Mission310Solutions.ipynb

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    "# Finding the Best Markets to Advertise In\n",
    "\n",
    "In this project, we'll aim to find the two best markets to advertise our product in — we're working for an e-learning company that offers courses on programming. Most of our courses are on web and mobile development, but we also cover many other domains, like data science, game development, etc.\n",
    "\n",
    "# Understanding the Data\n",
    "\n",
    "To avoid spending money on organizing a survey, we'll first try to make use of existing data to determine whether we can reach any reliable result.\n",
    "\n",
    "One good candidate for our purpose is [freeCodeCamp's 2017 New Coder Survey](https://medium.freecodecamp.org/we-asked-20-000-people-who-they-are-and-how-theyre-learning-to-code-fff5d668969). [freeCodeCamp](https://www.freecodecamp.org/) is a free e-learning platform that offers courses on web development. Because they run [a popular Medium publication](https://medium.freecodecamp.org/) (over 400,000 followers), their survey attracted new coders with varying interests (not only web development), which is ideal for the purpose of our analysis.\n",
    "\n",
    "The survey data is publicly available in [this GitHub repository](https://github.com/freeCodeCamp/2017-new-coder-survey). Below, we'll do a quick exploration of the `2017-fCC-New-Coders-Survey-Data.csv` file stored in the `clean-data` folder of the repository we just mentioned."
   ]
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     "text": [
      "(2882, 136)\n"
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       "  <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",
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       "      <th>CodeEventNodeSchool</th>\n",
       "      <th>CodeEventNone</th>\n",
       "      <th>CodeEventOther</th>\n",
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       "      <th>CodeEventWomenCode</th>\n",
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       "      <th>CommuteTime</th>\n",
       "      <th>CountryCitizen</th>\n",
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       "      <th>EmploymentField</th>\n",
       "      <th>EmploymentFieldOther</th>\n",
       "      <th>EmploymentStatus</th>\n",
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       "      <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",
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       "      <th>ID.x</th>\n",
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       "      <th>IsSoftwareDev</th>\n",
       "      <th>IsUnderEmployed</th>\n",
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       "      <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",
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       "  <tbody>\n",
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       "      <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",
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       "      <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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "fcc = pd.read_csv('2017-fCC-New-Coders-Survey-Data.csv')\n",
    "print(fcc.shape)\n",
    "\n",
    "pd.options.display.max_columns = 150 # to avoid truncated output \n",
    "fcc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for Sample Representativity\n",
    "\n",
    "As we mentioned in the introduction, most of our courses are on web and mobile development, but we also cover many other domains, like data science, game development, etc. For the purpose of our analysis, we want to answer questions about a population of new coders that are interested in the subjects we teach. We'd like to know:\n",
    "\n",
    "* Where are these new coders located.\n",
    "* What locations have the greatest densities of new coders.\n",
    "* How much money they're willing to spend on learning.\n",
    "\n",
    "So we first need to clarify whether the data set has the right categories of people for our purpose. The `JobRoleInterest` column describes for every participant the role(s) they'd be interested in working in. If a participant is interested in working in a certain domain, it means that they're also interested to learn about that domain. So let's take a look at the frequency distribution table of this column and determine whether the data we have is relevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Full-Stack Web Developer                                                                                                                                                                                  12.015810\n",
       "  Front-End Web Developer                                                                                                                                                                                  4.426877\n",
       "  Data Scientist                                                                                                                                                                                           2.371542\n",
       "  Mobile Developer                                                                                                                                                                                         1.660079\n",
       "Back-End Web Developer                                                                                                                                                                                     1.581028\n",
       "Full-Stack Web Developer,   Front-End Web Developer                                                                                                                                                        1.264822\n",
       "  Front-End Web Developer, Full-Stack Web Developer                                                                                                                                                        1.106719\n",
       "  Product Manager                                                                                                                                                                                          1.106719\n",
       "Data Engineer                                                                                                                                                                                              1.027668\n",
       "Information Security                                                                                                                                                                                       0.948617\n",
       "  Front-End Web Developer, Back-End Web Developer, Full-Stack Web Developer                                                                                                                                0.948617\n",
       "Back-End Web Developer, Full-Stack Web Developer                                                                                                                                                           0.948617\n",
       "  User Experience Designer,   Front-End Web Developer                                                                                                                                                      0.790514\n",
       "Back-End Web Developer, Full-Stack Web Developer,   Front-End Web Developer                                                                                                                                0.790514\n",
       "  User Experience Designer                                                                                                                                                                                 0.790514\n",
       "Game Developer                                                                                                                                                                                             0.711462\n",
       "Back-End Web Developer,   Front-End Web Developer, Full-Stack Web Developer                                                                                                                                0.632411\n",
       "Full-Stack Web Developer, Back-End Web Developer                                                                                                                                                           0.632411\n",
       "  Front-End Web Developer, Full-Stack Web Developer, Back-End Web Developer                                                                                                                                0.474308\n",
       "  Front-End Web Developer,   User Experience Designer                                                                                                                                                      0.474308\n",
       "Full-Stack Web Developer,   Mobile Developer                                                                                                                                                               0.395257\n",
       "Data Engineer,   Data Scientist                                                                                                                                                                            0.395257\n",
       "Back-End Web Developer,   Front-End Web Developer                                                                                                                                                          0.395257\n",
       "  Front-End Web Developer, Full-Stack Web Developer,   Mobile Developer                                                                                                                                    0.395257\n",
       "  Data Scientist, Full-Stack Web Developer                                                                                                                                                                 0.395257\n",
       "  DevOps / SysAdmin                                                                                                                                                                                        0.395257\n",
       "  Mobile Developer, Game Developer                                                                                                                                                                         0.316206\n",
       "Full-Stack Web Developer, Information Security                                                                                                                                                             0.316206\n",
       "  User Experience Designer, Full-Stack Web Developer,   Front-End Web Developer                                                                                                                            0.316206\n",
       "Full-Stack Web Developer,   Front-End Web Developer, Back-End Web Developer                                                                                                                                0.316206\n",
       "                                                                                                                                                                                                            ...    \n",
       "  Product Manager, Game Developer,   Front-End Web Developer,   Mobile Developer,   Data Scientist, Full-Stack Web Developer, Back-End Web Developer, Data Engineer,   User Experience Designer            0.079051\n",
       "Back-End Web Developer, Full-Stack Web Developer,   Mobile Developer, Game Developer,   Front-End Web Developer                                                                                            0.079051\n",
