{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Social Justice in Programming\n", "\n", "#### 1. Business Understanding\n", "\n", "This project investigates the level of equal opportunities in the field of professional developers, following the CRISP-DM process.
\n", "Survey results from Stackoverflow, which are freely accessible at https://insights.stackoverflow.com/survey, are used as the data basis.

\n", "**Note: The notebook does not claim to fully reflect the complexity of this question. It only elaborates on what the Stackoverflow survey results indicate.*\n", "\n", "**Framing the Problem**
\n", "The questions to be answered in this notebook are: \n", "- Q1 Is there equal opportunity in the programming profession?
\n", "

\n", "- Q2 How does your social environment influence your chances of being successful as a developer?
\n", "

\n", "- Q3 How important is an open mind and tolerance to succeed as a programmer?
\n", "

\n", "
To answer these questions, the first step is to analyze the data base in an exploratory manner.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Necessary libraries\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get a feel for the data set\n", "#### 2. Data Underestanding" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Read in the data - gather the data\n", "df = pd.read_csv(\"2017survey_results_public.csv\")\n", "schema = pd.read_csv(\"2017survey_results_schema.csv\")\n", "schema.set_index(\"Column\", inplace=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "shape of the data: (51392, 154)\n", "Survey participants: 51392, Questions asked: 154\n", "\n", "df.info:\n", "\n", "RangeIndex: 51392 entries, 0 to 51391\n", "Columns: 154 entries, Respondent to ExpectedSalary\n", "dtypes: float64(6), int64(1), object(147)\n", "memory usage: 60.4+ MB\n", "{None}\n" ] }, { "data": { "text/html": [ "
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01StudentYes, bothUnited StatesNoNot employed, and not looking for workSecondary schoolNaNNaNNaNNaN2 to 3 yearsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNWith a soft \"g,\" like \"jiff\"Strongly agreeStrongly agreeAgreeDisagreeStrongly agreeAgreeAgreeDisagreeSomewhat agreeDisagreeStrongly agreeStrongly agreeStrongly disagreeAgreeAgreeDisagreeAgreeI'm not actively looking, but I am open to new...0.0Not applicable/ neverVery importantVery importantImportantVery importantVery importantVery importantImportantVery importantVery importantVery importantVery importantVery importantSomewhat importantNot very importantSomewhat importantStock options; Vacation/days off; Remote optionsYesOtherNaNNaNImportantImportantImportantSomewhat importantImportantNot very importantNot very importantNot at all importantSomewhat importantVery importantNaNNaNTabsNaNOnline course; Open source contributionsNaNNaNNaN6:00 AMSwiftSwiftNaNNaNNaNNaNiOSiOSAtom; XcodeTurn on some musicNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSomewhat satisfiedNot very satisfiedNot at all satisfiedVery satisfiedSatisfiedNot very satisfiedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNI have created a CV or Developer Story on Stac...9.0Desktop; iOS appAt least once each weekHaven't done at allOnce or twiceHaven't done at allHaven't done at allSeveral timesSeveral timesOnce or twiceSomewhat agreeStrongly disagreeStrongly disagreeStrongly agreeAgreeStrongly agreeStrongly agreeStrongly disagreeMaleHigh schoolWhite or of European descentStrongly disagreeStrongly agreeDisagreeStrongly agreeNaNNaN
12StudentYes, bothUnited KingdomYes, full-timeEmployed part-timeSome college/university study without earning ...Computer science or software engineeringMore than half, but not all, the time20 to 99 employeesPrivately-held limited company, not in startup...9 to 10 yearsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNWith a hard \"g,\" like \"gift\"NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoOtherNaNSome other wayImportantImportantImportantImportantSomewhat importantSomewhat importantNot very importantSomewhat importantNot very importantVery importantBritish pounds sterling (£)NaNSpacesNaNOnline course; Self-taught; Hackathon; Open so...Official documentation; Stack Overflow Q&A; OtherNaNNaN10:00 AMJavaScript; Python; Ruby; SQLJava; Python; Ruby; SQL.NET Core.NET CoreMySQL; SQLiteMySQL; SQLiteAmazon Web Services (AWS)Linux Desktop; Raspberry Pi; Amazon Web Servic...Atom; Notepad++; Vim; PyCharm; RubyMine; Visua...Put on some ambient sounds (e.g. whale songs, ...NaNGitMultiple times a dayAgreeDisagreeStrongly disagreeAgreeSomewhat agreeDisagreeStrongly disagreeCustomer satisfaction; On time/in budget; Peer...Not very satisfiedSatisfiedSatisfiedSatisfiedSomewhat satisfiedSatisfiedNo influence at allNo influence at allNo influence at allNo influence at allNo influence at allNo influence at allNo influence at allNo influence at allNo influence at allNo influence at allNo influence at allI have created a CV or Developer Story on Stac...8.0Desktop; iOS browser; iOS app; Android browser...Several timesSeveral timesOnce or twiceOnce or twiceOnce or twiceHaven't done at allSeveral timesAt least once each weekDisagreeStrongly disagreeStrongly disagreeStrongly agreeAgreeStrongly agreeStrongly agreeStrongly disagreeMaleA master's degreeWhite or of European descentSomewhat agreeSomewhat agreeDisagreeStrongly agreeNaN37500.0
23Professional developerYes, bothUnited KingdomNoEmployed full-timeBachelor's degreeComputer science or software engineeringLess than half the time, but at least one day ...10,000 or more employeesPublicly-traded corporation20 or more years20 or more yearsNaNOtherNaNNaNNaN8.09.0NaNNaNNaNNaNNaNNaNNaNNaNWith a hard \"g,\" like \"gift\"Strongly agreeStrongly agreeStrongly agreeSomewhat agreeAgreeStrongly agreeAgreeSomewhat agreeDisagreeDisagreeAgreeSomewhat agreeDisagreeSomewhat agreeAgreeDisagreeAgreeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNYesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNBritish pounds sterling (£)Neither underpaid nor overpaidSpacesNot very importantSelf-taught; Coding competition; Hackathon; Op...Official documentation; Trade book; Textbook; ...NaNNaN9:00 AMJava; PHP; PythonC; Python; RustNaNNaNMySQLNaNNaNNaNSublime Text; Vim; IntelliJTurn on some musicAgile; Lean; Scrum; Extreme; Pair; KanbanMercurialMultiple times a dayAgreeDisagreeDisagreeAgreeAgreeDisagreeSomewhat agreeCustomer satisfaction; Benchmarked product per...Very satisfiedSomewhat satisfiedSatisfiedSatisfiedSomewhat satisfiedVery satisfiedA lot of influenceSome influenceSome influenceSome influenceA lot of influenceSome influenceSome influenceSome influenceSome influenceSome influenceSome influenceI have created a CV or Developer Story on Stac...8.0Desktop; iOS browser; iOS appOnce or twiceHaven't done at allHaven't done at allHaven't done at allHaven't done at allHaven't done at allAt least once each dayAt least once each dayDisagreeDisagreeStrongly disagreeStrongly agreeAgreeAgreeAgreeDisagreeMaleA professional degreeWhite or of European descentSomewhat agreeAgreeDisagreeAgree113750.0NaN
34Professional non-developer who sometimes write...Yes, bothUnited StatesNoEmployed full-timeDoctoral degreeA non-computer-focused engineering disciplineLess than half the time, but at least one day ...10,000 or more employeesNon-profit/non-governmental organization or pr...14 to 15 years9 to 10 yearsNaNNaNNaNNaNData scientist6.03.0NaNNaNNaNNaNNaNNaNNaNNaNWith a soft \"g,\" like \"jiff\"Strongly agreeStrongly agreeStrongly agreeDisagreeSomewhat agreeAgreeAgreeAgreeSomewhat agreeStrongly disagreeStrongly agreeAgreeDisagreeStrongly agreeStrongly agreeSomewhat agreeAgreeI am actively looking for a job5.0Between 2 and 4 years agoSomewhat importantSomewhat importantSomewhat importantImportantImportantVery importantImportantVery importantImportantSomewhat importantNot very importantVery importantImportantVery importantVery importantStock options; Annual bonus; Health benefits; ...YesLinkedIn; OtherNaNA friend, family member, or former colleague t...Somewhat importantSomewhat importantVery importantVery importantSomewhat importantSomewhat importantNot very importantNot very importantImportantVery importantNaNNaNSpacesNaNNaNNaNNaNNaN9:00 AMMatlab; Python; R; SQLMatlab; Python; R; SQLReactHadoop; Node.js; ReactMongoDB; Redis; SQL Server; MySQL; SQLiteMongoDB; Redis; SQL Server; MySQL; SQLiteWindows Desktop; Linux Desktop; Mac OS; Amazon...Windows Desktop; Linux Desktop; Mac OS; Amazon...Notepad++; Sublime Text; TextMate; Vim; IPytho...Turn on some musicAgileGitMultiple times a daySomewhat agreeAgreeSomewhat agreeSomewhat agreeStrongly agreeDisagreeSomewhat agreeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNI have created a CV or Developer Story on Stac...10.0Desktop; iOS browser; iOS appAt least once each weekSeveral timesAt least once each weekSeveral timesAt least once each weekSeveral timesAt least once each dayAt least once each dayAgreeStrongly disagreeStrongly disagreeStrongly agreeStrongly agreeAgreeStrongly agreeDisagreeMaleA doctoral degreeWhite or of European descentAgreeAgreeSomewhat agreeStrongly agreeNaNNaN
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\n", "
" ], "text/plain": [ " Respondent Professional \\\n", "0 1 Student \n", "1 2 Student \n", "2 3 Professional developer \n", "3 4 Professional non-developer who sometimes write... \n", "4 5 Professional developer \n", "\n", " ProgramHobby Country University \\\n", "0 Yes, both United States No \n", "1 Yes, both United Kingdom Yes, full-time \n", "2 Yes, both United Kingdom No \n", "3 Yes, both United States No \n", "4 Yes, I program as a hobby Switzerland No \n", "\n", " EmploymentStatus \\\n", "0 Not employed, and not looking for work \n", "1 Employed part-time \n", "2 Employed full-time \n", "3 Employed full-time \n", "4 Employed full-time \n", "\n", " FormalEducation \\\n", "0 Secondary school \n", "1 Some college/university study without earning ... \n", "2 Bachelor's degree \n", "3 Doctoral degree \n", "4 Master's degree \n", "\n", " MajorUndergrad \\\n", "0 NaN \n", "1 Computer science or software engineering \n", "2 Computer science or software engineering \n", "3 A non-computer-focused engineering discipline \n", "4 Computer science or software engineering \n", "\n", " HomeRemote \\\n", "0 NaN \n", "1 More than half, but not all, the time \n", "2 Less than half the time, but at least one day ... \n", "3 Less than half the time, but at least one day ... \n", "4 Never \n", "\n", " CompanySize \\\n", "0 NaN \n", "1 20 to 99 employees \n", "2 10,000 or more employees \n", "3 10,000 or more employees \n", "4 10 to 19 employees \n", "\n", " CompanyType YearsProgram \\\n", "0 NaN 2 to 3 years \n", "1 Privately-held limited company, not in startup... 9 to 10 years \n", "2 Publicly-traded corporation 20 or more years \n", "3 Non-profit/non-governmental organization or pr... 14 to 15 years \n", "4 Privately-held limited company, not in startup... 20 or more years \n", "\n", " YearsCodedJob YearsCodedJobPast \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 20 or more years NaN \n", "3 9 to 10 years NaN \n", "4 10 to 11 years NaN \n", "\n", " DeveloperType WebDeveloperType \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 Other NaN \n", "3 NaN NaN \n", "4 Mobile developer; Graphics programming; Deskto... NaN \n", "\n", " MobileDeveloperType NonDeveloperType CareerSatisfaction JobSatisfaction \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN 8.0 9.0 \n", "3 NaN Data scientist 6.0 3.0 \n", "4 NaN NaN 6.0 8.0 \n", "\n", " ExCoderReturn ExCoderNotForMe ExCoderBalance ExCoder10Years ExCoderBelonged \\\n", "0 NaN NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN NaN \n", "\n", " ExCoderSkills ExCoderWillNotCode ExCoderActive \\\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", " PronounceGIF ProblemSolving BuildingThings \\\n", "0 With a soft \"g,\" like \"jiff\" Strongly agree Strongly agree \n", "1 With a hard \"g,\" like \"gift\" NaN NaN \n", "2 With a hard \"g,\" like \"gift\" Strongly agree Strongly agree \n", "3 With a soft \"g,\" like \"jiff\" Strongly agree Strongly agree \n", "4 With a soft \"g,\" like \"jiff\" NaN NaN \n", "\n", " LearningNewTech BoringDetails JobSecurity DiversityImportant \\\n", "0 Agree Disagree Strongly agree Agree \n", "1 NaN NaN NaN NaN \n", "2 Strongly agree Somewhat agree Agree Strongly agree \n", "3 Strongly agree Disagree Somewhat agree Agree \n", "4 NaN NaN NaN NaN \n", "\n", " AnnoyingUI FriendsDevelopers RightWrongWay UnderstandComputers \\\n", "0 Agree Disagree Somewhat agree Disagree \n", "1 NaN NaN NaN NaN \n", "2 Agree Somewhat agree Disagree Disagree \n", "3 Agree Agree Somewhat agree Strongly disagree \n", "4 NaN NaN NaN NaN \n", "\n", " SeriousWork InvestTimeTools WorkPayCare KinshipDevelopers \\\n", "0 Strongly agree Strongly agree Strongly disagree Agree \n", "1 NaN NaN NaN NaN \n", "2 Agree Somewhat agree Disagree Somewhat agree \n", "3 Strongly agree Agree Disagree Strongly agree \n", "4 NaN NaN NaN NaN \n", "\n", " ChallengeMyself CompetePeers ChangeWorld \\\n", "0 Agree Disagree Agree \n", "1 NaN NaN NaN \n", "2 Agree Disagree Agree \n", "3 Strongly agree Somewhat agree Agree \n", "4 NaN NaN NaN \n", "\n", " JobSeekingStatus HoursPerWeek \\\n", "0 I'm not actively looking, but I am open to new... 0.0 \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 I am actively looking for a job 5.0 \n", "4 NaN NaN \n", "\n", " LastNewJob AssessJobIndustry AssessJobRole \\\n", "0 Not applicable/ never Very important Very important \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 Between 2 and 4 years ago Somewhat important Somewhat important \n", "4 NaN NaN NaN \n", "\n", " AssessJobExp AssessJobDept AssessJobTech AssessJobProjects \\\n", "0 Important Very important Very important Very important \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 Somewhat important Important Important Very important \n", "4 NaN NaN NaN NaN \n", "\n", " AssessJobCompensation AssessJobOffice AssessJobCommute AssessJobRemote \\\n", "0 Important Very important Very important Very important \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 Important Very important Important Somewhat important \n", "4 NaN NaN NaN NaN \n", "\n", " AssessJobLeaders AssessJobProfDevel AssessJobDiversity \\\n", "0 Very important Very important Somewhat important \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 Not very important Very important Important \n", "4 NaN NaN NaN \n", "\n", " AssessJobProduct AssessJobFinances \\\n", "0 Not very important Somewhat important \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 Very important Very important \n", "4 NaN NaN \n", "\n", " ImportantBenefits ClickyKeys \\\n", "0 Stock options; Vacation/days off; Remote options Yes \n", "1 NaN No \n", "2 NaN Yes \n", "3 Stock options; Annual bonus; Health benefits; ... Yes \n", "4 NaN NaN \n", "\n", " JobProfile ResumePrompted \\\n", "0 Other NaN \n", "1 Other NaN \n", "2 NaN NaN \n", "3 LinkedIn; Other NaN \n", "4 NaN NaN \n", "\n", " LearnedHiring \\\n", "0 NaN \n", "1 Some other way \n", "2 NaN \n", "3 A friend, family member, or former colleague t... \n", "4 NaN \n", "\n", " ImportantHiringAlgorithms ImportantHiringTechExp \\\n", "0 Important Important \n", "1 Important Important \n", "2 NaN NaN \n", "3 Somewhat important Somewhat important \n", "4 NaN NaN \n", "\n", " ImportantHiringCommunication ImportantHiringOpenSource ImportantHiringPMExp \\\n", "0 Important Somewhat important Important \n", "1 Important Important Somewhat important \n", "2 NaN NaN NaN \n", "3 Very important Very important Somewhat important \n", "4 NaN NaN NaN \n", "\n", " ImportantHiringCompanies ImportantHiringTitles ImportantHiringEducation \\\n", "0 Not very important Not very important Not at all important \n", "1 Somewhat important Not very important Somewhat important \n", "2 NaN NaN NaN \n", "3 Somewhat important Not very important Not very important \n", "4 NaN NaN NaN \n", "\n", " ImportantHiringRep ImportantHiringGettingThingsDone \\\n", "0 Somewhat important Very important \n", "1 Not very important Very important \n", "2 NaN NaN \n", "3 Important Very important \n", "4 NaN NaN \n", "\n", " Currency Overpaid TabsSpaces \\\n", "0 NaN NaN Tabs \n", "1 British pounds sterling (£) NaN Spaces \n", "2 British pounds sterling (£) Neither underpaid nor overpaid Spaces \n", "3 NaN NaN Spaces \n", "4 NaN NaN NaN \n", "\n", " EducationImportant EducationTypes \\\n", "0 NaN Online course; Open source contributions \n", "1 NaN Online course; Self-taught; Hackathon; Open so... \n", "2 Not very important Self-taught; Coding competition; Hackathon; Op... \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " SelfTaughtTypes TimeAfterBootcamp \\\n", "0 NaN NaN \n", "1 Official documentation; Stack Overflow Q&A; Other NaN \n", "2 Official documentation; Trade book; Textbook; ... NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " CousinEducation WorkStart HaveWorkedLanguage \\\n", "0 NaN 6:00 AM Swift \n", "1 NaN 10:00 AM JavaScript; Python; Ruby; SQL \n", "2 NaN 9:00 AM Java; PHP; Python \n", "3 NaN 9:00 AM Matlab; Python; R; SQL \n", "4 NaN NaN NaN \n", "\n", " WantWorkLanguage HaveWorkedFramework WantWorkFramework \\\n", "0 Swift NaN NaN \n", "1 Java; Python; Ruby; SQL .NET Core .NET Core \n", "2 C; Python; Rust NaN NaN \n", "3 Matlab; Python; R; SQL React Hadoop; Node.js; React \n", "4 NaN NaN NaN \n", "\n", " HaveWorkedDatabase \\\n", "0 NaN \n", "1 MySQL; SQLite \n", "2 MySQL \n", "3 MongoDB; Redis; SQL Server; MySQL; SQLite \n", "4 NaN \n", "\n", " WantWorkDatabase \\\n", "0 NaN \n", "1 MySQL; SQLite \n", "2 NaN \n", "3 MongoDB; Redis; SQL Server; MySQL; SQLite \n", "4 NaN \n", "\n", " HaveWorkedPlatform \\\n", "0 iOS \n", "1 Amazon Web Services (AWS) \n", "2 NaN \n", "3 Windows Desktop; Linux Desktop; Mac OS; Amazon... \n", "4 NaN \n", "\n", " WantWorkPlatform \\\n", "0 iOS \n", "1 Linux Desktop; Raspberry Pi; Amazon Web Servic... \n", "2 NaN \n", "3 Windows Desktop; Linux Desktop; Mac OS; Amazon... \n", "4 NaN \n", "\n", " IDE \\\n", "0 Atom; Xcode \n", "1 Atom; Notepad++; Vim; PyCharm; RubyMine; Visua... \n", "2 Sublime Text; Vim; IntelliJ \n", "3 Notepad++; Sublime Text; TextMate; Vim; IPytho... \n", "4 NaN \n", "\n", " AuditoryEnvironment \\\n", "0 Turn on some music \n", "1 Put on some ambient sounds (e.g. whale songs, ... \n", "2 Turn on some music \n", "3 Turn on some music \n", "4 NaN \n", "\n", " Methodology VersionControl \\\n", "0 NaN NaN \n", "1 NaN Git \n", "2 Agile; Lean; Scrum; Extreme; Pair; Kanban Mercurial \n", "3 Agile Git \n", "4 NaN NaN \n", "\n", " CheckInCode ShipIt OtherPeoplesCode ProjectManagement \\\n", "0 NaN NaN NaN NaN \n", "1 Multiple times a day Agree Disagree Strongly disagree \n", "2 Multiple times a day Agree Disagree Disagree \n", "3 Multiple times a day Somewhat agree Agree Somewhat agree \n", "4 NaN NaN NaN NaN \n", "\n", " EnjoyDebugging InTheZone DifficultCommunication CollaborateRemote \\\n", "0 NaN NaN NaN NaN \n", "1 Agree Somewhat agree Disagree Strongly disagree \n", "2 Agree Agree Disagree Somewhat agree \n", "3 Somewhat agree Strongly agree Disagree Somewhat agree \n", "4 NaN NaN NaN NaN \n", "\n", " MetricAssess \\\n", "0 NaN \n", "1 Customer satisfaction; On time/in budget; Peer... \n", "2 Customer satisfaction; Benchmarked product per... \n", "3 NaN \n", "4 NaN \n", "\n", " EquipmentSatisfiedMonitors EquipmentSatisfiedCPU EquipmentSatisfiedRAM \\\n", "0 Somewhat satisfied Not very satisfied Not at all satisfied \n", "1 Not very satisfied Satisfied Satisfied \n", "2 Very satisfied Somewhat satisfied Satisfied \n", "3 NaN NaN NaN \n", "4 Satisfied Satisfied Satisfied \n", "\n", " EquipmentSatisfiedStorage EquipmentSatisfiedRW InfluenceInternet \\\n", "0 Very satisfied Satisfied Not very satisfied \n", "1 Satisfied Somewhat satisfied Satisfied \n", "2 Satisfied Somewhat satisfied Very satisfied \n", "3 NaN NaN NaN \n", "4 Satisfied Satisfied Satisfied \n", "\n", " InfluenceWorkstation InfluenceHardware InfluenceServers \\\n", "0 NaN NaN NaN \n", "1 No influence at all No influence at all No influence at all \n", "2 A lot of influence Some influence Some influence \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " InfluenceTechStack InfluenceDeptTech InfluenceVizTools \\\n", "0 NaN NaN NaN \n", "1 No influence at all No influence at all No influence at all \n", "2 Some influence A lot of influence Some influence \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " InfluenceDatabase InfluenceCloud InfluenceConsultants \\\n", "0 NaN NaN NaN \n", "1 No influence at all No influence at all No influence at all \n", "2 Some influence Some influence Some influence \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " InfluenceRecruitment InfluenceCommunication \\\n", "0 NaN NaN \n", "1 No influence at all No influence at all \n", "2 Some influence Some influence \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " StackOverflowDescribes \\\n", "0 I have created a CV or Developer Story on Stac... \n", "1 I have created a CV or Developer Story on Stac... \n", "2 I have created a CV or Developer Story on Stac... \n", "3 I have created a CV or Developer Story on Stac... \n", "4 NaN \n", "\n", " StackOverflowSatisfaction \\\n", "0 9.0 \n", "1 8.0 \n", "2 8.0 \n", "3 10.0 \n", "4 NaN \n", "\n", " StackOverflowDevices StackOverflowFoundAnswer \\\n", "0 Desktop; iOS app At least once each week \n", "1 Desktop; iOS browser; iOS app; Android browser... Several times \n", "2 Desktop; iOS browser; iOS app Once or twice \n", "3 Desktop; iOS browser; iOS app At least once each week \n", "4 NaN NaN \n", "\n", " StackOverflowCopiedCode StackOverflowJobListing StackOverflowCompanyPage \\\n", "0 Haven't done at all Once or twice Haven't done at all \n", "1 Several times Once or twice Once or twice \n", "2 Haven't done at all Haven't done at all Haven't done at all \n", "3 Several times At least once each week Several times \n", "4 NaN NaN NaN \n", "\n", " StackOverflowJobSearch StackOverflowNewQuestion StackOverflowAnswer \\\n", "0 Haven't done at all Several times Several times \n", "1 Once or twice Haven't done at all Several times \n", "2 Haven't done at all Haven't done at all At least once each day \n", "3 At least once each week Several times At least once each day \n", "4 NaN NaN NaN \n", "\n", " StackOverflowMetaChat StackOverflowAdsRelevant \\\n", "0 Once or twice Somewhat agree \n", "1 At least once each week Disagree \n", "2 At least once each day Disagree \n", "3 At least once each day Agree \n", "4 NaN NaN \n", "\n", " StackOverflowAdsDistracting StackOverflowModeration StackOverflowCommunity \\\n", "0 Strongly disagree Strongly disagree Strongly agree \n", "1 Strongly disagree Strongly disagree Strongly agree \n", "2 Disagree Strongly disagree Strongly agree \n", "3 Strongly disagree Strongly disagree Strongly agree \n", "4 NaN NaN NaN \n", "\n", " StackOverflowHelpful StackOverflowBetter StackOverflowWhatDo \\\n", "0 Agree Strongly agree Strongly agree \n", "1 Agree Strongly agree Strongly agree \n", "2 Agree Agree Agree \n", "3 Strongly agree Agree Strongly agree \n", "4 NaN NaN NaN \n", "\n", " StackOverflowMakeMoney Gender HighestEducationParents \\\n", "0 Strongly disagree Male High school \n", "1 Strongly disagree Male A master's degree \n", "2 Disagree Male A professional degree \n", "3 Disagree Male A doctoral degree \n", "4 NaN NaN NaN \n", "\n", " Race SurveyLong QuestionsInteresting \\\n", "0 White or of European descent Strongly disagree Strongly agree \n", "1 White or of European descent Somewhat agree Somewhat agree \n", "2 White or of European descent Somewhat agree Agree \n", "3 White or of European descent Agree Agree \n", "4 NaN NaN NaN \n", "\n", " QuestionsConfusing InterestedAnswers Salary ExpectedSalary \n", "0 Disagree Strongly agree NaN NaN \n", "1 Disagree Strongly agree NaN 37500.0 \n", "2 Disagree Agree 113750.0 NaN \n", "3 Somewhat agree Strongly agree NaN NaN \n", "4 NaN NaN NaN NaN " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Size of the dataset - assess the data\n", "print(\"shape of the data:\", df.shape)\n", "print(f\"Survey participants: {df.shape[0]}, Questions asked: {df.shape[1]}\\n\")\n", "\n", "# Summary of the df\n", "print(f\"df.info:\")\n", "print({df.info()})\n", "\n", "pd.set_option('display.max_columns', 200)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The most interesting columns in regard to the questions are:\n", "

\n", ">Q1: Is there equal opportunity in the programming profession?\n", "- **Initial conditons:** Race, HighestEducationParents, Gender\n", "- **Partially influenceable conditions:** Country
\n", "*Note: The country is probably strongly related to ethnicity and needs to be considered in the study.*\n", "

\n", ">Q2: How does your social environment influence your chances of being successful as a developer?\n", "- **Social environment:** HighestEducationParents, FriendsDevelopers\n", "

\n", ">Q3: How important is an open mind and tolerance to succeed as a programmer?\n", "- **Mindset:** RightWrongWay, DiversityImportant\n", "


\n", "To evaluate whether a developer is successful, the following parameters are evaluated:\n", "- **Evaluation parameters:** JobSatisfaction, CareerSatisfaction, Salary\n", "\n", "

\n", "Of course, these evaluation criteria must take into account whether the survey participant is a professional programmer.
