "      <td>    ABBATACOLA,  ROBERT J</td>\n",
       "      <td>                           ELECTRICAL MECHANIC</td>\n",
       "      <td>         AVIATION</td>\n",
       "      <td>  $93600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10   </th>\n",
       "      <td>    ABBATEMARCO,  JAMES J</td>\n",
       "      <td>                             FIRE ENGINEER-EMT</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td> $100320.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11   </th>\n",
       "      <td>         ABBATE,  TERRY M</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $90618.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12   </th>\n",
       "      <td>         ABBOTT,  BETTY L</td>\n",
       "      <td>                            FOSTER GRANDPARENT</td>\n",
       "      <td> FAMILY &amp; SUPPORT</td>\n",
       "      <td>   $2756.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13   </th>\n",
       "      <td>        ABBOTT,  LYNISE M</td>\n",
       "      <td>                                     CLERK III</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $46896.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14   </th>\n",
       "      <td>   ABBRUZZESE,  WILLIAM J</td>\n",
       "      <td>                        INVESTIGATOR - IPRA II</td>\n",
       "      <td>             IPRA</td>\n",
       "      <td>  $73920.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15   </th>\n",
       "      <td>          ABDALLAH,  ZAID</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $74028.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16   </th>\n",
       "      <td>    ABDELHADI,  ABDALMAHD</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $81588.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17   </th>\n",
       "      <td>      ABDELLATIF,  AREF R</td>\n",
       "      <td> FIREFIGHTER (PER ARBITRATORS AWARD)-PARAMEDIC</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td>  $99228.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18   </th>\n",
       "      <td>       ABDELMAJEID,  AZIZ</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $81588.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19   </th>\n",
       "      <td>      ABDOLLAHZADEH,  ALI</td>\n",
       "      <td>                         FIREFIGHTER/PARAMEDIC</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td>  $88596.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20   </th>\n",
       "      <td> ABDUL-KARIM,  MUHAMMAD A</td>\n",
       "      <td>                     ENGINEERING TECHNICIAN VI</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td> $108228.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21   </th>\n",
       "      <td>      ABDULLAH,  DANIEL N</td>\n",
       "      <td>                               FIREFIGHTER-EMT</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td>  $92682.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22   </th>\n",
       "      <td>         ABDULLAH,  KEVIN</td>\n",
       "      <td>                                    LIEUTENANT</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td> $111474.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23   </th>\n",
       "      <td>     ABDULLAH,  LAKENYA N</td>\n",
       "      <td>                                CROSSING GUARD</td>\n",
       "      <td>             OEMC</td>\n",
       "      <td>  $17846.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24   </th>\n",
       "      <td>      ABDULLAH,  RASHAD J</td>\n",
       "      <td>       ELECTRICAL MECHANIC-AUTO-POLICE MTR MNT</td>\n",
       "      <td> GENERAL SERVICES</td>\n",
       "      <td>  $93600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25   </th>\n",
       "      <td>     ABDULSATTAR,  MUDHAR</td>\n",
       "      <td>                             CIVIL ENGINEER II</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td>  $58536.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26   </th>\n",
       "      <td>     ABDUL-SHAKUR,  TAHIR</td>\n",
       "      <td>                         GENERAL LABORER - DSS</td>\n",
       "      <td>    STREETS &amp; SAN</td>\n",
       "      <td>  $41849.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27   </th>\n",
       "      <td>   ABDULWAHAB,  ABUUBAIDA</td>\n",
       "      <td>                            FOSTER GRANDPARENT</td>\n",
       "      <td> FAMILY &amp; SUPPORT</td>\n",
       "      <td>   $2756.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28   </th>\n",
       "      <td>        ABEJERO,  JASON V</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $87384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29   </th>\n",
       "      <td>  ABERCROMBIE IV,  EARL S</td>\n",
       "      <td>                                     PARAMEDIC</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td>  $70380.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32033</th>\n",
       "      <td>          ZUNIGA,  JUAN M</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32034</th>\n",
       "      <td>           ZUNIGA,  OSCAR</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $74028.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32035</th>\n",
       "      <td>          ZUNIGA,  RONALD</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $87384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32036</th>\n",
       "      <td>          ZUNIGA,  THOMAS</td>\n",
       "      <td>                         GENERAL LABORER - DSS</td>\n",
       "      <td>    STREETS &amp; SAN</td>\n",
       "      <td>  $43201.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32037</th>\n",
       "      <td>         ZUNIGA,  VICENTE</td>\n",
       "      <td>                               FIREFIGHTER-EMT</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td>  $89568.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32038</th>\n",
       "      <td>              ZUNO,  ERIK</td>\n",
       "      <td>                          LABORER - APPRENTICE</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td>  $73382.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32039</th>\n",
       "      <td>           ZUPAN,  BILL M</td>\n",
       "      <td>                                LIEUTENANT-EMT</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td> $107592.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32040</th>\n",
       "      <td>         ZUPANCIC,  KELLY</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $66606.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32041</th>\n",
       "      <td>       ZURAWSKI,  CAROL A</td>\n",
       "      <td>                                CROSSING GUARD</td>\n",
       "      <td>             OEMC</td>\n",
       "      <td>  $22568.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32042</th>\n",
       "      <td>       ZURAWSKI,  JEFFREY</td>\n",
       "      <td>                FRM OF MACHINISTS - AUTOMOTIVE</td>\n",
       "      <td> GENERAL SERVICES</td>\n",
       "      <td>  $99528.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32043</th>\n",
       "      <td>         ZURAWSKI,  MAC Z</td>\n",
       "      <td>                            SANITATION LABORER</td>\n",
       "      <td>    STREETS &amp; SAN</td>\n",
       "      <td>  $73840.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32044</th>\n",
       "      <td>        ZURAWSKI,  MARY E</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $90618.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32045</th>\n",
       "      <td>          ZUREK,  FRANCIS</td>\n",
       "      <td>                           ELECTRICAL MECHANIC</td>\n",
       "      <td>             OEMC</td>\n",
       "      <td>  $93600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32046</th>\n",
       "      <td>           ZUREK,  MARY H</td>\n",
       "      <td>             SENIOR PUBLIC INFORMATION OFFICER</td>\n",
       "      <td>          FINANCE</td>\n",
       "      <td>  $83340.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32047</th>\n",
       "      <td>          ZURITA,  ADRIEL</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $66606.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32048</th>\n",
       "      <td>          ZVANJA,  TINA M</td>\n",
       "      <td>                               LEGAL SECRETARY</td>\n",
       "      <td>              LAW</td>\n",
       "      <td>  $71292.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32049</th>\n",
       "      <td>   ZWARYCZ MANN,  IRENE A</td>\n",
       "      <td>                                CROSSING GUARD</td>\n",
       "      <td>             OEMC</td>\n",
       "      <td>  $17024.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32050</th>\n",
       "      <td>       ZWARYCZ,  THOMAS J</td>\n",
       "      <td>                       POOL MOTOR TRUCK DRIVER</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td>  $72862.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32051</th>\n",
       "      <td>       ZWIESLER,  MATTHEW</td>\n",
       "      <td>                   AIRPORT OPERATIONS SUPVSR I</td>\n",
       "      <td>         AVIATION</td>\n",
       "      <td>  $75384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32052</th>\n",
       "      <td>         ZWIT,  JEFFREY J</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32053</th>\n",
       "      <td>      ZWOLFER,  MATTHEW W</td>\n",
       "      <td>                               FIREFIGHTER-EMT</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td>  $92682.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32054</th>\n",
       "      <td>           ZYCH,  MATEUSZ</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $46668.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32055</th>\n",
       "      <td>            ZYDEK,  BRYAN</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $81588.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32056</th>\n",
       "      <td>         ZYGADLO,  JOHN P</td>\n",
       "      <td>                        MACHINIST (AUTOMOTIVE)</td>\n",
       "      <td> GENERAL SERVICES</td>\n",
       "      <td>  $94328.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32057</th>\n",
       "      <td>      ZYGADLO,  MICHAEL J</td>\n",
       "      <td>                FRM OF MACHINISTS - AUTOMOTIVE</td>\n",
       "      <td> GENERAL SERVICES</td>\n",
       "      <td>  $99528.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32058</th>\n",
       "      <td>       ZYGOWICZ,  PETER J</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $87384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32059</th>\n",