       "Game Developer,   Mobile Developer, Back-End Web Developer, Full-Stack Web Developer,   Front-End Web Developer, Information Security                                                                      0.079051\n",
       "  Mobile Developer,   User Experience Designer,   Front-End Web Developer, Back-End Web Developer                                                                                                          0.079051\n",
       "  Product Manager, Back-End Web Developer,   DevOps / SysAdmin,   Front-End Web Developer,   Quality Assurance Engineer,   Data Scientist, Information Security, Full-Stack Web Developer                  0.079051\n",
       "  Front-End Web Developer,   DevOps / SysAdmin,   User Experience Designer, Back-End Web Developer, Full-Stack Web Developer                                                                               0.079051\n",
       "Back-End Web Developer, Full-Stack Web Developer,   Mobile Developer,   DevOps / SysAdmin                                                                                                                  0.079051\n",
       "Full-Stack Web Developer, Back-End Web Developer,   Front-End Web Developer, Information Security, Data Engineer,   Data Scientist                                                                         0.079051\n",
       "Information Security, Full-Stack Web Developer,   User Experience Designer,   Mobile Developer,   Front-End Web Developer, Back-End Web Developer                                                          0.079051\n",
       "  Front-End Web Developer,   User Experience Designer,   Product Manager, Back-End Web Developer, Information Security,   DevOps / SysAdmin, Full-Stack Web Developer, Data Engineer,   Data Scientist     0.079051\n",
       "Game Developer, Data Engineer,   Data Scientist,   DevOps / SysAdmin, Information Security,   Product Manager                                                                                              0.079051\n",
       "Full-Stack Web Developer, Game Developer,   Mobile Developer                                                                                                                                               0.079051\n",
       "Game Developer,   Mobile Developer                                                                                                                                                                         0.079051\n",
       "Full-Stack Web Developer,   Front-End Web Developer, Back-End Web Developer, Game Developer                                                                                                                0.079051\n",
       "  Data Scientist, Data Engineer,   Front-End Web Developer                                                                                                                                                 0.079051\n",
       "Full-Stack Web Developer, Information Security,   Front-End Web Developer, Game Developer,   User Experience Designer, Back-End Web Developer                                                              0.079051\n",
       "Data Engineer,   User Experience Designer, Full-Stack Web Developer, Game Developer,   Front-End Web Developer, Back-End Web Developer                                                                     0.079051\n",
       "Full-Stack Web Developer, Game Developer,   Front-End Web Developer,   Mobile Developer                                                                                                                    0.079051\n",
       "  Product Manager,   Data Scientist, Data Engineer                                                                                                                                                         0.079051\n",
       "Full-Stack Web Developer,   DevOps / SysAdmin,   Mobile Developer, Back-End Web Developer,   Quality Assurance Engineer                                                                                    0.079051\n",
       "Full-Stack Web Developer,   Mobile Developer, Back-End Web Developer,   DevOps / SysAdmin                                                                                                                  0.079051\n",
       "  Mobile Developer,   Front-End Web Developer,   Data Scientist,   User Experience Designer, Full-Stack Web Developer                                                                                      0.079051\n",
       "Back-End Web Developer, Data Engineer, Full-Stack Web Developer                                                                                                                                            0.079051\n",
       "  Front-End Web Developer, Back-End Web Developer, Full-Stack Web Developer,   User Experience Designer                                                                                                    0.079051\n",
       "Back-End Web Developer, Full-Stack Web Developer,   Quality Assurance Engineer,   Product Manager                                                                                                          0.079051\n",
       "  Data Scientist, Data Engineer,   User Experience Designer                                                                                                                                                0.079051\n",
       "  User Experience Designer,   Product Manager,   Mobile Developer,   Data Scientist, Full-Stack Web Developer, Back-End Web Developer                                                                      0.079051\n",
       "  Data Scientist, Game Developer,   Mobile Developer,   User Experience Designer, Full-Stack Web Developer,   Front-End Web Developer, Back-End Web Developer                                              0.079051\n",
       "Full-Stack Web Developer,   Mobile Developer,   Front-End Web Developer, Back-End Web Developer                                                                                                            0.079051\n",
       "Information Security,   Data Scientist, Data Engineer,   Mobile Developer, Full-Stack Web Developer, Game Developer                                                                                        0.079051\n",
       "Name: JobRoleInterest, Length: 732, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcc['JobRoleInterest'].value_counts(normalize = True) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The information in the table above is quite granular, but from a quick scan it looks like:\n",
    "\n",
    "* A lot of people are interested in web development (full-stack _web development_, front-end _web development_ and back-end _web development_).\n",
    "* A few people (2.4%) are interested in mobile development.\n",
    "* A few other people are interested in domains other than web and mobile development.\n",
    "\n",
    "It's also interesting to note that many people are interested in more than one subject. In the next code block, we'll:\n",
    "\n",
    "- Split each string in the `JobRoleInterest` to find the number of options for each participant.\n",
    "    - We'll first drop the null values because we can't split `Nan` values.\n",
    "- Generate a frequency table for the variable describing the number of options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     27.272727\n",
       "2     12.569170\n",
       "3     18.023715\n",
       "4     16.521739\n",
       "5     12.332016\n",
       "6      6.719368\n",
       "7      3.320158\n",
       "8      1.185771\n",
       "9      1.106719\n",
       "10     0.474308\n",
       "11     0.237154\n",
       "12     0.237154\n",
       "Name: JobRoleInterest, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interests_no_nulls = fcc['JobRoleInterest'].dropna()\n",
    "splitted_interests = interests_no_nulls.str.split(',')\n",
    "\n",
    "n_of_options = splitted_interests.apply(lambda x: len(x)) # x is a list of job options\n",
    "n_of_options.value_counts(normalize = True).sort_index() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that only 27.3% of the participants have a clear idea about what programming niche they'd like to work in, while the vast majority of students have mixed interests. But given that we offer courses on various subjects, the fact that new coders have mixed interest might be actually good for us.\n",
    "\n",
    "The focus of our courses is on web and mobile development, so let's find out how many people from this survey chose at least one of these two options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True     87.98419\n",
      "False    12.01581\n",
      "Name: JobRoleInterest, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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4deoUduzYgfDwcFy/fh2xsbEqtxVUtO+++w5Vq1ZFRESESsfulClTxKu915VnQgNefYmnp6cjMzOzzImtsGPy77//Avi/Y3LhwgXs27cPnTp1wm+//aa07/Py8vDDDz+UKQ5N1atXD0uXLsWSJUtw8+ZNnDx5EuvXr8eKFSvw+PFj/Pjjj0XOX5btKOw45u+jyMhItb/IdVF+zM7OzoiNjS3x/Or2Rf4yPTw8sG3bNo2X1a1bN3Tr1g3Pnz/HuXPncPToUfz0008YNWoUwsPDlQa5FCZ/3ePGjRN/BGs6z7///qsyOCs3N1dMlPqEfWqFqFmzJnx8fHD//n2YmJjgww8/LLJ+fp9NYU8CiIqKAqD+Eh149UXWv39/bNu2DW3atEFiYiJu3LghTjcwMFBqPqoot2/fRqNGjVQSWl5eXqmH5ub/wi5J/K1btwYAHDlypFTrLEhdIlYoFLh69SqqV68OR0dHAK+2HQB69+6t8mPi3LlzZbpBvCBN94dEIkGjRo3w8ccf48CBAzA2Ntbo/siK2I7843Hq1KkSz1uRirp6MzU1hbOzMxITE5Genl4u62vYsCHMzc1x7tw5tUP3i2NiYoIOHTpg9uzZmDt3LgRBULp9qKhzo1WrVjAwMCjRMcj/XlL3GTh9+nSle4iEJpjUijBjxgxs2rQJO3fuLHYIetu2bdGoUSOcO3dOqaMWeJXQwsPDUadOHfTu3RsAkJaWhrNnz6os58WLF+L9I9WqVRPL69Spg7S0tHL7Yi2MnZ0dbt++jQcPHohlgiAgODgY169fL9Uy8zuoCxtgoo6fnx8A4Ouvv8bdu3dVphd2W4U627dvF/sW8s2fPx9Pnz7Fhx9+KH7x5w+hjomJUar777//Ytq0aRqvrzhF7Y+rV6/izp07KuWPHj1CTk6O0jlRmIrYjhEjRkAqlWLhwoU4c+aMynRBEHDq1KlSfdGXRf6+vHfvntrpEydORE5ODiZMmKD2/rzMzEyx/1YTRkZG8PPzE/fls2fPVOqkp6cjISFB/HdkZKTaevmtN69/zgH150bdunUxePBgXLp0CUFBQWoT0v3793Hz5k3x3/mD1hYvXqx0VfbixQvMnTu32O2tjNj8WARra2uxc7o4EokEoaGh6Nevn3j/Wf59anv37kXVqlWxatUqsT/kwYMH6NGjBxwdHdG8eXNYW1vj6dOnOH78OP766y/07dsXDRo0EJffpUsX7NixAwMGDICbmxuMjY3RpEkTeHp6lus2T5gwAZ999hk6deqEDz74AEZGRuLITg8PDxw8eLDEy+zSpQt2796NyZMnw9vbGzVq1IC5uTnGjRtX6DydO3fG9OnTERwcDFdXV/E+tbS0NJw/fx41a9YUR5IWp0ePHvDw8ICPjw/kcjliY2MRFxcHBwcHpZGfLi4ucHV1RXh4OHr27AlXV1c8fPgQR48ehaOjI+rVq1fibVena9euWLZsGebMmYNr166JIxoDAgIQGRmJmTNnonXr1mjYsCEsLCyQmpqK/fv3Iy8vr9hm8Irajlq1amHDhg0YMWIEevbsiY4dO8LJyQlVqlTB/fv38eeff+LevXu4c+eO2Hf3JrRp0wampqbYtWsXqlatChsbG0gkEgwePBh2dnYYPnw4Ll68iDVr1qB58+bo1q0b7Ozs8PjxY9y9exexsbHo0qULtmzZovE6AwICcPXqVWzYsAGHDx9Gx44dYW1tjbS0NPz99984ffo0xowZg2bNmgEAvvrqK9y9exft27eHnZ0dqlWrhitXroj3SBYcgFbUuQEACxYswO3btxESEoLt27fDzc0NcrkcqampuHXrFs6ePYv58+eLT9hxdXXFuHHjsGbNGrRr1w4ffPCBeJ+aubk5LC0t8c8//5TX4dAJTGrlyMXFBZGRkVi4cCEiIyNx7NgxmJubw8vLC1OnThVPcuDVr+kZM2YgOjoaJ0+eRFpaGszNzfHuu+9i8uTJKrcFBAcHw8DAABEREYiLi0Nubi6GDh1a7klt9OjRqFq1KkJDQ7F161ZUq1YN7dq1w4oVK7B3795SJbURI0bg/v372LFjB1asWIGcnBzY2toWmdSAVzeNtmnTBqtXr8bx48eRmZmJunXronHjxvjoo480Xr+/vz/69OmDlStX4tatWzA1NcWIESPw9ddfKw1zNjQ0xNatWzFv3jwcPnwYq1evRr169eDr64tp06ahbdu2Jd52dTp16oQFCxbg559/xrp168RBGwEBAejWrRvu3buHU6dO4eDBg3jy5AksLCzQpk0b+Pn5oUuXLsUuv6K2o2PHjjh58iR+/PFHHDt2DGfOnIGRkRHkcjlat26Nb7755o0/f9Pc3BybN29GUFAQdu3aJQ6ScHV1Fa9YFyxYgJ49e+Knn35CTEwMMjIyYG5uDisrK3zyySfFdi28zsjICBs2bMDOnTuxefNmHDlyRBxoYmtri88++wxDhgwR60+dOhV//PEHLly4IHZPWFlZwd/fHxMmTFC6PaWocwN4NZBo37592LhxI3799Vfs27cP2dnZkMlksLOzw9dff41+/fopxRsSEoIGDRpg3bp1CAsLQ+3atdGnTx/MmjVLo768ykby/2+EJNI7/v7+2Lp1K8LDw+Hu7q7tcIjoDWCfGhER6Q0mNSIi0htMakREpDfYp0ZERHqDV2pERKQ3mNSIiEhvMKmREg8PD0il0hI9seNtNm7cOEil0hI9usjDw0PllSaRkZHiEztIMxs2bIBUKlV5gg+93ZjUdER8fDykUik6d+6sdvrhw4fFV7tfuHBBbZ0mTZpAKpWqfcySvstPClKpFM2aNSv0uYpZWVmwtbUV6+Y/J/FtkP+DJf+vTp06sLe3R6tWreDr64tffvlFfEQb6Z/S/ACrjPhEER3RrFkz1KpVCwkJCcjIyECtWrWUpp84cQISiQSCICAqKgotWrRQmn779m3cu3cP9vb2al+q+bYwMjLC3bt3ERkZia5du6pM37VrFzIzM8v1PVIltXbtWmRnZ2tl3QAwfPhw2NjYQBAEZGVlITk5GbGxsdi7dy++/fZbLF68GP3799dafERlwSs1HWFgYAB3d3fk5eWpfdJ/VFQUGjdujAYNGohP/H99OvDqMTtvs86dO8PY2Fh8Z9XrwsLCYGlpqdXXp9ja2opvBtCGESNG4Msvv8SMGTPw3XffYePGjbh69SpCQkLw9OlTjBkzBuHh4VqLj6gsmNR0SH5COnHihFL5o0ePcPnyZXTs2BHu7u44ffq0ykse8+dR13wZFRWFwYMHo