Finding_Best_Advertising_Markets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding the best markets to advertise in\n",
    "\n",
    "The premise of this exercise is that we are trying to determine the best two markets to advertise in for an e-learning company that mostly offers courses on web and mobile development. To do this we will use the 2017 new coders survey data from freeCodeCamp, made publicly available here: https://github.com/freeCodeCamp/2017-new-coder-survey."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Read in and look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dataquest/system/env/python3/lib/python3.4/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (17,62) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.max_columns = 150 # to avoid truncated output \n",
    "\n",
    "survey = pd.read_csv(\"2017-fCC-New-Coders-Survey-Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>AttendedBootcamp</th>\n",
       "      <th>BootcampFinish</th>\n",
       "      <th>BootcampLoanYesNo</th>\n",
       "      <th>BootcampName</th>\n",
       "      <th>BootcampRecommend</th>\n",
       "      <th>ChildrenNumber</th>\n",
       "      <th>CityPopulation</th>\n",
       "      <th>CodeEventConferences</th>\n",
       "      <th>CodeEventDjangoGirls</th>\n",
       "      <th>CodeEventFCC</th>\n",
       "      <th>CodeEventGameJam</th>\n",
       "      <th>CodeEventGirlDev</th>\n",
       "      <th>CodeEventHackathons</th>\n",
       "      <th>CodeEventMeetup</th>\n",
       "      <th>CodeEventNodeSchool</th>\n",
       "      <th>CodeEventNone</th>\n",
       "      <th>CodeEventOther</th>\n",
       "      <th>CodeEventRailsBridge</th>\n",
       "      <th>CodeEventRailsGirls</th>\n",
       "      <th>CodeEventStartUpWknd</th>\n",
       "      <th>CodeEventWkdBootcamps</th>\n",
       "      <th>CodeEventWomenCode</th>\n",
       "      <th>CodeEventWorkshops</th>\n",
       "      <th>CommuteTime</th>\n",
       "      <th>CountryCitizen</th>\n",
       "      <th>CountryLive</th>\n",
       "      <th>EmploymentField</th>\n",
       "      <th>EmploymentFieldOther</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>EmploymentStatusOther</th>\n",
       "      <th>ExpectedEarning</th>\n",
       "      <th>FinanciallySupporting</th>\n",
       "      <th>FirstDevJob</th>\n",
       "      <th>Gender</th>\n",
       "      <th>GenderOther</th>\n",
       "      <th>HasChildren</th>\n",
       "      <th>HasDebt</th>\n",
       "      <th>HasFinancialDependents</th>\n",
       "      <th>HasHighSpdInternet</th>\n",
       "      <th>HasHomeMortgage</th>\n",
       "      <th>HasServedInMilitary</th>\n",
       "      <th>HasStudentDebt</th>\n",
       "      <th>HomeMortgageOwe</th>\n",
       "      <th>HoursLearning</th>\n",
       "      <th>ID.x</th>\n",
       "      <th>ID.y</th>\n",
       "      <th>Income</th>\n",
       "      <th>IsEthnicMinority</th>\n",
       "      <th>IsReceiveDisabilitiesBenefits</th>\n",
       "      <th>IsSoftwareDev</th>\n",
       "      <th>IsUnderEmployed</th>\n",
       "      <th>JobApplyWhen</th>\n",
       "      <th>JobInterestBackEnd</th>\n",
       "      <th>JobInterestDataEngr</th>\n",
       "      <th>JobInterestDataSci</th>\n",
       "      <th>JobInterestDevOps</th>\n",
       "      <th>JobInterestFrontEnd</th>\n",
       "      <th>JobInterestFullStack</th>\n",
       "      <th>JobInterestGameDev</th>\n",
       "      <th>JobInterestInfoSec</th>\n",
       "      <th>JobInterestMobile</th>\n",
       "      <th>JobInterestOther</th>\n",
       "      <th>JobInterestProjMngr</th>\n",
       "      <th>JobInterestQAEngr</th>\n",
       "      <th>JobInterestUX</th>\n",
       "      <th>JobPref</th>\n",
       "      <th>JobRelocateYesNo</th>\n",
       "      <th>JobRoleInterest</th>\n",
       "      <th>JobWherePref</th>\n",
       "      <th>LanguageAtHome</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MoneyForLearning</th>\n",
       "      <th>MonthsProgramming</th>\n",
       "      <th>NetworkID</th>\n",
       "      <th>Part1EndTime</th>\n",
       "      <th>Part1StartTime</th>\n",
       "      <th>Part2EndTime</th>\n",
       "      <th>Part2StartTime</th>\n",
       "      <th>PodcastChangeLog</th>\n",
       "      <th>PodcastCodeNewbie</th>\n",
       "      <th>PodcastCodePen</th>\n",
       "      <th>PodcastDevTea</th>\n",
       "      <th>PodcastDotNET</th>\n",
       "      <th>PodcastGiantRobots</th>\n",
       "      <th>PodcastJSAir</th>\n",
       "      <th>PodcastJSJabber</th>\n",
       "      <th>PodcastNone</th>\n",
       "      <th>PodcastOther</th>\n",
       "      <th>PodcastProgThrowdown</th>\n",
       "      <th>PodcastRubyRogues</th>\n",
       "      <th>PodcastSEDaily</th>\n",
       "      <th>PodcastSERadio</th>\n",
       "      <th>PodcastShopTalk</th>\n",
       "      <th>PodcastTalkPython</th>\n",
       "      <th>PodcastTheWebAhead</th>\n",
       "      <th>ResourceCodecademy</th>\n",
       "      <th>ResourceCodeWars</th>\n",
       "      <th>ResourceCoursera</th>\n",
       "      <th>ResourceCSS</th>\n",
       "      <th>ResourceEdX</th>\n",
       "      <th>ResourceEgghead</th>\n",
       "      <th>ResourceFCC</th>\n",
       "      <th>ResourceHackerRank</th>\n",
       "      <th>ResourceKA</th>\n",
       "      <th>ResourceLynda</th>\n",
       "      <th>ResourceMDN</th>\n",
       "      <th>ResourceOdinProj</th>\n",
       "      <th>ResourceOther</th>\n",
       "      <th>ResourcePluralSight</th>\n",
       "      <th>ResourceSkillcrush</th>\n",
       "      <th>ResourceSO</th>\n",
       "      <th>ResourceTreehouse</th>\n",
       "      <th>ResourceUdacity</th>\n",
       "      <th>ResourceUdemy</th>\n",
       "      <th>ResourceW3S</th>\n",
       "      <th>SchoolDegree</th>\n",
       "      <th>SchoolMajor</th>\n",
       "      <th>StudentDebtOwe</th>\n",
       "      <th>YouTubeCodeCourse</th>\n",
       "      <th>YouTubeCodingTrain</th>\n",
       "      <th>YouTubeCodingTut360</th>\n",
       "      <th>YouTubeComputerphile</th>\n",
       "      <th>YouTubeDerekBanas</th>\n",
       "      <th>YouTubeDevTips</th>\n",
       "      <th>YouTubeEngineeredTruth</th>\n",
       "      <th>YouTubeFCC</th>\n",
       "      <th>YouTubeFunFunFunction</th>\n",
       "      <th>YouTubeGoogleDev</th>\n",
       "      <th>YouTubeLearnCode</th>\n",
       "      <th>YouTubeLevelUpTuts</th>\n",
       "      <th>YouTubeMIT</th>\n",
       "      <th>YouTubeMozillaHacks</th>\n",
       "      <th>YouTubeOther</th>\n",
       "      <th>YouTubeSimplilearn</th>\n",
       "      <th>YouTubeTheNewBoston</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15 to 29 minutes</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Canada</td>\n",
       "      <td>software development and IT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Employed for wages</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>02d9465b21e8bd09374b0066fb2d5614</td>\n",
       "      <td>eb78c1c3ac6cd9052aec557065070fbf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>start your own business</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>English</td>\n",
       "      <td>married or domestic partnership</td>\n",
       "      <td>150.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6f1fbc6b2b</td>\n",
       "      <td>2017-03-09 00:36:22</td>\n",
       "      <td>2017-03-09 00:32:59</td>\n",
       "      <td>2017-03-09 00:59:46</td>\n",
       "      <td>2017-03-09 00:36:26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>less than 100,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not working but looking for work</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5bfef9ecb211ec4f518cfc1d2a6f3e0c</td>\n",
       "      <td>21db37adb60cdcafadfa7dca1b13b6b1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Within 7 to 12 months</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>work for a nonprofit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Full-Stack Web Developer</td>\n",
       "      <td>in an office with other developers</td>\n",
       "      <td>English</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>f8f8be6910</td>\n",
       "      <td>2017-03-09 00:37:07</td>\n",
       "      <td>2017-03-09 00:33:26</td>\n",
       "      <td>2017-03-09 00:38:59</td>\n",
       "      <td>2017-03-09 00:37:10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15 to 29 minutes</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>software development and IT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Employed for wages</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14f1863afa9c7de488050b82eb3edd96</td>\n",
       "      <td>21ba173828fbe9e27ccebaf4d5166a55</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Within 7 to 12 months</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>work for a medium-sized company</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Front-End Web Developer, Back-End Web Develo...</td>\n",
       "      <td>no preference</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2ed189768e</td>\n",
       "      <td>2017-03-09 00:37:58</td>\n",
       "      <td>2017-03-09 00:33:53</td>\n",
       "      <td>2017-03-09 00:40:14</td>\n",
       "      <td>2017-03-09 00:38:02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Codenewbie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>high school diploma or equivalent (GED)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I work from home</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>software development and IT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Employed for wages</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>91756eb4dc280062a541c25a3d44cfb0</td>\n",
       "      <td>3be37b558f02daae93a6da10f83f0c77</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Within the next 6 months</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>work for a medium-sized company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Front-End Web Developer, Full-Stack Web Deve...</td>\n",
       "      <td>from home</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>married or domestic partnership</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>dbdc0664d1</td>\n",
       "      <td>2017-03-09 00:40:13</td>\n",
       "      <td>2017-03-09 00:37:45</td>\n",
       "      <td>2017-03-09 00:42:26</td>\n",
       "      <td>2017-03-09 00:40:18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not working but looking for work</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>aa3f061a1949a90b27bef7411ecd193f</td>\n",
       "      <td>d7c56bbf2c7b62096be9db010e86d96d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Within 7 to 12 months</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>work for a multinational corporation</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Full-Stack Web Developer, Information Security...</td>\n",
       "      <td>in an office with other developers</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>11b0f2d8a9</td>\n",
       "      <td>2017-03-09 00:42:45</td>\n",
       "      <td>2017-03-09 00:39:44</td>\n",
       "      <td>2017-03-09 00:45:42</td>\n",
       "      <td>2017-03-09 00:42:50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  AttendedBootcamp  BootcampFinish  BootcampLoanYesNo BootcampName  \\\n",
       "0  27.0               0.0             NaN                NaN          NaN   \n",
       "1  34.0               0.0             NaN                NaN          NaN   \n",
       "2  21.0               0.0             NaN                NaN          NaN   \n",
       "3  26.0               0.0             NaN                NaN          NaN   \n",
       "4  20.0               0.0             NaN                NaN          NaN   \n",
       "\n",
       "   BootcampRecommend  ChildrenNumber                 CityPopulation  \\\n",
       "0                NaN             NaN            more than 1 million   \n",
       "1                NaN             NaN              less than 100,000   \n",
       "2                NaN             NaN            more than 1 million   \n",
       "3                NaN             NaN  between 100,000 and 1 million   \n",
       "4                NaN             NaN  between 100,000 and 1 million   \n",
       "\n",
       "   CodeEventConferences  CodeEventDjangoGirls  CodeEventFCC  CodeEventGameJam  \\\n",
       "0                   NaN                   NaN           NaN               NaN   \n",
       "1                   NaN                   NaN           NaN               NaN   \n",
       "2                   NaN                   NaN           NaN               NaN   \n",
       "3                   NaN                   NaN           NaN               NaN   \n",
       "4                   NaN                   NaN           NaN               NaN   \n",
       "\n",
       "   CodeEventGirlDev  CodeEventHackathons  CodeEventMeetup  \\\n",
       "0               NaN                  NaN              NaN   \n",
       "1               NaN                  NaN              NaN   \n",
       "2               NaN                  1.0              NaN   \n",
       "3               NaN                  NaN              NaN   \n",
       "4               NaN                  NaN              NaN   \n",
       "\n",
       "   CodeEventNodeSchool  CodeEventNone CodeEventOther  CodeEventRailsBridge  \\\n",
       "0                  NaN            NaN            NaN                   NaN   \n",
       "1                  NaN            NaN            NaN                   NaN   \n",
       "2                  1.0            NaN            NaN                   NaN   \n",
       "3                  NaN            NaN            NaN                   NaN   \n",
       "4                  NaN            NaN            NaN                   NaN   \n",
       "\n",
       "   CodeEventRailsGirls  CodeEventStartUpWknd  CodeEventWkdBootcamps  \\\n",
       "0                  NaN                   NaN                    NaN   \n",
       "1                  NaN                   NaN                    NaN   \n",
       "2                  NaN                   NaN                    NaN   \n",
       "3                  NaN                   NaN                    NaN   \n",
       "4                  NaN                   NaN                    NaN   \n",
       "\n",
       "   CodeEventWomenCode  CodeEventWorkshops       CommuteTime  \\\n",
       "0                 NaN                 NaN  15 to 29 minutes   \n",
       "1                 NaN                 NaN               NaN   \n",
       "2                 NaN                 NaN  15 to 29 minutes   \n",
       "3                 NaN                 NaN  I work from home   \n",
       "4                 NaN                 NaN               NaN   \n",
       "\n",
       "             CountryCitizen               CountryLive  \\\n",
       "0                    Canada                    Canada   \n",
       "1  United States of America  United States of America   \n",
       "2  United States of America  United States of America   \n",
       "3                    Brazil                    Brazil   \n",
       "4                  Portugal                  Portugal   \n",
       "\n",
       "               EmploymentField EmploymentFieldOther  \\\n",
       "0  software development and IT                  NaN   \n",
       "1                          NaN                  NaN   \n",
       "2  software development and IT                  NaN   \n",
       "3  software development and IT                  NaN   \n",
       "4                          NaN                  NaN   \n",
       "\n",
       "                   EmploymentStatus EmploymentStatusOther  ExpectedEarning  \\\n",
       "0                Employed for wages                   NaN              NaN   \n",
       "1  Not working but looking for work                   NaN          35000.0   \n",
       "2                Employed for wages                   NaN          70000.0   \n",
       "3                Employed for wages                   NaN          40000.0   \n",
       "4  Not working but looking for work                   NaN         140000.0   \n",
       "\n",
       "   FinanciallySupporting  FirstDevJob  Gender GenderOther  HasChildren  \\\n",
       "0                    NaN          NaN  female         NaN          NaN   \n",
       "1                    NaN          NaN    male         NaN          NaN   \n",
       "2                    NaN          NaN    male         NaN          NaN   \n",
       "3                    0.0          NaN    male         NaN          0.0   \n",
       "4                    NaN          NaN  female         NaN          NaN   \n",
       "\n",
       "   HasDebt  HasFinancialDependents  HasHighSpdInternet  HasHomeMortgage  \\\n",
       "0      1.0                     0.0                 1.0              0.0   \n",
       "1      1.0                     0.0                 1.0              0.0   \n",
       "2      0.0                     0.0                 1.0              NaN   \n",
       "3      1.0                     1.0                 1.0              1.0   \n",
       "4      0.0                     0.0                 1.0              NaN   \n",
       "\n",
       "   HasServedInMilitary  HasStudentDebt  HomeMortgageOwe  HoursLearning  \\\n",
       "0                  0.0             0.0              NaN           15.0   \n",
       "1                  0.0             1.0              NaN           10.0   \n",
       "2                  0.0             NaN              NaN           25.0   \n",
       "3                  0.0             0.0          40000.0           14.0   \n",
       "4                  0.0             NaN              NaN           10.0   \n",
       "\n",
       "                               ID.x                              ID.y  \\\n",
       "0  02d9465b21e8bd09374b0066fb2d5614  eb78c1c3ac6cd9052aec557065070fbf   \n",
       "1  5bfef9ecb211ec4f518cfc1d2a6f3e0c  21db37adb60cdcafadfa7dca1b13b6b1   \n",
       "2  14f1863afa9c7de488050b82eb3edd96  21ba173828fbe9e27ccebaf4d5166a55   \n",
       "3  91756eb4dc280062a541c25a3d44cfb0  3be37b558f02daae93a6da10f83f0c77   \n",
       "4  aa3f061a1949a90b27bef7411ecd193f  d7c56bbf2c7b62096be9db010e86d96d   \n",
       "\n",
       "    Income  IsEthnicMinority  IsReceiveDisabilitiesBenefits  IsSoftwareDev  \\\n",
       "0      NaN               NaN                            0.0            0.0   \n",
       "1      NaN               0.0                            0.0            0.0   \n",
       "2  13000.0               1.0                            0.0            0.0   \n",
       "3  24000.0               0.0                            0.0            0.0   \n",
       "4      NaN               0.0                            0.0            0.0   \n",
       "\n",
       "   IsUnderEmployed              JobApplyWhen  JobInterestBackEnd  \\\n",
       "0              0.0                       NaN                 NaN   \n",
       "1              NaN     Within 7 to 12 months                 NaN   \n",
       "2              0.0     Within 7 to 12 months                 1.0   \n",
       "3              1.0  Within the next 6 months                 1.0   \n",
       "4              NaN     Within 7 to 12 months                 1.0   \n",
       "\n",
       "   JobInterestDataEngr  JobInterestDataSci  JobInterestDevOps  \\\n",
       "0                  NaN                 NaN                NaN   \n",
       "1                  NaN                 NaN                NaN   \n",
       "2                  NaN                 NaN                1.0   \n",
       "3                  NaN                 NaN                NaN   \n",
       "4                  NaN                 NaN                NaN   \n",
       "\n",
       "   JobInterestFrontEnd  JobInterestFullStack  JobInterestGameDev  \\\n",
       "0                  NaN                   NaN                 NaN   \n",
       "1                  NaN                   1.0                 NaN   \n",
       "2                  1.0                   1.0                 NaN   \n",
       "3                  1.0                   1.0                 NaN   \n",
       "4                  1.0                   1.0                 NaN   \n",
       "\n",
       "   JobInterestInfoSec  JobInterestMobile JobInterestOther  \\\n",
       "0                 NaN                NaN              NaN   \n",
       "1                 NaN                NaN              NaN   \n",
       "2                 NaN                1.0              NaN   \n",
       "3                 NaN                NaN              NaN   \n",
       "4                 1.0                1.0              NaN   \n",
       "\n",
       "   JobInterestProjMngr  JobInterestQAEngr  JobInterestUX  \\\n",
       "0                  NaN                NaN            NaN   \n",
       "1                  NaN                NaN            NaN   \n",
       "2                  NaN                NaN            NaN   \n",
       "3                  NaN                NaN            NaN   \n",
       "4                  NaN                NaN            NaN   \n",
       "\n",
       "                                JobPref  JobRelocateYesNo  \\\n",
       "0               start your own business               NaN   \n",
       "1                  work for a nonprofit               1.0   \n",
       "2       work for a medium-sized company               1.0   \n",
       "3       work for a medium-sized company               NaN   \n",
       "4  work for a multinational corporation               1.0   \n",
       "\n",
       "                                     JobRoleInterest  \\\n",
       "0                                                NaN   \n",
       "1                           Full-Stack Web Developer   \n",
       "2    Front-End Web Developer, Back-End Web Develo...   \n",
       "3    Front-End Web Developer, Full-Stack Web Deve...   \n",
       "4  Full-Stack Web Developer, Information Security...   \n",
       "\n",
       "                         JobWherePref LanguageAtHome  \\\n",
       "0                                 NaN        English   \n",
       "1  in an office with other developers        English   \n",
       "2                       no preference        Spanish   \n",
       "3                           from home     Portuguese   \n",
       "4  in an office with other developers     Portuguese   \n",
       "\n",
       "                     MaritalStatus  MoneyForLearning  MonthsProgramming  \\\n",
       "0  married or domestic partnership             150.0                6.0   \n",
       "1            single, never married              80.0                6.0   \n",
       "2            single, never married            1000.0                5.0   \n",
       "3  married or domestic partnership               0.0                5.0   \n",
       "4            single, never married               0.0               24.0   \n",
       "\n",
       "    NetworkID         Part1EndTime       Part1StartTime         Part2EndTime  \\\n",
       "0  6f1fbc6b2b  2017-03-09 00:36:22  2017-03-09 00:32:59  2017-03-09 00:59:46   \n",
       "1  f8f8be6910  2017-03-09 00:37:07  2017-03-09 00:33:26  2017-03-09 00:38:59   \n",
       "2  2ed189768e  2017-03-09 00:37:58  2017-03-09 00:33:53  2017-03-09 00:40:14   \n",
       "3  dbdc0664d1  2017-03-09 00:40:13  2017-03-09 00:37:45  2017-03-09 00:42:26   \n",
       "4  11b0f2d8a9  2017-03-09 00:42:45  2017-03-09 00:39:44  2017-03-09 00:45:42   \n",
       "\n",
       "        Part2StartTime  PodcastChangeLog  PodcastCodeNewbie  PodcastCodePen  \\\n",
       "0  2017-03-09 00:36:26               NaN                NaN             NaN   \n",
       "1  2017-03-09 00:37:10               NaN                1.0             NaN   \n",
       "2  2017-03-09 00:38:02               1.0                NaN             1.0   \n",
       "3  2017-03-09 00:40:18               NaN                NaN             NaN   \n",
       "4  2017-03-09 00:42:50               NaN                NaN             NaN   \n",
       "\n",
       "   PodcastDevTea  PodcastDotNET  PodcastGiantRobots  PodcastJSAir  \\\n",
       "0            1.0            NaN                 NaN           NaN   \n",
       "1            NaN            NaN                 NaN           NaN   \n",
       "2            NaN            NaN                 NaN           NaN   \n",
       "3            NaN            NaN                 NaN           NaN   \n",
       "4            NaN            NaN                 NaN           NaN   \n",
       "\n",
       "   PodcastJSJabber  PodcastNone PodcastOther  PodcastProgThrowdown  \\\n",
       "0              NaN          NaN          NaN                   NaN   \n",
       "1              NaN          NaN          NaN                   NaN   \n",
       "2              NaN          NaN   Codenewbie                   NaN   \n",
       "3              NaN          NaN          NaN                   NaN   \n",
       "4              NaN          NaN          NaN                   NaN   \n",
       "\n",
       "   PodcastRubyRogues  PodcastSEDaily  PodcastSERadio  PodcastShopTalk  \\\n",
       "0                NaN             NaN             NaN              NaN   \n",
       "1                NaN             NaN             NaN              NaN   \n",
       "2                NaN             NaN             NaN              1.0   \n",
       "3                NaN             NaN             NaN              NaN   \n",
       "4                NaN             NaN             NaN              NaN   \n",