\n", "- **Additional parameters:** Professional\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Columns of interest\n", "INITIAL_CONDITIONS = [\"Race\", \"HighestEducationParents\", \"Gender\"]\n", "PART_INFLUENCEABLE_CONDITIONS = [\"Country\"]\n", "SOCIAL_ENVIRONMENT = [\"HighestEducationParents\", \"FriendsDevelopers\"]\n", "MINDSET = [\"RightWrongWay\", \"DiversityImportant\"]\n", "EVALUATION_PARAMETERS = [\"JobSatisfaction\", \"CareerSatisfaction\", \"Salary\"]\n", "ADDITIONAL_PARAMETERS = [\"Professional\"]\n", "\n", "columns_of_interest = list(set([item for list in [INITIAL_CONDITIONS, PART_INFLUENCEABLE_CONDITIONS, SOCIAL_ENVIRONMENT, \n", " MINDSET, ADDITIONAL_PARAMETERS, EVALUATION_PARAMETERS] for item in list]))\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gender: Which of the following do you currently identify as?\n", "Salary: What is your current annual base salary, before taxes, and excluding bonuses, grants, or other compensation?\n", "Race: Which of the following do you identify as?\n", "RightWrongWay: There's a right and a wrong way to do everything\n", "FriendsDevelopers: Most of my friends are developers, engineers, or scientists\n", "HighestEducationParents: What is the highest level of education received by either of your parents?\n", "DiversityImportant: Diversity in the workplace is important\n", "CareerSatisfaction: Career satisfaction rating\n", "Professional: Which of the following best describes you?\n", "Country: In which country do you currently live?\n", "JobSatisfaction: Job satisfaction rating\n" ] } ], "source": [ "# Let's take a look at the questions behind the columns\n", "for index, row in schema.loc[columns_of_interest].iterrows():\n", " question = schema.loc[index, \"Question\"]\n", " print(f\"{index}: {question}\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Gender Salary Race RightWrongWay \\\n", "0 Male NaN White or of European descent Somewhat agree \n", "1 Male NaN White or of European descent NaN \n", "2 Male 113750.0 White or of European descent Disagree \n", "3 Male NaN White or of European descent Somewhat agree \n", "4 NaN NaN NaN NaN \n", "\n", " FriendsDevelopers HighestEducationParents DiversityImportant \\\n", "0 Disagree High school Agree \n", "1 NaN A master's degree NaN \n", "2 Somewhat agree A professional degree Strongly agree \n", "3 Agree A doctoral degree Agree \n", "4 NaN NaN NaN \n", "\n", " CareerSatisfaction Professional \\\n", "0 NaN Student \n", "1 NaN Student \n", "2 8.0 Professional developer \n", "3 6.0 Professional non-developer who sometimes write... \n", "4 6.0 Professional developer \n", "\n", " Country JobSatisfaction \n", "0 United States NaN \n", "1 United Kingdom NaN \n", "2 United Kingdom 9.0 \n", "3 United States 3.0 \n", "4 Switzerland 8.0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Build a df containing only the columns of interest\n", "df_filtered = df[columns_of_interest].copy()\n", "df_filtered.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gender object\n", "Salary float64\n", "Race object\n", "RightWrongWay object\n", "FriendsDevelopers object\n", "HighestEducationParents object\n", "DiversityImportant object\n", "CareerSatisfaction float64\n", "Professional object\n", "Country object\n", "JobSatisfaction float64\n", "dtype: object\n", "categorical cols: ['Gender', 'Race', 'RightWrongWay', 'FriendsDevelopers', 'HighestEducationParents', 'DiversityImportant', 'Professional', 'Country']\n" ] } ], "source": [ "# Datatypes in the dataset\n", "print(df_filtered.dtypes)\n", "cat_cols = list(df_filtered.columns[df_filtered.dtypes==\"object\"])\n", "print(f\"categorical cols: {cat_cols}\")\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SalaryCareerSatisfactionJobSatisfaction
count12891.00000042695.00000040376.000000
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" ], "text/plain": [ " Salary CareerSatisfaction JobSatisfaction\n", "count 12891.000000 42695.000000 40376.000000\n", "mean 56298.480641 7.300574 6.957078\n", "std 39880.905277 1.955444 2.167652\n", "min 0.000000 0.000000 0.000000\n", "25% 26440.371839 6.000000 6.000000\n", "50% 50000.000000 8.000000 7.000000\n", "75% 80000.000000 9.000000 8.000000\n", "max 197000.000000 10.000000 10.000000" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Ditribution of the quantitative parameters\n", "df_filtered.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3. Data Preparation" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing evaluation parameters\n", "Salary: 74.9 %\n", "JobSatisfaction: 21.4 %\n", "CareerSatisfaction: 16.9 %\n", "\n", "Data left after removing every row with missing evaluation parameters:\n", "35.6 %, -> 12847\n", "\n", "shape of the df: (12847, 11)\n" ] } ], "source": [ "# Proportions of missing evaluation parameters\n", "prop_missing_salary = df_filtered[\"Salary\"].isnull().mean()\n", "prop_missing_jobsf = df_filtered[\"JobSatisfaction\"].isnull().mean()\n", "prop_missing_careersf = df_filtered[\"CareerSatisfaction\"].isnull().mean()\n", "print(\"Missing evaluation parameters\")\n", "print(f\"Salary: {round(prop_missing_salary*100, 1)} %\")\n", "print(f\"JobSatisfaction: {round(prop_missing_jobsf*100, 1)} %\")\n", "print(f\"CareerSatisfaction: {round(prop_missing_careersf*100, 1)} %\")\n", "\n", "\n", "# Missing evaluation parameters combined\n", "print(\"\\nData left after removing every row with missing evaluation parameters:\")\n", "# We can safely drop rows where all parameters are nan \n", "df_filtered.dropna(subset=cat_cols, how=\"all\", inplace=True)\n", "\n", "# Build a df containing only rows with the needed evaluation criteria - clean the data\n", "df_eval = df_filtered.dropna(subset=EVALUATION_PARAMETERS, axis=0).copy()\n", "\n", "# Due to the questions above we are only interested in data from professional developers\n", "data_left = round((df_eval[df_eval[\"Professional\"]==\"Professional developer\"].shape[0]/\n", " df_filtered[df_filtered[\"Professional\"]==\"Professional developer\"].shape[0])*100, 1)\n", "print(f\"{data_left} %, -> {df_eval.shape[0]}\")\n", "print(f\"\\nshape of the df: {df_eval.shape}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order not to lose that much data, we could impute values in this step. However, this reduces the significance of our data, since we reduce the variability.
\n", "Because our df has a lot of rows in proportion to the columns, we continue without imputation for now." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the df df_eval, due to the filtering with the salary, there should be only survey participants whose professional status is professional developer.
\n", "This can be easily checked using the \"Professional\" column." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Professional developer 12847\n", "Name: Professional, dtype: int64\n" ] } ], "source": [ "print(df_eval[\"Professional\"].value_counts())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The df is now filtered so we can take a look at the distribution of the selected parameters in the data set." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# analyze the data\n", "# Plot only results where there are at least 50 survey participants\n", "ethnicity_counts = df_eval[\"Race\"].value_counts()[df_eval[\"Race\"].value_counts() >=50]\n", "\n", "# Plot only results where there are at least 400 survey participants\n", "country_counts = df_eval[\"Country\"].value_counts()[df_eval[\"Country\"].value_counts() >=400]\n", "\n", "# Plot only result where there are at least 10 survey participants\n", "gender_counts = df_eval[\"Gender\"].value_counts()[df_eval[\"Gender\"].value_counts()>=10]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "White or of European descent 8685\n", "South Asian 682\n", "Hispanic or Latino/Latina 419\n", "East Asian 308\n", "Middle Eastern 212\n", "Hispanic or Latino/Latina; White or of European descent 155\n", "Black or of African descent 155\n", "I prefer not to say 150\n", "I don’t know 131\n", "Middle Eastern; White or of European descent 66\n", "Name: Race, dtype: int64\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Default plot for data overview\n", "def do_standard_plot(df, title, plot_type, size=[6,6], use_y_grid=False):\n", " \"\"\"[Takes a df containing a distribution and creates a plot from it]\n", "\n", " Args:\n", " df ([pandas.DataFrame]): [df containing a distribution]\n", " title ([str]): [title of the plot]\n", " size (list, optional): [size of the figure]. Defaults to [8,8].\n", " size ([bool, optional]): [True for y-grid]. Defaults to False.\n", " \"\"\"\n", " if use_y_grid:\n", " _, ax = plt.subplots()\n", " df.plot(kind=plot_type, figsize=size);\n", " plt.title(title)\n", " if use_y_grid:\n", " plt.grid(axis=\"y\")\n", " ax.set_axisbelow(True)\n", " plt.ylabel(\"Survey participants\")\n", "\n", "# Ethnicity distribution\n", "print(ethnicity_counts)\n", "\n", "do_standard_plot(ethnicity_counts, \"Ethnicity distribution\", \"pie\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is clearly evident that one ethnicity in the chart is clearly overweighted.
\n", "Since only ethnicities to which at least 50 survey participants belong are shown here,
\n", "it should still be possible to make a reasonably statistically reliable statement with these.
\n", "We can neglect the answers 'I prefer not to say' and 'I don't know'.

\n", "So we focus on the following groups:
\n", "*['White or of European descent', 'South Asian',
\n", "'Hispanic or Latino/Latina', 'East Asian', 'Middle Eastern',
\n", "'Hispanic or Latino/Latina; White or of European descent',
\n", "'Black or of African descent', 'Middle Eastern; White or of European descent']*" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "ethnicities_in_focus = ['White or of European descent', 'South Asian',\n", " 'Hispanic or Latino/Latina', 'East Asian', 'Middle Eastern',\n", " 'Hispanic or Latino/Latina; White or of European descent',\n", " 'Black or of African descent', 'Middle Eastern; White or of European descent']" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Country distribution\n", "do_standard_plot(country_counts, \"Country distribution\", \"bar\", [8,6], True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In terms of countries, there is a bias towards the U.S., although the data is much more balanced on this point.
\n", "The countries that we want to consider in the evaluation are:
\n", "*['United States', 'United Kingdom', 'Germany', 'India', 'Canada', 'France']*" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "countries_in_focus = list(country_counts.index)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Gender distribution\n", "do_standard_plot(gender_counts, \"Gender distribution\", \"bar\", [8,6], True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you look at the gender distribution, you can see, as you would expect, that the profession of developer is strongly dominated by males.
\n", "Since we can't make reasonable statements, with too few survey participants for a gender, we will divide this property into 3 groups.
\n", "The first two groups are *female* and *male*. All remaining answers are summarized under the term *Other*.
" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Male 10632\n", "Female 816\n", "Other 222\n", "Name: Gender, dtype: int64\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Disable chained assignments\n", "pd.options.mode.chained_assignment = None # Source: https://stackoverflow.com/questions/49728421/pandas-dataframe-settingwithcopywarning-a-value-is-trying-to-be-set-on-a-copy\n", "\n", "# Reworked gender distribution\n", "df_eval[\"Gender\"][[element not in [\"Male\", \"Female\", np.nan] for element in df_eval[\"Gender\"]]] = \"Other\"\n", "print(df_eval[\"Gender\"].value_counts())\n", "\n", "do_standard_plot(df_eval[\"Gender\"].value_counts(), \"Gender distribution\", \"pie\")\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Gender 1177\n", "Salary 0\n", "Race 1605\n", "RightWrongWay 4017\n", "FriendsDevelopers 4000\n", "HighestEducationParents 1182\n", "DiversityImportant 4014\n", "CareerSatisfaction 0\n", "Professional 0\n", "Country 0\n", "JobSatisfaction 0\n", "dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Missing values left\n", "df_eval.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4. Evaluation\n", "This should be enough for a first overview.
\n", "So let's take a look at question number 1:
\n", "**Q1 Is there equal opportunity in the programming profession?**

\n", "*Note: The OverallSatisfaction is introduced as the sum of the job and the career satisfaction*" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def add_overall_sf(df):\n", " \"\"\"[Adds the OverallSatifsction column to a df as the sum of job and career satisfaction]\n", "\n", " Args:\n", " df ([pandas.DataFrame]): [Pandas df that contains the columns \"JobSatisfaction\" and \"CareerSatisfaction\"]\n", " \"\"\"\n", " df[\"OverallSatisfaction\"] = df[\"JobSatisfaction\"] + df[\"CareerSatisfaction\"]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SalaryCareerSatisfactionJobSatisfactionOverallSatisfaction
Race
Middle Eastern; White or of European descent69035.9267397.5757587.09090914.666667
White or of European descent61582.5253127.5591257.05711014.616235
Hispanic or Latino/Latina; White or of European descent60179.6261607.7419357.21935514.961290
East Asian52027.6128077.1136366.77922113.892857
Black or of African descent50068.6502507.2129036.18709713.400000
Hispanic or Latino/Latina41648.0646998.0692127.24821015.317422
Middle Eastern37802.3174567.5377366.84905714.386792
South Asian30528.1775067.0762466.37390013.450147
\n", "
" ], "text/plain": [ " Salary \\\n", "Race \n", "Middle Eastern; White or of European descent 69035.926739 \n", "White or of European descent 61582.525312 \n", "Hispanic or Latino/Latina; White or of European... 60179.626160 \n", "East Asian 52027.612807 \n", "Black or of African descent 50068.650250 \n", "Hispanic or Latino/Latina 41648.064699 \n", "Middle Eastern 37802.317456 \n", "South Asian 30528.177506 \n", "\n", " CareerSatisfaction \\\n", "Race \n", "Middle Eastern; White or of European descent 7.575758 \n", "White or of European descent 7.559125 \n", "Hispanic or Latino/Latina; White or of European... 7.741935 \n", "East Asian 7.113636 \n", "Black or of African descent 7.212903 \n", "Hispanic or Latino/Latina 8.069212 \n", "Middle Eastern 7.537736 \n", "South Asian 7.076246 \n", "\n", " JobSatisfaction \\\n", "Race \n", "Middle Eastern; White or of European descent 7.090909 \n", "White or of European descent 7.057110 \n", "Hispanic or Latino/Latina; White or of European... 7.219355 \n", "East Asian 6.779221 \n", "Black or of African descent 6.187097 \n", "Hispanic or Latino/Latina 7.248210 \n", "Middle Eastern 6.849057 \n", "South Asian 6.373900 \n", "\n", " OverallSatisfaction \n", "Race \n", "Middle Eastern; White or of European descent 14.666667 \n", "White or of European descent 14.616235 \n", "Hispanic or Latino/Latina; White or of European... 14.961290 \n", "East Asian 13.892857 \n", "Black or of African descent 13.400000 \n", "Hispanic or Latino/Latina 15.317422 \n", "Middle Eastern 14.386792 \n", "South Asian 13.450147 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Evaluation parameter by ethnicity\n", "ethnicity_satifaction = df_eval.groupby(\"Race\").mean().loc[ethnicities_in_focus].sort_values(\"Salary\", ascending=False)\n", "add_overall_sf(ethnicity_satifaction)\n", "ethnicity_satifaction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The table shows that a low salary does not necessarily mean low satisfaction.
\n", "The salary varies so significantly that in the next step we have to take the countries into account.