379+pDJZGjSpAkCAgKKfDlg/nu3WrVqBblcjiZNmmDWrFlKLyDUxOPHj/Htt9+Ky7Gzs0Pfvn3VPoz49u3bkEql8Pb2xr179zBu3Dg0bNgQtWrV0viZk7Vr10bfvn2xf/9+pKWlKU27fPkyzp07h+HDhxf5FunIyEgMGDAADg4OsLCwQPPmzfHll1+qLO91GzduRPv27WFpaYmGDRti8uTJaudR16dWFIVCgXnz5sHV1RX16tWDra0tPD098fvvv2u8jOJUrVoV48ePR3BwMPLy8vDll1+qfZGopueSi4sLZDJZoa98WbNmDaRSKYKDg5XKU1JSEBgYCBcXF8jlctjb22PAgAEqn4nixMfH46OPPoKjoyNkMhkaN26MSZMmISkpSaXuvHnzxL65/fv3o3v37rCysoKDgwNGjx6ttjm/YFPejh070LFjR9SrVw9OTk6YNWuW+LaCo0ePwtPTEzY2NrC3t8f48eMLfQt0SbY9vz9x4cKFuHjxIgYOHAg7OztYWVnBy8tL5U0Kzs7O2LFjBwDA09NTqQla3zCp6ZD8pPb6lVh0dDQEQRCT2vPnz5VOWkEQEB0dDYlEgo4dOyrNu3jxYnh7e+Ps2bPo0aMH/Pz80LBhQ6xduxZdu3ZFSkqK2li++OILLFmyBB07dsT48eNRs2ZNLF++HP3799f49SIZGRno0aMHlixZAlNTU/j5+cHHxwcJCQkYPnx4oS86TE9PR8+ePXHlyhX4+Phg1KhR4tPKNeHr64ucnBxs3bpVqTwsLAwSiaTIhyGvXbsWPj4+OH36NHr37o2JEyeiXr16CA0NRZcuXQodQPPDDz8gMDAQzZs3h7+/P+zt7REWFoaePXuW6VX29+7dQ5cuXbBo0SLUrl0bo0ePRv/+/fH3339j5MiR5T6wZOTIkbCyssK9e/dUXqxZknNp6NChyMnJEb9IX7d161ZIJBKlB/8mJCTA3d0da9aswbvvvouxY8eid+/eOHv2LPr166fxk/T/+OMP9OjRAwcPHkTHjh3x6aefwtnZGZs2bULnzp1x6dIltfPt2bMHvr6+sLe3h5+fH1xcXLB7925079690H7qlStXYtq0aXBychLfGL98+XJ8/vnn2LlzJ0aMGAFLS0uMGjUKNjY22L59O8aPH6+ynNJu+7lz59CrVy/k5ubC19cXPXr0QGxsLLy9vXHr1i2x3oQJE+Ds7AzgVfNzYGAgAgMD8cUXX2i0TysVhUIh8E93/qytrQUAwpUrV8Syjz/+WDA0NBTu3r0rJCYmCgCEadOmidOjo6MFAEKTJk2UlrV3714BgODq6iokJSUpTfvxxx8FAEK/fv2Uyl1dXQUAQt26dZViSE9PF3r16iUAEObMmaPRtowYMUIAIIwcOVLIyMgQyy9duiTIZDJBIpEIkZGRYvn58+cFAAIAYdiwYUJ6errG+23Pnj0CAGHQoEFCRkaG8O677wqOjo7i9JSUFMHc3Fzo3Lmz0naeP39erBMfHy9UqVJFMDMzE+Li4pSW/9lnnwkABE9PT6XyQYMGCQAEY2NjISYmRmnaxx9/LAAQxowZo7KPDQ0N1cY/c+ZMpfL27dsLEolE+OWXX5TK79y5IzRu3FiQSCRCbGysRvsof5sPHDhQZL0BAwaoxFLSc+ny5cuCgYGB0LRpU5Xlnz59WgAgdOjQQSxLS0sT3n33XaFatWoq8V29elWwtLQUqlevLty6dUss/+GHHwQAwurVq8Wy5ORkQSqVCgYGBsIff/yhtJwlS5YIAITGjRsrlU+bNk0873bu3Kk0bfbs2QIAoWfPnmqPe+3atYX4+Hil41KrVi3BwMBAqF27ttL5nZqaKjRq1EgAoHTMyrLtAIR169YpzbNw4UIBgDB27Fi1MRd3/Cv7H6/UdEz+lVbBJocTJ07AxcUFNWvWhEwmg5OTk8p0QLU/LTQ0FACwdOlSlZecjhgxAs7Ozti3bx+ePn2qEseECROU3iVnaGiIb7/9FgCwadOmYrfjxYsX+O2332BqaorZs2dDIpGI02xtbTFlyhQIgoCNGzeqzGtsbIy5c+eKbwEuKYlEAl9fXyQmJopv/P3999/x+PFjpXdXvW7btm3IycnB2LFjVd78/cUXX8DCwgIHDx5U22w7dOhQNGnSRKls5syZqF69OrZt26b2zczFuXjxIk6ePAlvb2+V14lIpVIEBgZCEAT8+uuvJV52USwtLQG8eqlovpKeSzY2NujYsSMuXbqEy5cvK9XPv+oo+HqlAwcO4Pbt2xg3bhzatWunVN/KygqTJk3Cs2fPiu3rCw8Ph0KhQP/+/dG+fXulaaNGjUKTJk1w5coVnDt3TmXerl27olu3bkplEyZMgJWVFY4cOSK+1PP16QUHZkmlUvTq1Qt5eXno27evUt+tsbExvL29AUBpn5Rl293d3TFw4EClMl9fXxgYGBQ6SlrfcfSjjunUqRO2bt2KqKgoDB06FA8ePMCtW7cwdepUsY67uzt+/vlnZGZmwszMrND+tLi4OFSpUgW7d+9Wu66XL1/i5cuXuH37Npo2bao07fUvBABwcnJCnTp1kJiYiOfPn8PExKTQ7bh+/Tqys7Ph6uqqMpKzYKyvv5EaAN55550yt/UPGzYM8+fPR1hYGNq3b4+wsDDUqVMHXl5ehc6TH8vrTbgAYGJigrZt2yI8PBwJCQno3r270nR1+6tOnTpo1KgRLly4gL/++kt8caOm4uLiALzqlwwKClKZnp90Cr7puDwV/CFSmnNp2LBhiIyMxJYtW/Ddd98BAHJzc/Hrr7/C1NRU/ILPXz4A3L17V+225jelFbetRR1DiUSCTp064fLly7h48SJatmypNF1CmBpxAAAgAElEQVTdMaxatSpat26N33//HZcuXYJcLleaXvAdifnyfxS8/pkqOK3gm+XLsu3qBjwZGxujbt26ZWr2rsyY1HRM/pd9fqLK718r+CF1d3fH2rVrcfLkSXTv3h2xsbGoUqWKyq+8jIwMCIKAkJCQItep7krNwsJCbV0LCwukp6cjMzOzyKT25MmTIpeT/+HOr6fJukvCwsICHh4eCA8Px5kzZ3Dq1ClMmjSpyLcyV0TM+eXq5inOo0ePAAARERGIiIgotJ6641cW+W9Crlu3rlhWmnOpb9++qFmzJn799VfMmTMHRkZGiIiIQEpKCoYNG4YaNWqIdfO3dc+ePRovX52yHEOZTKZ2nqKOoZmZmUpZ/iCkoqbl5OSIZWXZ9sJeympkZFSq1gF9wKSmYywtLdGoUSPcuHEDiYmJiIqKgrGxsdLbijt06ACJRIKoqCjUrl0bmZmZaNeuHUxNTZWWZWZmBgMDg1LdjP3w4UO88847asvzl12U/A9bYSMs87841X0oC14hlMWoUaMQHh6O0aNHA0CRTY8FY3n48KHYqV5QUTEXtp355aV5I3T+PMHBwfDz8yvx/KWRm5srDhBp3bq1WF6ac8nExAQ+Pj4ICwvD4cOH0bt3b7VNj8D/beuWLVvQu3fvUsdflvOuYHNrQWU5hpoor22nV9inpoPyr8qioqIQHR2N1q1bo1q1auL02rVro3HjxoiKiiry/rQ2bdpAoVDg2rVrJY4hvy+qoOvXryM9PR0NGzYs8ioNeNVUWa1aNVy+fBkZGRkq0/Pjrsj7xbp06QJbW1vcv38fbm5uxd4b9v777wOA2vsEs7OzcebMGUgkErVNTur2V3p6Om7cuAFTU1O8++67JY6/TZs2APBGnwARFhaGlJQU2NjYwM3NTSmW0pxL+clr69atePz4Mfbv3w97e3uVpr7y2taijmHBcnXnnbpj+N9//+HPP/+ERCJR25xYHt7UcTYwePV1r+9XcExqOig/Qa1fvx73799X2z/g7u6Oa9euiX0c6pLaxIkTAQCTJ09WO3Q/Ozu70A/SypUrlYav5+bm4ptvvgHwakhwcYyNjfHhhx8iKysLc+fOVZp2//59LF26FBKJBCNGjCh2WaVlYGCAjRs3YtOmTVi2bFmx9YcMGYIqVapg7dq1SsOhAWDRokVITU2Fh4eH2qatrVu3qgyImD9/Pp49e4bBgwcXeV9cYVq1agVXV1eEh4dj48aNEATVt0TdvHkT9+7dK/GyX5eTk4M1a9bgyy+/hIGBAYKDg5Waakt7LrVt2xaOjo44dOgQ1q1bh+zsbAwdOlTlarxPnz6wt7fHunXrcOjQIbUxxsfHF9tP1LdvX0ilUuzatQunT59WmrZhwwYkJCSgcePGcHFxUZn3+PHjOHbsmFJZ/uege/fuKv1p5aW8tr04+f3U5XG+6DI2P+qgDh06wNDQEFevXgWgvtPb3d0doaGhuHr1KkxNTdGqVSuVOl26dMHs2bPx7bffwsXFBT169ICDgwOePXuG5ORknDp1Cu+++y4iIyNV5m3Tpg06dOgAHx8fmJqa4ujRo7h69Spat24Nf39/jbZjzpw5iIuLw/r16xEfH4+OHTvi8ePH2L17NxQKBWbMmFHhT/Zo3ry5xutwcHDAd999hy+++AKdO3dGv379IJPJEBcXh9jYWNjY2GDRokVq5+3WrRt69uwJHx8fWFhY4OTJkzhz5gzeffddfPXVV6WO/6effoK3tzc+/fRTrFq1Cq1atUKtWrXw4MEDXLt2DZcuXcK2bdtgY2Oj8TI3bdokHvOnT5/i7t27iI2NRVpaGmrVqoVFixahT58+SvOU5VwaOnQo5syZg+DgYJV70/JVrVoVmzdvxoABAzB48GC0adMGzZo1g4mJCR48eICLFy/i1q1biI2NLfKeRTMzM6xYsQKjRo1C37594e3tDTs7O1y5cgWHDh2CVCrFqlWr1M7r6emJIUOG4IMPPoC9vT3i4+Nx/Phx1K1bt9B7KstDeW17cbp27YoVK1Zg9uzZuHz5MszNzWFgYKA0CE0fMKnpIKlUivfffx/nz5+Hqampyigt4NVILUNDQ+Tm5sLNzQ1VqlRRu6wpU6agXbt2WLVqFeLi4nDgwAGYmZnB0tISgwcPLvQZfyEhIdi9ezfCwsKQnJyMunXrYtKkSZg+fXqRgy0KqlWrFg4fPoylS5ciPDwcK1euRLVq1dCsWTP4+/sXORJRW8aOHQtHR0csX74c4eHhePbsGaysrODn54dp06YpDZ4o6NNPP4WnpydCQ0Px119/wczMDL6+vpg1a5ba0Z+asra2RmRkJNasWYO9e/di586dyMnJgYWFBRwdHbFgwQK4urqWaJmbN28G8Oo2jRo1akAmk6Fdu3bo2rUrfHx8Cv3iLO25NGTIEMybNw85OTno0KFDoc8mbdKkCU6ePImVK1fi4MGDYv+bhYUF3nvvPUyePFmjZlwvLy8cPnwYixcvRkREBB4/fgwLCwsMHz4cAQEBha6/X79++Oijj/D999/jwIEDqFKlCvr164fZs2dX+PNUy2vbi9KtWzcEBQUhLCwMa9euxYsXL2BoaKh3SY1vviait9q8efOwaNEirF69GoMHD9Z2OFRG7FMjIiK9waRGRER6g0mNiIj0BvvUiIhIb/BKjYiI9AaTGhER6Q0mNSIi0htMakREpDeY1IiISG8wqRERkd5gUiMiIr3BpEZERHqDSY2IiPQGkxoREekNJjUiItIbTGpERKQ3mNSIiEhvMKkREZHeYFIjIiK98caS2smTJzFkyBC89957kEql2Lx5s9J0QRAQFBQEJycnWFpawsvLC9euXVOqo1AoMG7cONjZ2cHOzg7jxo2DQqF4U5tAREQ67o0ltadPn8LZ2RnBwcEwMTFRmb5s2TKsWLECISEhOH78OGQyGXx8fJCZmSnWGTNmDBISEvDrr7/it99+Q0JCAsaPH/+mNoGIiHScVt58bW1tjQULFmD48OEAXl2lOTk5YezYsZg2bRoA4Pnz53B0dMTcuXMxevRo3LhxA23btsXBgwfh6uoKADh16hQ8PT1x9uxZODo6vunNICIiHaMTfWpJSUlITU1F165dxTITExO4ubkhLi4OAHDmzBmYmpqibdu2Yh1XV1fUqFFDrENERG83nUhqqampAACZTKZULpPJ8PDhQwDAw4cPUadOHUgkEnG6RCJB3bp1xTpERPR204mklq9gwgJeNUu+nsRe93odIiJ6e+lEUpPL5QCgcsWVlpYmXr1ZWFggLS0NgvB/XYCCICA9PV3lCo+IiN5OOpHU7O3tIZfLERERIZZlZ2fj1KlTYh9amzZtkJWVhTNnzoh1zpw5g6dPnyr1s5F2JSYmajsEojeG57vuMXpTK8rKysLt27cBAHl5ebh37x4SEhJQq1Yt2Nrawt/fH4sXL4ajoyMaNGiARYsWoUaNGhg4cCAAoFGjRujevTs+++wzLFu2DIIg4LPPPkOvXr048pGIiAC8wSH90dHR6Nu3r0r50KFDERoaCkEQEBwcjF9++QUKhQItW7bEokWL4OzsLNbNyMhAYGAgDhw4AADw9PTEggULIJVK38QmkAYSExP5I4PeGjzfdY9W7lMj/cUPOb1NeL7rHp3oUyMiIioPTGpERKQ3mNSIiEhvMKkREZHeYFIjIiK9waRGRER6g0mNiIj0BpMaERHpDSY1IiLSG0xqRESkN5jUiIhIbzCpERGR3mBSIyIivcGkRkREeoNJjYiI9AaTGhER6Q0mNSIi0htMakREpDeY1IiISG8wqRERkd5gUiMiIr3BpEZERHrDSNsBkCrpz/e1HUIZVAdiKmf8itHW2g6BiMqIV2pERKQ3mNSIiEhvMKkREZHeYFIjIiK9waRGRER6g0mNiIj0BpMaERHpDSY1IiLSG0xqRESkN5jUiIhIbzCpERGR3mBSIyIivcGkRkREekNnklpubi7mzZuHZs2aQS6Xo1mzZpg3bx5evnwp1hEEAUFBQXBycoKlpSW8vLxw7do1LUZNRES6RGeS2tKlS7Fu3TqEhITgzJkzCA4Oxtq1a/H999+LdZYtW4YVK1YgJCQEx48fh0wmg4+PDzIzM7UYORER6QqdSWpnzpyBh4cHPD09YW9vj969e8PT0xPnzp0D8OoqLTQ0FFOmTIG3tzecnZ0RGhqKrKws/Pbbb1qOnoiIdIHOJDVXV1fExMTg5s2bAIDr168jOjoaPXr0AAAkJSUhNTUVXbt2FecxMTGBm5sb4uLitBIzERHpFp158/WUKVOQlZWFtm3bwtDQEC9fvsS0adMwZswYAEBqaioAQCaTKc0nk8mQkpJS6HITExMrLugKU13bAbyVKue5QtrG8+bNcnR0LHK6ziS1Xbt2Ydu2bVi3bh2cnJxw6dIlTJ8+HXZ2dvD19RXrSSQSpfkEQVApK6i4HaCTYu5rO4K3UqU8V0irEhMTed7oGJ1Jal9//TUmTZqEAQMGAAAaN26M5ORkLFmyBL6+vpDL5QCAhw8fwsbGRpwvLS1N5eqNiIjeTjrTp/bs2TMYGhoqlRkaGiIvLw8AYG9vD7lcjoiICHF6dnY2Tp06hbZt277RWImISDfpzJWah4cHli5dCnt7ezg5OSEhIQErVqzAkCFDALxqdvT398fixYvh6OiIBg0aYNGiRahRowYGDhyo5eiJiEgX6ExSW7BgAebPn4+pU6ciLS0NcrkcI0eOxBdffCHWmTx5Mp4/f46AgAAoFAq0bNkSu3btgpmZmRYjJyIiXSFRKBSCtoMgZdKfOVBEGxSjrbUdAlUyHCiie3SmT42IiKismNSIiEhvMKkREZHeYFIjIiK9waRGRER6g0mNiIj0BpMaERHpDSY1IiLSG0xqRESkN5jUiIhIbzCpERGR3mBSIyIivcGkRkREeoNJjYiI9AaTGhER6Q0mNSIi0htMakREpDeY1IiISG8wqRERkd5gUiMiIr3BpEZERHqDSY2IiPQGkxoREekNJjUiItIbTGpERKQ3mNSIiEhvMKkREZHeYFIjIiK9waRGRER6g0mNiIj0BpMaERHpDSY1IiLSG0xqRESkN4xKUvnGjRv4/fff8fDhQyxatAg3b97Ef//9hyZNmlRUfERERBrT+Eptz5498PLyQkpKCrZv3w4AePr0KWbOnFlhwREREZWExkntu+++w65du7BkyRIYGLyarUmTJrh8+XKFBUdERFQSGie1f//9F02bNgUASCQS8b/5/18e/vnnH/j5+aF+/fqQy+Vo27YtYmJixOmCICAoKAhOTk6wtLSEl5cXrl27Vm7rJyKiyk3jpNa8eXNs27ZNqWznzp1o2bJluQSiUCjQq1cvCIKAHTt2IC4uDgsWLIBMJhPrLFu2DCtWrEBISAiOHz8OmUwGHx8fZGZmlksMRERUuUkUCoWgScWbN2+if//+sLOzw59//okOHTrg1q1b2L17N+rXr1/mQObMmYOTJ0/i0KFDaqcLggAnJyeMHTsW06ZNAwA8f/4cjo6OmDt3LkaPHl3mGHSF9Of72g7hraQYba3tEKiSSUxMhKOjo7bDoAI0vlJr2LAhzpw5g7Fjx+Krr77C8OHDERsbWy4JDQD++OMPtGzZEqNHj0aDBg3QoUMHrFmzBoLwKucmJSUhNTUVXbt2FecxMTGBm5sb4uLiyiUGIiKq3Eo0pL969erw8fGpkEDu3LmDn376CRMmTMCUKVNw6dIlBAYGAgDGjRuH1NRUAFBqjsz/d0pKSqHLTUxMrJB4K1Z1bQfwVqqc5wppG8+bN6u4K2ONk5qnp6facmNjY1hZWaFv376F1tFEXl4eWrRogW+++QYA8P777+P27dtYt24dxo0bJ9Z7fWCKIAhFDlaplE0DMWx+1IZKea6QVrH5Ufdo3PzYvn17JCcno3379hg8eLD47xYtWsDCwgKTJk3CsmXLSh2IXC5Ho0aNlMoaNmyIe/fuidMB4OHDh0p10tLSVK7eiIjo7aTxlVpERAR27typlHgGDRoEf39/HDt2DH379sXHH3+MyZMnlyoQV1dX3Lp1S6ns1q1bsLW1BQDY29tDLpcjIiICLi4uAIDs7GycOnUKc+bMKdU6iYhIv2h8pZaYmAgHBwelMltbW7E9uWXLlkhLSyt1IBMmTMDZs2exaNEi3L59G3v27MGaNWswZswYAK+aHf39/bF06VLs3bsXV69exYQJE1CjRg0MHDiw1OslIiL9ofGVWrt27TBx4kTMmDEDVlZWePDgAYKDg9GuXTsAwJUrV8QmwtJwcXHB5s2bMWfOHCxcuBA2NjaYMWOGmNQAYPLkyXj+/DkCAgKgUCjQsmVL7Nq1C2ZmZqVeLxER6Q+N71PLyMjA1KlTER4ejtzcXBgZGaFv375YsGAB6tSpg8TERGRlZaFFixYVHbPe431q2sH71KikOFBE92h8pVarVi2sX78eeXl5SEtLQ926dcVnQAIcOUZERNpXovvUgFdP5n/27Bnu3r0rlr3e10ZERKQNGie169evY+zYsbh8+TIkEonS/WGPHj2qsACJiIg0pfHox6lTp8Ld3R1///03zMzMcOfOHYwePRqhoaEVGR8REZHGNL5Su3z5Mvbs2YMqVapAEASYm5tjzpw5aNeuHQYPHlyRMRIREWlE4yu1atWqIScnBwBQp04dJCcnIy8vDxkZGRUWHBERUUlonNTatWuH3bt3AwC8vb3x4YcfwsvLC+7u7hUWHBERUUlo3Pz4yy+/iP//9ddf47333kNWVhaGDh1aEXERERGVmMZXasuXL/+/mQwMMHjwYHzyySdYv359hQRGRERUUhontQULFqgtX7RoUbkFQ0REVBbFNj9GRUUBAHJzc3HixAnxTdTAq7dRm5qaVlx0REREJVBsUvv0008BvHrNy6RJk8RyiUQCuVxe6BUcERHRm1ZsUktISAAAjB8/HqtXr67wgIiIiEpL49GPBRNaXl6e0rSCDzYmIiLSFo2zUXx8PHr06AErKyvUrVsXdevWRZ06dVC3bt2KjI+IiEhjGl+pTZgwAR4eHvjxxx9hYmJSkTERERGVisZJLTk5GbNmzRKfzE9ERKRrNG5+9PLywvHjxysyFiIiojLR+ErtxYsXGDFiBFxdXWFhYaE0jaMiiYhIF2ic1Bo1aoRGjRpVZCxERERlonFSmz59ekXGQUREVGYlusEsIiICkyZNEl8KeuHCBfExWkRERNqmcVJbvXo1Pv/8c9SvXx+nTp0C8OrFofPnz6+w4IiIiEpC46QWGhqKPXv24LPPPhOH9Tds2BCJiYkVFhwREVFJaJzUsrKyYGNjAwBiUsvJyUHVqlUrJjIiIqIS0jipubm5YcmSJUplq1evhru7e7kHRUREVBoaj35csGABhgwZgrCwMGRlZaFVq1YwMzPDtm3bKjI+IiIijWmc1CwtLREREYHz588jOTkZ1tbWaNmyJZ/QT0REOkPjpJaQkIDatWujZcuWaNmyJQDg3r17yMjIQNOmTSssQCIiIk1pfJk1fvx4vHz5UqksJycH48ePL/egiIiISkPjpHbv3j04ODgolb3zzjtITk4u75iIiIhKReOkZmVlhfj4eKWy+Ph4WFpalntQREREpVGil4QOHz4c//vf//DOO+/g77//xvLlyzFt2rSKjI+IiEhjGie1kSNHwtzcHBs3bsT9+/dhbW2N+fPnw9vbuyLjIyIi0phGSS03NxfBwcGYNm0a+vXrV9ExERERlYpGfWqGhoZYt24dqlSpUtHxiBYvXgypVIqAgACxTBAEBAUFwcnJCZaWlvDy8sK1a9feWExERKTbNB4oMnToUKxfv74iYxGdPXsWYWFhaNy4sVL5smXLsGLFCoSEhOD48eOQyWTw8fFBZmbmG4mLiIh0m8ZJ7fz585g+fTqaNm0KT09Ppb/y9PjxY4wdOxbLly+HVCoVywVBQGhoKKZMmQJvb284OzsjNDQUWVlZ+O2338o1BiIiqpw0Hiji6+sLX1/fiowFAMSk1alTJyxYsEAsT0pKQmpqKrp27SqWmZiYwM3NDXFxcRg9enSFx0ZERLpN46Q2bNiwiowDABAWFobbt29j9erVKtNSU1MBADKZTKlcJpMhJSWlwmMjIiLdp3FSEwQBGzZswG+//Yb09HTExsbi5MmTePjwIXx8fMocSGJiIubMmYMDBw4U+Y62/He5FYzr9bLXl1v5VNd2AG+lynmukLbxvHmzHB0di5yucVKbP38+IiMj4e/vj88++wwAYG1tjRkzZpRLUjtz5gzS09PRrl07sSw3NxexsbFYv349Tp8+DQB4+PCh+LJSAEhLS1O5eiuouB2gk2LuazuCt1KlPFdIqxITE3ne6BiNB4ps3boV27dvx4ABA8QrI3t7e9y5c6dcAvHy8kJsbCyio6PFvxYtWmDAgAGIjo5GgwYNIJfLERERIc6TnZ2NU6dOoW3btuUSAxERVW4aX6nl5uaiRo0aAP6vCTArKwumpqblEohUKlUa7QgA1atXR61ateDs7AwA8Pf3x+LFi+Ho6IgGDRpg0aJFqFGjBgYOHFguMRARUeWmcVLr3r07Zs6cie+++w7Aq76s+fPnw8PDo8KCe93kyZPx/PlzBAQEQKFQoGXLlti1axfMzMzeWAxERKS7JAqFQtCk4pMnT+Dv74+jR48iJycH1apVQ5cuXbBq1SomlXIm/Zl9atqgGG2t7RCokmGfmu4p9krt2bNnWLRoEa5evYpmzZohODgY//77L6ytrSGXy99EjERERBopNqkFBATgwoUL6N69O8LDw5GRkYGFCxe+idiIiIhKpNjRj0ePHsWuXbswZ84c/Prrrzh06NCbiIuIiKjEik1qz549E99ubWNjgydPnlR4UERERKVRbPPjy5cvceLECQjCq/Ekubm5Sv8GgE6dOlVchERERBoqdvRj06ZNi3wMlUQiwcWLF8s9sLcZRz9qB0c/Uklx9KPuKfZK7dKlS28iDiIiojLT+DFZREREuo5JjYiI9AaTGhER6Q0mNSIi0htMakREpDeY1IiISG8wqRERkd5gUiMiIr3BpEZERHqDSY2IiPQGkxoREekNJjUiItIbTGpERKQ3mNSIiEhvMKkREZHeYFIjIiK9waRGRER6g0mNiIj0BpMaERHpDSY1IiLSG0xqRESkN5jUiIhIbzCpERGR3mBSIyIivcGkRkREeoNJjYiI9AaTGhER6Q0mNSIi0htMakREpDd0Jql9//336NKlC2xtbVG/fn0MHjwYV69eVaojCAKCgoLg5OQES0tLeHl54dq1a1qKmIiIdI3OJLWYmBh88sknOHToEPbu3QsjIyP069cPGRkZYp1ly5ZhxYoVCAkJwfHjxyGTyeDj44PMzEwtRk5ERLpColAoBG0HoU5WVhbs7OywefNmeHp6QhAEODk5YezYsZg2bRoA4Pnz53B0dMTcuXMxevRoLUdcfqQ/39d2CG8lxWhrbYdAlUxiYiIcHR21HQYVoDNXaq/LyspCXl4epFIpACApKQmpqano2rWrWMfExARubm6Ii4vTVphERKRDjLQdQGGmT5+Opk2bok2bNgCA1NRUAIBMJlOqJ5PJkJKSUuhyEhMTKy7IClNd2wG8lSrnuULaxvPmzSruylgnk9qMGTNw+vRpHDx4EIaGhkrTJBKJ0r8FQVApK6hSNg3EsPlRGyrluUJaxeZH3aNzzY9ffvkldu7cib1798LBwUEsl8vlAICHDx8q1U9LS1O5eiMioreTTiW1wMBA/Pbbb9i7dy8aNmyoNM3e3h5yuRwRERFiWXZ2Nk6dOoW2bdu+6VCJiEgH6Uzz47Rp07B9+3Zs2rQJUqlU7EOrUaMGTE1NIZFI4O/vj8WLF8PR0RENGjTAokWLUKNGDQwcOFDL0RMRkS7QmaS2bt06AIC3t7dSeWBgIL788ksAwOTJk/H8+XMEBARAoVCgZcuW2LVrF8zMzN54vEREpHt09j61txnvU9MO3qdGJcWBIrpHp/rUiIiIyoJJjYiI9AaTGhER6Q0mNSIi0htMakREpDeY1IiISG8wqRERkd5gUiMiIr3BpEZERHqDSY2IiPQGkxoREekNJjUiItIbOvOUfiJ6O5mO7KztEEqthbYDKIOssEhth1AheKVGRER6g0mNiIj0BpMaERHpDSY1IiLSG0xqRESkN5jUiIhIbzCpERGR3mBSIyIivcGkRkREeoNJjYiI9AaTGhER6Q0mNSIi0htMakREpDeY1IiISG8wqRERkd5gUiMiIr3BpEZERHqDSY2IiPQGkxoREekNJjUiItIbTGpERKQ3mNSIiEhvMKkREZHeqJRJbd26dWjWrBnkcjk6deqE2NhYbYdEREQ6oNIltV27dmH69OmYOnUqTpw4gTZt2uDDDz9EcnKytkMjIiItq3RJbcWKFRg2bBhGjhyJRo0aYeHChZDL5Vi/fr22QyMiIi0z0nYAJfHff/8hPj4en376qVJ5165dERcXp6Woyp9itLW2QyB6Y7LCIrUdAumRSnWllp6ejtzcXMhkMqVymUyGhw8faikqIiLSFZUqqeWTSCRK/xYEQaWMiIjePpUqqdWpUweGhoYqV2VpaWkqV29ERPT2qVRJrWrVqmjevDkiIiKUyiMiItC2bVstRUVERLqiUiU1AJg4cSK2bNmCDRs24MaNGwgMDMQ///yD0aNHazs0nRQdHQ2pVIr09HRth1LupFIpfv/99xLV0WQeqlwq2zmelJQEqVSKCxculKkOqVfpklr//v0RFBSEhQsXwt3dHadPn8aOHTtgZ2en7dBKbf369bCyssJ///0nlv3333+oV68e3NzclOr+9ddfkEqliIqKetNhVko3btyAh4eHtsOgYjx48ACTJ0+Gs7MzZDIZ3nvvPfzvf//D/fv3lep5eXkhICBAS1G+OTY2Nrhx4waaNm1abst8W37QVbqkBgBjxozBpUuX8ILE1EkAABGRSURBVPDhQ0RFRaF9+/baDqlMOnbsiGfPnuHcuXNi2Z9//omaNWvi1q1bSEtLE8tjYmJgbGysc82teXl5yM3N1XYYKuRyOYyNjbUdBhXhzp076NKlC65du4bQ0FCcP38eq1evxvXr19G1a1ckJSVpJa6CPzLfNENDQ8jlchgZVaq7rnRCpUxq+qZBgwaoV68eoqOjxbLo6Gh06tQJLVq0QExMjFJ569atUa1aNQCvPnjffPMNnJ2dYWVlhS5duuDYsWMq6zh79iw6dOggPlosPj6+yJgUCgX8/Pxgb28PS0tLeHt749q1a+L0zZs3w9raGocPH0a7du0gk8lw48YNleXkN6Ps3LkTvXv3hqWlJdzd3XH58mVcvXoVPXv2hJWVFTw8PHDnzh2leX/++We0aNECMpkMLVq0QFhYmMryU1NTMWjQINSrVw9NmjTB9u3blaYX9+v0wYMH+Pjjj2Fvbw97e3sMGjQIf/31V5H7hspXQEAADAwMsGfPHnTq1Am2trbo2LEj9uzZAwMDA/HKzN/fHydPnsTatWshlUohlUqVEt7ly5fRrVs31KtXD507d1Y5x+Pi4tC7d2/Uq1cP7733Hj7//HM8efJEnO7l5YXPP/8cX331FerXr49evXqpjffevXsYOnQoHBwcUK9ePbRu3Ro7d+4EUHizobrz8NatW/Dw8IBcLkfr1q1x/PhxcZq65Vy/fh2DBg2CjY0NGjRogE8++QSpqalKy9yyZQvc3NxgYWEBR0dH+Pv7A4B4xTdy5EhIpdJyvQLUNUxqOsLd3V0lqXXo0AEdOnRQKo+JiYG7u7v474kTJ4of9NjYWAwdOhRDhgzBpUuXlJY/a9YsfPvtt4iIiICDgwMGDRqEZ8+eFRqPv78/zp07hy1btuDYsWMwMTHBwIED8fz5c7FOdnY2Fi1ahCVLliAuLg62traFLi8oKAhTpkzBiRMnYG5ujrFjx+KLL77AV199hWPHjiE7OxuBgYFi/fDwcAQEBMDf3x+nTp2Cn58fpk6digMHDqgs19PTE9HR0Rg1ahT8/Pw07od49uwZ+vbtC2NjY/zxxx84cuQI5HI5vL29i9w3VH4yMjJw9OhRjBkzBtWrV1eaVr16dXzyySc4cuQIFAoFgoOD0aZNGwwfPhw3btzAjRs3YGNjI9b/9ttv8c033yAqKgq1a9fGuHHjIAgCAODKlSvo378/PD09ERMTg40bN+LSpUuYNGmS0jp37NgBQRBw4MABrFq1Sm3MU6dOxfPnzxEeHo5Tp04hKCgI5ubmJd72b775BuPHj0d0dDQ6d+6MYcOG4cGDB2rr/vPP/2vv3oOiKt8Ajn/BoBHFS+YWrjLcQgQJhFDQFZthckrQJEUCzTEvm5qEa4Z3KaJMEcoLEJWOmn8kmIpO5l1uYiFelskLwrqYRRjTZKR4KXd/fzB7huMusJL5w+39zDCznMt73rOc5dn3Pe95nzpGjRrFgAEDOHz4MLt27eL69evExcVhMBiApi+BGo2G+Ph4jh07Rl5eHgMGDACQBtetXbuWyspKs8F2tkS0bTsIlUpFUlISt2/fxmg0Ul5ezrp16+jbty8LFy4E4OLFi9TV1REeHg6AXq9n+/btVFRUSAFFrVZTUFDApk2bSE9Pl8p/5513iIiIAJqmGvP19WX79u1MnjzZrC46nY5vv/2Wb775RurazcnJwd/fn7y8PGmfu3fvsmrVKgIDA9s8vzfffJORI0cCMGfOHF599VW2bNkinYspyJmsX7+e2NhY1Go10NSaPXPmDGvWrOGll16Sths9erQ0SGj+/PkUFxeTnZ3NZ5991madvv76a4xGI1lZWdJzjp988gleXl7s37+f6OjoNssQ/hmdTofRaMTb29vi+v79+2M0GtHpdAQHB+Pg4ICTkxNPPfWU2bZLliyRrqekpCRefPFFamtrUSqVrF27lujoaNlsROnp6YSHh1NfXy89EuTq6soHH3zQap2vXLnCmDFjpNaOm5tbe06dqVOnStfYypUrOXLkCBs3bmTp0qVm227YsIGBAwfy3nvvSctycnJwc3Pj9OnTBAcHk5aWxqxZs2SB2vTZfPLJJwHo3r27xffOloig1kGEh4dz69YtysrKMBqN9OrVC3d3dxQKBXq9nqtXr1JcXIyTkxPPPfccAFqtFqPRSGhoqKys27dvSx9uk8GDB0uvu3btip+fHxcuXLBYl8rKSuzt7WX7dO/eHV9fX9k+jz32mNXdGH5+ftJrhUJhcdmNGzdobGzEycmJyspKJk6cKCsjLCzMrKUWEhJi9vuBAwesqpNWq+Xy5cuyb/vQ1ILT6/VWlSE8GC1NnmBqaVkzuULz6+npp58GoL6+HqVSiVar5dKlS+zcudOsbL1eLwU1a76gzZw5k3nz5nH48GFGjBhBVFSUVfvdq/m1a29vT3BwcIufSa1WS2lpKUql+RR6er0eV1dXamtrGTFixH3Xw9aIoNZBuLm50a9fP0pKSjAajVILqUuXLgQGBlJSUkJJSQmhoaE4ODgATYMz7OzsOHLkiLTMxHTPrT1MH3ZLmv9zefzxx+nUqZNVZTavn6mM5jfBTctMXSn3Hqu1Ze1lMBjw9/e3OBl2z549H9hxhJZ5enpiZ2fHhQsXiIqKMlt/8eJF7OzscHd3b7MsS9eY6Vo2GAxMnjyZ2bNnm+3n4uIive7SpUubx5k8eTIREREcPHiQgoICRo4ciUajYdGiRdjb28uOC/DXX3+1WWZbDAYDI0eOJDU11Wxd7969ZbcF/uvEPbUOxHRfzXQ/zUSlUlFUVERJSYmsBfbss89iNBq5evUqHh4esp8+ffrIyj5x4oT0+saNG5w7d47+/ftbrIePjw8Gg4GysjJpWUNDQ6v7PGj9+/fnu+++ky07fvw4Pj4+smXl5eVmv1tbx4CAAC5dusQTTzxh9v6JoPZw9OzZk4iICDZs2GB2H7OxsZEvvviCF154Qfp7ODo6tmuUbUBAAOfPnzf7O3t4eNC5c+f7Lk+pVDJlyhQ2bdrE4sWLpUFMpm6+uro6adt772+bNL92jUYjp06davHaDQgI4MKFC/Tr18+s/s7OzigUCvr06dPqoz4ODg4dcoTygyaCWgcyfPhwysvLOXnypGwwyLBhw9ixYwf19fWy5V5eXkyYMIHZs2eTn59PTU0Np0+fZt26dezevVtW9urVqzl69Cjnz59nzpw5ODo6Mn78eIv18PT0ZNSoUWg0GkpLSzl79ixqtRpnZ2diYmL+nZO/R0JCAtu2bePzzz9Hp9ORk5NDXl4eb731lmy7PXv2sHnzZnQ6HRkZGRQWFkojvtoSExODQqEgPj6ekpISampqOHbsGEuWLBEjIB+itLQ0/v77b8aOHUthYSE//fQTxcXFREdHYzQaWbVqlbStq6srJ0+e5PLly/z222+yln1rEhMTOXXqFBqNRuqK3LdvH3Pnzr3v+i5YsIBDhw5RU1NDRUUFhw4dkoJR586dCQkJYc2aNZw/f57vv//e4j0yaHo+NT8/n6qqKhYuXMiVK1eYOnWqxW2nT59OQ0MDr7/+OuXl5dTU1FBQUEBiYiJ//vkn0DSAJTs7m8zMTKqrq6moqGDdunVSGa6urhQWFnL16lWuXbt23+f9qBBBrQMZPnw4d+7coXfv3rLultDQUG7evEm3bt3M+u4zMzOZOHEiy5cvJyQkhNjYWI4dO2b2MHpycjJLlixhxIgR6HQ6tm3b1mpXS1ZWFkFBQcTFxREREcHNmzfZvn17u77VtkdUVBSrVq0iKyuLIUOG8Omnn5Keni4bJAKwcOFCdu/ezbBhw9i4cSOZmZkEBQVZdQwnJyf27t2Lm5sbU6ZMYfDgwcyaNYtr167Ro0ePf+O0BAvc3d05evQoPj4+zJw5k8DAQGbMmIG3tzdHjhyRDcRISEjA0dGR0NBQPD09rU4OPHDgQPbu3cuPP/5IVFQUKpWKlJSUds0ZazAYSEpKYsiQIURHR6NQKMjOzpbWr1+/HmhKiaXRaFoMasnJyWRmZqJSqTh8+DBbt261eM8MmrpI9+/fj729PePGjSM0NJT58+fj6OgoPYc5bdo00tLS2LJlC2FhYYwfP152jy41NZXi4mL8/PxkX45tjd21a9davoEiCIIgPHRVVVWEhIRQVlbW4shQwTLRUhMEQehAfv/9d/Lz83F2dm712U/BMjH6URAEoQOZM2cOWq2WjIyMh9bdb0tE96MgCIJgM0T3oyAIgmAzRFB7BMXGxlo9bP1BeNTyVQmC8N8lgprwn+Tv7y97hkcQBNsggpogCIJgM0RQ6+AaGxuZNWsWSqWSZ555RjbzPrSeT81gMODr60tOTo5sn+rqanr06IFWqwXgjz/+IDExES8vL/r27cuoUaPaTN+ye/duKW+Tn58fq1evls135+/vz4oVK1Cr1SiVSry9vc1aRj169GDDhg3ExcXh4uJCcHAwRUVF/Pzzz7zyyiv06dMHlUrVrrxYb7/9NikpKXh4eODl5cXSpUul2SciIyO5cuUKy5Ytk/JyCYJgG0RQ6+CWLVtGQUEBW7ZsIT8/n4qKCkpLS6X1reVTM80+kJeXJyszNzcXHx8fAgICMBqNxMbG8ssvv7Bt2zaKiooYOnQoY8aMkc1f19yZM2eYMmUKUVFRlJaWkpyczMcff2yW7iUrKwtvb28KCwtZtGgRKSkpFqfvGjduHCUlJQwaNIjp06eTkJDAtGnTKCoqwsXFRTYJrbV5sfLy8ujUqRMHDhwgLS2N7OxsduzYASDN3JCUlCTl5RIEwTaIIf0d2PXr1/Hw8GD9+vVMmDBBWubr60tkZCRJSUkEBQXJ8qkBxMfH4+LiQnp6Oj/88AMqlYpTp07h4eEBQFBQEJMmTWLevHkUFhYSHx9PdXW17JkYlUpFTEwMiYmJFBcXM3r0aHQ6Hb169WLGjBnU1dWxZ88eafsVK1bw5Zdfcu7cOaCppebp6cmuXbukbRISEqiqqmLfvn1AU0tNo9GQnJwMwLlz5xg6dCipqalSkLr32G+88QYODg7SVEQAFRUVhIeHU1VVRe/evYmMjOTOnTscPHhQ2mbs2LH069dPai36+/ujVqtl+bUEQXj0iYevOzC9Xs+dO3cs5kID6/KpDRw4EF9fX/Ly8liwYAHl5eXo9XppMmOtVktjYyNeXl6yMm7dutViTrHKykop4adJWFgYK1eupKGhgW7dugGWc501D4RgXZ41aMqL1atXL6vzYjUvA5rya9XX11s8H0EQbIcIah1Ya3nNwPp8ahMmTGDr1q0sWLCA3NxcwsLCpAmPDQYDCoXCLPkmgLOzc4v1aimv2f3mO7vfPGvW5sW69/2ws7Nr8/0UBOHRJ4JaB+bh4YGDgwMnTpyQZio35UJzc3OT5VO7N9N1czExMaSkpHDixAl27twpmzU8ICCAX3/9FXt7e6vT0vv4+FjMdaZUKmWB8J/kOmtJ87xY/0R783IJgtCxiYEiHVjXrl157bXXePfdd2W50EytFmvzqSmVSoYOHYpGo6GhoYGXX35ZWvf8888TGhpKfHw8Bw8epKamhrKyMj788EPZgJTmTINTVqxYQXV1Nbm5uWRmZprlOisvLycjIwOdTsfmzZv56quvLLaw7seDyovl6urK8ePHqa2tFQ+VC4INES21Du7999/nxo0bTJo0ic6dO6NWq2UZgjMzM1m9ejXLly+ntraWnj17EhQUZJYvKTY2loSEBEaPHi0bwm5nZ0dubi6pqakkJiZSX1+PQqFgyJAhxMXFWaxTYGAgmzZt4qOPPiIjIwOFQsHcuXNRq9Wy7WbPns3Zs2dJT0/HycmJxYsXywJqe5jyYqWmphIVFcXdu3dxc3MjMjLyvspZvHgxc+fOZdCgQdy+fdumkyYKwn+JGP0o/CvE6EJBEP4fRPejIAiCYDNEUBMEQRBshuh+FARBEGyGaKkJgiAINkMENUEQBMFmiKAmCIIg2AwR1ARBEASbIYKaIAiCYDNEUBMEQRBsxv8AaHs2uEBvk5MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "web_or_mobile = interests_no_nulls.str.contains(\n",
    "    'Web Developer|Mobile Developer') # returns an array of booleans\n",
    "freq_table = web_or_mobile.value_counts(normalize = True) * 100\n",
    "print(freq_table)\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "freq_table.plot.bar()\n",
    "plt.title('Most Participants are Interested in \\nWeb or Mobile Development',\n",
    "          y = 1.08) # y pads the title upward\n",
    "plt.ylabel('Percentage', fontsize = 12)\n",
    "plt.xticks([0,1],['Web or mobile\\ndevelopment', 'Other subject'],\n",
    "           rotation = 0) # the initial xtick labels were True and False\n",
    "plt.ylim([0,100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that most people in this survey (roughly 88%) are interested in either web or mobile development. These figures offer us a strong reason to consider this sample representative for our population of interest. We want to advertise our courses to people interested in all sorts of programming niches but mostly web and mobile development.\n",
    "\n",
    "Now we need to figure out what are the best markets to invest money in for advertising our courses. We'd like to know:\n",
    "\n",
    "* Where are these new coders located.\n",
    "* What are the locations with the greatest number of new coders.\n",
    "* How much money new coders are willing to spend on learning.\n",
    "\n",
    "# New Coders - Locations and Densities\n",
    "\n",
    "Let's begin with finding out where these new coders are located, and what are the densities (how many new coders there are) for each location. This should be a good start for finding out the best two markets to run our ads campaign in.\n",
    "\n",
    "The data set provides information about the location of each participant at a country level. We can think of each country as an individual market, so we can frame our goal as finding the two best countries to advertise in.\n",
    "\n",
    "We can start by examining the frequency distribution table of the `CountryLive` variable, which describes what country each participant lives in (not their origin country). We'll only consider those participants who answered what role(s) they're interested in, to make sure we work with a representative sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>Absolute frequency</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>620</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>105</td>\n",
       "      <td>8.467742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64</td>\n",
       "      <td>5.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>49</td>\n",
       "      <td>3.951613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>26</td>\n",
       "      <td>2.096774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>24</td>\n",
       "      <td>1.935484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>23</td>\n",
       "      <td>1.854839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>19</td>\n",
       "      <td>1.532258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>14</td>\n",
       "      <td>1.129032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>13</td>\n",
       "      <td>1.048387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>12</td>\n",
       "      <td>0.967742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>11</td>\n",
       "      <td>0.887097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>11</td>\n",
       "      <td>0.887097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands (Holland, Europe)</th>\n",
       "      <td>10</td>\n",
       "      <td>0.806452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>9</td>\n",
       "      <td>0.725806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serbia</th>\n",
       "      <td>9</td>\n",
       "      <td>0.725806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>9</td>\n",
       "      <td>0.725806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>8</td>\n",
       "      <td>0.645161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>8</td>\n",
       "      <td>0.645161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>8</td>\n",
       "      <td>0.645161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>7</td>\n",
       "      <td>0.564516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia &amp; Herzegovina</th>\n",
       "      <td>7</td>\n",
       "      <td>0.564516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>7</td>\n",
       "      <td>0.564516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>7</td>\n",
       "      <td>0.564516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>6</td>\n",
       "      <td>0.483871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>6</td>\n",
       "      <td>0.483871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>6</td>\n",
       "      <td>0.483871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>6</td>\n",
       "      <td>0.483871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>6</td>\n",
       "      <td>0.483871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>5</td>\n",
       "      <td>0.403226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latvia</th>\n",
       "      <td>2</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>2</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>2</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>2</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>2</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <td>2</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haiti</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virgin Islands (USA)</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovakia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Panama</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honduras</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kyrgyzstan</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guam</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecuador</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senegal</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iraq</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <td>1</td>\n",
       "      <td>0.080645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Absolute frequency  Percentage\n",
       "United States of America                      620   50.000000\n",
       "India                                         105    8.467742\n",
       "United Kingdom                                 64    5.161290\n",
       "Canada                                         49    3.951613\n",
       "Germany                                        26    2.096774\n",
       "Poland                                         24    1.935484\n",
       "Brazil                                         23    1.854839\n",
       "Australia                                      19    1.532258\n",
       "Romania                                        14    1.129032\n",
       "Russia                                         13    1.048387\n",
       "Italy                                          12    0.967742\n",
       "France                                         11    0.887097\n",
       "Spain                                          11    0.887097\n",
       "Netherlands (Holland, Europe)                  10    0.806452\n",
       "Mexico                                          9    0.725806\n",
       "Serbia                                          9    0.725806\n",
       "Belgium                                         9    0.725806\n",
       "New Zealand                                     8    0.645161\n",
       "Sweden                                          8    0.645161\n",
       "Turkey                                          8    0.645161\n",
       "Singapore                                       7    0.564516\n",
       "Bosnia & Herzegovina                            7    0.564516\n",
       "Philippines                                     7    0.564516\n",
       "Argentina                                       7    0.564516\n",
       "Nigeria                                         6    0.483871\n",
       "Greece                                          6    0.483871\n",
       "Ukraine                                         6    0.483871\n",
       "Portugal                                        6    0.483871\n",
       "Ireland                                         6    0.483871\n",
       "China                                           5    0.403226\n",
       "...                                           ...         ...\n",
       "Latvia                                          2    0.161290\n",
       "Georgia                                         2    0.161290\n",
       "Pakistan                                        2    0.161290\n",
       "Iran                                            2    0.161290\n",
       "Vietnam                                         2    0.161290\n",
       "Sri Lanka                                       2    0.161290\n",
       "Hong Kong                                       2    0.161290\n",
       "Haiti                                           1    0.080645\n",
       "Virgin Islands (USA)                            1    0.080645\n",
       "Slovakia                                        1    0.080645\n",
       "Algeria                                         1    0.080645\n",
       "Albania                                         1    0.080645\n",
       "Turkmenistan                                    1    0.080645\n",
       "Morocco                                         1    0.080645\n",
       "Botswana                                        1    0.080645\n",
       "Panama                                          1    0.080645\n",
       "Iceland                                         1    0.080645\n",
       "Honduras                                        1    0.080645\n",
       "Kyrgyzstan                                      1    0.080645\n",
       "Czech Republic                                  1    0.080645\n",
       "Bahrain                                         1    0.080645\n",
       "Guam                                            1    0.080645\n",
       "Tunisia                                         1    0.080645\n",
       "Ecuador                                         1    0.080645\n",
       "Jamaica                                         1    0.080645\n",
       "Senegal                                         1    0.080645\n",
       "Chile                                           1    0.080645\n",
       "Iraq                                            1    0.080645\n",
       "Slovenia                                        1    0.080645\n",
       "Cyprus                                          1    0.080645\n",
       "\n",
       "[89 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcc_good = fcc[fcc['JobRoleInterest'].notnull()].copy()\n",
    "\n",
    "absolute_frequencies = fcc_good['CountryLive'].value_counts()\n",
    "relative_frequencies = fcc_good['CountryLive'].value_counts(normalize = True) * 100\n",
    "\n",
    "pd.DataFrame(data = {'Absolute frequency': absolute_frequencies, \n",
    "                     'Percentage': relative_frequencies}\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "50% of our potential customers are located in the US, and this definitely seems like the most interesting market. India has the second customer density, but it's just 8.5%, which is not too far from the United Kingdom (5.2%) or Canada (4.0%).\n",
    "\n",
    "This is useful information, but we need to go more in depth than this and figure out how much money people are actually willing to spend on learning. Advertising within markets where people are only willing to learn for free is extremely unlikely to be profitable for us.\n",
    "\n",
    "# Spending Money for Learning\n",
    "\n",
    "The `MoneyForLearning` column describes in American dollars the amount of money spent by participants from the moment they started coding until the moment they completed the survey. Our company sells subscriptions at a price of \\$59 per month, and for this reason we're interested in finding out how much money each student spends per month.\n",
    "\n",
    "We'll narrow down our analysis to only four countries: the US, India, the United Kingdom, and Canada. We do this for two reasons:\n",
    "\n",
    "* These are the countries having the highest frequency in the frequency table above, which means we have a decent amount of data for each.\n",
    "* Our courses are written in English, and English is an official language in all these four countries. The more people know English, the better our chances to target the right people with our ads.\n",
    "\n",
    "Let's start with creating a new column that describes the amount of money a student has spent per month so far. To do that, we'll need to divide the `MoneyForLearning` column to the `MonthsProgramming` column. The problem is that some students answered that they have been learning to code for 0 months (it might be that they have just started). To avoid dividing by 0, we'll replace 0 with 1 in the `MonthsProgramming` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "122"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcc_good['MonthsProgramming'].replace(0,1, inplace = True)\n",
    "fcc_good['money_per_month'] = fcc_good['MoneyForLearning'] / fcc_good['MonthsProgramming']\n",
    "fcc_good['money_per_month'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's keep only the rows that don't have null values for the `money_per_month` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fcc_good = fcc_good[fcc_good['money_per_month'].notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to group the data by country, and then measure the average amount money that students spend per month in each country. First, let's remove the rows having null values for the `CountryLive` column, and check out if we still have enough data for the four countries that interest us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States of America    582\n",
       "India                        92\n",
       "United Kingdom               58\n",
       "Canada                       45\n",
       "Germany                      23\n",
       "Name: CountryLive, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcc_good = fcc_good[fcc_good['CountryLive'].notnull()]\n",
    "fcc_good['CountryLive'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This should be enough, so let's compute the average value spent per month in each country by a student. We'll compute the average using the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountryLive\n",
       "United States of America    262.466988\n",
       "India                       119.467205\n",
       "United Kingdom               18.921319\n",
       "Canada                       35.808258\n",
       "Name: money_per_month, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_mean = fcc_good.groupby('CountryLive').mean()\n",
    "countries_mean['money_per_month'][['United States of America',\n",
    "                            'India', 'United Kingdom',\n",
    "                            'Canada']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results for the United Kingdom and Canada are a bit surprising relative to the values we see for India. If we considered a few socio-economical metrics (like <a href = 'https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita#Lists_of_countries_and_dependencies'>GDP per capita</a>), we'd intuitively expect people in the UK and Canada to spend more on learning than people in India.\n",
    "\n",
    "It might be that we don't have have enough representative data for the United Kingdom and Canada, or we have some outliers (maybe coming from wrong survey answers) making the mean too large for India, or too low for the UK and Canada. Or it might be that the results are correct.\n",
    "\n",
    "# Dealing with Extreme Outliers\n",
    "\n",
    "Let's use box plots to visualize the distribution of the `money_per_month` variable for each country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fed6c2cb470>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "only_4 = fcc_good[fcc_good['CountryLive'].str.contains(\n",
    "    'United States of America|India|United Kingdom|Canada')]\n",
    "sns.boxplot(y = 'money_per_month', x = 'CountryLive',\n",
    "            data = only_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's hard to see on the plot above if there's anything wrong with the data for the United Kingdom, India, or Canada, but we can see immediately that there's something really off for the US: at least one person spends roughly \\$50,000 each month for learning. This is not impossible, but it seems extremely unlikely, so we'll remove every value that goes over \\$10,000 per month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fcc_good = fcc_good[fcc_good['money_per_month'] < 10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's recompute the mean values and plot the box plots again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountryLive\n",
       "United States of America    176.860219\n",
       "India                       119.467205\n",
       "United Kingdom               18.921319\n",
       "Canada                       35.808258\n",
       "Name: money_per_month, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_mean = fcc_good.groupby('CountryLive').mean()\n",
    "countries_mean['money_per_month'][['United States of America',\n",
    "                            'India', 'United Kingdom',\n",
    "                            'Canada']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fed6c265a90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "only_4 = fcc_good[fcc_good['CountryLive'].str.contains(\n",
    "    'United States of America|India|United Kingdom|Canada')]\n",
    "sns.boxplot(y = 'money_per_month', x = 'CountryLive',\n",
    "            data = only_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see two extreme outliers for India, but it's unclear whether this is good data or not. Maybe these two persons attended several bootcamps, which tend to be very expensive. Let's examine these two data points to see if we can find anything relevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>money_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>24.0</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>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>India</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A stay-at-home parent or homemaker</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>30.0</td>\n",
       "      <td>d964ec629fd6d85a5bf27f7339f4fa6d</td>\n",
       "      <td>950a8cf9cef1ae6a15da470e572b1b7a</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 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>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>work for a startup</td>\n",
       "      <td>1.0</td>\n",
       "      <td>User Experience Designer,   Mobile Developer...</td>\n",
       "      <td>in an office with other developers</td>\n",
       "      <td>Bengali</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>38d312a990</td>\n",
       "      <td>2017-03-10 10:22:34</td>\n",
       "      <td>2017-03-10 10:17:42</td>\n",
       "      <td>2017-03-10 10:24:38</td>\n",
       "      <td>2017-03-10 10:22:40</td>\n",
       "      <td>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>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>1.0</td>\n",
       "      <td>1.0</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>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>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</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>more than 1 million</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>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>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>811bf953ef546460f5436fcf2baa532d</td>\n",
       "      <td>81e2a4cab0543e14746c4a20ffdae17c</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>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>1.0</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>1.0</td>\n",
       "      <td>Information Security, Full-Stack Web Developer...</td>\n",
       "      <td>no preference</td>\n",
       "      <td>Hindi</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4611a76b60</td>\n",
       "      <td>2017-03-10 10:48:31</td>\n",
       "      <td>2017-03-10 10:42:29</td>\n",
       "      <td>2017-03-10 10:51:37</td>\n",
       "      <td>2017-03-10 10:48:38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>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>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>1.0</td>\n",
       "      <td>1.0</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>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>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>3333.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  AttendedBootcamp  BootcampFinish  BootcampLoanYesNo BootcampName  \\\n",
       "1728  24.0               0.0             NaN                NaN          NaN   \n",
       "1755  20.0               0.0             NaN                NaN          NaN   \n",
       "\n",
       "      BootcampRecommend  ChildrenNumber                 CityPopulation  \\\n",
       "1728                NaN             NaN  between 100,000 and 1 million   \n",
       "1755                NaN             NaN            more than 1 million   \n",
       "\n",
       "      CodeEventConferences  CodeEventDjangoGirls  CodeEventFCC  \\\n",
       "1728                   NaN                   NaN           NaN   \n",
       "1755                   NaN                   NaN           1.0   \n",
       "\n",
       "      CodeEventGameJam  CodeEventGirlDev  CodeEventHackathons  \\\n",
       "1728               1.0               NaN                  NaN   \n",
       "1755               NaN               NaN                  1.0   \n",
       "\n",
       "      CodeEventMeetup  CodeEventNodeSchool  CodeEventNone  CodeEventOther  \\\n",
       "1728              NaN                  NaN            NaN             NaN   \n",
       "1755              1.0                  NaN            NaN             NaN   \n",
       "\n",
       "      CodeEventRailsBridge  CodeEventRailsGirls  CodeEventStartUpWknd  \\\n",
       "1728                   NaN                  NaN                   NaN   \n",
       "1755                   NaN                  NaN                   NaN   \n",
       "\n",
       "      CodeEventWkdBootcamps  CodeEventWomenCode  CodeEventWorkshops  \\\n",
       "1728                    NaN                 NaN                 NaN   \n",
       "1755                    NaN                 NaN                 NaN   \n",
       "\n",
       "     CommuteTime CountryCitizen CountryLive EmploymentField  \\\n",
       "1728         NaN          India       India             NaN   \n",
       "1755         NaN          India       India             NaN   \n",
       "\n",
       "     EmploymentFieldOther                      EmploymentStatus  \\\n",
       "1728                  NaN    A stay-at-home parent or homemaker   \n",
       "1755                  NaN  Not working and not looking for work   \n",
       "\n",
       "     EmploymentStatusOther  ExpectedEarning  FinanciallySupporting  \\\n",
       "1728                   NaN          70000.0                    NaN   \n",
       "1755                   NaN         100000.0                    NaN   \n",
       "\n",
       "      FirstDevJob Gender GenderOther  HasChildren  HasDebt  \\\n",
       "1728          NaN   male         NaN          NaN      0.0   \n",
       "1755          NaN   male         NaN          NaN      0.0   \n",
       "\n",
       "      HasFinancialDependents  HasHighSpdInternet  HasHomeMortgage  \\\n",
       "1728                     0.0                 1.0              NaN   \n",
       "1755                     0.0                 1.0              NaN   \n",
       "\n",
       "      HasServedInMilitary  HasStudentDebt  HomeMortgageOwe  HoursLearning  \\\n",
       "1728                  0.0             NaN              NaN           30.0   \n",
       "1755                  0.0             NaN              NaN           10.0   \n",
       "\n",
       "                                  ID.x                              ID.y  \\\n",
       "1728  d964ec629fd6d85a5bf27f7339f4fa6d  950a8cf9cef1ae6a15da470e572b1b7a   \n",
       "1755  811bf953ef546460f5436fcf2baa532d  81e2a4cab0543e14746c4a20ffdae17c   \n",
       "\n",
       "      Income  IsEthnicMinority  IsReceiveDisabilitiesBenefits  IsSoftwareDev  \\\n",
       "1728     NaN               0.0                            0.0            0.0   \n",
       "1755     NaN               0.0                            0.0            0.0   \n",
       "\n",
       "      IsUnderEmployed              JobApplyWhen  JobInterestBackEnd  \\\n",
       "1728              NaN  Within the next 6 months                 1.0   \n",
       "1755              NaN         I haven't decided                 NaN   \n",
       "\n",
       "      JobInterestDataEngr  JobInterestDataSci  JobInterestDevOps  \\\n",
       "1728                  NaN                 NaN                NaN   \n",
       "1755                  1.0                 NaN                1.0   \n",
       "\n",
       "      JobInterestFrontEnd  JobInterestFullStack  JobInterestGameDev  \\\n",
       "1728                  1.0                   NaN                 NaN   \n",
       "1755                  1.0                   1.0                 NaN   \n",
       "\n",
       "      JobInterestInfoSec  JobInterestMobile  JobInterestOther  \\\n",
       "1728                 NaN                1.0               NaN   \n",
       "1755                 1.0                NaN               NaN   \n",
       "\n",
       "      JobInterestProjMngr  JobInterestQAEngr  JobInterestUX  \\\n",
       "1728                  1.0                NaN            1.0   \n",
       "1755                  NaN                NaN            NaN   \n",
       "\n",
       "                                   JobPref  JobRelocateYesNo  \\\n",
       "1728                    work for a startup               1.0   \n",
       "1755  work for a multinational corporation               1.0   \n",
       "\n",
       "                                        JobRoleInterest  \\\n",
       "1728    User Experience Designer,   Mobile Developer...   \n",
       "1755  Information Security, Full-Stack Web Developer...   \n",
       "\n",
       "                            JobWherePref LanguageAtHome  \\\n",
       "1728  in an office with other developers        Bengali   \n",
       "1755                       no preference          Hindi   \n",
       "\n",
       "              MaritalStatus  MoneyForLearning  MonthsProgramming   NetworkID  \\\n",
       "1728  single, never married           20000.0                4.0  38d312a990   \n",
       "1755  single, never married           50000.0               15.0  4611a76b60   \n",
       "\n",
       "             Part1EndTime       Part1StartTime         Part2EndTime  \\\n",
       "1728  2017-03-10 10:22:34  2017-03-10 10:17:42  2017-03-10 10:24:38   \n",
       "1755  2017-03-10 10:48:31  2017-03-10 10:42:29  2017-03-10 10:51:37   \n",
       "\n",
       "           Part2StartTime  PodcastChangeLog  PodcastCodeNewbie  \\\n",
       "1728  2017-03-10 10:22:40               NaN                NaN   \n",
       "1755  2017-03-10 10:48:38               NaN                NaN   \n",
       "\n",
       "      PodcastCodePen  PodcastDevTea  PodcastDotNET  PodcastGiantRobots  \\\n",
       "1728             NaN            NaN            NaN                 NaN   \n",
       "1755             NaN            NaN            NaN                 NaN   \n",
       "\n",
       "      PodcastJSAir  PodcastJSJabber  PodcastNone PodcastOther  \\\n",
       "1728           1.0              NaN          NaN          NaN   \n",
       "1755           NaN              NaN          NaN          NaN   \n",
       "\n",
       "      PodcastProgThrowdown  PodcastRubyRogues  PodcastSEDaily  PodcastSERadio  \\\n",
       "1728                   NaN                NaN             NaN             NaN   \n",
       "1755                   NaN                NaN             1.0             NaN   \n",
       "\n",
       "      PodcastShopTalk  PodcastTalkPython  PodcastTheWebAhead  \\\n",
       "1728              NaN                NaN                 NaN   \n",
       "1755              NaN                NaN                 NaN   \n",
       "\n",
       "      ResourceCodecademy  ResourceCodeWars  ResourceCoursera  ResourceCSS  \\\n",
       "1728                 1.0               NaN               NaN          NaN   \n",
       "1755                 1.0               1.0               1.0          NaN   \n",
       "\n",
       "      ResourceEdX  ResourceEgghead  ResourceFCC  ResourceHackerRank  \\\n",
       "1728          NaN              NaN          NaN                 NaN   \n",
       "1755          1.0              NaN          1.0                 NaN   \n",
       "\n",
       "      ResourceKA  ResourceLynda  ResourceMDN  ResourceOdinProj ResourceOther  \\\n",
       "1728         NaN            NaN          NaN               NaN           NaN   \n",
       "1755         1.0            1.0          NaN               NaN           NaN   \n",
       "\n",
       "      ResourcePluralSight  ResourceSkillcrush  ResourceSO  ResourceTreehouse  \\\n",
       "1728                  NaN                 NaN         NaN                NaN   \n",
       "1755                  1.0                 NaN         NaN                NaN   \n",
       "\n",
       "      ResourceUdacity  ResourceUdemy  ResourceW3S       SchoolDegree  \\\n",
       "1728              NaN            1.0          1.0  bachelor's degree   \n",
       "1755              1.0            1.0          1.0  bachelor's degree   \n",
       "\n",
       "               SchoolMajor  StudentDebtOwe  YouTubeCodeCourse  \\\n",
       "1728  Computer Programming             NaN                NaN   \n",
       "1755      Computer Science             NaN                NaN   \n",
       "\n",
       "      YouTubeCodingTrain  YouTubeCodingTut360  YouTubeComputerphile  \\\n",
       "1728                 NaN                  NaN                   NaN   \n",
       "1755                 NaN                  1.0                   NaN   \n",
       "\n",
       "      YouTubeDerekBanas  YouTubeDevTips  YouTubeEngineeredTruth  YouTubeFCC  \\\n",
       "1728                NaN             NaN                     NaN         1.0   \n",
       "1755                NaN             NaN                     NaN         1.0   \n",
       "\n",
       "      YouTubeFunFunFunction  YouTubeGoogleDev  YouTubeLearnCode  \\\n",
       "1728                    NaN               NaN               NaN   \n",
       "1755                    NaN               NaN               1.0   \n",
       "\n",
       "      YouTubeLevelUpTuts  YouTubeMIT  YouTubeMozillaHacks YouTubeOther  \\\n",
       "1728                 NaN         NaN                  NaN          NaN   \n",
       "1755                 NaN         1.0                  NaN          NaN   \n",
       "\n",
       "      YouTubeSimplilearn  YouTubeTheNewBoston  money_per_month  \n",
       "1728                 NaN                  NaN      5000.000000  \n",
       "1755                 NaN                  NaN      3333.333333  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_4[(only_4['CountryLive'] == 'India') & (only_4['money_per_month'] > 3000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that neither participant attended a bootcamp. Overall, it's really hard to figure out from the data whether these two persons really spent that much money with learning. The actual question of the survey was _\"Aside from university tuition, about how much money have you spent on learning to code so far (in US dollars)?\"_, so they might have misunderstood and thought university tuition is included. It seems safer to remove these two rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_4 = only_4.drop([1728,1755]) # using the row labels from above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking back at the box plot above, we can also see two extreme outliers for the US. Let's examine these participants more in depth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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",
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       "        text-align: right;\n",
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       "</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>money_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The Coding Boot Camp at UCLA Extension</td>\n",
       "      <td>1.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>architecture or physical engineering</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Employed for wages</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50000.0</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>35.0</td>\n",
       "      <td>796ae14c2acdee36eebc250a252abdaf</td>\n",
       "      <td>d9e44d73057fa5d322a071adc744bf07</td>\n",
       "      <td>44500.0</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>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 startup</td>\n",
       "      <td>1.0</td>\n",
       "      <td>User Experience Designer, Full-Stack Web Dev...</td>\n",
       "      <td>in an office with other developers</td>\n",
       "      <td>English</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50dab3f716</td>\n",
       "      <td>2017-03-09 21:26:35</td>\n",
       "      <td>2017-03-09 21:21:58</td>\n",
       "      <td>2017-03-09 21:29:10</td>\n",
       "      <td>2017-03-09 21:26:39</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>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>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>Architecture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The Iron Yard</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>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 and not looking for work</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50000.0</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>50.0</td>\n",
       "      <td>bfabebb4293ac002d26a1397d00c7443</td>\n",
       "      <td>590f0be70e80f1daf5a23eb7f4a72a3d</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 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>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>1.0</td>\n",
       "      <td>Front-End Web Developer,   Mobile Developer,...</td>\n",
       "      <td>in an office with other developers</td>\n",
       "      <td>English</td>\n",
       "      <td>single, never married</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>e512c4bdd0</td>\n",
       "      <td>2017-03-10 02:14:11</td>\n",
       "      <td>2017-03-10 02:10:07</td>\n",
       "      <td>2017-03-10 02:15:32</td>\n",
       "      <td>2017-03-10 02:14:16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>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>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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>Anthropology</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>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>6500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  AttendedBootcamp  BootcampFinish  BootcampLoanYesNo  \\\n",
       "718   26.0               1.0             0.0                0.0   \n",
       "1222  32.0               1.0             0.0                0.0   \n",
       "\n",
       "                                BootcampName  BootcampRecommend  \\\n",
       "718   The Coding Boot Camp at UCLA Extension                1.0   \n",
       "1222                           The Iron Yard                1.0   \n",
       "\n",
       "      ChildrenNumber                 CityPopulation  CodeEventConferences  \\\n",
       "718              NaN            more than 1 million                   1.0   \n",
       "1222             NaN  between 100,000 and 1 million                   NaN   \n",
       "\n",
       "      CodeEventDjangoGirls  CodeEventFCC  CodeEventGameJam  CodeEventGirlDev  \\\n",
       "718                    NaN           NaN               NaN               NaN   \n",
       "1222                   NaN           NaN               NaN               NaN   \n",
       "\n",
       "      CodeEventHackathons  CodeEventMeetup  CodeEventNodeSchool  \\\n",
       "718                   NaN              NaN                  NaN   \n",
       "1222                  NaN              1.0                  NaN   \n",
       "\n",
       "      CodeEventNone  CodeEventOther  CodeEventRailsBridge  \\\n",
       "718             NaN             NaN                   NaN   \n",
       "1222            NaN             NaN                   NaN   \n",
       "\n",
       "      CodeEventRailsGirls  CodeEventStartUpWknd  CodeEventWkdBootcamps  \\\n",
       "718                   NaN                   NaN                    NaN   \n",
       "1222                  NaN                   NaN                    NaN   \n",
       "\n",
       "      CodeEventWomenCode  CodeEventWorkshops       CommuteTime  \\\n",
       "718                  NaN                 NaN  15 to 29 minutes   \n",
       "1222                 NaN                 NaN               NaN   \n",
       "\n",
       "                CountryCitizen               CountryLive  \\\n",
       "718   United States of America  United States of America   \n",
       "1222  United States of America  United States of America   \n",
       "\n",
       "                           EmploymentField EmploymentFieldOther  \\\n",
       "718   architecture or physical engineering                  NaN   \n",
       "1222                                   NaN                  NaN   \n",
       "\n",
       "                          EmploymentStatus EmploymentStatusOther  \\\n",
       "718                     Employed for wages                   NaN   \n",
       "1222  Not working and not looking for work                   NaN   \n",
       "\n",
       "      ExpectedEarning  FinanciallySupporting  FirstDevJob  Gender GenderOther  \\\n",
       "718           50000.0                    NaN          NaN    male         NaN   \n",
       "1222          50000.0                    NaN          NaN  female         NaN   \n",
       "\n",
       "      HasChildren  HasDebt  HasFinancialDependents  HasHighSpdInternet  \\\n",
       "718           NaN      0.0                     0.0                 0.0   \n",
       "1222          NaN      1.0                     0.0                 1.0   \n",
       "\n",
       "      HasHomeMortgage  HasServedInMilitary  HasStudentDebt  HomeMortgageOwe  \\\n",
       "718               NaN                  0.0             NaN              NaN   \n",
       "1222              0.0                  0.0             0.0              NaN   \n",
       "\n",
       "      HoursLearning                              ID.x  \\\n",
       "718            35.0  796ae14c2acdee36eebc250a252abdaf   \n",
       "1222           50.0  bfabebb4293ac002d26a1397d00c7443   \n",
       "\n",
       "                                  ID.y   Income  IsEthnicMinority  \\\n",
       "718   d9e44d73057fa5d322a071adc744bf07  44500.0               0.0   \n",
       "1222  590f0be70e80f1daf5a23eb7f4a72a3d      NaN               0.0   \n",
       "\n",
       "      IsReceiveDisabilitiesBenefits  IsSoftwareDev  IsUnderEmployed  \\\n",
       "718                             0.0            0.0              1.0   \n",
       "1222                            0.0            0.0              NaN   \n",
       "\n",
       "                  JobApplyWhen  JobInterestBackEnd  JobInterestDataEngr  \\\n",
       "718   Within the next 6 months                 1.0                  NaN   \n",
       "1222  Within the next 6 months                 NaN                  NaN   \n",
       "\n",
       "      JobInterestDataSci  JobInterestDevOps  JobInterestFrontEnd  \\\n",
       "718                  NaN                NaN                  1.0   \n",
       "1222                 NaN                NaN                  1.0   \n",
       "\n",
       "      JobInterestFullStack  JobInterestGameDev  JobInterestInfoSec  \\\n",
       "718                    1.0                 NaN                 NaN   \n",
       "1222                   NaN                 NaN                 NaN   \n",
       "\n",
       "      JobInterestMobile  JobInterestOther  JobInterestProjMngr  \\\n",
       "718                 1.0               NaN                  NaN   \n",
       "1222                1.0               NaN                  NaN   \n",
       "\n",
       "      JobInterestQAEngr  JobInterestUX               JobPref  \\\n",
       "718                 NaN            1.0    work for a startup   \n",
       "1222                NaN            1.0  work for a nonprofit   \n",
       "\n",
       "      JobRelocateYesNo                                    JobRoleInterest  \\\n",
       "718                1.0    User Experience Designer, Full-Stack Web Dev...   \n",
       "1222               1.0    Front-End Web Developer,   Mobile Developer,...   \n",
       "\n",
       "                            JobWherePref LanguageAtHome  \\\n",
       "718   in an office with other developers        English   \n",
       "1222  in an office with other developers        English   \n",
       "\n",
       "              MaritalStatus  MoneyForLearning  MonthsProgramming   NetworkID  \\\n",
       "718   single, never married            8000.0                1.0  50dab3f716   \n",
       "1222  single, never married           13000.0                2.0  e512c4bdd0   \n",
       "\n",
       "             Part1EndTime       Part1StartTime         Part2EndTime  \\\n",
       "718   2017-03-09 21:26:35  2017-03-09 21:21:58  2017-03-09 21:29:10   \n",
       "1222  2017-03-10 02:14:11  2017-03-10 02:10:07  2017-03-10 02:15:32   \n",
       "\n",
       "           Part2StartTime  PodcastChangeLog  PodcastCodeNewbie  \\\n",
       "718   2017-03-09 21:26:39               NaN                1.0   \n",
       "1222  2017-03-10 02:14:16               NaN                NaN   \n",
       "\n",
       "      PodcastCodePen  PodcastDevTea  PodcastDotNET  PodcastGiantRobots  \\\n",
       "718              1.0            NaN            NaN                 NaN   \n",
       "1222             NaN            NaN            NaN                 NaN   \n",
       "\n",
       "      PodcastJSAir  PodcastJSJabber  PodcastNone PodcastOther  \\\n",
       "718            NaN              NaN          NaN          NaN   \n",
       "1222           NaN              1.0          NaN          NaN   \n",
       "\n",
       "      PodcastProgThrowdown  PodcastRubyRogues  PodcastSEDaily  PodcastSERadio  \\\n",
       "718                    NaN                NaN             1.0             NaN   \n",
       "1222                   NaN                NaN             NaN             NaN   \n",
       "\n",
       "      PodcastShopTalk  PodcastTalkPython  PodcastTheWebAhead  \\\n",
       "718               NaN                NaN                 NaN   \n",
       "1222              NaN                NaN                 NaN   \n",
       "\n",
       "      ResourceCodecademy  ResourceCodeWars  ResourceCoursera  ResourceCSS  \\\n",
       "718                  NaN               1.0               NaN          NaN   \n",
       "1222                 1.0               NaN               NaN          1.0   \n",
       "\n",
       "      ResourceEdX  ResourceEgghead  ResourceFCC  ResourceHackerRank  \\\n",
       "718           NaN              NaN          1.0                 NaN   \n",
       "1222          NaN              1.0          1.0                 NaN   \n",
       "\n",
       "      ResourceKA  ResourceLynda  ResourceMDN  ResourceOdinProj ResourceOther  \\\n",
       "718          NaN            NaN          NaN               NaN           NaN   \n",
       "1222         NaN            NaN          1.0               NaN           NaN   \n",
       "\n",
       "      ResourcePluralSight  ResourceSkillcrush  ResourceSO  ResourceTreehouse  \\\n",
       "718                   NaN                 NaN         NaN                NaN   \n",
       "1222                  1.0                 1.0         1.0                1.0   \n",
       "\n",
       "      ResourceUdacity  ResourceUdemy  ResourceW3S       SchoolDegree  \\\n",
       "718               NaN            NaN          NaN  bachelor's degree   \n",
       "1222              1.0            1.0          NaN  bachelor's degree   \n",
       "\n",
       "       SchoolMajor  StudentDebtOwe  YouTubeCodeCourse  YouTubeCodingTrain  \\\n",
       "718   Architecture             NaN                NaN                 NaN   \n",
       "1222  Anthropology             NaN                NaN                 1.0   \n",
       "\n",
       "      YouTubeCodingTut360  YouTubeComputerphile  YouTubeDerekBanas  \\\n",
       "718                   NaN                   NaN                NaN   \n",
       "1222                  NaN                   NaN                NaN   \n",
       "\n",
       "      YouTubeDevTips  YouTubeEngineeredTruth  YouTubeFCC  \\\n",
       "718              NaN                     NaN         1.0   \n",
       "1222             1.0                     NaN         1.0   \n",
       "\n",
       "      YouTubeFunFunFunction  YouTubeGoogleDev  YouTubeLearnCode  \\\n",
       "718                     NaN               NaN               NaN   \n",
       "1222                    NaN               NaN               1.0   \n",
       "\n",
       "      YouTubeLevelUpTuts  YouTubeMIT  YouTubeMozillaHacks YouTubeOther  \\\n",
       "718                  NaN         NaN                  NaN          NaN   \n",
       "1222                 NaN         NaN                  NaN          NaN   \n",
       "\n",
       "      YouTubeSimplilearn  YouTubeTheNewBoston  money_per_month  \n",
       "718                  NaN                  NaN           8000.0  \n",
       "1222                 NaN                  NaN           6500.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_4[\n",
    "    (only_4['CountryLive'] == 'United States of America') & \n",
    "    (only_4['money_per_month'] > 6000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both persons attended bootcamps, but it seems that they had been programming for no more than two months when they completed the survey. They most likely paid a large sum of money for a bootcamp that was going to last for several months, so the money spent per month it's unrealistic and should be significantly lower (because they probably didn't spend anything for the next couple of months after the survey). Consequently, we'll remove these rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_4 = only_4.drop([718,1222]) # using the row labels from above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's recompute the mean values and generate the final box plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountryLive\n",
       "Canada                       35.808258\n",
       "India                        29.529439\n",
       "United Kingdom               18.921319\n",
       "United States of America    152.427957\n",
       "Name: money_per_month, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_4.groupby('CountryLive').mean()['money_per_month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fed6c185240>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y = 'money_per_month', x = 'CountryLive',\n",
    "            data = only_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviously, one country we should advertise in is the US. Lots of new coders live there and they are willing to pay a good amount of money each month. We want to choose one more market though, but how can we decide between the other three countries?\n",
    "\n",
    "# Finding the Second Best Market Using Z-scores\n",
    "\n",
    "At a first look, it seems that Canada is the best choice because people there are willing to pay roughly \\$36 per month, compared to India (\\$30) and the United Kingdom (\\$19).\n",
    "\n",
    "We sell subscription at a price of \\$59 per month, and Canada seems to be the closest to this figure. But we need to take into account the variability of each distribution before drawing any conclusion. We could use z-scores to figure out for which of these three markets \\$59 feels like the least expensive. To do that, we'll compute a z-score for \\$59 for each market, and the market with the lowest z-score will be our choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "India: 0.2301503759200336\n",
      "Canada: 0.21909241155098433\n",
      "United Kingdom: 0.8967313005116766\n"
     ]
    }
   ],
   "source": [
    "for country in ['India', 'Canada', 'United Kingdom']:\n",
    "    one_country = only_4[only_4['CountryLive'] == country]\n",
    "    mean = one_country['money_per_month'].mean()\n",
    "    st_dev = one_country['money_per_month'].std(ddof = 1)\n",
    "    z = (59 - mean) / st_dev\n",
    "    print(country + ': ' + str(z))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Canada has the lowest z-score (0.22), but India's is just slightly greater (0.23). We can stop considering the United Kingdom at this point because its z-score is quite big compared to the other two countries.\n",
    "\n",
    "With these two z-scores that are very close to one other, it might make sense to actually choose India because there are more potential customers there:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States of America    579\n",
       "India                        90\n",
       "United Kingdom               58\n",
       "Canada                       45\n",
       "Name: CountryLive, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_4['CountryLive'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we have several options:\n",
    "\n",
    "1. Advertise only in the US and India. Probably it make sense to split the advertisement budget unequally. For instance:\n",
    "    - 70% for the US, and 30% for India.\n",
    "    - 65% for the US, and 35% for India; etc.\n",
    "2. Advertise in the US, India, and Canada by splitting the advertisement budget in various combinations:\n",
    "    - 60% for the US, 25% for India, 15% for Canada.\n",
    "    - 50% for the US, 30% for India, 20% for Canada; etc.\n",
    "3. Advertise only in the US.\n",
    "\n",
    "At this point, it's probably best to send our analysis to the marketing team and let them use their domain knowledge to decide. They might want to do some extra surveys in India and Canada and then get back to us for analyzing the new survey data.\n",
    "\n",
    "# Conclusion\n",
    "\n",
    "In this project, we analyzed survey data from new coders to find the best two markets to advertise in. The only solid conclusion we reached is that the US would be a good market to advertise in.\n",
    "\n",
    "For the second best market, it wasn't clear-cut what to choose between India and Canada. We decided to send the results to the marketing team so they can use their domain knowledge to take the best decision."
   ]
  }
 ],
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