Project - Statistics - Finding Best Markets for Advertisement.ipynb

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   "source": [
    "# Project - Finding the Best Markets for Advertisement\n",
    "\n",
    "**Background and Aim**\n",
    "\n",
    "As a marketing analyst working for an an e-learning company that offers courses on programming we are interested in finding markets for advertising. Most of our courses are on \"web\" and \"mobile development\", but we also cover many other domains, like \"data science\", \"game development\", etc. We want to promote our product and we'd like to invest some money in advertisement. Our goal in this project is to find out the two best markets to advertise our product in.\n",
    "\n",
    "**Dataset**\n",
    "\n",
    "Conducting a physical survey being costly, we will initially explore available datasets. Presently, we wil work with data from [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://www.freecodecamp.org/news/) (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 their [GitHub repository](https://github.com/freeCodeCamp/2017-new-coder-survey)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "# Setting pandas display options for large data\n",
    "pd.options.display.max_rows = 200\n",
    "pd.options.display.max_columns = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
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    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18175 entries, 0 to 18174\n",
      "Columns: 136 entries, Age to YouTubeTheNewBoston\n",
      "dtypes: float64(105), object(31)\n",
      "memory usage: 18.9+ MB\n",
      "None\n",
      "    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  \n"
     ]
    }
   ],
   "source": [
    "survey = pd.read_csv('2017-fCC-New-Coders-Survey-Data.csv', keep_default_na = True, low_memory = False)\n",
    "print(survey.info())\n",
    "print(survey.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a Frequency Table\n",
    "\n",
    "Below we generate a frequency table in the form of a dictionary, in order to assess the major categories for which the respondents to the survey expressed interest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Full-Stack Web Developer': 460, '  Front-End Web Developer': 416, '  Data Scientist': 266, 'Back-End Web Developer': 382, '  Mobile Developer': 306, 'Game Developer': 256, 'Information Security': 209, '   Front-End Web Developer': 1582, ' Full-Stack Web Developer': 1761, '  Product Manager': 141, 'Data Engineer': 209, '  User Experience Designer': 218, ' Back-End Web Developer': 1509, '  DevOps / SysAdmin': 162, '   Mobile Developer': 1347, '   User Experience Designer': 907, '   Data Scientist': 974, ' Data Engineer': 859, ' Game Developer': 948, '  Quality Assurance Engineer': 86, ' Information Security': 893, '   DevOps / SysAdmin': 643, '   Product Manager': 543, 'Software Engineer': 1, 'Software Developer': 1, '   Quality Assurance Engineer': 375, 'Unsure': 1, 'Not sure': 1, 'Artificial Intelligence': 1, 'undecided': 1, 'Not sure yet': 1, ' data analyst': 2, ' plc': 1, 'Network': 1, ' Teacher': 1, ' software engineer': 1, ' Bioinformatics/science ': 1, ' security expert': 1, ' Business Analyst': 2, 'All - whatever is required to develop tools to revolutionize the mechanical engineering process': 1, ' Software Developer or Front-End Web Developer': 1, 'Developer Evangelist': 1, ' Project Management': 1, ' Technical Writer': 1, 'Research ': 1, 'improving in my current career as a Learning technologist': 1, 'Data Visualization Specialist': 1, 'Data visualisation': 1, ' Desktop Application Developer': 3, ' Software Engineer': 6, ' Product Designer': 2, 'Not Sure': 1, ' I am interested in Game Development': 1, ' Mobile Development': 1, ' Web Design': 1, ' Front End Web Development': 1, ' Program Manager': 1, 'Software Engineers': 1, 'Support Engineer or API Support': 1, 'Front-End Web Designer': 1, ' computer engineer': 1, ' System Software': 1, ' Software engineer': 2, ' milatary engineer': 1, ' Technology-Business Liaison': 1, ' Researcher': 2, 'non-programmer': 1, 'Full Stack Developer ': 1, 'Non technical ': 1, 'Computer Architect': 1, 'Quant (Algorithmic Trader)': 1, ' UI Design': 1, ' network admin': 1, \" This futurist's dream of using some tech in a way that inspires critical amounts of people to influence the changes we need to protect \": 1, ' Entrepreneur': 1, ' Artificial Intelligence engineer': 1, 'Research': 1, ' Application Support Analyst': 1, ' Networking': 1, ' Machine Learning Engineer ': 1, 'AI': 1, ' Artificial Intelligence ': 1, 'Informatician': 1, 'Web Designer': 1, \"I'm just learning code to increase my skill-set. I see it as a literacy issue.\": 1, 'Urban Planner': 1, ' Embedded Developer': 1, ' Data Analyst': 2, 'Not Sure Yet': 1, ' virtual reality developer': 1, 'lab scientist': 1, 'Software Engineer (Computer Science Based)': 1, ' Campaign Manager': 1, 'IoT Developer': 1, ' creative coder / generative artist/designer': 1, ' Information Technology': 1, ' UX developer/designer': 1, 'Software Specialist ': 1, 'Security Business Analyst ': 1, ' Desktop applications developer': 1, 'Remote Support': 1, ' Marketing': 1, 'BA or developer': 1, ' Software Development': 1, 'Real-time systems': 1, 'Desktop Applications': 1, ' Bitcoin/Crypto': 1, ' Bioinformatics': 1, ' SWE': 1, 'Digital Humanitites': 1, 'Research and education': 1, 'code developer...in whatever format': 1, ' front-end': 1, ' back-end': 1, ' app dev etc.': 1, ' Analyst': 1, 'full-stack developer': 1, ' support scientific resaerch ': 1, \"i don't know what the difference is between most of these soz lol\": 1, ' Robotics': 1, ' Web Designer': 1, ' Artificial intelligence': 1, 'Ceo': 1, 'Desings': 1, 'data journalist / data visualist': 1, 'Ethical Hacker': 1, 'idk': 1, ' System Administrator/Network': 1, ' Founder': 1, 'VR Technology developer': 1, 'Technology Management': 1, 'software developer': 1, ' Entreprenuer / Web Dev Hustler ': 1, 'Web development ': 1, 'Any of them.': 1, ' Bioinformatitian': 1, ' Project Manager': 1, ' Operating Systems': 1, ' Compilers': 1, ' etc...': 1, 'Physicist ': 1, ' Software developer': 1, 'Information Developer': 1, ' designer': 1, \"Don't know yet\": 1, ' SEO': 1, ' Criminal Defense Attorney-- focusing on cyber crimes ': 1, 'philosopher': 1, 'Web developer': 1, 'Programming': 1, 'Not sure!': 1, 'Pharmaceutical industry': 1, \"I don't know yet!