       "      <td>        ZYMANTAS,  MARK E</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32060</th>\n",
       "      <td>      ZYRKOWSKI,  CARLO E</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $87384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32061</th>\n",
       "      <td>      ZYSKOWSKI,  DARIUSZ</td>\n",
       "      <td>                       CHIEF DATA BASE ANALYST</td>\n",
       "      <td>             DoIT</td>\n",
       "      <td> $113664.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32062</th>\n",
       "      <td>                      NaN</td>\n",
       "      <td>                                           NaN</td>\n",
       "      <td>              NaN</td>\n",
       "      <td>        NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32063 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name  \\\n",
       "0               AARON,  ELVIA J   \n",
       "1             AARON,  JEFFERY M   \n",
       "2                AARON,  KARINA   \n",
       "3           AARON,  KIMBERLEI R   \n",
       "4           ABAD JR,  VICENTE M   \n",
       "5               ABARCA,  ANABEL   \n",
       "6             ABARCA,  EMMANUEL   \n",
       "7             ABASCAL,  REECE E   \n",
       "8          ABBASI,  CHRISTOPHER   \n",
       "9         ABBATACOLA,  ROBERT J   \n",
       "10        ABBATEMARCO,  JAMES J   \n",
       "11             ABBATE,  TERRY M   \n",
       "12             ABBOTT,  BETTY L   \n",
       "13            ABBOTT,  LYNISE M   \n",
       "14       ABBRUZZESE,  WILLIAM J   \n",
       "15              ABDALLAH,  ZAID   \n",
       "16        ABDELHADI,  ABDALMAHD   \n",
       "17          ABDELLATIF,  AREF R   \n",
       "18           ABDELMAJEID,  AZIZ   \n",
       "19          ABDOLLAHZADEH,  ALI   \n",
       "20     ABDUL-KARIM,  MUHAMMAD A   \n",
       "21          ABDULLAH,  DANIEL N   \n",
       "22             ABDULLAH,  KEVIN   \n",
       "23         ABDULLAH,  LAKENYA N   \n",
       "24          ABDULLAH,  RASHAD J   \n",
       "25         ABDULSATTAR,  MUDHAR   \n",
       "26         ABDUL-SHAKUR,  TAHIR   \n",
       "27       ABDULWAHAB,  ABUUBAIDA   \n",
       "28            ABEJERO,  JASON V   \n",
       "29      ABERCROMBIE IV,  EARL S   \n",
       "...                         ...   \n",
       "32033           ZUNIGA,  JUAN M   \n",
       "32034            ZUNIGA,  OSCAR   \n",
       "32035           ZUNIGA,  RONALD   \n",
       "32036           ZUNIGA,  THOMAS   \n",
       "32037          ZUNIGA,  VICENTE   \n",
       "32038               ZUNO,  ERIK   \n",
       "32039            ZUPAN,  BILL M   \n",
       "32040          ZUPANCIC,  KELLY   \n",
       "32041        ZURAWSKI,  CAROL A   \n",
       "32042        ZURAWSKI,  JEFFREY   \n",
       "32043          ZURAWSKI,  MAC Z   \n",
       "32044         ZURAWSKI,  MARY E   \n",
       "32045           ZUREK,  FRANCIS   \n",
       "32046            ZUREK,  MARY H   \n",
       "32047           ZURITA,  ADRIEL   \n",
       "32048           ZVANJA,  TINA M   \n",
       "32049    ZWARYCZ MANN,  IRENE A   \n",
       "32050        ZWARYCZ,  THOMAS J   \n",
       "32051        ZWIESLER,  MATTHEW   \n",
       "32052          ZWIT,  JEFFREY J   \n",
       "32053       ZWOLFER,  MATTHEW W   \n",
       "32054            ZYCH,  MATEUSZ   \n",
       "32055             ZYDEK,  BRYAN   \n",
       "32056          ZYGADLO,  JOHN P   \n",
       "32057       ZYGADLO,  MICHAEL J   \n",
       "32058        ZYGOWICZ,  PETER J   \n",
       "32059         ZYMANTAS,  MARK E   \n",
       "32060       ZYRKOWSKI,  CARLO E   \n",
       "32061       ZYSKOWSKI,  DARIUSZ   \n",
       "32062                       NaN   \n",
       "\n",
       "                                      Position Title        Department  \\\n",
       "0                                   WATER RATE TAKER       WATER MGMNT   \n",
       "1                                     POLICE OFFICER            POLICE   \n",
       "2                                     POLICE OFFICER            POLICE   \n",
       "3                           CHIEF CONTRACT EXPEDITER  GENERAL SERVICES   \n",
       "4                                  CIVIL ENGINEER IV       WATER MGMNT   \n",
       "5                               ASST TO THE ALDERMAN      CITY COUNCIL   \n",
       "6                              GENERAL LABORER - DSS     STREETS & SAN   \n",
       "7                        TRAFFIC CONTROL AIDE-HOURLY              OEMC   \n",
       "8                         STAFF ASST TO THE ALDERMAN      CITY COUNCIL   \n",
       "9                                ELECTRICAL MECHANIC          AVIATION   \n",
       "10                                 FIRE ENGINEER-EMT              FIRE   \n",
       "11                                    POLICE OFFICER            POLICE   \n",
       "12                                FOSTER GRANDPARENT  FAMILY & SUPPORT   \n",
       "13                                         CLERK III            POLICE   \n",
       "14                            INVESTIGATOR - IPRA II              IPRA   \n",
       "15                                    POLICE OFFICER            POLICE   \n",
       "16                                    POLICE OFFICER            POLICE   \n",
       "17     FIREFIGHTER (PER ARBITRATORS AWARD)-PARAMEDIC              FIRE   \n",
       "18                                    POLICE OFFICER            POLICE   \n",
       "19                             FIREFIGHTER/PARAMEDIC              FIRE   \n",
       "20                         ENGINEERING TECHNICIAN VI       WATER MGMNT   \n",
       "21                                   FIREFIGHTER-EMT              FIRE   \n",
       "22                                        LIEUTENANT              FIRE   \n",
       "23                                    CROSSING GUARD              OEMC   \n",
       "24           ELECTRICAL MECHANIC-AUTO-POLICE MTR MNT  GENERAL SERVICES   \n",
       "25                                 CIVIL ENGINEER II       WATER MGMNT   \n",
       "26                             GENERAL LABORER - DSS     STREETS & SAN   \n",
       "27                                FOSTER GRANDPARENT  FAMILY & SUPPORT   \n",
       "28                                    POLICE OFFICER            POLICE   \n",
       "29                                         PARAMEDIC              FIRE   \n",
       "...                                              ...               ...   \n",
       "32033                                 POLICE OFFICER            POLICE   \n",
       "32034                                 POLICE OFFICER            POLICE   \n",
       "32035                                 POLICE OFFICER            POLICE   \n",
       "32036                          GENERAL LABORER - DSS     STREETS & SAN   \n",
       "32037                                FIREFIGHTER-EMT              FIRE   \n",
       "32038                           LABORER - APPRENTICE       WATER MGMNT   \n",
       "32039                                 LIEUTENANT-EMT              FIRE   \n",
       "32040                                 POLICE OFFICER            POLICE   \n",
       "32041                                 CROSSING GUARD              OEMC   \n",
       "32042                 FRM OF MACHINISTS - AUTOMOTIVE  GENERAL SERVICES   \n",
       "32043                             SANITATION LABORER     STREETS & SAN   \n",
       "32044                                 POLICE OFFICER            POLICE   \n",
       "32045                            ELECTRICAL MECHANIC              OEMC   \n",
       "32046              SENIOR PUBLIC INFORMATION OFFICER           FINANCE   \n",
       "32047                                 POLICE OFFICER            POLICE   \n",
       "32048                                LEGAL SECRETARY               LAW   \n",
       "32049                                 CROSSING GUARD              OEMC   \n",
       "32050                        POOL MOTOR TRUCK DRIVER       WATER MGMNT   \n",
       "32051                    AIRPORT OPERATIONS SUPVSR I          AVIATION   \n",
       "32052                                 POLICE OFFICER            POLICE   \n",
       "32053                                FIREFIGHTER-EMT              FIRE   \n",
       "32054                                 POLICE OFFICER            POLICE   \n",
       "32055                                 POLICE OFFICER            POLICE   \n",
       "32056                         MACHINIST (AUTOMOTIVE)  GENERAL SERVICES   \n",
       "32057                 FRM OF MACHINISTS - AUTOMOTIVE  GENERAL SERVICES   \n",
       "32058                                 POLICE OFFICER            POLICE   \n",
       "32059                                 POLICE OFFICER            POLICE   \n",
       "32060                                 POLICE OFFICER            POLICE   \n",
       "32061                        CHIEF DATA BASE ANALYST              DoIT   \n",
       "32062                                            NaN               NaN   \n",
       "\n",
       "      Employee Annual Salary  \n",
       "0                  $90744.00  \n",
       "1                  $84450.00  \n",
       "2                  $84450.00  \n",
       "3                  $89880.00  \n",
       "4                 $106836.00  \n",
       "5                  $70764.00  \n",
       "6                  $41849.60  \n",
       "7                  $20051.20  \n",
       "8                  $49452.00  \n",
       "9                  $93600.00  \n",
       "10                $100320.00  \n",
       "11                 $90618.00  \n",
       "12                  $2756.00  \n",
       "13                 $46896.00  \n",
       "14                 $73920.00  \n",
       "15                 $74028.00  \n",
       "16                 $81588.00  \n",
       "17                 $99228.00  \n",
       "18                 $81588.00  \n",
       "19                 $88596.00  \n",
       "20                $108228.00  \n",
       "21                 $92682.00  \n",
       "22                $111474.00  \n",
       "23                 $17846.40  \n",
       "24                 $93600.00  \n",
       "25                 $58536.00  \n",
       "26                 $41849.60  \n",
       "27                  $2756.00  \n",
       "28                 $87384.00  \n",
       "29                 $70380.00  \n",
       "...                      ...  \n",
       "32033              $84450.00  \n",
       "32034              $74028.00  \n",
       "32035              $87384.00  \n",
       "32036              $43201.60  \n",
       "32037              $89568.00  \n",