       "\n",
       "   PodcastTalkPython  PodcastTheWebAhead  ResourceCodecademy  \\\n",
       "0                NaN                 NaN                 1.0   \n",
       "1                NaN                 NaN                 1.0   \n",
       "2                NaN                 NaN                 1.0   \n",
       "3                NaN                 NaN                 NaN   \n",
       "4                NaN                 NaN                 NaN   \n",
       "\n",
       "   ResourceCodeWars  ResourceCoursera  ResourceCSS  ResourceEdX  \\\n",
       "0               NaN               NaN          NaN          NaN   \n",
       "1               NaN               NaN          1.0          NaN   \n",
       "2               NaN               NaN          1.0          NaN   \n",
       "3               NaN               NaN          NaN          NaN   \n",
       "4               NaN               NaN          NaN          NaN   \n",
       "\n",
       "   ResourceEgghead  ResourceFCC  ResourceHackerRank  ResourceKA  \\\n",
       "0              NaN          1.0                 NaN         NaN   \n",
       "1              NaN          1.0                 NaN         NaN   \n",
       "2              NaN          1.0                 NaN         NaN   \n",
       "3              1.0          1.0                 NaN         NaN   \n",
       "4              NaN          NaN                 NaN         NaN   \n",
       "\n",
       "   ResourceLynda  ResourceMDN  ResourceOdinProj ResourceOther  \\\n",
       "0            NaN          1.0               NaN           NaN   \n",
       "1            NaN          NaN               NaN           NaN   \n",
       "2            NaN          1.0               NaN           NaN   \n",
       "3            NaN          1.0               NaN           NaN   \n",
       "4            NaN          NaN               NaN           NaN   \n",
       "\n",
       "   ResourcePluralSight  ResourceSkillcrush  ResourceSO  ResourceTreehouse  \\\n",
       "0                  NaN                 NaN         NaN                NaN   \n",
       "1                  NaN                 NaN         1.0                NaN   \n",
       "2                  NaN                 NaN         NaN                NaN   \n",
       "3                  NaN                 NaN         1.0                NaN   \n",
       "4                  NaN                 NaN         1.0                NaN   \n",
       "\n",
       "   ResourceUdacity  ResourceUdemy  ResourceW3S  \\\n",
       "0              NaN            1.0          1.0   \n",
       "1              NaN            1.0          1.0   \n",
       "2              1.0            1.0          NaN   \n",
       "3              NaN            NaN          NaN   \n",
       "4              NaN            NaN          NaN   \n",
       "\n",
       "                              SchoolDegree             SchoolMajor  \\\n",
       "0           some college credit, no degree                     NaN   \n",
       "1           some college credit, no degree                     NaN   \n",
       "2  high school diploma or equivalent (GED)                     NaN   \n",
       "3           some college credit, no degree                     NaN   \n",
       "4                        bachelor's degree  Information Technology   \n",
       "\n",
       "   StudentDebtOwe  YouTubeCodeCourse  YouTubeCodingTrain  YouTubeCodingTut360  \\\n",
       "0             NaN                NaN                 NaN                  NaN   \n",
       "1             NaN                NaN                 NaN                  NaN   \n",
       "2             NaN                NaN                 NaN                  1.0   \n",
       "3             NaN                NaN                 NaN                  NaN   \n",
       "4             NaN                NaN                 NaN                  NaN   \n",
       "\n",
       "   YouTubeComputerphile  YouTubeDerekBanas  YouTubeDevTips  \\\n",
       "0                   NaN                NaN             NaN   \n",
       "1                   NaN                NaN             NaN   \n",
       "2                   NaN                1.0             1.0   \n",
       "3                   NaN                NaN             1.0   \n",
       "4                   NaN                NaN             NaN   \n",
       "\n",
       "   YouTubeEngineeredTruth  YouTubeFCC  YouTubeFunFunFunction  \\\n",
       "0                     NaN         NaN                    NaN   \n",
       "1                     NaN         1.0                    NaN   \n",
       "2                     NaN         NaN                    NaN   \n",
       "3                     NaN         1.0                    1.0   \n",
       "4                     NaN         NaN                    NaN   \n",
       "\n",
       "   YouTubeGoogleDev  YouTubeLearnCode  YouTubeLevelUpTuts  YouTubeMIT  \\\n",
       "0               NaN               NaN                 NaN         NaN   \n",
       "1               NaN               NaN                 NaN         NaN   \n",
       "2               NaN               1.0                 1.0         NaN   \n",
       "3               NaN               NaN                 1.0         NaN   \n",
       "4               NaN               NaN                 NaN         NaN   \n",
       "\n",
       "   YouTubeMozillaHacks YouTubeOther  YouTubeSimplilearn  YouTubeTheNewBoston  \n",
       "0                  NaN          NaN                 NaN                  NaN  \n",
       "1                  NaN          NaN                 NaN                  NaN  \n",
       "2                  NaN          NaN                 NaN                  NaN  \n",
       "3                  NaN          NaN                 NaN                  NaN  \n",
       "4                  NaN          NaN                 NaN                  NaN  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>AttendedBootcamp</th>\n",
       "      <th>BootcampFinish</th>\n",
       "      <th>BootcampLoanYesNo</th>\n",
       "      <th>BootcampName</th>\n",
       "      <th>BootcampRecommend</th>\n",
       "      <th>ChildrenNumber</th>\n",
       "      <th>CityPopulation</th>\n",
       "      <th>CodeEventConferences</th>\n",
       "      <th>CodeEventDjangoGirls</th>\n",
       "      <th>CodeEventFCC</th>\n",
       "      <th>CodeEventGameJam</th>\n",
       "      <th>CodeEventGirlDev</th>\n",
       "      <th>CodeEventHackathons</th>\n",
       "      <th>CodeEventMeetup</th>\n",
       "      <th>CodeEventNodeSchool</th>\n",
       "      <th>CodeEventNone</th>\n",
       "      <th>CodeEventOther</th>\n",
       "      <th>CodeEventRailsBridge</th>\n",
       "      <th>CodeEventRailsGirls</th>\n",
       "      <th>CodeEventStartUpWknd</th>\n",
       "      <th>CodeEventWkdBootcamps</th>\n",
       "      <th>CodeEventWomenCode</th>\n",
       "      <th>CodeEventWorkshops</th>\n",
       "      <th>CommuteTime</th>\n",
       "      <th>CountryCitizen</th>\n",
       "      <th>CountryLive</th>\n",
       "      <th>EmploymentField</th>\n",
       "      <th>EmploymentFieldOther</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>EmploymentStatusOther</th>\n",
       "      <th>ExpectedEarning</th>\n",
       "      <th>FinanciallySupporting</th>\n",
       "      <th>FirstDevJob</th>\n",
       "      <th>Gender</th>\n",
       "      <th>GenderOther</th>\n",
       "      <th>HasChildren</th>\n",
       "      <th>HasDebt</th>\n",
       "      <th>HasFinancialDependents</th>\n",
       "      <th>HasHighSpdInternet</th>\n",
       "      <th>HasHomeMortgage</th>\n",
       "      <th>HasServedInMilitary</th>\n",
       "      <th>HasStudentDebt</th>\n",
       "      <th>HomeMortgageOwe</th>\n",
       "      <th>HoursLearning</th>\n",
       "      <th>ID.x</th>\n",
       "      <th>ID.y</th>\n",
       "      <th>Income</th>\n",
       "      <th>IsEthnicMinority</th>\n",
       "      <th>IsReceiveDisabilitiesBenefits</th>\n",
       "      <th>IsSoftwareDev</th>\n",
       "      <th>IsUnderEmployed</th>\n",
       "      <th>JobApplyWhen</th>\n",
       "      <th>JobInterestBackEnd</th>\n",
       "      <th>JobInterestDataEngr</th>\n",
       "      <th>JobInterestDataSci</th>\n",
       "      <th>JobInterestDevOps</th>\n",
       "      <th>JobInterestFrontEnd</th>\n",
       "      <th>JobInterestFullStack</th>\n",
       "      <th>JobInterestGameDev</th>\n",
       "      <th>JobInterestInfoSec</th>\n",
       "      <th>JobInterestMobile</th>\n",
       "      <th>JobInterestOther</th>\n",
       "      <th>JobInterestProjMngr</th>\n",
       "      <th>JobInterestQAEngr</th>\n",
       "      <th>JobInterestUX</th>\n",
       "      <th>JobPref</th>\n",
       "      <th>JobRelocateYesNo</th>\n",
       "      <th>JobRoleInterest</th>\n",
       "      <th>JobWherePref</th>\n",
       "      <th>LanguageAtHome</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MoneyForLearning</th>\n",
       "      <th>MonthsProgramming</th>\n",
       "      <th>NetworkID</th>\n",
       "      <th>Part1EndTime</th>\n",
       "      <th>Part1StartTime</th>\n",
       "      <th>Part2EndTime</th>\n",
       "      <th>Part2StartTime</th>\n",
       "      <th>PodcastChangeLog</th>\n",
       "      <th>PodcastCodeNewbie</th>\n",
       "      <th>PodcastCodePen</th>\n",
       "      <th>PodcastDevTea</th>\n",
       "      <th>PodcastDotNET</th>\n",
       "      <th>PodcastGiantRobots</th>\n",
       "      <th>PodcastJSAir</th>\n",
       "      <th>PodcastJSJabber</th>\n",
       "      <th>PodcastNone</th>\n",
       "      <th>PodcastOther</th>\n",
       "      <th>PodcastProgThrowdown</th>\n",
       "      <th>PodcastRubyRogues</th>\n",
       "      <th>PodcastSEDaily</th>\n",
       "      <th>PodcastSERadio</th>\n",
       "      <th>PodcastShopTalk</th>\n",
       "      <th>PodcastTalkPython</th>\n",
       "      <th>PodcastTheWebAhead</th>\n",
       "      <th>ResourceCodecademy</th>\n",
       "      <th>ResourceCodeWars</th>\n",
       "      <th>ResourceCoursera</th>\n",
       "      <th>ResourceCSS</th>\n",
       "      <th>ResourceEdX</th>\n",
       "      <th>ResourceEgghead</th>\n",
       "      <th>ResourceFCC</th>\n",
       "      <th>ResourceHackerRank</th>\n",
       "      <th>ResourceKA</th>\n",
       "      <th>ResourceLynda</th>\n",
       "      <th>ResourceMDN</th>\n",
       "      <th>ResourceOdinProj</th>\n",
       "      <th>ResourceOther</th>\n",
       "      <th>ResourcePluralSight</th>\n",
       "      <th>ResourceSkillcrush</th>\n",
       "      <th>ResourceSO</th>\n",
       "      <th>ResourceTreehouse</th>\n",
       "      <th>ResourceUdacity</th>\n",
       "      <th>ResourceUdemy</th>\n",
       "      <th>ResourceW3S</th>\n",
       "      <th>SchoolDegree</th>\n",
       "      <th>SchoolMajor</th>\n",
       "      <th>StudentDebtOwe</th>\n",
       "      <th>YouTubeCodeCourse</th>\n",
       "      <th>YouTubeCodingTrain</th>\n",
       "      <th>YouTubeCodingTut360</th>\n",
       "      <th>YouTubeComputerphile</th>\n",
       "      <th>YouTubeDerekBanas</th>\n",
       "      <th>YouTubeDevTips</th>\n",
       "      <th>YouTubeEngineeredTruth</th>\n",
       "      <th>YouTubeFCC</th>\n",
       "      <th>YouTubeFunFunFunction</th>\n",
       "      <th>YouTubeGoogleDev</th>\n",
       "      <th>YouTubeLearnCode</th>\n",
       "      <th>YouTubeLevelUpTuts</th>\n",
       "      <th>YouTubeMIT</th>\n",
       "      <th>YouTubeMozillaHacks</th>\n",
       "      <th>YouTubeOther</th>\n",
       "      <th>YouTubeSimplilearn</th>\n",
       "      <th>YouTubeTheNewBoston</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15367.000000</td>\n",
       "      <td>17709.000000</td>\n",
       "      <td>1069.000000</td>\n",
       "      <td>1079.000000</td>\n",
       "      <td>949</td>\n",
       "      <td>1073.000000</td>\n",
       "      <td>2314.000000</td>\n",
       "      <td>15354</td>\n",
       "      <td>1609.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>1708.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>2472.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>570</td>\n",
       "      <td>133.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>1753.0</td>\n",
       "      <td>9246</td>\n",
       "      <td>15382</td>\n",
       "      <td>15336</td>\n",
       "      <td>8116</td>\n",
       "      <td>1078</td>\n",
       "      <td>14345</td>\n",
       "      <td>835</td>\n",
       "      <td>7200.000000</td>\n",
       "      <td>3833.000000</td>\n",
       "      <td>4508.000000</td>\n",
       "      <td>15454</td>\n",
       "      <td>55</td>\n",
       "      <td>3858.000000</td>\n",
       "      <td>15291.000000</td>\n",
       "      <td>15329.000000</td>\n",
       "      <td>15380.000000</td>\n",
       "      <td>6397.000000</td>\n",
       "      <td>15256.000000</td>\n",
       "      <td>6402.000000</td>\n",
       "      <td>1371.000000</td>\n",
       "      <td>16714.000000</td>\n",
       "      <td>18175</td>\n",
       "      <td>15743</td>\n",
       "      <td>7623.000000</td>\n",
       "      <td>15293.000000</td>\n",
       "      <td>15222.000000</td>\n",
       "      <td>18068.000000</td>\n",
       "      <td>9223.000000</td>\n",
       "      <td>8138</td>\n",
       "      <td>3201.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>4047.0</td>\n",
       "      <td>4831.0</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>2677.0</td>\n",
       "      <td>266</td>\n",
       "      <td>939.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>1691.0</td>\n",
       "      <td>13483</td>\n",
       "      <td>6503.000000</td>\n",
       "      <td>6992</td>\n",
       "      <td>8118</td>\n",
       "      <td>15344</td>\n",
       "      <td>15335</td>\n",
       "      <td>16577.000000</td>\n",
       "      <td>17084.000000</td>\n",
       "      <td>18175</td>\n",
       "      <td>18175</td>\n",
       "      <td>18175</td>\n",
       "      <td>15743</td>\n",
       "      <td>15743</td>\n",
       "      <td>403.0</td>\n",
       "      <td>1636.0</td>\n",
       "      <td>773.0</td>\n",
       "      <td>728.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>1061.0</td>\n",
       "      <td>850</td>\n",
       "      <td>345.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>9390.0</td>\n",
       "      <td>1848.0</td>\n",
       "      <td>4402.0</td>\n",
       "      <td>4671.0</td>\n",
       "      <td>3243.0</td>\n",
       "      <td>1341.0</td>\n",
       "      <td>13803.0</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>2558.0</td>\n",
       "      <td>6417.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>1024</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>11207.0</td>\n",
       "      <td>2260.0</td>\n",
       "      <td>3837.0</td>\n",
       "      <td>5120.0</td>\n",
       "      <td>9765.0</td>\n",
       "      <td>15368</td>\n",
       "      <td>8727</td>\n",
       "      <td>3362.000000</td>\n",
       "      <td>956.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>1397.0</td>\n",
       "      <td>1453.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>2619.0</td>\n",
       "      <td>993.0</td>\n",
       "      <td>6036.0</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>3539.0</td>\n",
       "      <td>2662.0</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>3327.0</td>\n",
       "      <td>622.0</td>\n",
       "      <td>1214</td>\n",
       "      <td>201.0</td>\n",
       "      <td>2960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>463</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>181</td>\n",
       "      <td>172</td>\n",
       "      <td>14</td>\n",
       "      <td>724</td>\n",
       "      <td>10</td>\n",
       "      <td>383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18175</td>\n",
       "      <td>15743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3213</td>\n",
       "      <td>3</td>\n",
       "      <td>167</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17738</td>\n",
       "      <td>18054</td>\n",
       "      <td>18065</td>\n",
       "      <td>15657</td>\n",
       "      <td>15654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>575</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>General Assembly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15 to 29 minutes</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>software development and IT</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Employed for wages</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>non-binary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37ffca418c7c2817c5e4f762a1d1651d</td>\n",
       "      <td>22ffb4b8e1450ae56bfce78d782360f0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Within the next 6 months</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>work for a medium-sized company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Full-Stack Web Developer</td>\n",
       "      <td>in an office with other developers</td>\n",
       "      <td>English</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86af4fcb21</td>\n",
       "      <td>2017-03-09 22:54:01</td>\n",
       "      <td>2017-03-15 12:10:33</td>\n",
       "      <td>2017-03-19 20:47:42</td>\n",
       "      <td>2017-03-09 22:02:48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YouTube</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6534</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2478</td>\n",
       "      <td>5480</td>\n",
       "      <td>5791</td>\n",
       "      <td>4721</td>\n",
       "      <td>32</td>\n",
       "      <td>7624</td>\n",
       "      <td>278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12344</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2251</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>823</td>\n",
       "      <td>4069</td>\n",
       "      <td>7124</td>\n",
       "      <td>10291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5851</td>\n",
       "      <td>1345</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>27.691872</td>\n",
       "      <td>0.062002</td>\n",
       "      <td>0.699719</td>\n",
       "      <td>0.305839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.818267</td>\n",
       "      <td>1.832325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56169.169306</td>\n",
       "      <td>0.227498</td>\n",
       "      <td>0.572981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.609383</td>\n",
       "      <td>0.420901</td>\n",
       "      <td>0.252463</td>\n",
       "      <td>0.854811</td>\n",
       "      <td>0.269970</td>\n",
       "      <td>0.075511</td>\n",
       "      <td>0.568416</td>\n",
       "      <td>166541.105033</td>\n",
       "      <td>15.609250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43169.357864</td>\n",
       "      <td>0.238083</td>\n",
       "      <td>0.024241</td>\n",
       "      <td>0.250609</td>\n",
       "      <td>0.434674</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.771644</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>957.299753</td>\n",
       "      <td>20.790447</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31602.625521</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.559239</td>\n",
       "      <td>0.241167</td>\n",
       "      <td>0.458594</td>\n",
       "      <td>0.460975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.385805</td>\n",
       "      <td>0.972813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74811.725422</td>\n",
       "      <td>0.419271</td>\n",
       "      <td>0.494700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.487952</td>\n",
       "      <td>0.493720</td>\n",
       "      <td>0.434439</td>\n",
       "      <td>0.352302</td>\n",
       "      <td>0.443979</td>\n",
       "      <td>0.264223</td>\n",
       "      <td>0.495336</td>\n",
       "      <td>137761.450348</td>\n",
       "      <td>15.283681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61812.293272</td>\n",
       "      <td>0.425924</td>\n",
       "      <td>0.153802</td>\n",
       "      <td>0.433376</td>\n",
       "      <td>0.495741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.419806</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5003.003169</td>\n",
       "      <td>41.722826</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39383.187732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>72000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9536.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>140000.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>215000.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210000.000000</td>\n",
       "      <td>744.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Age  AttendedBootcamp  BootcampFinish  BootcampLoanYesNo  \\\n",
       "count   15367.000000      17709.000000     1069.000000        1079.000000   \n",
       "unique           NaN               NaN             NaN                NaN   \n",
       "top              NaN               NaN             NaN                NaN   \n",
       "freq             NaN               NaN             NaN                NaN   \n",
       "mean       27.691872          0.062002        0.699719           0.305839   \n",
       "std         8.559239          0.241167        0.458594           0.460975   \n",
       "min         0.000000          0.000000        0.000000           0.000000   \n",
       "25%        22.000000          0.000000        0.000000           0.000000   \n",
       "50%        26.000000          0.000000        1.000000           0.000000   \n",
       "75%        32.000000          0.000000        1.000000           1.000000   \n",
       "max        90.000000          1.000000        1.000000           1.000000   \n",
       "\n",
       "            BootcampName  BootcampRecommend  ChildrenNumber  \\\n",
       "count                949        1073.000000     2314.000000   \n",
       "unique               141                NaN             NaN   \n",
       "top     General Assembly                NaN             NaN   \n",
       "freq                  58                NaN             NaN   \n",
       "mean                 NaN           0.818267        1.832325   \n",
       "std                  NaN           0.385805        0.972813   \n",
       "min                  NaN           0.000000        1.000000   \n",
       "25%                  NaN           1.000000        1.000000   \n",
       "50%                  NaN           1.000000        2.000000   \n",
       "75%                  NaN           1.000000        2.000000   \n",
       "max                  NaN           1.000000        9.000000   \n",
       "\n",
       "             CityPopulation  CodeEventConferences  CodeEventDjangoGirls  \\\n",
       "count                 15354                1609.0                 165.0   \n",
       "unique                    3                   NaN                   NaN   \n",
       "top     more than 1 million                   NaN                   NaN   \n",
       "freq                   6534                   NaN                   NaN   \n",
       "mean                    NaN                   1.0                   1.0   \n",
       "std                     NaN                   0.0                   0.0   \n",
       "min                     NaN                   1.0                   1.0   \n",
       "25%                     NaN                   1.0                   1.0   \n",
       "50%                     NaN                   1.0                   1.0   \n",
       "75%                     NaN                   1.0                   1.0   \n",
       "max                     NaN                   1.0                   1.0   \n",
       "\n",
       "        CodeEventFCC  CodeEventGameJam  CodeEventGirlDev  CodeEventHackathons  \\\n",
       "count         1708.0             290.0             297.0               1977.0   \n",
       "unique           NaN               NaN               NaN                  NaN   \n",
       "top              NaN               NaN               NaN                  NaN   \n",
       "freq             NaN               NaN               NaN                  NaN   \n",
       "mean             1.0               1.0               1.0                  1.0   \n",
       "std              0.0               0.0               0.0                  0.0   \n",
       "min              1.0               1.0               1.0                  1.0   \n",
       "25%              1.0               1.0               1.0                  1.0   \n",
       "50%              1.0               1.0               1.0                  1.0   \n",
       "75%              1.0               1.0               1.0                  1.0   \n",
       "max              1.0               1.0               1.0                  1.0   \n",
       "\n",
       "        CodeEventMeetup  CodeEventNodeSchool  CodeEventNone CodeEventOther  \\\n",
       "count            2472.0                446.0         1076.0            570   \n",
       "unique              NaN                  NaN            NaN            463   \n",
       "top                 NaN                  NaN            NaN             No   \n",
       "freq                NaN                  NaN            NaN             21   \n",
       "mean                1.0                  1.0            1.0            NaN   \n",
       "std                 0.0                  0.0            0.0            NaN   \n",
       "min                 1.0                  1.0            1.0            NaN   \n",