\": 1, ' Scientific Programming': 1, ' Growth Hacker': 1, 'Network Engineer': 1, ' AI Engineer': 1, ' IoT': 1, ' Cybersecurity': 1, 'College professor': 1, '  Bioinformatics ': 1, 'User Interface Designer': 1, ' Journalist': 1, 'Document Controller': 1, ' Machine Learning': 1, ' developer': 1, ' Artificial Intelligence Engineer': 1, ' Journalist/Graphic Designer/Marketing': 1, 'full stack developer': 1, 'Java developer': 1, 'Astrophysicist': 1, 'Cloud computing ': 1, 'Systems Engineer': 1, 'Data Reporter': 1, ' AI and neuroscience': 1, 'Full Stack Software Engineer': 1, ' Programmer': 2, ' Anything that engages me': 1, 'Marketing Automation ': 1, ' Tech lobbiest': 1, ' Information Architect': 1, 'System Engineer': 1, ' Library Developer': 1, 'Software engineer': 1, 'Teacher. Teaching students to code. ': 1, ' Databases': 1, ' Ethical Hacker': 1, ' Software enginner': 1, ' Artificial Intelligence': 1, 'Robotics Process Automation Specialist': 1, 'Natural Language Processing': 1, ' Machine Learning Engineer': 2, 'Systems Programmer': 1, 'Systems Programming': 1, ' Tech art': 1, 'Software Developper': 1, ' programmer': 1, ' AI and Machine Learning': 1, ' a job in which I can use coding skills to create valuable portals to advance human rights': 1, ' Machine Learning ': 1, 'Machine learning engineer': 1, ' GIS Database Admin': 1, 'Robotics and AI Engineer': 1, ' IT specialist ': 1, ' User Interface Design': 1, 'Machine learning and AI ': 1, 'Embedded hardware': 1, ' i dunno!!!!': 1, ' Data analyst': 1, ' User Interface Designer': 1, ' I dont yet know': 1, 'AI Developer': 1, ' Python Developer': 1, ' UI Designer': 1, 'Desktop Applications Programmer': 1, 'email coder': 1, 'Front end': 1, ' back end': 1, ' game': 1, ' web': 1, ' mobile developer': 1, 'undeceided': 1, 'Software engineer ': 1, 'programmer': 1, 'GIS Developer': 1, ' Software Developer': 2, ' Java developer': 1, ' Infrastructure Architect ': 1, ' Software Projects Manager': 1, 'Project Manager': 1, 'Data/Interactive Journalist': 1, 'Education': 1, 'Pharmacy tech': 1, 'Project manager': 1, 'Financial Services': 1, 'Software Engineering': 1}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "236"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = survey.JobRoleInterest.value_counts(normalize = True)*100\n",
    "\n",
    "# Coders have given multiple options in the form of strings; we have to split the string\n",
    "\n",
    "counts_split = counts.index.str.split(',')\n",
    "\n",
    "# Generate frequency table in the form of a dictionary \n",
    "\n",
    "freq_table = {}\n",
    "for row in counts_split:\n",
    "    for i in range(len(row)):\n",
    "        role = row[i] \n",
    "    \n",
    "        if role in freq_table:\n",
    "            freq_table[role] += 1\n",
    "        else:\n",
    "            freq_table[role] = 1\n",
    "\n",
    "print(freq_table)\n",
    "\n",
    "len(freq_table)\n",
    "        \n",
    "                 \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above generated dictionary is a long list of categories (236), and has many repetitive values due to inconsistent formatting and inconsistent use of spacing by respondents.\n",
    "Below, we concrentrate on respondents who included `'web developer'` and `'mobile developer'` as a future role that they were interested in, using regex. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum_web_mobile:  7769 \n",
      "\n",
      "Full-Stack Web Developer                           460\n",
      "  Front-End Web Developer                          416\n",
      "Back-End Web Developer                             382\n",
      "  Mobile Developer                                 306\n",
      "   Front-End Web Developer                        1582\n",
      " Full-Stack Web Developer                         1761\n",
      " Back-End Web Developer                           1509\n",
      "   Mobile Developer                               1347\n",
      " Software Developer or Front-End Web Developer       1\n",
      " Mobile Development                                  1\n",
      " Front End Web Development                           1\n",
      "Web development                                      1\n",
      "Web developer                                        1\n",
      " mobile developer                                    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "freq_series = pd.Series(freq_table, index = freq_table.keys()) # uses keys() to get dictionary keys as index of series\n",
    "web_mobile = freq_series[freq_series.index.str.contains('[wW]eb [dD]evel*|[mM]obile [dD]evel*')] # caters for development and developer\n",
    "\n",
    "print('sum_web_mobile: ', sum(web_mobile), '\\n')\n",
    "print(web_mobile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6992\n",
      "11183\n"
     ]
    }
   ],
   "source": [
    "# Checking null entries\n",
    "print(survey.JobRoleInterest.notnull().sum())\n",
    "print(survey.JobRoleInterest.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see above the use of inconsistent spacing and non-uniform formatting due to which the data is segmented. However, it is clear that there are a total of 7769 expressions of some interest in web and/ or mobile development under 14 categories. 6992 respondents have answered this question, but due to multiple options exercised by them the interest for web and mobile development is almost uniform. For instance, check following entries which contain the string 'web developer' multiple times. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "Name: JobRoleInterest, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(survey.JobRoleInterest.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can identify the categories which are other than web or mobile development."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum_not_web_mobile:  7911 \n",
      "\n",
      "  Data Scientist        266\n",
      "Game Developer          256\n",
      "Information Security    209\n",
      "  Product Manager       141\n",
      "Data Engineer           209\n",
      "                       ... \n",
      "Education                 1\n",
      "Pharmacy tech             1\n",
      "Project manager           1\n",
      "Financial Services        1\n",
      "Software Engineering      1\n",
      "Length: 222, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "not_web_mobile = freq_series[freq_series.index.str.contains('[wW]eb [dD]evel*|[mM]obile [dD]evel*') == False]\n",
    "print('sum_not_web_mobile: ', sum(not_web_mobile), '\\n')\n",
    "print(not_web_mobile) # Remove all single digit entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7911 expressions of intersts are there in 222 `not_web_mobile` categories. Most of them have frequencies in single digit though implying that these are not very popular choices. So below, we have removed the single digit categories from both criteria i.e. `web_mobile` as well as `not_web_mobile` to focus on major categories only.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum_web_mobile_dd:  7763 \n",
      "\n",
      " Full-Stack Web Developer     1761\n",
      "   Front-End Web Developer    1582\n",
      " Back-End Web Developer       1509\n",
      "   Mobile Developer           1347\n",
      "Full-Stack Web Developer       460\n",