       "32038              $73382.40  \n",
       "32039             $107592.00  \n",
       "32040              $66606.00  \n",
       "32041              $22568.00  \n",
       "32042              $99528.00  \n",
       "32043              $73840.00  \n",
       "32044              $90618.00  \n",
       "32045              $93600.00  \n",
       "32046              $83340.00  \n",
       "32047              $66606.00  \n",
       "32048              $71292.00  \n",
       "32049              $17024.80  \n",
       "32050              $72862.40  \n",
       "32051              $75384.00  \n",
       "32052              $84450.00  \n",
       "32053              $92682.00  \n",
       "32054              $46668.00  \n",
       "32055              $81588.00  \n",
       "32056              $94328.00  \n",
       "32057              $99528.00  \n",
       "32058              $87384.00  \n",
       "32059              $84450.00  \n",
       "32060              $87384.00  \n",
       "32061             $113664.00  \n",
       "32062                    NaN  \n",
       "\n",
       "[32063 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is at least one row filled with 'NaN's -- let's drop all rows with NaNs present\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Position Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Employee Annual Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0    </th>\n",
       "      <td>          AARON,  ELVIA J</td>\n",
       "      <td>                              WATER RATE TAKER</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td>  $90744.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1    </th>\n",
       "      <td>        AARON,  JEFFERY M</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2    </th>\n",
       "      <td>           AARON,  KARINA</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3    </th>\n",
       "      <td>      AARON,  KIMBERLEI R</td>\n",
       "      <td>                      CHIEF CONTRACT EXPEDITER</td>\n",
       "      <td> GENERAL SERVICES</td>\n",
       "      <td>  $89880.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4    </th>\n",
       "      <td>      ABAD JR,  VICENTE M</td>\n",
       "      <td>                             CIVIL ENGINEER IV</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td> $106836.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5    </th>\n",
       "      <td>          ABARCA,  ANABEL</td>\n",
       "      <td>                          ASST TO THE ALDERMAN</td>\n",
       "      <td>     CITY COUNCIL</td>\n",
       "      <td>  $70764.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6    </th>\n",
       "      <td>        ABARCA,  EMMANUEL</td>\n",
       "      <td>                         GENERAL LABORER - DSS</td>\n",
       "      <td>    STREETS &amp; SAN</td>\n",
       "      <td>  $41849.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7    </th>\n",
       "      <td>        ABASCAL,  REECE E</td>\n",
       "      <td>                   TRAFFIC CONTROL AIDE-HOURLY</td>\n",
       "      <td>             OEMC</td>\n",
       "      <td>  $20051.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8    </th>\n",
       "      <td>     ABBASI,  CHRISTOPHER</td>\n",
       "      <td>                    STAFF ASST TO THE ALDERMAN</td>\n",
       "      <td>     CITY COUNCIL</td>\n",
       "      <td>  $49452.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9    </th>\n",
       "      <td>    ABBATACOLA,  ROBERT J</td>\n",
       "      <td>                           ELECTRICAL MECHANIC</td>\n",
       "      <td>         AVIATION</td>\n",
       "      <td>  $93600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10   </th>\n",
       "      <td>    ABBATEMARCO,  JAMES J</td>\n",
       "      <td>                             FIRE ENGINEER-EMT</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td> $100320.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11   </th>\n",
       "      <td>         ABBATE,  TERRY M</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $90618.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12   </th>\n",
       "      <td>         ABBOTT,  BETTY L</td>\n",
       "      <td>                            FOSTER GRANDPARENT</td>\n",
       "      <td> FAMILY &amp; SUPPORT</td>\n",
       "      <td>   $2756.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13   </th>\n",
       "      <td>        ABBOTT,  LYNISE M</td>\n",
       "      <td>                                     CLERK III</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $46896.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14   </th>\n",
       "      <td>   ABBRUZZESE,  WILLIAM J</td>\n",
       "      <td>                        INVESTIGATOR - IPRA II</td>\n",
       "      <td>             IPRA</td>\n",
       "      <td>  $73920.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15   </th>\n",
       "      <td>          ABDALLAH,  ZAID</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $74028.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16   </th>\n",
       "      <td>    ABDELHADI,  ABDALMAHD</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $81588.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17   </th>\n",
       "      <td>      ABDELLATIF,  AREF R</td>\n",
       "      <td> FIREFIGHTER (PER ARBITRATORS AWARD)-PARAMEDIC</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td>  $99228.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18   </th>\n",
       "      <td>       ABDELMAJEID,  AZIZ</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $81588.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19   </th>\n",
       "      <td>      ABDOLLAHZADEH,  ALI</td>\n",
       "      <td>                         FIREFIGHTER/PARAMEDIC</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td>  $88596.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20   </th>\n",
       "      <td> ABDUL-KARIM,  MUHAMMAD A</td>\n",
       "      <td>                     ENGINEERING TECHNICIAN VI</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td> $108228.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21   </th>\n",
       "      <td>      ABDULLAH,  DANIEL N</td>\n",
       "      <td>                               FIREFIGHTER-EMT</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td>  $92682.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22   </th>\n",
       "      <td>         ABDULLAH,  KEVIN</td>\n",
       "      <td>                                    LIEUTENANT</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td> $111474.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23   </th>\n",
       "      <td>     ABDULLAH,  LAKENYA N</td>\n",
       "      <td>                                CROSSING GUARD</td>\n",
       "      <td>             OEMC</td>\n",
       "      <td>  $17846.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24   </th>\n",
       "      <td>      ABDULLAH,  RASHAD J</td>\n",
       "      <td>       ELECTRICAL MECHANIC-AUTO-POLICE MTR MNT</td>\n",
       "      <td> GENERAL SERVICES</td>\n",
       "      <td>  $93600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25   </th>\n",
       "      <td>     ABDULSATTAR,  MUDHAR</td>\n",
       "      <td>                             CIVIL ENGINEER II</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td>  $58536.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26   </th>\n",
       "      <td>     ABDUL-SHAKUR,  TAHIR</td>\n",
       "      <td>                         GENERAL LABORER - DSS</td>\n",
       "      <td>    STREETS &amp; SAN</td>\n",
       "      <td>  $41849.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27   </th>\n",
       "      <td>   ABDULWAHAB,  ABUUBAIDA</td>\n",
       "      <td>                            FOSTER GRANDPARENT</td>\n",
       "      <td> FAMILY &amp; SUPPORT</td>\n",
       "      <td>   $2756.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28   </th>\n",
       "      <td>        ABEJERO,  JASON V</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $87384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29   </th>\n",
       "      <td>  ABERCROMBIE IV,  EARL S</td>\n",
       "      <td>                                     PARAMEDIC</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td>  $70380.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32032</th>\n",
       "      <td>        ZUNIGA JR,  JAMES</td>\n",
       "      <td>                              GARAGE ATTENDANT</td>\n",
       "      <td> GENERAL SERVICES</td>\n",
       "      <td>  $47528.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32033</th>\n",
       "      <td>          ZUNIGA,  JUAN M</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32034</th>\n",
       "      <td>           ZUNIGA,  OSCAR</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $74028.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32035</th>\n",
       "      <td>          ZUNIGA,  RONALD</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $87384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32036</th>\n",
       "      <td>          ZUNIGA,  THOMAS</td>\n",
       "      <td>                         GENERAL LABORER - DSS</td>\n",
       "      <td>    STREETS &amp; SAN</td>\n",
       "      <td>  $43201.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32037</th>\n",
       "      <td>         ZUNIGA,  VICENTE</td>\n",
       "      <td>                               FIREFIGHTER-EMT</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td>  $89568.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32038</th>\n",
       "      <td>              ZUNO,  ERIK</td>\n",
       "      <td>                          LABORER - APPRENTICE</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td>  $73382.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32039</th>\n",
       "      <td>           ZUPAN,  BILL M</td>\n",
       "      <td>                                LIEUTENANT-EMT</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td> $107592.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32040</th>\n",
       "      <td>         ZUPANCIC,  KELLY</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $66606.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32041</th>\n",
       "      <td>       ZURAWSKI,  CAROL A</td>\n",
       "      <td>                                CROSSING GUARD</td>\n",
       "      <td>             OEMC</td>\n",
       "      <td>  $22568.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32042</th>\n",
       "      <td>       ZURAWSKI,  JEFFREY</td>\n",
       "      <td>                FRM OF MACHINISTS - AUTOMOTIVE</td>\n",
       "      <td> GENERAL SERVICES</td>\n",
       "      <td>  $99528.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32043</th>\n",
       "      <td>         ZURAWSKI,  MAC Z</td>\n",
       "      <td>                            SANITATION LABORER</td>\n",
       "      <td>    STREETS &amp; SAN</td>\n",
       "      <td>  $73840.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32044</th>\n",
       "      <td>        ZURAWSKI,  MARY E</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $90618.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32045</th>\n",
       "      <td>          ZUREK,  FRANCIS</td>\n",
       "      <td>                           ELECTRICAL MECHANIC</td>\n",
       "      <td>             OEMC</td>\n",
       "      <td>  $93600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32046</th>\n",
       "      <td>           ZUREK,  MARY H</td>\n",
       "      <td>             SENIOR PUBLIC INFORMATION OFFICER</td>\n",
       "      <td>          FINANCE</td>\n",
       "      <td>  $83340.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32047</th>\n",
       "      <td>          ZURITA,  ADRIEL</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $66606.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32048</th>\n",
       "      <td>          ZVANJA,  TINA M</td>\n",
       "      <td>                               LEGAL SECRETARY</td>\n",
       "      <td>              LAW</td>\n",
       "      <td>  $71292.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32049</th>\n",
       "      <td>   ZWARYCZ MANN,  IRENE A</td>\n",
       "      <td>                                CROSSING GUARD</td>\n",
       "      <td>             OEMC</td>\n",
       "      <td>  $17024.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32050</th>\n",
       "      <td>       ZWARYCZ,  THOMAS J</td>\n",
       "      <td>                       POOL MOTOR TRUCK DRIVER</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td>  $72862.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32051</th>\n",
       "      <td>       ZWIESLER,  MATTHEW</td>\n",
       "      <td>                   AIRPORT OPERATIONS SUPVSR I</td>\n",
       "      <td>         AVIATION</td>\n",
       "      <td>  $75384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32052</th>\n",
       "      <td>         ZWIT,  JEFFREY J</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32053</th>\n",
       "      <td>      ZWOLFER,  MATTHEW W</td>\n",
       "      <td>                               FIREFIGHTER-EMT</td>\n",
       "      <td>             FIRE</td>\n",
       "      <td>  $92682.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32054</th>\n",
       "      <td>           ZYCH,  MATEUSZ</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $46668.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32055</th>\n",
       "      <td>            ZYDEK,  BRYAN</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $81588.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32056</th>\n",
       "      <td>         ZYGADLO,  JOHN P</td>\n",
       "      <td>                        MACHINIST (AUTOMOTIVE)</td>\n",
       "      <td> GENERAL SERVICES</td>\n",
       "      <td>  $94328.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32057</th>\n",
       "      <td>      ZYGADLO,  MICHAEL J</td>\n",
       "      <td>                FRM OF MACHINISTS - AUTOMOTIVE</td>\n",
       "      <td> GENERAL SERVICES</td>\n",
       "      <td>  $99528.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32058</th>\n",
       "      <td>       ZYGOWICZ,  PETER J</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $87384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32059</th>\n",
       "      <td>        ZYMANTAS,  MARK E</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32060</th>\n",
       "      <td>      ZYRKOWSKI,  CARLO E</td>\n",
       "      <td>                                POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $87384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32061</th>\n",
       "      <td>      ZYSKOWSKI,  DARIUSZ</td>\n",
       "      <td>                       CHIEF DATA BASE ANALYST</td>\n",
       "      <td>             DoIT</td>\n",
       "      <td> $113664.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32062 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name  \\\n",
       "0               AARON,  ELVIA J   \n",
       "1             AARON,  JEFFERY M   \n",
       "2                AARON,  KARINA   \n",
       "3           AARON,  KIMBERLEI R   \n",
       "4           ABAD JR,  VICENTE M   \n",
       "5               ABARCA,  ANABEL   \n",
       "6             ABARCA,  EMMANUEL   \n",
       "7             ABASCAL,  REECE E   \n",
       "8          ABBASI,  CHRISTOPHER   \n",
       "9         ABBATACOLA,  ROBERT J   \n",
       "10        ABBATEMARCO,  JAMES J   \n",
       "11             ABBATE,  TERRY M   \n",
       "12             ABBOTT,  BETTY L   \n",
       "13            ABBOTT,  LYNISE M   \n",
       "14       ABBRUZZESE,  WILLIAM J   \n",
       "15              ABDALLAH,  ZAID   \n",
       "16        ABDELHADI,  ABDALMAHD   \n",
       "17          ABDELLATIF,  AREF R   \n",
       "18           ABDELMAJEID,  AZIZ   \n",
       "19          ABDOLLAHZADEH,  ALI   \n",
       "20     ABDUL-KARIM,  MUHAMMAD A   \n",
       "21          ABDULLAH,  DANIEL N   \n",
       "22             ABDULLAH,  KEVIN   \n",
       "23         ABDULLAH,  LAKENYA N   \n",
       "24          ABDULLAH,  RASHAD J   \n",
       "25         ABDULSATTAR,  MUDHAR   \n",
       "26         ABDUL-SHAKUR,  TAHIR   \n",
       "27       ABDULWAHAB,  ABUUBAIDA   \n",
       "28            ABEJERO,  JASON V   \n",
       "29      ABERCROMBIE IV,  EARL S   \n",
       "...                         ...   \n",
       "32032         ZUNIGA JR,  JAMES   \n",
       "32033           ZUNIGA,  JUAN M   \n",
       "32034            ZUNIGA,  OSCAR   \n",
       "32035           ZUNIGA,  RONALD   \n",
       "32036           ZUNIGA,  THOMAS   \n",
       "32037          ZUNIGA,  VICENTE   \n",
       "32038               ZUNO,  ERIK   \n",
       "32039            ZUPAN,  BILL M   \n",
       "32040          ZUPANCIC,  KELLY   \n",
       "32041        ZURAWSKI,  CAROL A   \n",
       "32042        ZURAWSKI,  JEFFREY   \n",
       "32043          ZURAWSKI,  MAC Z   \n",
       "32044         ZURAWSKI,  MARY E   \n",
       "32045           ZUREK,  FRANCIS   \n",
       "32046            ZUREK,  MARY H   \n",
       "32047           ZURITA,  ADRIEL   \n",
       "32048           ZVANJA,  TINA M   \n",
       "32049    ZWARYCZ MANN,  IRENE A   \n",
       "32050        ZWARYCZ,  THOMAS J   \n",
       "32051        ZWIESLER,  MATTHEW   \n",
       "32052          ZWIT,  JEFFREY J   \n",
       "32053       ZWOLFER,  MATTHEW W   \n",
       "32054            ZYCH,  MATEUSZ   \n",
       "32055             ZYDEK,  BRYAN   \n",
       "32056          ZYGADLO,  JOHN P   \n",
       "32057       ZYGADLO,  MICHAEL J   \n",
       "32058        ZYGOWICZ,  PETER J   \n",
       "32059         ZYMANTAS,  MARK E   \n",
       "32060       ZYRKOWSKI,  CARLO E   \n",
       "32061       ZYSKOWSKI,  DARIUSZ   \n",
       "\n",
       "                                      Position Title        Department  \\\n",
       "0                                   WATER RATE TAKER       WATER MGMNT   \n",
       "1                                     POLICE OFFICER            POLICE   \n",
       "2                                     POLICE OFFICER            POLICE   \n",
       "3                           CHIEF CONTRACT EXPEDITER  GENERAL SERVICES   \n",
       "4                                  CIVIL ENGINEER IV       WATER MGMNT   \n",
       "5                               ASST TO THE ALDERMAN      CITY COUNCIL   \n",
       "6                              GENERAL LABORER - DSS     STREETS & SAN   \n",
       "7                        TRAFFIC CONTROL AIDE-HOURLY              OEMC   \n",
       "8                         STAFF ASST TO THE ALDERMAN      CITY COUNCIL   \n",
       "9                                ELECTRICAL MECHANIC          AVIATION   \n",
       "10                                 FIRE ENGINEER-EMT              FIRE   \n",
       "11                                    POLICE OFFICER            POLICE   \n",
       "12                                FOSTER GRANDPARENT  FAMILY & SUPPORT   \n",
       "13                                         CLERK III            POLICE   \n",
       "14                            INVESTIGATOR - IPRA II              IPRA   \n",
       "15                                    POLICE OFFICER            POLICE   \n",
       "16                                    POLICE OFFICER            POLICE   \n",
       "17     FIREFIGHTER (PER ARBITRATORS AWARD)-PARAMEDIC              FIRE   \n",
       "18                                    POLICE OFFICER            POLICE   \n",
       "19                             FIREFIGHTER/PARAMEDIC              FIRE   \n",
       "20                         ENGINEERING TECHNICIAN VI       WATER MGMNT   \n",
       "21                                   FIREFIGHTER-EMT              FIRE   \n",
       "22                                        LIEUTENANT              FIRE   \n",
       "23                                    CROSSING GUARD              OEMC   \n",
       "24           ELECTRICAL MECHANIC-AUTO-POLICE MTR MNT  GENERAL SERVICES   \n",
       "25                                 CIVIL ENGINEER II       WATER MGMNT   \n",
       "26                             GENERAL LABORER - DSS     STREETS & SAN   \n",
       "27                                FOSTER GRANDPARENT  FAMILY & SUPPORT   \n",
       "28                                    POLICE OFFICER            POLICE   \n",
       "29                                         PARAMEDIC              FIRE   \n",
       "...                                              ...               ...   \n",
       "32032                               GARAGE ATTENDANT  GENERAL SERVICES   \n",
       "32033                                 POLICE OFFICER            POLICE   \n",
       "32034                                 POLICE OFFICER            POLICE   \n",
       "32035                                 POLICE OFFICER            POLICE   \n",
       "32036                          GENERAL LABORER - DSS     STREETS & SAN   \n",
       "32037                                FIREFIGHTER-EMT              FIRE   \n",
       "32038                           LABORER - APPRENTICE       WATER MGMNT   \n",
       "32039                                 LIEUTENANT-EMT              FIRE   \n",
       "32040                                 POLICE OFFICER            POLICE   \n",
       "32041                                 CROSSING GUARD              OEMC   \n",
       "32042                 FRM OF MACHINISTS - AUTOMOTIVE  GENERAL SERVICES   \n",
       "32043                             SANITATION LABORER     STREETS & SAN   \n",
       "32044                                 POLICE OFFICER            POLICE   \n",
       "32045                            ELECTRICAL MECHANIC              OEMC   \n",
       "32046              SENIOR PUBLIC INFORMATION OFFICER           FINANCE   \n",
       "32047                                 POLICE OFFICER            POLICE   \n",
       "32048                                LEGAL SECRETARY               LAW   \n",
       "32049                                 CROSSING GUARD              OEMC   \n",
       "32050                        POOL MOTOR TRUCK DRIVER       WATER MGMNT   \n",
       "32051                    AIRPORT OPERATIONS SUPVSR I          AVIATION   \n",
       "32052                                 POLICE OFFICER            POLICE   \n",
       "32053                                FIREFIGHTER-EMT              FIRE   \n",
       "32054                                 POLICE OFFICER            POLICE   \n",
       "32055                                 POLICE OFFICER            POLICE   \n",
       "32056                         MACHINIST (AUTOMOTIVE)  GENERAL SERVICES   \n",
       "32057                 FRM OF MACHINISTS - AUTOMOTIVE  GENERAL SERVICES   \n",
       "32058                                 POLICE OFFICER            POLICE   \n",
       "32059                                 POLICE OFFICER            POLICE   \n",
       "32060                                 POLICE OFFICER            POLICE   \n",
       "32061                        CHIEF DATA BASE ANALYST              DoIT   \n",
       "\n",
       "      Employee Annual Salary  \n",
       "0                  $90744.00  \n",
       "1                  $84450.00  \n",
       "2                  $84450.00  \n",
       "3                  $89880.00  \n",
       "4                 $106836.00  \n",
       "5                  $70764.00  \n",
       "6                  $41849.60  \n",
       "7                  $20051.20  \n",
       "8                  $49452.00  \n",
       "9                  $93600.00  \n",
       "10                $100320.00  \n",
       "11                 $90618.00  \n",
       "12                  $2756.00  \n",
       "13                 $46896.00  \n",
       "14                 $73920.00  \n",
       "15                 $74028.00  \n",
       "16                 $81588.00  \n",
       "17                 $99228.00  \n",
       "18                 $81588.00  \n",
       "19                 $88596.00  \n",
       "20                $108228.00  \n",
       "21                 $92682.00  \n",
       "22                $111474.00  \n",
       "23                 $17846.40  \n",
       "24                 $93600.00  \n",
       "25                 $58536.00  \n",
       "26                 $41849.60  \n",
       "27                  $2756.00  \n",
       "28                 $87384.00  \n",
       "29                 $70380.00  \n",
       "...                      ...  \n",
       "32032              $47528.00  \n",
       "32033              $84450.00  \n",
       "32034              $74028.00  \n",
       "32035              $87384.00  \n",
       "32036              $43201.60  \n",
       "32037              $89568.00  \n",
       "32038              $73382.40  \n",
       "32039             $107592.00  \n",
       "32040              $66606.00  \n",
       "32041              $22568.00  \n",
       "32042              $99528.00  \n",
       "32043              $73840.00  \n",
       "32044              $90618.00  \n",
       "32045              $93600.00  \n",
       "32046              $83340.00  \n",
       "32047              $66606.00  \n",
       "32048              $71292.00  \n",
       "32049              $17024.80  \n",
       "32050              $72862.40  \n",
       "32051              $75384.00  \n",
       "32052              $84450.00  \n",
       "32053              $92682.00  \n",
       "32054              $46668.00  \n",
       "32055              $81588.00  \n",
       "32056              $94328.00  \n",
       "32057              $99528.00  \n",
       "32058              $87384.00  \n",
       "32059              $84450.00  \n",
       "32060              $87384.00  \n",
       "32061             $113664.00  \n",
       "\n",
       "[32062 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column names are a little long, and we might be typing them often, let's make them shorter\n",
    "--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.columns = ['Name', 'Title', 'Department', 'Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>     AARON,  ELVIA J</td>\n",
       "      <td>         WATER RATE TAKER</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td>  $90744.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>   AARON,  JEFFERY M</td>\n",
       "      <td>           POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>      AARON,  KARINA</td>\n",
       "      <td>           POLICE OFFICER</td>\n",
       "      <td>           POLICE</td>\n",
       "      <td>  $84450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> AARON,  KIMBERLEI R</td>\n",
       "      <td> CHIEF CONTRACT EXPEDITER</td>\n",
       "      <td> GENERAL SERVICES</td>\n",
       "      <td>  $89880.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> ABAD JR,  VICENTE M</td>\n",
       "      <td>        CIVIL ENGINEER IV</td>\n",
       "      <td>      WATER MGMNT</td>\n",
       "      <td> $106836.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name                     Title        Department      Salary\n",
       "0      AARON,  ELVIA J          WATER RATE TAKER       WATER MGMNT   $90744.00\n",
       "1    AARON,  JEFFERY M            POLICE OFFICER            POLICE   $84450.00\n",
       "2       AARON,  KARINA            POLICE OFFICER            POLICE   $84450.00\n",
       "3  AARON,  KIMBERLEI R  CHIEF CONTRACT EXPEDITER  GENERAL SERVICES   $89880.00\n",
       "4  ABAD JR,  VICENTE M         CIVIL ENGINEER IV       WATER MGMNT  $106836.00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we use the describe method (method is a fancy word for function) to get a sense of our data\n",
    "--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>              32062</td>\n",
       "      <td>          32062</td>\n",
       "      <td>  32062</td>\n",
       "      <td>     32062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>              31776</td>\n",
       "      <td>           1093</td>\n",
       "      <td>     35</td>\n",
       "      <td>      1156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td> HERNANDEZ,  JUAN C</td>\n",
       "      <td> POLICE OFFICER</td>\n",
       "      <td> POLICE</td>\n",
       "      <td> $87384.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>                  4</td>\n",
       "      <td>           9184</td>\n",
       "      <td>  12618</td>\n",
       "      <td>      2394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name           Title Department     Salary\n",
       "count                32062           32062      32062      32062\n",
       "unique               31776            1093         35       1156\n",
       "top     HERNANDEZ,  JUAN C  POLICE OFFICER     POLICE  $87384.00\n",
       "freq                     4            9184      12618       2394"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there are 4 Juan C Hernandezes, 9184 cops, and 2394 people make $87384 a year. But this is an odd way to describe numerical data like salary. What datatypes do we have?\n",
    "--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name          object\n",
       "Title         object\n",
       "Department    object\n",
       "Salary        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For categorical variables like names and titles, objects work, but we should convert salary to a numerical representation. We will use the pandas map method to assign a new value via a function applied to each value in a column (i.e. series) \n",
    "--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def dollars_to_float(dollars):\n",
    "    return float(dollars.replace('$', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "df['Salary'] = df['Salary'].map(dollars_to_float, na_action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name           object\n",
       "Title          object\n",
       "Department     object\n",
       "Salary        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>  32062.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>  80204.178634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>  25098.329868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>      0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>  72862.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>  84450.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>  93240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 300000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Salary\n",
       "count   32062.000000\n",
       "mean    80204.178634\n",
       "std     25098.329868\n",
       "min         0.960000\n",
       "25%     72862.400000\n",
       "50%     84450.000000\n",
       "75%     93240.000000\n",
       "max    300000.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>              32062</td>\n",
       "      <td>          32062</td>\n",
       "      <td>  32062</td>\n",
       "      <td>  32062.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>              31776</td>\n",
       "      <td>           1093</td>\n",
       "      <td>     35</td>\n",
       "      <td>           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td> HERNANDEZ,  JUAN C</td>\n",
       "      <td> POLICE OFFICER</td>\n",
       "      <td> POLICE</td>\n",
       "      <td>           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>                  4</td>\n",
       "      <td>           9184</td>\n",
       "      <td>  12618</td>\n",
       "      <td>           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>                NaN</td>\n",
       "      <td>            NaN</td>\n",
       "      <td>    NaN</td>\n",
       "      <td>  80204.178634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>                NaN</td>\n",
       "      <td>            NaN</td>\n",
       "      <td>    NaN</td>\n",
       "      <td>  25098.329868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>                NaN</td>\n",
       "      <td>            NaN</td>\n",
       "      <td>    NaN</td>\n",
       "      <td>      0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>                NaN</td>\n",
       "      <td>            NaN</td>\n",
       "      <td>    NaN</td>\n",
       "      <td>  72862.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>                NaN</td>\n",
       "      <td>            NaN</td>\n",
       "      <td>    NaN</td>\n",
       "      <td>  84450.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>                NaN</td>\n",
       "      <td>            NaN</td>\n",
       "      <td>    NaN</td>\n",
       "      <td>  93240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>                NaN</td>\n",
       "      <td>            NaN</td>\n",
       "      <td>    NaN</td>\n",
       "      <td> 300000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name           Title Department         Salary\n",
       "count                32062           32062      32062   32062.000000\n",
       "unique               31776            1093         35            NaN\n",
       "top     HERNANDEZ,  JUAN C  POLICE OFFICER     POLICE            NaN\n",
       "freq                     4            9184      12618            NaN\n",
       "mean                   NaN             NaN        NaN   80204.178634\n",
       "std                    NaN             NaN        NaN   25098.329868\n",
       "min                    NaN             NaN        NaN       0.960000\n",
       "25%                    NaN             NaN        NaN   72862.400000\n",
       "50%                    NaN             NaN        NaN   84450.000000\n",
       "75%                    NaN             NaN        NaN   93240.000000\n",
       "max                    NaN             NaN        NaN  300000.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a99d2d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BdZn5QETMB7YAS4AxYHVm7j6OPiRJHVC0JRIRFwDnZuZ5wEXAGcDtVNdQvwCYExFXRsRS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a99d490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Salary.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see who makes 300k\n",
    "--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8184</th>\n",
       "      <td> EVANS,  GINGER S</td>\n",
       "      <td> COMMISSIONER OF AVIATION</td>\n",
       "      <td> AVIATION</td>\n",
       "      <td> 300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name                     Title Department  Salary\n",
       "8184  EVANS,  GINGER S  COMMISSIONER OF AVIATION   AVIATION  300000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Salary==300000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I know what job to get.\n",
    "--\n",
    "\n",
    "Just to hedge my bets, who makes 200k or more?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7954 </th>\n",
       "      <td>    EMANUEL,  RAHM</td>\n",
       "      <td>                    MAYOR</td>\n",
       "      <td> MAYOR'S OFFICE</td>\n",
       "      <td> 216210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8184 </th>\n",
       "      <td>  EVANS,  GINGER S</td>\n",
       "      <td> COMMISSIONER OF AVIATION</td>\n",
       "      <td>       AVIATION</td>\n",
       "      <td> 300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25532</th>\n",
       "      <td> SANTIAGO,  JOSE A</td>\n",
       "      <td>        FIRE COMMISSIONER</td>\n",
       "      <td>           FIRE</td>\n",
       "      <td> 202728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name                     Title      Department  Salary\n",
       "7954      EMANUEL,  RAHM                     MAYOR  MAYOR'S OFFICE  216210\n",
       "8184    EVANS,  GINGER S  COMMISSIONER OF AVIATION        AVIATION  300000\n",
       "25532  SANTIAGO,  JOSE A         FIRE COMMISSIONER            FIRE  202728"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Salary >= 200000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps there will be a job opening soon. \n",
    "--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "IFrame(\"http://www.salon.com/2016/01/07/rahm_emanuel_is_a_national_disgrace_why_he_represents_every_worst_instinct_of_the_democratic_party/\", width=700, height=700);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15102</th>\n",
       "      <td> KOCH,  STEVEN</td>\n",
       "      <td> ADMINISTRATIVE SECRETARY</td>\n",
       "      <td> MAYOR'S OFFICE</td>\n",
       "      <td> 0.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name                     Title      Department  Salary\n",
       "15102  KOCH,  STEVEN  ADMINISTRATIVE SECRETARY  MAYOR'S OFFICE    0.96"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Salary==0.96]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df=df.drop(15102, axis=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>  32061.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>  80206.680216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>  25094.723809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>   2756.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>  72862.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>  84450.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>  93240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 300000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Salary\n",
       "count   32061.000000\n",
       "mean    80206.680216\n",
       "std     25094.723809\n",
       "min      2756.000000\n",
       "25%     72862.400000\n",
       "50%     84450.000000\n",
       "75%     93240.000000\n",
       "max    300000.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "POLICE                   12618\n",
       "FIRE                      4796\n",
       "STREETS & SAN             2152\n",
       "OEMC                      1982\n",
       "WATER MGMNT               1840\n",
       "AVIATION                  1521\n",
       "TRANSPORTN                1073\n",
       "PUBLIC LIBRARY             961\n",
       "GENERAL SERVICES           930\n",
       "FAMILY & SUPPORT           654\n",
       "FINANCE                    577\n",
       "HEALTH                     529\n",
       "LAW                        414\n",
       "CITY COUNCIL               396\n",
       "BUILDINGS                  262\n",
       "COMMUNITY DEVELOPMENT      208\n",
       "BUSINESS AFFAIRS           161\n",
       "BOARD OF ELECTION          117\n",
       "DoIT                       102\n",
       "CITY CLERK                  94\n",
       "MAYOR'S OFFICE              85\n",
       "PROCUREMENT                 83\n",
       "IPRA                        82\n",
       "CULTURAL AFFAIRS            74\n",
       "HUMAN RESOURCES             70\n",
       "ANIMAL CONTRL               67\n",
       "INSPECTOR GEN               57\n",
       "BUDGET & MGMT               39\n",
       "ADMIN HEARNG                39\n",
       "DISABILITIES                28\n",
       "TREASURER                   22\n",
       "HUMAN RELATIONS             16\n",
       "BOARD OF ETHICS              9\n",
       "POLICE BOARD                 2\n",
       "LICENSE APPL COMM            1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Department.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "POLICE OFFICER                            9184\n",
       "FIREFIGHTER-EMT                           1208\n",
       "SERGEANT                                  1185\n",
       "POOL MOTOR TRUCK DRIVER                    918\n",
       "POLICE OFFICER (ASSIGNED AS DETECTIVE)     896\n",
       "MOTOR TRUCK DRIVER                         717\n",
       "SANITATION LABORER                         673\n",
       "FIREFIGHTER                                671\n",
       "CROSSING GUARD                             560\n",
       "TRAFFIC CONTROL AIDE-HOURLY                442\n",
       "CONSTRUCTION LABORER                       428\n",
       "LIEUTENANT-EMT                             391\n",
       "PARAMEDIC                                  344\n",
       "LIEUTENANT                                 332\n",
       "FIREFIGHTER-EMT (RECRUIT)                  301\n",
       "...\n",
       "RISK MANAGER                                   1\n",
       "SUPERVISING BOILER INSPECTOR                   1\n",
       "CHAIRPERSON - COMMISSION ON HUMAN RELATIONS    1\n",
       "SUPERVISING BUDGET ANALYST                     1\n",
       "PROGRAM COORD - DISABILITY SERVICES            1\n",
       "CITY CLERK                                     1\n",
       "MANAGER OF QUALITY ASSURANCE                   1\n",
       "TELEPHONE SYSTEMS ADMINISTRATOR                1\n",
       "SENIOR LAND DISPOSITION OFFICER                1\n",
       "SUPVR OF POLLING-ELECTIONS                     1\n",
       "ANIMAL PLACEMENT COORD                         1\n",
       "A/MGR COM SVC-ELECTIONS                        1\n",
       "EXEC DIR EMERG MGMT & COMM                     1\n",
       "EMERGENCY MANAGEMENT COORD                     1\n",
       "MOBILE UNIT OPERATOR                           1\n",
       "Length: 1093, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Title.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: there is only one risk manager. A very risky way of managing risk!\n",
    "\n",
    "## Fitting distributions with Scipy\n",
    "\n",
    "scipy.stats has a lot of tools for playing with various statistical distributions. We can use it to fit the parameters of a given distribution to data using Maximum Likelihood Estimation (black-boxed). \n",
    "\n",
    "Before we fit a distribution to our "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fitted_normal = scipy.stats.norm(loc=df.Salary.mean(), scale=df.Salary.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25094.723809012736, 80206.680215838554)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_normal.std(), fitted_normal.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.linspace(df.Salary.min(), df.Salary.max(), 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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awlvd9x4yxuS3TIY5VZEsEI2CqhpPWZb61bkMONhMm5iIxJuse6iZbaSPB25CRL4FfA64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\nrPgYY4zpcFZ8jDHGdLj/H37EAYxK0WGXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ac872d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.hist(df.Salary.dropna().values, normed=True, histtype='stepfilled')\n",
    "plt.plot(x, fitted_normal.pdf(x), 'r', lw=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6jbsphmxEpLuoGEhE7eFHR2JVTz+pR2KKlDAVA4lo2H5HGtdZNxKvvu2WGLIR\nke6gYiBRySS1hx4ZCVffdgs0NMSQkIgUmoqB5FV78KFkU6kWsYovZsGDD8aUkYgUkoqB5JVdcUXq\n99gr2nDttd2fjIgUnIqBtGnx4UdFgxMnUvH+9G7PRUQKS8VA2tSw0y5k1lo7Eu91/TXdn4yIFJSK\ngbQtmWTxMcdHwunbbyMxb24MCYlIoaQ6GsDMEsBVwDCgFjjO3WfktI8Ffgo0AG+4+08KlKvEoPaQ\nw+jzx9+RWLTw21hy4QKqb7uFxSeeEmNmItKVOrNlMAZIu/t2wDnAZc0NZlYNXAjs7O47AgPMLM9R\nRylW2f4DqD34kEi81/XXQiYTQ0YiUgidKQY7ABMB3P0FYIuctjpgO3dvfnJ6imDrQUrI4uNOjMQq\nZn5I1SMTY8hGRAqhM8WgH5C7g7jRzJIA7p5199kAZnYq0MfdH+v6NCVOmfXWp27X3SLxXtddHUM2\nIlIIHR4zAOYBNTmvk+7e1PwiPKbwJ2B9YN/OTHTIkJqOBypiJdm/s86Axx9tEap6ZjJDPnkPhg+P\nKanCKMn5FyrlvkHp96+QOlMMpgJ7AXeZ2TbAG63arwUWu/uYzk509uz5nc+wyAwZUlOa/Ru+LSus\nvwGp6e+2CNf+5iLmX39zTEl1vZKdf5R236A8+ldIiWw22+4AOWcTbRKGjgY2B/oA04CXgClhWxb4\nq7uPb2eU2VKfYaXav+qbb6TmrJ+1iGUTCb555iUy628QU1Zdq5TnXyn3Dcqif4lCjr/DYlAAKgbF\nqraWIVsPg88+axk+6BDmX/H3mJLqWqU8/0q5b1AW/StoMdBFZ9J51dVw5pmRcPquf5Oc+VEMCYlI\nV1ExkKVzwgk0DRzYIpTIZOh95V9iSkhEuoKKgSydPn1YfHz0IvPq224h+flned4gIsVAxUCW2uJj\nj6eppl+LWKK+nl5X/DmmjERkeakYyFLL9h9A7TE/jsR7/eMGPSdZpEipGMgyWXTCyWR792kRSzQ0\n0Ofi38aUkYgsDxUDWSbZwYNZ9JNTI/Hqe+4k9fp/YshIRJaHioEss8U/OZWmwUMi8T4XXhBDNiKy\nPFQMZJll+9aw8IxfROJVk5+k8qknYshIRJaVioEsl9rDjyKz9jqReN/fnAeNjTFkJCLLQsVAlk9V\nFQt/Fd0tlHrrTXpdXxq3qBApByoGstzqRo2hYfhmkXjvP/6e5P99GkNGIrK0VAxk+SWTLPj9JWQT\nLe+jlVwD5WgKAAAJBklEQVS4gL7n/TKmpERkaagYSJdo3HxLao88JhJPPzieqkf1eEyRnk7FQLrM\nwl9dkPdU077nnAULFsSQkYh0loqBdJls/wEsuOgPkXjFzI/oe752F4n0ZCoG0qXq9j2A+h13icR7\n3TqOqgfaewCeiMRJxUC6ViLBgksuI9u7d6Sp5oxTdXaRSA+lYiBdLrPueiy46OJIPDlnDjWnnACZ\nTAxZiUh7VAykIGoPO5K6PUdF4lXPTKb3JdHjCiISLxUDKYxEgvmXXU5m5VUiTX0u+xPpe+6MISkR\naYuKgRRMduAg5l95TeRiNICan/6E1LSXYshKRPJRMZCCathpFxb9/OxIPFFXR/8jxpL89JMYshKR\n1lQMpOAWnXUOtaP3icSTs7+g/4FjSMyaFUNWIpJLxUAKL5lk/uVX07Dp8EhTavq7DNh3pAqCSMxU\nDKR79O7NvHG3k1ll1UiTCoJI/FQMpNs0rbwK8/75b5r6D4i0paa/y4C9d6fi/ekxZCYiKgbSrRq/\nN4y5d95HU7/+kbbUjPcZsPuuVD79ZAyZiZQ3FQPpdo2bbtZmQUjOnUP/g/el+oZrIJuNITuR8qRi\nILFoHL45c++4N29BSGQy1JxzFv2OOFjHEUS6iYqBxKZxsy2YM+FRMmuvk7c9PelhBu60Fen77tZW\ngkiBqRhIrDK2Id9MfIL67XfM25785hv6HX80/fffm9Tr/+nm7ETKh4qBxC47cBBz77iPxUce2+Yw\nVVOeYoURO1Fz4rE640ikAFQMpGeorGTBJX9m7k230jR4cJuDVd9zJwO33Zx+Y/ej8onHtPtIpIuo\nGEiPUj9yFF9PfpG6vfZud7j0448y4OB9GbjlMPpcdEGwC0mFQWSZqRhIj5MdPJh5N4xj7o3/pHGd\nddsdtmLmh/S+4s+sMGInBm45jL5nnEb6vrtJzJ7dTdmKlIZEtvvXprKzZ8/v7ml2myFDalD/ulBD\nA9XjbqLPpReT/PLLpXprZo01adxkUxo3GUajbUTmO+sFZy6l022+p5TnXyn3Dcqif9F7wXehDouB\nmSWAq4BhQC1wnLvPyGkfBZwPNAA3ufv1HUxTxaCIxdW/xIL5VN9yM71uuJaKmR8u83iyySRNq6xK\n0yqrkll1NZpWXpnsoME0DRxE08BB9F9rFb5pSpHt149sn75ke/Ui27sPpFJd15mY6LtZ3HpCMdgH\nGOXux5jZ1sA57j4mbEsBbwObA4uBqcBId29vG13FoIjF3r9MhqpHJtLrxmupnPI0iaambplsNpWC\nqjTZdBXZqjRUVUFFBdnKSkilyFakIFUBFRWQSEIySTYZ/E8iEfxP+FtOJII/E4ngX3MMggcBtX4Y\nUJ6HA+XVwXDpqhR19Y2d73SRKfn+TXq4oMWgM6s7OwATAdz9BTPbIqdtI2C6u88DMLNngJ2Au7s6\nUREAKiqo32Mk9XuMJPHFF6Qn3E96/D1UPv9sQQtDorERGhtJLFpYsGl0h7Z3kJWGUu9fIXXmAHI/\nYG7O60YzS7bRNh+I3l9ApACyK65I7dHHMfe+h/jKP2TuuNtZ9OMTadxo42CtXEQ6rTNbBvOAmpzX\nSXdvymnrl9NWA8zpYHyJIUNqOhikuKl/MRhSA+sdBIcfFHcmIkWpM6tPU4E9AcxsG+CNnLa3gfXM\nbICZVRHsInquy7MUEZGCWpqziTYJQ0cTHDDu4+7Xm9lI4AKCQ2I3uPvfC5iviIgUQBzXGYiISA+j\no2wiIqJiICIiKgYiIkLnTi3tEh3d1qInMrNpLLmO4gPg98A/gCbgTXc/ORzux8DxBLfk+J27TzCz\nauCfwIoEp+Ae6e5fhWdk/SUc9lF3v7Abu0SY79bAxe7+fTP7TqH6ZGa/BkaG8dPd/aUY+rcp8CDw\nbth8tbvfWYz9C6/4vxFYG6gCfge8RYnMvzb69zGlM/+SwHWAEcyvE4E6esj8684tgzFA2t23A84B\nLuvGaS81M0sDuPsPwn/HEuR8rrvvDCTNbG8zWwk4FdgW2B34g5lVAicBr7v7TsAtBPdvArgaONjd\ndwS2NrNh3dyvswi+kM0XaxakT2Y2HNjJ3bcGxgJ/i6l/mwOX5szHO4u4f4cBX4b57Q5cSWnNv9z+\n7RH2bzNKZ/6NArLuvkOY2+/pQfOvO4tBi9taAFu0P3jshgF9zGySmT0Wrm1u5u5TwvaHgd2ArYBn\n3L0xvC3H9PC93/Y3HHZXM6sBqtz9wzA+CRjRPd351nvAPjmvNy9An3YLh30EwN0/BirMbFDBerVE\npH/ASDN72syuM7O+FG//7mDJAqACaKQw38me0L8kwRrt5sBepTD/3H08wdo+wFrAN/Sg+dedxaC9\n21r0RIuAS9z9RwQV+Va+vdMYENx6ox/BVde5/VpAcEuO3Pj8nNi8VuPo1tt3uPu9BAuRZoXqU1vj\nKKg8/XsBOCtc85pBcE1M6+9iUfTP3Re5+8JwAXAn8CtKaP7l6d95wIvAmaUw/wDcvcnMbgIuB26j\nB82/7lwYt3dbi57oXYICgLtPB74CVsppb771Rr5bcnxDy/42Dzs/z7Ad3b6j0HLnQVf1qfWwucN3\nt/vc/dXmv4FNCX4kRdk/M1sDeAK42d1vp8TmX57+ldT8A3D3o4ENgOuBXnlyiGX+dWcxaO+2Fj3R\n0cClAGa2KsEH/oiZ7Ry27wFMAV4CdjCzKjPrD2wIvAk8S9jf8P8p7j4fqDOzdcID6j8KxxGnV8xs\np/DvruzTs8CPzCxhZmsCCXf/uvu69a2JOXfa3RWYRpH2L9yXPAk4291vDsOvlsr8a6N/pTT/Djez\nc8KXtUAGeLlAy5Sl7l93PrHjXmA3M5savj66G6e9LG4AbjSzyUAWOIpg6+D68GDO28Bd7p41s8uB\nZwg2+c5193ozuxq42cymEJwxcEg43hMJNg+TwCPddYZNO84EritEn8LhngvHcXJ3dirHicDfzKwe\n+Bw43t0XFGn/zgEGAOdbcKZIFvgpcEWJzL98/fsZ8JcSmX93Af8ws6cJlr2nAe9QoGXK0vZPt6MQ\nERFddCYiIioGIiKCioGIiKBiICIiqBiIiAgqBiIigoqBiIigYiAiIsD/A4rll2bHqh0yAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10accda50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,fitted_normal.sf(x), 'r', lw=5)\n",
    "plt.title('Survival Function of Fitted Normal Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.8891561727224314e-19"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_normal.sf(300000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(629745163.05063069, -0.68450872985583655, 1.8892442465118831)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Salary.var(), df.Salary.skew(), df.Salary.kurtosis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80206.680215838554, 25094.332446929646)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.norm.fit(df.Salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<scipy.stats._distn_infrastructure.rv_frozen at 0x10aca4690>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lognorm_fit = scipy.stats.lognorm.fit(df.Salary)\n",
    "lognorm_distribution = scipy.stats.lognorm(s=lognorm_fit[0], loc=lognorm_fit[1], scale=lognorm_fit[2])\n",
    "lognorm_distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10aca4490>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p6R2PcR+79GZM/rHiUwjicSKrV7nC0fNtvKeldwd5rHBtN5sak3es+BSAUPVm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aca41d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.Salary, normed=True, histtype='stepfilled')\n",
    "plt.plot(x, lognorm_distribution.pdf(x), 'r', lw=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10b0caf90>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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984Dz6bg/08+ttfOMMZXAX4DBdFyPcZa1dnX69NzfpdddZK29poAjke53X+CX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\nMwYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b0503d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, lognorm_distribution.sf(x), 'r', lw=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lognorm_distribution.sf(300000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.7170069978756147e-15, -69.306421423452804, 28307.253810282185)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha_fit = scipy.stats.alpha.fit(df.Salary)\n",
    "alpha_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fitted_alpha = scipy.stats.alpha(alpha_fit[0], alpha_fit[1], alpha_fit[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10b08e090>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9BSkvwsMHnzqILRzetmzZ1r/f3yDr9+c3wpRz/8q5bzD0wVpI+NwLnGpmTwav\nLzSzzwBjgpFtV5I+FOYBtznn1plZzjrB41XALcGAglXA3c4538x+AiwOtnGdc67TzH4O/NrMFgEd\nwAXBNr4I3GNm24CXgVsG0H+8kL0ev6YGotGBbHZYWzb3VLa8284nn/1jj/IIPl97+GdE/NSo2wMS\nkeLy/PI/0ez39tdJ5P11TJhlPcpSDZPY+PLrQ92uQbEzf33d+pu7eOqd8XzzgR/zkVWPh9a5+eRL\nuX/2GYPRxGGrq6OVfzyhllNPOrFkbRgNfz2Xa//KuW8ADQ21Xt+1dt6ov8g0bGqdVJkNNgjjexF+\n/NErWLLv0aHLv/LILzn/qd9rFJyIDAmFT5lNKtofyWiMfzvrKp6ccUzo8s8+9XuueOinRJOJIrdM\nRMqdwmcUDLPuTTIa44Yzv8Wi/Y4LXf7Rlx/h+j/Op7qjrcgtE5FypvAZ5eED6QC68cwreWxm7k3o\nAOaseZ4Fd17NHhvfLnLLRKRcKXzK8HYKOyMVifLvp3+du474ZOjy3Te/y413Xs2xry0pcstEpBwp\nfEbxOZ9svhfhv074B24++VKSXu4/jZqudq79879x0d9uI57oLEELRaRcKHx02C3H/bPPYP7HrqEt\nnjsVD8C5y//Ej+68mr3WNxa3YSJSNhQ+YZOKjtI9n0zL9jmSb11wA++M3y10+fQNa1hw59Wc+9x9\nmhVbRPpN4RO656P5mgDembAHV15wQ95rgSqSXVz0+O3c8LtrmLb+rSK3TkRGMoVP6F1MtefTbVtl\nDd8/55+5dd6FdEXDZ2Pa74PV3PTbb/H5Rf9FZVd7kVsoIiORwqc17LBbedzFdNB4HvfNOZcrL7iB\ntRP2CK0S9VN8atk9/PJXX+EjrzyG56dC64mIgMJHh936obFhOt+84Eb+99Cz89aZ0LqJKx/4MTf8\n7p854J2Xi9g6ERlJCpnVuqwVe7TbLf/137i3t/RdsUCVFVE6Ovt3wr+5pRXGTdip9+uMV3LLRy5i\n8X7H8dWHf8aem94JrWfvr+aHf/gXlu91CL857nO8vuu+O/V+IlKeFD5FPuezqaWDTdFB/CJOAv29\n+8O4gb/tqt1m8vXP/TufWnYPn37mLuJ55n87bM0LHLbmBZ7e50juPuKTuKkWWk9ERheFj4Za77RE\nLM7vj/l7Ht9/Lhc+cQfHvLE0b92j33iGo994hpd2P4j/OeITLJ92KHhDOmO7iAxjCh9dZDpg68ZP\n4XvnXstIc9VsAAAP8klEQVSstS9x0d9uY/qGNXnrznpnJbPeWcnaXXbnz4eexWMzT6S9orqIrRWR\n4UADDhQ+g+alPWfxjc8t4Eenf4N19ZN7rbvnpnf4yiO/5PZbvsQlj/4n0zRbgsioMrr3fFKp8Nto\n67DbTktFovztgBNZZMdz8suP8vdL72JSy/q89cd2tHHOC3/hnBf+wmuTZ/DXg05i8X7H0VJdV8RW\ni0ixje7waWvDy7pTp19TA9H+nsGXbMlojIdmfZRHDjyJea8+wSeX3ZN3ZFy3/T5YzX4frObix27j\nuemH8fj+c1m29+F05JljTkRGrlEVPitfeYVNm5q2v67atJGzsup0xCt4YvHQ3TZg44YPoXL3Idv+\ncJOMxnj0wJN47IATOeqNZzhn+Z+Z9c7KXteJpxLbByh0xCp4dvocnpxxLM9NP4y2Sl0ALFIORlX4\n/OquR/gwteMK/d2a1uWEzya/gv98+MMha0MkOnN0/dIDvhfh6X2P5ul9j2ba+kbOfn4hJ776OJV9\n3JqhMtHJcauf4rjVT5GIRFmx+0E8s88RLJt+OB+M27VIrReRwTaqvgfjFVVUeDvOJdQ154bMtsox\nVOh8w5BqbJjGTz96Ob+a9wXmvrqIj658mBkfvNHnerFUkkPXvsiha1/k0sdu5b1xU1i+16E8P+0Q\nVu5+oPaKREaQURU+2ao7cyfB3KZhv0XTVjmGB2afzgOzT2fa+rc46ZW/MffVJ5jQurmg9ac2rWNq\n0zrOfvEvJL0Ir0/ehxV7HMzK3Q5g1W4zFUYiw9joDp+ubTll7Tq5XRKNDdP51bzp3HHCP3Lw2ys5\nwS3mmNefpq499yLgMFE/hb2/Gnt/NZ9adg8pPBob9mLV1Jm8OsV4darxfv2uurBVZJgY3eHTmRs+\n2vMprVQkyot7zebFvWZz8ymXcdA7Kzlu9VMc8cYyGrZuLHg7EXz2Xt/I3usbOevF+wHYUl3H65P3\nZfXkfVi96wxc/a7ga1i9SCmM6vAZ39qUU6bwGT5SkSgv7Tmbl/aczc9PupS917/FkW88w5zG55nx\n/mqi/bxtQ/22ZuY0LmdO4/LtZe331hI5ZA6Jgw4mMfMAkgceRGKGQZX2gEWG0qgOn/3efy2nbN24\nKSVoifTJ83hz0t68OWlvfn/M+Yzd1sIha1/k0DUvcPDbK5iy5YOd2mxVSwss+hsVi/62vcyPREhO\nm07SZpKw/UnuO4PkjP1I7jsDv1aDUUQGw6gOH1vncspenaJZl0eCrdW1LLbjWWzHA9DQ/CGz3l7B\nge+8woHvvsLUpnU7vW0vlSL25hvE3nyDyvv/3GNZctJkknvvQ3KffUlO35vktOmkpk0nOW06fl39\ngPokMpqM2vAZv3UTk5t7TvuS9CKs1n1nRqT1dZN45MCTeeTAk4H05zvzvVexda+x/zrHPh+8QWWy\n92uKChH98AOiH34AT+deiJwaN47kHnuR2nMvkrvvQWqPPUjuviep3XcnOXV3/AkTNOBBJDBqw2f/\nkL2exoZpmsqlTGweuwtL9juWJfsdC0A0mWDPjWuZ8cEbzHh/NXt/+BZ7bWikMtk1aO8ZaWoi0tQE\nK14MXe5XVZGcMpXUlKmkdp1CaupusO80KsaOJzV5CqnJk0lNmgw1NYPWJpHhatSGjw65jS7JaIy3\nJu3NW5P25qGDT02XbWvm8r23cMKYKmKrXiH6ysr043vvDkkbvPZ2Ym+9CW+92aM8+2BdqraO1KRJ\n+BMbSDVMItUQPE6YSGpiA/7Eiennu0zAHz9ecxHKiDRqwydsz8dN2a8ELZFSSUWibNljLzpOOpGO\njHKveQvR1xwx9yrR1a8Rff01oq+vJtr4Fl6qfyPsdkakpZlISzO88XqfdX3Pwx83Lh1Eu0wgtcsu\n+OPGkxo3Hn98xmP9OPxx6Z9U3Tj8+nqIx4e8LyL5jMrwiSW72Pf93OlcXp2yfwlaI8ONX1dP4vAj\nSRx+ZM8FnZ1E164h+ubrRN94g2jjm0Qb3yLS+BbRd97G6xq8Q3iF8nwfb/NmIps3FxRWmfyaMaTq\n6/Hr6vBr6/Dr6kh1P6+tw6+txa+tJVVbhz+2Fn/s2OCn53OFmOyMURk+09c35px83lJdxzpNVCm9\nqahID7ved0busmSSyAfvE127hsjaNUTfeZvIO28TfXstkffeJfruu6H3jiolr62VaFsrrHtvQNvx\nKyrSQTRmLP6YMfg1NennNTUwvp6x0Yr085pgWU0NfnXGY3U1VFfjV1fjV+14pCZ4VLiVpVEZPnnP\n92gk0qgSicR46IllvOh6v8/QTqmeCjOmwoyj0q99n+r2bdQ1N1HfvJn65ibqm5sYv62F3fwuJna0\nE/nwAyLrP8RLJAa/PUPI6+zE27QJNm0KXT7Qy7b9aDQdQtVV6XCqqoLKKvyqynR5ZWVQXolfWQUV\nlfhVlek6FRXp+hUV+BWV6boVFfiVO5ZTWYkfjwePFVBZgR8P6lfEg8cKiMX0HTGIRmX47P+ezvcI\nROOVbIzPYeOWIr5pVfAzaUfRsbtt4KJ/+HT6RSqVPowWBNH2n40b8TasJ7JhPZENG4hs3IC3aROR\n5mI2vjS8ZBKvdSu05t7yvtj8inQwUZEOq12iMYjH0yEWi+NXxIPH4HU8BvGKdJ3guR+PQyyWfuxe\nPx4L6neXxfBj6TJi3c8z1onF0ut0P4/GIBYN6qbXSZdH0wNSuutEozvqbX8dg0ik6L/LPsPHzDzg\nZmA20A5c5Jx7M2P5OcD1QBdwu3Pu1nzrmNk+wB1ACljpnLs82MbFwCXBNuY75xaaWRXwW9L/TZuB\nzzvnNprZ0cBNQd2HnXPf7W+nwwYb6HyPDAuRCP6ECSQnTCA584C+63d2Bud8NhHZtDH9vGlzOpia\nNuM1NQWPm/G2bCHS1IS3pQmveUvOXXylb15nJ15nJwRHUMtlnKHveRlBFE2H3Zbc6ccGUyF7PucB\nlc65Y83sKGBBUIaZxYLXc4BtwJNmdh9wfJ51FgDXOecWmdnPzexc4GngCuAwoAZYbGYPAV8GXnLO\nfdfM/p50wH0D+Dnwcedco5ktNLPZzrnwCytCjGvdzOSs+/jo4lIZsSoq8CdPJjl5Msn+rJdK4bVu\nxduyBa+5mUjzlnQgNTfjtbTgtTQTaW7G29oSvG5J19/agrd1a/p1W1v6dRFGAMrQ8nwfEon0D1CM\ng4uFhM/xwAMAzrmlZnZ4xrKZwGrnXDOAmS0C5gHHZK0zJ6g/xzm3KHh+P/BR0ntBi51zCaDZzFaT\n3mM6HvhhRt1/NbNaoMI51xiUPwicAvQePh0dVN/6S776v7+jpjV3RNKaiXvSrglFZTSJRLaPagP6\nF1yZfB+2bUsHUetWvNbWdDi1teK1tVEfTdLy/sb08rZWvG3bYFtbxut2vG1t6fL24HlbG177Nrxt\n7dC+TeFWpgoJnzog88BywswizrlUyLKtpK+Zq80qT5pZlJ6B2hKsn103bBstGWXNWduY3lcHar/+\nFaruuYvD8izXITcppeWvvsN3FtxR0jbM2HMXPvupj/V/Rc+D7hFsEyfmLm+opX19YfdkCuX70NWF\n19EO29rTodTevuN1R/C8vSO9rLMzHWIdHdDZEdQNnnd0pusGz7c/dnXidXZAR/rR6+wM3rMj/djV\nCR0deMmdjmgJUUj4NJP+0u/WHTzdyzKn+a0FNudZJ2lmqay6TQVuo7tuS0jd3g9MbttG5Z/+2GuV\nVXU1pDau6LXOcBWNRUgmyvcvw9HQv7ZElMZ1pR04sHzJ/Sx+7P5B325lZZyOjsKuf+rsaKe+bojv\nrxSciKcm/C63vu8TyXPy3fN9Yqkk0WSKeCpFTTxCYlsn8VSSaCpFNJUilkwR81M7XqdCnvv+9rLt\nP/6O5ZFk+nV3vUhm/ayy7cv8FNGUTyR4jPopIhmvI35Q1/eJpHo+j5Xo3F8h4fMkcDZwd3CyP/Nb\nehWwr5mNA9qAE4AbgmVh6yw3s7nOuSeAM4BHgWXAfDOrID0qc39gJbAEOBN4Nnhc5JxrMbMOM5sO\nNAKnAd/ptfXV1Z7X2fuEkt8KfkREpDg8v4/Uyxi5NisoupD0AIMxwci2s4Bvkz6kdptz7hdh6zjn\nXjOzGcAtQJx0cF3snPPN7EvApcE25jvn/mhm1cCvgSlAB3CBc+5DMzsS+DEQAR5yzl0/OL8KEREp\nlj7DR0REZLAV/8oiEREZ9RQ+IiJSdAofEREpOoWPiIgUXdlOLNrXnHTDlZk9x46La98CvscwmA9v\nIIIpln7gnPvIUM7vZ2b/BzgrKP+mc25ZCfp3CPBn4LVg8c+dc3eNxP4F02f9CpgGVADzgVcok88v\nT//epnw+vwjp0cVG+vO6jPTI4TsYBp9fOe/5bJ+TDriW9Lxyw5qZVQI4504Kfr7Ejvnw5gERMzvX\nzCaTng/vGOB04PtmFmfHfHhzgd+Qng8P0vPhne+cOwE4ysxmF7FPV5P+D1AZFA1Jf8zsUGCuc+4o\n4DPAz0rUvznAjzI+w7tGcP8+B2wI2nc68FPK6/PL7N8ZQf8Oo3w+v3MA3zl3fNC27zGMPr9yDp8e\nc9IBh/defViYDYwxswfN7K/BX9SHZc2HdypwJMF8eMG8epnz4T2QUffkXubDK5bXgY9nvM6e328w\n+nNqUPchAOfc20DUzCYMWa92yOkfcJaZPW5mt5jZWEZu//7Aji+cKJBgaP49Dof+RUj/xT4HOLsc\nPj/n3H2k92YA9iI9c8yw+fzKOXxC56QrVWMK1Abc4Jw7jfRfHf+PoZkPr34oGh/GOXcv6S+tbkPV\nn3zbGFIh/VsKXB38Zfkm6QuwC5kDcdj1zznX5pxrDb5w7gL+hTL6/EL696/AM8BV5fD5ATjnUmZ2\nO/AT4E6G0ec33L+MB6K3OemGq9dIBw7OudXARmByxvLizIc3tIZifr+w+QRL1c8/Ouee734OHEL6\nP+WI7J+Z7UF6GqxfO+d+T5l9fiH9K6vPD8A5dyGwH3ArPW8sW9LPr5zD50nSc8IRMifdcHUh8CMA\nM5tK+kN+yMzmBcvPABaRng/veDOrMLN6cufDg4z58IAOM5seDMI4LdhGqSw3s7nB88HszxLgNDPz\nzGxPwHPOhd/XeWg9YDtuO3Iy8BwjtH/BuYAHgX9yzv06KH6+XD6/PP0rp8/vH8zs2uBlO+k7Zzw7\nRN8n/e5f2Y52A+4FTjWzJ4PXF5ayMQW6DfiVmT0B+MAXSO/93BqcAFwF3O3S8+H9BFhMejf6Oudc\np5n9HPi1pe+r1AFcEGz3MtK73N3z4RVlFFgeVwG3DEV/gnpPBdu4vJidynAZ8DMz6wTeBy5xzm0d\nof27FhgHXG/pkUw+8HXgP8rk8wvr3zeAm8rk87sbuMPMHif9Xf814FWG6Pukv/3T3G4iIlJ05XzY\nTUREhimFj4iIFJ3CR0REik7hIyIiRafwERGRolP4iIhI0Sl8RESk6BQ+IiJSdP8fhR08RWuEMCkA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10af64290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.Salary, normed=True, histtype='stepfilled')\n",
    "plt.plot(x, fitted_alpha.pdf(x), 'r', lw=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10b3abc50>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B0vTvsdbGU8/vBKqAHwP/A/h1Y8xHprfE3HTDugZWLqhkx752ntqssYtEJLs5E/3Vaoz5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\n4P8DFlEiml8UUKYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b1dc410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, fitted_alpha.sf(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.075157523343034049"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_alpha.sf(300000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.30468726666024171, -2.2624896896805069, 2758.2624836242585)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pareto_fit = scipy.stats.pareto.fit(df.Salary)\n",
    "pareto_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fitted_pareto = scipy.stats.pareto(pareto_fit[0], pareto_fit[0], pareto_fit[0],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10b466110>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dy0MX/I+S9Hfsf4FPbL6LdG40hl6JiBz7FHwm8G9nfIDOeQtL0pd5Bzf+x1e0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b466290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.Salary, normed=True, histtype='stepfilled')\n",
    "plt.plot(x, fitted_pareto.pdf(x), 'r', lw=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10b65ac50>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TyC1fC7wduN0YkwRuArLW2vW44fHFGe73eWttqudPf381Ab/D136wTbO+ioicopiAWA88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\niIgUpIAQEZGCFBAiIlKQAkJERAr6/wAGrj+JgOhnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b5d3c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, fitted_pareto.sf(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(274.16496251762089, -346299.13591695158, 1554.274382092929)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gamma_fit = scipy.stats.gamma.fit(df.Salary)\n",
    "gamma_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10b47f990>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hik+eC+zZ5by5tLSUyPkX5Cij/BT3+dl2lk25NqZQWPHJc8F1Lqfcxo6HUCgH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b612cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitted_gamma = scipy.stats.gamma(gamma_fit[0], gamma_fit[1], gamma_fit[2])\n",
    "\n",
    "plt.hist(df.Salary, normed=True, histtype='stepfilled')\n",
    "plt.plot(x, fitted_gamma.pdf(x), 'r', lw=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10af58f90>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b7fd950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, fitted_gamma.sf(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.4260764837065559e-14"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_gamma.sf(300000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1190543027354106e-05"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.0/df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66786.091192358232, 29380.924368910561)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gumbel_r_fit = scipy.stats.gumbel_r.fit(df.Salary)\n",
    "gumbel_r_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10af4b750>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vnw3tX/rK5L4lWjzFZwkwGZglIsOBdVFtm4CeIlIEVAGjgXsjbXbrrBKRMcaYhcAk4G1g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\nSrr/DyqCQ3R8tyWbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10af3fed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitted_gumbel_r = scipy.stats.gumbel_r(gumbel_r_fit[0], gumbel_r_fit[1])\n",
    "\n",
    "plt.hist(df.Salary, normed=True, histtype='stepfilled')\n",
    "plt.plot(x, fitted_gumbel_r.pdf(x), 'r', lw=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10b99fc90>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0fM4oAFZvOeFyJSKSKhQGaaiqPJ/Jo4vYd7yJmroWt8sRkRSgMEhTd82rAuCVzTUuVyIi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\nCgMREQH+PwU1ejCEdPhzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10af0e6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, fitted_gumbel_r.sf(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00035700007621530805"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_gumbel_r.sf(300000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91439.361960652546, 29025.701813899137)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gumbel_l_fit = scipy.stats.gumbel_l.fit(df.Salary)\n",
    "gumbel_l_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10aec8850>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MBX4c7WMq8HMRWQYMAv7Us29BZvPs2E7x3//miDdcdoWt49YNIa+P96uGOOJ2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\nAOnt7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10af04710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitted_gumbel_l = scipy.stats.gumbel_l(gumbel_l_fit[0], gumbel_l_fit[1])\n",
    "\n",
    "plt.hist(df.Salary, normed=True, histtype='stepfilled')\n",
    "plt.plot(x, fitted_gumbel_l.pdf(x), 'r', lw=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2755.9999999619604, 77391.088743183529)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expon_fit = scipy.stats.expon.fit(df.Salary)\n",
    "expon_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10aeecfd0>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fzP+6HRGzvkP5Jm6fNYHh78+0j+GMaWPiST79gLkAqvo+ELm7ZC9ghaqWqGoN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\n0lnyMcYYk3SWfIwxxiTd/wfdHFjSkaNDSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bb1ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitted_expon = scipy.stats.expon(expon_fit[0], expon_fit[1])\n",
    "\n",
    "plt.hist(df.Salary, normed=True, histtype='stepfilled')\n",
    "plt.plot(x, fitted_expon.pdf(x), 'r', lw=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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13j2BEgEQv2sr4v/2cBA60x+/AkCSpDQhRPHHQRMAZBf73iWEMEqS5Cn2mjsr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\nTjEAiIh0igFARKRTDAAiIp36f7UmmkbwaRY0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bbfd050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.Salary, normed=True, histtype='stepfilled')\n",
    "plt.plot(x, fitted_gumbel_r.pdf(x), 'r', lw=5)\n",
    "\n",
    "plt.gca().set_xscale('log')\n",
    "plt.gca().set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-45.511487173979518, 0.0)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.skewtest(df.Salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8.2547357385747517, 300675.80545293575, 232038.99643075338)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frechet_l_fit = scipy.stats.frechet_l.fit(df.Salary)\n",
    "frechet_l_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c001350>]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NwEmxbZYBVzZ/MxEpAYKqui0Weo5o4eu8khWfLD/ltk8gQH2Sx3sXPGE3nBqT71IpPqXA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\nMs6KjzHGmIyz4mOMMSbjrPgYY4zJuP8P3oi9Zmaih4EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bbee810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitted_frechet_l = scipy.stats.frechet_l(frechet_l_fit[0], frechet_l_fit[1], frechet_l_fit[2])\n",
    "\n",
    "plt.hist(df.Salary, normed=True, histtype='stepfilled')\n",
    "plt.plot(x, fitted_frechet_l.pdf(x), 'r', lw=5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.8"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}