\n", "It is important to note that different countries have different salary levels and that there is an uneven distribution of ethnic groups across countries.\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_multiple_bar_plot(data, x=\"Country\", y=\"Salary\", hue=\"Race\", title=\"Salary by country and ethnicity\", \n", " palette=[\"tab:blue\",\"tab:green\",\"tab:red\",\"tab:cyan\",\"tab:pink\",\"tab:olive\",\"tab:gray\",\"tab:orange\"]):\n", " \"\"\"[Creates a plot in which each x-tick is divided again using several bars in different colors]\n", "\n", " Args:\n", " data ([pandas.DataFrame]): [A df containing the x-ticks, the categories for the hues, and the corresponding y-values]\n", " x (str, optional): [Name of the column with the values for the x-ticks]. Defaults to \"Country\".\n", " y (str, optional): [Name of the column with the values for the y-axis]. Defaults to \"Salary\".\n", " hue (str, optional): [Name of the column with the categories that are divided into different colored bars.]. Defaults to \"Race\".\n", " title (str, optional): [Title of the plot]. Defaults to \"Salary by country and ethnicity\".\n", " palette (list, optional): [seaborn.color_palette or list of matplotlib colors.]. Defaults to [\"tab:blue\",\"tab:green\",\"tab:red\",\n", " \"tab:cyan\",\"tab:pink\",\"tab:olive\",\n", " \"tab:gray\",\"tab:orange\"].\n", " \"\"\"\n", " _, ax = plt.subplots()\n", " sns.barplot(x=x, y=y, hue=hue, data=data, palette=palette)\n", " plt.gcf().set_size_inches(14, 7)\n", " plt.legend(loc=\"upper center\")\n", " plt.title(title)\n", " plt.grid(axis=\"y\")\n", " ax.set_axisbelow(True)\n", "\n", "\n", "# Plot salary by country and ethnicity\n", "ethnicity_sf_countries = df_eval.groupby([\"Race\", \"Country\"], as_index=False)[EVALUATION_PARAMETERS].mean()\n", "\n", "# Select only ethnicities and countries in focus\n", "ethnicity_sf_countries_if = ethnicity_sf_countries[(ethnicity_sf_countries[\"Race\"].isin(ethnicities_in_focus)) & \n", " (ethnicity_sf_countries[\"Country\"].isin(countries_in_focus))]\n", "\n", "# visualize results\n", "plot_multiple_bar_plot(ethnicity_sf_countries_if)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can quickly see that there is not enough data available for India, so the plot is made again without India." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Select only ethnicities and countries in focus - without India\n", "countries_in_focus_no_india = countries_in_focus.copy()\n", "countries_in_focus_no_india.remove(\"India\")\n", "\n", "ethnicity_sf_countries_if_mod = ethnicity_sf_countries[(ethnicity_sf_countries[\"Race\"].isin(ethnicities_in_focus)) & \n", " (ethnicity_sf_countries[\"Country\"].isin(countries_in_focus_no_india))]\n", "\n", "\n", "plot_multiple_bar_plot(ethnicity_sf_countries_if_mod)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can clearly see that some ethnicities are disadvantaged. The \"Black or of African descent\" group in particular has the lowest or second lowest salary in all countries considered." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Standard diagram for the visualization of the distribution of the evaluation parameters\n", "def plot_eval_param_dist(df, title, parameter, x_lim=[13, 16], \n", " palette=[\"tab:blue\",\"tab:green\",\"tab:red\",\"tab:cyan\",\"tab:pink\",\"tab:olive\",\n", " \"tab:gray\",\"tab:orange\",\"tab:purple\",\"tab:brown\"]):\n", " \"\"\"[Plots the distribution of a parameter in a bar chart]\n", "\n", " Args:\n", " df ([pandas.DataFrame]): [A df containing a distribution over \"parameter\"]\n", " title ([str]): [Title of the plot]\n", " parameter ([str]): [Evaluation parameter to plot]\n", " x_lim (list, optional): [x-Axis limit of the plot]. Defaults to [12, 16].\n", " palette (list, optional): [seaborn.color_palette or list of matplotlib colors.]. Defaults to [\"tab:blue\",\"tab:green\",\"tab:red\",\n", " \"tab:cyan\",\"tab:pink\",\"tab:olive\",\n", " \"tab:gray\",\"tab:orange\",\"tab:purple\", \n", " \"tab:brown\"].\n", " \"\"\"\n", " df.sort_values(parameter, ascending=False, inplace=True)\n", " _, ax = plt.subplots()\n", " sns.barplot(x=df[parameter], y=df.index, palette=palette)\n", " plt.title(title)\n", " plt.xlim(x_lim)\n", " plt.grid(axis=\"x\")\n", " ax.set_axisbelow(True)\n", " \n", "\n", "# Plot overall satisfaction by ethnicity\n", "plot_eval_param_dist(ethnicity_satifaction, \"Overall satisfaction by ethnicity\", \"OverallSatisfaction\", \n", " palette=[\"tab:red\", \"tab:cyan\", \"tab:olive\", \"tab:orange\", \"tab:pink\", \"tab:green\", \"tab:gray\", \"tab:blue\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see again that the group \"Black or of African descent\" is clearly disadvantaged. People from Asia also only achieve a low level of overall satisfaction." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to see which parameters affect general satisfaction and to what extent,
\n", "the ranges between maximum and minimum satisfaction are stored in a dict." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "range_oa_sf = {}\n", "def save_range_in_oa_sf(df, parameter):\n", " \"\"\"[The function saves the range between maximum and minimum OverallSatisfaction depending on the category/parameter in a dict]\n", "\n", " Args:\n", " df ([pandas.DataFrame]): [A df containing a distribution over the OverallSatisfaction]\n", " parameter ([str]): [The category to which the distribution belongs]\n", " \"\"\"\n", " global range_oa_sf\n", " range_oa_sf[parameter] = round(df[\"OverallSatisfaction\"].max()-df[\"OverallSatisfaction\"].min(),2)\n", "\n", "\n", "# Safe range \n", "save_range_in_oa_sf(ethnicity_satifaction, \"Ethnicity\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Available data HighestEducationParents: 11665\n" ] }, { "data": { "text/html": [ "
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SalaryCareerSatisfactionJobSatisfactionOverallSatisfaction
HighestEducationParents
A doctoral degree68414.4720927.5532267.18548414.738710
A professional degree64326.3066647.4800006.99818214.478182
A master's degree60409.9098277.5223777.03395114.556327
Some college/university study, no bachelor's degree59926.8310127.6633606.99690014.660260
A bachelor's degree57102.2909907.4947736.94518214.439955
High school50924.0492817.4638676.89990214.363770
I don't know/not sure49553.5584127.2335336.89820414.131737
I prefer not to answer45081.7225017.5230776.86153814.384615
Primary/elementary school39351.8551257.2469736.73365613.980630
No education38277.7735917.5517246.68965514.241379
\n", "
" ], "text/plain": [ " Salary \\\n", "HighestEducationParents \n", "A doctoral degree 68414.472092 \n", "A professional degree 64326.306664 \n", "A master's degree 60409.909827 \n", "Some college/university study, no bachelor's de... 59926.831012 \n", "A bachelor's degree 57102.290990 \n", "High school 50924.049281 \n", "I don't know/not sure 49553.558412 \n", "I prefer not to answer 45081.722501 \n", "Primary/elementary school 39351.855125 \n", "No education 38277.773591 \n", "\n", " CareerSatisfaction \\\n", "HighestEducationParents \n", "A doctoral degree 7.553226 \n", "A professional degree 7.480000 \n", "A master's degree 7.522377 \n", "Some college/university study, no bachelor's de... 7.663360 \n", "A bachelor's degree 7.494773 \n", "High school 7.463867 \n", "I don't know/not sure 7.233533 \n", "I prefer not to answer 7.523077 \n", "Primary/elementary school 7.246973 \n", "No education 7.551724 \n", "\n", " JobSatisfaction \\\n", "HighestEducationParents \n", "A doctoral degree 7.185484 \n", "A professional degree 6.998182 \n", "A master's degree 7.033951 \n", "Some college/university study, no bachelor's de... 6.996900 \n", "A bachelor's degree 6.945182 \n", "High school 6.899902 \n", "I don't know/not sure 6.898204 \n", "I prefer not to answer 6.861538 \n", "Primary/elementary school 6.733656 \n", "No education 6.689655 \n", "\n", " OverallSatisfaction \n", "HighestEducationParents \n", "A doctoral degree 14.738710 \n", "A professional degree 14.478182 \n", "A master's degree 14.556327 \n", "Some college/university study, no bachelor's de... 14.660260 \n", "A bachelor's degree 14.439955 \n", "High school 14.363770 \n", "I don't know/not sure 14.131737 \n", "I prefer not to answer 14.384615 \n", "Primary/elementary school 13.980630 \n", "No education 14.241379 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Available data HighestEducationParents\n", "available_data_hep = len(df_eval[\"HighestEducationParents\"]) - sum(df_eval[\"HighestEducationParents\"].isnull())\n", "print(f\"Available data HighestEducationParents: {available_data_hep}\")\n", "\n", "# Evaluation parameter by HighestEducationParents\n", "pe_satifaction = df_eval.groupby(\"HighestEducationParents\").mean().sort_values(\"Salary\", ascending=False)\n", "add_overall_sf(pe_satifaction)\n", "pe_satifaction" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot salary by HighestEducationParents\n", "plot_eval_param_dist(pe_satifaction, \"Salary by HighestEducationParents\", \"Salary\", x_lim=[37500,70000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The chart clearly shows that the salary earned is heavily dependent on the education of the parents." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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53Z0R8VpeFvDrnFx9DPQDPgf8t4ljairePqTE9IZCLJ/NvzcjJcYAVwNnF9p7OCKezsvbA4Mk7Z5fL05KArfPP4/k8j65fEwxsIgYTkpIWfTzq/ju92ZmHcTJoBkgaWlgW2BdSQH0AELS8fHpx/S0JHFpsteuBfXeKSzvAywLbBQRH0qaCfSsuNVcleJdCHgjIgbXGF+lWETqWRxZrCDpq8DpEXFRC9s2M7MO4GFis2R34PKI6B8RAyJiJeBpUo9d0RhgV0m9cq/fTgARMQt4XdKWud5+wD0R8SbwnKRdACR9VlJv4C2gb1m7++Q6awIrA49XiHNx4OWcCG4D9K9yXE3F+ybwtKQ98j4laf28zVhgt7zc3IU0I4FDJS1SilvSorn8oNz7iKR+kparEqeZmXUQJ4NmyVDglrKymyi74jciJgLXAZPy+nsLq/cHzpI0hTRX7pRcvh9wZC5/APg8MAX4KF9Ucgxpvl4PSVNz+wdExPsV4rwKGJLn2e0DzGjuoKrEuw9wsKTJwHSgdDHK0cCPJD0MLA/MaqL5i4FHgYmSpgEXAQtHxCjS8PKD+XhuZN7E18zMOhF9egTMzLqz3HP5XkSEpL2BoRFR9arl+bHo51eJtfbzRcdm1jYmnPWdVm3X2NhIQ0ND2wZTR5ImRMSQ+W3HcwbNrNxGwPn5KuU3gIM6NhwzM6snJ4NmNo+IuBdYv2pFMzNbIHjOoJmZmVk35p5BM+twX1hxaca3co5PNV1tDlBXixccc3voavFC14y5u3LPoJmZmVk35mTQzMzMrBtzMmhmZmbWjfk+g2bW4XqvtmasceZ5HR2GmXWwybt/tUP339XmObbVfQbdM2hmZmbWjTkZNDMzM+vGnAyamZmZdWNOBs3MzMy6MSeDZmZmZt1Y3ZJBSSdImi5piqRJkr5Yr33ND0kHSDo/Lw+TdGyd9nORpM1bue0/JC3RxiEV2z9F0nZ5+WhJvdugzQMkrdCK7WZKWmZ+99+K/bbJey9phKTdW7jN263Yz8yWbpO3a5B0W2u2NTOzBVNdkkFJmwHfADaMiEHAdsCz9dhXF/JFYGxrNoyIr0XEG/OzcyUV3++IODEi/pVfHg3MdzIIHAC0OBm05knq0dExlHSmWMzMrPXq1TO4PPBqRLwPEBGvRsQLAJK+LOkRSVMlXSLps7l8pqRfS3pQ0nhJG0oaKenfkn5QaljScZLG5R7HkyvtXNIOkiZKmixpdC5bStJf83ZjJQ1q7gAkrSbpDkkTJN0raa1C+dgcwynFXp2mYpP0BeCJiJgjqVHSkFy+TKmHJ/ek3Zz3+aSk3xS2n5nrninpsEL5MEk/bmrfkgZIekzSBcBEYKXcczUtn/9jcr0RknaXdCQpgbtb0t2SDpb0+8L+vifpd2XnqUd5m7lnbAhwVe4V7lXs8ZM0RFJjXl5a0qj8mbgIUC4/VdJRhf2cluNr7j17O9ebnN+jz+Xy/pJG53MzWtLKTTSxvqS78vn/Xt62T95mYj6+nQv7+05uc7KkKwrtbCXpAUlPqdBLWO2zq+SswrncK5c35PfjamBqrv5KXre8pDH5PE+TtGWFdneQNEPSfcC3CuWLKn0Hx+Xzv3Mu7y3p+hzndZIeKnxm386f+4eAzSTtK+nhvP+LlBNESdsrfZcnSrpBUp/m3jszM+s49UoGR5ESjyckXSBpawBJPYERwF4RsR6wMHBoYbtnI2Iz4N5cb3dgU+CUvP32wBrAJsBgYCNJWxV3LGlZ4M/AbhGxPrBHXnUy8Ejuqfw5cHmVYxgOHBERGwHHAhfk8nOAcyJiY+CFwn6bi21H4I4q+yNvtxewHrCXpJXK1l+b15fsCdxQZd8DgcsjYgNgGaBfRKybz/+lxcYj4tx8TNtExDZ5f9