      "  Front-End Web Developer      416\n",
      "Back-End Web Developer         382\n",
      "  Mobile Developer             306\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Removing single digit categories\n",
    "web_mobile_dd = web_mobile[web_mobile > 9].sort_values(ascending = False) ## dd denotes double digit\n",
    "\n",
    "print('sum_web_mobile_dd: ', sum(web_mobile_dd), '\\n')\n",
    "print(web_mobile_dd) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum_not_web_mobile_dd:  7689 \n",
      "\n",
      "   Data Scientist                974\n",
      " Game Developer                  948\n",
      "   User Experience Designer      907\n",
      " Information Security            893\n",
      " Data Engineer                   859\n",
      "   DevOps / SysAdmin             643\n",
      "   Product Manager               543\n",
      "   Quality Assurance Engineer    375\n",
      "  Data Scientist                 266\n",
      "Game Developer                   256\n",
      "  User Experience Designer       218\n",
      "Data Engineer                    209\n",
      "Information Security             209\n",
      "  DevOps / SysAdmin              162\n",
      "  Product Manager                141\n",
      "  Quality Assurance Engineer      86\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Removing single digit categories\n",
    "not_web_mobile_dd = not_web_mobile[not_web_mobile > 9].sort_values(ascending = False) ## dd denotes double digit\n",
    "\n",
    "print('sum_not_web_mobile_dd: ', sum(not_web_mobile_dd), '\\n')\n",
    "print(not_web_mobile_dd) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have removed all single digit categories. After this we are reduced to only 16 categories instead of the original 222. categories. There are some duplications in these categories as well, for instance, `Data Scientist`, `Data Engineer` and `Game Developer` are repeated due to formatting inconsistencies. However, we get a broad idea of popular categories from `not_web_mobile` criteria.\n",
    "In many cases, the respondents have stacked them together with `web_mobile` categories while answering the question as shown below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18170                                                  NaN\n",
      "18171      DevOps / SysAdmin,   Mobile Developer,   Pro...\n",
      "18172                                                  NaN\n",
      "18173                                                  NaN\n",
      "18174    Back-End Web Developer, Data Engineer,   Data ...\n",
      "Name: JobRoleInterest, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(survey.JobRoleInterest.tail(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the Data\n",
    "\n",
    "We will now, plot the data for `web_mobile` and `not_web_mobile` as a bar chart for each criteria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 12))\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "web_mobile_dd.plot.barh(color = 'red', title = 'Respondents Interest: Development Streams', fontsize = 8, width = 0.25)\n",
    "\n",
    "\n",
    "plt.xlim(0, 1800)\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "not_web_mobile_dd.plot.barh(color = 'blue', title = 'Respondents Interest: Non-Development Streams', fontsize = 8)\n",
    "plt.xlabel('frequency', fontsize = 12)\n",
    "\n",
    "plt.xlim(0, 1800)\n",
    "plt.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the data above can be further refined by combining similar columns, but nonethelss, it is quite clear even from this picture that the respondents had expressed interest not only in web and mobile development but also in some major non-development categories such as data science, game development and data engineering. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Locations and Densities of New Coders\n",
    "\n",
    "We will now focus on respondents who had answered the question for the `JobRoleInterest` column and make absolute and normalized frequency tables based on their current locations i.e. the column `CountryLive` to generate the density of new coders by their countries of residence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>3125</td>\n",
       "      <td>45.700497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>528</td>\n",
       "      <td>7.721556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>315</td>\n",
       "      <td>4.606610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>260</td>\n",
       "      <td>3.802281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>131</td>\n",
       "      <td>1.915765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>129</td>\n",
       "      <td>1.886517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>125</td>\n",
       "      <td>1.828020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>112</td>\n",
       "      <td>1.637906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>102</td>\n",
       "      <td>1.491664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>89</td>\n",
       "      <td>1.301550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>84</td>\n",
       "      <td>1.228429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>77</td>\n",
       "      <td>1.126060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>75</td>\n",
       "      <td>1.096812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>71</td>\n",
       "      <td>1.038315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands (Holland, Europe)</th>\n",
       "      <td>65</td>\n",
       "      <td>0.950570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>62</td>\n",
       "      <td>0.906698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>52</td>\n",
       "      <td>0.760456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serbia</th>\n",
       "      <td>52</td>\n",
       "      <td>0.760456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>46</td>\n",
       "      <td>0.672711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>43</td>\n",
       "      <td>0.628839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>39</td>\n",
       "      <td>0.570342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>37</td>\n",
       "      <td>0.541094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>36</td>\n",
       "      <td>0.526470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>34</td>\n",
       "      <td>0.497221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>34</td>\n",
       "      <td>0.497221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>33</td>\n",
       "      <td>0.482597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <td>32</td>\n",
       "      <td>0.467973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>32</td>\n",
       "      <td>0.467973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>31</td>\n",