       "25%                 1.0                  1.0            1.0            NaN   \n",
       "50%                 1.0                  1.0            1.0            NaN   \n",
       "75%                 1.0                  1.0            1.0            NaN   \n",
       "max                 1.0                  1.0            1.0            NaN   \n",
       "\n",
       "        CodeEventRailsBridge  CodeEventRailsGirls  CodeEventStartUpWknd  \\\n",
       "count                  133.0                132.0                 544.0   \n",
       "unique                   NaN                  NaN                   NaN   \n",
       "top                      NaN                  NaN                   NaN   \n",
       "freq                     NaN                  NaN                   NaN   \n",
       "mean                     1.0                  1.0                   1.0   \n",
       "std                      0.0                  0.0                   0.0   \n",
       "min                      1.0                  1.0                   1.0   \n",
       "25%                      1.0                  1.0                   1.0   \n",
       "50%                      1.0                  1.0                   1.0   \n",
       "75%                      1.0                  1.0                   1.0   \n",
       "max                      1.0                  1.0                   1.0   \n",
       "\n",
       "        CodeEventWkdBootcamps  CodeEventWomenCode  CodeEventWorkshops  \\\n",
       "count                   547.0               481.0              1753.0   \n",
       "unique                    NaN                 NaN                 NaN   \n",
       "top                       NaN                 NaN                 NaN   \n",
       "freq                      NaN                 NaN                 NaN   \n",
       "mean                      1.0                 1.0                 1.0   \n",
       "std                       0.0                 0.0                 0.0   \n",
       "min                       1.0                 1.0                 1.0   \n",
       "25%                       1.0                 1.0                 1.0   \n",
       "50%                       1.0                 1.0                 1.0   \n",
       "75%                       1.0                 1.0                 1.0   \n",
       "max                       1.0                 1.0                 1.0   \n",
       "\n",
       "             CommuteTime            CountryCitizen               CountryLive  \\\n",
       "count               9246                     15382                     15336   \n",
       "unique                 6                       181                       172   \n",
       "top     15 to 29 minutes  United States of America  United States of America   \n",
       "freq                2478                      5480                      5791   \n",
       "mean                 NaN                       NaN                       NaN   \n",
       "std                  NaN                       NaN                       NaN   \n",
       "min                  NaN                       NaN                       NaN   \n",
       "25%                  NaN                       NaN                       NaN   \n",
       "50%                  NaN                       NaN                       NaN   \n",
       "75%                  NaN                       NaN                       NaN   \n",
       "max                  NaN                       NaN                       NaN   \n",
       "\n",
       "                    EmploymentField EmploymentFieldOther    EmploymentStatus  \\\n",
       "count                          8116                 1078               14345   \n",
       "unique                           14                  724                  10   \n",
       "top     software development and IT            Marketing  Employed for wages   \n",
       "freq                           4721                   32                7624   \n",
       "mean                            NaN                  NaN                 NaN   \n",
       "std                             NaN                  NaN                 NaN   \n",
       "min                             NaN                  NaN                 NaN   \n",
       "25%                             NaN                  NaN                 NaN   \n",
       "50%                             NaN                  NaN                 NaN   \n",
       "75%                             NaN                  NaN                 NaN   \n",
       "max                             NaN                  NaN                 NaN   \n",
       "\n",
       "       EmploymentStatusOther  ExpectedEarning  FinanciallySupporting  \\\n",
       "count                    835      7200.000000            3833.000000   \n",
       "unique                   383              NaN                    NaN   \n",
       "top                  Student              NaN                    NaN   \n",
       "freq                     278              NaN                    NaN   \n",
       "mean                     NaN     56169.169306               0.227498   \n",
       "std                      NaN     74811.725422               0.419271   \n",
       "min                      NaN      6000.000000               0.000000   \n",
       "25%                      NaN     30000.000000               0.000000   \n",
       "50%                      NaN     50000.000000               0.000000   \n",
       "75%                      NaN     65000.000000               0.000000   \n",
       "max                      NaN   1000000.000000               1.000000   \n",
       "\n",
       "        FirstDevJob Gender GenderOther  HasChildren       HasDebt  \\\n",
       "count   4508.000000  15454          55  3858.000000  15291.000000   \n",
       "unique          NaN      5          50          NaN           NaN   \n",
       "top             NaN   male  non-binary          NaN           NaN   \n",
       "freq            NaN  12344           3          NaN           NaN   \n",
       "mean       0.572981    NaN         NaN     0.609383      0.420901   \n",
       "std        0.494700    NaN         NaN     0.487952      0.493720   \n",
       "min        0.000000    NaN         NaN     0.000000      0.000000   \n",
       "25%        0.000000    NaN         NaN     0.000000      0.000000   \n",
       "50%        1.000000    NaN         NaN     1.000000      0.000000   \n",
       "75%        1.000000    NaN         NaN     1.000000      1.000000   \n",
       "max        1.000000    NaN         NaN     1.000000      1.000000   \n",
       "\n",
       "        HasFinancialDependents  HasHighSpdInternet  HasHomeMortgage  \\\n",
       "count             15329.000000        15380.000000      6397.000000   \n",
       "unique                     NaN                 NaN              NaN   \n",
       "top                        NaN                 NaN              NaN   \n",
       "freq                       NaN                 NaN              NaN   \n",
       "mean                  0.252463            0.854811         0.269970   \n",
       "std                   0.434439            0.352302         0.443979   \n",
       "min                   0.000000            0.000000         0.000000   \n",
       "25%                   0.000000            1.000000         0.000000   \n",
       "50%                   0.000000            1.000000         0.000000   \n",
       "75%                   1.000000            1.000000         1.000000   \n",
       "max                   1.000000            1.000000         1.000000   \n",
       "\n",
       "        HasServedInMilitary  HasStudentDebt  HomeMortgageOwe  HoursLearning  \\\n",
       "count          15256.000000     6402.000000      1371.000000   16714.000000   \n",
       "unique                  NaN             NaN              NaN            NaN   \n",
       "top                     NaN             NaN              NaN            NaN   \n",
       "freq                    NaN             NaN              NaN            NaN   \n",
       "mean               0.075511        0.568416    166541.105033      15.609250   \n",
       "std                0.264223        0.495336    137761.450348      15.283681   \n",
       "min                0.000000        0.000000      1000.000000       0.000000   \n",
       "25%                0.000000        0.000000     72000.000000       5.000000   \n",
       "50%                0.000000        1.000000    140000.000000      10.000000   \n",
       "75%                0.000000        1.000000    215000.000000      20.000000   \n",
       "max                1.000000        1.000000   1000000.000000     168.000000   \n",
       "\n",
       "                                    ID.x                              ID.y  \\\n",
       "count                              18175                             15743   \n",
       "unique                             18175                             15743   \n",
       "top     37ffca418c7c2817c5e4f762a1d1651d  22ffb4b8e1450ae56bfce78d782360f0   \n",
       "freq                                   1                                 1   \n",
       "mean                                 NaN                               NaN   \n",
       "std                                  NaN                               NaN   \n",
       "min                                  NaN                               NaN   \n",
       "25%                                  NaN                               NaN   \n",
       "50%                                  NaN                               NaN   \n",
       "75%                                  NaN                               NaN   \n",
       "max                                  NaN                               NaN   \n",
       "\n",
       "                Income  IsEthnicMinority  IsReceiveDisabilitiesBenefits  \\\n",
       "count      7623.000000      15293.000000                   15222.000000   \n",
       "unique             NaN               NaN                            NaN   \n",
       "top                NaN               NaN                            NaN   \n",
       "freq               NaN               NaN                            NaN   \n",
       "mean      43169.357864          0.238083                       0.024241   \n",
       "std       61812.293272          0.425924                       0.153802   \n",
       "min        6000.000000          0.000000                       0.000000   \n",
       "25%       17000.000000          0.000000                       0.000000   \n",
       "50%       33000.000000          0.000000                       0.000000   \n",
       "75%       55000.000000          0.000000                       0.000000   \n",
       "max     1000000.000000          1.000000                       1.000000   \n",
       "\n",
       "        IsSoftwareDev  IsUnderEmployed              JobApplyWhen  \\\n",
       "count    18068.000000      9223.000000                      8138   \n",
       "unique            NaN              NaN                         5   \n",
       "top               NaN              NaN  Within the next 6 months   \n",
       "freq              NaN              NaN                      2251   \n",
       "mean         0.250609         0.434674                       NaN   \n",
       "std          0.433376         0.495741                       NaN   \n",
       "min          0.000000         0.000000                       NaN   \n",
       "25%          0.000000         0.000000                       NaN   \n",
       "50%          0.000000         0.000000                       NaN   \n",
       "75%          1.000000         1.000000                       NaN   \n",
       "max          1.000000         1.000000                       NaN   \n",
       "\n",
       "        JobInterestBackEnd  JobInterestDataEngr  JobInterestDataSci  \\\n",
       "count               3201.0               1470.0              1940.0   \n",
       "unique                 NaN                  NaN                 NaN   \n",
       "top                    NaN                  NaN                 NaN   \n",
       "freq                   NaN                  NaN                 NaN   \n",
       "mean                   1.0                  1.0                 1.0   \n",
       "std                    0.0                  0.0                 0.0   \n",
       "min                    1.0                  1.0                 1.0   \n",
       "25%                    1.0                  1.0                 1.0   \n",
       "50%                    1.0                  1.0                 1.0   \n",
       "75%                    1.0                  1.0                 1.0   \n",
       "max                    1.0                  1.0                 1.0   \n",
       "\n",
       "        JobInterestDevOps  JobInterestFrontEnd  JobInterestFullStack  \\\n",
       "count              1092.0               4047.0                4831.0   \n",
       "unique                NaN                  NaN                   NaN   \n",
       "top                   NaN                  NaN                   NaN   \n",
       "freq                  NaN                  NaN                   NaN   \n",
       "mean                  1.0                  1.0                   1.0   \n",
       "std                   0.0                  0.0                   0.0   \n",
       "min                   1.0                  1.0                   1.0   \n",
       "25%                   1.0                  1.0                   1.0   \n",
       "50%                   1.0                  1.0                   1.0   \n",
       "75%                   1.0                  1.0                   1.0   \n",
       "max                   1.0                  1.0                   1.0   \n",
       "\n",
       "        JobInterestGameDev  JobInterestInfoSec  JobInterestMobile  \\\n",
       "count               1875.0              1560.0             2677.0   \n",
       "unique                 NaN                 NaN                NaN   \n",
       "top                    NaN                 NaN                NaN   \n",
       "freq                   NaN                 NaN                NaN   \n",
       "mean                   1.0                 1.0                1.0   \n",
       "std                    0.0                 0.0                0.0   \n",
       "min                    1.0                 1.0                1.0   \n",
       "25%                    1.0                 1.0                1.0   \n",
       "50%                    1.0                 1.0                1.0   \n",
       "75%                    1.0                 1.0                1.0   \n",
       "max                    1.0                 1.0                1.0   \n",
       "\n",
       "       JobInterestOther  JobInterestProjMngr  JobInterestQAEngr  \\\n",
       "count               266                939.0              584.0   \n",
       "unique              177                  NaN                NaN   \n",
       "top           Undecided                  NaN                NaN   \n",
       "freq                 23                  NaN                NaN   \n",
       "mean                NaN                  1.0                1.0   \n",
       "std                 NaN                  0.0                0.0   \n",
       "min                 NaN                  1.0                1.0   \n",
       "25%                 NaN                  1.0                1.0   \n",
       "50%                 NaN                  1.0                1.0   \n",
       "75%                 NaN                  1.0                1.0   \n",
       "max                 NaN                  1.0                1.0   \n",
       "\n",
       "        JobInterestUX                          JobPref  JobRelocateYesNo  \\\n",
       "count          1691.0                            13483       6503.000000   \n",
       "unique            NaN                                7               NaN   \n",
       "top               NaN  work for a medium-sized company               NaN   \n",
       "freq              NaN                             2979               NaN   \n",
       "mean              1.0                              NaN          0.771644   \n",
       "std               0.0                              NaN          0.419806   \n",
       "min               1.0                              NaN          0.000000   \n",
       "25%               1.0                              NaN          1.000000   \n",
       "50%               1.0                              NaN          1.000000   \n",
       "75%               1.0                              NaN          1.000000   \n",
       "max               1.0                              NaN          1.000000   \n",
       "\n",
       "                 JobRoleInterest                        JobWherePref  \\\n",
       "count                       6992                                8118   \n",
       "unique                      3213                                   3   \n",
       "top     Full-Stack Web Developer  in an office with other developers   \n",
       "freq                         823                                4069   \n",
       "mean                         NaN                                 NaN   \n",
       "std                          NaN                                 NaN   \n",
       "min                          NaN                                 NaN   \n",
       "25%                          NaN                                 NaN   \n",
       "50%                          NaN                                 NaN   \n",
       "75%                          NaN                                 NaN   \n",
       "max                          NaN                                 NaN   \n",
       "\n",
       "       LanguageAtHome          MaritalStatus  MoneyForLearning  \\\n",
       "count           15344                  15335      16577.000000   \n",
       "unique            167                      5               NaN   \n",
       "top           English  single, never married               NaN   \n",
       "freq             7124                  10291               NaN   \n",
       "mean              NaN                    NaN        957.299753   \n",
       "std               NaN                    NaN       5003.003169   \n",
       "min               NaN                    NaN          0.000000   \n",
       "25%               NaN                    NaN          0.000000   \n",
       "50%               NaN                    NaN         10.000000   \n",
       "75%               NaN                    NaN        200.000000   \n",
       "max               NaN                    NaN     210000.000000   \n",
       "\n",
       "        MonthsProgramming   NetworkID         Part1EndTime  \\\n",
       "count        17084.000000       18175                18175   \n",
       "unique                NaN       17738                18054   \n",
       "top                   NaN  86af4fcb21  2017-03-09 22:54:01   \n",
       "freq                  NaN           7                    3   \n",
       "mean            20.790447         NaN                  NaN   \n",
       "std             41.722826         NaN                  NaN   \n",
       "min              0.000000         NaN                  NaN   \n",
       "25%              3.000000         NaN                  NaN   \n",
       "50%              9.000000         NaN                  NaN   \n",
       "75%             24.000000         NaN                  NaN   \n",
       "max            744.000000         NaN                  NaN   \n",
       "\n",
       "             Part1StartTime         Part2EndTime       Part2StartTime  \\\n",
       "count                 18175                15743                15743   \n",
       "unique                18065                15657                15654   \n",
       "top     2017-03-15 12:10:33  2017-03-19 20:47:42  2017-03-09 22:02:48   \n",
       "freq                      2                    2                    2   \n",
       "mean                    NaN                  NaN                  NaN   \n",
       "std                     NaN                  NaN                  NaN   \n",
       "min                     NaN                  NaN                  NaN   \n",
       "25%                     NaN                  NaN                  NaN   \n",
       "50%                     NaN                  NaN                  NaN   \n",
       "75%                     NaN                  NaN                  NaN   \n",
       "max                     NaN                  NaN                  NaN   \n",
       "\n",
       "        PodcastChangeLog  PodcastCodeNewbie  PodcastCodePen  PodcastDevTea  \\\n",
       "count              403.0             1636.0           773.0          728.0   \n",
       "unique               NaN                NaN             NaN            NaN   \n",
       "top                  NaN                NaN             NaN            NaN   \n",
       "freq                 NaN                NaN             NaN            NaN   \n",
       "mean                 1.0                1.0             1.0            1.0   \n",
       "std                  0.0                0.0             0.0            0.0   \n",
       "min                  1.0                1.0             1.0            1.0   \n",
       "25%                  1.0                1.0             1.0            1.0   \n",
       "50%                  1.0                1.0             1.0            1.0   \n",
       "75%                  1.0                1.0             1.0            1.0   \n",
       "max                  1.0                1.0             1.0            1.0   \n",
       "\n",
       "        PodcastDotNET  PodcastGiantRobots  PodcastJSAir  PodcastJSJabber  \\\n",
       "count           327.0               187.0         726.0           1123.0   \n",
       "unique            NaN                 NaN           NaN              NaN   \n",
       "top               NaN                 NaN           NaN              NaN   \n",
       "freq              NaN                 NaN           NaN              NaN   \n",
       "mean              1.0                 1.0           1.0              1.0   \n",
       "std               0.0                 0.0           0.0              0.0   \n",
       "min               1.0                 1.0           1.0              1.0   \n",
       "25%               1.0                 1.0           1.0              1.0   \n",
       "50%               1.0                 1.0           1.0              1.0   \n",
       "75%               1.0                 1.0           1.0              1.0   \n",
       "max               1.0                 1.0           1.0              1.0   \n",
       "\n",
       "        PodcastNone PodcastOther  PodcastProgThrowdown  PodcastRubyRogues  \\\n",
       "count        1061.0          850                 345.0              333.0   \n",
       "unique          NaN          575                   NaN                NaN   \n",
       "top             NaN           no                   NaN                NaN   \n",
       "freq            NaN           26                   NaN                NaN   \n",
       "mean            1.0          NaN                   1.0                1.0   \n",
       "std             0.0          NaN                   0.0                0.0   \n",
       "min             1.0          NaN                   1.0                1.0   \n",
       "25%             1.0          NaN                   1.0                1.0   \n",
       "50%             1.0          NaN                   1.0                1.0   \n",
       "75%             1.0          NaN                   1.0                1.0   \n",
       "max             1.0          NaN                   1.0                1.0   \n",
       "\n",
       "        PodcastSEDaily  PodcastSERadio  PodcastShopTalk  PodcastTalkPython  \\\n",
       "count            790.0           408.0            318.0              655.0   \n",
       "unique             NaN             NaN              NaN                NaN   \n",
       "top                NaN             NaN              NaN                NaN   \n",
       "freq               NaN             NaN              NaN                NaN   \n",
       "mean               1.0             1.0              1.0                1.0   \n",
       "std                0.0             0.0              0.0                0.0   \n",
       "min                1.0             1.0              1.0                1.0   \n",
       "25%                1.0             1.0              1.0                1.0   \n",
       "50%                1.0             1.0              1.0                1.0   \n",
       "75%                1.0             1.0              1.0                1.0   \n",
       "max                1.0             1.0              1.0                1.0   \n",
       "\n",
       "        PodcastTheWebAhead  ResourceCodecademy  ResourceCodeWars  \\\n",
       "count                311.0              9390.0            1848.0   \n",
       "unique                 NaN                 NaN               NaN   \n",
       "top                    NaN                 NaN               NaN   \n",
       "freq                   NaN                 NaN               NaN   \n",
       "mean                   1.0                 1.0               1.0   \n",
       "std                    0.0                 0.0               0.0   \n",
       "min                    1.0                 1.0               1.0   \n",
       "25%                    1.0                 1.0               1.0   \n",
       "50%                    1.0                 1.0               1.0   \n",
       "75%                    1.0                 1.0               1.0   \n",
       "max                    1.0                 1.0               1.0   \n",
       "\n",
       "        ResourceCoursera  ResourceCSS  ResourceEdX  ResourceEgghead  \\\n",
       "count             4402.0       4671.0       3243.0           1341.0   \n",
       "unique               NaN          NaN          NaN              NaN   \n",
       "top                  NaN          NaN          NaN              NaN   \n",
       "freq                 NaN          NaN          NaN              NaN   \n",
       "mean                 1.0          1.0          1.0              1.0   \n",
       "std                  0.0          0.0          0.0              0.0   \n",
       "min                  1.0          1.0          1.0              1.0   \n",
       "25%                  1.0          1.0          1.0              1.0   \n",
       "50%                  1.0          1.0          1.0              1.0   \n",
       "75%                  1.0          1.0          1.0              1.0   \n",
       "max                  1.0          1.0          1.0              1.0   \n",
       "\n",
       "        ResourceFCC  ResourceHackerRank  ResourceKA  ResourceLynda  \\\n",
       "count       13803.0              2049.0      3800.0         2558.0   \n",
       "unique          NaN                 NaN         NaN            NaN   \n",
       "top             NaN                 NaN         NaN            NaN   \n",
       "freq            NaN                 NaN         NaN            NaN   \n",
       "mean            1.0                 1.0         1.0            1.0   \n",
       "std             0.0                 0.0         0.0            0.0   \n",
       "min             1.0                 1.0         1.0            1.0   \n",
       "25%             1.0                 1.0         1.0            1.0   \n",
       "50%             1.0                 1.0         1.0            1.0   \n",
       "75%             1.0                 1.0         1.0            1.0   \n",
       "max             1.0                 1.0         1.0            1.0   \n",
       "\n",
       "        ResourceMDN  ResourceOdinProj ResourceOther  ResourcePluralSight  \\\n",
       "count        6417.0             976.0          1024               2392.0   \n",
       "unique          NaN               NaN           643                  NaN   \n",
       "top             NaN               NaN       YouTube                  NaN   \n",
       "freq            NaN               NaN            28                  NaN   \n",
       "mean            1.0               1.0           NaN                  1.0   \n",
       "std             0.0               0.0           NaN                  0.0   \n",
       "min             1.0               1.0           NaN                  1.0   \n",
       "25%             1.0               1.0           NaN                  1.0   \n",
       "50%             1.0               1.0           NaN                  1.0   \n",
       "75%             1.0               1.0           NaN                  1.0   \n",
       "max             1.0               1.0           NaN                  1.0   \n",
       "\n",
       "        ResourceSkillcrush  ResourceSO  ResourceTreehouse  ResourceUdacity  \\\n",
       "count                452.0     11207.0             2260.0           3837.0   \n",
       "unique                 NaN         NaN                NaN              NaN   \n",
       "top                    NaN         NaN                NaN              NaN   \n",
       "freq                   NaN         NaN                NaN              NaN   \n",
       "mean                   1.0         1.0                1.0              1.0   \n",
       "std                    0.0         0.0                0.0              0.0   \n",
       "min                    1.0         1.0                1.0              1.0   \n",
       "25%                    1.0         1.0                1.0              1.0   \n",
       "50%                    1.0         1.0                1.0              1.0   \n",
       "75%                    1.0         1.0                1.0              1.0   \n",
       "max                    1.0         1.0                1.0              1.0   \n",
       "\n",
       "        ResourceUdemy  ResourceW3S       SchoolDegree       SchoolMajor  \\\n",
       "count          5120.0       9765.0              15368              8727   \n",
       "unique            NaN          NaN                 10               472   \n",
       "top               NaN          NaN  bachelor's degree  Computer Science   \n",
       "freq              NaN          NaN               5851              1345   \n",
       "mean              1.0          1.0                NaN               NaN   \n",
       "std               0.0          0.0                NaN               NaN   \n",
       "min               1.0          1.0                NaN               NaN   \n",
       "25%               1.0          1.0                NaN               NaN   \n",
       "50%               1.0          1.0                NaN               NaN   \n",
       "75%               1.0          1.0                NaN               NaN   \n",
       "max               1.0          1.0                NaN               NaN   \n",
       "\n",
       "        StudentDebtOwe  YouTubeCodeCourse  YouTubeCodingTrain  \\\n",
       "count      3362.000000              956.0               976.0   \n",
       "unique             NaN                NaN                 NaN   \n",
       "top                NaN                NaN                 NaN   \n",
       "freq               NaN                NaN                 NaN   \n",
       "mean      31602.625521                1.0                 1.0   \n",
       "std       39383.187732                0.0                 0.0   \n",
       "min         100.000000                1.0                 1.0   \n",
       "25%        9536.500000                1.0                 1.0   \n",
       "50%       20000.000000                1.0                 1.0   \n",
       "75%       40000.000000                1.0                 1.0   \n",
       "max      500000.000000                1.0                 1.0   \n",
       "\n",
       "        YouTubeCodingTut360  YouTubeComputerphile  YouTubeDerekBanas  \\\n",
       "count                1397.0                1453.0             1847.0   \n",
       "unique                  NaN                   NaN                NaN   \n",
       "top                     NaN                   NaN                NaN   \n",
       "freq                    NaN                   NaN                NaN   \n",
       "mean                    1.0                   1.0                1.0   \n",
       "std                     0.0                   0.0                0.0   \n",
       "min                     1.0                   1.0                1.0   \n",
       "25%                     1.0                   1.0                1.0   \n",
       "50%                     1.0                   1.0                1.0   \n",
       "75%                     1.0                   1.0                1.0   \n",
       "max                     1.0                   1.0                1.0   \n",
       "\n",
       "        YouTubeDevTips  YouTubeEngineeredTruth  YouTubeFCC  \\\n",
       "count           2619.0                   993.0      6036.0   \n",
       "unique             NaN                     NaN         NaN   \n",
       "top                NaN                     NaN         NaN   \n",
       "freq               NaN                     NaN         NaN   \n",
       "mean               1.0                     1.0         1.0   \n",
       "std                0.0                     0.0         0.0   \n",
       "min                1.0                     1.0         1.0   \n",
       "25%                1.0                     1.0         1.0   \n",
       "50%                1.0                     1.0         1.0   \n",
       "75%                1.0                     1.0         1.0   \n",
       "max                1.0                     1.0         1.0   \n",
       "\n",
       "        YouTubeFunFunFunction  YouTubeGoogleDev  YouTubeLearnCode  \\\n",
       "count                  1261.0            3539.0            2662.0   \n",
       "unique                    NaN               NaN               NaN   \n",
       "top                       NaN               NaN               NaN   \n",
       "freq                      NaN               NaN               NaN   \n",
       "mean                      1.0               1.0               1.0   \n",
       "std                       0.0               0.0               0.0   \n",
       "min                       1.0               1.0               1.0   \n",
       "25%                       1.0               1.0               1.0   \n",
       "50%                       1.0               1.0               1.0   \n",
       "75%                       1.0               1.0               1.0   \n",
       "max                       1.0               1.0               1.0   \n",
       "\n",
       "        YouTubeLevelUpTuts  YouTubeMIT  YouTubeMozillaHacks YouTubeOther  \\\n",
       "count               1396.0      3327.0                622.0         1214   \n",
       "unique                 NaN         NaN                  NaN          736   \n",
       "top                    NaN         NaN                  NaN         none   \n",
       "freq                   NaN         NaN                  NaN           78   \n",
       "mean                   1.0         1.0                  1.0          NaN   \n",
       "std                    0.0         0.0                  0.0          NaN   \n",
       "min                    1.0         1.0                  1.0          NaN   \n",
       "25%                    1.0         1.0                  1.0          NaN   \n",
       "50%                    1.0         1.0                  1.0          NaN   \n",
       "75%                    1.0         1.0                  1.0          NaN   \n",
       "max                    1.0         1.0                  1.0          NaN   \n",
       "\n",
       "        YouTubeSimplilearn  YouTubeTheNewBoston  \n",
       "count                201.0               2960.0  \n",
       "unique                 NaN                  NaN  \n",
       "top                    NaN                  NaN  \n",
       "freq                   NaN                  NaN  \n",
       "mean                   1.0                  1.0  \n",
       "std                    0.0                  0.0  \n",
       "min                    1.0                  1.0  \n",
       "25%                    1.0                  1.0  \n",
       "50%                    1.0                  1.0  \n",
       "75%                    1.0                  1.0  \n",
       "max                    1.0                  1.0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18175, 136)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "freeCodeCamp's survey dataset consists of 18,175 participant surveys for 136 variables that describe the participants, their bootcamp experience, and whether they watched various youtube videos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Determine if sample data is representative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN                                                                                                                                                                                                                                                                         61.529574\n",
       "Full-Stack Web Developer                                                                                                                                                                                                                                                     4.528198\n",
       "  Front-End Web Developer                                                                                                                                                                                                                                                    2.475928\n",
       "  Data Scientist                                                                                                                                                                                                                                                             0.836314\n",
       "Back-End Web Developer                                                                                                                                                                                                                                                       0.781293\n",
       "  Mobile Developer                                                                                                                                                                                                                                                           0.643741\n",
       "Game Developer                                                                                                                                                                                                                                                               0.627235\n",
       "Information Security                                                                                                                                                                                                                                                         0.506190\n",
       "Full-Stack Web Developer,   Front-End Web Developer                                                                                                                                                                                                                          0.352132\n",
       "  Front-End Web Developer, Full-Stack Web Developer                                                                                                                                                                                                                          0.308116\n",
       "  Product Manager                                                                                                                                                                                                                                                            0.302613\n",
       "Data Engineer                                                                                                                                                                                                                                                                0.291609\n",
       "  User Experience Designer                                                                                                                                                                                                                                                   0.286107\n",
       "  User Experience Designer,   Front-End Web Developer                                                                                                                                                                                                                        0.236589\n",
       "  Front-End Web Developer, Back-End Web Developer, Full-Stack Web Developer                                                                                                                                                                                                  0.214580\n",
       "Back-End Web Developer, Full-Stack Web Developer,   Front-End Web Developer                                                                                                                                                                                                  0.198074\n",
       "Back-End Web Developer,   Front-End Web Developer, Full-Stack Web Developer                                                                                                                                                                                                  0.198074\n",
       "  DevOps / SysAdmin                                                                                                                                                                                                                                                          0.198074\n",
       "Full-Stack Web Developer,   Front-End Web Developer, Back-End Web Developer                                                                                                                                                                                                  0.170564\n",
       "  Front-End Web Developer, Full-Stack Web Developer, Back-End Web Developer                                                                                                                                                                                                  0.165062\n",
       "  Front-End Web Developer,   User Experience Designer                                                                                                                                                                                                                        0.159560\n",
       "Full-Stack Web Developer,   Mobile Developer                                                                                                                                                                                                                                 0.159560\n",
       "Back-End Web Developer, Full-Stack Web Developer                                                                                                                                                                                                                             0.148556\n",
       "Full-Stack Web Developer, Back-End Web Developer                                                                                                                                                                                                                             0.143054\n",
       "Back-End Web Developer,   Front-End Web Developer                                                                                                                                                                                                                            0.110041\n",
       "Data Engineer,   Data Scientist                                                                                                                                                                                                                                              0.104539\n",
       "Full-Stack Web Developer, Back-End Web Developer,   Front-End Web Developer                                                                                                                                                                                                  0.104539\n",
       "  Front-End Web Developer,   Mobile Developer                                                                                                                                                                                                                                0.099037\n",
       "Full-Stack Web Developer,   Data Scientist                                                                                                                                                                                                                                   0.093535\n",
       "  Data Scientist, Data Engineer                                                                                                                                                                                                                                              0.088033\n",
       "                                                                                                                                                                                                                                                                              ...    \n",
       "  Front-End Web Developer, Back-End Web Developer,   Quality Assurance Engineer, Full-Stack Web Developer,   Product Manager                                                                                                                                                 0.005502\n",
       "  Mobile Developer,   Data Scientist,   Front-End Web Developer, Data Engineer, Full-Stack Web Developer                                                                                                                                                                     0.005502\n",
       "Game Developer,   Mobile Developer,   Front-End Web Developer, Back-End Web Developer, Full-Stack Web Developer                                                                                                                                                              0.005502\n",
       "Game Developer,   User Experience Designer,   Front-End Web Developer,   Mobile Developer, Back-End Web Developer, Full-Stack Web Developer, Data Engineer,   Data Scientist                                                                                                 0.005502\n",
       "  Front-End Web Developer, Data Engineer,   User Experience Designer, Game Developer,   Data Scientist                                                                                                                                                                       0.005502\n",
       "Game Developer,   User Experience Designer, Back-End Web Developer,   Front-End Web Developer, Full-Stack Web Developer,   Mobile Developer                                                                                                                                  0.005502\n",
       "  DevOps / SysAdmin, Information Security, Game Developer, Data Engineer,   Front-End Web Developer,   User Experience Designer,   Mobile Developer                                                                                                                          0.005502\n",
       "  Mobile Developer,   Data Scientist,   Front-End Web Developer, Full-Stack Web Developer, Back-End Web Developer                                                                                                                                                            0.005502\n",
       "AI                                                                                                                                                                                                                                                                           0.005502\n",
       "  User Experience Designer,   Front-End Web Developer, Full-Stack Web Developer,   Quality Assurance Engineer,   DevOps / SysAdmin, Data Engineer,   Mobile Developer, Information Security, Back-End Web Developer                                                          0.005502\n",
       "Full-Stack Web Developer, Back-End Web Developer,   DevOps / SysAdmin, Ethical Hacker                                                                                                                                                                                        0.005502\n",
       "Game Developer, Full-Stack Web Developer,   User Experience Designer                                                                                                                                                                                                         0.005502\n",
       "Information Security, Data Engineer, Full-Stack Web Developer,   Product Manager,   Mobile Developer,   Front-End Web Developer, Game Developer                                                                                                                              0.005502\n",
       "Full-Stack Web Developer,   Data Scientist,   User Experience Designer,   Mobile Developer,   Front-End Web Developer                                                                                                                                                        0.005502\n",
       "Back-End Web Developer,   Mobile Developer, Information Security,   Data Scientist, Data Engineer                                                                                                                                                                            0.005502\n",
       "  Front-End Web Developer, Full-Stack Web Developer,   User Experience Designer,   Mobile Developer, Information Security                                                                                                                                                    0.005502\n",
       "Game Developer, Data Engineer, Information Security, Full-Stack Web Developer, Back-End Web Developer,   Front-End Web Developer,   Data Scientist                                                                                                                           0.005502\n",
       "  Front-End Web Developer,   Data Scientist, Information Security, Full-Stack Web Developer, Back-End Web Developer,   Mobile Developer, Data Engineer                                                                                                                       0.005502\n",
       "  DevOps / SysAdmin, Back-End Web Developer,   User Experience Designer                                                                                                                                                                                                      0.005502\n",
       "Game Developer,   Product Manager, Back-End Web Developer,   Front-End Web Developer, Full-Stack Web Developer,   Mobile Developer,   Data Scientist                                                                                                                         0.005502\n",
       "Full-Stack Web Developer,   Mobile Developer,   Data Scientist, Game Developer, Back-End Web Developer                                                                                                                                                                       0.005502\n",
       "Data Engineer, Information Security,   Front-End Web Developer,   User Experience Designer, Full-Stack Web Developer,   Data Scientist                                                                                                                                       0.005502\n",
       "  Front-End Web Developer,   Quality Assurance Engineer, Full-Stack Web Developer                                                                                                                                                                                            0.005502\n",
       "Full-Stack Web Developer, Information Security,   Data Scientist, Data Engineer, Game Developer,   Product Manager,   Quality Assurance Engineer,   Mobile Developer,   DevOps / SysAdmin, Back-End Web Developer,   User Experience Designer,   Front-End Web Developer     0.005502\n",
       "  Data Scientist,   User Experience Designer, Data Engineer, Full-Stack Web Developer,   Front-End Web Developer                                                                                                                                                             0.005502\n",
       "  Front-End Web Developer, Information Security, Game Developer,   Data Scientist                                                                                                                                                                                            0.005502\n",
       "Back-End Web Developer, Information Security, Data Engineer                                                                                                                                                                                                                  0.005502\n",
       "  Data Scientist, Data Engineer, Full-Stack Web Developer,   Front-End Web Developer, Back-End Web Developer                                                                                                                                                                 0.005502\n",
       "  Front-End Web Developer,   Quality Assurance Engineer,   User Experience Designer,   DevOps / SysAdmin, Back-End Web Developer, Data Engineer,   Product Manager, Full-Stack Web Developer,   Data Scientist                                                               0.005502\n",
       "  Mobile Developer, Full-Stack Web Developer,   Data Scientist, Back-End Web Developer, Data Engineer, Information Security                                                                                                                                                  0.005502\n",
       "Name: JobRoleInterest, Length: 3214, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a look at the jobs the survey participants said they would be interested\n",
    "# in, note that we are primarily interested in mobile and web development \n",
    "\n",
    "job_pcts = survey[\"JobRoleInterest\"].value_counts(normalize=True,dropna=False)*100\n",
    "job_pcts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3214\n"
     ]
    }
   ],
   "source": [
    "# total number of different job options\n",
    "print(len(job_pcts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow, it looks like there are 3,213 different possibilities for the job categories that a person could be interested in! However, the job categories seem to be a subset of some list of jobs (of which the survey participant could select). In addition, it looks like this survey question is left blank about 61.5% of the time.\n",
    "\n",
    "To get a better idea of the proportion of people who are interested in web or mobile development, we can subset to the job categories that contain the words \"Web\" or \"Mobile\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Full-Stack Web Developer                                                                                                                                                                                                                                                    11.770595\n",
       "  Front-End Web Developer                                                                                                                                                                                                                                                    6.435927\n",
       "Back-End Web Developer                                                                                                                                                                                                                                                       2.030892\n",
       "  Mobile Developer                                                                                                                                                                                                                                                           1.673341\n",
       "Full-Stack Web Developer,   Front-End Web Developer                                                                                                                                                                                                                          0.915332\n",
       "  Front-End Web Developer, Full-Stack Web Developer                                                                                                                                                                                                                          0.800915\n",
       "  User Experience Designer,   Front-End Web Developer                                                                                                                                                                                                                        0.614989\n",
       "  Front-End Web Developer, Back-End Web Developer, Full-Stack Web Developer                                                                                                                                                                                                  0.557780\n",
       "Back-End Web Developer,   Front-End Web Developer, Full-Stack Web Developer                                                                                                                                                                                                  0.514874\n",
       "Back-End Web Developer, Full-Stack Web Developer,   Front-End Web Developer                                                                                                                                                                                                  0.514874\n",
       "Full-Stack Web Developer,   Front-End Web Developer, Back-End Web Developer                                                                                                                                                                                                  0.443364\n",
       "  Front-End Web Developer, Full-Stack Web Developer, Back-End Web Developer                                                                                                                                                                                                  0.429062\n",
       "  Front-End Web Developer,   User Experience Designer                                                                                                                                                                                                                        0.414760\n",
       "Full-Stack Web Developer,   Mobile Developer                                                                                                                                                                                                                                 0.414760\n",
       "Back-End Web Developer, Full-Stack Web Developer                                                                                                                                                                                                                             0.386156\n",
       "Full-Stack Web Developer, Back-End Web Developer                                                                                                                                                                                                                             0.371854\n",
       "Back-End Web Developer,   Front-End Web Developer                                                                                                                                                                                                                            0.286041\n",
       "Full-Stack Web Developer, Back-End Web Developer,   Front-End Web Developer                                                                                                                                                                                                  0.271739\n",
       "  Front-End Web Developer,   Mobile Developer                                                                                                                                                                                                                                0.257437\n",
       "Full-Stack Web Developer,   Data Scientist                                                                                                                                                                                                                                   0.243135\n",
       "  Mobile Developer, Game Developer                                                                                                                                                                                                                                           0.228833\n",
       "Game Developer,   Front-End Web Developer                                                                                                                                                                                                                                    0.214531\n",
       "Full-Stack Web Developer, Game Developer                                                                                                                                                                                                                                     0.214531\n",
       "  Mobile Developer,   Front-End Web Developer                                                                                                                                                                                                                                0.214531\n",
       "  User Experience Designer, Full-Stack Web Developer,   Front-End Web Developer                                                                                                                                                                                              0.214531\n",
       "  Data Scientist, Full-Stack Web Developer                                                                                                                                                                                                                                   0.214531\n",
       "  User Experience Designer,   Front-End Web Developer, Full-Stack Web Developer                                                                                                                                                                                              0.214531\n",
       "  Mobile Developer, Full-Stack Web Developer                                                                                                                                                                                                                                 0.200229\n",
       "  Front-End Web Developer, Full-Stack Web Developer,   Mobile Developer                                                                                                                                                                                                      0.171625\n",
       "Game Developer,   Mobile Developer                                                                                                                                                                                                                                           0.171625\n",
       "                                                                                                                                                                                                                                                                              ...    \n",
       "  DevOps / SysAdmin,   Product Manager, Information Security, Back-End Web Developer, Game Developer, Full-Stack Web Developer                                                                                                                                               0.014302\n",
       "  Front-End Web Developer, Back-End Web Developer,   Quality Assurance Engineer, Full-Stack Web Developer,   Product Manager                                                                                                                                                 0.014302\n",
       "  Mobile Developer,   Data Scientist,   Front-End Web Developer, Data Engineer, Full-Stack Web Developer                                                                                                                                                                     0.014302\n",
       "Game Developer,   Mobile Developer,   Front-End Web Developer, Back-End Web Developer, Full-Stack Web Developer                                                                                                                                                              0.014302\n",
       "Game Developer,   User Experience Designer,   Front-End Web Developer,   Mobile Developer, Back-End Web Developer, Full-Stack Web Developer, Data Engineer,   Data Scientist                                                                                                 0.014302\n",
       "  Front-End Web Developer, Data Engineer,   User Experience Designer, Game Developer,   Data Scientist                                                                                                                                                                       0.014302\n",
       "Game Developer,   User Experience Designer, Back-End Web Developer,   Front-End Web Developer, Full-Stack Web Developer,   Mobile Developer                                                                                                                                  0.014302\n",
       "  DevOps / SysAdmin, Information Security, Game Developer, Data Engineer,   Front-End Web Developer,   User Experience Designer,   Mobile Developer                                                                                                                          0.014302\n",
       "  Mobile Developer,   Data Scientist,   Front-End Web Developer, Full-Stack Web Developer, Back-End Web Developer                                                                                                                                                            0.014302\n",
       "  User Experience Designer,   Front-End Web Developer, Full-Stack Web Developer,   Quality Assurance Engineer,   DevOps / SysAdmin, Data Engineer,   Mobile Developer, Information Security, Back-End Web Developer                                                          0.014302\n",
       "Full-Stack Web Developer, Back-End Web Developer,   DevOps / SysAdmin, Ethical Hacker                                                                                                                                                                                        0.014302\n",
       "Game Developer, Full-Stack Web Developer,   User Experience Designer                                                                                                                                                                                                         0.014302\n",
       "Information Security, Data Engineer, Full-Stack Web Developer,   Product Manager,   Mobile Developer,   Front-End Web Developer, Game Developer                                                                                                                              0.014302\n",
       "Full-Stack Web Developer,   Data Scientist,   User Experience Designer,   Mobile Developer,   Front-End Web Developer                                                                                                                                                        0.014302\n",
       "Back-End Web Developer,   Mobile Developer, Information Security,   Data Scientist, Data Engineer                                                                                                                                                                            0.014302\n",
       "  Front-End Web Developer, Full-Stack Web Developer,   User Experience Designer,   Mobile Developer, Information Security                                                                                                                                                    0.014302\n",
       "Game Developer, Data Engineer, Information Security, Full-Stack Web Developer, Back-End Web Developer,   Front-End Web Developer,   Data Scientist                                                                                                                           0.014302\n",
       "  Front-End Web Developer,   Data Scientist, Information Security, Full-Stack Web Developer, Back-End Web Developer,   Mobile Developer, Data Engineer                                                                                                                       0.014302\n",
       "  DevOps / SysAdmin, Back-End Web Developer,   User Experience Designer                                                                                                                                                                                                      0.014302\n",
       "Game Developer,   Product Manager, Back-End Web Developer,   Front-End Web Developer, Full-Stack Web Developer,   Mobile Developer,   Data Scientist                                                                                                                         0.014302\n",
       "Full-Stack Web Developer,   Mobile Developer,   Data Scientist, Game Developer, Back-End Web Developer                                                                                                                                                                       0.014302\n",
       "Data Engineer, Information Security,   Front-End Web Developer,   User Experience Designer, Full-Stack Web Developer,   Data Scientist                                                                                                                                       0.014302\n",
       "  Front-End Web Developer,   Quality Assurance Engineer, Full-Stack Web Developer                                                                                                                                                                                            0.014302\n",
       "Full-Stack Web Developer, Information Security,   Data Scientist, Data Engineer, Game Developer,   Product Manager,   Quality Assurance Engineer,   Mobile Developer,   DevOps / SysAdmin, Back-End Web Developer,   User Experience Designer,   Front-End Web Developer     0.014302\n",
       "  Data Scientist,   User Experience Designer, Data Engineer, Full-Stack Web Developer,   Front-End Web Developer                                                                                                                                                             0.014302\n",
       "  Front-End Web Developer, Information Security, Game Developer,   Data Scientist                                                                                                                                                                                            0.014302\n",
       "Back-End Web Developer, Information Security, Data Engineer                                                                                                                                                                                                                  0.014302\n",
       "  Data Scientist, Data Engineer, Full-Stack Web Developer,   Front-End Web Developer, Back-End Web Developer                                                                                                                                                                 0.014302\n",
       "  Front-End Web Developer,   Quality Assurance Engineer,   User Experience Designer,   DevOps / SysAdmin, Back-End Web Developer, Data Engineer,   Product Manager, Full-Stack Web Developer,   Data Scientist                                                               0.014302\n",
       "  Mobile Developer, Back-End Web Developer,   Front-End Web Developer,   User Experience Designer, Full-Stack Web Developer, Information Security                                                                                                                            0.014302\n",
       "Name: JobRoleInterest, Length: 2923, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# focus just on web and mobile development\n",
    "import numpy as np\n",
    "job_pcts_noNA = survey[\"JobRoleInterest\"].value_counts(normalize=True)*100\n",
    "web_mobile_idxs = [(\"WEB\" in job.upper()) | (\"MOBILE\" in job.upper()) for job in job_pcts_noNA.index]\n",
    "job_pcts_noNA[web_mobile_idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.31292906178975"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(job_pcts_noNA[web_mobile_idxs])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like when this survey question is filled out, 86.3% of the time the participant is interested in either web or mobile development. Hence if we drop the surveys where the participants did not fill in this information, then the data is fairly representative of the population that we'd be hoping to advertise our e-learning service to (which includes courses mostly in web and mobile development).\n",
    "\n",
    "Below we make a bar plot which sumarizes these results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd23064470>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAAE6CAYAAAASvogaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn+HGWeiKoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd26ca6e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% matplotlib inline\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "pcts = [0,0,0]\n",
    "pcts[0] = job_pcts[0]\n",
    "pcts[1] = sum(job_pcts_noNA[web_mobile_idxs])*(1-job_pcts[0]/100)\n",
    "pcts[2] = 100 - pcts[0] - pcts[1]\n",
    "percentages = pd.Series(pcts,index=[\"Missing\",\"Web/Mobile Related\",\"Not Web/Mobile Related\"])\n",
    "percentages.plot.barh()\n",
    "plt.xlabel(\"Percentage\")\n",
    "plt.title(\"Job Interests of Survey Participants\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd2203ec88>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAFNCAYAAAAQDCuCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEREREbnBsERERETkBsMSERERkRv/D0eziQxnSkboAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd27096400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "percentages[1:].plot.pie(figsize=(5,5),autopct='%.1f')\n",
    "plt.ylabel('')\n",
    "plt.title(\"Job Interests of Survey Participants (when not missing)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Find where people are located so we can determine the two countries with the largest markets to advertise in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18175, 136)\n",
      "(6992, 136)\n"
     ]
    }
   ],
   "source": [
    "# remove the surveys in which the job interest question was not answered\n",
    "print(survey.shape)\n",
    "survey_job = survey[survey[\"JobRoleInterest\"].isna()==False]\n",
    "print(survey_job.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States of America         3125\n",
       "India                             528\n",
       "United Kingdom                    315\n",
       "Canada                            260\n",
       "Poland                            131\n",
       "Brazil                            129\n",
       "Germany                           125\n",
       "Australia                         112\n",
       "Russia                            102\n",
       "Ukraine                            89\n",
       "Nigeria                            84\n",
       "Spain                              77\n",
       "France                             75\n",
       "Romania                            71\n",
       "Netherlands (Holland, Europe)      65\n",
       "Italy                              62\n",
       "Serbia                             52\n",
       "Philippines                        52\n",
       "Greece                             46\n",
       "Ireland                            43\n",
       "South Africa                       39\n",
       "Mexico                             37\n",
       "Turkey                             36\n",
       "Hungary                            34\n",
       "Singapore                          34\n",
       "New Zealand                        33\n",
       "Croatia                            32\n",
       "Argentina                          32\n",
       "Indonesia                          31\n",
       "Sweden                             31\n",
       "                                 ... \n",
       "Cameroon                            1\n",
       "Gambia                              1\n",
       "Vanuatu                             1\n",
       "Nicaragua                           1\n",
       "Jordan                              1\n",
       "Rwanda                              1\n",
       "Guatemala                           1\n",
       "Qatar                               1\n",
       "Kyrgyzstan                          1\n",
       "Papua New Guinea                    1\n",
       "Samoa                               1\n",
       "Cayman Islands                      1\n",
       "Panama                              1\n",
       "Aruba                               1\n",
       "Yemen                               1\n",
       "Cuba                                1\n",
       "Myanmar                             1\n",
       "Botswana                            1\n",
       "Somalia                             1\n",
       "Mozambique                          1\n",
       "Channel Islands                     1\n",
       "Guadeloupe                          1\n",
       "Anguilla                            1\n",
       "Bolivia                             1\n",
       "Liberia                             1\n",
       "Trinidad & Tobago                   1\n",
       "Sudan                               1\n",
       "Nambia                              1\n",
       "Gibraltar                           1\n",
       "Angola                              1\n",
       "Name: CountryLive, Length: 137, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate frequency tables for the CountryLive variable\n",
    "# absolute frequencies\n",
    "survey_job[\"CountryLive\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States of America         45.700497\n",
       "India                             7.721556\n",
       "United Kingdom                    4.606610\n",
       "Canada                            3.802281\n",
       "Poland                            1.915765\n",
       "Brazil                            1.886517\n",
       "Germany                           1.828020\n",
       "Australia                         1.637906\n",
       "Russia                            1.491664\n",
       "Ukraine                           1.301550\n",
       "Nigeria                           1.228429\n",
       "Spain                             1.126060\n",
       "France                            1.096812\n",
       "Romania                           1.038315\n",
       "Netherlands (Holland, Europe)     0.950570\n",
       "Italy                             0.906698\n",
       "Serbia                            0.760456\n",
       "Philippines                       0.760456\n",
       "Greece                            0.672711\n",
       "Ireland                           0.628839\n",
       "South Africa                      0.570342\n",
       "Mexico                            0.541094\n",
       "Turkey                            0.526470\n",
       "Hungary                           0.497221\n",
       "Singapore                         0.497221\n",
       "New Zealand                       0.482597\n",
       "Croatia                           0.467973\n",
       "Argentina                         0.467973\n",
       "Indonesia                         0.453349\n",
       "Sweden                            0.453349\n",
       "                                   ...    \n",
       "Cameroon                          0.014624\n",
       "Gambia                            0.014624\n",
       "Vanuatu                           0.014624\n",
       "Nicaragua                         0.014624\n",
       "Jordan                            0.014624\n",
       "Rwanda                            0.014624\n",
       "Guatemala                         0.014624\n",
       "Qatar                             0.014624\n",
       "Kyrgyzstan                        0.014624\n",
       "Papua New Guinea                  0.014624\n",
       "Samoa                             0.014624\n",
       "Cayman Islands                    0.014624\n",
       "Panama                            0.014624\n",
       "Aruba                             0.014624\n",
       "Yemen                             0.014624\n",
       "Cuba                              0.014624\n",
       "Myanmar                           0.014624\n",
       "Botswana                          0.014624\n",
       "Somalia                           0.014624\n",
       "Mozambique                        0.014624\n",
       "Channel Islands                   0.014624\n",
       "Guadeloupe                        0.014624\n",
       "Anguilla                          0.014624\n",
       "Bolivia                           0.014624\n",
       "Liberia                           0.014624\n",
       "Trinidad & Tobago                 0.014624\n",
       "Sudan                             0.014624\n",
       "Nambia                            0.014624\n",
       "Gibraltar                         0.014624\n",
       "Angola                            0.014624\n",
       "Name: CountryLive, Length: 137, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# relative frequencies\n",
    "survey_job[\"CountryLive\"].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on these results, the two largest markets are the United States and India. Together these would cover 53.4% of the sample participants. However, we note that some other countries like the United Kingdom and Canada, also have a sizable fraction of people interested in learning Web/Mobile development. We need to take this one step further and determine if the people in these countries are actually willing to spend money on their learning as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Determine willingness to pay for learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the `MoneyForLearning` column which describes the amount of money spent by participants since they started coding (in American dollars) and the `MonthsProgramming` column to determine payments made by users each month.\n",
    "\n",
    "We will also restrict our further analyses to the United States, India, the United Kingdom, and Canada since they have the largest markets and English is an official language in all of these countries (and our e-learning courses are written in English)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States of America    3125\n",
       "India                        528\n",
       "United Kingdom               315\n",
       "Canada                       260\n",
       "Name: CountryLive, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we will use the MoneyForLearning column which describes the amount of money\n",
    "# spent by participants since they started coding (in American dollars)\n",
    "\n",
    "country_idxs = survey_job[\"CountryLive\"].isin([\"United States of America\", \"India\", \"United Kingdom\", \"Canada\"])\n",
    "\n",
    "survey_sub = survey_job[country_idxs]\n",
    "\n",
    "# check results\n",
    "survey_sub[\"CountryLive\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0    150\n",
      "1.0    480\n",
      "2.0    401\n",
      "3.0    380\n",
      "4.0    209\n",
      "Name: MonthsProgramming, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:6: 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 caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/dataquest/system/env/python3/lib/python3.4/site-packages/ipykernel/__main__.py:8: 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 caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# calculate monthly payment amounts\n",
    "\n",
    "print(survey_sub[\"MonthsProgramming\"].value_counts().sort_index().head())\n",
    "# replace zero months with one month\n",
    "\n",
    "survey_sub[\"CleanMonths\"] = survey_sub[\"MonthsProgramming\"].replace(0.0,1.0)\n",
    "survey_sub[\"MoneyPerMonth\"] = (survey_sub[\"MoneyForLearning\"] / \n",
    "                               survey_sub[\"CleanMonths\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313\n",
      "(4228, 138)\n",
      "(3915, 138)\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "print(sum(survey_sub[\"MoneyPerMonth\"].isna()))\n",
    "\n",
    "# drop missing values\n",
    "print(survey_sub.shape)\n",
    "survey_sub = survey_sub[survey_sub[\"MoneyPerMonth\"].isna() == False]\n",
    "print(survey_sub.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MoneyPerMonth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountryLive</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>113.510961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>135.100982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>45.534443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>227.997996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          MoneyPerMonth\n",
       "CountryLive                            \n",
       "Canada                       113.510961\n",
       "India                        135.100982\n",
       "United Kingdom                45.534443\n",
       "United States of America     227.997996"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get average monthly payments in the four countries of interest\n",
    "survey_sub[[\"MoneyPerMonth\",\"CountryLive\"]].groupby([\"CountryLive\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MoneyPerMonth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountryLive</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          MoneyPerMonth\n",
       "CountryLive                            \n",
       "Canada                         0.000000\n",
       "India                          0.000000\n",
       "United Kingdom                 0.000000\n",
       "United States of America       3.333333"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a look at the median as well\n",
    "survey_sub[[\"MoneyPerMonth\",\"CountryLive\"]].groupby([\"CountryLive\"]).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MoneyPerMonth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountryLive</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          MoneyPerMonth\n",
       "CountryLive                            \n",
       "Canada                              0.0\n",
       "India                               0.0\n",
       "United Kingdom                      0.0\n",
       "United States of America            0.0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and the mode\n",
    "from statistics import mode\n",
    "survey_sub[[\"MoneyPerMonth\",\"CountryLive\"]].groupby([\"CountryLive\"]).agg(lambda x:\n",
    "                                                                        x.mode()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the average monthly payment is driven mostly by outliers given that the means and modes for each country are zero. The presence of outliers could also explain why the average monthly payments in the United Kingdom and Canada are much lower than the average monthly payments from India (which is the opposite of what I would expect based on socio-ecomonics).\n",
    "\n",
    "We should look into this a bit more. Let's look at box plots of money per month for each of the four countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd230f9ba8>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAccAAAGHCAYAAADFpJneAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nng0XGGsa2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd230a1940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survey_sub[[\"MoneyPerMonth\",\"CountryLive\"]].boxplot(by=\"CountryLive\",\n",
    "                                                    rot=45)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Money Per Month')\n",
    "plt.title('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 200)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbUAAAGHCAYAAAA3GMx7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niGZDQk0IIUSzIaEmhBCi2fhfshNf5VDDKWMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd23329eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# zoom in to see the boxes!\n",
    "survey_sub[[\"MoneyPerMonth\",\"CountryLive\"]].boxplot(by=\"CountryLive\",\n",
    "                                                    rot=45)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Money Per Month')\n",
    "plt.title('')\n",
    "plt.ylim(0,200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many extreme outliers for each country!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       MoneyPerMonth               CountryLive\n",
      "14024   80000.000000  United States of America\n",
      "2478    50000.000000  United States of America\n",
      "16650   16666.666667  United States of America\n",
      "17231   15000.000000  United States of America\n",
      "6805    15000.000000  United States of America\n",
      "7198    14000.000000  United States of America\n",
      "16997   12500.000000  United States of America\n",
      "3930    10833.333333  United States of America\n",
      "13398   10000.000000                     India\n",
      "3184     9000.000000  United States of America\n",
      "9778     8000.000000  United States of America\n",
      "718      8000.000000  United States of America\n",
      "7505     6666.666667  United States of America\n",
      "1222     6500.000000  United States of America\n",
      "3145     5666.666667  United States of America\n",
      "13145    5333.333333  United States of America\n",
      "7989     5000.000000                     India\n",
      "8202     5000.000000  United States of America\n",
      "8901     5000.000000  United States of America\n",
      "5769     5000.000000  United States of America\n",
      "1728     5000.000000                     India\n",
      "8126     5000.000000                     India\n",
      "6528     5000.000000  United States of America\n",
      "13659    5000.000000                    Canada\n",
      "16211    5000.000000  United States of America\n",
      "5059     4666.666667  United States of America\n",
      "16290    4550.000000  United States of America\n",
      "16700    4500.000000  United States of America\n",
      "2480     4250.000000  United States of America\n",
      "15587    4166.666667                     India\n",
      "...              ...                       ...\n",
      "9441        0.000000                     India\n",
      "9490        0.000000  United States of America\n",
      "9484        0.000000  United States of America\n",
      "9480        0.000000  United States of America\n",
      "9479        0.000000  United States of America\n",
      "9475        0.000000  United States of America\n",
      "4658        0.000000                     India\n",
      "1833        0.000000  United States of America\n",
      "9466        0.000000  United States of America\n",
      "3006        0.000000  United States of America\n",
      "9464        0.000000  United States of America\n",
      "9450        0.000000  United States of America\n",
      "1848        0.000000  United States of America\n",
      "9443        0.000000  United States of America\n",
      "4669        0.000000                     India\n",
      "4671        0.000000  United States of America\n",
      "9432        0.000000  United States of America\n",
      "9430        0.000000                     India\n",
      "9420        0.000000  United States of America\n",
      "9419        0.000000  United States of America\n",
      "9418        0.000000  United States of America\n",
      "9415        0.000000                    Canada\n",
      "1880        0.000000  United States of America\n",
      "9397        0.000000                     India\n",
      "9392        0.000000  United States of America\n",
      "9389        0.000000  United States of America\n",
      "9385        0.000000  United States of America\n",
      "1894        0.000000  United States of America\n",
      "9376        0.000000                     India\n",
      "3253        0.000000  United States of America\n",
      "\n",
      "[3915 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(survey_sub[[\"MoneyPerMonth\",\"CountryLive\"]].sort_values(\n",
    "                                            by=\"MoneyPerMonth\",\n",
    "                                            ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's investigate the participants who reported spending at least $10,000:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>AttendedBootcamp</th>\n",
       "      <th>BootcampFinish</th>\n",
       "      <th>BootcampLoanYesNo</th>\n",
       "      <th>BootcampName</th>\n",
       "      <th>BootcampRecommend</th>\n",
       "      <th>ChildrenNumber</th>\n",
       "      <th>CityPopulation</th>\n",
       "      <th>CodeEventConferences</th>\n",
       "      <th>CodeEventDjangoGirls</th>\n",
       "      <th>CodeEventFCC</th>\n",
       "      <th>CodeEventGameJam</th>\n",
       "      <th>CodeEventGirlDev</th>\n",
       "      <th>CodeEventHackathons</th>\n",
       "      <th>CodeEventMeetup</th>\n",
       "      <th>CodeEventNodeSchool</th>\n",
       "      <th>CodeEventNone</th>\n",
       "      <th>CodeEventOther</th>\n",
       "      <th>CodeEventRailsBridge</th>\n",
       "      <th>CodeEventRailsGirls</th>\n",
       "      <th>CodeEventStartUpWknd</th>\n",
       "      <th>CodeEventWkdBootcamps</th>\n",
       "      <th>CodeEventWomenCode</th>\n",
       "      <th>CodeEventWorkshops</th>\n",
       "      <th>CommuteTime</th>\n",
       "      <th>CountryCitizen</th>\n",
       "      <th>CountryLive</th>\n",
       "      <th>EmploymentField</th>\n",
       "      <th>EmploymentFieldOther</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>EmploymentStatusOther</th>\n",
       "      <th>ExpectedEarning</th>\n",
       "      <th>FinanciallySupporting</th>\n",
       "      <th>FirstDevJob</th>\n",
       "      <th>Gender</th>\n",
       "      <th>GenderOther</th>\n",
       "      <th>HasChildren</th>\n",
       "      <th>HasDebt</th>\n",
       "      <th>HasFinancialDependents</th>\n",
       "      <th>HasHighSpdInternet</th>\n",
       "      <th>HasHomeMortgage</th>\n",
       "      <th>HasServedInMilitary</th>\n",
       "      <th>HasStudentDebt</th>\n",
       "      <th>HomeMortgageOwe</th>\n",
       "      <th>HoursLearning</th>\n",
       "      <th>ID.x</th>\n",
       "      <th>ID.y</th>\n",
       "      <th>Income</th>\n",
       "      <th>IsEthnicMinority</th>\n",
       "      <th>IsReceiveDisabilitiesBenefits</th>\n",
       "      <th>IsSoftwareDev</th>\n",
       "      <th>IsUnderEmployed</th>\n",
       "      <th>JobApplyWhen</th>\n",
       "      <th>JobInterestBackEnd</th>\n",
       "      <th>JobInterestDataEngr</th>\n",
       "      <th>JobInterestDataSci</th>\n",
       "      <th>JobInterestDevOps</th>\n",
       "      <th>JobInterestFrontEnd</th>\n",
       "      <th>JobInterestFullStack</th>\n",
       "      <th>JobInterestGameDev</th>\n",
       "      <th>JobInterestInfoSec</th>\n",
       "      <th>JobInterestMobile</th>\n",
       "      <th>JobInterestOther</th>\n",
       "      <th>JobInterestProjMngr</th>\n",
       "      <th>JobInterestQAEngr</th>\n",
       "      <th>JobInterestUX</th>\n",
       "      <th>JobPref</th>\n",
       "      <th>JobRelocateYesNo</th>\n",
       "      <th>JobRoleInterest</th>\n",
       "      <th>JobWherePref</th>\n",
       "      <th>LanguageAtHome</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MoneyForLearning</th>\n",
       "      <th>MonthsProgramming</th>\n",
       "      <th>NetworkID</th>\n",
       "      <th>Part1EndTime</th>\n",
       "      <th>Part1StartTime</th>\n",
       "      <th>Part2EndTime</th>\n",
       "      <th>Part2StartTime</th>\n",
       "      <th>PodcastChangeLog</th>\n",
       "      <th>PodcastCodeNewbie</th>\n",
       "      <th>PodcastCodePen</th>\n",
       "      <th>PodcastDevTea</th>\n",
       "      <th>PodcastDotNET</th>\n",
       "      <th>PodcastGiantRobots</th>\n",
       "      <th>PodcastJSAir</th>\n",
       "      <th>PodcastJSJabber</th>\n",
       "      <th>PodcastNone</th>\n",
       "      <th>PodcastOther</th>\n",
       "      <th>PodcastProgThrowdown</th>\n",
       "      <th>PodcastRubyRogues</th>\n",
       "      <th>PodcastSEDaily</th>\n",
       "      <th>PodcastSERadio</th>\n",
       "      <th>PodcastShopTalk</th>\n",
       "      <th>PodcastTalkPython</th>\n",
       "      <th>PodcastTheWebAhead</th>\n",
       "      <th>ResourceCodecademy</th>\n",
       "      <th>ResourceCodeWars</th>\n",
       "      <th>ResourceCoursera</th>\n",
       "      <th>ResourceCSS</th>\n",
       "      <th>ResourceEdX</th>\n",
       "      <th>ResourceEgghead</th>\n",
       "      <th>ResourceFCC</th>\n",
       "      <th>ResourceHackerRank</th>\n",
       "      <th>ResourceKA</th>\n",
       "      <th>ResourceLynda</th>\n",
       "      <th>ResourceMDN</th>\n",
       "      <th>ResourceOdinProj</th>\n",
       "      <th>ResourceOther</th>\n",
       "      <th>ResourcePluralSight</th>\n",
       "      <th>ResourceSkillcrush</th>\n",
       "      <th>ResourceSO</th>\n",
       "      <th>ResourceTreehouse</th>\n",
       "      <th>ResourceUdacity</th>\n",
       "      <th>ResourceUdemy</th>\n",
       "      <th>ResourceW3S</th>\n",
       "      <th>SchoolDegree</th>\n",
       "      <th>SchoolMajor</th>\n",
       "      <th>StudentDebtOwe</th>\n",
       "      <th>YouTubeCodeCourse</th>\n",
       "      <th>YouTubeCodingTrain</th>\n",
       "      <th>YouTubeCodingTut360</th>\n",
       "      <th>YouTubeComputerphile</th>\n",
       "      <th>YouTubeDerekBanas</th>\n",
       "      <th>YouTubeDevTips</th>\n",
       "      <th>YouTubeEngineeredTruth</th>\n",
       "      <th>YouTubeFCC</th>\n",
       "      <th>YouTubeFunFunFunction</th>\n",
       "      <th>YouTubeGoogleDev</th>\n",
       "      <th>YouTubeLearnCode</th>\n",
       "      <th>YouTubeLevelUpTuts</th>\n",
       "      <th>YouTubeMIT</th>\n",
       "      <th>YouTubeMozillaHacks</th>\n",
       "      <th>YouTubeOther</th>\n",
       "      <th>YouTubeSimplilearn</th>\n",
       "      <th>YouTubeTheNewBoston</th>\n",
       "      <th>CleanMonths</th>\n",
       "      <th>MoneyPerMonth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2478</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not working and not looking for work</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>trans</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>b84e33311d235660ad1b7a918d7d7df4</td>\n",
       "      <td>b3229f43317af29a4d23734f9d543222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Within 7 to 12 months</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>work for a nonprofit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Front-End Web Developer, Full-Stack Web Deve...</td>\n",
       "      <td>from home</td>\n",
       "      <td>English</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>befcbda814</td>\n",
       "      <td>2017-03-10 23:30:16</td>\n",
       "      <td>2017-03-10 23:26:33</td>\n",
       "      <td>2017-03-10 23:31:50</td>\n",
       "      <td>2017-03-10 23:30:19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not working and not looking for work</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>e1d790033545934fbe5bb5b60e368cd9</td>\n",
       "      <td>7cf1e41682462c42ce48029abf77d43c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Within the next 6 months</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>work for a startup</td>\n",
       "      <td>1.0</td>\n",
       "      <td>DevOps / SysAdmin,   Front-End Web Developer...</td>\n",
       "      <td>no preference</td>\n",
       "      <td>English</td>\n",
       "      <td>married or domestic partnership</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>75759e5a1c</td>\n",
       "      <td>2017-03-13 10:06:46</td>\n",
       "      <td>2017-03-13 09:56:13</td>\n",
       "      <td>2017-03-13 10:10:00</td>\n",
       "      <td>2017-03-13 10:06:50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reactivex.io/learnrx/ &amp; jafar husain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>Biology</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>various conf presentations</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6805</th>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sabio.la</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not working but looking for work</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>69096aacf4245694303cf8f7ce68a63f</td>\n",
       "      <td>4c56f82a348836e76dd90d18a3d5ed88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Within the next 6 months</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>work for a multinational corporation</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Full-Stack Web Developer, Game Developer,   Pr...</td>\n",
       "      <td>no preference</td>\n",
       "      <td>English</td>\n",
       "      <td>married or domestic partnership</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53d13b58e9</td>\n",
       "      <td>2017-03-21 20:13:08</td>\n",
       "      <td>2017-03-21 20:10:25</td>\n",
       "      <td>2017-03-21 20:14:36</td>\n",
       "      <td>2017-03-21 20:13:11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>Business Administration and Management</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7198</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15 to 29 minutes</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>education</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Employed for wages</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>cb2754165344e6be79da8a4c76bf3917</td>\n",
       "      <td>272219fbd28a3a7562cb1d778e482e1e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>I'm already applying</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>work for a multinational corporation</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Full-Stack Web Developer, Back-End Web Developer</td>\n",
       "      <td>no preference</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>439a4adaf6</td>\n",
       "      <td>2017-03-23 01:37:46</td>\n",
       "      <td>2017-03-23 01:35:01</td>\n",
       "      <td>2017-03-23 01:39:37</td>\n",
       "      <td>2017-03-23 01:37:49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>professional degree (MBA, MD, JD, etc.)</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13398</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unable to work</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>b7fe7bc4edefc3a60eb48f977e4426e3</td>\n",
       "      <td>80ff09859ac475b70ac19b7b7369e953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I haven't decided</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>work for a multinational corporation</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mobile Developer</td>\n",
       "      <td>no preference</td>\n",
       "      <td>Hindi</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51a6f9a1a7</td>\n",
       "      <td>2017-04-01 00:31:25</td>\n",
       "      <td>2017-04-01 00:28:17</td>\n",
       "      <td>2017-04-01 00:33:44</td>\n",
       "      <td>2017-04-01 00:31:32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14024</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not working but looking for work</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11d884a40e5662ef57566556206b7754</td>\n",
       "      <td>4f77cc788154cb155fb513ece8ea40da</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I'm already applying</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>work for a nonprofit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Back-End Web Developer,   Front-End Web Develo...</td>\n",
       "      <td>in an office with other developers</td>\n",
       "      <td>English</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b01f8f18a7</td>\n",
       "      <td>2017-03-16 23:14:34</td>\n",
       "      <td>2017-03-16 23:09:32</td>\n",
       "      <td>2017-03-16 23:16:45</td>\n",
       "      <td>2017-03-16 23:14:39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16650</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not working but looking for work</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>e1925d408c973b91cf3e9a9285238796</td>\n",
       "      <td>7e9e3c31a3dc2cafe3a09269398c4de8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I'm already applying</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>work for a multinational corporation</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Product Manager, Data Engineer, Full-Stack W...</td>\n",
       "      <td>in an office with other developers</td>\n",
       "      <td>English</td>\n",
       "      <td>married or domestic partnership</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1a45f4a3ef</td>\n",
       "      <td>2017-03-14 02:42:57</td>\n",
       "      <td>2017-03-14 02:40:10</td>\n",
       "      <td>2017-03-14 02:45:55</td>\n",
       "      <td>2017-03-14 02:43:05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>Computer Programming</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16666.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16997</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15 to 29 minutes</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>health care</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Employed for wages</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>624914ce07c296c866c9e16a14dc01c7</td>\n",
       "      <td>6384a1e576caf4b6b9339fe496a51f1f</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Within 7 to 12 months</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>work for a medium-sized company</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mobile Developer, Game Developer,   User Exp...</td>\n",
       "      <td>in an office with other developers</td>\n",
       "      <td>English</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ad1a21217c</td>\n",
       "      <td>2017-03-20 05:43:28</td>\n",
       "      <td>2017-03-20 05:40:08</td>\n",
       "      <td>2017-03-20 05:45:28</td>\n",
       "      <td>2017-03-20 05:43:32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17231</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>less than 100,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not working but looking for work</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>d4bc6ae775b20816fcd41048ef75417c</td>\n",
       "      <td>606749cd07b124234ab6dff81b324c02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Within the next 6 months</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>work for a nonprofit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Front-End Web Developer</td>\n",
       "      <td>in an office with other developers</td>\n",
       "      <td>English</td>\n",
       "      <td>married or domestic partnership</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38c1b478d0</td>\n",
       "      <td>2017-03-24 18:48:23</td>\n",
       "      <td>2017-03-24 18:46:01</td>\n",
       "      <td>2017-03-24 18:51:20</td>\n",
       "      <td>2017-03-24 18:48:27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>Computer Programming</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  AttendedBootcamp  BootcampFinish  BootcampLoanYesNo BootcampName  \\\n",
       "2478   38.0               1.0             0.0                1.0          NaN   \n",
       "3930   31.0               0.0             NaN                NaN          NaN   \n",
       "6805   46.0               1.0             1.0                1.0     Sabio.la   \n",
       "7198   32.0               0.0             NaN                NaN          NaN   \n",
       "13398  19.0               0.0             NaN                NaN          NaN   \n",
       "14024  45.0               0.0             NaN                NaN          NaN   \n",
       "16650  29.0               0.0             NaN                NaN          NaN   \n",
       "16997  27.0               0.0             NaN                NaN          NaN   \n",
       "17231  50.0               0.0             NaN                NaN          NaN   \n",
       "\n",
       "       BootcampRecommend  ChildrenNumber                 CityPopulation  \\\n",
       "2478                 1.0             NaN  between 100,000 and 1 million   \n",
       "3930                 NaN             NaN  between 100,000 and 1 million   \n",
       "6805                 0.0             NaN  between 100,000 and 1 million   \n",
       "7198                 NaN             NaN            more than 1 million   \n",
       "13398                NaN             NaN            more than 1 million   \n",
       "14024                NaN             NaN  between 100,000 and 1 million   \n",
       "16650                NaN             2.0            more than 1 million   \n",
       "16997                NaN             1.0            more than 1 million   \n",
       "17231                NaN             2.0              less than 100,000   \n",
       "\n",
       "       CodeEventConferences  CodeEventDjangoGirls  CodeEventFCC  \\\n",
       "2478                    NaN                   NaN           NaN   \n",
       "3930                    NaN                   NaN           NaN   \n",
       "6805                    NaN                   NaN           NaN   \n",
       "7198                    1.0                   NaN           NaN   \n",
       "13398                   NaN                   NaN           NaN   \n",
       "14024                   NaN                   NaN           NaN   \n",
       "16650                   NaN                   NaN           NaN   \n",
       "16997                   NaN                   NaN           NaN   \n",
       "17231                   NaN                   NaN           NaN   \n",
       "\n",
       "       CodeEventGameJam  CodeEventGirlDev  CodeEventHackathons  \\\n",
       "2478                NaN               NaN                  NaN   \n",
       "3930                NaN               NaN                  NaN   \n",
       "6805                NaN               NaN                  NaN   \n",
       "7198                NaN               NaN                  NaN   \n",
       "13398               NaN               NaN                  NaN   \n",
       "14024               NaN               NaN                  NaN   \n",
       "16650               NaN               NaN                  NaN   \n",
       "16997               NaN               NaN                  NaN   \n",
       "17231               NaN               NaN                  NaN   \n",
       "\n",
       "       CodeEventMeetup  CodeEventNodeSchool  CodeEventNone CodeEventOther  \\\n",
       "2478               NaN                  NaN            NaN            NaN   \n",
       "3930               NaN                  NaN            NaN            NaN   \n",
       "6805               NaN                  NaN            NaN            NaN   \n",
       "7198               1.0                  NaN            NaN            NaN   \n",
       "13398              NaN                  NaN            1.0            NaN   \n",
       "14024              NaN                  NaN            1.0            NaN   \n",
       "16650              1.0                  NaN            NaN            NaN   \n",
       "16997              NaN                  NaN            NaN            NaN   \n",
       "17231              NaN                  NaN            NaN            NaN   \n",
       "\n",
       "       CodeEventRailsBridge  CodeEventRailsGirls  CodeEventStartUpWknd  \\\n",
       "2478                    NaN                  NaN                   NaN   \n",
       "3930                    NaN                  NaN                   NaN   \n",
       "6805                    NaN                  NaN                   NaN   \n",
       "7198                    NaN                  NaN                   NaN   \n",
       "13398                   NaN                  NaN                   NaN   \n",
       "14024                   NaN                  NaN                   NaN   \n",
       "16650                   NaN                  NaN                   NaN   \n",
       "16997                   NaN                  NaN                   NaN   \n",
       "17231                   NaN                  NaN                   NaN   \n",
       "\n",
       "       CodeEventWkdBootcamps  CodeEventWomenCode  CodeEventWorkshops  \\\n",
       "2478                     NaN                 NaN                 NaN   \n",
       "3930                     NaN                 NaN                 NaN   \n",
       "6805                     NaN                 NaN                 NaN   \n",
       "7198                     NaN                 NaN                 NaN   \n",
       "13398                    NaN                 NaN                 NaN   \n",
       "14024                    NaN                 NaN                 NaN   \n",
       "16650                    NaN                 NaN                 NaN   \n",
       "16997                    NaN                 NaN                 NaN   \n",
       "17231                    NaN                 1.0                 NaN   \n",
       "\n",
       "            CommuteTime            CountryCitizen               CountryLive  \\\n",
       "2478                NaN  United States of America  United States of America   \n",
       "3930                NaN  United States of America  United States of America   \n",
       "6805                NaN  United States of America  United States of America   \n",
       "7198   15 to 29 minutes  United States of America  United States of America   \n",
       "13398               NaN                     India                     India   \n",
       "14024               NaN  United States of America  United States of America   \n",
       "16650               NaN  United States of America  United States of America   \n",
       "16997  15 to 29 minutes  United States of America  United States of America   \n",
       "17231               NaN                     Kenya  United States of America   \n",
       "\n",
       "      EmploymentField EmploymentFieldOther  \\\n",
       "2478              NaN                  NaN   \n",
       "3930              NaN                  NaN   \n",
       "6805              NaN                  NaN   \n",
       "7198        education                  NaN   \n",
       "13398             NaN                  NaN   \n",
       "14024             NaN                  NaN   \n",
       "16650             NaN                  NaN   \n",
       "16997     health care                  NaN   \n",
       "17231             NaN                  NaN   \n",
       "\n",
       "                           EmploymentStatus EmploymentStatusOther  \\\n",
       "2478   Not working and not looking for work                   NaN   \n",
       "3930   Not working and not looking for work                   NaN   \n",
       "6805       Not working but looking for work                   NaN   \n",
       "7198                     Employed for wages                   NaN   \n",
       "13398                        Unable to work                   NaN   \n",
       "14024      Not working but looking for work                   NaN   \n",
       "16650      Not working but looking for work                   NaN   \n",
       "16997                    Employed for wages                   NaN   \n",
       "17231      Not working but looking for work                   NaN   \n",
       "\n",
       "       ExpectedEarning  FinanciallySupporting  FirstDevJob  Gender  \\\n",
       "2478          100000.0                    NaN          NaN   trans   \n",
       "3930          100000.0                    NaN          NaN    male   \n",
       "6805           70000.0                    NaN          NaN    male   \n",
       "7198           55000.0                    NaN          NaN    male   \n",
       "13398         100000.0                    NaN          NaN    male   \n",
       "14024          30000.0                    NaN          NaN    male   \n",
       "16650              NaN                    1.0          NaN    male   \n",
       "16997          60000.0                    0.0          NaN  female   \n",
       "17231          40000.0                    0.0          NaN  female   \n",
       "\n",
       "      GenderOther  HasChildren  HasDebt  HasFinancialDependents  \\\n",
       "2478          NaN          NaN      1.0                     0.0   \n",
       "3930          NaN          NaN      1.0                     0.0   \n",
       "6805          NaN          NaN      1.0                     0.0   \n",
       "7198          NaN          NaN      1.0                     0.0   \n",
       "13398         NaN          NaN      0.0                     0.0   \n",
       "14024         NaN          NaN      1.0                     0.0   \n",
       "16650         NaN          1.0      1.0                     1.0   \n",
       "16997         NaN          1.0      1.0                     1.0   \n",
       "17231         NaN          1.0      0.0                     1.0   \n",
       "\n",
       "       HasHighSpdInternet  HasHomeMortgage  HasServedInMilitary  \\\n",
       "2478                  1.0              0.0                  0.0   \n",
       "3930                  1.0              0.0                  0.0   \n",
       "6805                  1.0              0.0                  0.0   \n",
       "7198                  1.0              0.0                  0.0   \n",
       "13398                 0.0              NaN                  0.0   \n",
       "14024                 1.0              0.0                  0.0   \n",
       "16650                 1.0              1.0                  0.0   \n",
       "16997                 1.0              0.0                  0.0   \n",
       "17231                 1.0              NaN                  0.0   \n",
       "\n",
       "       HasStudentDebt  HomeMortgageOwe  HoursLearning  \\\n",
       "2478              1.0              NaN           40.0   \n",
       "3930              1.0              NaN           50.0   \n",
       "6805              1.0              NaN           45.0   \n",
       "7198              1.0              NaN            4.0   \n",
       "13398             NaN              NaN           30.0   \n",
       "14024             1.0              NaN           20.0   \n",
       "16650             1.0         400000.0           40.0   \n",
       "16997             1.0              NaN           12.0   \n",
       "17231             NaN              NaN            1.0   \n",
       "\n",
       "                                   ID.x                              ID.y  \\\n",
       "2478   b84e33311d235660ad1b7a918d7d7df4  b3229f43317af29a4d23734f9d543222   \n",
       "3930   e1d790033545934fbe5bb5b60e368cd9  7cf1e41682462c42ce48029abf77d43c   \n",
       "6805   69096aacf4245694303cf8f7ce68a63f  4c56f82a348836e76dd90d18a3d5ed88   \n",
       "7198   cb2754165344e6be79da8a4c76bf3917  272219fbd28a3a7562cb1d778e482e1e   \n",
       "13398  b7fe7bc4edefc3a60eb48f977e4426e3  80ff09859ac475b70ac19b7b7369e953   \n",
       "14024  11d884a40e5662ef57566556206b7754  4f77cc788154cb155fb513ece8ea40da   \n",
       "16650  e1925d408c973b91cf3e9a9285238796  7e9e3c31a3dc2cafe3a09269398c4de8   \n",
       "16997  624914ce07c296c866c9e16a14dc01c7  6384a1e576caf4b6b9339fe496a51f1f   \n",
       "17231  d4bc6ae775b20816fcd41048ef75417c  606749cd07b124234ab6dff81b324c02   \n",
       "\n",
       "        Income  IsEthnicMinority  IsReceiveDisabilitiesBenefits  \\\n",
       "2478       NaN               0.0                            0.0   \n",
       "3930       NaN               1.0                            0.0   \n",
       "6805       NaN               1.0                            0.0   \n",
       "7198       NaN               1.0                            0.0   \n",
       "13398      NaN               0.0                            0.0   \n",
       "14024      NaN               0.0                            0.0   \n",
       "16650      NaN               1.0                            1.0   \n",
       "16997  40000.0               1.0                            0.0   \n",
       "17231      NaN               1.0                            0.0   \n",
       "\n",
       "       IsSoftwareDev  IsUnderEmployed              JobApplyWhen  \\\n",
       "2478             0.0              NaN     Within 7 to 12 months   \n",
       "3930             0.0              NaN  Within the next 6 months   \n",
       "6805             0.0              NaN  Within the next 6 months   \n",
       "7198             0.0              0.0      I'm already applying   \n",
       "13398            0.0              NaN         I haven't decided   \n",
       "14024            0.0              NaN      I'm already applying   \n",
       "16650            0.0              NaN      I'm already applying   \n",
       "16997            0.0              0.0     Within 7 to 12 months   \n",
       "17231            0.0              NaN  Within the next 6 months   \n",
       "\n",
       "       JobInterestBackEnd  JobInterestDataEngr  JobInterestDataSci  \\\n",
       "2478                  1.0                  NaN                 NaN   \n",
       "3930                  1.0                  NaN                 NaN   \n",
       "6805                  NaN                  1.0                 1.0   \n",
       "7198                  1.0                  NaN                 NaN   \n",
       "13398                 NaN                  NaN                 NaN   \n",
       "14024                 1.0                  NaN                 NaN   \n",
       "16650                 1.0                  1.0                 NaN   \n",
       "16997                 NaN                  NaN                 NaN   \n",
       "17231                 NaN                  NaN                 NaN   \n",
       "\n",
       "       JobInterestDevOps  JobInterestFrontEnd  JobInterestFullStack  \\\n",
       "2478                 NaN                  1.0                   1.0   \n",
       "3930                 1.0                  1.0                   1.0   \n",
       "6805                 NaN                  NaN                   1.0   \n",
       "7198                 NaN                  NaN                   1.0   \n",
       "13398                NaN                  NaN                   NaN   \n",
       "14024                NaN                  1.0                   1.0   \n",
       "16650                NaN                  1.0                   1.0   \n",
       "16997                NaN                  1.0                   1.0   \n",
       "17231                NaN                  1.0                   NaN   \n",
       "\n",
       "       JobInterestGameDev  JobInterestInfoSec  JobInterestMobile  \\\n",
       "2478                  NaN                 NaN                1.0   \n",
       "3930                  NaN                 NaN                NaN   \n",
       "6805                  1.0                 NaN                NaN   \n",
       "7198                  NaN                 NaN                NaN   \n",
       "13398                 NaN                 NaN                1.0   \n",
       "14024                 NaN                 NaN                NaN   \n",
       "16650                 1.0                 NaN                NaN   \n",
       "16997                 1.0                 NaN                1.0   \n",
       "17231                 NaN                 NaN                NaN   \n",
       "\n",
       "      JobInterestOther  JobInterestProjMngr  JobInterestQAEngr  JobInterestUX  \\\n",
       "2478               NaN                  NaN                NaN            1.0   \n",
       "3930               NaN                  NaN                NaN            NaN   \n",
       "6805               NaN                  1.0                NaN            NaN   \n",
       "7198               NaN                  NaN                NaN            NaN   \n",
       "13398              NaN                  NaN                NaN            NaN   \n",
       "14024              NaN                  1.0                NaN            NaN   \n",
       "16650              NaN                  1.0                NaN            NaN   \n",
       "16997              NaN                  1.0                NaN            1.0   \n",
       "17231              NaN                  NaN                NaN            NaN   \n",
       "\n",
       "                                    JobPref  JobRelocateYesNo  \\\n",
       "2478                   work for a nonprofit               NaN   \n",
       "3930                     work for a startup               1.0   \n",
       "6805   work for a multinational corporation               1.0   \n",
       "7198   work for a multinational corporation               0.0   \n",
       "13398  work for a multinational corporation               1.0   \n",
       "14024                  work for a nonprofit               0.0   \n",
       "16650  work for a multinational corporation               1.0   \n",
       "16997       work for a medium-sized company               1.0   \n",
       "17231                  work for a nonprofit               0.0   \n",
       "\n",
       "                                         JobRoleInterest  \\\n",
       "2478     Front-End Web Developer, Full-Stack Web Deve...   \n",
       "3930     DevOps / SysAdmin,   Front-End Web Developer...   \n",
       "6805   Full-Stack Web Developer, Game Developer,   Pr...   \n",
       "7198    Full-Stack Web Developer, Back-End Web Developer   \n",
       "13398                                   Mobile Developer   \n",
       "14024  Back-End Web Developer,   Front-End Web Develo...   \n",
       "16650    Product Manager, Data Engineer, Full-Stack W...   \n",
       "16997    Mobile Developer, Game Developer,   User Exp...   \n",
       "17231                            Front-End Web Developer   \n",
       "\n",
       "                             JobWherePref LanguageAtHome  \\\n",
       "2478                            from home        English   \n",
       "3930                        no preference        English   \n",
       "6805                        no preference        English   \n",
       "7198                        no preference        Spanish   \n",
       "13398                       no preference          Hindi   \n",
       "14024  in an office with other developers        English   \n",
       "16650  in an office with other developers        English   \n",
       "16997  in an office with other developers        English   \n",
       "17231  in an office with other developers        English   \n",
       "\n",
       "                         MaritalStatus  MoneyForLearning  MonthsProgramming  \\\n",
       "2478             single, never married          100000.0                2.0   \n",
       "3930   married or domestic partnership           65000.0                6.0   \n",
       "6805   married or domestic partnership           15000.0                1.0   \n",
       "7198             single, never married           70000.0                5.0   \n",
       "13398            single, never married           20000.0                2.0   \n",
       "14024            single, never married           80000.0                1.0   \n",
       "16650  married or domestic partnership          200000.0               12.0   \n",
       "16997            single, never married           12500.0                1.0   \n",
       "17231  married or domestic partnership           30000.0                2.0   \n",
       "\n",
       "        NetworkID         Part1EndTime       Part1StartTime  \\\n",
       "2478   befcbda814  2017-03-10 23:30:16  2017-03-10 23:26:33   \n",
       "3930   75759e5a1c  2017-03-13 10:06:46  2017-03-13 09:56:13   \n",
       "6805   53d13b58e9  2017-03-21 20:13:08  2017-03-21 20:10:25   \n",
       "7198   439a4adaf6  2017-03-23 01:37:46  2017-03-23 01:35:01   \n",
       "13398  51a6f9a1a7  2017-04-01 00:31:25  2017-04-01 00:28:17   \n",
       "14024  b01f8f18a7  2017-03-16 23:14:34  2017-03-16 23:09:32   \n",
       "16650  1a45f4a3ef  2017-03-14 02:42:57  2017-03-14 02:40:10   \n",
       "16997  ad1a21217c  2017-03-20 05:43:28  2017-03-20 05:40:08   \n",
       "17231  38c1b478d0  2017-03-24 18:48:23  2017-03-24 18:46:01   \n",
       "\n",
       "              Part2EndTime       Part2StartTime  PodcastChangeLog  \\\n",
       "2478   2017-03-10 23:31:50  2017-03-10 23:30:19               NaN   \n",
       "3930   2017-03-13 10:10:00  2017-03-13 10:06:50               NaN   \n",
       "6805   2017-03-21 20:14:36  2017-03-21 20:13:11               NaN   \n",
       "7198   2017-03-23 01:39:37  2017-03-23 01:37:49               NaN   \n",
       "13398  2017-04-01 00:33:44  2017-04-01 00:31:32               NaN   \n",
       "14024  2017-03-16 23:16:45  2017-03-16 23:14:39               NaN   \n",
       "16650  2017-03-14 02:45:55  2017-03-14 02:43:05               NaN   \n",
       "16997  2017-03-20 05:45:28  2017-03-20 05:43:32               NaN   \n",
       "17231  2017-03-24 18:51:20  2017-03-24 18:48:27               NaN   \n",
       "\n",
       "       PodcastCodeNewbie  PodcastCodePen  PodcastDevTea  PodcastDotNET  \\\n",
       "2478                 1.0             NaN            NaN            NaN   \n",
       "3930                 NaN             NaN            NaN            NaN   \n",
       "6805                 NaN             NaN            NaN            NaN   \n",
       "7198                 NaN             NaN            NaN            NaN   \n",
       "13398                NaN             NaN            NaN            NaN   \n",
       "14024                NaN             NaN            NaN            NaN   \n",
       "16650                NaN             NaN            NaN            NaN   \n",
       "16997                NaN             NaN            NaN            NaN   \n",
       "17231                NaN             NaN            NaN            NaN   \n",
       "\n",
       "       PodcastGiantRobots  PodcastJSAir  PodcastJSJabber  PodcastNone  \\\n",
       "2478                  NaN           NaN              NaN          NaN   \n",
       "3930                  NaN           1.0              1.0          NaN   \n",
       "6805                  NaN           NaN              NaN          NaN   \n",
       "7198                  NaN           NaN              NaN          NaN   \n",
       "13398                 NaN           NaN              NaN          NaN   \n",
       "14024                 NaN           NaN              NaN          1.0   \n",
       "16650                 NaN           NaN              NaN          NaN   \n",
       "16997                 NaN           NaN              NaN          NaN   \n",
       "17231                 NaN           NaN              NaN          NaN   \n",
       "\n",
       "      PodcastOther  PodcastProgThrowdown  PodcastRubyRogues  PodcastSEDaily  \\\n",
       "2478           NaN                   NaN                NaN             NaN   \n",
       "3930           NaN                   NaN                NaN             NaN   \n",
       "6805           NaN                   NaN                NaN             NaN   \n",
       "7198           NaN                   NaN                NaN             NaN   \n",
       "13398          NaN                   NaN                NaN             NaN   \n",
       "14024          NaN                   NaN                NaN             NaN   \n",
       "16650          NaN                   NaN                NaN             NaN   \n",
       "16997          NaN                   NaN                NaN             NaN   \n",
       "17231          NaN                   NaN                NaN             1.0   \n",
       "\n",
       "       PodcastSERadio  PodcastShopTalk  PodcastTalkPython  PodcastTheWebAhead  \\\n",
       "2478              NaN              NaN                NaN                 NaN   \n",
       "3930              NaN              NaN                NaN                 NaN   \n",
       "6805              NaN              NaN                NaN                 NaN   \n",
       "7198              NaN              NaN                NaN                 NaN   \n",
       "13398             NaN              NaN                NaN                 NaN   \n",
       "14024             NaN              NaN                NaN                 NaN   \n",
       "16650             NaN              NaN                NaN                 NaN   \n",
       "16997             NaN              NaN                NaN                 NaN   \n",
       "17231             NaN              NaN                NaN                 NaN   \n",
       "\n",
       "       ResourceCodecademy  ResourceCodeWars  ResourceCoursera  ResourceCSS  \\\n",
       "2478                  1.0               NaN               NaN          NaN   \n",
       "3930                  NaN               NaN               NaN          NaN   \n",
       "6805                  1.0               NaN               NaN          NaN   \n",
       "7198                  1.0               NaN               1.0          1.0   \n",
       "13398                 NaN               NaN               NaN          NaN   \n",
       "14024                 1.0               NaN               NaN          NaN   \n",
       "16650                 NaN               NaN               1.0          NaN   \n",
       "16997                 1.0               NaN               NaN          NaN   \n",
       "17231                 NaN               NaN               NaN          NaN   \n",
       "\n",
       "       ResourceEdX  ResourceEgghead  ResourceFCC  ResourceHackerRank  \\\n",
       "2478           NaN              NaN          1.0                 NaN   \n",
       "3930           NaN              1.0          1.0                 NaN   \n",
       "6805           NaN              NaN          1.0                 NaN   \n",
       "7198           NaN              NaN          1.0                 NaN   \n",
       "13398          NaN              NaN          NaN                 NaN   \n",
       "14024          NaN              NaN          1.0                 NaN   \n",
       "16650          NaN              NaN          1.0                 NaN   \n",
       "16997          NaN              NaN          1.0                 NaN   \n",
       "17231          NaN              NaN          NaN                 NaN   \n",
       "\n",
       "       ResourceKA  ResourceLynda  ResourceMDN  ResourceOdinProj  \\\n",
       "2478          NaN            NaN          1.0               NaN   \n",
       "3930          NaN            NaN          1.0               NaN   \n",
       "6805          NaN            NaN          NaN               NaN   \n",
       "7198          1.0            NaN          1.0               NaN   \n",
       "13398         NaN            NaN          NaN               NaN   \n",
       "14024         NaN            NaN          NaN               NaN   \n",
       "16650         NaN            NaN          NaN               NaN   \n",
       "16997         NaN            NaN          NaN               NaN   \n",
       "17231         NaN            NaN          NaN               NaN   \n",
       "\n",
       "                              ResourceOther  ResourcePluralSight  \\\n",
       "2478                                    NaN                  NaN   \n",
       "3930   reactivex.io/learnrx/ & jafar husain                  NaN   \n",
       "6805                                    NaN                  NaN   \n",
       "7198                                    NaN                  NaN   \n",
       "13398                                   NaN                  NaN   \n",
       "14024                                   NaN                  NaN   \n",
       "16650                                   NaN                  NaN   \n",
       "16997                                   NaN                  NaN   \n",
       "17231                                   NaN                  NaN   \n",
       "\n",
       "       ResourceSkillcrush  ResourceSO  ResourceTreehouse  ResourceUdacity  \\\n",
       "2478                  NaN         1.0                1.0              NaN   \n",
       "3930                  NaN         1.0                NaN              NaN   \n",
       "6805                  NaN         1.0                1.0              1.0   \n",
       "7198                  NaN         1.0                NaN              1.0   \n",
       "13398                 NaN         1.0                NaN              NaN   \n",
       "14024                 NaN         NaN                NaN              NaN   \n",
       "16650                 NaN         NaN                NaN              1.0   \n",
       "16997                 NaN         1.0                NaN              NaN   \n",
       "17231                 NaN         NaN                NaN              NaN   \n",
       "\n",
       "       ResourceUdemy  ResourceW3S                             SchoolDegree  \\\n",
       "2478             NaN          1.0           some college credit, no degree   \n",
       "3930             NaN          NaN                        bachelor's degree   \n",
       "6805             1.0          1.0                        bachelor's degree   \n",
       "7198             NaN          1.0  professional degree (MBA, MD, JD, etc.)   \n",
       "13398            NaN          1.0                        bachelor's degree   \n",
       "14024            NaN          NaN                        bachelor's degree   \n",
       "16650            NaN          1.0                       associate's degree   \n",
       "16997            1.0          1.0           some college credit, no degree   \n",
       "17231            1.0          NaN                        bachelor's degree   \n",
       "\n",
       "                                  SchoolMajor  StudentDebtOwe  \\\n",
       "2478                                      NaN         48000.0   \n",
       "3930                                  Biology         40000.0   \n",
       "6805   Business Administration and Management         45000.0   \n",
       "7198                         Computer Science             NaN   \n",
       "13398                        Computer Science             NaN   \n",
       "14024                  Information Technology        100000.0   \n",
       "16650                    Computer Programming         30000.0   \n",
       "16997                                     NaN         12500.0   \n",
       "17231                    Computer Programming             NaN   \n",
       "\n",
       "       YouTubeCodeCourse  YouTubeCodingTrain  YouTubeCodingTut360  \\\n",
       "2478                 NaN                 NaN                  NaN   \n",
       "3930                 NaN                 NaN                  NaN   \n",
       "6805                 NaN                 NaN                  NaN   \n",
       "7198                 NaN                 NaN                  NaN   \n",
       "13398                NaN                 NaN                  NaN   \n",
       "14024                NaN                 NaN                  NaN   \n",
       "16650                NaN                 NaN                  NaN   \n",
       "16997                NaN                 NaN                  NaN   \n",
       "17231                NaN                 NaN                  NaN   \n",
       "\n",
       "       YouTubeComputerphile  YouTubeDerekBanas  YouTubeDevTips  \\\n",
       "2478                    NaN                NaN             NaN   \n",
       "3930                    NaN                NaN             NaN   \n",
       "6805                    NaN                NaN             NaN   \n",
       "7198                    NaN                NaN             NaN   \n",
       "13398                   NaN                NaN             NaN   \n",
       "14024                   NaN                NaN             NaN   \n",
       "16650                   NaN                NaN             NaN   \n",
       "16997                   NaN                NaN             NaN   \n",
       "17231                   NaN                1.0             NaN   \n",
       "\n",
       "       YouTubeEngineeredTruth  YouTubeFCC  YouTubeFunFunFunction  \\\n",
       "2478                      NaN         NaN                    NaN   \n",
       "3930                      1.0         1.0                    1.0   \n",
       "6805                      NaN         1.0                    NaN   \n",
       "7198                      NaN         1.0                    NaN   \n",
       "13398                     NaN         NaN                    NaN   \n",
       "14024                     NaN         NaN                    NaN   \n",
       "16650                     NaN         NaN                    NaN   \n",
       "16997                     NaN         NaN                    NaN   \n",
       "17231                     NaN         NaN                    NaN   \n",
       "\n",
       "       YouTubeGoogleDev  YouTubeLearnCode  YouTubeLevelUpTuts  YouTubeMIT  \\\n",
       "2478                NaN               NaN                 NaN         NaN   \n",
       "3930                1.0               1.0                 1.0         1.0   \n",
       "6805                NaN               NaN                 NaN         NaN   \n",
       "7198                1.0               1.0                 1.0         NaN   \n",
       "13398               NaN               NaN                 NaN         NaN   \n",
       "14024               NaN               NaN                 NaN         NaN   \n",
       "16650               NaN               NaN                 NaN         NaN   \n",
       "16997               NaN               NaN                 NaN         NaN   \n",
       "17231               NaN               NaN                 NaN         NaN   \n",
       "\n",
       "       YouTubeMozillaHacks                YouTubeOther  YouTubeSimplilearn  \\\n",
       "2478                   NaN                         NaN                 NaN   \n",
       "3930                   NaN  various conf presentations                 NaN   \n",
       "6805                   NaN                         NaN                 NaN   \n",
       "7198                   NaN                         NaN                 NaN   \n",
       "13398                  NaN                         NaN                 NaN   \n",
       "14024                  NaN                        None                 NaN   \n",
       "16650                  NaN                         NaN                 NaN   \n",
       "16997                  NaN                         NaN                 NaN   \n",
       "17231                  NaN                         NaN                 NaN   \n",
       "\n",
       "       YouTubeTheNewBoston  CleanMonths  MoneyPerMonth  \n",
       "2478                   NaN          2.0   50000.000000  \n",
       "3930                   NaN          6.0   10833.333333  \n",
       "6805                   NaN          1.0   15000.000000  \n",
       "7198                   NaN          5.0   14000.000000  \n",
       "13398                  NaN          2.0   10000.000000  \n",
       "14024                  NaN          1.0   80000.000000  \n",
       "16650                  1.0         12.0   16666.666667  \n",
       "16997                  NaN          1.0   12500.000000  \n",
       "17231                  NaN          2.0   15000.000000  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_sub[survey_sub[\"MoneyPerMonth\"]>=10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of these participants attended bootcamps which might be expensive. This is a legitimate expense, but if we're interested in average long-term monthly payments, we should probably remove the people who were programming for < 3 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MoneyPerMonth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountryLive</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>102.823423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>79.633596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>45.850150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>161.152724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          MoneyPerMonth\n",
       "CountryLive                            \n",
       "Canada                       102.823423\n",
       "India                         79.633596\n",
       "United Kingdom                45.850150\n",
       "United States of America     161.152724"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get average monthly payments in the four countries of interest\n",
    "avg_pmt_gt3mo = survey_sub[survey_sub[\"MonthsProgramming\"]>3][[\"MoneyPerMonth\",\"CountryLive\"]].groupby([\"CountryLive\"]).mean()\n",
    "avg_pmt_gt3mo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd239807b8>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAccAAAGHCAYAAADFpJneAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd231e3160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survey_sub[survey_sub[\"MonthsProgramming\"]>3][[\"MoneyPerMonth\",\"CountryLive\"]].boxplot(by=\"CountryLive\",\n",
    "                                                    rot=45)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Money Per Month')\n",
    "plt.title('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 200)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbUAAAGHCAYAAAA3GMx7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd231efe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# zoom in to see the boxes!\n",
    "survey_sub[survey_sub[\"MonthsProgramming\"]>3][[\"MoneyPerMonth\",\"CountryLive\"]].boxplot(by=\"CountryLive\",\n",
    "                                                    rot=45)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Money Per Month')\n",
    "plt.title('')\n",
    "plt.ylim(0,200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Restricting to those enrolled for more than 3 months removes some of the more extreme outliers (though there are still some outliers that are quite high). It's certainly possible that the survey question was interpreted incorrectly.  In this case, we can also try removing outliers using a statistical methodology."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# remove all participants that had PaymentPerMonth > Q3+1.5*IQR\n",
    "quantiles = survey_sub[survey_sub[\"CountryLive\"]==\"Canada\"][\"MoneyPerMonth\"].quantile([0.25,0.75])\n",
    "Canada_limit = quantiles[0.75] + 1.5*(quantiles[0.75]-quantiles[0.25])\n",
    "\n",
    "quantiles = survey_sub[survey_sub[\"CountryLive\"]==\"India\"][\"MoneyPerMonth\"].quantile([0.25,0.75])\n",
    "India_limit = quantiles[0.75] + 1.5*(quantiles[0.75]-quantiles[0.25])\n",
    "\n",
    "quantiles = survey_sub[survey_sub[\"CountryLive\"]==\"United Kingdom\"][\"MoneyPerMonth\"].quantile([0.25,0.75])\n",
    "UK_limit = quantiles[0.75] + 1.5*(quantiles[0.75]-quantiles[0.25])\n",
    "\n",
    "quantiles = survey_sub[survey_sub[\"CountryLive\"]==\"United States of America\"][\"MoneyPerMonth\"].quantile([0.25,0.75])\n",
    "US_limit = quantiles[0.75] + 1.5*(quantiles[0.75]-quantiles[0.25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "survey_sub_outl = survey_sub[(((survey_sub[\"CountryLive\"]==\"Canada\") & \n",
    "                             (survey_sub[\"MoneyPerMonth\"] <= Canada_limit)) |\n",
    "                            ((survey_sub[\"CountryLive\"]==\"India\") & \n",
    "                             (survey_sub[\"MoneyPerMonth\"] <= India_limit)) |\n",
    "                            ((survey_sub[\"CountryLive\"]==\"United Kingdom\") & \n",
    "                             (survey_sub[\"MoneyPerMonth\"] <= UK_limit)) |\n",
    "                            ((survey_sub[\"CountryLive\"]==\"United States of America\") & \n",
    "                             (survey_sub[\"MoneyPerMonth\"] <= US_limit)) )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MoneyPerMonth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountryLive</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>6.826765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>2.530940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>6.096647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>14.428544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          MoneyPerMonth\n",
       "CountryLive                            \n",
       "Canada                         6.826765\n",
       "India                          2.530940\n",
       "United Kingdom                 6.096647\n",
       "United States of America      14.428544"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get average monthly payments in the four countries of interest\n",
    "outl_avg_pmt = survey_sub_outl[[\"MoneyPerMonth\",\"CountryLive\"]].groupby([\"CountryLive\"]).mean()\n",
    "outl_avg_pmt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow, the mean values for `MoneyPerMonth` are really driven by outliers! Let's take a look at the total estimated amount of earnings using the two outlier removal methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "United States of America    1880\n",
      "India                        335\n",
      "United Kingdom               175\n",
      "Canada                       155\n",
      "Name: CountryLive, dtype: int64\n",
      "MoneyPerMonth    15937.630637\n",
      "Name: Canada, dtype: float64\n",
      "MoneyPerMonth    26677.254651\n",
      "Name: India, dtype: float64\n",
      "MoneyPerMonth    8023.776305\n",
      "Name: United Kingdom, dtype: float64\n",
      "MoneyPerMonth    302967.121808\n",
      "Name: United States of America, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Method I: restrict to months > 3\n",
    "\n",
    "# number of participants:\n",
    "counts = survey_sub[survey_sub[\"MonthsProgramming\"]>3][\"CountryLive\"].value_counts()\n",
    "print(counts)\n",
    "\n",
    "# Canada\n",
    "print(avg_pmt_gt3mo.iloc[0] * counts[\"Canada\"])\n",
    "\n",
    "# India\n",
    "print(avg_pmt_gt3mo.iloc[1] * counts[\"India\"])\n",
    "\n",
    "# UK\n",
    "print(avg_pmt_gt3mo.iloc[2] * counts[\"United Kingdom\"])\n",
    "\n",
    "# US\n",
    "print(avg_pmt_gt3mo.iloc[3] * counts[\"United States of America\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "United States of America    2485\n",
      "India                        371\n",
      "United Kingdom               234\n",
      "Canada                       202\n",
      "Name: CountryLive, dtype: int64\n",
      "MoneyPerMonth    1379.006539\n",
      "Name: Canada, dtype: float64\n",
      "MoneyPerMonth    938.978677\n",
      "Name: India, dtype: float64\n",
      "MoneyPerMonth    1426.615383\n",
      "Name: United Kingdom, dtype: float64\n",
      "MoneyPerMonth    35854.930911\n",
      "Name: United States of America, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Method II: remove statistical outliers\n",
    "counts = survey_sub_outl[\"CountryLive\"].value_counts()\n",
    "print(counts)\n",
    "\n",
    "# Canada\n",
    "print(outl_avg_pmt.iloc[0] * counts[\"Canada\"])\n",
    "\n",
    "# India\n",
    "print(outl_avg_pmt.iloc[1] * counts[\"India\"])\n",
    "\n",
    "# UK\n",
    "print(outl_avg_pmt.iloc[2] * counts[\"United Kingdom\"])\n",
    "\n",
    "# US\n",
    "print(outl_avg_pmt.iloc[3] * counts[\"United States of America\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "* Method I indicates that advertisting would be best in the US, followed by India, Canada, and then the UK.\n",
    "* Method II indicates that advertising would be best in the US, followed by the UK, then Canada, then India.\n",
    "* Since the expected returns from the US are more than an order or magnitude larger than the returns from the other countries, the best approaches might be to:\n",
    "    * only advertise in the US, or\n",
    "    * spend most of the money (>= 90%) on advertising in the US and split the rest between the UK, India, and Canada."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}