+UtEiucmD5Nnl/87QZETcC44F9ImJwRLzXzDGfBNyX47sVKCVqfwH2B1Dq0dwbuKqZdgAWBcbm930M8L1cfn4+B4NyG+c2sf0g4OvAZsCJSsPcs4FdI2JDYBvgtzlpWwc4Adg27++oQjvLA1uQesfPyMdQ9bNLStQGA+uTetPPkrR8XrcJcEJErA2QP38A3wZGRkRpu0nFBvN37s/ATsCWwOcLq08A7sptbZP3tyhwGPB6Pl+nAhsVtlkUmBYRXwT+R/o8bp73PwfYJyf9vwC2y+dtPPCjsmNF0iFK//EbX77OzMzaT12SwYh4m/QH5BBSD8Z1kg4gJSZPR8QTueplQPEP4q3591TgoYh4KyJeAWYrzZnbPv88QurpWov0B7ZoU2BMRDydY3ktl28BXJHL7gKWlrR4pfhzL8aXSInWJOAi0h94SInCDXn56sJmzcX2VWpLBkdHxKyImA08CvQvroyIR4DlJK0gaX3SH+z/VNn3MxFRGp5+ClhV0nmSdgDebC6YiHgHuAv4hlLP6CIRMbWsWovarGAr4Mq8v9uB1/PyTOB/kjYoHVtE/K9KWx8ApflwE4ABeXkz5r5XV5A+C5X8LSLei4hXgbtJCZiAX0uaAvwL6Ad8DtgWuDHXLX7OAP4aER9HxKO5LtT22d0CuCYi5kTES8A9QCnpe7j0mS4zDjhQ0jBgvYh4q2z9WqTv3JORHjd0ZWHd9sBP82e8EehJSsa3IP1HgIiYBkwpbDMHuCkvf5n0PR+X2/gysCrpO7g2cH8u35+yz3Jue3hEDGmLu+ebmVnrLVyvhiNiDukPTKOkqaQ/CJOqbPZ+/v1xYbn0emHSH+bTI+KiZtoQUOkZe6oUZhNtLAS8kXs7alUxNqWLMZYoDZMDHzE3Ce9Z1kbxmOdQ+f25kdRj+nnyH+xm9j0AeKf0OiJez0nkV4EfknoWD6pyXBeTelJn8OlewZa02dxxN/U+XEyae/h54JIqcQJ8GHOfr9jU+Wtuf+XlAewDLAtsFBEfKg3r96TpzxnM+z6q8LuWz25T3qlUGBFjcg/j14ErJJ0VEeW93k3FKVIP+uPzFErNxTE7f7dL218WET8r234n4M6IGNpMO2Zm1knU6wKSgZKKvR6DgWdICcUASavn8v1IvR+1GgkcVJp/JKmfpOXK6jwIbC1plVxnqVw+hvSHHUkNpDmNFXuxcvnTkvbI9ZUTHkgXgeyWl/euIbZtSL1MJTOZO+zWoqtOs2vzfncnJYbN7XseefhuoYi4CfglsGGF9t8C+pZeRMRDwEqk4chrWtDmPO0w73HvVigvvi87AksW1t0C7EDqHRtZ2OeMCnE35wHmvlf7APc1UW9nST0lLQ00kHrdFgdezongNszt4RoN7JnrFj9nTanlPRpDmh7QI0932Ap4uLlGJfXP8f2ZNLRe/p7OAFaRtFp+XUzQRgJHlJK/3AsL6fzsmcvWJk1bqGQ0sHvpOJTm5fYnfUc2L33P8xzENZs7DjMz6zj16hnsA5yXh3Y/Av4POCQiZks6kDT8ujDpj+2fam00IkYpXYzxYP779TawL/Byoc4rkg4Bbs5zzV4GvgIMAy7Nw33vkuejNWMf4EJJvwAWISVhk0lX216pdOHG7cCsKrHtyNykDeBs4HpJ+5GGYFskIqZL6gs8HxEvVtn3nLLN++VzUPpPwM/4tOHAPyW9mOcNAlwPDI6I1yvUb6rNEcCfJL1HGqY9GfiLpJ8DDxW2Pxm4RtJE0n8M/lM41g8k3U3qpZ0DnySfzfVcVXIkcImk40jTFg5sot7DpPd0ZeDUiHhB0lXA3/O8tkmk5Kr0PpwG3CNpDmn494CmAqjls0tKfjcjfc4COD4i/puH6JvSABwn6cPc5nfK9js7fx9ul/QqKdFbN68+FfgDMCUnhDNJ8xwvAC7L35VHSMPEsyoc06P5+zEqv/8fAj+MiLF5Wsg1yheIkeYQPlHehpmZdTzNHVWzWuRh3/ciIiTtDQyNiJ2bqT8R+GJEfNhuQbYxpfvS/T4iRrfzfhciza/bIyKezGXfAFbNF7tYHShdEbxITiRXI/UArhkRH9Rrn71XWzPWOPO8ejVvZl3E5N2/2qH7b2xspKGhoUNjaAlJE9pi3nXd5gwuwDYCzs89KW9QZc5dvpqyS8o9uw8DkzsgEVybdDHILaVEECAifMPk+utNur3QIqRe2EPrmQiamVnHcjLYQhFxL+kWHgu8SDe67pC5XvlK3FU7Yt/dXb4i2Vf4mpl1E342sZmZmVk35p5BM+tway+5GOPrNFeoq80B6mrxgmNuD10tXuiaMXdX7hk0MzMz68acDJqZmZl1Y04GzczMzLoxzxk0sw731uOPc89WW9elbdGyxxx1tK4WLzjm9tCZ4t16TGeJxNqKewbNzMzMujEng2ZmZmbdmJNBMzMzs26spmRQ0h6S+ublX0i6WVKXfcyamZmZmSW19gz+MiLekrQF8FXgMuDC+oVlZmZmZu2h1mRwTv79deDCiPgb8Jn6hGTW/iTtKikkrdWO+1xC0mGt3HZmK7cbIGlaa7Y1M7MFU63J4POSLgL2BP4h6bMt2NasKxgK3Afs3Y77XAJoUTKopFN89yT16OgYzMxs/tX6R2VPYCSwQ0S8ASwFHFevoMzak6Q+wObAwTSRDOYetRmSLpY0TdJVkraTdL+kJyVtkuttIukBSY/k3wNz+TqSHpY0SdIUSWsAZwCr5bKzcr3jJI3LdU4u7PsxSRcAE4GVgFfyukUl3S5pco5rrwqxb5TXPwj8sFDeQ9JZhf19P5cvJOkCSdMl3SbpH5J2z+tmSjpR0n3AHpK2l/SgpImSbsjnsrTPeyRNkDRS0vLz/06ZmVk91JoMXhQRN0fEkwAR8SKwX/3CMmtXuwB3RMQTwGvNXBy1OnAOMAhYC/g2sAVwLPDzXGcGsFVEbACcCPw6l/8AOCciBgNDgOeAnwL/jojBEXGcpO2BNYBNgMHARpK2ytsPBC6PiA0i4pmI2DiX7wC8EBHrR8S6wB0V4r4UODIiNisrPxiYldvaGPiepFWAbwEDgPWA7wLl282OiC2AfwG/ALaLiA2B8cCPJC0CnAfsHhEbAZcAp5UHJekQSeMlja8Qs5mZtZNan0CyTvFFHh7aqO3DMesQQ4E/5OVr8+uJFeo9HRFTASRNB0ZHREiaSkqeABYHLss9fwEskssfBE6QtCJwc0Q8Kam8/e3zzyP5dR9Scvgf4JmIGFshpqnA2ZLOBG6LiHuLKyUtDiwREaVHBlwB7FjY36BSr1+OfQ1SgntDRHwM/FfS3WX7vC7/3hRYG7g/H8tn8nEOBNYF7szlPYAXywOPiOHAcICBfftGhWMzM7N20GwyKOlnpB6PXpLeLBUDH5D/ETfryiQtDWwLrCspSIlLSDo+IsoTlPcLyx8XXn/M3O/SqcDdEbGrpAFAI0BEXC3pIdJFWCMlfRd4qjwc4PSIuKgsxgHAO5Xij4gnJG0EfA04XdKoiDilrM2mEi0BR0TEyLL9fb2J+iWlWATcGRFDy7ZfD5heoSfSzMw6oWaHiSPi9IjoC5wVEYvln74RsXRE/KydYjSrp91Jw6/9I2JARKwEPE3qHWuNxYHn8/IBpUJJqwJPRcS5wK2koea3gL6FbUcCBxXm3fWTtFxzO5O0AvBuRFwJnA3MM8Sd5/jOyreFAtinbH+H5mFdJK0paVHShTS75bmDnwMamtj9WGBzSavn7XtLWhN4HFhW0ma5fBFJ6zTRhpmZdbCahokj4meS+gH9i9tExJh6BWbWToaSLuQouok0H/DeT1ev6jekYeIfAXcVyvcC9pX0IfBf4JSIeC1fgDIN+GeeN/gF4ME8vPo2sC9zb+1UyXrAWZI+Bj4EDq1Q50DgEknvkhLAkotJw9sTlXb4Cmn+5E3Al4FpwBPAQ8Cs8kYj4hVJBwDXKN1hAOAXubdyd+DcPEy9MGkYfnozx2FmZh1Enx4Jq1BJOoN0leWjzP3DFBHxzTrGZmYdRFKfiHg7D6M/DGweEf+t1/4G9u0bwzfwQ43MuoKtx9xTvRLQ2NhIQ0NDfYNpY10tZkkTImLI/LZT6wUkuwIDI+L9qjXNbEFwm6QlSBeFnFrPRNDMzDpWrcngU6SrIp0MmnUDEdHQ0TGYmVn7qDUZfBeYJGk0hYQwIo6sS1Rm1q30HTiw5qGnlupqwz5dLV5wzO2hq8VrXUutyeCt+cfMzMzMFiC1Xk18maRewMoR8XidYzIzMzOzdlLT4+gk7QRMIj/qStJgSe4pNDMzM+vian028TDS81LfAIiIScAqdYnIzMzMzNpNrXMGP4qIWWXPUvWzRM2sTcx4eQabn7d5/XYwtX5N10VXixccc3tog3jvP+L++W/EFji1JoPTJH0b6CFpDeBI4IH6hWVmZmZm7aHWYeIjgHVIt5W5mvRoqqPrFJOZmZmZtZOqPYOSegC3RsR2wAn1D8nMzMzM2kvVnsGImAO8mx84b2ZmZmYLkFqHiWcDUyX9RdK5pZ96BmZWK0m7SgpJa9VxH3tIekzS3W3U3imStmuLtgptNki6rYZ6jZLm+8HmZma2YKj1ApLb849ZZzQUuA/Ym3QbpFaR1CP3hFdyMHBYRLRJMhgRJ7ZFOx1J0sIR8VFHx2FmZvOnpp7BiLis0k+9gzOrRlIfYHNSsrZ3E3UGSJoh6TJJUyTdKKl3XjdT0omS7gP2kDRU0lRJ0ySdmeucCGwB/EnSWZJ65N/jcnvfz/WWlzRG0qS8/Za57oj8eqqkY3LdEZJ2z8tflvRIXn+JpM8WYjtZ0sS8bq1cvomkB/I2D0gaWOUc9ZJ0bY71OqBXYd32kh7M+7ghn08kfS2fs/vySMBtuXyYpOGSRgGXS1pW0k35XIyTtHmut2g+lnE5zp1b8/6amVn91foEkjXyH9BHJT1V+ql3cGY12AW4IyKeAF6TtGET9QYCwyNiEPAmcFhh3eyI2AIYA5wJbAsMBjaWtEtEnAKMB/aJiONIieesiNgY2Bj4nqRVgG8DIyNiMLA+6ak9g4F+EbFuRKwHXFoMSlJPYASwV16/MHBoocqrEbEhcCFwbC6bAWwVERsAJwK/rnKODgXezcd+GrBR3vcywC+A7fI+xgM/yjFdBOyYz8uyZe1tBOwcEd8GzgF+n8/FbsDFuc4JwF25fBvgLEmLlh37IZLGSxpfJX4zM6ujWucMXkr6Y/QR6R/2y4Er6hWUWQsMBa7Ny9fm15U8GxGlu61eSerpK7ku/94YaIyIV/Lw51XAVhXa2h74jqRJwEPA0sAawDjgQEnDgPUi4i3gKWBVSedJ2oGUiBYNBJ7OySzAZWX7vDn/ngAMyMuLAzdImgb8nnTbp+ZslY+ZiJgCTMnlmwJrA/fnY9kf6A+sBTwVEU/neteUtXdrRLyXl7cDzs/b3wosJqkv6Rz9NJc3Aj2BlYuNRMTwiBgSEZ6/aGbWgWqdM9grIkZLUkQ8AwyTdC9wUh1jM2uWpKVJvXjrSgqgBxCSjo+I8ifkNPf6nVKTte4aOCIiRlaIaSvg68AVks6KiMslrQ98FfghsCdwUFlbzXk//57D3O/rqcDdEbGrpAGkZKuaSk8MEnBnRMyTQEvaoEpb7xSWFwI2KySHpTYE7BYRj9cQm5mZdaCaryaWtBDwpKTDJe0KLFfHuMxqsTtweUT0j4gBEbES8DTz9vqVrCxps7xcuuCk3EPA1pKWUbq/5lDgngr1RgKHSloEQNKaeY5cf+DliPgz8BdgwzwUu1BE3AT8Eigfxp4BDJC0en69XxP7LFoceD4vH1ClLqTh731yrOsCg3L5WGDz0r4l9Za0Zo5p1ZxoAuzVTNujgMNLLyQNzosjgSNyUlhLgmlmZh2k1mTwaKA36TF0GwH7koaUzDrSUOCWsrKbSHP3yj0G7C9pCrAUadrDPCLiReBnwN3AZGBiRPytQlsXA48CE/NQ7UWkXrsGYJKkR0jz584B+gGNebh0RG6/uM/ZwIGkYd+pwMfAn6oc92+A0yXdT+oNreZCoE8+9uOBh/O+XyElk9fkdWOBtXIv32HAHUoX1rxEeupQJUcCQ/LFKY8CP8jlpwKLAFPyOTq1hjjNzKwD6NOjaYWV0nLAz4HVSY/IPj0iyuc8mXVquYfrtohYt6Nj6Sok9YmIt3PP3h+BJyPi9/XaX5+V+8T6x61fr+bNLLv/iPurV2ojjY2NNDQ0tNv+2kJXi1nShLaYd12tZ/By0vyg84A+gG80bdY9fC/3Zk4nDUtf1LHhmJlZvVS7gOTzEVF6HvFISRPrHZBZW4uImYB7BVsg9wLWrSfQzMw6j2rJoCQtydwrHnsUX0fEa/UMzszMzMzqq1oyuDjp/mbF21+UegcDWLUeQZlZ97LWcmvVbS5TV5sD1NXiBcfcHrpavNa1NJsMRsSAdorDzMzMzDpArTedRlI/0tMJPtkmIsbUIygzMzMzax81JYOSziTdePZR0pMQIA0TOxk0MzMz68Jq7RncBRgYEe9Xq2hmZmZmXUetyeBTpKcJOBk0szY3+8W3eOy0u+rS9ueAx+6vT9v10NXiBcfcHmqJ9wsnbNs+wdgCp9Zk8F3SY7ZGU0gII+LIukRlZmZmZu2i1mTw1vxjZmZmZguQmpLBiLhM0meANXPR4xHxYf3CMjMzM7P2UOvVxA3AZcBM0g2oV5K0v28tY2ZmZta1LVRjvd8C20fE1hGxFfBV/NxS60Ik7SopJK3VxPoBkqa1wX4aJN3Wwm1GSNq9Fds0tGSbwrYzJS3Tmm3NzGzBU2syuEhEPF56ERFPkK4uNusqhgL3AXt3dCDzS1KPjo4BQFLNN603M7POq9ZkcLykv+RejwZJfyY9s9is05PUB9gcOJjmk8GFJV0maYqkGyX1ztufKGmcpGmShktSLl9d0r8kTZY0UdJquZ0+efsZkq4q1N9I0j2SJkgaKWn5CrF+WdIjkqZKukTSZ3P5zBzHfcAewCzgg7zuDEmP5rjPrtDm0pJG5XYvovCscUn7SnpY0iRJF5USTUkHS3pCUqOkP0s6P5ePkPQ7SXcDZ0paTdId+ZjuLfW8SlpW0k35vI2TtHnt75iZmbWnWpPBQ4HpwJHAUaQnkfygXkGZtbFdgDtyj/ZrkjZsot5AYHhEDALeBA7L5edHxMYRsS7QC/hGLr8K+GNErA98CXgxl28AHA2sDawKbC5pEeA8YPeI2Ai4BDituHNJPYERwF4RsR5pTu+hhSqzI2KLiLg2Io6KiAckLQXsCqyT4/5VheM6CbgvIjYg3RVg5by/L5CeLLR5RAwmPV1oH0krAL8ENgW+ApQPra8JbBcRPwaGA0fkYzoWuCDXOQf4fURsDOwGXFwelKRDJI2XNL5CzGZm1k5qvZr4feB3+cesqxkK/CEvX5tfT6xQ79mIuD8vX0n6z8/ZwDaSjgd6A0sB0yU1Av0i4haAiJgNkDsBH46I5/LrScAA4A1gXeDOXKcHc5PHkoHA0zlphXTR1g8LsV9XIeY3gdnAxZJuByrNV9wK+FaO83ZJr+fyLwMbAeNyTL2Al4FNgHsi4rV8DDcw904CADdExJzc4/ol4Ia8PcBn8+/tgLUL5YtJ6hsRb5UKImI4KZlk3X4Do0LcZmbWDppNBiVdHxF7SppKehbxPHJPhFmnJWlpYFtgXUlBSsJC0vERUf6Z/tTr3Ft3ATAkIp6VNAzoSWGotYLik3rmkL5nAqZHxGbNhVvlcN4pL4iIjyRtQkrs9gYOJx3vp6o2sb/LIuJn8xRKu9YYx0LAG7lXsdxCwGYR8V6VtszMrINVGyY+Kv/+BrBThR+zzm534PKI6B8RAyJiJeBpYIsKdVeWVErWShec9MyvX809YbsDRMSbwHOSdgGQ9NnSHMMmPA4sW2pf0iKS1imrMwMYIGn1/Ho/4J7mDi7HtHhE/IM0ND24QrUxwD65/o7Akrl8NLC7pOXyuqUk9QceBraWtGS+SGS3SvvO5+BpSXvk7SVp/bx6FCkxLcVZKS4zM+sEmk0GI6I0jHVYRDxT/GHufCqzzmwocEtZ2U3AtyvUfQzYX9IU0nDwhRHxBvBnYCrwV2Bcof5+wJG5/gPA55sKIiI+ICWSZ0qaDEwiDbEW68wGDiQNu04FPgb+VOX4+gK35RjuAY6pUOdkYCtJE4Htgf/k/T0K/AIYlbe/E1g+Ip4Hfg08BPyLNEd4VhP73wc4OB/TdGDnXH4kMCRf1OI5xmZmnZg+PVJWoZI0MSI2LCub4mFiswWTpD4R8XbuGbwFuKQ0P7Ie1u03MG447MJ6NW/WLXzhhEozRDpOY2MjDQ0NHR1Gi3S1mCVNiIgh89tOtTmDh5J6AFfNPQclfYH7K29lZguAYZK2Iw2TjyL1ipqZ2QKo2tXEVwP/BE4Hfloof6t0paGZLXgi4tiOjsHMzNpHs8lgRMwizRUaCpAnmvck3VS3T0T8p/4hmpmZmVm91HSfQUk7ke4xuALpPmT9SZPty6+GNDNrsZ7L963bfKeuNgeoq8ULjrk9dLV4rWup9QkkvyI9jeCJiFiFdE8zzxk0MzMz6+JqTQY/jIj/AQtJWigi7qby/czMzMzMrAupaZgYeCPf3HYMcJWkl4GP6heWmZmZmbWHWpPBnYH3SDe03QdYHDilXkGZWffywYvT+c8p69Wl7VWB/4ypS9N10dXiBcdcLyufOLWjQ7BuotZkcDngxfyEhMsk9QI+B/yvbpGZmZmZWd3VOmfwBtKjsUrm5DIzMzMz68JqTQYXzs9WBT55zupn6hOSmZmZmbWXWpPBVyR9s/RC0s7Aq/UJyczMzMzaS61zBn9Auor4fEDAs8B36haVmZmZmbWLmpLBiPg3sGm+vYwi4q36hmVWO0lvR0SfOrS7JfAn4ENgs4h4r633UWGfDcAHEfFAS9aZmZm1Vq2Pozux7DUAEeHby1iXJqlHRMxpYvU+wNkRcWmNbYn0n6WPq1ZuWgPwNlAp4WtuXacnaeGI8P1Jzcw6mVrnDL5T+JkD7AgMqFNMZvNN0gBJMyRdJmmKpBsl9c7rZko6UdJ9wB6Stpf0oKSJkm6Q1EfSd4E9gRMlXZW3O07SuNzeyYX9PCbpAmAisFJZHDMlnZzbnipprVy+lKS/5rbGShokaQBpSsYxkiblnslPjqd8naT+kkbnNkZLWrnCedhE0gOSHsm/B+byAyTdLOkOSU9K+k0u7yFphKRpOd5jJC0naUJev76kKO1L0r8l9Za0rKSb8vkZJ2nzvH6YpOGSRgGXt827a2ZmbanWYeLfFl9LOhu4tS4RmbWdgcDBEXG/pEuAw4Cz87rZEbGFpGWAm4HtIuIdST8BfhQRp0jaArgtIm6UtD2wBrAJad7srZK2Av6T93NgRBzWRByvRsSGkg4DjgW+C5wMPBIRu0jaFrg8IgZL+hPwdkScXWwgImaWr5P097zdZZIOAs4Fdinb9wxgq4j4SNJ2wK+B3fK6wcAGwPvA45LOI91TtF9ErJv3sUREvCGpp6TFgC2B8cCWOZl+OSLelXQx8PuIuC8niiOBL+T9bARsUT7MLukQ4BCA9Vbo2cSpMzOzeqv1ApJyvUk3cDfrzJ6NiPvz8pXAkcxNBq/LvzcF1gbuz9MfPgM8WKGt7fPPI/l1H1Jy+B/gmYgY20wcN+ffE4Bv5eUtyElZRNwlaWlJi9d+aABsVmjvCuA3FeosTrpR/BpAAIsU1o2OiFkAkh4F+gPTgVVzYng7MCrXfQDYHNiKlFDuQEqK783rtwPWLk0hARaT1Dcv31ppvmVEDAeGAwzq1ytqP2wzM2tLtc4ZnEr6QwLQA1gWP47OOr/yBKP4+p38W8CdETG0SlsCTo+Ii+YpTMO371TcYq738+85zP3OqUK9+U2IKm1/KnB3ROyaY22sENcnsUXE65LWB74K/JA0VH4QKenbkpQw/g34Sd7fbXn7hahwkU1ODqudHzMz60C1zhn8BrBT/tkeWCEizq9bVGZtY2VJm+XlocB9FeqMBTaXtDpAnv+2ZoV6I4GDlK6oR1I/ScvNR2xjSBeolK4SfjUi3gTeAvo2sU35ugeAvfPyPlQ+vsWB5/PyAdWCysPmC0XETcAvgQ0L8e4LPJkvkHkN+BpQ6nkdBRxeaGdwtX2ZmVnn0GwymCe5L0X6I1T6eY80BLRUO8RnNj8eA/aXNAVYCriwvEJEvEJKkq7J9cYCa1WoNwq4Gngw95TfSNNJWy2GAUPyPs8A9s/lfwd2Lb+ApIl1RwIH5jb2A46qsJ/fAKdLup/Uq19NP6BR0iRgBPAzSHMW8/ox+fd9wBsR8Xp+fWTpePKQ8w9q2JeZmXUCimh6ZErS06ShIAErA6/n5SWA/0TEKu0Qo1mL5SHR20oXQljnNqhfr7jt+6t3dBhmncrKJ079ZLmxsZGGhoaOC6YVHHP9SZoQEUPmt51mewYjYpWIWJU0RLZTRCwTEUuTho1vbm5bMzMzM+v8ap0zuHFE/KP0IiL+CWxdn5DM5l9EzHSvoJmZWXW13lrmVUm/IN2eI0gTyf9Xt6jMzMzMrF3UmgwOBU4CbiElg2NymZnZfPvM8uuw8onj69J2V5sD1NXiBcds1tXV+gSS1yi7UlFSa29YbWZmZmadRLVby9xXWL6ibPXDdYnIzMzMzNpNtQtIFi0sl0/Gr/QEBTMzMzPrQqoN9UYTy5Vem5m1yuuvP8b1N2xSt/avv6FuTddFV4sXHPP82nMPD7ZZx6mWDC4haVdSD+ISkr6Vy0V6zJWZmZmZdWHVksF7gG8WlncqrBvz6epmZmZm1pU0mwxGxIEAklaJiKeL6yT5UXRmZmZmXVytTyC5qULZjW0ZiJmZmZm1v2Z7BiWtBawDLF6YLwiwGNCznoGZmZmZWf1V6xkcCHwDWII0X7D0syHwvbpGZtaNSHq77PUBks7Pyz+Q9J0q239Sv43iGSBpWhu1NULS7m3RlpmZtb1qcwb/BvxN0mYR8WA7xWRmBRHxp46OwczMFly1zhncVdJikhaRNFrSq5L2rWtkZgaApGGSjs3LG0uaIulBSWeV9d6tIOkOSU9K+k0TbZ0h6dHcxtm57HOSbpE0Of98KVfvIenPkqZLGiWpV64/WNLY3MYtkpZsrtzMzDq3WpPB7SPiTdKQ8XPAmsBxdYvKrPvpJWlS6Qc4pYl6lwI/iIjNgDll6wYDewHrAXtJWqm4UtJSwK7AOhExCPhVXnUucE9ErE+aAjI9l68B/DEi1gHeAHbL5ZcDP8ltTAVOqlJekaRDJI2XNL65emZmVl+1JoOL5N9fA66JiNfqFI9Zd/VeRAwu/QAnlleQtATQNyIeyEVXl1UZHRGzImI28CjQv2z9m8Bs4OJ8Qdi7uXxb4EKAiJgTEbNy+dMRMSkvTwAGSFocWCIi7snllwFbNVXe3AFHxPCIGBIRQ5qrZ2Zm9VVrMvh3STOAIcBoScuS/qiYWfup9jzw9wvLcyibExwRHwGbkG4VtQtwx/y0Z2ZmC4aaksGI+CmwGTAkIj4k9SjsXM/AzGxeEfE68JakTXPR3i3ZXlIfYPGI+AdwNGlYGWA0cGiu00PSYs3EMAt4XdKWuWg/0hBzxfKWxGdmZh2jpv/pS+oN/BBYGTgEWIF025nb6heamVVwMPBnSe8AjcCs5qvPoy/p7gA9Sb2Mx+Tyo4Dhkg4m9QAeCrzYTDv7A3/K/y48BRxYpdzMzDqxWod9LiXNGSpdZfgccANOBs3aRET0KXs9AhiRl4cVVk3PF2gg6afA+PL6+fU3KuzjRdIwcXn5S1Tu6V+3UOfswvIkYNPyys2UH1ChbTMz6yRqnTO4WkT8BvgQICLeo/r8JTNre1/PVxxPA7Zk7hXBZmZmrVJrz+AH+R5jASBpNeadXG5m7SAirgOu6+g4zMxswVFrMngS6crDlSRdBWwOHFCvoMyse1lyyS+w5x4P16XtxsZGGhoa6tJ2PXS1eMExm3V1NSWDEXGnpImk+UACjoqIV+samZmZmZnVXUvuG9YTeD1vs7YkImJMfcIyMzMzs/ZQ661lziQ95mo68HEuDsDJoJmZmVkXVmvP4C7AwIjwRSNmZmZmC5Bak8GnSM8ndjJoZm3uxaf/zWn77l639u+/+Py6tV0PXS1eWDBjPuHKG9spErOO1WwyKOk80nDwu8AkSaMpJIQRcWR9wzMzMzOzeqrWMzg+/54A3FrnWMzMzMysnTWbDEbEZe0ViJmZmZm1v1qvJp5KfvpIwSxSz+GvIuJ/bR2YmZmZmdVfrc8m/idwO7BP/vk7cC/wX2BEXSIzW4BICkm/Lbw+VtKwOuynUdKQNm7z52WvH2jL9s3MrGPVmgxuHhE/i4ip+ecEYOuIOBMYUL/wzBYY7wPfkrRMRwfSCvMkgxHxpY4KxMzM2l6tyWAfSV8svZC0CdAnv/yozaMyW/B8BAwHjilfIam/pNGSpuTfK1eos6ikSySNk/SIpJ1zeS9J1+ZtrwN6FbZ5u7C8u6QReflzkm6RNDn/fCmX/1XSBEnTJR2Sy84AekmalJ9L/km7Ss6SNE3SVEl75fKG3EN5o6QZkq6SpDY6j2Zm1sZqvc/gd4FLJPUhPZv4TeC7khYFTq9XcGYLmD8CUyT9pqz8fODyiLhM0kHAuaQbvRedANwVEQdJWgJ4WNK/gO8D70bEIEmDgIk1xHEucE9E7CqpB3P/Y3dQRLwmqRcwTtJNEfFTSYdHxOAK7XwLGAysDyyTtyk9lWgDYB3gBeB+YHPgvuLGOeE8BGCFpZaoIWwzM6uHmpLBiBgHrCdpcUAR8UZh9fX1CMxsQRMRb0q6HDgSeK+wajNSYgVwBVCeLAJsD3xT0rH5dU9gZWArUnJHREyRNKWGULYFvpO3mUO6GAzgSEm75uWVgDWA5i4O2wK4JrfxkqR7gI1J/1l8OCKeA5A0iTSdZJ5kMCKGk3pL6bf0kuUXqJmZWTupdtPpfSPiSkk/KisHICJ+V8fYzBZEfyD13l3aTJ1KiZGA3SLi8XkK03exqUSqWN6zuaAkNQDbAZtFxLuSGqttk2NqSvFpRXOofRTCzMzaWbU5g4vm332b+DGzFoiI10i96QcXih8A9s7L+1DWg5aNBI4ozb2TtEEuH5O3QdK6wKDCNi9J+oKkhYBdC+WjgUPzNj0kLQYsDryeE8G1gE0L9T+UtEiFmMYAe+U2liX1Uj7c7AkwM7NOp9pNpy/Kv09un3DMuoXfAocXXh9JmpN7HPAKcGCFbU4l9SpOyQnhTOAbwIXApXl4eBLzJmM/BW4DngWmMXdu4FHAcEkHk3rtDgXuAH6Q23kcGFtoZ3je78SI2KdQfgtpiHsyqRfy+Ij4b04mzcysi6g2THxuc+v9bGKz2kREn8LyS0DvwuuZpHl8zW3/HulikUrle396C4iIG4EbK5S/BOxcYZMdm2jnJ8BPCq/75N8BHJd/ivUbgcbC62Lia2ZmnUy1eTwTCssnAyfVMRYzMzMza2c1P5tY0tF+VrGZmZnZgqXWm05D01csmpmZmVkX5ds9mFmHW36V1Tjhyk9Nb2wTjY2NNDQ01KXteuhq8YJjNuvqql1A8hZzewR7S3qztIo0f3yxegZnZmZmZvVVbc6g7yVoZmZmtgBryZxBMzMzM1vAeM6gmXW4F154gWHDhtWt/cbGxrq1XQ9dLV5o/5jr+Xkx627cM2hmZmbWjTkZNDMzM+vGnAyamZmZdWNOBs3MzMy6MSeDZmZmZt2Yk0FbYEh6u4Y6wyQd28r2GySNKCx/qa330cq47pDUrxXbNXkMZmbWfTgZNGudBqDDEylJvYClIuL5VmzeQB2OQVKPtm7TzMzqx8mgLfAknSDpcUn/AgYWygdLGitpiqRbJC2ZyxslnSnpYUlPSNoyb/IBMEvSAOAHwDGSJhXWV9r39yT9U1KvptqV1FPSpZKmSnpE0ja5/B+SBuXlRySdmJdPlfTdvIsGoDGXz5R0sqSJua21cvlSkv6aj3OspEHVjkHS1rl8Ut5339yTeFuhzvmSDijs+0RJ9wF7SNpe0oM5lhsk9Wnh22ZmZu3EyaAt0CRtBOwNbAB8C9i4sPpy4CcRMQiYCpxUWLdwRGwCHF0qj4gHIuKoiJgJ/An4fUQMjoh7m9j34cBOwC4R8V5T7QI/zO2vBwwFLpPUExgDbClpMeAjYPNcfwugtM8dgTsKu301IjYELgRKQ9UnA4/k4/w5cHkNx3As8MOIGAxsCbxHdbMjYgvgX8AvgO1yLOOBH1U4P4dIGi9pfA1tm5lZnTgZtAXdlsAtEfFuRLwJ3AogaXFgiYi4J9e7DNiqsN3N+fcEYEAr9rsfKVHbLSLer9LuFsAVABExA3gGWJOU8G2V198O9JHUGxgQEY/nbTcH7mtB+3cBS+fjb879wO8kHUk6Tx9VP2Suy783BdYG7pc0Cdgf6F9eOSKGR8SQiBhSQ9tmZlYnfhyddQfRim1KCdwcWvc9mQYMBlYEnq7SrppoYxwwBHgKuBNYBvgeKdFD0qrAsxHxQSvab/acRMQZkm4HvgaMlbQdqXey+B/InmWbvVPY350RMbS5fZiZWefgnkFb0I0Bds1z9vqShm2JiFnA64W5cvsB9zTRRiVvAX2bWf8I8H3gVkkr1BDjPgCS1gRWBh7PSd6zwJ7AWFJP4bE0PURcS/sNpKHkN5s7BkmrRcTUiDiTNMy7FqnHcm1Jn809i19uYn9jgc0lrZ7b6p2Py8zMOiEng7ZAi4iJpOHLScBNzE2kIA1fniVpCqkX75QWNP13UpLZ5AUkEXEfKXm7XdIyzbR1AdBD0tQc6wGFoeV7gZci4t28vGLhGHagtmRwGDAkH+cZpOOudgxHS5omaTJpvuA/I+JZ4HpgCnAVKeGtdNyvAAcA1+R9jiUlk2Zm1gl5mNgWGBFR8YrViDgNOK1C+STS/Lby8obC8qtUmDMYEU8Ag5rY37DC8khgZH5Zsd2ImE1Kniq19Uvgl3n5BfKQr6TPAsvnC0FKdQcUlseX9hcRrwE7t/AYjmii/Hjg+ArlA8pe38W8F+uYmVkn5WTQrAvKPYe+8MLMzOabh4nNzMzMujEng2ZmZmbdmIeJzazDrbDCCgwbNqwubTc2NtLQ0FCXtuuhq8ULXTNmM5vLPYNmZmZm3ZiTQTMzM7NuzMmgmZmZWTfmOYNm1uFefm4W5//473Vrf9rf69d2PXT2eA//7U4dHYKZtSH3DJqZmZl1Y04GzczMzLoxJ4NmZmZm3ZiTQTMzM7NuzMmgmZmZWTfmZNDqQtIcSZMkTZN0g6TeTdR7oANiW0TShCp1Zkpapr1iKtv30U2dr3aO4+02ameYpGPboi0zM2t7TgatXt6LiMERsS7wAfCD4kpJPQAi4kttvWNJ1W6ZtAXQ7kloCxwNtCgZLJ1PMzOzlnIyaO3hXmB1SQ2S7pZ0NTAV5vY+5XX3SLpe0hOSzpC0j6SHJU2VtFqut5OkhyQ9Iulfkj6Xy4dJGi5pFHC5pHslDS4FIOl+SYPyyx2Af+byffM+Jkm6qFJS1VQdSW9LOlPShBzLJpIaJT0l6Zu5Tg9JZ0kaJ2mKpO8XjrdR0o2SZki6SsmRwArA3ZLuznUvlDRe0nRJJxfiminpREn3AT+VNLGwbo1KvZ+SjpT0aI7l2lzWR9Kl+TxPkbRbof5pkiZLGls41/0ljc51R0taublyMzPr3JwMWl3lXrodyckfsAlwQkSsXaH6+sBRwHrAfsCaEbEJcDFwRK5zH7BpRGwAXAscX9h+I2DniPh23uaAHMOawGcjYkqutw3QKOkLwF7A5hExGJgD7FMWf3N1FgUaI2Ij4C3gV8BXgF2BU3Kdg4FZEbExsDHwPUmr5HUbkHoB1wZWzfs4F3gB2CYitsn1ToiIIcAgYOtCUgswOyK2iIjTgFmFBPhAYMSnTzE/BTaIiEHM7a39ZY5xvVx+V+H4xkbE+sAY4Hu5/Hzg8lz3KuDcKuUVSTokJ7njm6tnZmb15WTQ6qWXpEnAeOA/wF9y+cMR8XQT24yLiBcj4n3g38CoXD4VGJCXVwRGSpoKHAesU9j+1oh4Ly/fAHxD0iLAQeTESNIKwGsR8S7wZVICOS7H+mVSUlbUXJ0PgDsKMd4TER+Wxbs98J287UPA0sAahXPxXER8DEwqbFNuz9zr90g+3mIifV1h+WLgwNxzuRdwdYW2pgBXSdoX+CiXbQf8sVQhIl4vHN9teXlCIb7NCm1fQRp2b668oogYHhFDcqJrZmYdxI+js3p5L/ekfUISwDvNbPN+YfnjwuuPmftZPQ/4XUTcKqkBGFbY5pO2I+JdSXcCOwN7AqWEY0dgZCkk4LKI+FkzMTVX58OIiPJ4I+LjwrxFAUdExMjihjn24vHOocL3MfciHgtsHBGvSxoB9Kx0zMBNwEmknr0JEfG/CjF/HdgK+CbwS0nr5BijQt3i8VWML6u0bXPlZmbWibhn0LqaxYHn8/L+VepeTBqqHBcRr+WyT+YLAqOB3SUtByBpKUn9y9qopU5zRgKH5h5KJK0padEq27wF9M3Li5ESvll5zt6OTW0UEbPz/i4ELi1fL2khYKWIuJs0vL4E0IfUA3t4od6SVeJ7ANg7L+9DGrpvrtzMzDoxJ4PW1QwDbpB0L/BqcxUjYgLwJjkxysOna0TEjLz+UeAXwChJU4A7geXL2qhap4qLgUeBiZKmARdRvUd+OPBPSXdHxGTS8PB04BLg/irbXkXqkRtVYV0P4Mo8xP4I8PuIeIM013FJpdsATSbNqWzOkaTh6CmkuZ1HVSk3M7NOzMPEVhcR0adCWSPQWKle+bqIaKi0XUT8DfhbhbaHlZfl+YELMTcx2gwYW7bddcw7765UPqCGOn0Ky8MqrcvzAX+ef4o+OaZc7/DC8nmk4fDS6wPK910eY8EWwCURMadC/Q+pMI8vIt6mQi9r2fHdCNyYl2cC21ao31T5sErxm5lZ5+Bk0BZIkr4DnAb8KCdkRMR9LMBDl5JuAVajQkJmZmbWFCeDtkCKiMuByzs6jvYUEbt2dAxmZtb1eM6gmZmZWTfmnkEz63DLrbg4h/92p7q03djYSENDQ13aroeuFq+ZdX3uGTQzMzPrxjT3nrJmZh1D0ivAM3Vqfhmq3Iaok+lq8YJjbg9dLV5wzO1hYET0rV6teR4mNrMOFxHL1qttSeO70iPvulq84JjbQ1eLFxxze2irZ7t7mNjMzMysG3MyaGZmZtaNORk0swXd8I4OoIW6WrzgmNtDV4sXHHN7aJN4fQGJmZmZWTfmnkEzMzOzbszJoJmZmVk35mTQzLokSZdIelnStELZqZKmSJokaZSkFZrYdglJN0qaIekxSZt18niPkTRd0jRJ10jqWe94m4q5sO5YSSFpmSa23UHS45L+T9JP6x/tJ/ttVcySVpJ0d/48TJd0VGeOt1Cnh6RHJN1W30jn2ef8fC7a/buX9zs/Mbf796+Jfy+GSXo+/3sxSdLXmti2xd89J4Nm1lWNAHYoKzsrIgZFxGDgNuDEJrY9B7gjItYC1gceq1eQBSNoRbyS+gFHAkMiYl2gB7B3fUP9xAg+HTOSVgK+Avyn0kaSegB/BHYE1gaGSlq7fmHOYwStiBn4CPhxRHwB2BT4YTvFPILWxVtyFO3z+S0aQetj7ojvHrT+s9xR378RVIgX+H1EDM4//yhf2drvnpNBM+uSImIM8FpZ2ZuFl4sCn7pCTtJiwFbAX/I2H0TEG/WL9JPYWhVvtjDQS9LCQG/ghboEWaZSzNnvgeNpOt5NgP+LiKci4gPgWmDn+kQ5r9bGHBEvRsTEvPwWKUnpV684C/tt7TlG0orA14GL6xNdZa2NuaO+e3lfrT7PdMD3r5l4q2nVd8/JoJktUCSdJulZYB8q9wyuCrwCXJqH1y6WtGi7BllQLd6IeB44m9Rz8SIwKyJGtW+Uc0n6JvB8RExuplo/4NnC6+doh8SqKTXGXKw/ANgAeKiecTWz/1rj/QMpkfm47kFVUWPMne27VzXmzvb9Aw7PU0sukbRkhfWt+u45GTSzBUpEnBARKwFXAYdXqLIwsCFwYURsALwDtNuctnLV4s3/4O8MrAKsACwqad/2jfKTWHoDJ9D08PsnVSuUdch9zFoQc6l+H+Am4Oiyntt2UWu8kr4BvBwRE9olsOZjqfUcd5rvXgvOc6f5/gEXAqsBg0mJ6W8r1GnVd8/JoJktqK4GdqtQ/hzwXESUen1uJP2B6mhNxbsd8HREvBIRHwI3A19q18jmWo30R3GypJnAisBESZ8vq/ccsFLh9Yq009B2BbXGjKRFSIngVRFxc7tGOVet8W4OfDPXuRbYVtKV7RloQUs+F53lu1drzJ3m+xcRL0XEnIj4GPgzaUi4XKu+e04GzWyBIWmNwstvAjPK60TEf4FnJQ3MRV8GHm2H8D6llnhJw1ObSuotSaR42/uCAQAiYmpELBcRAyJiAOkPz4b5nBaNA9aQtIqkz5Am3N/azuECtcecz+1fgMci4ncdECpQe7wR8bOIWDHX2Ru4KyI6pMeqBTF3mu9eCz7Lneb7J2n5wstdgU9dGU0rv3tOBs2sS5J0DfAgMFDSc5IOBs7It3+YAmxPutISSStIKl55dwRwVa43GPh1Z40396LcCEwEppL+3W6XR2Y1EXNTdYsxf0Qa8h5J+sN5fURM78wxk3ra9iP1sDV7645OEm+Hmc+Y2/27l+No7We5Q75/TcT7G0lT87nbBjimQryt+u75cXRmZmZm3Zh7Bs3MzMy6MSeDZmZmZt2Yk0EzMzOzbszJoJmZmVk35mTQzMzMrBtzMmhmZu1C0oqS/ibpSUn/lnROvhdaPff5dv49QNK0vNxb0lX5Nh3TJN2XnzzSXDs/L3v9QJX6e0h6TNLdrYj5AEkrFF5fLGntlrZjVisng2ZmVnf5hr03A3+NiDWANYE+wGnz2e7CrdjsKOCliFgvItYFDgY+rLLNPMlgRFR7CsXBwGERsU0r4juA9Oiz0r6+GxEdcnNm6x6cDJqZWXvYFpgdEZcCRMQc0k1zD5I0TtI6pYqSGiVtJGlRSZfk9Y9I2jmvP0DSDZL+DoyS1EfSaEkTc2/fzlViWR54vvQiIh6PiPdz23+VNEHSdEmH5LIzgF75RtRX5bJSj+PyksbkddMkbSnpRGAL4E+Szsq9kvfm+CZK+iSRlHR8jnmypDMk7Q4MId2YeZKkXvl8DMn1hxZ6NM8stPO2pNNyO2Mlfa5V75J1S77ptJmZ1Z2kI4FVIuKYsvJHgL8CC0XESUqP3LonItaU9Gvg0Yi4UtISwMPABsAewK+AQRHxWu4d7B0Rb0paBhgLrBERIentiOgjaQBwW0SsK2kwMAr4NzAauCwinszxLJXb7EV6tNfWEfG/UjuFuEvt/hjoGRGnSeqR43hLUiNwbESMl9Qb+DgiZis9gvCaiBgiaUfgl8B2EfFuYd+fbJv31QgcS3rG7FhgI+D1fAznRsRfJQXwzYj4u6TfAG9GxK/m/52z7sA9g2Zm1h4EVOp9ENBISvAA9gRuyMvbAz+VNCnX6QmsnNfdGRGvFdr4dX5M17+AfkCTPWMRMQlYFTgLWAoYJ+kLefWRkiaTkq6VgDUqNjLXOOBAScOA9SLirQp1FgH+LGlqPrbS/L/tgEsj4t0c12sVti3aGGiMiFfyY8euArbK6z4AbsvLE4ABVdoy+0Rr5lqYmZm11HRgt2KBpMVICdc44H+SBgF7Ad8vVQF2i4jHy7b7IvBOoWgfYFlgo4j4UNJMUuLYpIh4mzSH8WZJHwNfy0Or2wGb5Z66xhraGSNpK+DrwBWSzoqIy8uqHQO8BKxP6oSZXTi+lgzPqZl1H8bcob45+O+7tYB7Bs3MrD2MBnpL+g5AHlL9LTAi94xdCxwPLB4RU/M2I4Ej8sUnSNqgibYXB17OieA2QP/mApG0uaQl8/JnSD11z+R2Xs+J4FrApoXNPpS0SIW2+ud9/xn4C7BhE/G9GBEfA/sBPXL5KNKcyd65raVy+VtA3wrtPARsLWmZfP6GAvc0d6xmtXAyaGZmdZd7rXYF9pD0JPAEqYesdJXujcDewPWFzU4lDbFOUbotzKlNNH8VMETSeFIv4Ywq4awG3JOHbR8BxgM3AXcAC+fh5lNJQ8Ulw3McV5W11QBMynMfdwPOqbC/C4D9JY0lXUX9DkBE3AHcCozPQ+HH5vojSBefTMpzF8n1XwR+BtwNTAYmRsTfqhyrWVW+gMTMzMysG3PPoJmZmVk35mTQzMzMrBtzMmhmZmbWjTkZNDMzM+vGnAyamZmZdWNOBs3MzMy6MSeDZmZmZt3Y/wMVEzWikaTcWgAAAABJRU5ErkJggg==", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot overall satisfaction by HighestEducationParents\n", "plot_eval_param_dist(pe_satifaction, \"Overall satisfaction by HighestEducationParents\", \"OverallSatisfaction\", x_lim=[13.5,15],\n", " palette=[\"tab:blue\",\"tab:cyan\",\"tab:red\",\"tab:green\",\"tab:pink\",\"tab:orange\",\"tab:olive\",\"tab:brown\",\"tab:gray\",\"tab:purple\"])\n", "\n", "# Save satisfaction range for HighestEducationParents\n", "save_range_in_oa_sf(pe_satifaction, \"HighestEducationParents\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As far as overall satisfaction is concerned, the graph is a little less clear. Nevertheless, we can clearly see that a high level
\n", " of overall satisfaction is more likely to be achieved by people whose parents have achieved a high academic degree. The parents of
\n", " the three most satisfied groups on average all have an academic degree." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SalaryCareerSatisfactionJobSatisfactionOverallSatisfaction
Gender
Other61633.9219187.1621626.57657713.738739
Female57969.6282767.4093146.98529414.394608
Male56924.5462697.5256776.97789714.503574
\n", "
" ], "text/plain": [ " Salary CareerSatisfaction JobSatisfaction OverallSatisfaction\n", "Gender \n", "Other 61633.921918 7.162162 6.576577 13.738739\n", "Female 57969.628276 7.409314 6.985294 14.394608\n", "Male 56924.546269 7.525677 6.977897 14.503574" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Evaluation parameter by Gender\n", "gender_satifaction = df_eval.groupby(\"Gender\").mean().sort_values(\"Salary\", ascending=False)\n", "add_overall_sf(gender_satifaction)\n", "gender_satifaction" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot salary by Gender\n", "plot_eval_param_dist(gender_satifaction, \"Salary by gender\", \"Salary\", x_lim=[55000,63000],\n", " palette=[\"tab:red\",\"tab:blue\",\"tab:green\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Contrary to what one might expect, men in the developer profession earn the lowest salaries.
\n", "One reason for this could be that the proportion of men in lower-income countries is significantly higher.