       "      <td>0.453349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>31</td>\n",
       "      <td>0.453349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>31</td>\n",
       "      <td>0.453349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>31</td>\n",
       "      <td>0.453349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>30</td>\n",
       "      <td>0.438725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>29</td>\n",
       "      <td>0.424101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Egypt</th>\n",
       "      <td>29</td>\n",
       "      <td>0.424101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>29</td>\n",
       "      <td>0.424101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malaysia</th>\n",
       "      <td>28</td>\n",
       "      <td>0.409476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>28</td>\n",
       "      <td>0.409476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>28</td>\n",
       "      <td>0.409476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>28</td>\n",
       "      <td>0.409476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>26</td>\n",
       "      <td>0.380228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kenya</th>\n",
       "      <td>26</td>\n",
       "      <td>0.380228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>24</td>\n",
       "      <td>0.350980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>23</td>\n",
       "      <td>0.336356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>23</td>\n",
       "      <td>0.336356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Great Britain</th>\n",
       "      <td>21</td>\n",
       "      <td>0.307107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia &amp; Herzegovina</th>\n",
       "      <td>20</td>\n",
       "      <td>0.292483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>20</td>\n",
       "      <td>0.292483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>19</td>\n",
       "      <td>0.277859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>19</td>\n",
       "      <td>0.277859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>17</td>\n",
       "      <td>0.248611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nepal</th>\n",
       "      <td>17</td>\n",
       "      <td>0.248611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea South</th>\n",
       "      <td>17</td>\n",
       "      <td>0.248611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>16</td>\n",
       "      <td>0.233987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>16</td>\n",
       "      <td>0.233987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>15</td>\n",
       "      <td>0.219362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>14</td>\n",
       "      <td>0.204738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Serbia</th>\n",
       "      <td>14</td>\n",
       "      <td>0.204738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>14</td>\n",
       "      <td>0.204738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>13</td>\n",
       "      <td>0.190114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latvia</th>\n",
       "      <td>13</td>\n",
       "      <td>0.190114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ghana</th>\n",
       "      <td>12</td>\n",
       "      <td>0.175490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <td>11</td>\n",
       "      <td>0.160866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kazakhstan</th>\n",
       "      <td>11</td>\n",
       "      <td>0.160866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macedonia</th>\n",
       "      <td>10</td>\n",
       "      <td>0.146242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>10</td>\n",
       "      <td>0.146242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>10</td>\n",
       "      <td>0.146242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>9</td>\n",
       "      <td>0.131617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>9</td>\n",
       "      <td>0.131617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovakia</th>\n",
       "      <td>9</td>\n",
       "      <td>0.131617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>9</td>\n",
       "      <td>0.131617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>8</td>\n",
       "      <td>0.116993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>8</td>\n",
       "      <td>0.116993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>8</td>\n",
       "      <td>0.116993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dominican Republic</th>\n",
       "      <td>8</td>\n",
       "      <td>0.116993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Costa Rica</th>\n",
       "      <td>7</td>\n",
       "      <td>0.102369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puerto Rico</th>\n",
       "      <td>7</td>\n",
       "      <td>0.102369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>6</td>\n",
       "      <td>0.087745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>6</td>\n",
       "      <td>0.087745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virgin Islands (USA)</th>\n",
       "      <td>6</td>\n",
       "      <td>0.087745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>6</td>\n",
       "      <td>0.087745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>5</td>\n",
       "      <td>0.073121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>5</td>\n",
       "      <td>0.073121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>5</td>\n",
       "      <td>0.073121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>5</td>\n",
       "      <td>0.073121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>4</td>\n",
       "      <td>0.058497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambodia</th>\n",
       "      <td>4</td>\n",
       "      <td>0.058497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>4</td>\n",
       "      <td>0.058497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>4</td>\n",
       "      <td>0.058497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>3</td>\n",
       "      <td>0.043872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niger</th>\n",
       "      <td>3</td>\n",
       "      <td>0.043872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paraguay</th>\n",
       "      <td>3</td>\n",
       "      <td>0.043872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherland Antilles</th>\n",
       "      <td>3</td>\n",
       "      <td>0.043872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>3</td>\n",
       "      <td>0.043872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senegal</th>\n",
       "      <td>3</td>\n",
       "      <td>0.043872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>3</td>\n",
       "      <td>0.043872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haiti</th>\n",
       "      <td>2</td>\n",
       "      <td>0.029248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mauritius</th>\n",
       "      <td>2</td>\n",
       "      <td>0.029248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <td>2</td>\n",
       "      <td>0.029248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecuador</th>\n",
       "      <td>2</td>\n",
       "      <td>0.029248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moldova</th>\n",
       "      <td>2</td>\n",
       "      <td>0.029248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guam</th>\n",
       "      <td>2</td>\n",
       "      <td>0.029248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>2</td>\n",
       "      <td>0.029248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>2</td>\n",
       "      <td>0.029248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iraq</th>\n",
       "      <td>2</td>\n",
       "      <td>0.029248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honduras</th>\n",
       "      <td>2</td>\n",
       "      <td>0.029248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kyrgyzstan</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guatemala</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gibraltar</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guadeloupe</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Somalia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Panama</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanuatu</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cameroon</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jordan</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Myanmar</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mozambique</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anguilla</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gambia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicaragua</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Papua New Guinea</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nambia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel Islands</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samoa</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liberia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad &amp; Tobago</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cayman Islands</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cuba</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rwanda</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Frequency  Percentage\n",
       "United States of America            3125   45.700497\n",
       "India                                528    7.721556\n",
       "United Kingdom                       315    4.606610\n",
       "Canada                               260    3.802281\n",
       "Poland                               131    1.915765\n",
       "Brazil                               129    1.886517\n",
       "Germany                              125    1.828020\n",
       "Australia                            112    1.637906\n",
       "Russia                               102    1.491664\n",
       "Ukraine                               89    1.301550\n",
       "Nigeria                               84    1.228429\n",
       "Spain                                 77    1.126060\n",
       "France                                75    1.096812\n",
       "Romania                               71    1.038315\n",
       "Netherlands (Holland, Europe)         65    0.950570\n",
       "Italy                                 62    0.906698\n",
       "Philippines                           52    0.760456\n",
       "Serbia                                52    0.760456\n",
       "Greece                                46    0.672711\n",
       "Ireland                               43    0.628839\n",
       "South Africa                          39    0.570342\n",
       "Mexico                                37    0.541094\n",
       "Turkey                                36    0.526470\n",
       "Hungary                               34    0.497221\n",
       "Singapore                             34    0.497221\n",
       "New Zealand                           33    0.482597\n",
       "Croatia                               32    0.467973\n",
       "Argentina                             32    0.467973\n",
       "Indonesia                             31    0.453349\n",
       "Pakistan                              31    0.453349\n",
       "Norway                                31    0.453349\n",
       "Sweden                                31    0.453349\n",
       "Denmark                               30    0.438725\n",
       "Finland                               29    0.424101\n",
       "Egypt                                 29    0.424101\n",
       "Israel                                29    0.424101\n",
       "Malaysia                              28    0.409476\n",
       "Portugal                              28    0.409476\n",
       "Vietnam                               28    0.409476\n",
       "China                                 28    0.409476\n",
       "Czech Republic                        26    0.380228\n",
       "Kenya                                 26    0.380228\n",
       "Japan                                 24    0.350980\n",
       "Bangladesh                            23    0.336356\n",
       "Lithuania                             23    0.336356\n",
       "Great Britain                         21    0.307107\n",
       "Bosnia & Herzegovina                  20    0.292483\n",
       "Belarus                               20    0.292483\n",
       "United Arab Emirates                  19    0.277859\n",
       "Belgium                               19    0.277859\n",
       "Austria                               17    0.248611\n",
       "Nepal                                 17    0.248611\n",
       "Korea South                           17    0.248611\n",
       "Colombia                              16    0.233987\n",
       "Venezuela                             16    0.233987\n",
       "Bulgaria                              15    0.219362\n",
       "Taiwan                                14    0.204738\n",
       "Republic of Serbia                    14    0.204738\n",
       "Switzerland                           14    0.204738\n",
       "Thailand                              13    0.190114\n",
       "Latvia                                13    0.190114\n",
       "Ghana                                 12    0.175490\n",
       "Hong Kong                             11    0.160866\n",
       "Kazakhstan                            11    0.160866\n",
       "Macedonia                             10    0.146242\n",
       "Sri Lanka                             10    0.146242\n",
       "Morocco                               10    0.146242\n",
       "Slovenia                               9    0.131617\n",
       "Jamaica                                9    0.131617\n",
       "Slovakia                               9    0.131617\n",
       "Saudi Arabia                           9    0.131617\n",
       "Peru                                   8    0.116993\n",
       "Algeria                                8    0.116993\n",
       "Estonia                                8    0.116993\n",
       "Dominican Republic                     8    0.116993\n",
       "Costa Rica                             7    0.102369\n",
       "Puerto Rico                            7    0.102369\n",
       "Albania                                6    0.087745\n",
       "Chile                                  6    0.087745\n",
       "Virgin Islands (USA)                   6    0.087745\n",
       "Luxembourg                             6    0.087745\n",
       "Azerbaijan                             5    0.073121\n",
       "Iran                                   5    0.073121\n",
       "Tunisia                                5    0.073121\n",
       "Uruguay                                5    0.073121\n",
       "Zimbabwe                               4    0.058497\n",
       "Cambodia                               4    0.058497\n",
       "Georgia                                4    0.058497\n",
       "Afghanistan                            4    0.058497\n",
       "Iceland                                3    0.043872\n",
       "Niger                                  3    0.043872\n",
       "Paraguay                               3    0.043872\n",
       "Netherland Antilles                    3    0.043872\n",
       "Uzbekistan                             3    0.043872\n",
       "Senegal                                3    0.043872\n",
       "Uganda                                 3    0.043872\n",
       "Haiti                                  2    0.029248\n",
       "Mauritius                              2    0.029248\n",
       "Cyprus                                 2    0.029248\n",
       "Ecuador                                2    0.029248\n",
       "Moldova                                2    0.029248\n",
       "Guam                                   2    0.029248\n",
       "Lebanon                                2    0.029248\n",
       "Bahrain                                2    0.029248\n",
       "Iraq                                   2    0.029248\n",
       "Honduras                               2    0.029248\n",
       "Kyrgyzstan                             1    0.014624\n",
       "Turkmenistan                           1    0.014624\n",
       "Guatemala                              1    0.014624\n",
       "Gibraltar                              1    0.014624\n",
       "Guadeloupe                             1    0.014624\n",
       "Bolivia                                1    0.014624\n",
       "Somalia                                1    0.014624\n",
       "Panama                                 1    0.014624\n",
       "Vanuatu                                1    0.014624\n",
       "Cameroon                               1    0.014624\n",
       "Jordan                                 1    0.014624\n",
       "Myanmar                                1    0.014624\n",
       "Mozambique                             1    0.014624\n",
       "Angola                                 1    0.014624\n",
       "Anguilla                               1    0.014624\n",
       "Sudan                                  1    0.014624\n",
       "Gambia                                 1    0.014624\n",
       "Aruba                                  1    0.014624\n",
       "Nicaragua                              1    0.014624\n",
       "Papua New Guinea                       1    0.014624\n",
       "Nambia                                 1    0.014624\n",
       "Qatar                                  1    0.014624\n",
       "Botswana                               1    0.014624\n",
       "Channel Islands                        1    0.014624\n",
       "Samoa                                  1    0.014624\n",
       "Liberia                                1    0.014624\n",
       "Trinidad & Tobago                      1    0.014624\n",
       "Cayman Islands                         1    0.014624\n",
       "Cuba                                   1    0.014624\n",
       "Rwanda                                 1    0.014624\n",
       "Yemen                                  1    0.014624"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_notnull = survey[survey.JobRoleInterest.notnull()]\n",
    "\n",
    "# generate absolute frquency table by location\n",
    "abs_freq = survey_notnull.CountryLive.value_counts(ascending = False)\n",
    "\n",
    "# generate relative frquency table by location\n",
    "rel_freq = survey_notnull.CountryLive.value_counts(normalize = True, ascending = False)*100\n",
    "\n",
    "# Combine data in a new dataframe\n",
    "\n",
    "survey_bylocation = pd.DataFrame({'Frequency': abs_freq, 'Percentage': rel_freq}, index = abs_freq.index)\n",
    "\n",
    "survey_bylocation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "United States is the biggest market followed by a wide margin by India, UK and Canada. To illustrate the distribution, below, we plot a bar graph depicting relative frequencies country wise only for countries with at least 1% respondents. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 6))\n",
    "from numpy import arange\n",
    "plt.style.use('fivethirtyeight')\n",
    "survey_bylocation[survey_bylocation['Percentage'] >= 1]['Percentage'].plot.barh(label = 'location distribution', legend = True, colormap = 'tab10')\n",
    "\n",
    "# lines indicating mean, and standard deviation\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Relative Frequency')\n",
    "plt.title(\"Distribution of Coders by Location\", fontsize = 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spending Money for Learning\n",
    "\n",
    "An important factor to consider while identifying markets for our products is the financial capacity and preferences of the new coders i.e. how much money they are willing to spend on 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 usd 59 per month, and for this reason we're interested in finding out how much money each student spends per month on learning.\n",
    "\n",
    "We will also narrow down our analysis to only four top countries: the US, India, the United Kingdom, and Canada being the most representative of typical digital learners. We will find the mean of money spent per month by new coders in these four countries and plot a bar chart for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CountryLive\n",
      "United States of America    227.997996\n",
      "India                       135.100982\n",
      "United Kingdom               45.534443\n",
      "Canada                      113.510961\n",
      "Name: money_per_month, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.py:6746: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._update_inplace(new_data)\n",
      "<ipython-input-14-9a0b4ce6bda7>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survey_notnull['money_per_month'] = survey_notnull.MoneyForLearning / survey_notnull.MonthsProgramming\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new column \"money_per_month\" based on past spending and the duration spent coding till the time of survey\n",
    "# Analysis will be restricted to the US, India, the United Kingdon and Canada\n",
    "\n",
    "# Replace the '0' values in the column 'MonthsProgramming' with 1\n",
    "survey_notnull.MonthsProgramming.replace(0, 1, inplace = True)\n",
    "# Create the column and keep only not-null values\n",
    "survey_notnull['money_per_month'] = survey_notnull.MoneyForLearning / survey_notnull.MonthsProgramming\n",
    "\n",
    "# Remove null values from 'CountryLive' column\n",
    "\n",
    "survey_notnull = survey_notnull[survey_notnull.CountryLive.notnull()].copy()\n",
    "\n",
    "# Groupby 'CountryLive', find mean and isolate data to four countries of concern\n",
    "survey_mean_spending = survey_notnull.groupby('CountryLive')['money_per_month'].mean()[['United States of America',\n",
    "                            'India', 'United Kingdom',\n",
    "                            'Canada']]\n",
    "print(survey_mean_spending)\n",
    "\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "from numpy import arange\n",
    "plt.style.use('fivethirtyeight')\n",
    "survey_mean_spending.plot.bar(label = 'mean spending', legend = True, colormap = 'winter', rot = 0)\n",
    "\n",
    "# lines indicating mean, and standard deviation\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.ylabel('mean spending')\n",
    "plt.xlabel('')\n",
    "plt.title(\"Mean Spending by Coders by Location\", fontsize = 16)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for Extreme Outliers\n",
    "\n",
    "Above spending profiles are somewhat inconsistent with socio-economic indicators for respective countries. We have to, therefore, check for extreme outliers which might be responsible for inflating mean values for India and deflating for USA, UK and Canada. \n",
    "For this we will plot box plots for the four countries in question. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# isolate four countries of interest\n",
    "countries = ['United States of America', 'India', 'United Kingdom', 'Canada']\n",
    "top_four = pd.DataFrame()\n",
    "for country in countries:\n",
    "    country_wise = survey_notnull[survey_notnull['CountryLive'] == country]\n",
    "    top_four = top_four.append(country_wise)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot data for all four countries as boxplots\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.boxplot(x = 'CountryLive', y = 'money_per_month', data = top_four)\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('money spent')\n",
    "plt.title(' Distribution of Money Spent Per Month Per Country')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There ae some extreme outliers far above the mean in terms of money spent. We start rationalizing by removing outliers who spent above usd 10,000. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CountryLive\n",
      "Canada                      113.510961\n",
      "India                       113.748387\n",
      "United Kingdom               45.534443\n",
      "United States of America    155.459187\n",
      "Name: money_per_month, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove outliers above $10000\n",
    "top_four = top_four[top_four['money_per_month'] < 10000]\n",
    "print(top_four.groupby('CountryLive')['money_per_month'].mean())\n",
    "\n",
    "# Plot data for all four countries as boxplots again\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.boxplot(x = 'CountryLive', y = 'money_per_month', data = top_four)\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('money spent')\n",
    "plt.title(' Distribution of Money Spent Per Month Per Country')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This has driven the mean down for the US and India while not affecting UK and Canada. We need to identify the high level of spending for these outliers for the US and India and a couple of outliers left for Canada. One possible reason could be attendance of an expensive bootcamp by those reporting spending over USD 2000 per month. We can identify that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1728     0.0\n",
       "1755     0.0\n",
       "7989     0.0\n",
       "8126     0.0\n",
       "15587    0.0\n",
       "Name: AttendedBootcamp, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check india's coders who attended bootcamp\n",
    "india_2000 = top_four[(top_four['CountryLive'] == 'India') & (top_four['money_per_month'] > 2000)]\n",
    "india_2000['AttendedBootcamp']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "None of these outliers report having attended an expensive bootcamp. The most likely possibility is that they misinterpreted the survey question which required them to report monthly expenses on tuition other than university. Perhaps, they included the university tuition as well. \n",
    "So, we are going to remove these outliers. But first, we will check the US and Canadian outliers who spent over usd 2000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "415      1.0\n",
       "441      1.0\n",
       "484      1.0\n",
       "718      1.0\n",
       "723      1.0\n",
       "1222     1.0\n",
       "1334     1.0\n",
       "2432     0.0\n",
       "2480     0.0\n",
       "3013     1.0\n",
       "3144     1.0\n",
       "3145     1.0\n",
       "3184     1.0\n",
       "3260     0.0\n",
       "3304     1.0\n",
       "4014     1.0\n",
       "4884     0.0\n",
       "5059     1.0\n",
       "5769     0.0\n",
       "5894     1.0\n",
       "6018     0.0\n",
       "6444     1.0\n",
       "6528     0.0\n",
       "6949     1.0\n",
       "7167     1.0\n",
       "7194     0.0\n",
       "7505     1.0\n",
       "7925     1.0\n",
       "8030     0.0\n",
       "8120     0.0\n",
       "8202     1.0\n",
       "8901     1.0\n",
       "9145     1.0\n",
       "9248     1.0\n",
       "9559     1.0\n",
       "9778     1.0\n",
       "12283    1.0\n",
       "12877    1.0\n",
       "13051    0.0\n",
       "13145    1.0\n",
       "13357    1.0\n",
       "13587    1.0\n",
       "13815    1.0\n",
       "16211    1.0\n",
       "16290    0.0\n",
       "16410    1.0\n",
       "16616    1.0\n",
       "16672    1.0\n",
       "16700    0.0\n",
       "16719    0.0\n",
       "16971    0.0\n",
       "17265    1.0\n",
       "17361    1.0\n",
       "Name: AttendedBootcamp, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# US Outliers being checked whether they attended bootcamp\n",
    "us_2000 = top_four[(top_four['CountryLive'] == 'United States of America') & (top_four['money_per_month'] > 2000)]\n",
    "us_2000['AttendedBootcamp']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many US respondents who reported spending over usd 2000 also attended a bootcamp which explains for their high spending. Those who did not, may have misinterpreted the survey question, so we are going to remove them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6590     1.0\n",
       "13659    1.0\n",
       "Name: AttendedBootcamp, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Canadian Outliers being checked whether they attended bootcamp\n",
    "can_2000 = top_four[(top_four['CountryLive'] == 'Canada') & (top_four['money_per_month'] > 2000)]\n",
    "can_2000['AttendedBootcamp']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bootcamps are Part of Coding Community's Culture\n",
    "Canadian outliers have spent large sumns as they attended the bootcamp. Coding bootcamps are very much a part of the coding community's culture, especially in the West, so the money spent by those attending the bootcamps is justified. We are going to keep these outliers and remove Indian and US outliers who did not attend bootcamp. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Indian Outliers\n",
    "top_four = top_four.drop(labels = india_2000.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop US Outliers who did not attend bootcamp\n",
    "\n",
    "us_no_bootcamp = us_2000[us_2000['AttendedBootcamp'] == 0]\n",
    "\n",
    "top_four = top_four.drop(labels = us_no_bootcamp.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CountryLive\n",
      "Canada                      113.510961\n",
      "India                        65.758763\n",
      "United Kingdom               45.534443\n",
      "United States of America    137.729595\n",
      "Name: money_per_month, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check again for mean and outliers\n",
    "print(top_four.groupby('CountryLive')['money_per_month'].mean())\n",
    "\n",
    "# Plot data for all four countries as boxplots again\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.boxplot(x = 'CountryLive', y = 'money_per_month', data = top_four)\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('money spent')\n",
    "plt.title(' Distribution of Money Spent Per Month Per Country')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing Top Two Markets\n",
    "\n",
    "We can check the absolute and relative frequencies of individuals left in our dataset after removal of outliers to verify the size of market."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "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>market_size</th>\n",
       "      <th>share_market</th>\n",
       "      <th>spending_power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>240</td>\n",
       "      <td>6.176016</td>\n",
       "      <td>113.510961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>457</td>\n",
       "      <td>11.760165</td>\n",
       "      <td>65.758763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>279</td>\n",
       "      <td>7.179619</td>\n",
       "      <td>45.534443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>2910</td>\n",
       "      <td>74.884200</td>\n",
       "      <td>137.729595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          market_size  share_market  spending_power\n",
       "Canada                            240      6.176016      113.510961\n",
       "India                             457     11.760165       65.758763\n",
       "United Kingdom                    279      7.179619       45.534443\n",
       "United States of America         2910     74.884200      137.729595"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate frequency tables\n",
    "abs_market_size = top_four['CountryLive'].value_counts(ascending = False)\n",
    "relative_market_size = top_four['CountryLive'].value_counts(normalize = True, ascending = False)*100\n",
    "average_monthly_spending = top_four.groupby('CountryLive')['money_per_month'].mean()\n",
    "\n",
    "# Combine metrics above into a dataframe for comparison\n",
    "combined = {'market_size': abs_market_size, 'share_market': relative_market_size, 'spending_power': average_monthly_spending}\n",
    "market_comparison = pd.DataFrame(data = combined)\n",
    "\n",
    "market_comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Country')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a pie chart to compare comparative market size\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "plt.style.use('fivethirtyeight')\n",
    "market_comparison.market_size.plot.pie(title = \"Comparative Market Size\", figsize = (10,10), colormap = \"summer\", autopct = '%.1f%%')\n",
    "plt.ylabel('')\n",
    "plt.xlabel(\"Country\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Country')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a bar chart to compare spending power\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "plt.style.use('fivethirtyeight')\n",
    "market_comparison.spending_power.plot.bar(title = \"Comparative Spending Power\", figsize = (10,10), colormap = \"winter\", rot = 45)\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.xlabel(\"Country\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recommendations and Conclusions (Top Two Markets)\n",
    "\n",
    "- From above comparisons, it can be noted that the US has the largest share of market at about 75% as well as the highest spending power at about usd 140, so **United States** is the automatic selection as the top market.\n",
    "- UK is at the bottom in terms of spending at only usd 45 though nearly tied with Canada in terms of market share at between 6-7%. It is not a promising market for our products.\n",
    "\n",
    "- We have to choose between Canada and India. Canada has a decent market share at between 6-7% and high spending power next only to the USA at usd 113%. On the other hand, India, though has a higher market share at about 11.8% does not have the required spending power which stands at only about usd 66. And though we offer products at usd 59 per month subscription, but still it is too close. \n",
    "\n",
    "- With a more aggressive marketing strategy, the higher spending power of Canada can be capitalized to increase the market share, whereas India is a higher risk market. So, the second recommended market is **Canada**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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