\n", "In order to check this, the salary distribution of the different genders across the countries must be considered." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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0MwyuTTMb4E9r+beA7yS5laan6qC6r/uSfAq4um73yVLKfcN1nJIkSZI0bMGqlPLGflbt2c/2RwNH91E+F9i+j/JHqMGsj3XfBr496MZKkiRJUgujZfIKSZKeZLfjdhvS/V12+GUr3SYJb3rTm/jOd74DwLJly9hss83YZZddOO+88/qtN2fOHL74xS8OuI0kafwa6WusJEka1f7mb/6GG264gYcffhiACy64gM037/NbOyRJWsFgJUlSL6985Sv58Y9/DMBpp53GG9/4+Oj2q666il133ZUdd9yRXXfdlZtvvvlJ9R966CEOOeQQdtppJ3bccUd++MMfPmkbSdL4YrCSJKmXgw46iNNPP51HHnmE6667jl122WXFuuc973lceumlXHvttXzyk5/kwx/+8JPqH3300bzsZS/j6quv5uKLL+YDH/gADz300EgegiRphHmNlSRJveywww7cdtttnHbaaey7775PWLdkyRJmz57NLbfcQhKWLl36pPo/+9nPOPfcc/niF78IwCOPPMLvf/97tt122xFpvyRp5BmsJEnqw3777cf73/9+5syZw7333rui/GMf+xgvfelLOeecc7jtttuYNWvWk+qWUjj77LPZZpttRrDFktS/oZ4MqI3PjNMI4lBASZL6cMghh/Dxj3+c5z//+U8oX7JkyYrJLE488cQ+6+69994cd9xxNN9bD9dee+2wtlWS1H3jMy5KksaFwUyPPlymTJnCu9/97ieVH3HEEcyePZsvfelLvOxlL+uz7sc+9jHe8573sMMOO1BKYerUqU7DLknjXHo+TVvdzZw5s8ydO7fbzZCk1drChQu9DmkAPj6SnqpRNRTwe6Onb2ePSy9Z5TpJ5pVSZvYudyigJEmSJLVksJIkSZKklgxWkiRJktSSwUqSJEmSWjJYSZIkSVJLBitJkiRJamn0zHUoSVIvl+y+x5DubzDT6i5evJjDDjuMG2+8kccee4xXv/rVfOELX+DGG2/kjjvuYN999wXgyCOPZN111+X973//kLZRkjQ22WMlSVJVSuG1r30tBxxwALfccgu//vWvefDBB/nIRz7C/Pnz+clPfjJk97V8+fIh25ckqfsMVpIkVRdddBGTJk3iX//1XwGYMGECxxxzDN/85jc54ogjOOOMM5g+fTpnnHEGADfeeCOzZs3i2c9+Nscee+yK/ZxyyinsvPPOTJ8+nbe97W0rQtS6667Lxz/+cXbZZRcuv/zykT9ASdKwMVhJklQtWLCAGTNmPKFs/fXXZ+rUqXz0ox/lwAMPZP78+Rx44IEA3HTTTZx//vlcddVVHHXUUSxdupSFCxdyxhlncNlllzF//nwmTJjAqaeeCsBDDz3E9ttvz5VXXslLXvKSET8+SdLw8RorSZKqUgpJBl3+qle9iokTJzJx4kQ23XRT7rrrLi688ELmzZvHTjvtBMDDDz/MpptuCjQ9YK973euG9yAkSV1hsJIkqZo2bRpnn332E8r+/Oc/s2jRIiZMmPCk7SdOnLhiecKECSxbtoxSCrNnz+azn/3sk7afNGlSn/uRJI19DgWUJKnac889+ctf/sLJJ58MNBNMvO997+PNb34zkydP5oEHHhjUPs466yzuvvtuAO677z5uv/32YW23JKn77LGSJI1ag5kefSgl4ZxzzuGd73wnn/rUp3jsscfYd999+cxnPsNDDz3E5z73OaZPn86HPvShfvex3Xbb8elPf5q99tqLxx57jDXXXJPjjz+eLbfccgSPRJI00gxWkiR12GKLLfjRj370pPKJEydy9dVX91vvhhtuWLF84IEHrpjgotODDz44NI2UJI06DgWUJEmSpJYMVpIkSZLUksFKkjSqlFK63YRRycdFkkY3g5UkadSYNGkS9957ryGil1IK9957L5MmTep2UyRJ/XDyCknSqDFlyhQWL17MH//4x243ZdSZNGkSU6ZM6XYzJEn9MFhJkkaNNddck6222qrbzZAkaZU5FFCSJEmSWjJYSZIkSVJLDgWUJEmShsGMD5zc7SasMOlZ3W7B+GePlSRJkiS1ZLCSJEmSpJYMVpIkSZLUksFKkiRJkloyWEmSJElSSwYrSZIkSWrJYCVJkiRJLRmsJEmSJKklg5UkSZIktWSwkiRJkqSWDFaSJEmS1JLBSpIkSZJaMlhJkiRJUksGK0mSJElqyWAlSZIkSS0ZrCRJkiSpJYOVJEmSJLVksJIkSZKklgxWkiRJktSSwUqSJEmSWjJYSZIkSVJLBitJkiRJaslgJUmSJEktGawkSZIkqSWDlSRJkiS1ZLCSJEmSpJYMVpIkSZLUksFKkiRJkloyWEmSJElSSwYrSZIkSWqpK8EqyXuTLEhyQ5LTkkxKslGSC5LcUn9v2LH9h5LcmuTmJHt3lM9Icn1dd2yS1PKJSc6o5VcmmdqFw5QkSZK0mhjxYJVkc+BdwMxSyvbABOAg4IPAhaWUrYEL622SbFfXTwP2Ab6aZELd3deAQ4Gt688+tfwtwP2llOcAxwCfH4FDkyRJkrSa6tZQwDWAtZOsAawD3AHsD5xU158EHFCX9wdOL6U8Wkr5HXArsHOSzYD1SymXl1IKcHKvOj37OgvYs6c3S5IkSZKG2hojfYellP9N8kXg98DDwM9KKT9LMrmUcmfd5s4km9YqmwNXdOxicS1bWpd7l/fUWVT3tSzJEmBj4J7OtiQ5lKbHi8mTJzNnzpwhO05JkiRJo9tQvv8f8WBVr53aH9gK+BPwvSRvGqhKH2VlgPKB6jyxoJQTgBMAZs6cWWbNmjVAMyRJkqRV8OOTu90CrcRQvv/vxlDAlwO/K6X8sZSyFPg+sCtwVx3eR/19d91+MbBFR/0pNEMHF9fl3uVPqFOHG24A3DcsRyNJkiRptdeNYPV74EVJ1qnXPe0JLATOBWbXbWYDP6zL5wIH1Zn+tqKZpOKqOmzwgSQvqvs5uFednn29HrioXoclSZIkSUOuG9dYXZnkLOAaYBlwLc1wvHWBM5O8hSZ8vaFuvyDJmcCNdfvDSinL6+7eAZwIrA38tP4AfAv4TpJbaXqqDhqBQ5MkSZK0mhrxYAVQSvkE8IlexY/S9F71tf3RwNF9lM8Ftu+j/BFqMJMkSZKk4dat6dYlSZIkadwwWEmSJElSSwYrSZIkSWrJYCVJkiRJLRmsJEmSJKklg5UkSZIktWSwkiRJkqSWDFaSJEmS1JLBSpIkSZJaMlhJkiRJUksGK0mSJElqyWAlSZIkSS0ZrCRJkiSpJYOVJEmSJLVksJIkSZKklgxWkiRJktSSwUqSJEmSWjJYSZIkSVJLBitJkiRJaslgJUmSJEktGawkSZIkqSWDlSRJkiS1ZLCSJEmSpJYMVpIkSZLUksFKkiRJkloyWEmSJElSSwYrSZIkSWrJYCVJkiRJLRmsJEmSJKklg5UkSZIktWSwkiRJkqSWDFaSJEmS1JLBSpIkSZJaMlhJkiRJUksGK0mSJElqyWAlSZIkSS0ZrCRJkiSpJYOVJEmSJLVksJIkSZKklgxWkiRJktSSwUqSJEmSWjJYSZIkSVJLBitJkiRJaslgJUmSJEktGawkSZIkqSWDlSRJkiS1ZLCSJEmSpJYMVpIkSZLUksFKkiRJkloyWEmSJElSSwYrSZIkSWrJYCVJkiRJLRmsJEmSJKklg5UkSZIktWSwkiRJkqSWDFaSJEmS1JLBSpIkSZJaMlhJkiRJUksGK0mSJElqyWAlSZIkSS0ZrCRJkiSpJYOVJEmSJLVksJIkSZKklgxWkiRJktRSV4JVkqcnOSvJTUkWJnlxko2SXJDklvp7w47tP5Tk1iQ3J9m7o3xGkuvrumOTpJZPTHJGLb8yydQuHKYkSZKk1US3eqz+E/h/pZTnAS8AFgIfBC4spWwNXFhvk2Q74CBgGrAP8NUkE+p+vgYcCmxdf/ap5W8B7i+lPAc4Bvj8SByUJEmSpNXTiAerJOsDuwPfAiil/LWU8idgf+CkutlJwAF1eX/g9FLKo6WU3wG3Ajsn2QxYv5RyeSmlACf3qtOzr7OAPXt6syRJkiRpqK3Rhft8NvBH4L+TvACYB7wbmFxKuROglHJnkk3r9psDV3TUX1zLltbl3uU9dRbVfS1LsgTYGLinsyFJDqXp8WLy5MnMmTNniA5RkiRJ0mg3lO//uxGs1gBeCBxeSrkyyX9Sh/31o6+epjJA+UB1nlhQygnACQAzZ84ss2bNGqAZkiRJ0ir48cndboFWYijf/3fjGqvFwOJSypX19lk0QeuuOryP+vvuju236Kg/Bbijlk/po/wJdZKsAWwA3DfkRyJJkiRJdCFYlVL+ACxKsk0t2hO4ETgXmF3LZgM/rMvnAgfVmf62opmk4qo6bPCBJC+q108d3KtOz75eD1xUr8OSJEmSpCHXjaGAAIcDpyZZC/gt8K80Ie/MJG8Bfg+8AaCUsiDJmTThaxlwWClled3PO4ATgbWBn9YfaCbG+E6SW2l6qg4aiYOSJEmStHrqSrAqpcwHZvaxas9+tj8aOLqP8rnA9n2UP0INZpIkSZI03LrVY6Uhtttxu3W7CStcdvhl3W6CJEmSNKK69QXBkiRJkjRuGKwkSZIkqSWDlSRJkiS1ZLCSJEmSpJYMVpIkSZLUksFKkiRJkloaVLBKMmG4GyJJkiRJY9Vge6xuTfKFJNsNa2skSZIkaQwabLDaAfg18M0kVyQ5NMn6w9guSZIkSRozBhWsSikPlFL+q5SyK3AE8AngziQnJXnOsLZQkiRJkka5QV9jlWS/JOcA/wn8B/Bs4EfAT4axfZIkSZI06q0xyO1uAS4GvlBK+WVH+VlJdh/6ZkmSJEnS2LHSYFVnBDyxlPLJvtaXUt415K2SJEmSpDFkpUMBSynLgZeOQFskSZIkaUwa7FDAXyb5CnAG8FBPYSnlmmFplSRJkiSNIYMNVrvW353DAQvwsqFtjiRJkiSNPYMKVqUUhwJKkiRJUj8G22NFklcB04BJPWX9TWghSZKk0WG343brdhNWuOzwy7rdBGnYDPZ7rL4OHAgcDgR4A7DlMLZLkiRJksaMQQUrYNdSysHA/aWUo4AXA1sMX7MkSZIkaewYbLB6uP7+S5JnAkuBrYanSZIkSZI0tgz2Gqvzkjwd+AJwDc2MgN8crkZJGvsu2X2PbjdhhT0uvaTbTRhxo+WaCq+nkCStLgY7K+Cn6uLZSc4DJpVSlgxfsyRJkiRp7BgwWCV57QDrKKV8f+ibJEmSJEljy8p6rP5hgHUFMFhJkiRJWu0NGKxKKf86Ug2RJEmSpLHKLwiWJEmSpJb8gmBJkiRJaskvCJYkSZKklgY7FLD3FwTfh18QLElPMOMDJ3e7CStMela3WyBJ0uplVb8g+N+BebXMLwiWJEmSJFb+PVY7AYt6viA4ybrA9cBNwDHD37zRzU+nJUmSJMHKr7H6BvBXgCS7A5+rZUuAE4a3aZIkSZI0NqxsKOCEUsp9dflA4IRSytnA2UnmD2vLJEmSJGmMWFmP1YQkPeFrT+CijnWD/g4sSZIkSRrPVhaOTgMuSXIPzcyA/wOQ5Dk0wwElSZKkQblk9z263QQA9rj0km43QePQgMGqlHJ0kguBzYCflVJKXfU0mi8LliRJkqTV3kqH85VSruij7NfD0xxJkiRJGntWdo2VJEmSJGklDFaSJEmS1JLBSpIkSZJacsp0SZKkITbjAyd3uwkrTHpWt1sgrR7ssZIkSZKklgxWkiRJktSSwUqSJEmSWjJYSZIkSVJLBitJkiRJaslgJUmSJEktGawkSZIkqSWDlSRJkiS1ZLCSJEmSpJYMVpIkSZLUksFKkiRJkloyWEmSJElSSwYrSZIkSWppjW43QNLQ2e243brdhBU+48uLJElajdhjJUmSJEktGawkSZIkqSXH6kiShs0lu+/R7SassMell3S7CZKkccweK0mSJElqyWAlSZIkSS0ZrCRJkiSppa4FqyQTklyb5Lx6e6MkFyS5pf7esGPbDyW5NcnNSfbuKJ+R5Pq67tgkqeUTk5xRy69MMnXED1CSJEnSaqObPVbvBhZ23P4gcGEpZWvgwnqbJNsBBwHTgH2AryaZUOt8DTgU2Lr+7FPL3wLcX0p5DnAM8PnhPRRJkiRJq7OuBKskU4BXAd/sKN4fOKkunwQc0FF+einl0VLK74BbgZ2TbAasX0q5vJRSgJN71enZ11nAnj29WZIkSZI01Lo13fqXgSOA9TrKJpdS7gQopdyZZNNavjlwRcd2i2vZ0rrcu7ynzqK6r2VJlgAbA/d0NiLJoTQ9XkyePJk5c+a0PS6Bj6NGHc9JgeeBpMf5eqAeQ3kujHiwSvJq4O5SyrwkswZTpY+yMkD5QHWeWFDKCcAJADNnziyzZg2mOR1+fPKqbb+aWOXHUUPn+m43YHQasXPS14RRzdcmjShfD0Y1/y+ox1CeC93osdoN2C/JvsAkYP0kpwB3Jdms9lZtBtxdt18MbNFRfwpwRy2f0kd5Z53FSdYANgDuG64DkiRJkrR6G/FrrEopHyqlTCmlTKWZlOKiUsqbgHOB2XWz2cAP6/K5wEF1pr+taCapuKoOG3wgyYvq9VMH96rTs6/X1/t4Uo+VJEmSJA2Fbl1j1ZfPAWcmeQvwe+ANAKWUBUnOBG4ElgGHlVKW1zrvAE4E1gZ+Wn8AvgV8J8mtND1VB43UQUiSJEla/XQ1WJVS5gBz6vK9wJ79bHc0cHQf5XOB7fsof4QazCRJkiRpuHXze6wkSZIkaVwYTUMBNU5csvse3W7CCntcekm3myBJkqTVgD1WkiRJktSSPVbSEJjxgdHxPRWTntXtFkiSJK2e7LGSJEmSpJYMVpIkSZLUksFKkiRJkloyWEmSJElSSwYrSZIkSWrJYCVJkiRJLRmsJEmSJKklg5UkSZIktWSwkiRJkqSWDFaSJEmS1JLBSpIkSZJaMlhJkiRJUksGK0mSJElqyWAlSZIkSS0ZrCRJkiSpJYOVJEmSJLVksJIkSZKklgxWkiRJktSSwUqSJEmSWjJYSZIkSVJLBitJkiRJaslgJUmSJEktGawkSZIkqSWDlSRJkiS1ZLCSJEmSpJYMVpIkSZLUksFKkiRJkloyWEmSJElSSwYrSZIkSWrJYCVJkiRJLRmsJEmSJKklg5UkSZIktWSwkiRJkqSWDFaSJEmS1JLBSpIkSZJaMlhJkiRJUksGK0mSJElqyWAlSZIkSS0ZrCRJkiSpJYOVJEmSJLVksJIkSZKklgxWkiRJktSSwUqSJEmSWjJYSZIkSVJLBitJkiRJaslgJUmSJEktGawkSZIkqSWDlSRJkiS1ZLCSJEmSpJYMVpIkSZLUksFKkiRJkloyWEmSJElSSwYrSZIkSWrJYCVJkiRJLRmsJEmSJKklg5UkSZIktWSwkiRJkqSWDFaSJEmS1JLBSpIkSZJaGvFglWSLJBcnWZhkQZJ31/KNklyQ5Jb6e8OOOh9KcmuSm5Ps3VE+I8n1dd2xSVLLJyY5o5ZfmWTqSB+nJEmSpNVHN3qslgHvK6VsC7wIOCzJdsAHgQtLKVsDF9bb1HUHAdOAfYCvJplQ9/U14FBg6/qzTy1/C3B/KeU5wDHA50fiwCRJkiStnkY8WJVS7iylXFOXHwAWApsD+wMn1c1OAg6oy/sDp5dSHi2l/A64Fdg5yWbA+qWUy0spBTi5V52efZ0F7NnTmyVJkiRJQ22Nbt55HaK3I3AlMLmUcic04SvJpnWzzYErOqotrmVL63Lv8p46i+q+liVZAmwM3NPr/g+l6fFi8uTJzJkzZ6gOTaOEz6nA80ANzwNJPXw9UI+hPBe6FqySrAucDbynlPLnATqU+lpRBigfqM4TC0o5ATgBYObMmWXWrFkraXUvPz551bbXiFvl5/Sp8lwY1TwPBCN4Hkjg68Eo5/8F9RjKc6ErswImWZMmVJ1aSvl+Lb6rDu+j/r67li8GtuioPgW4o5ZP6aP8CXWSrAFsANw39EciSZIkSd2ZFTDAt4CFpZQvdaw6F5hdl2cDP+woP6jO9LcVzSQVV9Vhgw8keVHd58G96vTs6/XARfU6LEmSJEkact0YCrgb8C/A9Unm17IPA58DzkzyFuD3wBsASikLkpwJ3Egzo+BhpZTltd47gBOBtYGf1h9ogtt3ktxK01N10DAfkyRJkqTV2IgHq1LKL+j7GiiAPfupczRwdB/lc4Ht+yh/hBrMJEmSJGm4deUaK0mSJEkaTwxWkiRJktSSwUqSJEmSWjJYSZIkSVJLBitJkiRJaslgJUmSJEktGawkSZIkqSWDlSRJkiS1ZLCSJEmSpJYMVpIkSZLUksFKkiRJkloyWEmSJElSSwYrSZIkSWrJYCVJkiRJLRmsJEmSJKklg5UkSZIktWSwkiRJkqSWDFaSJEmS1JLBSpIkSZJaWqPbDZAkSePfJbvv0e0mALDHpZd0uwmSxil7rCRJkiSpJYOVJEmSJLVksJIkSZKklgxWkiRJktSSwUqSJEmSWjJYSZIkSVJLBitJkiRJaslgJUmSJEktGawkSZIkqaU1ut0ASZI0PHY7brduN2GFz/iWQ9I4Z4+VJEmSJLVksJIkSZKklgxWkiRJktSSwUqSJEmSWjJYSZIkSVJLBitJkiRJaslgJUmSJEktGawkSZIkqSWDlSRJkiS1ZLCSJEmSpJYMVpIkSZLUksFKkiRJkloyWEmSJElSSwYrSZIkSWrJYCVJkiRJLRmsJEmSJKmlNbrdAEmSxpMZHzi5201YYdKzut0CSVp92GMlSZIkSS0ZrCRJkiSpJYOVJEmSJLVksJIkSZKklgxWkiRJktSSwUqSJEmSWjJYSZIkSVJLBitJkiRJaslgJUmSJEktGawkSZIkqSWDlSRJkiS1ZLCSJEmSpJYMVpIkSZLUksFKkiRJkloyWEmSJElSSwYrSZIkSWrJYCVJkiRJLY3rYJVknyQ3J7k1yQe73R5JkiRJ49O4DVZJJgDHA68EtgPemGS77rZKkiRJ0ng0boMVsDNwaynlt6WUvwKnA/t3uU2SJEmSxqE1ut2AYbQ5sKjj9mJgl84NkhwKHFpvPpjk5hFq22i2CXBPmx3MGpp2DI2k2y0YqzwPBJ4HarQ+D2AUnQueB0+V54F6+L8BtuyrcDwHq74epfKEG6WcAJwwMs0ZG5LMLaXM7HY71F2eBwLPAzU8DwSeB3qc50L/xvNQwMXAFh23pwB3dKktkiRJksax8Rysrga2TrJVkrWAg4Bzu9wmSZIkSePQuB0KWEpZluT/AOcDE4Bvl1IWdLlZY4FDIwWeB2p4Hgg8D9TwPFAPz4V+pJSy8q0kSZIkSf0az0MBJUmSJGlEGKwkSZIkqSWD1TiR5G+TnJ7kN0luTPKTJM8dxvt7cLj2rfaSLE8yv+NnarfbpOGVZHKS7yb5bZJ5SS5P8pput0uj06q+hieZleS8urxfkg8OT8tWX0mmJrmhV9mRSd6/knozkxxbl2cl2fUp3PdtSTYZqDzJjCS/S7LjUJ4DneeWBjZKz5FDklyf5LokNyTZv5a/OckzB7HfQW03VozbyStWJ0kCnAOcVEo5qJZNByYDv+5i09Q9D5dSpve1op4vKaU8NrJN0nCpz+kPaF4D/qmWbQnsN8j6E0opy4evhRpPSinn4iy7o0YpZS4wt96cBTwI/HIo7yPJDsBZwIGllGuBa/EcGDOG6xxJMgX4CPDCUsqSJOsCz6ir3wzcwMq/6miw240J9liNDy8FlpZSvt5TUEqZD1yb5MIk19RPE3o+RZiaZGGS/0qyIMnPkqxd1/1bkquT/CrJ2UnWqeVb1U/Ar07yqZ77SbJuX/eh0aXjOf8qcA2wRZKvJZlbz4GjOra9LclRHc/p82r5ukn+u+OTqdfV8r3quXFNku/VF1aNrJcBf+31GnB7KeW4JBOSfKH+7V6X5G2w4lPLi5N8F7i+3r4kyZlJfp3kc0n+OclV9Tn/u1rvH5JcmeTaJD9PMrmWH5nk20nmpOk1e1ct/1SSd/e0K8nRPevUffV5n5PkrCQ3JTm1BnWS7FPLfgG8tqPOm5N8pS73eT5o6NXn6fP1b/LXSf6+ls9Kcl6akQlvB96bZqTC3yd5Rv1ffnX92a3W2bj+7782yTeADHDX29J8cPMvpZSrav3Oc+DEJMcm+WX92399LX9akq/W/zHnpRlJ07Ouv3NroyQ/qK9VV6QJdD2vLyfVNt+W5LVJ/r2+Nv2/JGsO6YM9RnXhHNkUeIAmqFFKebCU8rv6PM8ETq33s3aSj9f935DkhDT62m5G/V80L8n5STar7XlXmhFZ1yU5fZgfyqeulOLPGP8B3gUc00f5GsD6dXkT4FaaP4ypwDJgel13JvCmurxxR/1PA4fX5XOBg+vyYcCDA91Htx+T1f0HWA7Mrz/n1Of8MeBFHdtsVH9PAOYAO9Tbt3U87+8EvlmXPw98uaP+hvU5vxT4m1r2f4GPd/v4V7ef/l4D6rpDgY/W5Yk0n1puRfOp5UPAVnXdLOBPwGZ1u/8Fjqrr3t3z3NfnvWdG2bcC/1GXj6T5BHRiPS/uBdas5941dZunAb/pfJ3xp2vnTM9r+CxgCTClPj+XAy8BJgGLgK3r/40zgfNqnTcDXxnofPDnKT0nU4EbepUdCby/Ls/p+HvbF/h5x3N4Xu/t6+3vAi+py88CFtblY3teq4FXAQXYpI823QbcB+zbq7zzHDgR+F49f7YDbq3lrwd+Usv/Fri/lg10bh0HfKIuvwyY33Fcv6ivKS8A/gK8sq47Bzig28/f6niO0Lx/OB/4PfDfwD90rJsDzOy4vVHH8nd6tu3crj6/vwSeUW8fSPN1SdD0aE2sy0/v9nPR349DAce3AJ9JsjvNm+rNaYYHAvyuNL1aAPNo/lgBtk/yaeDpwLo0fzAAuwGvq8vfoXmTPdB9/GHoD0er4AlDAeunVLeXUq7o2OYfkxxKE443o/mHeF1d9/36ex6Pf5r4cpov2gaglHJ/klfXepfVD7nXonljpi5KcjzNm+O/ArcDO/R8UgxsQPOG5q/AVaWU33VUvbqUcmfdx2+An9Xy62l6xqF5A35G/RRxLaCz/o9LKY8Cjya5G5hcSrktyb1JdqR5bbi2lHLvEB+y2rmqlLIYIMl8mv8HD9L8n7illp9CE9J7G+h80Krp7/tvOss7X5unDmKfLwe2q6/PAOsnWQ/YnfraXkr5cZL7B9jHz4G3Jjm/9D9k+AelGV5+Y0ev5UuA79XyPyS5uJY/j/7PrZdQ32uUUi6qvSYb1HU/LaUsTXI9zRv6/1fLr2dwj8V4MKrOkVLK8iT7ADsBewLHJJlRSjmyj/t5aZIjgHWAjYAFwI96bbMNsD1wQW3PBODOuu46mp6tH9D0oI5KBqvxYQHNp0C9/TPNWNcZ9cXoNppPigAe7dhuObB2XT6R5pOfXyV5M82nHD36+oMe6D40ujzUs5BkK+D9wE41IJ3IE5+3nvNjOY+/ToQnnwMBLiilvHFYWqzBWsDjH3xQSjkszUXGc2k+STy8lHJ+Z4Uks+g4J6rO14XHOm4/xuPnwXHAl0op59Z9HNlP/c5z55s0n3D/LfDtwR6URkx/z9tgvuhyoPNBq+Zemh7AThvxxLDa12vzQJ4GvLiU8nBnYX3TOtgvMv0/wNeBrwJv62ebznMovX73pb/77qtOz7aPApRSHkuytNSuC574+jTejbpzpD4PVwFXJbmApufqyF77mkRz/swspSxKciR9v1cMsKCU8uI+1r2KJuztB3wsybRSyrKVtW+keY3V+HARMDHJv/UUJNkJ2BK4uwael9bbK7MecGcdr/zPHeWX8XhvRWf5Bk/hPtR969O8qV5SP1185SDq/IzmHywASTYErgB2S/KcWrZOhnE2SvXrImBSknd0lK1Tf58PvKPnGoQkz03yNy3uawOaYYIAswdZ5xyg51PN81eyrUaHm4CtUq+tA/r78OSpnA/qQynlQZr/v3tCc70Rzd/NL1ZhNw/Q/B/v0ft1e3pdvJT6vzzJK3nym/VOj9E8/9sk+eQqtOUXwOvSXGs1mcc/qB3o3Ops1yzgnlLKn1fhPse10XaOJHlmkhd2FE2nGSXR+356QtQ9aa7D7uwM6NzuZuAZSV5c979mkmlJngZsUUq5GDiCx0dVjToGq3GgflrwGuAVaaZbX0DzacFPgJlJ5tL8cdw0iN19DLgSuKDX9u8GDktyNc0/0h6nPoX7UJeVUn5FM6vTApoehMsGUe3TwIb1wtNfAS8tpfyRpifitCTX0QSt5w1Pq9Wf+hpwALBHmumQrwJOornm7ZvAjcA1aabp/QbtPt09Evhekv8B7hlk+/4KXAycOcBQIo0ipZRHaIZn/TjNBAO397Ppkazi+aABHQx8tA7JvIjmOsffrEL9HwGvqRMB/D3N9Zcz6wX/N9JMXABwFLB7kmuAvWh6tvtVh/juD+yX5LBBtuVsYDHNjG/foHlvsWQl59aRPe0FPodhvS+j6RxZE/himolI5tNcE9UzWdGJwNdr+aPAf9EM2/wBcHXHPjq3m0ATuj5f32fMB3at5afUYaDX0lxT/KdVOOYRk8d7UiVJGnr108ZrgDf0XFchafxLsm4p5cEkG9MMF9utlOI12Bq3VpcxqZKkLkiyHXAecI6hSlrtnJfk6TQTm3zKUKXxzh4rSZIkSWrJa6wkSZIkqSWDlSRJkiS1ZLCSJEmSpJYMVpKkcSfJ3yY5vX4FxY1JfjKU37GWZFaSXYdqf5Kksc9gJUkaV5KE5kuJ55RS/q6Ush3wYWDyEN7NLJrvV+nr/p1xV5JWQwYrSdJ481JgaSnl6z0FpZT5wC+SfKF+yfX1SQ6EFb1P5/Vsm+QrSd5cl29LclSSa2qd5yWZSvMlmu/t+ZLNJCcm+VKSi4EvJLklyTPqPp6W5NYkm4zUAyBJGnl+qiZJGm+2B+b1Uf5aYDrwAmAT4Ooklw5if/eUUl6Y5J3A+0spb03ydeDBUsoXAZK8BXgu8PJSyvIkfwL+Gfgy8HLgV6WUe9odliRpNLPHSpK0ungJcFopZXkp5S7gEmCnQdT7fv09D5g6wHbfK6Usr8vfBg6uy4cA/73qzZUkjSUGK0nSeLMAmNFHefrZfhlP/H84qdf6R+vv5Qw80uOhnoVSyiLgriQvA3YBfjpQgyVJY5/BSpI03lwETEzybz0FSXYC7gcOTDKhXv+0O3AVcDuwXZKJSTYA9hzEfTwArLeSbb4JnAKc2dGTJUkapwxWkqRxpZRSgNcAr6jTrS8AjgS+C1wH/IomfB1RSvlD7V06s647Fbh2EHfzI+A1PZNX9LPNucC6OAxQklYLaf7/SJKkoZRkJnBMKaW/4CVJGkecFVCSpCGW5IPAO2hmBpQkrQbssZIkSZKklrzGSpIkSZJaMlhJkiRJUksGK0mSJElqyWAlSZIkSS0ZrCRJkiSppf8fNxneDcZWB5kAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot salary by country and Gender\n", "gender_sf_countries = df_eval.groupby([\"Gender\", \"Country\"], as_index=False)[EVALUATION_PARAMETERS].mean()\n", "\n", "# Select only countries in focus\n", "gender_sf_countries_if = gender_sf_countries[gender_sf_countries[\"Country\"].isin(countries_in_focus)]\n", "\n", "plot_multiple_bar_plot(gender_sf_countries_if, hue=\"Gender\", title=\"Salary by country and gender\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When comparing countries, we can see that women are clearly disadvantaged.
\n", "For people who assign themselves to a gender other than male or female,
\n", "whether they belong to the best or the lowest paid group depends heavily on the country." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot overall satisfaction by Gender\n", "plot_eval_param_dist(gender_satifaction, \"Overall satisfaction by gender\", \"OverallSatisfaction\", x_lim=[13.5,14.8],\n", " palette=[\"tab:green\",\"tab:blue\",\"tab:red\"])\n", "\n", "# Save satisfaction range for Gender\n", "save_range_in_oa_sf(gender_satifaction, \"Gender\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When it comes to general job and career satisfaction, the graph is reversed. Here the men are the happiest." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Ethnicity': 1.92, 'HighestEducationParents': 0.76, 'Gender': 0.76}" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# range overall satifaction for the initial conditions\n", "range_oa_sf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Overall, we can state that ethnicity has the greatest impact on the level of satisfaction achieved.
\n", "As the graphics show, all initial conditions have an impact on salary and satisfaction.
\n", "Question Q1 can be clearly answered in the negative. **There is no equal opportunity in the job profile of professional developers.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's continue with question 2:
\n", "**Q2 How does your social environment influence your chances of being successful as a developer?**

\n", "We have already seen clearly that the parents education not only has an extreme impact on salary, but also on job satisfaction.
\n", "Now we have to check how the circle of friends affect the success. Here, however, it must be taken into account that a correlation does not necessarily mean a causality.
\n", "If you are successful as a programmer, you have a lot to do with other developers and the probability is high that you will find new friends among them.
\n", "In addition, many friendships are made during an academic career and in our society people with high degrees tend to earn higher incomes." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Available data FriendsDevelopers: 8847\n" ] }, { "data": { "text/html": [ "
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SalaryCareerSatisfactionJobSatisfactionOverallSatisfaction
FriendsDevelopers
Disagree60290.4518497.5561887.02418214.580370
Somewhat agree56516.2778127.4923906.95210414.444494
Strongly disagree55530.7015407.4367826.95977014.396552
Agree53857.0076337.4472466.87441614.321662
Strongly agree52200.1286577.5399246.97845414.518378
\n", "
" ], "text/plain": [ " Salary CareerSatisfaction JobSatisfaction \\\n", "FriendsDevelopers \n", "Disagree 60290.451849 7.556188 7.024182 \n", "Somewhat agree 56516.277812 7.492390 6.952104 \n", "Strongly disagree 55530.701540 7.436782 6.959770 \n", "Agree 53857.007633 7.447246 6.874416 \n", "Strongly agree 52200.128657 7.539924 6.978454 \n", "\n", " OverallSatisfaction \n", "FriendsDevelopers \n", "Disagree 14.580370 \n", "Somewhat agree 14.444494 \n", "Strongly disagree 14.396552 \n", "Agree 14.321662 \n", "Strongly agree 14.518378 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Available data FriendsDevelopers\n", "available_data_fd = len(df_eval[\"FriendsDevelopers\"]) - sum(df_eval[\"FriendsDevelopers\"].isnull())\n", "print(f\"Available data FriendsDevelopers: {available_data_fd}\")\n", "\n", "# Evaluation parameter by FriendsDevelopers\n", "friends_dev_satifaction = df_eval.groupby(\"FriendsDevelopers\").mean().sort_values(\"Salary\", ascending=False)\n", "add_overall_sf(friends_dev_satifaction)\n", "friends_dev_satifaction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The table shows that the general level of satisfaction is very balanced.
What is more interesting for a graphical visualisation is the correlation between social circle and salary." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot salary in relation to friends from the same professional field\n", "plot_eval_param_dist(friends_dev_satifaction, \"Salary / Are your friends developers?\", \"Salary\", x_lim=[50000,62000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Contrary to my original assumption, we can see from the graph that people whose circle of friends includes few developers have the highest salary.
\n", "The people who mainly surround themselves with developers earn even the lowest salary." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With regard to Q2, it can be said that the social environment has a significant impact on success as a programmer.
\n", "The parents in particular have a major impact. The higher the educational level of the parents,
\n", "the more likely it is to achieve a high salary and the greater the chances of being satisfied in one's job/career.
\n", "As far as the circle of friends is concerned, it is advantageous in terms of salary not only to surround oneself
\n", "with friends of the same professional orientation. However, this parameter is irrelevant for the level of satisfaction achieved." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we continue with question 3:
\n", "**Q3 How important is an open mind and tolerance to succeed as a programmer?**" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Available data RightWrongWay: 8830\n" ] }, { "data": { "text/html": [ "
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SalaryCareerSatisfactionJobSatisfactionOverallSatisfaction
RightWrongWay
Strongly disagree64572.1232117.5486867.13446714.683153
Disagree63007.0507587.4699457.02858314.498529
Somewhat agree60474.8537317.5292516.97434114.503592
Agree49374.5583307.5236616.92511714.448778
Strongly agree38073.5524837.3935286.69833014.091858
\n", "
" ], "text/plain": [ " Salary CareerSatisfaction JobSatisfaction \\\n", "RightWrongWay \n", "Strongly disagree 64572.123211 7.548686 7.134467 \n", "Disagree 63007.050758 7.469945 7.028583 \n", "Somewhat agree 60474.853731 7.529251 6.974341 \n", "Agree 49374.558330 7.523661 6.925117 \n", "Strongly agree 38073.552483 7.393528 6.698330 \n", "\n", " OverallSatisfaction \n", "RightWrongWay \n", "Strongly disagree 14.683153 \n", "Disagree 14.498529 \n", "Somewhat agree 14.503592 \n", "Agree 14.448778 \n", "Strongly agree 14.091858 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Available data RightWrongWay\n", "available_data_rw = len(df_eval[\"RightWrongWay\"]) - sum(df_eval[\"RightWrongWay\"].isnull())\n", "print(f\"Available data RightWrongWay: {available_data_rw}\")\n", "\n", "# Evaluation parameter by RightWrongWay\n", "right_wrong_satifaction = df_eval.groupby(\"RightWrongWay\").mean().sort_values(\"Salary\", ascending=False)\n", "add_overall_sf(right_wrong_satifaction)\n", "right_wrong_satifaction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with the circle of friends, this parameter does not have a high impact on satisfaction.
\n", "It remains to be said, however, that the people who believe that there is a right and wrong way for everything are the most unsatisfied.
\n", "In terms of salary, the more open a developer is to different solutions, the higher the salary he achieves. \n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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lTQAul7S6pGsl3ZsfO+VyAyVdkudNk7R/tx0hMzNbJs1KoBOAdST9VdIFknYDiIgfA/OA3SNi91x2IDA7IrYHXgWOAbYHdgA+LWnLXG5D4GcRsQnwInBQnv9L4PiIGAUs1XqNiDdJLd7D86w9gRkRMb+k6DeBOyNiS+B6YN0y9fo4cFNEbAG8D5ie55+eb8S6ObCbpM0lrQT8Atg7InYGVi/Z1tbA/hHxceBHwHkRsW2u18Wd2wVuyfN3B74vaWBpUJKOywl8SpmYzcysCZqSQCNiISlBHAc8B4yXdHSF4ouBa/P0zsB1EfFK3sZvgV3ysscjYnqengqMkDQUGBwRnecof1NhH5cAR+bpY0lJt9SupERLRNwIvFCmzL3AMZLGAJtFxII8/2OS7gOmAZsAGwMbAY9FxOO5zLiSbV0fEa/m6T2Bn0qaTkreq0gaDOwFnJbndwArUSaxR8TYiNimq3dTNzOzxjXlHChAPpfZAXRImgUcBVxapuhrhfOeKrO806LC9GJg5Rrli7HMlfSMpD1IrdvDKxWtsZ3bJe0K7Av8StL3gTuALwHbRsQLki4lJbpasb1SmF4OGFVIqABIEnBQRDxcY1tmZtbDmtIClTRS0oaFWVsAT+TpBcDgCqveDhwgaUDuqjyQlKDKiogXgAWSdsizDq0S1sWkFuZVFQYq3U5OrJL2Bt5RWkDScODZiLgI+F9gK2AVUjJ8SdIawN65+EPAepJG5OeHVIltAvD5wn62yJM3ASfmREqhO9vMzHpZs86BDgIuk/SApJmkLs0xedlY4I+dg4iKIuI+Uiv1HmAycHFETKuxr08CYyVNIrX6XqpQ7vocV7nuW4AzgV1zV+xewN/LlBkNTJc0jXSu8kcRMYPUdXs/qat4Yq7Lq8AJwJ8k3Qk8UyW2k4Bt8mCoB4Dj8/yzgP7AzDw46qwK65uZWQ9TRNVey5YnaVA+X4qk04BhEXFymXLbkAbq7FK6rNmx5Rbkz4BHIuK8Zu5z07VGxtUnXNjMXZiZ9Zj3nr7HUvM6OjoYPXp0t+5H0tSujiPpC1ci2rfzZyWkAUf/U1ogJ9Zrga/1cGyfzgOA7geGkEblmplZH9C0QUQ9JSLGA+NrlDkHOKdnIlpiv+cBTW1xmplZ7+gLLVAzM7Me5wRqZmbWACdQMzOzBjiBmpmZNcAJ1MzMrAFOoGZmZg1wAjUzM2uAE6iZmVkDnEDNzMwa0PZXIrIl/XtQ+WtHtqtmXPOyt/W1Ork+ra8v1qkVuAVqZmbWACdQMzOzBjiBmpmZNcAJ1MzMrAFOoGZmZg1wAjUzM2uAE6iZmVkDnEDNzMwa4Asp9DFPvfoUO/1kp94Oo3vN6u0AmqCv1cn1aX29VKeJJ07snR33ALdAzczMGuAEamZm1gAnUDMzswY4gZqZmTXACdTMzKwBTqBmZmYNcAI1MzNrgBOomZlZA5xAzczMGuAEamZm1gAnUDMzswY4gZqZmTXACdTMzKwBTqCApMWSpku6X9IMSadKWi4v20bSj3s7RjMzay2+nVnyakRsASDpXcBvgCHANyNiCjClWTuWtHxEvNGs7ZuZWXO4BVoiIp4FjgM+r2S0pBsAJO2WW6rTJU2TNFjSIEl/kXSfpFmS9u/clqT/lvSQpJsljZP0pTy/Q9LZkm4DTpa0taTbJE2VdJOkYbnc+pL+lOffIWmjXnhJzMysDLdAy4iIx3IX7rtKFn0J+FxETJQ0CHgtzz8wIl6WtBpwt6Trga2Bg4AtSa/zfcDUwraGRsRukvoDtwH7R8Rzkg4Bvg0cC4wFjo+IRyRtD1wA7NGUSpuZWZc4gVamMvMmAj+U9GvgtxHxZE6AZ0vaFXgTWAtYA9gZ+F1EvAog6fcl2xqf/44ENgVulgTQD3g6J+gdgavzfIAVywYqHUdqNTNwnYENVNXMzLrKCbQMSesBi4Fngfd2zo+IcyTdCOxDamnuCewArA5sHRH/ljQHWInyCbjolc7dAfdHxKiSGFYBXuw8N1tNRIwltVYZtO6gqFlBMzNbZj4HWkLS6sDPgZ9GRJQsWz8iZkXEd0kDizYiDTZ6NifP3YHhufidwH6SVsqtyX0r7PJhYHVJo/I++kvaJCJeBh6X9NE8X5Le183VNTOzBrkFmqwsaTrQH3gD+BXwwzLlTslJcjHwAPBHYDDwe0lTgOnAQwARcW8+FzoDeIKUcF8q3WBEvC7pYODHkoaQjsn5wP3A4cCFks7IsV2Zt2dmZr3MCRSIiH5VlnUAHXn6xDJFFgGjyswH+EFEjJE0ALgdODdvZ3TJPqYDu5bZ9+PAh2rFb2ZmPc8JtLnGStqYdE70soi4r7cDMjOz7lFXApW0aUTMbnYwfU1EfLy3YzAzs+aodxDRzyXdI+kESUObGZCZmVk7qCuBRsTOpAEt6wBTJP1G0geaGpmZmVkLq/tnLBHxCHAG8FVgN9Ko0YckfaRZwZmZmbWquhKopM0lnQc8SLqU3H4R8d48fV4T4zMzM2tJ9Y7C/SlwEfD1zkvTAUTEvPwbRTMzs7eVuhJoRCz1G8XCsl91XzhmZmbtod6fsWwIfAfo/E0jABGxXpPiMjMza2n1DiL6JXAh6TJ3uwOXky53Z2Zm9rZUbwJdOSL+AiginoiIMfi+lGZm9jZW7yCi1/INph+R9HngKZa+2bSZmdnbRr0J9BRgAHAScBap9XlUk2KyZbDWymsx8cSJvR1Gt+no6GD06NG9HUa36mt1cn1aX1+sUyuomkAlzSDd1/Iu4LmImAMc0wNxmZmZtbRa50APJ91/8gPABElPSbpa0hckbd/88MzMzFpT1RZovgPLbGAsgKTVgENJXbo/ACreR9PMzKwvq9WF2w/YEtgR2AlYnzSA6GJgUtOjMzMza1G1BhG9TLr+7c+A0yLi8eaHZGZm1vpqJdBPAaPy32Mk3UtqeU6KiKeaHZyZmVmrqnUOdBwwDkDSAGA7UlfudyStEBHDmx+imZlZ66n5O1BJA4Htees86LbAXKDv/NjQzMysi2oNIpoGrAtMIf0W9Fzg7ohY2AOxWSPmzeO2XXfr7Si6jYDbejuIbtbX6uT6tL7SOu12e1+rYe+o1QI9CpgVEdETwZiZmbWLWudAZwJIOrXM4peAqRExvQlxmZmZtbR678ayDXA8sFZ+HAeMBi6S9JXmhGZmZta66r2Y/KrAVp3nPiV9E7gG2BWYCnyvOeGZmZm1pnpboOsCrxee/xsYHhGvAou6PSozM7MWV28L9DfA3ZJ+l5/vB4zLP3F5oCmRmZmZtbC6EmhEnCXpj6TfgQo4PiKm5MWHNys4MzOzVlVvCxRgGjCvcx1J60bE35sSlZmZWYurK4FKOhH4JvAMsJjUCg1g8+aFZmZm1rrqbYGeDIyMiOebGYyZmVm7qHcU7lzShRPMzMyM+lugjwEdkm6k8LOViPhhU6IyMzNrcfUm0L/nxwr5YWZm9rZW789YzgSQNDg97frdWCSdDnycNAjpTeAzETG5q9vpLpIWRsSgLpQfDbweEXc1LSgzM2sb9Y7C3RT4FfDO/Hw+cGRE3F/n+qOAD5MuB7hI0mq0X0t2NLCQdFu3biWpX0Qs7u7tmplZ89Q7iGgscGpEDI+I4cAXgYu6sJ9hwPyIWAQQEfMjYh6ApPdLmiZplqRLJK2Y58+RdLakSZKmSNpK0k2S/ibp+M4NS/qypHslzZTU2VL+iqST8vR5km4p7OuKwrrfljRD0t2S1sjz9pM0Ocf0Z0lrSBpBupj+FyRNl7RLsXKStpN0V17nLkkj8/wBkq7KsY3P290mL1so6VuSJgOjJB0h6Z68/V9I6pfL7ZVfg/skXS2p7lazmZk1T70JdGBE3Nr5JCI6gIFd2M8EYB1Jf5V0gaTdACStBFwKHBIRm5FaxJ8trDc3IkYBd+RyBwM7AN/K6+8FbAhsB2wBbC1pV+B2oDPJbQMMktQf2Dlvixz/3RHxvlz+03n+ncAOEbElcCXwlYiYA/wcOC8itoiIzm10egjYNa/zDeDsPP8E4IWI2Bw4C9i6sM5AYHZEbA88DxwC7BQRW5C6uQ/PLfUzgD0jYivSjc2XurWcpOPyl4wppcvMzKw56h6FK+m/Sd24AEcAj9e7k4hYKGlrUlLbHRgv6TTS1Y0ej4i/5qKXAZ8Dzs/Pr89/ZwGDImIBsEDSa5KGAnvlx7RcbhApoV5OSqaDSaOG7yMl0l2Ak3LZ14Eb8vRU4AN5eu0c3zBSN3M99RwCXCZpQ9IFJvrn+TsDP8qvwWxJMwvrLAauzdPvJyXXeyUBrAw8S/qysDEwMc9fAZhUuvOIGEvqJWDk4MG++bmZWQ+oN4EeC5wJ/DY/vx04pis7yuf4Okg/h5kFHAVMr7Fa509m3mTJu768SYpdwHci4helK0qak2O8C5hJStzrAw/mIv+OiM5ks5i3XoufAD+MiOvzwKExdVTvLODWiDgwd/d2dIZRZZ3XCuc9BVwWEV8rqcN+wM0RcVgdMZiZWQ+q2YWbz8VdHREnRcRW+XFKRLxQ704kjcyts05bAE+Quj5HSNogz/8EcFv94XMTcGzneUFJa0l6V152O/Cl/PcO0jnM6YWkWckQ4Kk8fVRh/gJgcB3rHF2YfyfwsRzbxsBmFdb/C3BwZ+yS3ilpOHA3sFPn65PPqb6nRvxmZtYDaibQ3Er6l6Qhy7CfQaQuzgdyN+bGwJiIeI3USrw6t0rfJJ1rrEtETCDdam1SXv8a3kpyd5AGL02KiGeA13jr/Gc1Y3I8dwDzC/N/DxxYbhAR6Ybi35E0EehXmH8BsHqu81dJLeGlrugUEQ+QznVOyGVvBoZFxHOkhDwuz78b2KiOOpiZWZOpdoMMJF1FOh93M/BK5/yIOKniStbZeu8fEa9JWp/U0nxPRLxeY9WGjRw8OMZuuVWzNm9mfcBut3elo6/1dHR0MHr06G7dpqSpEbFNV9ap9xzojflhXTMAuDWPABbw2WYmTzMz6zlVE6ikGaTzeHcBE/PPOaxOedRwl77RmJlZe6h1DvRwYAbpJx4TJD2Vf8z/BUnbNT88MzOz1lS1BRoRs4HZ5N8Y5h/2HwqcAvyAJQfMmJmZvW3U6sLtB2wJ7AjsRPod5VPAxZT5Qb+ZmdnbRa1BRC+TLjzwM+C0iKj76kNmZmZ9Wa0E+ilgVP57jKR7SS3PSRHxVNU1zczM+rBa50DHAeMgXQWHdNH2nUgXDVgh35nFzMzsbafm70AlDQS2563zoNsCc4GJzQ3NzMysddUaRDQNWBfo7Lo9l3QLsIU9EJuZmVnLqtUCPYp0K7F3R8RjxQWS3u1BRWZm9nZV6xzoTABJ1wClF1i9hiVvEG2tYM012/46l0XNuOZlb+trdXJ9Wl9frFMrqNWFuxGwCTBE0kcKi1YBVmpmYGZmZq2sVhfuSODDwFBgv8L8BcCnmxSTmZlZy6vVhfs74HeSRkWErzxkZmaW1Xs7s0clfR0YUVwnIo5tRlBmZmatrt4E+jvgDuDPwOLmhWNmZtYe6k2gAyLiq02NxMzMrI3Uuh9opxsk7dPUSMzMzNpIrZ+xLAACEPB1SYuAf+fnERGrND9EMzOz1lNrFO7gngrEuseTL73O1l++vLfD6F439rH6QNvVaer3j+ztEMxaTl3nQCWVXoUI4CXgiYh4o3tDMjMza331DiK6gHQpv1n5+WbADGBVScdHxIRmBGdmZtaq6h1ENAfYMiK2joitgS2A2cCewPeaE5qZmVnrqjeBbhQR93c+iYgHSAn1sSrrmJmZ9Vn1duE+LOlC4Mr8/BDgr5JWJI3KNTMze1uptwV6NPAocArwBeCxPO/fwO5NiMvMzKyl1dUCjYhXgXPzo9TCbo3IzMysDdS6kMJVEfExSbNIF1RYQkRs3rTIzMzMWlitFujJ+e+Hmx2ImZlZO6l1JaKn898nivMl9QMOBZ4ot56ZmVlfV3UQkaRVJH1N0k8l7aXkRNIgoo/1TIhmZmatp1YX7q+AF4BJwKeALwMrAPtHxPTmhmZmZta6aiXQ9SJiMwBJFwPzgXUjYkHTIzMzM2thtX4H+p+LJETEYuBxJ8/yJB0oKSRt1NuxmJlZ89VKoO+T9HJ+LAA275yW9HJPBNhGDgPuJA2uqlsekGVmZm2magKNiH4RsUp+DI6I5QvTvpl2JmkQsBPwSXIClbScpAsk3S/pBkl/kHRwXjZH0jck3Ql8NA/QmiTpPklX5+0haWtJt0maKukmScN6q45mZrakei/lZ9UdAPwpIv4K/DPfP/UjwAjSrd8+BYwqWee1iNgZ+DNwBrBnRGwFTAFOldQf+AlwcL4DziXAt3ugLmZmVod6LyZv1R0GnJ+nr8zP+wNXR8SbwD8k3Vqyzvj8dwdgY2CiJEijnCcBI4FNgZvz/H7A0+V2Luk44DiAAWuM6I76mJlZDU6gy0jSqsAewKaSgpToAriuxqqvdG4CuDkiDivZ7mbA/RFR2nJdSkSMBcYCDPx/717qkotmZtb93IW77A4GLo+I4RExIiLWAR4n/eTnoHwudA1gdIX17wZ2krQBgKQBkt4DPAysLmlUnt9f0ibNroyZmdXHCXTZHcbSrc1rgTWBJ4HZwC+AycBLpStHxHOkW8ONkzSTlFA3iojXScn5u5JmANOBHZtTBTMz6yp34S6jiBhdZt6PIY3OjYiFuZv3HmBWXj6ipPwtwLZltjMd2LXbgzYzs2XmBNpcN0gaShoYdFZE/KOX4zEzs27iBNpE5VqnZmbWN/gcqJmZWQOcQM3MzBrgBGpmZtYAJ1AzM7MGOIGamZk1wAnUzMysAU6gZmZmDXACNTMza4ATqJmZWQOcQM3MzBrgS/n1MWsPWYGp3z+yt8PoNh0dHYwePbq3w+hWfbFOZm9HboGamZk1wAnUzMysAU6gZmZmDXACNTMza4ATqJmZWQOcQM3MzBrgBGpmZtYAJ1AzM7MG+EIKfczcN4L3XXNTr+1/xsEf7LV9m5n1JLdAzczMGuAEamZm1gAnUDMzswY4gZqZmTXACdTMzKwBTqBmZmYNcAI1MzNrgBOomZlZA5xAzczMGuAEamZm1gAnUDMzswY4gZqZmTXACdTMzKwBLZlAJZ0u6X5JMyVNl7R9nn+KpAE9sP/Rkm5o9n7MzKx9tdztzCSNAj4MbBURiyStBqyQF58CXAH8q8x6/SJicY8F2o3aOXYzs7erVmyBDgPmR8QigIiYHxHzJJ0ErAncKulWAEkLJX1L0mRglKRTJc3Oj1NymRGSHpR0UW7VTpC0cl62bW7lTpL0fUmzi4FIWk7SI5JWLzx/NCf1YrntJN0laVr+OzLPHyDpqryP8ZImS9qmQuxHSLont7h/IalfLrdXju8+SVdLGtSsF97MzOrXigl0ArCOpL9KukDSbgAR8WNgHrB7ROyeyw4EZkfE9sCrwDHA9sAOwKclbZnLbQj8LCI2AV4EDsrzfwkcHxGjgKVagBHxJqnFe3ietScwIyLmlxR9CNg1IrYEvgGcneefALwQEZsDZwFbF9Ypxv48cAiwU0RskWM5PCfqM4A9I2IrYApwammcko6TNEXSlKVfTjMza4aWS6ARsZCUaI4DngPGSzq6QvHFwLV5emfguoh4JW/jt8AuednjETE9T08FRkgaCgyOiLvy/N9U2MclwJF5+lhS0i01BLg6t2DPAzYpxHRlrtdsYGaF2N+f63yvpOn5+XqkLwIbAxPz/KOA4aU7j4ixEbFNRGxToQ5mZtbNWu4cKEA+H9gBdEiaRUocl5Yp+lrh3KGqbHJRYXoxsHKN8sVY5kp6RtIepNbt4WWKnQXcGhEHShqRY68VU2nsl0XE14oFJO0H3BwRh9UTq5mZ9ZyWa4FKGilpw8KsLYAn8vQCYHCFVW8HDsjnHQcCBwJ3VNpPRLwALJC0Q551aJWwLiZ15V5VYbDPEOCpPH10Yf6dwMcAJG0MbFZh+38BDpb0rlz2nZKGA3cDO0naIM8fIOk9VeI0M7Me0nIJFBgEXCbpAUkzSV2YY/KyscAfOwcRFUXEfaRW6j3AZODiiJhWY1+fBMZKmkRqBb5Uodz1Oa5y3bcA3wO+I2ki0K8w/wJg9VyPr5K6cJfaR0Q8QDrXOSGXvRkYFhHPkRLyuDz/bmCjGnUyM7Me0HJduBExFdixwrKfAD8pPB9UsvyHwA9L5s0BNi08/0Fh8f15gA+STiMN0iEiOnirGxbgfaTBQw9ViGsSUGwZ/nf++xpwRES8Jml9UkvziQqxjwfGl9n2LcC25fZrZma9p+USaA/bV9LXSK/DEyzZ/Qr8J7F+lvLnPmsZQPrZTX9SC/ezEfF64+GamVmreFsn0EqtvpIy5wDnNLj9BYBHxpqZ9UGteA7UzMys5TmBmpmZNcAJ1MzMrAFOoGZmZg1wAjUzM2uAE6iZmVkDnEDNzMwa4ARqZmbWACdQMzOzBrytr0TUF62zvJhx8Ad7Owwzsz7PLVAzM7MGOIGamZk1wAnUzMysAU6gZmZmDVBE9HYM1o0kLQAe7u04utFqwPzeDqKb9bU6uT6tr6/VqRn1GR4Rq3dlBY/C7Xsejog+cw9SSVP6Un2g79XJ9Wl9fa1OrVIfd+GamZk1wAnUzMysAU6gfc/Y3g6gm/W1+kDfq5Pr0/r6Wp1aoj4eRGRmZtYAt0DNzMwa4ARqZmbWACfQFiRpJUn3SJoh6X5JZ+b5YyQ9JWl6fuxTWOdrkh6V9LCkDxbmby1pVl72Y0nK81eUND7PnyxpRA/Uq5+kaZJuyM/fKelmSY/kv+9o8/q0+/GZk2OZLmlKnte2x6hCfdr2GEkaKukaSQ9JelDSqHY+PlXq1D7HKCL8aLEHIGBQnu4PTAZ2AMYAXypTfmNgBrAi8G7gb0C/vOweYFTe5h+BvfP8E4Cf5+lDgfE9UK9Tgd8AN+Tn3wNOy9OnAd9t8/q0+/GZA6xWMq9tj1GF+rTtMQIuAz6Vp1cAhrbz8alSp7Y5Rm6BtqBIFuan/fOj2miv/YErI2JRRDwOPApsJ2kYsEpETIr0DrocOKCwzmV5+hrg/Z3f2ppB0trAvsDFJXF3xnBZSWztWJ9KWr4+VbTtMeqilq6PpFWAXYH/BYiI1yPiRdr4+FSpUyUtVycn0Bal1D04HXgWuDkiJudFn5c0U9Ilhe6atYC5hdWfzPPWytOl85dYJyLeAF4CVm1GXbLzga8AbxbmrRERT+cYngbeVRpb1i71gfY9PpC+pE2QNFXScXleOx+jcvWB9jxG6wHPAb9UOm1wsaSBtPfxqVQnaJNj5ATaoiJicURsAaxN+pa1KXAhsD6wBfA0cG4uXu4bVVSZX22dbifpw8CzETG13lXKzGuH+rTl8SnYKSK2AvYGPidp1ypl26FO5erTrsdoeWAr4MKI2BJ4hdRlW0mr1wcq16ltjpETaIvLXRodwIci4pmcWN8ELgK2y8WeBNYprLY2MC/PX7vM/CXWkbQ8MAT4Z3NqwU7Af0maA1wJ7CHpCuCZ3P1C/vtsaWwlcbd0fdr4+AAQEfPy32eB60jxt+sxKlufNj5GTwJPFnqiriEln7Y9PlSoUzsdIyfQFiRpdUlD8/TKwJ7AQ50flOxAYHaevh44NI84ezewIXBP7tJZIGmH3O9/JPC7wjpH5emDgVvy+YNuFxFfi4i1I2IE6UT+LRFxREkMR5XE1nb1adfjAyBpoKTBndPAXjn+tjxGlerTrscoIv4BzJU0Ms96P/AAbXp8qtWprY5RV0cd+dH8B7A5MA2Ymd8838jzfwXMyvOvB4YV1jmdNCrtYfIItDx/m7yNvwE/5a2rT60EXE06EX8PsF4P1W00b41aXRX4C/BI/vvONq9P2x4f0vmoGflxP3B6Ox+jKvVp52O0BTAlx/5/wDva9fjUqFPbHCNfys/MzKwB7sI1MzNrgBOomZlZA5xAzczMGuAEamZm1gAnUDMzswY4gZpZl0g6XekuQTOV7paxfZWyl0o6uCfjM+spy/d2AGbWPiSNAj5MumLMIkmrke6i0V3bXz7SNUvNWp5boGbWFcOA+RGxCCAi5kfEPEnfkHSvpNmSxpa740WlMpI6JJ0t6TbgdEmPS+qfl62idF/P/j1ZSbN6OIGaWVdMANaR9FdJF0jaLc//aURsGxGbAiuTWqmlqpUZGhG7RcSZpGs/75vnHwpcGxH/bkptzJaBE6iZ1S3SfWq3Bo4j3YpqvKSjgd0lTZY0C9gD2KTM6tXKjC9MXwwck6ePAX7ZvbUw6x4+B2pmXRIRi0mtxI6cDD9Dun7zNhExV9IY0jVI/0PSSsAFVcq8Utj+REkjcuu2X0TMxqwFuQVqZnWTNFLShoVZW5Au7A0wX9Ig0l0vSq1UR5miy4FxuPVpLcwtUDPrikHAT/Lt9t4g3eXiOOBF0h005gD3lq4UES9KuqhamRK/Bv6HlETNWpLvxmJmLSf/dnT/iPhEb8diVolboGbWUiT9BNgb2Ke3YzGrxi1QMzOzBngQkZmZWQOcQM3MzBrgBGpmZtYAJ1AzM7MGOIGamZk14P8DF77O58YwqpEAAAAASUVORK5CYII=", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the relation of salary to whether people believe there is a right way and a wrong way to do everything\n", "plot_eval_param_dist(right_wrong_satifaction, \"Salary / There's a right and a wrong way to do everything\", \"Salary\", x_lim=[35000,67000], \n", " palette=[\"tab:pink\",\"tab:green\",\"tab:red\",\"tab:blue\",\"tab:cyan\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the difference in salary is so significant, we should look at this again in a country comparison." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot salary by country and RightWrongWay\n", "right_wrong_sf_countries = df_eval.groupby([\"RightWrongWay\", \"Country\"], as_index=False)[EVALUATION_PARAMETERS].mean()\n", "\n", "# Select only countries in focus\n", "right_wrong_sf_countries_if = right_wrong_sf_countries[right_wrong_sf_countries[\"Country\"].isin(countries_in_focus)]\n", "\n", "plot_multiple_bar_plot(right_wrong_sf_countries_if, hue=\"RightWrongWay\", title=\"Salary / There's a right and a wrong way to do everything\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that in all the countries considered, people who believe that there is only one right way to solve a problem earn less than those who strongly reject this belief." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Available data DiversityImportant: 8833\n" ] }, { "data": { "text/html": [ "
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SalaryCareerSatisfactionJobSatisfactionOverallSatisfaction
DiversityImportant
Strongly disagree63195.7137917.2607366.68711713.947853
Strongly agree61762.8947207.6199687.07853214.698500
Disagree57684.2927447.3588656.80992914.168794
Agree53805.3463547.4875276.94856814.436095
Somewhat agree53270.8273457.4557156.91984214.375557
\n", "
" ], "text/plain": [ " Salary CareerSatisfaction JobSatisfaction \\\n", "DiversityImportant \n", "Strongly disagree 63195.713791 7.260736 6.687117 \n", "Strongly agree 61762.894720 7.619968 7.078532 \n", "Disagree 57684.292744 7.358865 6.809929 \n", "Agree 53805.346354 7.487527 6.948568 \n", "Somewhat agree 53270.827345 7.455715 6.919842 \n", "\n", " OverallSatisfaction \n", "DiversityImportant \n", "Strongly disagree 13.947853 \n", "Strongly agree 14.698500 \n", "Disagree 14.168794 \n", "Agree 14.436095 \n", "Somewhat agree 14.375557 " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Available data DiversityImportant\n", "available_data_di = len(df_eval[\"DiversityImportant\"]) - sum(df_eval[\"DiversityImportant\"].isnull())\n", "print(f\"Available data DiversityImportant: {available_data_di}\")\n", "\n", "# Evaluation parameter by DiversityImportant\n", "diversity_imp_satifaction = df_eval.groupby(\"DiversityImportant\").mean().sort_values(\"Salary\", ascending=False)\n", "add_overall_sf(diversity_imp_satifaction)\n", "diversity_imp_satifaction" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the relation of overall satisfaction to whether people believe that Diversity in the workplace is important in percent\n", "diversity_imp_satifaction.sort_values(\"OverallSatisfaction\", ascending=False, inplace=True)\n", "_, ax = plt.subplots()\n", "sns.barplot(x=100*(diversity_imp_satifaction[\"OverallSatisfaction\"]/max(diversity_imp_satifaction[\"OverallSatisfaction\"])), y=diversity_imp_satifaction.index)\n", "plt.title(\"Overall satisfaction / Diversity in the workplace is important\")\n", "plt.xlabel(\"OverallSatisfaction[%]\")\n", "plt.xlim([90, 100])\n", "plt.grid(axis=\"x\")\n", "ax.set_axisbelow(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On the question of whether diversity is important in the workplace, it appears that people with a clear opinion on this earn the most.
\n", "The group that is strongly opposed to diversity has an average income that is about €1500 higher, but is also more than 5% less satisfied than those for whom diversity is important." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To answer Q3, it is important to note that it is extremely important to be open to different solution approaches when it comes to maximizing salary.
\n", "In addition, a clear opinion can help to maximize the salary. It should be taken into account that open people are happier.\n", "\n", "#### 5. Depleoyment\n", "For the condensed presentation of the results, check out this medium post:\n", "https://medium.com/@florianpue/an-inconvenient-truth-about-social-justice-in-programming-f78fb17c614b" ] } ], "metadata": { "interpreter": { "hash": "b452309f084dbde4ee04ee96a4964316dc909f027a0d6a69b3a024fcdd223501" }, "kernelspec": { "display_name": "Python 3.9.7 64-bit ('base': conda)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }